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Zhao C, Su KJ, Wu C, Cao X, Sha Q, Li W, Luo Z, Qing T, Qiu C, Zhao LJ, Liu A, Jiang L, Zhang X, Shen H, Zhou W, Deng HW. Multi-scale variational autoencoder for imputation of missing values in untargeted metabolomics using whole-genome sequencing data. Comput Biol Med 2024; 179:108813. [PMID: 38955127 PMCID: PMC11324385 DOI: 10.1016/j.compbiomed.2024.108813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/04/2024]
Abstract
BACKGROUND Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies. METHOD In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information. RESULTS We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R2-scores > 0.01 for 71.55 % of metabolites. CONCLUSION The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.
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Affiliation(s)
- Chen Zhao
- Department of Computer Science, Kennesaw State University, 680 Arntson Dr, Marietta, GA, 30060, USA
| | - Kuan-Jui Su
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Chong Wu
- Department of Biostatistics, University of Texas MD Anderson, Pickens Academic Tower, 1400 Pressler St., Houston, TX, 77030, USA
| | - Xuewei Cao
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Qiuying Sha
- Department of Mathematical Sciences, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA
| | - Wu Li
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Zhe Luo
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Tian Qing
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Chuan Qiu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Lan Juan Zhao
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Anqi Liu
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Lindong Jiang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Xiao Zhang
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Hui Shen
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
| | - Weihua Zhou
- Department of Applied Computing, Michigan Technological University, 1400 Townsend Dr, Houghton, MI, 49931, USA; Center for Biocomputing and Digital Health, Institute of Computing and Cybersystems, and Health Research Institute, Michigan Technological University, Houghton, MI, 49931, USA.
| | - Hong-Wen Deng
- Division of Biomedical Informatics and Genomics, Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, New Orleans, LA, 70112, USA
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Raheel A. Emotion analysis and recognition in 3D space using classifier-dependent feature selection in response to tactile enhanced audio-visual content using EEG. Comput Biol Med 2024; 179:108807. [PMID: 38970831 DOI: 10.1016/j.compbiomed.2024.108807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/13/2024] [Accepted: 06/24/2024] [Indexed: 07/08/2024]
Abstract
Traditional media such as text, images, audio, and video primarily target specific senses like vision and hearing. In contrast, multiple sensorial media aims to create immersive experiences by integrating additional sensory modalities such as touch, smell, and taste where applicable. Tactile enhanced audio-visual content leverages the sense of touch in addition to visual and auditory stimuli, aiming to create a more immersive and engaging interaction for users. Previously, tactile enhanced content has been explored in 2D emotional space (valence and arousal). In this paper, EEG data against tactile enhanced audio-visual content is labeled based on a self-assessment manikin scale in 3 dimensions i.e., valence, arousal, and dominance. Statistical significance (with a 95% confidence interval) is also established based on gathered scores, highlighting a significant difference in the arousal and dominance dimension of traditional media and tactile enhanced media. A new methodology is proposed using classifier-dependent feature selection approach to classify valence, arousal, and dominance states using three different classifiers. A highest accuracy of 75%, 73.8%, and 75% is achieved for classifying valence, arousal, and dominance states, respectively. The proposed scheme outperforms previous emotion recognition based studies in response to enhanced multimedia content in terms of accuracy, F-score, and other error parameters.
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Affiliation(s)
- Aasim Raheel
- Department of Computer Engineering, University of Engineering and Technology Taxila, Pakistan.
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Chen B, Sun X, Huang H, Feng C, Chen W, Wu D. An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes. J Affect Disord 2024; 359:22-32. [PMID: 38754597 DOI: 10.1016/j.jad.2024.05.061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 03/24/2024] [Accepted: 05/12/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop and validate our model. Following removal of batch effect, differentially expressed genes (DEGs) between respective disease and control groups were identified. Shared DEGs of the conditions then underwent functional enrichment analyses. Additionally, immune infiltration analysis was quantified through ssGSEA. A diagnostic model for MDD was constructed by exploring 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training sets following by external validation on test sets. Finally, the "Enrichr" platform was utilized to identify potential drugs for MDD. RESULTS Totally, 21 key genes closely associated with both MDD and IC were identified, predominantly involved in immune processes based on enrichment analyses. Immune infiltration analysis revealed distinct profiles of immune cell infiltration in MDD and IC compared to healthy controls. From these genes, a robust 11-gene (ABCD2, ATP8B4, TNNT1, AKR1C3, SLC26A8, S100A12, PTX3, FAM3B, ITGA2B, OLFM4, BCL7A) diagnostic signature was constructed, which exhibited superior performance over existing MDD diagnostic models both in training and testing cohorts. Additionally, epigallocatechin gallate and 10 other drugs emerged as potential targets for MDD. CONCLUSION Our work developed a diagnostic model for MDD employing a combination of bioinformatic techniques and machine learning methods, focusing on shared genes between MDD and IC.
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Affiliation(s)
- Bohong Chen
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, China
| | - Xinyue Sun
- Department of neurology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, China
| | - Haoxiang Huang
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, China
| | - Cong Feng
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, China
| | - Wei Chen
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, China.
| | - Dapeng Wu
- Department of Urology, The First Affiliated Hospital of Xi'an Jiaotong University, 710061 Xi'an, Shaanxi, China.
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4
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Cansiz B, Kilinc CU, Serbes G. Tunable Q-factor wavelet transform based lung signal decomposition and statistical feature extraction for effective lung disease classification. Comput Biol Med 2024; 178:108698. [PMID: 38861896 DOI: 10.1016/j.compbiomed.2024.108698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/07/2024] [Accepted: 06/01/2024] [Indexed: 06/13/2024]
Abstract
The auscultation is a non-invasive and cost-effective method used for the diagnosis of lung diseases, which are one of the leading causes of death worldwide. However, the efficacy of the auscultation suffers from the limitations of the analog stethoscopes and the subjective nature of human interpretation. To overcome these limitations, the accurate diagnosis of these diseases by employing the computer based automated algorithms applied to the digitized lung sounds has been studied for the last decades. This study proposes a novel approach that uses a Tunable Q-factor Wavelet Transform (TQWT) based statistical feature extraction followed by individual and ensemble learning model training with the aim of lung disease classification. During the learning stage various machine learning algorithms are utilized as the individual learners as well as the hard and soft voting fusion approaches are employed for performance enhancement with the aid of the predictions of individual models. For an objective evaluation of the proposed approach, the study was structured into two main tasks that were investigated in detail by using several sub-tasks to comparison with state-of-the-art studies. Among the sub-tasks which investigates patient-based classification, the highest accuracy obtained for the binary classification was achieved as 97.63% (healthy vs. non-healthy), while accuracy values up to 66.32% for three-class classification (obstructive-related, restrictive-related, and healthy), and 53.42% for five-class classification (asthma, chronic obstructive pulmonary disease, interstitial lung disease, pulmonary infection, and healthy) were obtained. Regarding the other sub-task, which investigates sample-based classification, the proposed approach was superior to almost all previous findings. The proposed method underscores the potential of TQWT based signal decomposition that leverages the power of its adaptive time-frequency resolution property satisfied by Q-factor adjustability. The obtained results are very promising and the proposed approach paves the way for more accurate and automated digital auscultation techniques in clinical settings.
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Affiliation(s)
- Berke Cansiz
- Department of Biomedical Engineering, Yildiz Technical University, Esenler, Istanbul 34220, Turkey
| | - Coskuvar Utkan Kilinc
- Department of Biomedical Engineering, Yildiz Technical University, Esenler, Istanbul 34220, Turkey
| | - Gorkem Serbes
- Department of Biomedical Engineering, Yildiz Technical University, Esenler, Istanbul 34220, Turkey.
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Asad M, Hassan A, Wang W, Alonazi WB, Khan MS, Ogunyemi SO, Ibrahim M, Bin L. An integrated in silico approach for the identification of novel potential drug target and chimeric vaccine against Neisseria meningitides strain 331401 serogroup X by subtractive genomics and reverse vaccinology. Comput Biol Med 2024; 178:108738. [PMID: 38870724 DOI: 10.1016/j.compbiomed.2024.108738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/15/2024] [Accepted: 06/08/2024] [Indexed: 06/15/2024]
Abstract
Neisseria meningitidis, commonly known as the meningococcus, leads to substantial illness and death among children and young adults globally, revealing as either epidemic or sporadic meningitis and/or septicemia. In this study, we have designed a novel peptide-based chimeric vaccine candidate against the N. meningitidis strain 331,401 serogroup X. Through rigorous analysis of subtractive genomics, two essential cytoplasmic proteins, namely UPI000012E8E0(UDP-3-O-acyl-GlcNAc deacetylase) and UPI0000ECF4A9(UDP-N-acetylglucosamine acyltransferase) emerged as potential drug targets. Additionally, using reverse vaccinology, the outer membrane protein UPI0001F4D537 (Membrane fusion protein MtrC) identified by subcellular localization and recognized for its known indispensable role in bacterial survival was identified as a novel chimeric vaccine target. Following a careful comparison of MHC-I, MHC-II, T-cell, and B-cell epitopes, three epitopes derived from UPI0001F4D537 were linked with three types of linkers-GGGS, EAAAK, and the essential PADRE-for vaccine construction. This resulted in eight distinct vaccine models (V1-V8). Among them V1 model was selected as the final vaccine construct. It exhibits exceptional immunogenicity, safety, and enhanced antigenicity, with 97.7 % of its residues in the Ramachandran plot's most favored region. Subsequently, the vaccine structure was docked with the TLR4/MD2 complex and six different HLA allele receptors using the HADDOCK server. The docking resulted in the lowest HADDOCK score of 39.3 ± 9.0 for TLR/MD2. Immune stimulation showed a strong immune response, including antibodies creation and the activation of B-cells, T Cytotoxic cells, T Helper cells, Natural Killer cells, and interleukins. Furthermore, the vaccine construct was successfully expressed in the Escherichia coli system by reverse transcription, optimization, and ligation in the pET-28a (+) vector for the expression study. The current study proposes V1 construct has the potential to elicit both cellular and humoral responses, crucial for the developing an epitope-based vaccine against N. meningitidis strain 331,401 serogroup X.
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Affiliation(s)
- Muhammad Asad
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan
| | - Ahmad Hassan
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan
| | - Weiyu Wang
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Wadi B Alonazi
- Health Administration Department, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
| | | | - Solabomi Olaitan Ogunyemi
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
| | - Muhammad Ibrahim
- Department of Biosciences, COMSATS University Islamabad, Sahiwal Campus, Pakistan.
| | - Li Bin
- State Key Laboratory of Rice Biology and Breeding, Key Laboratory of Molecular Biology of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, 310058, China
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Ekinci E, Garip Z, Serbest K. Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms. Comput Biol Med 2024; 178:108812. [PMID: 38943945 DOI: 10.1016/j.compbiomed.2024.108812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/19/2024] [Accepted: 06/24/2024] [Indexed: 07/01/2024]
Abstract
The sit-to-stand (STS) movement is fundamental in daily activities, involving coordinated motion of the lower extremities and trunk, which leads to the generation of joint moments based on joint angles and limb properties. Traditional methods for determining joint moments often involve sensors or complex mathematical approaches, posing limitations in terms of movement restrictions or expertise requirements. Machine learning (ML) algorithms have emerged as promising tools for joint moment estimation, but the challenge lies in efficiently selecting relevant features from diverse datasets, especially in clinical research settings. This study aims to address this challenge by leveraging metaheuristic optimization algorithms to predict joint moments during STS using minimal input data. Motion analysis data from 20 participants with varied mass and inertia properties are utilized, and joint angles are computed alongside simulations of joint moments. Feature selection is performed using the Manta Ray Foraging Optimization (MRFO), Marine Predators Algorithm (MPA), and Equilibrium Optimizer (EO) algorithms. Subsequently, Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Tree Regression (ETR), and eXtreme Gradient Boosting Regression (XGBoost Regression) ML algorithms are deployed for joint moment prediction. The results reveal EO-ETR as the most effective algorithm for ankle, knee, and neck joint moment prediction, while MPA-ETR exhibits superior performance for hip joint prediction. This approach demonstrates potential for enhancing accuracy in joint moment estimation with minimal feature input, offering implications for biomechanical research and clinical applications.
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Affiliation(s)
- Ekin Ekinci
- Department of Computer Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
| | - Zeynep Garip
- Department of Computer Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
| | - Kasim Serbest
- Department of Mechatronics Engineering, Faculty of Technology, Sakarya University of Applied Sciences, Sakarya, Turkey.
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Biswas B, Kumar N, Sugimoto M, Hoque MA. scHD4E: Novel ensemble learning-based differential expression analysis method for single-cell RNA-sequencing data. Comput Biol Med 2024; 178:108769. [PMID: 38897145 DOI: 10.1016/j.compbiomed.2024.108769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/14/2024] [Accepted: 06/15/2024] [Indexed: 06/21/2024]
Abstract
Differential expression (DE) analysis between cell types for scRNA-seq data by capturing its complicated features is crucial. Recently, different methods have been developed for targeting the scRNA-seq data analysis based on different modeling frameworks, assumptions, strategies and test statistic in considering various data features. The scDEA is an ensemble learning-based DE analysis method developed recently, yielding p-values using Lancaster's combination, generated by 12 individual DE analysis methods, and producing more accurate and stable results than individual methods. The objective of our study is to propose a new ensemble learning-based DE analysis method, scHD4E, using top performers in only 4 separate methods. The top performer 4 methods have been selected through an evaluation process using six real scRNA-seq data sets. We conducted comprehensive experiments for five experimental data sets to evaluate our proposed method based on the sample size effects, batch effects, type I error control, gene ontology enrichment analysis, runtime, identified matched DE genes, and semantic similarity measurement between methods. We also perform similar analyses (except the last 3 terms) and compute performance measures like accuracy, F1 score, Mathew's correlation coefficient etc. for a simulated data set. The results show that scHD4E is performs better than all the individual and scDEA methods in all the above perspectives. We expect that scHD4E will serve the modern data scientists for detecting the DEGs in scRNA-seq data analysis. To implement our proposed method, a Github R package scHD4E and its shiny application has been developed, and available in the following links: https://github.com/bbiswas1989/scHD4E and https://github.com/bbiswas1989/scHD4E-Shiny.
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Affiliation(s)
- Biplab Biswas
- Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh; Department of Statistics, Faculty of Science, University of Rajshahi, Rajshahi, 6205, Bangladesh.
| | - Nishith Kumar
- Department of Statistics, Faculty of Science, Bangabandhu Sheikh Mujibur Rahman Science & Technology University, Gopalganj, 8100, Bangladesh.
| | - Masahiro Sugimoto
- Institute for Advanced Biosciences, Keio University 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata, 997-0052, Japan.
| | - Md Aminul Hoque
- Department of Statistics, Faculty of Science, University of Rajshahi, Rajshahi, 6205, Bangladesh.
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Islam MA, Majumder MZH, Miah MS, Jannaty S. Precision healthcare: A deep dive into machine learning algorithms and feature selection strategies for accurate heart disease prediction. Comput Biol Med 2024; 176:108432. [PMID: 38744014 DOI: 10.1016/j.compbiomed.2024.108432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Revised: 04/06/2024] [Accepted: 04/07/2024] [Indexed: 05/16/2024]
Abstract
This paper presents a comprehensive exploration of machine learning algorithms (MLAs) and feature selection techniques for accurate heart disease prediction (HDP) in modern healthcare. By focusing on diverse datasets encompassing various challenges, the research sheds light on optimal strategies for early detection. MLAs such as Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and others were studied, with precision and recall metrics emphasized for robust predictions. Our study addresses challenges in real-world data through data cleaning and one-hot encoding, enhancing the integrity of our predictive models. Feature extraction techniques-Recursive Feature Extraction (RFE), Principal Component Analysis (PCA), and univariate feature selection-play a crucial role in identifying relevant features and reducing data dimensionality. Our findings showcase the impact of these techniques on improving prediction accuracy. Optimized models for each dataset have been achieved through grid search hyperparameter tuning, with configurations meticulously outlined. Notably, a remarkable 99.12 % accuracy was achieved on the first Kaggle dataset, showcasing the potential for accurate HDP. Model robustness across diverse datasets was highlighted, with caution against overfitting. The study emphasizes the need for validation of unseen data and encourages ongoing research for generalizability. Serving as a practical guide, this research aids researchers and practitioners in HDP model development, influencing clinical decisions and healthcare resource allocation. By providing insights into effective algorithms and techniques, the paper contributes to reducing heart disease-related morbidity and mortality, supporting the healthcare community's ongoing efforts.
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Affiliation(s)
- Md Ariful Islam
- Department of Robotics and Mechatronics Engineering, University of Dhaka, Dhaka, 1000, Bangladesh.
| | | | - Md Sohel Miah
- Department of Computer Science and Technology, Moulvibazar Polytechnic Institute, Bangladesh
| | - Sumaia Jannaty
- Gonoshasthaya Samaj Vittik Medical College, Savar, Dhaka, Bangladesh
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9
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Chen Z, Ge R, Wang C, Elazab A, Fu X, Min W, Qin F, Jia G, Fan X. Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm. Mamm Genome 2024; 35:241-255. [PMID: 38512459 DOI: 10.1007/s00335-024-10034-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/07/2024] [Indexed: 03/23/2024]
Abstract
Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient's quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia.
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Affiliation(s)
| | - Ruiquan Ge
- Hangzhou Dianzi University, Hangzhou, China.
- Hangzhou Institute of Advanced Technology, Hangzhou, China.
- Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou, China.
| | - Changmiao Wang
- Shenzhen Research Institute of Big Data, Shenzhen, China
| | - Ahmed Elazab
- Computer Science Department, Misr Higher Institute for Commerce and Computers, Mansoura, Egypt
| | - Xianjun Fu
- School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou, China
| | - Wenwen Min
- School of Information Science and Engineering, Yunnan University, Kunming, China
| | - Feiwei Qin
- Hangzhou Dianzi University, Hangzhou, China
| | | | - Xiaopeng Fan
- Hangzhou Institute of Advanced Technology, Hangzhou, China
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Zong Y, Yang Y, Zhao J, Li L, Luo D, Hu J, Gao Y, Xie X, Shen L, Chen S, Ning L, Jiang L. Identification of key mitochondria-related genes and their relevance to the immune system linking Parkinson's disease and primary Sjögren's syndrome through integrated bioinformatics analyses. Comput Biol Med 2024; 175:108511. [PMID: 38677173 DOI: 10.1016/j.compbiomed.2024.108511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 04/14/2024] [Accepted: 04/21/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND Mitochondria are the metabolic hubs of cells, regulating energy production and antigen presentation, which are essential for activation, proliferation, and function of immune cells. Recent evidence indicates that mitochondrial antigen presentation may have an impact on diseases such as Parkinson's disease (PD) and autoimmune diseases. However, there is limited knowledge about the mechanisms that regulate the presentation of mitochondrial antigens in these diseases. METHODS In the current study, RNA sequencing was performed on labial minor salivary gland (LSG) from 25 patients with primary Sjögren's syndrome (pSS) and 14 non-pSS aged controls. Additionally, we obtained gene expression omnibus datasets associated with PD patients from NCBI Gene Expression Omnibus (GEO) databases. Single-sample Gene Set Enrichment Analysis (ssGSEA), ESTIMATE and Spearman correlations were conducted to explore the association between mitochondrial related genes and the immune system. Furthermore, we applied weighted Gene Co-expression Network Analysis (WGCNA) to identify hub mitochondria-related genes and investigate the correlated networks in both diseases. Single cell transcriptome analysis, immunohistochemical (IHC) staining and quantitative real-time PCR (qRT-PCR) were used to verify the activation of the hub mitochondria-related pathway. Pearson correlations and the CIBERSORT algorithms were employed to further reveal the correlation between hub mitochondria-related pathways and immune infiltration. RESULTS The transcriptome analysis revealed the presence of overlapping mitochondria-related genes and mitochondrial DNA damage in patients with pSS and PD. Reactive oxygen species (ROS), the senescence marker p53, and the inflammatory markers CD45 and Bcl-2 were found to be regionally distributed in LSGs of pSS patients. WGCNA analysis identified the STING pathway as the central mitochondria-related pathway closely associated with the immune system. Single cell analysis, IHC staining, and qRT-PCR confirmed the activation of the STING pathway. Subsequent, bioinformatic analysis revealed the proportion of infiltrating immune cells in the STING-high and STING-low groups of pSS and PD. Furthermore, the study demonstrated the association of the STING pathway with innate and adaptive immune cells, as well as functional cells, in the immune microenvironment of PD and pSS. CONCLUSION Our study uncovered a central pathway that connects mitochondrial dysfunction and the immune microenvironment in PD and pSS, potentially offering valuable insights into therapeutic targets for these conditions.
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Affiliation(s)
- Yuan Zong
- Department of Stomatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
| | - Yi Yang
- Department of Stomatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
| | - Jiawen Zhao
- Department of Stomatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
| | - Lei Li
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Danyang Luo
- Department of Stomatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
| | - Jiawei Hu
- Department of Stomatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
| | - Yiming Gao
- Department of Stomatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China
| | - Xianfei Xie
- Hainan Branch, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Qionghai, China; Department of Orthopaedics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Linhui Shen
- Department of Geriatrics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sheng Chen
- Department of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Li Ning
- Department of Stomatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.
| | - Liting Jiang
- Department of Stomatology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, College of Stomatology, Shanghai Jiao Tong University, Shanghai, China.
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11
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Rashid MM, Selvarajoo K. Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data. Brief Bioinform 2024; 25:bbae300. [PMID: 38904542 PMCID: PMC11190965 DOI: 10.1093/bib/bbae300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 05/30/2024] [Accepted: 06/11/2024] [Indexed: 06/22/2024] Open
Abstract
The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class-specific feature selection algorithms, which identifies multi-modal and -omics-associated interpretable components. MOMLIN was applied to 147 patients' breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context-specific multi-omics network biomarkers and better predict drug-response classifications.
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Affiliation(s)
- Md Mamunur Rashid
- Biomolecular Sequence to Function Division, BII, (ASTAR), Singapore 138671, Republic of Singapore
| | - Kumar Selvarajoo
- Biomolecular Sequence to Function Division, BII, (ASTAR), Singapore 138671, Republic of Singapore
- Synthetic Biology Translational Research Program, Yong Loo Lin School of Medicine, NUS, Singapore 117456, Republic of Singapore
- School of Biological Sciences, Nanyang Technological University (NTU), Singapore 639798, Republic of Singapore
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12
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Fu Y, Wang C, Wu Z, Zhang X, Liu Y, Wang X, Liu F, Chen Y, Zhang Y, Zhao H, Wang Q. Discovery of the potential biomarkers for early diagnosis of endometrial cancer via integrating metabolomics and transcriptomics. Comput Biol Med 2024; 173:108327. [PMID: 38552279 DOI: 10.1016/j.compbiomed.2024.108327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/07/2024] [Accepted: 03/17/2024] [Indexed: 04/17/2024]
Abstract
Endometrial cancer (EC) is one of the most common malignant tumors in women, and the increasing incidence and mortality pose a serious threat to the public health. Early diagnosis of EC could prolong the survival period and optimize the survivorship, greatly alleviating patients' suffering and social medical pressure. In this study, we collected urine and serum samples from the recruited patients, analyzed the samples using LC-MS approach, and identified the differential metabolites through metabolomic analysis. Then, the differentially expressed genes were identified through the systematic transcriptomic analysis of EC-related dataset from Gene Expression Omnibus (GEO), followed by network profiling of metabolic-reaction-enzyme-gene. In this experiment, a total of 83 differential metabolites and 19 hub genes were discovered, of which 10 different metabolites and 3 hub genes were further evaluated as more potential biomarkers based on network analysis. According to the KEGG enrichment analysis, the potential biomarkers and gene-encoded proteins were found to be involved in the arginine and proline metabolism, histidine metabolism, and pyrimidine metabolism, which was of significance for the early diagnosis of EC. In particular, the combination of metabolites (histamine, 1-methylhistamine, and methylimidazole acetaldehyde) as well as the combination of RRM2, TYMS and TK1 exerted more accurate discrimination abilities between EC and healthy groups, providing more criteria for the early diagnosis of EC.
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Affiliation(s)
- Yan Fu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China; Core Facilities and Centers, Hebei Medical University, Shijiazhuang, 050017, China
| | - Chengzhao Wang
- College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Zhimin Wu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xiaoguang Zhang
- Core Facilities and Centers, Hebei Medical University, Shijiazhuang, 050017, China; College of Basic Medicine, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yan Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Xu Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Fangfang Liu
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yujuan Chen
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China
| | - Yang Zhang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
| | - Huanhuan Zhao
- Department of Obstetrics and Gynecology, The Fourth Hospital of Hebei Medical University, 050011, China.
| | - Qiao Wang
- School of Pharmacy, Hebei Medical University, Shijiazhuang, 050017, China.
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13
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Chen J, Wen B. Bi-level gene selection of cancer by combining clustering and sparse learning. Comput Biol Med 2024; 172:108236. [PMID: 38471351 DOI: 10.1016/j.compbiomed.2024.108236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 02/07/2024] [Accepted: 02/25/2024] [Indexed: 03/14/2024]
Abstract
The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines the k-means clustering algorithm and signal-to-noise ratio ranking method, and then, a weighted gene co-expression network has been applied to the reduced data set to identify modules corresponding to biological pathways. Moreover, we choose the key genes by using group bridge and sparse group lasso as wrapper methods. Finally, we conduct some numerical experiments on six cancer datasets. The numerical results show that our proposed method has achieved good performance in gene selection and cancer classification.
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Affiliation(s)
- Junnan Chen
- School of Science, Hebei University of Technology, Tianjin, PR China.
| | - Bo Wen
- Institute of Mathematics, Hebei University of Technology, Tianjin, PR China.
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14
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Zhou Y, Chen Z, Yang M, Chen F, Yin J, Zhang Y, Zhou X, Sun X, Ni Z, Chen L, Lv Q, Zhu F, Liu S. FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response. Brief Bioinform 2024; 25:bbae223. [PMID: 38742521 PMCID: PMC11091744 DOI: 10.1093/bib/bbae223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/25/2024] [Accepted: 04/21/2024] [Indexed: 05/16/2024] Open
Abstract
Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.
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Affiliation(s)
- Yuan Zhou
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Mengjie Yang
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Fengyun Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Jiayi Yin
- Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xuheng Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Ziheng Ni
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Lu Chen
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
| | - Qun Lv
- Department of Respiratory, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, 311121, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
| | - Shuiping Liu
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, Engineering Laboratory of Development and Application of Traditional Chinese Medicines, Collaborative Innovation Center of Traditional Chinese Medicines of Zhejiang Province, School of Pharmacy, and Department of Respiratory Medicine of Affiliated Hospital, Hangzhou Normal University, Hangzhou, 311121, China
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15
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Tang J, Mou M, Zheng X, Yan J, Pan Z, Zhang J, Li B, Yang Q, Wang Y, Zhang Y, Gao J, Li S, Yang H, Zhu F. Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma. Anal Chem 2024; 96:4745-4755. [PMID: 38417094 DOI: 10.1021/acs.analchem.3c03796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2024]
Abstract
Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.
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Affiliation(s)
- Jing Tang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xin Zheng
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Jin Yan
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Bo Li
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Qingxia Yang
- School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Ying Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Song Li
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Hui Yang
- Multidisciplinary Center for Pituitary Adenoma of Chongqing, Department of Neuosurgery, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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16
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Villikudathil AT, Mc Guigan DH, English A. Exploring metformin monotherapy response in Type-2 diabetes: Computational insights through clinical, genomic, and proteomic markers using machine learning algorithms. Comput Biol Med 2024; 171:108106. [PMID: 38368755 DOI: 10.1016/j.compbiomed.2024.108106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Revised: 01/24/2024] [Accepted: 02/04/2024] [Indexed: 02/20/2024]
Abstract
BACKGROUND In 2016, the UK had 4.5 million people with diabetes, predominantly Type-2 Diabetes Mellitus (T2DM). The NHS allocates £10 billion (9% of its budget) to manage diabetes. Metformin is the primary treatment for T2DM, but 35% of patients don't benefit from it, leading to complications. This study aims to delve into metformin's efficacy using clinical, genomic, and proteomic data to uncover new biomarkers and build a Machine Learning predictor for early metformin response detection. METHODS Here we report analysis from a T2DM dataset of individuals prescribed metformin monotherapy from the Diastrat cohort recruited at the Altnagelvin Area Hospital, Northern Ireland. RESULTS In the clinical data analysis, comparing responders (those achieving HbA1c ≤ 48 mmol/mol) to non-responders (with HbA1c > 48 mmol/mol), we identified that creatinine levels and bodyweight were more negatively correlated with response than non-response. In genomic analysis, we identified statistically significant (p-value <0.05) variants rs6551649 (LPHN3), rs6551654 (LPHN3), rs4495065 (LPHN3) and rs7940817 (TRPC6) which appear to differentiate the responders and non-responders. In proteomic analysis, we identified 15 statistically significant (p-value <0.05, q-value <0.05) proteomic markers that differentiate controls, responders, non-responders and treatment groups, out of which the most significant were HAOX1, CCL17 and PAI that had fold change ∼2. A machine learning model was build; the best model predicted non-responders with 83% classification accuracy. CONCLUSION Further testing in prospective validation cohorts is required to determine the clinical utility of the proposed model.
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Affiliation(s)
- Angelina Thomas Villikudathil
- Ulster University, Centre for Stratified Medicine, Faculty of Life and Health Sciences, Magee Campus, Londonderry, Northern Ireland, United Kingdom.
| | - Declan H Mc Guigan
- Ulster University, Centre for Stratified Medicine, Faculty of Life and Health Sciences, Magee Campus, Londonderry, Northern Ireland, United Kingdom
| | - Andrew English
- School of Health and Life Sciences, Teesside University, England, United Kingdom
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17
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Zheng T, Zheng Z, Zhou H, Guo Y, Li S. The multifaceted roles of COL4A4 in lung adenocarcinoma: An integrated bioinformatics and experimental study. Comput Biol Med 2024; 170:107896. [PMID: 38217972 DOI: 10.1016/j.compbiomed.2023.107896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 12/03/2023] [Accepted: 12/23/2023] [Indexed: 01/15/2024]
Abstract
BACKGROUND Abnormal expression of collagen IV subunits has been reported in cancers, but the significance is not clear. No study has reported the significance of COL4A4 in lung adenocarcinoma (LUAD). METHODS COL4A4 expression data, single-cell sequencing data and clinical data were downloaded from public databases. A range of bioinformatics and experimental methods were adopted to analyze the association of COL4A4 expression with clinical parameters, tumor microenvironment (TME), drug resistance and immunotherapy response, and to investigate the roles and underlying mechanism of COL4A4 in LUAD. RESULTS COL4A4 is differentially expressed in most of cancers analyzed, being associated with prognosis, tumor stemness, immune checkpoint gene expression and TME parameters. In LUAD, COL4A4 expression is down-regulated and associated with various TME parameters, response to immunotherapy and drug resistance. LUAD patients with lower COL4A4 have worse prognosis. Knockdown of COL4A4 significantly inhibited the expression of cell-cycle associated genes, and the expression and activation of signaling pathways including JAK/STAT3, p38, and ERK pathways, and induced quiescence in LUAD cells. Besides, it significantly induced the expression of a range of bioactive molecule genes that have been shown to have critical roles in TME remodeling and immune regulation. CONCLUSIONS COL4A4 is implicated in the pathogenesis of cancers including LUAD. Its function may be multifaceted. It can modulate the activity of LUAD cells, TME remodeling and tumor stemness, thus affecting the pathological process of LUAD. COL4A4 may be a prognostic molecular marker and a potential therapeutic target.
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Affiliation(s)
- Tiaozhan Zheng
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, Zhuang Autonomous Region, 530021, PR China
| | - Zhiwen Zheng
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, Zhuang Autonomous Region, 530021, PR China
| | - Hanxi Zhou
- Department of Pathology, Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, PR China
| | - Yiqing Guo
- Department of Pathology, Taizhou Hospital, Wenzhou Medical University, Linhai, Zhejiang Province, PR China
| | - Shikang Li
- Department of Thoracic and Cardiovascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, Zhuang Autonomous Region, 530021, PR China.
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18
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Li Q, Yin YH, Liu ZW, Liu LF, Xin GZ. FNICM: A New Methodology To Identify Core Metabolites Based on Significantly Perturbed Metabolic Subnetworks. Anal Chem 2024; 96:3335-3344. [PMID: 38363654 DOI: 10.1021/acs.analchem.3c04131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2024]
Abstract
Metabolomics has emerged as a powerful tool in biomedical research to understand the pathophysiological processes and metabolic biomarkers of diseases. Nevertheless, it is a significant challenge in metabolomics to identify the reliable core metabolites that are closely associated with the occurrence or progression of diseases. Here, we proposed a new research framework by integrating detection-based metabolomics with computational network biology for function-guided and network-based identification of core metabolites, namely, FNICM. The proposed FNICM methodology is successfully utilized to uncover ulcerative colitis (UC)-related core metabolites based on the significantly perturbed metabolic subnetwork. First, seed metabolites were screened out using prior biological knowledge and targeted metabolomics. Second, by leveraging network topology, the perturbations of the detected seed metabolites were propagated to other undetected ones. Ultimately, 35 core metabolites were identified by controllability analysis and were further hierarchized into six levels based on confidence level and their potential significance. The specificity and generalizability of the discovered core metabolites, used as UC's diagnostic markers, were further validated using published data sets of UC patients. More importantly, we demonstrated the broad applicability and practicality of the FNICM framework in different contexts by applying it to multiple clinical data sets, including inflammatory bowel disease, colorectal cancer, and acute coronary syndrome. In addition, FNICM was also demonstrated as a practicality methodology to identify core metabolites correlated with the therapeutic effects of Clematis saponins. Overall, the FNICM methodology is a new framework for identifying reliable core metabolites for disease diagnosis and drug treatment from a systemic and a holistic perspective.
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Affiliation(s)
- Qi Li
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Ying-Hao Yin
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
- Shenzhen Key Laboratory of Hospital Chinese Medicine Preparation, Shenzhen Traditional Chinese Medicine Hospital, The Fourth Clinical Medical College of Guangzhou University of Chinese Medicine, Shenzhen 518033, China
| | - Zi-Wei Liu
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Li-Fang Liu
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Gui-Zhong Xin
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, Nanjing 210009, China
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19
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Yang Q, Chen S, Jiang W, Mi L, Liu J, Hu Y, Ji X, Wang J, Zhu F. MultiClassMetabo: A Superior Classification Model Constructed Using Metabolic Markers in Multiclass Metabolomics. Anal Chem 2024; 96:1410-1418. [PMID: 38221713 DOI: 10.1021/acs.analchem.3c03212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
Multiclass metabolomics has become a popular technique for revealing the mechanisms underlying certain physiological processes, different tumor types, or different therapeutic responses. In multiclass metabolomics, it is highly important to uncover the underlying biological information on biosamples by identifying the metabolic markers with the most associations and classifying the different sample classes. The classification problem of multiclass metabolomics is more difficult than that of the binary problem. To date, various methods exist for constructing classification models and identifying metabolic markers consisting of well-established techniques and newly emerging machine learning algorithms. However, how to construct a superior classification model using these methods remains unclear for a given multiclass metabolomic data set. Herein, MultiClassMetabo has been developed for constructing a superior classification model using metabolic markers identified in multiclass metabolomics. MultiClassMetabo can enable online services, including (a) identifying metabolic markers by marker identification methods, (b) constructing classification models by classification methods, and (c) performing a comprehensive assessment from multiple perspectives to construct a superior classification model for multiclass metabolomics. In summary, MultiClassMetabo is distinguished for its capability to construct a superior classification model using the most appropriate method through a comprehensive assessment, which makes it an important complement to other available tools in multiclass metabolomics. MultiClassMetabo can be accessed at http://idrblab.cn/multiclassmetabo/.
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Affiliation(s)
- Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Shuman Chen
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Wenyu Jiang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Lan Mi
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jiarui Liu
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Yu Hu
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Xinglai Ji
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Jun Wang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China
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Yin J, Chen Z, You N, Li F, Zhang H, Xue J, Ma H, Zhao Q, Yu L, Zeng S, Zhu F. VARIDT 3.0: the phenotypic and regulatory variability of drug transporter. Nucleic Acids Res 2024; 52:D1490-D1502. [PMID: 37819041 PMCID: PMC10767864 DOI: 10.1093/nar/gkad818] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/01/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.
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Affiliation(s)
- Jiayi Yin
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Nanxin You
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Fengcheng Li
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- The Children's Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310052, China
| | - Hanyu Zhang
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Jia Xue
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Hui Ma
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Qingwei Zhao
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Lushan Yu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Su Zeng
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Department of Clinical Pharmacy, The First Affiliated Hospital, Zhejiang University School of Medicine, National Key Laboratory of Advanced Drug Delivery and Release Systems, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, Zhou Y, Wang S, Qiu Y, Chen Y, Zhu F. TTD: Therapeutic Target Database describing target druggability information. Nucleic Acids Res 2024; 52:D1465-D1477. [PMID: 37713619 PMCID: PMC10767903 DOI: 10.1093/nar/gkad751] [Citation(s) in RCA: 56] [Impact Index Per Article: 56.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/31/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
Target discovery is one of the essential steps in modern drug development, and the identification of promising targets is fundamental for developing first-in-class drug. A variety of methods have emerged for target assessment based on druggability analysis, which refers to the likelihood of a target being effectively modulated by drug-like agents. In the therapeutic target database (TTD), nine categories of established druggability characteristics were thus collected for 426 successful, 1014 clinical trial, 212 preclinical/patented, and 1479 literature-reported targets via systematic review. These characteristic categories were classified into three distinct perspectives: molecular interaction/regulation, human system profile and cell-based expression variation. With the rapid progression of technology and concerted effort in drug discovery, TTD and other databases were highly expected to facilitate the explorations of druggability characteristics for the discovery and validation of innovative drug target. TTD is now freely accessible at: https://idrblab.org/ttd/.
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Affiliation(s)
- Ying Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Yintao Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Donghai Zhao
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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22
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Zhang Y, Zhou Y, Zhou Y, Yu X, Shen X, Hong Y, Zhang Y, Wang S, Mou M, Zhang J, Tao L, Gao J, Qiu Y, Chen Y, Zhu F. TheMarker: a comprehensive database of therapeutic biomarkers. Nucleic Acids Res 2024; 52:D1450-D1464. [PMID: 37850638 PMCID: PMC10767989 DOI: 10.1093/nar/gkad862] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/21/2023] [Accepted: 09/29/2023] [Indexed: 10/19/2023] Open
Abstract
Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.
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Affiliation(s)
- Yintao Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Ying Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuan Zhou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyuan Yu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Xinyi Shen
- Department of Environmental Health Sciences, Yale School of Public Health, Yale University, New Haven 06510, USA
| | - Yanfeng Hong
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Yuxin Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Shanshan Wang
- Qian Xuesen Collaborative Research Center of Astrochemistry and Space Life Sciences, Institute of Drug Discovery Technology, Ningbo University, Ningbo 315211, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Jinsong Zhang
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lin Tao
- Key Laboratory of Elemene Class Anti-Cancer Chinese Medicines, School of Pharmacy, Hangzhou Normal University, Hangzhou 311121, China
| | - Jianqing Gao
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yunqing Qiu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- National Key Laboratory of Diagnosis and Treatment of Severe Infectious Disease, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Zhejiang Provincial Key Laboratory for Drug Clinical Research and Evaluation, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310000, China
| | - Yuzong Chen
- State Key Laboratory of Chemical Oncogenomics, Key Laboratory of Chemical Biology, The Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, China
- Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518000, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The First Affiliated Hospital, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Shen L, Sun X, Chen Z, Guo Y, Shen Z, Song Y, Xin W, Ding H, Ma X, Xu W, Zhou W, Che J, Tan L, Chen L, Chen S, Dong X, Fang L, Zhu F. ADCdb: the database of antibody-drug conjugates. Nucleic Acids Res 2024; 52:D1097-D1109. [PMID: 37831118 PMCID: PMC10768060 DOI: 10.1093/nar/gkad831] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/07/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.
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Affiliation(s)
- Liteng Shen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Xiuna Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
| | - Zhen Chen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yu Guo
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Zheyuan Shen
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Yi Song
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Wenxiu Xin
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
| | - Haiying Ding
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
| | - Xinyue Ma
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Weiben Xu
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Wanying Zhou
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Jinxin Che
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
| | - Lili Tan
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Liangsheng Chen
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
| | - Siqi Chen
- School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Xiaowu Dong
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
| | - Luo Fang
- Department of Pharmacy, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou 310005, China
- Postgraduate Training Base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou 310022, China
- College of Pharmaceutical Science, Zhejiang University of Technology, Hangzhou 310014, China
- School of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou 310053, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou 330110, China
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Wang J, Zhang L, Sun J, Yang X, Wu W, Chen W, Zhao Q. Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints. Methods 2024; 221:18-26. [PMID: 38040204 DOI: 10.1016/j.ymeth.2023.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 11/14/2023] [Accepted: 11/25/2023] [Indexed: 12/03/2023] Open
Abstract
Drug-induced liver injury (DILI) is a significant issue in drug development and clinical treatment due to its potential to cause liver dysfunction or damage, which, in severe cases, can lead to liver failure or even fatality. DILI has numerous pathogenic factors, many of which remain incompletely understood. Consequently, it is imperative to devise methodologies and tools for anticipatory assessment of DILI risk in the initial phases of drug development. In this study, we present DMFPGA, a novel deep learning predictive model designed to predict DILI. To provide a comprehensive description of molecular properties, we employ a multi-head graph attention mechanism to extract features from the molecular graphs, representing characteristics at the level of compound nodes. Additionally, we combine multiple fingerprints of molecules to capture features at the molecular level of compounds. The fusion of molecular fingerprints and graph features can more fully express the properties of compounds. Subsequently, we employ a fully connected neural network to classify compounds as either DILI-positive or DILI-negative. To rigorously evaluate DMFPGA's performance, we conduct a 5-fold cross-validation experiment. The obtained results demonstrate the superiority of our method over four existing state-of-the-art computational approaches, exhibiting an average AUC of 0.935 and an average ACC of 0.934. We believe that DMFPGA is helpful for early-stage DILI prediction and assessment in drug development.
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Affiliation(s)
- Jifeng Wang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Li Zhang
- School of Life Science, Liaoning University, Shenyang 110036, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi 276000, China
| | - Xin Yang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Wei Wu
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Wei Chen
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China.
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China.
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Zhou Q, Wang Y, Xin C, Wei X, Yao Y, Xia L. Identification of telomere-associated gene signatures to predict prognosis and drug sensitivity in glioma. Comput Biol Med 2024; 168:107750. [PMID: 38029531 DOI: 10.1016/j.compbiomed.2023.107750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 11/11/2023] [Accepted: 11/20/2023] [Indexed: 12/01/2023]
Abstract
OBJECTIVE Gliomas are a heterogeneous group of brain tumors with distinct biological and clinical properties, leading to significant mortality and morbidity. Emerging evidence shows telomere maintenance has implicated in glioma susceptibility and prognosis. In this study, we comprehensively analyzed gene signatures related to telomere maintenance in glioma and their predictive values for predicting the prognosis and drug sensitivity in glioma. METHODS We initially identified telomere-related genes differentially expressed between low-grade glioma (LGG) and glioblastoma (GBM) and accordingly developed a risk model by univariate and multivariate Cox analysis to assess the expressions of telomere-related genes across the risk groups. Finally, to assess these genes in immune function the anti-tumor medications often used in the clinical treatment of glioma, we computed immune cell infiltration analysis and drug sensitivity analysis. RESULTS The consensus clustering analysis identified 20 telomere-related genes which split LGG patients into two distinct subtypes. The patient survival, the expressions of key telomere-related DEGs, and immune cell infiltration significantly differed between Cluster 1 and Cluster 2. The LASSO risk model [riskScore=(0.086)*HOXA7+(0.242)*WEE1+(0.247)*IGF2BP3+(0.052)*DUSP10] showed significant differences regarding the 1-, 3-, 5-year overall survival, immune cell infiltration, and drug sensitivity between high- and low-risk groups. The predictive nomogram constructed to quantify the survival probability of each sample at 1, 3, and 5 years was consistent with the actual patient survival. CONCLUSION Our comprehensive characterization of telomere-associated gene signatures in glioma reveals their possible roles in the development, tumor microenvironment, and prognosis. The study provides some suggestive relationships between four telomere-related genes (HOXA7, WEE1, IGF2BP3, and DUSP10) and glioma prognosis.
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Affiliation(s)
- Qingqing Zhou
- Department of Neurosurgery, The First Affiliated Hospital of Yangtze University, Jingzhou First People's Hospital, Jingzhou, 434000, People's Republic of China
| | - Yamei Wang
- Department of Neurology, The First Affiliated Hospital of Yangtze University, Jingzhou First People's Hospital, Jingzhou, 434000, People's Republic of China
| | - Chenqi Xin
- Department of Scientific Research, The First Affiliated Hospital of Yangtze University, Jingzhou First People's Hospital, Jingzhou, 434000, People's Republic of China
| | - XiaoMing Wei
- Department of Neurosurgery, The First Affiliated Hospital of Yangtze University, Jingzhou First People's Hospital, Jingzhou, 434000, People's Republic of China
| | - Yuan Yao
- Department of Neurosurgery, The First Affiliated Hospital of Yangtze University, Jingzhou First People's Hospital, Jingzhou, 434000, People's Republic of China.
| | - Liang Xia
- Department of Neurosurgery, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital) Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, People's Republic of China.
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26
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Gong Y, Ding W, Wang P, Wu Q, Yao X, Yang Q. Evaluating Machine Learning Methods of Analyzing Multiclass Metabolomics. J Chem Inf Model 2023; 63:7628-7641. [PMID: 38079572 DOI: 10.1021/acs.jcim.3c01525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2023]
Abstract
Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.
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Affiliation(s)
- Yaguo Gong
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Wei Ding
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Panpan Wang
- College of Chemistry and Pharmaceutical Engineering, Huanghuai University, Zhumadian 463000, China
| | - Qibiao Wu
- State Key Laboratory of Quality Research in Chinese Medicine, School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Xiaojun Yao
- Centre for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China
| | - Qingxia Yang
- Zhejiang Provincial Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Fu M, He R, Zhang Z, Ma F, Shen L, Zhang Y, Duan M, Zhang Y, Wang Y, Zhu L, He J. Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome. Sci Rep 2023; 13:20535. [PMID: 37996510 PMCID: PMC10667512 DOI: 10.1038/s41598-023-47783-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 11/18/2023] [Indexed: 11/25/2023] Open
Abstract
A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.
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Affiliation(s)
- Meijiao Fu
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Ruhua He
- Department of Cardiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Zhihan Zhang
- Department of Cardiology, Hanzhong Central Hospital, Hanzhong, 723200, Shanxi, China
| | - Fuqing Ma
- Department of Cardiology, The Fifth People's Hospital of Ningxia, Shizuishan, 753000, Ningxia, China
| | - Libo Shen
- Center for Cardiovascular Diseases, People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, 750002, Ningxia, China
| | - Yu Zhang
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Mingyu Duan
- Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Yameng Zhang
- Department of Cardiology, The Second Affiliated Hospital of Henan University of Science and Technology, Luoyang, 471000, Henan, China
| | - Yifan Wang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China
| | - Li Zhu
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
| | - Jun He
- Department of Cardiology, General Hospital of Ningxia Medical University, Yinchuan, 750004, Ningxia, China.
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Wu Z, Wang Z, Wu H, Zheng N, Huang D, Huang Z, Han H, Bao J, Xu H, Zhang R, Du Z, Wu D. The pan-cancer multi-omics landscape of key genes of sialylation combined with RNA-sequencing validation. Comput Biol Med 2023; 166:107556. [PMID: 37801920 DOI: 10.1016/j.compbiomed.2023.107556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 09/12/2023] [Accepted: 09/28/2023] [Indexed: 10/08/2023]
Abstract
BACKGROUND Sialylation, the process of salivary acid glycan synthesis, plays a pivotal function in tumor growth, immune escape, tumor metastasis, and resistance to drugs. However, the association between sialylation and prognosis, tumor microenvironment (TME), and treatment response in a variety of cancers remains unclear. METHODS A comprehensive survey of the expression profile, prognostic value, and genetic and epigenetic alterations of sialylation-related genes was performed in pan-cancer. Subsequently, the single-sample gene set enrichment analysis (ssGSEA) algorithm was used to compute sialylation pathway scores in pan-cancer. Correlations of sialylation pathway scores with clinical features, prognosis, and TME were evaluated using multiple algorithms. Finally, the efficacy of the sialylation pathway score in determining the effect of immunotherapy was evaluated. The expression of sialylation-related genes were verified by RNA-sequencing. RESULTS Significant differences were observed in sialylation-related genes expression between tumors and adjacent normal tissues for most cancer types. Sialylation pathway scores differed according to the type of tumor, where the poor prognosis was correlated with high sialylation pathway scores in uveal melanoma (UVM) and pancreatic adenocarcinoma (PAAD). In addition, sialylation pathway scores were positively associated with the ImmuneScore, StromalScore and immune-related pathways. Moreover, the level of immune cells infiltration was higher in tumors with higher sialylation pathway scores. Finally, patients with high sialylation pathway scores were more sensitive to immunotherapy. CONCLUSION Sialylation-related genes are essential in pan-cancer. The sialylation pathway score may be used as a biomarker in oncology patients.
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Affiliation(s)
- Zhixuan Wu
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Ziqiong Wang
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Haodong Wu
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Na Zheng
- Department of Hernia and Abdominal Wall Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Dongdong Huang
- Department of Hernia and Abdominal Wall Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Zhipeng Huang
- Department of Hernia and Abdominal Wall Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Hui Han
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Jingxia Bao
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Hongjie Xu
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China
| | - Rongrong Zhang
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China.
| | - Zhou Du
- Department of Hernia and Abdominal Wall Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China.
| | - Dazhou Wu
- Department of Hernia and Abdominal Wall Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325015, Zhejiang, People's Republic of China.
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Chen T, Yao L, Liu W, Luan J, Wang Y, Yang C, Zhou X, Ji C, Guo X, Wang Z, Song N. Epididymal segment-specific miRNA and mRNA regulatory network at the single cell level. Cell Cycle 2023; 22:2194-2209. [PMID: 37982230 PMCID: PMC10732646 DOI: 10.1080/15384101.2023.2280170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 11/01/2023] [Indexed: 11/21/2023] Open
Abstract
Spermatozoa released from the testis cannot fertilize an egg before becoming mature and motile in the epididymis. Based on three bulk and one single-cell RNA-seq (scRNA-seq) data series, we compared mRNA or miRNA expression between epididymal segment-specific samples and the other samples. Hereby, we identified 570 differentially expressed mRNAs (DE-mRNAs) and 23 differentially expressed miRNAs (DE-miRNAs) in the caput, 175 DE-mRNAs and 15 DE-miRNAs in the corpus, 946 DE-mRNAs and 12 DE-miRNAs in the cauda. In accordance with respective DE-miRNAs, we predicted upstream transcription factors (TFs) and downstream target genes. Subsequently, we intersected target genes of respective DE-miRNAs with corresponding DE-mRNAs, thereby obtaining 127 upregulated genes in the caput and 92 upregulated genes in cauda. Enriched upregulated pathways included cell motility-related pathways for the caput, smooth muscle-related pathways for the corpus, and immune-associated pathways for the cauda. Protein-protein interaction (PPI) network was constructed to extract key module for the caput and cauda, followed by identifying hub genes through cytohubba. Epididymis tissues from six mice were applied to validate hub genes expression using qRT-PCR, and 7 of the 10 genes displayed identical expression trends in mice caput/cauda. These hub genes were found to be predominantly distributed in spermatozoa using scRNA-seq data. In addition, target genes of DE-miRNAs were intersected with genes in the PPI network for each segment. Subsequently, the miRNA and mRNA regulatory networks for the caput and cauda were constructed. Conclusively, we uncover segment-specific miRNA-mRNA regulatory network, upstream TFs, and downstream pathways of the human epididymis, warranting further investigation into epididymal segment-specific functions.
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Affiliation(s)
- Tong Chen
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Liangyu Yao
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Wen Liu
- Center for Reproductive Medicine, Cheeloo College of Medicine, Shandong University, Jinan, China
- Key Laboratory of Reproductive Endocrinology of Ministry of Education, National Research Center for Assisted Reproductive Technology and Reproductive Genetics, Shandong Key Laboratory of Reproductive Medicine, Shandong Provincial Clinical Research Center for Reproductive Health, Jinan, China
| | - Jiaochen Luan
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yichun Wang
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chao Yang
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xiang Zhou
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Chengjian Ji
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Xuejiang Guo
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, China
| | - Zengjun Wang
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Ninghong Song
- Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Tang X, Zhan Y, Yang B, Du B, Huang J. Exploring the mechanism of Semen Strychni in treating amyotrophic lateral sclerosis based on network pharmacology. Medicine (Baltimore) 2023; 102:e35101. [PMID: 37682161 PMCID: PMC10489316 DOI: 10.1097/md.0000000000035101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 08/16/2023] [Indexed: 09/09/2023] Open
Abstract
Semen Strychni (SS), known as an agonist of central nervous system, is a traditional herb widely used in treating amyotrophic lateral sclerosis (ALS) in small doses to relieve muscle weakness and improve muscle strength. However, the potential mechanisms and the main components of SS in treating ALS remain unclear. To explore the underlying mechanism of SS in treating ALS based on network pharmacology and molecular docking. The active components of SS were obtained using TCMSP, Herb, ETCM, and BATMAN-TCM. The targets of SS were gained from PharmMapper. The targets of ALS were searched on Genecards, Drugbank, DisGeNET, OMIM, TTD and GEO database. After obtaining the coincidence targets, we submitted them to the STRING database to build a protein-protein interaction network. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed subsequently. The active components and targets were further investigated using molecular docking technology. 395 targets of SS and 1925 targets of ALS were obtained with 125 common targets. The protein-protein interaction analysis indicated that SRC, AKT1, MAPK1, EGFR, and HSP90AA1 received the higher degree value and were considered the central genes. The Ras, PI3K-Akt, and MAPK signaling pathway could be involved in the treatment of ALS. Brucine-N-oxide obtained the lowest binding energy in molecular docking. This study explored the mechanism of SS in the treatment of ALS and provides a new perspective for future study. However, further experimental studies are needed to validate the therapeutic effect.
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Affiliation(s)
- Xiaohui Tang
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Yingshi Zhan
- The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Biying Yang
- Department of Neurology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Baoxin Du
- Department of Neurology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
| | - Jingyan Huang
- Department of Neurology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, China
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Liang S, Zhao Y, Jin J, Qiao J, Wang D, Wang Y, Wei L. Rm-LR: A long-range-based deep learning model for predicting multiple types of RNA modifications. Comput Biol Med 2023; 164:107238. [PMID: 37515874 DOI: 10.1016/j.compbiomed.2023.107238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/16/2023] [Accepted: 07/07/2023] [Indexed: 07/31/2023]
Abstract
Recent research has highlighted the pivotal role of RNA post-transcriptional modifications in the regulation of RNA expression and function. Accurate identification of RNA modification sites is important for understanding RNA function. In this study, we propose a novel RNA modification prediction method, namely Rm-LR, which leverages a long-range-based deep learning approach to accurately predict multiple types of RNA modifications using RNA sequences only. Rm-LR incorporates two large-scale RNA language pre-trained models to capture discriminative sequential information and learn local important features, which are subsequently integrated through a bilinear attention network. Rm-LR supports a total of ten RNA modification types (m6A, m1A, m5C, m5U, m6Am, Ψ, Am, Cm, Gm, and Um) and significantly outperforms the state-of-the-art methods in terms of predictive capability on benchmark datasets. Experimental results show the effectiveness and superiority of Rm-LR in prediction of various RNA modifications, demonstrating the strong adaptability and robustness of our proposed model. We demonstrate that RNA language pretrained models enable to learn dense biological sequential representations from large-scale long-range RNA corpus, and meanwhile enhance the interpretability of the models. This work contributes to the development of accurate and reliable computational models for RNA modification prediction, providing insights into the complex landscape of RNA modifications.
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Affiliation(s)
- Sirui Liang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yanxi Zhao
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Junru Jin
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Jianbo Qiao
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Ding Wang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Yu Wang
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China
| | - Leyi Wei
- School of Software, Shandong University, Jinan, 250101, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, 250101, China.
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Ollà I, Pardiñas AF, Parras A, Hernández IH, Santos-Galindo M, Picó S, Callado LF, Elorza A, Rodríguez-López C, Fernández-Miranda G, Belloc E, Walters JTR, O'Donovan MC, Méndez R, Toma C, Meana JJ, Owen MJ, Lucas JJ. Pathogenic Mis-splicing of CPEB4 in Schizophrenia. Biol Psychiatry 2023; 94:341-351. [PMID: 36958377 DOI: 10.1016/j.biopsych.2023.03.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/16/2023] [Accepted: 03/15/2023] [Indexed: 03/25/2023]
Abstract
BACKGROUND Schizophrenia (SCZ) is caused by an interplay of polygenic risk and environmental factors, which may alter regulators of gene expression leading to pathogenic misexpression of SCZ risk genes. The CPEB family of RNA-binding proteins (CPEB1-4) regulates translation of target RNAs (approximately 40% of overall genes). We previously identified CPEB4 as a key dysregulated translational regulator in autism spectrum disorder (ASD) because its neuronal-specific microexon (exon 4) is mis-spliced in ASD brains, causing underexpression of numerous ASD risk genes. The genetic factors and pathogenic mechanisms shared between SCZ and ASD led us to hypothesize CPEB4 mis-splicing in SCZ leading to underexpression of multiple SCZ-related genes. METHODS We performed MAGMA-enrichment analysis on Psychiatric Genomics Consortium genome-wide association study data and analyzed RNA sequencing data from the PsychENCODE Consortium. Reverse transcriptase polymerase chain reaction and Western blot were performed on postmortem brain tissue, and the presence/absence of antipsychotics was assessed through toxicological analysis. Finally, mice with mild overexpression of exon 4-lacking CPEB4 (CPEB4Δ4) were generated and analyzed biochemically and behaviorally. RESULTS First, we found enrichment of SCZ-associated genes for CPEB4-binder transcripts. We also found decreased usage of CPEB4 microexon in SCZ probands, which was correlated with decreased protein levels of CPEB4-target SCZ-associated genes only in antipsychotic-free individuals. Interestingly, differentially expressed genes fit those reported for SCZ, specifically in the SCZ probands with decreased CPEB4-microexon inclusion. Finally, we demonstrated that mice with mild overexpression of CPEB4Δ4 showed decreased protein levels of CPEB4-target SCZ genes and SCZ-linked behaviors. CONCLUSIONS We identified aberrant CPEB4 splicing and downstream misexpression of SCZ risk genes as a novel etiological mechanism in SCZ.
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Affiliation(s)
- Ivana Ollà
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Networking Research Center on Neurodegenerative Diseases (Centro de Investigación Biomédica en Red|Enfermedades Neurodegenerativas), Instituto de Salud Carlos III, Madrid, Spain
| | - Antonio F Pardiñas
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Alberto Parras
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Networking Research Center on Neurodegenerative Diseases (Centro de Investigación Biomédica en Red|Enfermedades Neurodegenerativas), Instituto de Salud Carlos III, Madrid, Spain
| | - Ivó H Hernández
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Networking Research Center on Neurodegenerative Diseases (Centro de Investigación Biomédica en Red|Enfermedades Neurodegenerativas), Instituto de Salud Carlos III, Madrid, Spain
| | - María Santos-Galindo
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Networking Research Center on Neurodegenerative Diseases (Centro de Investigación Biomédica en Red|Enfermedades Neurodegenerativas), Instituto de Salud Carlos III, Madrid, Spain
| | - Sara Picó
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Networking Research Center on Neurodegenerative Diseases (Centro de Investigación Biomédica en Red|Enfermedades Neurodegenerativas), Instituto de Salud Carlos III, Madrid, Spain
| | - Luis F Callado
- Department of Pharmacology, University of the Basque Country, UPV/EHU, Biocruces Bizkaia Health Research Institute and Networking Research Center on Mental Health (Centro de investigación Biomédica en Red | Salud Mental), Leioa, Bizkaia, Spain
| | - Ainara Elorza
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Networking Research Center on Neurodegenerative Diseases (Centro de Investigación Biomédica en Red|Enfermedades Neurodegenerativas), Instituto de Salud Carlos III, Madrid, Spain
| | - Claudia Rodríguez-López
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Networking Research Center on Neurodegenerative Diseases (Centro de Investigación Biomédica en Red|Enfermedades Neurodegenerativas), Instituto de Salud Carlos III, Madrid, Spain
| | - Gonzalo Fernández-Miranda
- Institute for Research in Biomedicine, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Eulàlia Belloc
- Institute for Research in Biomedicine, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - James T R Walters
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Michael C O'Donovan
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - Raúl Méndez
- Institute for Research in Biomedicine, Barcelona Institute of Science and Technology, Barcelona, Spain; Institució Catalana de RIcerca i Estudis Avançats, Barcelona, Spain
| | - Claudio Toma
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Neuroscience Research Australia, Sydney, New South Wales, Australia; School of Medical Sciences, University of New South Wales, Sydney, New South Wales, Australia
| | - J Javier Meana
- Department of Pharmacology, University of the Basque Country, UPV/EHU, Biocruces Bizkaia Health Research Institute and Networking Research Center on Mental Health (Centro de investigación Biomédica en Red | Salud Mental), Leioa, Bizkaia, Spain
| | - Michael J Owen
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, UK
| | - José J Lucas
- Center for Molecular Biology "Severo Ochoa," Spanish National Research Council/Autonomous University of Madrid, Madrid, Spain; Networking Research Center on Neurodegenerative Diseases (Centro de Investigación Biomédica en Red|Enfermedades Neurodegenerativas), Instituto de Salud Carlos III, Madrid, Spain.
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Xu PH, Chen S, Wang Y, Jin S, Wang J, Ye D, Zhu X, Shen Y. FGFR3 mutation characterization identifies prognostic and immune-related gene signatures in bladder cancer. Comput Biol Med 2023; 162:106976. [PMID: 37301098 DOI: 10.1016/j.compbiomed.2023.106976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 03/31/2023] [Accepted: 04/22/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Immunotherapy and FGFR3-targeted therapy play an important role in the management of locally advanced and metastatic bladder cancer (BLCA). Previous studies indicated that FGFR3 mutation (mFGFR3) may be involved in the alterations of immune infiltration, which may affect the priority or combination of these two treatment regimes. However, the specific impact of mFGFR3 on the immunity and how FGFR3 regulates the immune response in BLCA to affect prognosis remain unclear. In this study, we aimed to elucidate the immune landscape associated with mFGFR3 status in BLCA, screen immune-related gene signatures with prognostic value, and construct and validate a prognostic model. METHODS ESTIMATE and TIMER were used to assess the immune infiltration within tumors in the TCGA BLCA cohort based on transcriptome data. Further, the mFGFR3 status and mRNA expression profiles were analyzed to identify immune-related genes that were differentially expressed between patients with BLCA with wild-type FGFR3 or mFGFR3 in the TCGA training cohort. An FGFR3-related immune prognostic score (FIPS) model was established in the TCGA training cohort. Furthermore, we validated the prognostic value of FIPS with microarray data in the GEO database and tissue microarray from our center. Multiple fluorescence immunohistochemical analysis was performed to confirm the relationship between FIPS and immune infiltration. RESULTS mFGFR3 resulted in differential immunity in BLCA. In total, 359 immune-related biological processes were enriched in the wild-type FGFR3 group, whereas none were enriched in the mFGFR3 group. FIPS could effectively distinguish high-risk patients with poor prognosis from low-risk patients. The high-risk group was characterized by a higher abundance of neutrophils; macrophages; and follicular helper, CD4, and CD8 T-cells than the low-risk group. In addition, the high-risk group exhibited higher expression of PD-L1, PD-1, CTLA-4, LAG-3, and TIM-3 than the low-risk group, indicating an immune-infiltrated but functionally suppressed immune microenvironment. Furthermore, patients in the high-risk group exhibited a lower mutation rate of FGFR3 than those in the low-risk group. CONCLUSIONS FIPS effectively predicted survival in BLCA. Patients with different FIPS exhibited diverse immune infiltration and mFGFR3 status. FIPS might be a promising tool for selecting targeted therapy and immunotherapy for patients with BLCA.
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Affiliation(s)
- Pei-Hang Xu
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Siyuan Chen
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Yanhao Wang
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shengming Jin
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jun Wang
- Department of Urology, Sun Yat-sen University Cancer Center, Guangzhou, China; State Key Laboratory of Oncology in Southern China, Guangzhou, China; State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
| | - Dingwei Ye
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Xiaodong Zhu
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China; Department of Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
| | - Yijun Shen
- Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
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Luo X, Wang Y, Zou Q, Xu L. Recall DNA methylation levels at low coverage sites using a CNN model in WGBS. PLoS Comput Biol 2023; 19:e1011205. [PMID: 37315069 DOI: 10.1371/journal.pcbi.1011205] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 05/22/2023] [Indexed: 06/16/2023] Open
Abstract
DNA methylation is an important regulator of gene transcription. WGBS is the gold-standard approach for base-pair resolution quantitative of DNA methylation. It requires high sequencing depth. Many CpG sites with insufficient coverage in the WGBS data, resulting in inaccurate DNA methylation levels of individual sites. Many state-of-arts computation methods were proposed to predict the missing value. However, many methods required either other omics datasets or other cross-sample data. And most of them only predicted the state of DNA methylation. In this study, we proposed the RcWGBS, which can impute the missing (or low coverage) values from the DNA methylation levels on the adjacent sides. Deep learning techniques were employed for the accurate prediction. The WGBS datasets of H1-hESC and GM12878 were down-sampled. The average difference between the DNA methylation level at 12× depth predicted by RcWGBS and that at >50× depth in the H1-hESC and GM2878 cells are less than 0.03 and 0.01, respectively. RcWGBS performed better than METHimpute even though the sequencing depth was as low as 12×. Our work would help to process methylation data of low sequencing depth. It is beneficial for researchers to save sequencing costs and improve data utilization through computational methods.
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Affiliation(s)
- Ximei Luo
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Yansu Wang
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou, Zhejiang, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, Guangdong, China
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Vahabzadeh V, Moattar MH. Robust microarray data feature selection using a correntropy based distance metric learning approach. Comput Biol Med 2023; 161:107056. [PMID: 37235945 DOI: 10.1016/j.compbiomed.2023.107056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 04/18/2023] [Accepted: 05/20/2023] [Indexed: 05/28/2023]
Abstract
Classification of high-dimensional microarray data is a challenge in bioinformatics and genetic data processing. One of the challenging issues of feature selection is the presence of outliers. The Euclidean distance metric is sensitive to outliers. In this study, a distance metric learning based feature selection approach that uses the correntropy function as the discrimination metric is proposed. For this purpose, the metric learning problem is formulated as an optimization problem and solved using the Lagrange method. The output of the approach signifies the most important and robust features. After feature selection, different classification methods such as SVM, decision trees, and NN classifiers are used to investigate the classification accuracy of the proposed method as well as precision, recall, and F-measure. Experiments are carried out on 13 high-dimensional datasets and show that the proposed method outperforms the previous models in terms of accuracy and robustness.
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Affiliation(s)
- Venus Vahabzadeh
- Department of Software Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
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36
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Sadaqat M, Qasim M, Tahir Ul Qamar M, Masoud MS, Ashfaq UA, Noor F, Fatima K, Allemailem KS, Alrumaihi F, Almatroudi A. Advanced network pharmacology study reveals multi-pathway and multi-gene regulatory molecular mechanism of Bacopa monnieri in liver cancer based on data mining, molecular modeling, and microarray data analysis. Comput Biol Med 2023; 161:107059. [PMID: 37244150 DOI: 10.1016/j.compbiomed.2023.107059] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 05/05/2023] [Accepted: 05/20/2023] [Indexed: 05/29/2023]
Abstract
Liver cancer is a malignant tumor that grows on the surface or inside the liver. The leading cause is a viral infection with hepatitis B or C virus. Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer. A list of studies evidences the therapeutic efficacy of Bacopa monnieri against liver cancer, but the precise molecular mechanism is yet to be discovered. This study combines data mining, network pharmacology, and molecular docking analysis to potentially revolutionize liver cancer treatment by identifying effective phytochemicals. Initially, the information on active constituents of B. monnieri and target genes of both liver cancer and B. monnieri were retrieved from literature as well as from publicly available databases. Based on the matching results between B. monnieri potential targets and liver cancer targets, the protein-protein interaction (PPI) network was constructed using the STRING database and imported into Cytoscape for screening of hub genes based on their degree of connectivity. Later, the interactions network between compounds and overlapping genes was constructed using Cytoscape software to analyze the network pharmacological prospective effects of B. monnieri on liver cancer. Gene Ontology (GO) and KEGG pathway analysis of hub genes revealed that these genes are involved in the cancer-related pathway. Lastly, the expression level of core targets was analyzed using microarray data (GSE39791, GSE76427, GSE22058, GSE87630, and GSE112790). Further, the GEPIA server and PyRx software were used for survival and molecular docking analysis, respectively. In summary, we proposed that quercetin, luteolin, apigenin, catechin, epicatechin, stigmasterol, beta-sitosterol, celastrol, and betulic acid inhibit tumor growth by affecting tumor protein 53 (TP53), interleukin 6 (IL6), RAC-alpha serine/threonine protein kinases 1 (AKT1), caspase-3 (CASP3), tumor necrosis factor (TNF), jun proto-oncogene (JUN), heat shot protein 90 AA1 (HSP90AA1), vascular endothelial growth factor A (VEGFA), epidermal growth factor receptor (EGFR), and SRC proto-oncogene (SRC). Through, microarray data analysis, the expression level of JUN and IL6 were found to be upregulated while the expression level of HSP90AA1 was found to be downregulated. Kaplan-Meier survival analysis indicated that HSP90AA1 and JUN are promising candidate genes that can serve as diagnostic and prognostic biomarkers for liver cancer. Moreover, the molecular docking and molecular dynamic simulation of 60ns well complemented the binding affinity of the compound and revealed strong stability of predicted compounds at the docked site. Calculation of binding free energies using MMPBSA and MMGBSA validated the strong binding affinity between the compound and binding pockets of HSP90AA1 and JUN. Despite that, in vivo and in vitro studies are mandatory to unveil pharmacokinetics and biosafety profiles to completely track the candidature status of B. monnieri in liver cancer.
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Affiliation(s)
- Muhammad Sadaqat
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Muhammad Qasim
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Muhammad Tahir Ul Qamar
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Muhammad Shareef Masoud
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Usman Ali Ashfaq
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Fatima Noor
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Kinza Fatima
- Department of Bioinformatics and Biotechnology, Government College University Faisalabad (GCUF), Faisalabad, 38000, Pakistan.
| | - Khaled S Allemailem
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia.
| | - Faris Alrumaihi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia.
| | - Ahmad Almatroudi
- Department of Medical Laboratories, College of Applied Medical Sciences, Qassim University, Buraydah, 51452, Saudi Arabia.
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Zhang Y, Sun H, Lian X, Tang J, Zhu F. ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2207061. [PMID: 36950745 DOI: 10.1002/advs.202207061] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 02/13/2023] [Indexed: 05/27/2023]
Abstract
ANPELA is widely used for quantifying traditional bulk proteomic data. Recently, there is a clear shift from bulk proteomics to the single-cell ones (SCP), for which powerful cytometry techniques demonstrate the fantastic capacity of capturing cellular heterogeneity that is completely overlooked by traditional bulk profiling. However, the in-depth and high-quality quantification of SCP data is still challenging and severely affected by the large numbers of quantification workflows and extreme performance dependence on the studied datasets. In other words, the proper selection of well-performing workflow(s) for any studied dataset is elusory, and it is urgently needed to have a significantly enhanced and accelerated tool to address this issue. However, no such tool is developed yet. Herein, ANPELA is therefore updated to its 2.0 version (https://idrblab.org/anpela/), which is unique in providing the most comprehensive set of quantification alternatives (>1000 workflows) among all existing tools, enabling systematic performance evaluation from multiple perspectives based on machine learning, and identifying the optimal workflow(s) using overall performance ranking together with the parallel computation. Extensive validation on different benchmark datasets and representative application scenarios suggest the great application potential of ANPELA in current SCP research for gaining more accurate and reliable biological insights.
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Affiliation(s)
- Ying Zhang
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Xichen Lian
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
| | - Jing Tang
- Department of Bioinformatics, Chongqing Medical University, Chongqing, 400016, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310058, China
- Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare, Hangzhou, 330110, China
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Wang H, Yao Z, Luo R, Liu J, Wang Z, Zhang G. LaCOme: Learning the latent convolutional patterns among transcriptomic features to improve classifications. Gene 2023; 862:147246. [PMID: 36736509 DOI: 10.1016/j.gene.2023.147246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 12/22/2022] [Accepted: 01/27/2023] [Indexed: 02/04/2023]
Abstract
OMIC is a novel approach that analyses entire genetic or molecular profiles in humans and other organisms. It involves identifying and quantifying biological molecules that contribute to a species' structure, function, and dynamics. Finding the secrets of OMIC is like deciphering the biochemical code, but building data-driven models to mine the hidden phenotypic trait information has been a research hotspot. Transcriptome analysis is a popular biological technology for characterizing living systems' overall health, including cells and tissues. Individual transcript expression levels are known to be correlated with those of other transcripts. Nevertheless, most computational studies do not fully exploit these inter-feature correlations. Differential expression analyses, for example, assume that the expression levels of the transcripts are independent. Thus, we propose extracting these inter-feature correlations using the convolutional neural network (CNN) and transforming the transcriptomic features into a new space of convolutional transcriptomic (LaCOme) features. On most transcriptomic datasets in use, a series of comprehensive experiments have demonstrated that engineered LaCOme features outperform the original transcriptomic features in classification performances. Based on experimental results, OMIC data from biological samples could be further enriched using CNN to enhance computational analysis results. Also, feature rough screening can be used to extract valuable information from OMIC, regardless of the algorithm used to select features. It may always be better to create a novel feature than to keep the original. Furthermore, we investigated the feasibility of the feature construction method through cross-validation and independent verification, hoping to develop a more efficient and effective method.
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Affiliation(s)
- Hongyu Wang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Software, Jilin University, Changchun, Jilin 130012, China
| | - Zhaomin Yao
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Renli Luo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China
| | - Jiahao Liu
- School of Mathematical Sciences, Chongqing Normal University, Chongqing 401331, China
| | - Zhiguo Wang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China.
| | - Guoxu Zhang
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China.
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39
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Nie X, Qin D, Zhou X, Duo H, Hao Y, Li B, Liang G. Clustering ensemble in scRNA-seq data analysis: Methods, applications and challenges. Comput Biol Med 2023; 159:106939. [PMID: 37075602 DOI: 10.1016/j.compbiomed.2023.106939] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 04/21/2023]
Abstract
With the rapid development of single-cell RNA-sequencing techniques, various computational methods and tools were proposed to analyze these high-throughput data, which led to an accelerated reveal of potential biological information. As one of the core steps of single-cell transcriptome data analysis, clustering plays a crucial role in identifying cell types and interpreting cellular heterogeneity. However, the results generated by different clustering methods showed distinguishing, and those unstable partitions can affect the accuracy of the analysis to a certain extent. To overcome this challenge and obtain more accurate results, currently clustering ensemble is frequently applied to cluster analysis of single-cell transcriptome datasets, and the results generated by all clustering ensembles are nearly more reliable than those from most of the single clustering partitions. In this review, we summarize applications and challenges of the clustering ensemble method in single-cell transcriptome data analysis, and provide constructive thoughts and references for researchers in this field.
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Affiliation(s)
- Xiner Nie
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China; College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Dan Qin
- Department of Biology, College of Science, Northeastern University, Boston, MA, 02115, USA
| | - Xinyi Zhou
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Hongrui Duo
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Youjin Hao
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China
| | - Bo Li
- College of Life Sciences, Chongqing Normal University, Chongqing, 400044, PR China.
| | - Guizhao Liang
- Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing, 400044, China.
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Zhou Y, Liu C, Zhang Z, Chen J, Zhao D, Li L, Tong M, Zhang G. Identification and validation of diagnostic biomarkers of coronary artery disease progression in type 1 diabetes via integrated computational and bioinformatics strategies. Comput Biol Med 2023; 159:106940. [PMID: 37075605 DOI: 10.1016/j.compbiomed.2023.106940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 04/03/2023] [Accepted: 04/14/2023] [Indexed: 04/21/2023]
Abstract
OBJECTIVE Our study aimed to identify early peripheral blood diagnostic biomarkers and elucidate the immune mechanisms of coronary artery disease (CAD) progression in patients with type 1 diabetes mellitus (T1DM). METHODS Three transcriptome datasets were retrieved from the Gene Expression Omnibus (GEO) database. Gene modules associated with T1DM were selected with weighted gene co-expression network analysis. Differentially expressed genes (DEGs) between CAD and acute myocardial infarction (AMI) peripheral blood tissues were identified using limma. Candidate biomarkers were selected with functional enrichment analysis, node gene selection from a constructed protein-protein interaction (PPI) network, and 3 machine learning algorithms. Candidate expression was compared, and the receiver operating characteristic curve (ROC) and nomogram were constructed. Immune cell infiltration was assessed with the CIBERSORT algorithm. RESULTS A total of 1283 genes comprising 2 modules were detected as the most associated with T1DM. In addition, 451 DEGs related to CAD progression were identified. Among them, 182 were common to both diseases and mainly enriched in immune and inflammatory response regulation. The PPI network yielded 30 top node genes, and 6 were selected using the 3 machine learning algorithms. Upon validation, 4 genes (TLR2, CLEC4D, IL1R2, and NLRC4) were recognized as diagnostic biomarkers with the area under the curve (AUC) > 0.7. All 4 genes were positively correlated with neutrophils in patients with AMI. CONCLUSION We identified 4 peripheral blood biomarkers and provided a nomogram for early diagnosing CAD progression to AMI in patients with T1DM. The biomarkers were positively associated with neutrophils, indicating potential therapeutic targets.
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Affiliation(s)
- Yufei Zhou
- Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Chunjiang Liu
- Department of General Surgery, Division of Vascular Surgery, Shaoxing People's Hospital, Shaoxing, 312000, China
| | - Zhongzheng Zhang
- Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, Anhui, 230000, China
| | - Jian Chen
- Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, Anhui, 230000, China
| | - Di Zhao
- Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Linnan Li
- Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Mingyue Tong
- Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, Anhui, 230000, China.
| | - Gang Zhang
- Department of Rehabilitation, The First Affiliated Hospital of Anhui Medical University, Anhui Public Health Clinical Center, Hefei, Anhui, 230000, China.
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Chatterjee D, Rahman MM, Saha AK, Siam MKS, Sharif Shohan MU. Transcriptomic analysis of esophageal cancer reveals hub genes and networks involved in cancer progression. Comput Biol Med 2023; 159:106944. [PMID: 37075603 DOI: 10.1016/j.compbiomed.2023.106944] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 04/09/2023] [Accepted: 04/14/2023] [Indexed: 04/21/2023]
Abstract
Esophageal carcinoma (ESCA) has a 5-year survival rate of fewer than 20%. The study aimed to identify new predictive biomarkers for ESCA through transcriptomics meta-analysis to address the problems of ineffective cancer therapy, lack of efficient diagnostic tools, and costly screening and contribute to developing more efficient cancer screening and treatments by identifying new marker genes. Nine GEO datasets of three kinds of esophageal carcinoma were analyzed, and 20 differentially expressed genes were detected in carcinogenic pathways. Network analysis revealed four hub genes, namely RAR Related Orphan Receptor A (RORA), lysine acetyltransferase 2B (KAT2B), Cell Division Cycle 25B (CDC25B), and Epithelial Cell Transforming 2 (ECT2). Overexpression of RORA, KAT2B, and ECT2 was identified with a bad prognosis. These hub genes modulate immune cell infiltration. These hub genes modulate immune cell infiltration. Although this research needs lab confirmation, we found interesting biomarkers in ESCA that may aid in diagnosis and treatment.
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Affiliation(s)
- Dipankor Chatterjee
- Department of Biochemistry and Molecular Biology, University of Dhaka, Dhaka, Bangladesh
| | - Md Mostafijur Rahman
- Department of Microbiology, Jashore University of Science and Technology, Bangladesh
| | - Anik Kumar Saha
- Institute of Food Science and Technology, Bangladesh Council of Scientific and Industrial Research, Dhaka, Bangladesh
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Fajarda O, Almeida JR, Duarte-Pereira S, Silva RM, Oliveira JL. Methodology to identify a gene expression signature by merging microarray datasets. Comput Biol Med 2023; 159:106867. [PMID: 37060770 DOI: 10.1016/j.compbiomed.2023.106867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/01/2023] [Accepted: 03/30/2023] [Indexed: 04/17/2023]
Abstract
A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.
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Affiliation(s)
- Olga Fajarda
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal.
| | - João Rafael Almeida
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Computation, University of A Coruña, A Coruña, Spain.
| | - Sara Duarte-Pereira
- DETI/IEETA, LASI, University of Aveiro, Aveiro, Portugal; Department of Medical Sciences and iBiMED-Institute of Biomedicine, University of Aveiro, Aveiro, Portugal.
| | - Raquel M Silva
- Universidade Católica Portuguesa, Faculty of Dental Medicine (FMD), Center for Interdisciplinary Research in Health (CIIS), Viseu, Portugal.
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Azim R, Wang S, Dipu SA, Islam N, Ala Muid MR, Elahe MF. A patient-specific functional module and path identification technique from RNA-seq data. Comput Biol Med 2023; 158:106871. [PMID: 37030265 DOI: 10.1016/j.compbiomed.2023.106871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 02/12/2023] [Accepted: 03/30/2023] [Indexed: 04/05/2023]
Abstract
With the advancement of new technologies, a huge amount of high dimensional data is being generated which is opening new opportunities and challenges to the study of cancer and diseases. In particular, distinguishing the patient-specific key components and modules which drive tumorigenesis is necessary to analyze. A complex disease generally does not initiate from the dysregulation of a single component but it is the result of the dysfunction of a group of components and networks which differs from patient to patient. However, a patient-specific network is required to understand the disease and its molecular mechanism. We address this requirement by constructing a patient-specific network by sample-specific network theory with integrating cancer-specific differentially expressed genes and elite genes. By elucidating patient-specific networks, it can identify the regulatory modules, driver genes as well as personalized disease networks which can lead to personalized drug design. This method can provide insight into how genes are associating with each other and characterized the patient-specific disease subtypes. The results show that this method can be beneficial for the detection of patient-specific differential modules and interaction between genes. Extensive analysis using existing literature, gene enrichment and survival analysis for three cancer types STAD, PAAD and LUAD shows the effectiveness of this method over other existing methods. In addition, this method can be useful for personalized therapeutics and drug design. This methodology is implemented in the R language and is available at https://github.com/riasatazim/PatientSpecificRNANetwork.
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Yang Q, Gong Y, Zhu F. Critical Assessment of the Biomarker Discovery and Classification Methods for Multiclass Metabolomics. Anal Chem 2023; 95:5542-5552. [PMID: 36944135 DOI: 10.1021/acs.analchem.2c04402] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2023]
Abstract
Multiclass metabolomics has been widely applied in clinical practice to understand pathophysiological processes involved in disease progression and diagnostic biomarkers of various disorders. In contrast to the binary problem, the multiclass classification problem is more difficult in terms of obtaining reliable and stable results due to the increase in the complexity of determining exact class decision boundaries. In particular, methods of biomarker discovery and classification have a significant effect on the multiclass model because different methods with significantly varied theories produce conflicting results even for the same dataset. However, a systematic assessment for selecting the most appropriate methods of biomarker discovery and classification for multiclass metabolomics is still lacking. Therefore, a comprehensive assessment is essential to measure the suitability of methods in multiclass classification models from multiple perspectives. In this study, five biomarker discovery methods and nine classification methods were assessed based on four benchmark datasets of multiclass metabolomics. The performance assessment of the biomarker discovery and classification methods was performed using three evaluation criteria: assessment a (cluster analysis of sample grouping), assessment b (biomarker consistency in multiple subgroups), and assessment c (accuracy in the classification model). As a result, 13 combining strategies with superior performance were selected under multiple criteria based on these benchmark datasets. In conclusion, superior strategies that performed consistently well are suggested for the discovery of biomarkers and the construction of a classification model for multiclass metabolomics.
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Affiliation(s)
- Qingxia Yang
- Department of Bioinformatics, School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yaguo Gong
- School of Pharmacy, Macau University of Science and Technology, Macau 999078, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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Mortezapour M, Tapak L, Bahreini F, Najafi R, Afshar S. Identification of key genes in colorectal cancer diagnosis by co-expression analysis weighted gene co-expression network analysis. Comput Biol Med 2023; 157:106779. [PMID: 36931200 DOI: 10.1016/j.compbiomed.2023.106779] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Revised: 01/10/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND The purpose of this study was using bioinformatics tools to identify biomarkers and molecular factors involved in the diagnosis of colorectal cancer, which are effective for the diagnosis and treatment of the disease. METHODS We determined differentially expressed genes (DEGs) related to colorectal cancer (CRC) using the data series retrieved from GEO database. Then the weighted gene co-expression network analysis (WGCNA) was conducted to explore co-expression modules related to CRC diagnosis. Next, the relationship between the integrated modules with clinical features such as the stage of CRC was evaluated. Other downstream analyses were performed on selected module genes. RESULTS In this study, after performing the WGCNA method, a module named blue module which was more significantly associated with the CRC stage was selected for further evaluation. Afterward, the Protein-protein interaction network through sting software for 154 genes of the blue module was constructed and eight hub genes were identified through the evaluation of constructed network with Cytoscape. Among these eight hub genes, upregulation of MMP9, SERPINH1, COL1A2, COL5A2, COL1A1, SPARC, and COL5A1 in CRC was validated in other microarray and TCGA data. Based on the results of the mRNA-miRNA interaction network, SERPINH1 was found as a target gene of miR-940. Finally, results of the DGIDB database indicated that Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, and Bevacizumab, could be used as a therapeutic agent for targeting the MMP9. Furthermore, Ocriplasmin and Collagenase clostridium histolyticum could target COL1A1, COL1A2, COL5A1, and COL5A2. CONCLUSION Taken together, the results of the current study indicated that seven hub genes including COL1A2, COL5A1, COL5A2, SERPINH1, MMP9, SPARC, and COL1A1 which were upregulated in CRC could be used as a diagnostic and progression biomarker of CRC. On the other hand, miR-940 which targets SERPINH1 could be used as a potential biomarker of CRC. More ever, Andecaliximab, Carboxylated glucosamine, Marimastat, Tozuleristide, S-3304, Incyclinide, Curcumin, Prinomastat, Demethylwedelolactone, Bevacizumab, Ocriplasmin , and Collagenase clostridium histolyticum were introduced as therapeutic agents for CRC which their therapeutic potential should be evaluated experimentally.
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Affiliation(s)
- Mahdie Mortezapour
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Leili Tapak
- Department of Biostatistics, School of Public Health and Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Fatemeh Bahreini
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Rezvan Najafi
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
| | - Saeid Afshar
- Department of Molecular Medicine and Genetics, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran; Research Center for Molecular Medicine, Hamadan University of Medical Sciences, Hamadan, Iran.
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Nalepa J, Kotowski K, Machura B, Adamski S, Bozek O, Eksner B, Kokoszka B, Pekala T, Radom M, Strzelczak M, Zarudzki L, Krason A, Arcadu F, Tessier J. Deep learning automates bidimensional and volumetric tumor burden measurement from MRI in pre- and post-operative glioblastoma patients. Comput Biol Med 2023; 154:106603. [PMID: 36738710 DOI: 10.1016/j.compbiomed.2023.106603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/11/2023] [Accepted: 01/22/2023] [Indexed: 02/05/2023]
Abstract
Tumor burden assessment by magnetic resonance imaging (MRI) is central to the evaluation of treatment response for glioblastoma. This assessment is, however, complex to perform and associated with high variability due to the high heterogeneity and complexity of the disease. In this work, we tackle this issue and propose a deep learning pipeline for the fully automated end-to-end analysis of glioblastoma patients. Our approach simultaneously identifies tumor sub-regions, including the enhancing tumor, peritumoral edema and surgical cavity in the first step, and then calculates the volumetric and bidimensional measurements that follow the current Response Assessment in Neuro-Oncology (RANO) criteria. Also, we introduce a rigorous manual annotation process which was followed to delineate the tumor sub-regions by the human experts, and to capture their segmentation confidences that are later used while training deep learning models. The results of our extensive experimental study performed over 760 pre-operative and 504 post-operative adult patients with glioma obtained from the public database (acquired at 19 sites in years 2021-2020) and from a clinical treatment trial (47 and 69 sites for pre-/post-operative patients, 2009-2011) and backed up with thorough quantitative, qualitative and statistical analysis revealed that our pipeline performs accurate segmentation of pre- and post-operative MRIs in a fraction of the manual delineation time (up to 20 times faster than humans). Volumetric measurements were in strong agreement with experts with the Intraclass Correlation Coefficient (ICC): 0.959, 0.703, 0.960 for ET, ED, and cavity. Similarly, automated RANO compared favorably with experienced readers (ICC: 0.681 and 0.866) producing consistent and accurate results. Additionally, we showed that RANO measurements are not always sufficient to quantify tumor burden. The high performance of the automated tumor burden measurement highlights the potential of the tool for considerably improving and simplifying radiological evaluation of glioblastoma in clinical trials and clinical practice.
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Affiliation(s)
- Jakub Nalepa
- Graylight Imaging, Gliwice, Poland; Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
| | | | | | | | - Oskar Bozek
- Department of Radiodiagnostics and Invasive Radiology, School of Medicine in Katowice, Medical University of Silesia in Katowice, Katowice, Poland
| | - Bartosz Eksner
- Department of Radiology and Nuclear Medicine, ZSM Chorzów, Chorzów, Poland
| | - Bartosz Kokoszka
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland
| | - Tomasz Pekala
- Department of Radiodiagnostics, Interventional Radiology and Nuclear Medicine, University Clinical Centre, Katowice, Poland
| | - Mateusz Radom
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Marek Strzelczak
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Lukasz Zarudzki
- Department of Radiology and Diagnostic Imaging, Maria Skłodowska-Curie National Research Institute of Oncology, Gliwice Branch, Gliwice, Poland
| | - Agata Krason
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
| | - Filippo Arcadu
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Informatics, Roche Innovation Center Basel, Basel, Switzerland
| | - Jean Tessier
- Roche Pharmaceutical Research & Early Development, Early Clinical Development Oncology, Roche Innovation Center Basel, Basel, Switzerland
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Yang Y, Cao Y, Han X, Ma X, Li R, Wang R, Xiao L, Xie L. Revealing EXPH5 as a potential diagnostic gene biomarker of the late stage of COPD based on machine learning analysis. Comput Biol Med 2023; 154:106621. [PMID: 36746116 DOI: 10.1016/j.compbiomed.2023.106621] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 01/19/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023]
Abstract
Chronic obstructive pulmonary disease is a kind of chronic lung disease characterized by persistent air flow obstruction, which was the third leading cause of death in China. The incidence of COPD is steadily and increasing and has been a globally sever disease. Accordingly, it is urgently needed to explore how to diagnose and treat COPD timely. This study aims to find key genes to diagnose COPD as soon as possible to avoid COPD processing and analyze immune cell infiltration between COPD early stage and late stage. Two GEO datasets were merged as the merge data for analyses. 157 DEGs were used for GSEA analysis to find the pathway between COPD early stage and late stage. Above all, gene EXPH5 stood out from the screen as the most likely candidate diagnosis biomarker of COPD indicating the late-stage by least LASSO and SVM-RFE. ROC curves of EXPH5 were applied to represent the discriminatory ability through the area under the curve which is the gold standard to evaluate the accuracy of diagnosis and survival rate. The CIBERSORT algorithm was used to assess the distribution of tissue-infiltrating immune cells between two COPD stages. The diagnosis biomarker, gene EXPH5 had a positive correlation with NK cells resting; mast cell resting, eosinophils, and negative correlation with T cell gamma delta, macrophages M1, which underscore the role of gene and immune cell infiltration. To make results more reliable, we further analyzed the gene EXPH5 expression in single-cell transcriptome data and showed again that EXPH5 genes significantly downregulated in the late stage of COPD especially in the main lung cell types AT1 and AT2. In a word, our study identified genes EXPH5 as a marker gene, which adds to the knowledge for clinical diagnosis and pharmaceutical design of COPD.
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Affiliation(s)
- Yuwei Yang
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Yan Cao
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Xiaobo Han
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Xihui Ma
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Rui Li
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Hebei North Universit, Zhangjiakou, 075000, China.
| | - Rentao Wang
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Li Xiao
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
| | - Lixin Xie
- College of Pulmonary & Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100091, China; Beijing Key Laboratory of OTIR, Beijing, 100091, China.
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Baran Y, Doğan B. scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies. Comput Biol Med 2023; 155:106634. [PMID: 36774895 DOI: 10.1016/j.compbiomed.2023.106634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 01/28/2023] [Accepted: 02/04/2023] [Indexed: 02/11/2023]
Abstract
Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.
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Affiliation(s)
- Yusuf Baran
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey
| | - Berat Doğan
- Department of Biomedical Engineering, Inonu University, Malatya, Turkey.
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Segura-Ortiz A, García-Nieto J, Aldana-Montes JF, Navas-Delgado I. GENECI: A novel evolutionary machine learning consensus-based approach for the inference of gene regulatory networks. Comput Biol Med 2023; 155:106653. [PMID: 36803795 DOI: 10.1016/j.compbiomed.2023.106653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 01/09/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023]
Abstract
Gene regulatory networks define the interactions between DNA products and other substances in cells. Increasing knowledge of these networks improves the level of detail with which the processes that trigger different diseases are described and fosters the development of new therapeutic targets. These networks are usually represented by graphs, and the primary sources for their correct construction are usually time series from differential expression data. The inference of networks from this data type has been approached differently in the literature. Mostly, computational learning techniques have been implemented, which have finally shown some specialization in specific datasets. For this reason, the need arises to create new and more robust strategies for reaching a consensus based on previous results to gain a particular capacity for generalization. This paper presents GENECI (GEne NEtwork Consensus Inference), an evolutionary machine learning approach that acts as an organizer for constructing ensembles to process the results of the main inference techniques reported in the literature and to optimize the consensus network derived from them, according to their confidence levels and topological characteristics. After its design, the proposal was confronted with datasets collected from academic benchmarks (DREAM challenges and IRMA network) to quantify its accuracy. Subsequently, it was applied to a real-world biological network of melanoma patients whose results could be contrasted with medical research collected in the literature. Finally, it has been proved that its ability to optimize the consensus of several networks leads to outstanding robustness and accuracy, gaining a certain generalization capacity after facing the inference of multiple datasets. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a python package available at PyPI: https://pypi.org/project/geneci/.
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Affiliation(s)
- Adrián Segura-Ortiz
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain
| | - José García-Nieto
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain.
| | - José F Aldana-Montes
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
| | - Ismael Navas-Delgado
- Dept. de Lenguajes y Ciencias de la Computación, ITIS Software, Universidad de Málaga, Málaga, 29071, Spain; Biomedical Research Institute of Málaga (IBIMA), Universidad de Málaga, Málaga, Spain
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50
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Huang H, Cai X, Lin J, Wu Q, Zhang K, Lin Y, Liu B, Lin J. A novel five-gene metabolism-related risk signature for predicting prognosis and immune infiltration in endometrial cancer: A TCGA data mining. Comput Biol Med 2023; 155:106632. [PMID: 36805217 DOI: 10.1016/j.compbiomed.2023.106632] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 01/01/2023] [Accepted: 02/04/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND Metabolism dysfunction can affect the biological behavior of tumor cells and result in carcinogenesis and the development of various cancers. However, few thoughtful studies focus on the predictive value and efficacy of immunotherapy of metabolism-related gene signatures in endometrial cancer (EC). This research aims to construct a predictive metabolism-related gene signature in EC with prognostic and therapeutic implications. METHODS We downloaded the RNA profile and clinical data of 503 EC patients and screened out different expressions of metabolism-related genes with prognosis influence of EC from The Cancer Genome Atlas (TCGA) database. We first established a metabolism-related genes model using univariate and multivariate Cox regression and Lasso regression analysis. To internally validate the predictive model, 503 samples (entire set) were randomly assigned into the test set and the train set. Then, we applied the receiver operating characteristic (ROC) curve to confirm our previous predictive model and depicted a nomogram integrating the risk score and the clinicopathological feature. We employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways of the model. Afterward, we used ESTIMATE to evaluate the TME. Also, we adopted CIBERSORT and ssGSEA to estimate the fraction of immune infiltrating cells and immune function. At last, we investigated the relationship between the predictive model and immune checkpoint genes. RESULTS We first constructed a predictive model based on five metabolism-related genes (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA). This model showed the ability to predict EC patients' prognosis accurately and performed well in the train set, test set, and entire set. Then we confirmed the predictive signature was a novel independent prognostic factor in EC patients. In addition, we drew and validated a nomogram to precisely predict the survival rate of EC patients at 1-, 3-, and 5-years (ROC1-year = 0.714, ROC3-year = 0.750, ROC5-year = 0.767). Furthermore, GSEA unveiled that the cell cycle, certain malignant tumors, and cell metabolism were the main biological functions enriched in this identified model. We found the five metabolism-related genes signature was associated with the immune infiltrating cells and immune functions. Most importantly, it was linked with specific immune checkpoints (PD-1, CTLA4, and CD40) that could predict immunotherapy's clinical response. CONCLUSION The metabolism-related genes signature (INPP5K, PLPP2, MBOAT2, DDC, and ITPKA) is a valuable index for predicting the survival outcomes and efficacy of immunotherapy for EC in clinical settings.
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Affiliation(s)
- Huaqing Huang
- Department of Pain Medicine, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China; Pain Research Institute of Fujian Medical University, Fuzhou, Fujian Province, China
| | - Xintong Cai
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Jiexiang Lin
- Shengli Clinical Medical College, Fujian Medical University, Fuzhou, China
| | - Qiaoling Wu
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Kailin Zhang
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Yibin Lin
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Bin Liu
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China
| | - Jie Lin
- Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China.
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