1
|
Wu W, Li Y, He J, Yang J, Liu Y. Resveratrol shields against cisplatin-induced ototoxicity through epigenetic lncRNA GAS5 modulation of miR-455-5p/PTEN pathway. Int Immunopharmacol 2024; 138:112464. [PMID: 38917526 DOI: 10.1016/j.intimp.2024.112464] [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: 04/08/2024] [Revised: 06/06/2024] [Accepted: 06/08/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND Our previous research demonstrated that resveratrol counters DDP-induced ototoxicity by upregulating miR-455-5p, which targets PTEN. This study aimed to elucidate the underlying mechanisms involving GAS5 and DNA methyltransferase 1 (DNMT1) in resveratrol's protective action. METHODS A luciferase reporter assay and RNA immunoprecipitation (RIP) assay were employed to study the binding between GAS5 and miR-455-5p, as well as between miR-455-5p and PTEN. HEI-OC1 cells treated with DDP were transfected with vectors for GAS5, si-GAS5, DNMT1, si-DNMT1, and miR-455-5p mimics, as well as PTEN. Subsequently, they were treated with resveratrol and exposed to DDP, both separately and in combination. The distribution of CpG islands in the GAS5 promoter was identified using MethyPrimer, and methylation-specific PCR (MSP) was conducted to determine the methylation levels of GAS5. Chromatin immunoprecipitation (ChIP) was utilized to examine the interaction between DNMT1 and GAS5. The viability of HEI-OC1 cells, catalase (CAT) activity, apoptosis, and ROS levels were assessed using the CCK-8 assay, CAT assay, TUNEL staining, and flow cytometry, respectively. An in vivo mouse model was developed to measure auditory brainstem response (ABR) thresholds, while RT-qPCR and Western blot analysis were employed to evaluate molecular levels. RESULTS Our study discovered that GAS5 acts as a sponge for miR-455-5p, thereby increasing PTEN expression in DDP-treated HEI-OC1 cells. This process was reversed upon treatment with resveratrol. Importantly, DNMT1 promoted the methylation of the GAS5 promoter, leading to the suppression of GAS5 expression. This suppression enhanced the effectiveness of resveratrol in combating DDP-induced apoptosis and ROS in HEI-OC1 cells and amplified its protective effect against DDP's ototoxicity in vivo. CONCLUSIONS Our research emphasizes the significance of the DNMT1/GAS5/miR-455-5p/PTEN axis as a promising new route to boost resveratrol's effectiveness against DDP-induced ototoxicity.
Collapse
Affiliation(s)
- Wenjin Wu
- Department of Otorhinolaryngology-Head& Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Yingru Li
- Department of Otorhinolaryngology-Head& Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Jingchun He
- Department of Otorhinolaryngology-Head& Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Jun Yang
- Department of Otorhinolaryngology-Head& Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China
| | - Yupeng Liu
- Department of Otorhinolaryngology-Head& Neck Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China; Shanghai Jiaotong University School of Medicine Ear Institute, Shanghai, China; Shanghai Key Laboratory of Translational Medicine on Ear and Nose Diseases, Shanghai, China.
| |
Collapse
|
2
|
Lin L, Deng L, Bao Y. Identifying crucial lncRNAs and mRNAs in hypoxia-induced A549 lung cancer cells and investigating their underlying mechanisms via high-throughput sequencing. PLoS One 2024; 19:e0307954. [PMID: 39236027 PMCID: PMC11376552 DOI: 10.1371/journal.pone.0307954] [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: 03/07/2024] [Accepted: 07/01/2024] [Indexed: 09/07/2024] Open
Abstract
BACKGROUND Rapid proliferation and outgrowth of tumor cells frequently result in localized hypoxia, which has been implicated in the progression of lung cancer. The present study aimed to identify key long non-coding RNAs (lncRNAs) and messenger RNAs (mRNAs) involved in hypoxia-induced A549 lung cancer cells, and to investigate their potential underlying mechanisms of action. METHODS High-throughput sequencing was utilized to obtain the expression profiles of lncRNA and mRNA in both hypoxia-induced and normoxia A549 lung cancer cells. Subsequently, a bioinformatics analysis was conducted on the differentially expressed molecules, encompassing functional enrichment analysis, protein-protein interaction (PPI) network analysis, and competitive endogenous RNA (ceRNA) analysis. Finally, the alterations in the expression of key lncRNAs and mRNAs were validated using real-time quantitative PCR (qPCR). RESULTS In the study, 1155 mRNAs and 215 lncRNAs were identified as differentially expressed between the hypoxia group and the normoxia group. Functional enrichment analysis revealed that the differentially expressed mRNAs were significantly enriched in various pathways, including the p53 signaling pathway, DNA replication, and the cell cycle. Additionally, key lncRNA-miRNA-mRNA relationships, such as RP11-58O9.2-hsa-miR-6749-3p-XRCC2 and SNAP25-AS1-hsa-miR-6749-3p-TENM4, were identified. Notably, the qPCR assay demonstrated that the expression of SNAP25-AS1, RP11-58O9.2, TENM4, and XRCC2 was downregulated in the hypoxia group compared to the normoxia group. Conversely, the expression of LINC01164, VLDLR-AS1, RP11-14I17.2, and CDKN1A was upregulated. CONCLUSION Our findings suggest a potential involvement of SNAP25-AS1, RP11-58O9.2, TENM4, XRCC2, LINC01164, VLDLR-AS1, RP11-14I17.2, and CDKN1A in the development of hypoxia-induced lung cancer. These key lncRNAs and mRNAs exert their functions through diverse mechanisms, including the competitive endogenous RNA (ceRNA) pathway.
Collapse
Affiliation(s)
- Lin Lin
- Department of Respiratory Medicine, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, People's Republic of China
| | - Lili Deng
- Department of Oncology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, People's Republic of China
| | - Yongxia Bao
- Department of Respiratory Medicine, The Second Affiliated Hospital, Harbin Medical University, Harbin, Heilongjiang, People's Republic of China
| |
Collapse
|
3
|
Yu CQ, Wang XF, Li LP, You ZH, Ren ZH, Chu P, Guo F, Wang ZY. RBNE-CMI: An Efficient Method for Predicting circRNA-miRNA Interactions via Multiattribute Incomplete Heterogeneous Network Embedding. J Chem Inf Model 2024. [PMID: 39231016 DOI: 10.1021/acs.jcim.4c01118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/06/2024]
Abstract
Circular RNA (circRNA)-microRNA (miRNA) interaction (CMI) plays crucial roles in cellular regulation, offering promising perspectives for disease diagnosis and therapy. Therefore, it is necessary to employ computational methods for the rapid and cost-effective prediction of potential circRNA-miRNA interactions. However, the existing methods are limited by incomplete data; therefore, it is difficult to model molecules with different attributes on a large scale, which greatly hinders the efficiency and performance of prediction. In this study, we propose an effective method for predicting circRNA-miRNA interactions, called RBNE-CMI, and introduce a framework that can embed incomplete multiattribute CMI heterogeneous networks. By combining the proposed method, we integrate different data sets in the CMI prediction field into one incomplete network for modeling, achieving superior performance in 5-fold cross-validation. Moreover, in the prediction task based on complete data, the proposed method still achieves better performance than the known model. In addition, in the case study, we successfully predicted 18 of the 20 potential cancer biomarkers. The data and source code can be found at https://github.com/1axin/RBNE-CMI.
Collapse
Affiliation(s)
- Chang-Qing Yu
- School of Information Engineering, Xijing University, Xi'an 710123 China
| | - Xin-Fei Wang
- College of Computer Science and Technology, Jilin University, Changchun 130012 China
| | - Li-Ping Li
- Yizhi School of Agriculture and Forestry, Xiangyang Polytechnic Institute, Xianyang 712000, China
| | - Zhu-Hong You
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Zhong-Hao Ren
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Peng Chu
- School of Information Engineering, Xijing University, Xi'an 710123 China
| | - Feng Guo
- School of Information Engineering, Xijing University, Xi'an 710123 China
| | - Zhen-Yu Wang
- School of Telecommunications, Lanzhou University of Technology, Lanzhou 730000, China
| |
Collapse
|
4
|
Guo W, Liu J, Dong F, Hong H. Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals. JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH. PART C, TOXICOLOGY AND CARCINOGENESIS 2024:1-28. [PMID: 39228157 DOI: 10.1080/26896583.2024.2396731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effective, suffer from being costly and time-consuming. In response to this challenge, new alternative methodologies, notably machine learning and deep learning techniques, have attracted attention for their potential in developing carcinogenicity prediction models. This article reviews the progress in predicting carcinogenicity using various machine learning and deep learning algorithms. A comparative analysis on these developed models reveals that support vector machine, random forest, and ensemble learning are commonly preferred for their robustness and effectiveness in predicting chemical carcinogenicity. Conversely, models based on deep learning algorithms, such as feedforward neural network, convolutional neural network, graph convolutional neural network, capsule neural network, and hybrid neural networks, exhibit promising capabilities but are limited by the size of available carcinogenicity datasets. This review provides a comprehensive analysis of current machine learning and deep learning models for carcinogenicity prediction, underscoring the importance of high-quality and large datasets. These observations are anticipated to catalyze future advancements in developing effective and generalizable machine learning and deep learning models for predicting chemical carcinogenicity.
Collapse
Affiliation(s)
- Wenjing Guo
- National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR
| | - Jie Liu
- National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR
| | - Fan Dong
- National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR
| | - Huixiao Hong
- National Center for Toxicological Research (NCTR), U.S. Food & Drug Administration (FDA), Jefferson, AR
| |
Collapse
|
5
|
Chen Z, Zhang L, Li J, Chen H. Microbe-disease associations prediction by graph regularized non-negative matrix factorization with L 2 , 1 $$ {L}_{2,1} $$ norm regularization terms. J Cell Mol Med 2024; 28:e18553. [PMID: 39239860 PMCID: PMC11377990 DOI: 10.1111/jcmm.18553] [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: 05/15/2024] [Revised: 06/19/2024] [Accepted: 07/09/2024] [Indexed: 09/07/2024] Open
Abstract
Microbes are involved in a wide range of biological processes and are closely associated with disease. Inferring potential disease-associated microbes as the biomarkers or drug targets may help prevent, diagnose and treat complex human diseases. However, biological experiments are time-consuming and expensive. In this study, we introduced a new method called iPALM-GLMF, which modelled microbe-disease association prediction as a problem of non-negative matrix factorization with graph dual regularization terms andL 2 , 1 $$ {L}_{2,1} $$ norm regularization terms. The graph dual regularization terms were used to capture potential features in the microbe and disease space, and theL 2 , 1 $$ {L}_{2,1} $$ norm regularization terms were used to ensure the sparsity of the feature matrices obtained from the non-negative matrix factorization and to improve the interpretability. To solve the model, iPALM-GLMF used a non-negative double singular value decomposition to initialize the matrix factorization and adopted an inertial Proximal Alternating Linear Minimization iterative process to obtain the final matrix factorization results. As a result, iPALM-GLMF performed better than other existing methods in leave-one-out cross-validation and fivefold cross-validation. In addition, case studies of different diseases demonstrated that iPALM-GLMF could effectively predict potential microbial-disease associations. iPALM-GLMF is publicly available at https://github.com/LiangzheZhang/iPALM-GLMF.
Collapse
Affiliation(s)
- Ziwei Chen
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
| | - Liangzhe Zhang
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
| | - Jingyi Li
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
| | - Hang Chen
- School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing, China
| |
Collapse
|
6
|
Sun X, Qiu P, He Z, Zhu Y, Zhang R, Li X, Wang X. HERC5: a comprehensive in silico analysis of its diagnostic, prognostic, and therapeutic potential in cancer. APMIS 2024. [PMID: 39199018 DOI: 10.1111/apm.13462] [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: 06/27/2024] [Accepted: 08/08/2024] [Indexed: 09/01/2024]
Abstract
HERC5, a vital protein in the HERC family, plays crucial roles in immune response, cancer progression, and antiviral defense. This bioinformatic study comprehensively assessed HERC5's significance across various malignancies by analyzing its gene expression, immune and molecular subtype expressions, target proteins, biological functions, and prognostic and diagnostic values in pan-cancer. We further examined its correlation with clinical features, co-expressed and differentially expressed genes, and prognosis in clinical subgroups, focusing on endometrial cancer (UCEC). Our findings showed that HERC5 RNA is expressed at low levels in most cancers and significantly differs across immune and molecular subtypes. HERC5 accurately predicts cancer and correlates with most cancer prognoses. In UCEC, HERC5 was significantly associated with age, hormonal status, clinical stage, treatment status, and metastasis. Elevated HERC5 expression was linked to worse progression-free interval, disease-specific survival, and overall survival in UCEC, particularly in diverse clinical subgroups. Significant differences in HERC5 expression were also observed in various human cancer cell line validations. In summary, HERC5 may be a critical biomarker for pan-cancer prognosis, progression, and diagnosis, as well as a promising new target for cancer therapy.
Collapse
Affiliation(s)
- Xianqing Sun
- Department of Traumatology and Orthopedics, The First People's Hospital of Qujing, Yunnan, China
| | - Peng Qiu
- Department of Traumatology and Orthopedics, The First People's Hospital of Qujing, Yunnan, China
| | - Zhennan He
- Department of Traumatology and Orthopedics, The First People's Hospital of Qujing, Yunnan, China
| | - Yuan Zhu
- Department of Traumatology and Orthopedics, The First People's Hospital of Qujing, Yunnan, China
| | - Rui Zhang
- Department of Traumatology and Orthopedics, The First People's Hospital of Qujing, Yunnan, China
| | - Xiang Li
- Department of Traumatology and Orthopedics, The First People's Hospital of Qujing, Yunnan, China
| | - Xiaoyan Wang
- Department of Traumatology and Orthopedics, The First People's Hospital of Qujing, Yunnan, China
| |
Collapse
|
7
|
Zhang Q, Mao D, Tu Y, Wu YY. A New Fingerprint and Graph Hybrid Neural Network for Predicting Molecular Properties. J Chem Inf Model 2024; 64:5853-5866. [PMID: 39052623 DOI: 10.1021/acs.jcim.4c00586] [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: 07/27/2024]
Abstract
Machine learning plays a role in accelerating drug discovery, and the design of effective machine learning models is crucial for accurately predicting molecular properties. Characterizing molecules typically involves the use of molecular fingerprints and molecular graphs. These are input into a multilayer perceptron (MLP) and variants of graph neural networks, such as graph attention networks (GATs). Due to the diverse types and large dimension of fingerprints, models may contain many features that are relatively irrelevant or redundant; meanwhile, although the GAT excels in handling heterogeneous graph tasks, it lacks the ability to extract collaborative information from neighboring nodes, which is crucial in scenarios where it cannot capture the joint influence of adjacent groups on atoms. To overcome these challenges, we introduce a hybrid model, combining improved GAT and MLP. In GAT, the recurrent neural network is employed to capture collaborative information. To address the dimensionality issue, we propose a feature selection algorithm, which is based on the principle of maximizing relevance while minimizing redundancy. Through experiments on 13 public data sets and 14 breast cell lines, our model demonstrates superior performance compared to state-of-the-art deep learning and traditional machine learning algorithms. Additionally, a series of ablation experiments were conducted to demonstrate the advantages of our improved version, as well as its antinoise capability and interpretability. These results indicate that our model holds promising prospects for practical applications.
Collapse
Affiliation(s)
- Qingtian Zhang
- College of Physics Science and Technology, Yangzhou University, Jiangsu 225009, China
| | - Dangxin Mao
- College of Physics Science and Technology, Yangzhou University, Jiangsu 225009, China
| | - Yusong Tu
- College of Physics Science and Technology, Yangzhou University, Jiangsu 225009, China
| | - Yuan-Yan Wu
- College of Physics Science and Technology, Yangzhou University, Jiangsu 225009, China
| |
Collapse
|
8
|
Martins D, Abbasi M, Egas C, Arrais JP. Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models. Genomics 2024; 116:110910. [PMID: 39111546 DOI: 10.1016/j.ygeno.2024.110910] [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: 04/15/2024] [Revised: 07/08/2024] [Accepted: 07/31/2024] [Indexed: 08/20/2024]
Abstract
This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neural networks' capabilities to efficiently handle genetic interactions. A locally connected network initially routes input data from variants to their corresponding genes. The second step employs an Encoder-Decoder to capture relationships among identified genes. The final model integrates knowledge from the first two and incorporates a parallel component to consider the effects of additional genes. This expansion enhances prediction scores by considering a larger number of genes. Trained models achieved an average AUC of 0.83, surpassing other genotype-trained models and matching gene expression dataset-based approaches. Additionally, tests on held-out sets reported an average sensitivity of 0.72 and an accuracy of 0.76, aligning with schizophrenia heritability predictions. Moreover, the study addresses genetic heterogeneity challenges by considering diverse population subsets.
Collapse
Affiliation(s)
- Daniel Martins
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal; Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal
| | - Maryam Abbasi
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal; Polytechnic Institute of Coimbra, Applied Research Institute, Coimbra, Portugal; Research Centre for Natural Resources Environment and Society (CERNAS), Polytechnic Institute of Coimbra, Coimbra, Portugal.
| | - Conceição Egas
- Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, Coimbra, Portugal; Biocant - Transfer Technology Association, Cantanhede, Portugal; CNC - CNC Center for Neuroscience and Cell Biology, Coimbra, Portugal
| | - Joel P Arrais
- Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| |
Collapse
|
9
|
Chen J, Zhu Y, Yuan Q. Predicting potential microbe-disease associations based on dual branch graph convolutional network. J Cell Mol Med 2024; 28:e18571. [PMID: 39086148 PMCID: PMC11291560 DOI: 10.1111/jcmm.18571] [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: 05/15/2024] [Revised: 06/15/2024] [Accepted: 06/27/2024] [Indexed: 08/02/2024] Open
Abstract
Studying the association between microbes and diseases not only aids in the prevention and diagnosis of diseases, but also provides crucial theoretical support for new drug development and personalized treatment. Due to the time-consuming and costly nature of laboratory-based biological tests to confirm the relationship between microbes and diseases, there is an urgent need for innovative computational frameworks to anticipate new associations between microbes and diseases. Here, we propose a novel computational approach based on a dual branch graph convolutional network (GCN) module, abbreviated as DBGCNMDA, for identifying microbe-disease associations. First, DBGCNMDA calculates the similarity matrix of diseases and microbes by integrating functional similarity and Gaussian association spectrum kernel (GAPK) similarity. Then, semantic information from different biological networks is extracted by two GCN modules from different perspectives. Finally, the scores of microbe-disease associations are predicted based on the extracted features. The main innovation of this method lies in the use of two types of information for microbe/disease similarity assessment. Additionally, we extend the disease nodes to address the issue of insufficient features due to low data dimensionality. We optimize the connectivity between the homogeneous entities using random walk with restart (RWR), and then use the optimized similarity matrix as the initial feature matrix. In terms of network understanding, we design a dual branch GCN module, namely GlobalGCN and LocalGCN, to fine-tune node representations by introducing side information, including homologous neighbour nodes. We evaluate the accuracy of the DBGCNMDA model using five-fold cross-validation (5-fold-CV) technique. The results show that the area under the receiver operating characteristic curve (AUC) and area under the precision versus recall curve (AUPR) of the DBGCNMDA model in the 5-fold-CV are 0.9559 and 0.9630, respectively. The results from the case studies using published experimental data confirm a significant number of predicted associations, indicating that DBGCNMDA is an effective tool for predicting potential microbe-disease associations.
Collapse
Affiliation(s)
- Jing Chen
- School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhouChina
| | - Yongjun Zhu
- School of Electronic and Information EngineeringSuzhou University of Science and TechnologySuzhouChina
| | - Qun Yuan
- Department of Respiratory Medicine, The Affiliated Suzhou Hospital of NanjingUniversity Medical SchoolSuzhouChina
| |
Collapse
|
10
|
Le NQK, Tran TX, Nguyen PA, Ho TT, Nguyen VN. Recent progress in machine learning approaches for predicting carcinogenicity in drug development. Expert Opin Drug Metab Toxicol 2024; 20:621-628. [PMID: 38742542 DOI: 10.1080/17425255.2024.2356162] [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: 02/03/2024] [Accepted: 05/13/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies in overcoming challenges related to data interpretation, ethical considerations, and regulatory acceptance. AREAS COVERED The review comprehensively examines the integration of ML, deep learning, and diverse artificial intelligence (AI) approaches in various aspects of drug development safety assessments. It explores applications ranging from early-phase compound screening to clinical trial optimization, highlighting the versatility of ML in enhancing predictive accuracy and efficiency. EXPERT OPINION Through the analysis of traditional approaches such as in vivo rodent bioassays and in vitro assays, the review underscores the limitations and resource intensity associated with these methods. It provides expert insights into how ML offers innovative solutions to address these challenges, revolutionizing safety assessments in drug development.
Collapse
Affiliation(s)
- Nguyen Quoc Khanh Le
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- AIBioMed Research Group, Taipei Medical University, Taipei, Taiwan
- Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
- Translational Imaging Research Center, Taipei Medical University Hospital, Taipei, Taiwan
| | - Thi-Xuan Tran
- University of Economics and Business Administration, Thai Nguyen University, Thai Nguyen, Vietnam
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Vietnam
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Vietnam
| | - Trang-Thi Ho
- Department of Computer Science and Information Engineering, TamKang University, New Taipei, Taiwan
| | - Van-Nui Nguyen
- University of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Vietnam
| |
Collapse
|
11
|
Yang Q, Fan L, Hao E, Hou X, Deng J, Du Z, Xia Z. Construction of an explanatory model for predicting hepatotoxicity: a case study of the potentially hepatotoxic components of Gardenia jasminoides. Drug Chem Toxicol 2024:1-13. [PMID: 38938098 DOI: 10.1080/01480545.2024.2364905] [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: 01/18/2024] [Accepted: 06/01/2024] [Indexed: 06/29/2024]
Abstract
It is well-known that the hepatotoxicity of drugs can significantly influence their clinical use. Despite their effective therapeutic efficacy, many drugs are severely limited in clinical applications due to significant hepatotoxicity. In response, researchers have created several machine learning-based hepatotoxicity prediction models for use in drug discovery and development. Researchers aim to predict the potential hepatotoxicity of drugs to enhance their utility. However, current hepatotoxicity prediction models often suffer from being unverified, and they fail to capture the detailed toxicological structures of predicted hepatotoxic compounds. Using the 56 chemical constituents of Gardenia jasminoides as examples, we validated the trained hepatotoxicity prediction model through literature reviews, principal component analysis (PCA), and structural comparison methods. Ultimately, we successfully developed a model with strong predictive performance and conducted visual validation. Interestingly, we discovered that the predicted hepatotoxic chemical constituents of Gardenia possess both toxic and therapeutic effects, which are likely dose-dependent. This discovery greatly contributes to our understanding of the dual nature of drug-induced hepatotoxicity.
Collapse
Affiliation(s)
- Qi Yang
- School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
| | - Lili Fan
- School of Pharmacy, Guangxi University of Chinese Medicine, Nanning, China
| | - Erwei Hao
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Xiaotao Hou
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Jiagang Deng
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| | - Zhengcai Du
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Scientific Research Center of Traditional Chinese Medicine, Guangxi University of Chinese Medicine, Nanning, China
| | - Zhongshang Xia
- Guangxi Key Laboratory of Efficacy Study on Chinese Materia Medica, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Collaborative Innovation Center for Research on Functional Ingredients of Agricultural Residues, Guangxi University of Chinese Medicine, Nanning, China
- Guangxi Key Laboratory of Traditional Chinese Medicine Formulas Theory and Transformation for Damp Diseases, Guangxi University of Chinese Medicine, Nanning, China
| |
Collapse
|
12
|
Gupta RK, Bhushan R, Kumar S, Prasad SB. In silico analysis unveiling potential biomarkers in gallbladder carcinogenesis. Sci Rep 2024; 14:14570. [PMID: 38914609 PMCID: PMC11196699 DOI: 10.1038/s41598-024-61762-4] [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: 01/17/2024] [Accepted: 05/09/2024] [Indexed: 06/26/2024] Open
Abstract
Gallbladder cancer (GBC) is a rare but very aggressive most common digestive tract cancer with a high mortality rate due to delayed diagnosis at the advanced stage. Moreover, GBC progression shows asymptomatic characteristics making it impossible to detect at an early stage. In these circumstances, conventional therapy like surgery, chemotherapy, and radiotherapy becomes refractive. However, few studies reported some molecular markers like KRAS (Kirsten Rat Sarcoma) mutation, upregulation of HER2/neu, EGFR (Epidermal Growth Factor Receptor), and microRNAs in GBC. However, the absence of some specific early diagnostic and prognostic markers is the biggest hurdle for the therapy of GBC to date. The present study has been designed to identify some specific molecular markers for precise diagnosis, and prognosis, for successful treatment of the GBC. By In Silico a network-centric analysis of two microarray datasets; (GSE202479) and (GSE13222) from the Gene Expression Omnibus (GEO) database, shows 50 differentially expressed genes (DEGs) associated with GBC. Further network analysis revealed that 12 genes are highly interconnected based on the highest MCODE (Molecular Complex Detection) value, among all three genes; TRIP13 (Thyroid Receptor Interacting Protein), NEK2 (Never in Mitosis gene-A related Kinase 2), and TPX2 (Targeting Protein for Xklp2) having highest network interaction with transcription factors and miRNA suggesting critically associated with GBC. Further survival analysis data corroborate the association of these genes; TRIP13, NEK2, and TPX2 with GBC. Thus, TRIP13, NEK2, and TPX2 genes are significantly correlated with a greater risk of mortality, transforming them from mere biomarkers of the GBC for early detections and may emerge as prognostic markers for treatment.
Collapse
Affiliation(s)
- Raviranjan Kumar Gupta
- Department of Zoology, School of Life Sciences, Mahatma Gandhi Central University Bihar (MGCUB), Motihari, 845401, India
| | - Ravi Bhushan
- Department of Zoology, Munsi Singh College, Motihari, 845401, India
| | - Saket Kumar
- Department of Surgical Gastroenterology, Indira Gandhi Institute of Medical Sciences (IGIMS), Sheikhpura, Patna, India
| | - Shyam Babu Prasad
- Department of Zoology, School of Life Sciences, Mahatma Gandhi Central University Bihar (MGCUB), Motihari, 845401, India.
| |
Collapse
|
13
|
Almotairi S, Badr E, Abdelbaky I, Elhakeem M, Abdul Salam M. Hybrid transformer-CNN model for accurate prediction of peptide hemolytic potential. Sci Rep 2024; 14:14263. [PMID: 38902287 PMCID: PMC11190137 DOI: 10.1038/s41598-024-63446-5] [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: 04/02/2024] [Accepted: 05/29/2024] [Indexed: 06/22/2024] Open
Abstract
Hemolysis is a crucial factor in various biomedical and pharmaceutical contexts, driving our interest in developing advanced computational techniques for precise prediction. Our proposed approach takes advantage of the unique capabilities of convolutional neural networks (CNNs) and transformers to detect complex patterns inherent in the data. The integration of CNN and transformers' attention mechanisms allows for the extraction of relevant information, leading to accurate predictions of hemolytic potential. The proposed method was trained on three distinct data sets of peptide sequences known as recurrent neural network-hemolytic (RNN-Hem), Hlppredfuse, and Combined. Our computational results demonstrated the superior efficacy of our models compared to existing methods. The proposed approach demonstrated impressive Matthews correlation coefficients of 0.5962, 0.9111, and 0.7788 respectively, indicating its effectiveness in predicting hemolytic activity. With its potential to guide experimental efforts in peptide design and drug development, this method holds great promise for practical applications. Integrating CNNs and transformers proves to be a powerful tool in the fields of bioinformatics and therapeutic research, highlighting their potential to drive advancement in this area.
Collapse
Affiliation(s)
- Sultan Almotairi
- Department of Computer Science, Faculty of College of Computer and Information Sciences, Majmaah University, 11952, Majmaah, Saudi Arabia
- Department of Computer Science, Faculty of Computer and Information Systems, Islamic University of Madinah, 42351, Medinah, Saudi Arabia
| | - Elsayed Badr
- Scientific Computing Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
- The Egyptian School of Data Science (ESDS), Benha, Egypt.
| | - Ibrahim Abdelbaky
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Mohamed Elhakeem
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt.
| | - Mustafa Abdul Salam
- Artificial Intelligence Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
- Department of Computer Science, College of Arts and Science, Wadi Addawasir, Prince Sattam Bin Abdulaziz University, 16273, Al-Kharj, Saudi Arabia
| |
Collapse
|
14
|
Wang Z, Niu D. To explore the prognostic characteristics of colon cancer based on tertiary lymphoid structure-related genes and reveal the characteristics of tumor microenvironment and drug prediction. Sci Rep 2024; 14:13555. [PMID: 38867070 PMCID: PMC11169531 DOI: 10.1038/s41598-024-64308-w] [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: 03/25/2024] [Accepted: 06/07/2024] [Indexed: 06/14/2024] Open
Abstract
In order to construct a prognostic evaluation model of TLS features in COAD and better realize personalized precision medicine in COAD. Colon adenocarcinoma (COAD) is a common malignant tumor of the digestive system. At present, there is no effective prognostic marker to predict the prognosis of patients. Tertiary lymphoid structure (TLS) affects cancer progression by regulating immune microenvironment. Mining COAD biomarkers based on TLS-related genes helps to improve the prognosis of patients. In order to construct a prognostic evaluation model of TLS features in COAD and better realize personalized precision medicine in COAD. The mRNA expression data and clinical information of COAD and adjacent tissues were downloaded from the Cancer Genome Atlas database. The differentially expressed TLS-related genes of COAD relative to adjacent tissues were obtained by differential analysis. TLS gene co-expression analysis was used to mine genes highly related to TLS, and the intersection of the two was used to obtain candidate genes. Univariate, LASSO, and multivariate Cox regression analysis were performed on candidate genes to screen prognostic markers to construct a risk assessment model. The differences of immune characteristics were evaluated by ESTIMATE, ssGSEA and CIBERSORT in high and low risk groups of prognostic model. The difference of genomic mutation between groups was evaluated by tumor mutation burden score. Screening small molecule drugs through the GDSC library. Finally, a nomogram was drawn to evaluate the clinical value of the prognostic model. Seven TLS-related genes ADAM8, SLC6A1, PAXX, RIMKLB, PTH1R, CD1B, and MMP10 were screened to construct a prognostic model. Survival analysis showed that patients in the high-risk group had significantly lower overall survival rates. Immune microenvironment analysis showed that patients in the high-risk group had higher immune indicators, indicating higher immunity. The genomic mutation patterns of the high-risk and low-risk groups were significantly different, especially the KRAS mutation frequency was significantly higher in the high-risk group. Drug sensitivity analysis showed that the low-risk group was more sensitive to Erlotinib, Savolitinib and VE _ 822, which may be used as a potential drug for COAD treatment. Finally, the nomogram constructed by pathological features combined with RiskScore can accurately evaluate the prognosis of COAD patients. This study constructed and verified a TLS model that can predict COAD. More importantly, it provides a reference standard for guiding the prognosis and immunotherapy of COAD patients.
Collapse
Affiliation(s)
- Zhanmei Wang
- Department of Oncology, Qilu Hospital of Shandong University, Qingdao, 266000, China
| | - Dongguang Niu
- Department of Gastrointestinal Surgery, Affiliated Hospital of Qingdao University, Qingdao City, 266000, Shandong Province, China.
| |
Collapse
|
15
|
Yang X, Sun J, Jin B, Lu Y, Cheng J, Jiang J, Zhao Q, Shuai J. Multi-task aquatic toxicity prediction model based on multi-level features fusion. J Adv Res 2024:S2090-1232(24)00226-1. [PMID: 38844122 DOI: 10.1016/j.jare.2024.06.002] [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: 02/18/2024] [Revised: 05/21/2024] [Accepted: 06/02/2024] [Indexed: 06/09/2024] Open
Abstract
INTRODUCTION With the escalating menace of organic compounds in environmental pollution imperiling the survival of aquatic organisms, the investigation of organic compound toxicity across diverse aquatic species assumes paramount significance for environmental protection. Understanding how different species respond to these compounds helps assess the potential ecological impact of pollution on aquatic ecosystems as a whole. Compared with traditional experimental methods, deep learning methods have higher accuracy in predicting aquatic toxicity, faster data processing speed and better generalization ability. OBJECTIVES This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity. METHODS The model integrates molecular fingerprints and molecule graphs to characterize molecules, enabling the simultaneous prediction of acute toxicity for the same organic compound across four distinct fish species. Furthermore, to validate the advantages of multi-task learning, we independently construct prediction models, named ATFPGT-single, for each fish species. We employ cross-validation in our experiments to assess the performance and generalization ability of ATFPGT-multi. RESULTS The experimental results indicate, first, that ATFPGT-multi outperforms ATFPGT-single on four fish datasets with AUC improvements of 9.8%, 4%, 4.8%, and 8.2%, respectively, demonstrating the superiority of multi-task learning over single-task learning. Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. Moreover, ATFPGT-multi utilizes attention scores to identify molecular fragments associated with fish toxicity in organic molecules, as demonstrated by two organic molecule examples in the main text, demonstrating the interpretability of ATFPGT-multi. CONCLUSION In summary, ATFPGT-multi provides important support and reference for the further development of aquatic toxicity assessment. All of codes and datasets are freely available online at https://github.com/zhaoqi106/ATFPGT-multi.
Collapse
Affiliation(s)
- Xin Yang
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China; Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi 276000, China
| | - Bingyu Jin
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China
| | - Yuer Lu
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Jinyan Cheng
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China
| | - Jiaju Jiang
- College of Life Sciences, Sichuan University, Chengdu 610064, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan 114051, China.
| | - Jianwei Shuai
- Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou 325001, China; Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou 325001, China.
| |
Collapse
|
16
|
Qin C, Zhang J, Ma L. EMCMDA: predicting miRNA-disease associations via efficient matrix completion. Sci Rep 2024; 14:12761. [PMID: 38834687 DOI: 10.1038/s41598-024-63582-y] [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: 03/11/2024] [Accepted: 05/30/2024] [Indexed: 06/06/2024] Open
Abstract
Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.
Collapse
Affiliation(s)
- Chao Qin
- School of Information Science and Engineering, Qilu Normal University, Jinan, 250200, China.
| | - Jiancheng Zhang
- School of Information Science and Engineering, Qilu Normal University, Jinan, 250200, China
| | - Lingyu Ma
- School of Control Science and Engineering, Harbin Institute of Technology, Weihai, 250200, China
| |
Collapse
|
17
|
Sulaimany S, Farahmandi K, Mafakheri A. Computational prediction of new therapeutic effects of probiotics. Sci Rep 2024; 14:11932. [PMID: 38789535 PMCID: PMC11126595 DOI: 10.1038/s41598-024-62796-4] [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: 12/12/2023] [Accepted: 05/21/2024] [Indexed: 05/26/2024] Open
Abstract
Probiotics are living microorganisms that provide health benefits to their hosts, potentially aiding in the treatment or prevention of various diseases, including diarrhea, irritable bowel syndrome, ulcerative colitis, and Crohn's disease. Motivated by successful applications of link prediction in medical and biological networks, we applied link prediction to the probiotic-disease network to identify unreported relations. Using data from the Probio database and International Classification of Diseases-10th Revision (ICD-10) resources, we constructed a bipartite graph focused on the relationship between probiotics and diseases. We applied customized link prediction algorithms for this bipartite network, including common neighbors, Jaccard coefficient, and Adamic/Adar ranking formulas. We evaluated the results using Area under the Curve (AUC) and precision metrics. Our analysis revealed that common neighbors outperformed the other methods, with an AUC of 0.96 and precision of 0.6, indicating that basic formulas can predict at least six out of ten probable relations correctly. To support our findings, we conducted an exact search of the top 20 predictions and found six confirming papers on Google Scholar and Science Direct. Evidence suggests that Lactobacillus jensenii may provide prophylactic and therapeutic benefits for gastrointestinal diseases and that Lactobacillus acidophilus may have potential activity against urologic and female genital illnesses. Further investigation of other predictions through additional preclinical and clinical studies is recommended. Future research may focus on deploying more powerful link prediction algorithms to achieve better and more accurate results.
Collapse
Affiliation(s)
- Sadegh Sulaimany
- Social and Biological Network Analysis Laboratory (SBNA), Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran.
| | - Kajal Farahmandi
- Department of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
| | - Aso Mafakheri
- Social and Biological Network Analysis Laboratory (SBNA), Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
| |
Collapse
|
18
|
Qin Y, Sheng Y, Ren M, Hou Z, Xiao L, Chen R. Identification of necroptosis-related gene signatures for predicting the prognosis of ovarian cancer. Sci Rep 2024; 14:11133. [PMID: 38750159 PMCID: PMC11096311 DOI: 10.1038/s41598-024-61849-y] [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: 01/10/2024] [Accepted: 05/10/2024] [Indexed: 05/18/2024] Open
Abstract
Ovarian cancer (OC) is one of the most prevalent and fatal malignant tumors of the female reproductive system. Our research aimed to develop a prognostic model to assist inclinical treatment decision-making.Utilizing data from The Cancer Genome Atlas (TCGA) and copy number variation (CNV) data from the University of California Santa Cruz (UCSC) database, we conducted analyses of differentially expressed genes (DEGs), gene function, and tumor microenvironment (TME) scores in various clusters of OC samples.Next, we classified participants into low-risk and high-risk groups based on the median risk score, thereby dividing both the training group and the entire group accordingly. Overall survival (OS) was significantly reduced in the high-risk group, and two independent prognostic factors were identified: age and risk score. Additionally, three genes-C-X-C Motif Chemokine Ligand 10 (CXCL10), RELB, and Caspase-3 (CASP3)-emerged as potential candidates for an independent prognostic signature with acceptable prognostic value. In Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, pathways related to immune responses and inflammatory cell chemotaxis were identified. Cellular experiments further validated the reliability and precision of our findings. In conclusion, necroptosis-related genes play critical roles in tumor immunity, and our model introduces a novel strategy for predicting the prognosis of OC patients.
Collapse
Affiliation(s)
- Yuling Qin
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China
| | - Yawen Sheng
- Shandong University of Traditional Chinese Medicine, Jinan, 250014, Shandong, China
| | - Mengxue Ren
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China
| | - Zitong Hou
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China
| | - Lu Xiao
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China
| | - Ruixue Chen
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, No. 5, Beixiange Road, Xicheng District, Beijing, 100053, China.
| |
Collapse
|
19
|
Deng D, Xu X, Cui T, Xu M, Luo K, Zhang H, Wang Q, Song C, Li C, Li G, Shang D. PBAC: A pathway-based attention convolution neural network for predicting clinical drug treatment responses. J Cell Mol Med 2024; 28:e18298. [PMID: 38683133 PMCID: PMC11057419 DOI: 10.1111/jcmm.18298] [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: 12/07/2023] [Revised: 03/05/2024] [Accepted: 03/25/2024] [Indexed: 05/01/2024] Open
Abstract
Precise and personalized drug application is crucial in the clinical treatment of complex diseases. Although neural networks offer a new approach to improving drug strategies, their internal structure is difficult to interpret. Here, we propose PBAC (Pathway-Based Attention Convolution neural network), which integrates a deep learning framework and attention mechanism to address the complex biological pathway information, thereby provide a biology function-based robust drug responsiveness prediction model. PBAC has four layers: gene-pathway layer, attention layer, convolution layer and fully connected layer. PBAC improves the performance of predicting drug responsiveness by focusing on important pathways, helping us understand the mechanism of drug action in diseases. We validated the PBAC model using data from four chemotherapy drugs (Bortezomib, Cisplatin, Docetaxel and Paclitaxel) and 11 immunotherapy datasets. In the majority of datasets, PBAC exhibits superior performance compared to traditional machine learning methods and other research approaches (area under curve = 0.81, the area under the precision-recall curve = 0.73). Using PBAC attention layer output, we identified some pathways as potential core cancer regulators, providing good interpretability for drug treatment prediction. In summary, we presented PBAC, a powerful tool to predict drug responsiveness based on the biology pathway information and explore the potential cancer-driving pathways.
Collapse
Affiliation(s)
- Dexun Deng
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
| | - Xiaoqiang Xu
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cardiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
| | - Ting Cui
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
| | - Mingcong Xu
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
| | - Kunpeng Luo
- Department of Gastroenterology and HepatologySecond Affiliated Hospital of Harbin Medical UniversityHarbinHeilongjiangChina
| | - Han Zhang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
| | - Qiuyu Wang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cardiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
| | - Chao Song
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cardiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
| | - Chao Li
- Department of AnesthesiologyThe First Affiliated Hospital of University of South ChinaHengyangPR China
| | - Guohua Li
- Department of Pathophysiology, Key Laboratory for Arteriosclerology of Hunan Province, MOE Key Lab of Rare Pediatric Diseases, Hengyang Medical SchoolInstitute of Cardiovascular Disease, Hunan International Scientific and Technological Cooperation Base of Arteriosclerotic Disease, University of South ChinaHengyangHunanChina
| | - Desi Shang
- The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Hunan Provincial Key Laboratory of Multi‐omics And Artificial Intelligence of Cardiovascular DiseasesUniversity of South ChinaHengyangHunanChina
- School of ComputerUniversity of South ChinaHengyangHunanChina
- The First Affiliated Hospital, Institute of Cardiovascular Disease, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cardiology, The First Affiliated Hospital, Hengyang Medical SchoolUniversity of South ChinaHengyangChina
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
- Department of Cell Biology and Genetics, School of Basic Medical Sciences, Hengyang Medical SchoolUniversity of South ChinaHengyangHunanChina
| |
Collapse
|
20
|
Liu C, Wu H, Li K, Chi Y, Wu Z, Xing C. Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses. J Cell Mol Med 2024; 28:e18398. [PMID: 38785203 PMCID: PMC11117452 DOI: 10.1111/jcmm.18398] [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: 12/26/2023] [Revised: 04/03/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024] Open
Abstract
Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.
Collapse
Affiliation(s)
- Chunjiang Liu
- Department of General SurgeryThe Second Affiliated Hospital of Soochow UniversitySuzhouChina
| | - Huadong Wu
- Department of vascular surgeryFirst affiliated Hospital of Huzhou UniversityHuzhouChina
| | - Kuan Li
- Department of General SurgeryKunshan Hospital of Traditional Chinese MedicineKunshanChina
| | - Yongxing Chi
- Department of General SurgeryThe Second Affiliated Hospital of Soochow UniversitySuzhouChina
| | - Zhaoying Wu
- Department of General SurgeryThe Second Affiliated Hospital of Soochow UniversitySuzhouChina
| | - Chungen Xing
- Department of General SurgeryThe Second Affiliated Hospital of Soochow UniversitySuzhouChina
| |
Collapse
|
21
|
Zhou L, Wang X, Peng L, Chen M, Wen H. SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference. J Cell Mol Med 2024; 28:e18372. [PMID: 38747737 PMCID: PMC11095317 DOI: 10.1111/jcmm.18372] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 04/10/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024] Open
Abstract
Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.
Collapse
Affiliation(s)
- Liqian Zhou
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Xiwen Wang
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Lihong Peng
- School of Life Sciences and ChemistryHunan University of TechnologyHunanChina
| | - Min Chen
- School of Computer ScienceHunan Institute of TechnologyHengyangChina
| | - Hong Wen
- School of Computer ScienceHunan University of TechnologyHunanChina
| |
Collapse
|
22
|
Chen M, Deng Y, Li Z, Ye Y, Zeng L, He Z, Peng G. SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm. J Cell Mol Med 2024; 28:e18345. [PMID: 38693850 PMCID: PMC11063733 DOI: 10.1111/jcmm.18345] [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: 12/31/2023] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024] Open
Abstract
Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.
Collapse
Affiliation(s)
- Min Chen
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Yingwei Deng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Zejun Li
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Yifan Ye
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Lijun Zeng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Ziyi He
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| | - Guofang Peng
- Hunan Institute of TechnologySchool of Computer Science and EngineeringHengyang 421002China
| |
Collapse
|
23
|
Oh AR, Kwon JH, Jin G, Kong SM, Lee DJ, Park J. Association between inflammation-based prognostic markers and mortality after hip replacement. Sci Rep 2024; 14:9263. [PMID: 38649407 PMCID: PMC11035583 DOI: 10.1038/s41598-024-58646-y] [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: 12/07/2023] [Accepted: 04/02/2024] [Indexed: 04/25/2024] Open
Abstract
We aimed to evaluate the association between inflammation-based prognostic markers and mortality after hip replacement. From March 2010 to June 2020, we identified 5,369 consecutive adult patients undergoing hip replacement with C-reactive protein (CRP), albumin, and complete blood count measured within six months before surgery. Receiver operating characteristic (ROC) curves were generated to evaluate predictabilities and estimate thresholds of CRP-to-albumin ratio (CAR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). Patients were divided according to threshold, and mortality risk was compared. The primary outcome was one-year mortality, and overall mortality was also analyzed. One-year mortality was 2.9%. Receiver operating characteristics analysis revealed areas under the curve of 0.838, 0.832, 0.701, and 0.732 for CAR, NLR, PLR, and modified Glasgow Prognostic Score, respectively. The estimated thresholds were 2.10, 3.16, and 11.77 for CAR, NLR, and PLR, respectively. According to the estimated threshold, high CAR and NLR were associated with higher one-year mortality after adjustment (1.0% vs. 11.7%; HR = 2.16; 95% CI 1.32-3.52; p = 0.002 for CAR and 0.8% vs. 9.6%; HR = 2.05; 95% CI 1.24-3.39; p = 0.01 for NLR), but PLR did not show a significant mortality increase (1.4% vs. 7.4%; HR = 1.12; 95% CI 0.77-1.63; p = 0.57). Our study demonstrated associations of preoperative levels of CAR and NLR with postoperative mortality in patients undergoing hip replacement. Our findings may be helpful in predicting mortality in patients undergoing hip replacement.
Collapse
Affiliation(s)
- Ah Ran Oh
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Ji-Hye Kwon
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Gayoung Jin
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - So Myung Kong
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Dong Jae Lee
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea
| | - Jungchan Park
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-Ro, Gangnam-Gu, Seoul, 06351, Korea.
| |
Collapse
|
24
|
Song J, Li J, Pei X, Chen J, Wang L. Identification of cuproptosis-realated key genes and pathways in Parkinson's disease via bioinformatics analysis. PLoS One 2024; 19:e0299898. [PMID: 38626069 PMCID: PMC11020840 DOI: 10.1371/journal.pone.0299898] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 02/17/2024] [Indexed: 04/18/2024] Open
Abstract
INTRODUCTION Parkinson's disease (PD) is the second most common worldwide age-related neurodegenerative disorder without effective treatments. Cuproptosis is a newly proposed conception of cell death extensively studied in oncological diseases. Currently, whether cuproptosis contributes to PD remains largely unclear. METHODS The dataset GSE22491 was studied as the training dataset, and GSE100054 was the validation dataset. According to the expression levels of cuproptosis-related genes (CRGs) and differentially expressed genes (DEGs) between PD patients and normal samples, we obtained the differentially expressed CRGs. The protein-protein interaction (PPI) network was achieved through the Search Tool for the Retrieval of Interacting Genes. Meanwhile, the disease-associated module genes were screened from the weighted gene co-expression network analysis (WGCNA). Afterward, the intersection genes of WGCNA and PPI were obtained and enriched using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the key genes were identified from the datasets. The receiver operating characteristic curves were plotted and a PPI network was constructed, and the PD-related miRNAs and key genes-related miRNAs were intersected and enriched. Finally, the 2 hub genes were verified via qRT-PCR in the cell model of the PD and the control group. RESULTS 525 DEGs in the dataset GSE22491 were identified, including 128 upregulated genes and 397 downregulated genes. Based on the PPI network, 41 genes were obtained. Additionally, the dataset was integrated into 34 modules by WGCNA. 36 intersection genes found from WGCNA and PPI were significantly abundant in 7 pathways. The expression levels of the genes were validated, and 2 key genes were obtained, namely peptidase inhibitor 3 (PI3) and neuroserpin family I member 1 (SERPINI1). PD-related miRNAs and key genes-related miRNAs were intersected into 29 miRNAs including hsa-miR-30c-2-3p. At last, the qRT-PCR results of 2 hub genes showed that the expressions of mRNA were up-regulated in PD. CONCLUSION Taken together, this study demonstrates the coordination of cuproptosis in PD. The key genes and miRNAs offer novel perspectives in the pathogenesis and molecular targeting treatment for PD.
Collapse
Affiliation(s)
- Jia Song
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jia Li
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Xiaochen Pei
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Jiajun Chen
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Lin Wang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| |
Collapse
|
25
|
Singh S, Zeh G, Freiherr J, Bauer T, Türkmen I, Grasskamp AT. Classification of substances by health hazard using deep neural networks and molecular electron densities. J Cheminform 2024; 16:45. [PMID: 38627862 PMCID: PMC11302296 DOI: 10.1186/s13321-024-00835-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 03/23/2024] [Indexed: 08/09/2024] Open
Abstract
In this paper we present a method that allows leveraging 3D electron density information to train a deep neural network pipeline to segment regions of high, medium and low electronegativity and classify substances as health hazardous or non-hazardous. We show that this can be used for use-cases such as cosmetics and food products. For this purpose, we first generate 3D electron density cubes using semiempirical molecular calculations for a custom European Chemicals Agency (ECHA) subset consisting of substances labelled as hazardous and non-hazardous for cosmetic usage. Together with their 3-class electronegativity maps we train a modified 3D-UNet with electron density cubes to segment reactive sites in molecules and classify substances with an accuracy of 78.1%. We perform the same process on a custom food dataset (CompFood) consisting of hazardous and non-hazardous substances compiled from European Food Safety Authority (EFSA) OpenFoodTox, Food and Drug Administration (FDA) Generally Recognized as Safe (GRAS) and FooDB datasets to achieve a classification accuracy of 64.1%. Our results show that 3D electron densities and particularly masked electron densities, calculated by taking a product of original electron densities and regions of high and low electronegativity can be used to classify molecules for different use-cases and thus serve not only to guide safe-by-design product development but also aid in regulatory decisions. SCIENTIFIC CONTRIBUTION: We aim to contribute to the diverse 3D molecular representations used for training machine learning algorithms by showing that a deep learning network can be trained on 3D electron density representation of molecules. This approach has previously not been used to train machine learning models and it allows utilization of the true spatial domain of the molecule for prediction of properties such as their suitability for usage in cosmetics and food products and in future, to other molecular properties. The data and code used for training is accessible at https://github.com/s-singh-ivv/eDen-Substances .
Collapse
Affiliation(s)
- Satnam Singh
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Gina Zeh
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany
| | - Jessica Freiherr
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany
- Department of Psychiatry and Psychotherapy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schwabachanlage 6, 91054, Erlangen, Germany
| | - Thilo Bauer
- Computer Chemistry Center, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nägelsbachstr. 25, 91052, Erlangen, Germany
| | - Isik Türkmen
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany
| | - Andreas T Grasskamp
- Department of Sensory Analytics and Technologies, Fraunhofer Institute for Process Engineering and Packaging IVV, Giggenhauser Str. 35, 85354, Freising, Germany.
| |
Collapse
|
26
|
Cho SF, Yeh TJ, Wang HC, Du JS, Gau YC, Lin YY, Chuang TM, Liu YC, Hsiao HH, Moi SH. Prognostic mutation signature would serve as a potential prognostic predictor in patients with diffuse large B-cell lymphoma. Sci Rep 2024; 14:6161. [PMID: 38485750 PMCID: PMC10940711 DOI: 10.1038/s41598-024-56583-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024] Open
Abstract
The present study aimed to elucidate the prognostic mutation signature (PMS) associated with long-term survival in a diffuse large B-cell lymphoma (DLBCL) cohort. All data including derivation and validation cohorts were retrospectively retrieved from The Cancer Genome Atlas (TCGA) database and whole-exome sequencing (WES) data. The Lasso Cox regression analysis was used to construct the PMS based on WES data, and the PMS was determined using the area under the receiver operating curve (AUC). The predictive performance of eligible PMS was analyzed by time-dependent receiver operating curve (ROC) analyses. After the initial evaluation, a PMS composed of 94 PFS-related genes was constructed. Notably, this constructed PMS accurately predicted the 12-, 36-, and 60-month PFS, with AUC values of 0.982, 0.983, and 0.987, respectively. A higher level of PMS was closely linked to a significantly worse PFS, regardless of the molecular subtype. Further evaluation by forest plot revealed incorporation of international prognostic index or tumor mutational burden into PMS increased the prediction capability for PFS. The drug-gene interaction and pathway exploration revealed the PFS-related genes were associated with DNA damage, TP53, apoptosis, and immune cell functions. In conclusion, this study utilizing a high throughput genetic approach demonstrated that the PMS could serve as a prognostic predictor in DLBCL patients. Furthermore, the identification of the key signaling pathways for disease progression also provides information for further investigation to gain more insight into novel drug-resistant mechanisms.
Collapse
Affiliation(s)
- Shih-Feng Cho
- Division of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Center for Cancer Research, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Center for Liquid Biopsy and Cohort Research, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Tsung-Jang Yeh
- Division of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Center for Cancer Research, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Hui-Ching Wang
- Division of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Jeng-Shiun Du
- Division of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Yuh-Ching Gau
- Division of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Yu-Yin Lin
- Health Management Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Tzer-Ming Chuang
- Division of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Yi-Chang Liu
- Division of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Hui-Hua Hsiao
- Division of Hematology & Oncology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan
| | - Sin-Hua Moi
- Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
- Research Center for Precision Environmental Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
- Department of Medical Research, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, 807, Taiwan.
| |
Collapse
|
27
|
Qi L, Tang Z. Prognostic model revealing pyroptosis-related signatures in oral squamous cell carcinoma based on bioinformatics analysis. Sci Rep 2024; 14:6149. [PMID: 38480853 PMCID: PMC10937718 DOI: 10.1038/s41598-024-56694-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 03/09/2024] [Indexed: 03/17/2024] Open
Abstract
One of the most common oral carcinomas is oral squamous cell carcinoma (OSCC), bringing a heavy burden to global health. Although progresses have been made in the intervention of OSCC, 5 years survival of patients suffering from OSCC is poor like before regarding to the high invasiveness of OSCC, which causes metastasis and recurrence of the tumor. The relationship between pyroptosis and OSCC remains to be further investigated as pyroptosis in carcinomas has gained much attention. Herein, the key pyroptosis-related genes were identified according to The Cancer Genome Atlas (TCGA) dataset. Additionally, a prognostic model was constructed based upon three key genes (CTLA4, CD5, and IL12RB2) through least absolute shrinkage and selection operator (LASSO) analyses, as well as univariate and multivariate COX regression in OSCC. It was discovered that the high expression of these three genes was associated with the low-risk group. We also identified LAIR2 as a hub gene, whose expression negatively correlated with the risk score and the different immune cell infiltration. Finally, we proved that these three genes were independent prognostic factors linked to overall survival (OS), and reliable consequences could be predicted by this model. Our study revealed the relationship between pyroptosis and OSCC, providing insights into new treatment targets for preventing and treating OSCC.
Collapse
Affiliation(s)
- Lu Qi
- Hunan Key Laboratory of Oral Health Research, Hunan Clinical Research Center of Oral Major Diseases and Oral Health, Xiangya Stomatological Hospital, Xiangya School of Stomatology, Central South University, Changsha, 410000, China
| | - Zhangui Tang
- Hunan Key Laboratory of Oral Health Research, Hunan Clinical Research Center of Oral Major Diseases and Oral Health, Xiangya Stomatological Hospital, Xiangya School of Stomatology, Central South University, Changsha, 410000, China.
| |
Collapse
|
28
|
Hu H, Hu L, Deng Z, Jiang Q. A prognostic nomogram for recurrence survival in post-surgical patients with varicose veins of the lower extremities. Sci Rep 2024; 14:5486. [PMID: 38448552 PMCID: PMC10918178 DOI: 10.1038/s41598-024-55812-0] [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: 01/26/2024] [Accepted: 02/28/2024] [Indexed: 03/08/2024] Open
Abstract
Varicose veins of the lower extremities (VVLEs) are prevalent globally. This study aims to identify prognostic factors and develop a prediction model for recurrence survival (RS) in VVLEs patients after surgery. A retrospective analysis of VVLEs patients from the Third Hospital of Nanchang was conducted between April 2017 and March 2022. A LASSO (Least Absolute Shrinkage and Selection Operator) regression model pinpointed significant recurrence predictors, culminating in a prognostic nomogram. The model's performance was evaluated by C-index, receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). The LASSO regression identified seven predictors for the nomogram predicting 1-, 2-, and 5-year RS. These predictors were age, body mass index (BMI), hypertension, diabetes, the Clinical Etiological Anatomical Pathophysiological (CEAP) grade, iliac vein compression syndrome (IVCS), and postoperative compression stocking duration (PCSD). The nomogram's C-index was 0.716, with AUCs (Area Under the Curve scores) of 0.705, 0.725, and 0.758 for 1-, 2-, and 5-year RS, respectively. Calibration and decision curve analyses validated the model's predictive accuracy and clinical utility. Kaplan-Meier analysis distinguished between low and high-risk groups with significant prognostic differences (P < 0.05). This study has successfully developed and validated a nomogram for predicting RS in patients with VVLEs after surgery, enhancing personalized care and informing clinical decision-making.
Collapse
Affiliation(s)
- Hai Hu
- Department of General Surgery, The Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang, Jiangxi, China
| | - Lili Hu
- Department of pediatrics, The Third Hospital of Nanchang, Nanchang, China
| | - Ziqing Deng
- Department of General Surgery, The Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang, Jiangxi, China
| | - Qihua Jiang
- Department of General Surgery, The Third Hospital of Nanchang, No. 2, Xiangshan South Road, Xihu District, Nanchang, Jiangxi, China.
- Department of Breast Surgery, The Third Hospital of Nanchang, Nanchang, China.
| |
Collapse
|
29
|
Emami N, Ferdousi R. HormoNet: a deep learning approach for hormone-drug interaction prediction. BMC Bioinformatics 2024; 25:87. [PMID: 38418979 PMCID: PMC10903040 DOI: 10.1186/s12859-024-05708-7] [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: 12/05/2023] [Accepted: 02/16/2024] [Indexed: 03/02/2024] Open
Abstract
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is essential to understand the hormone-drug associations. Here, we present HormoNet to predict the HDI pairs and their risk level by integrating features derived from hormone and drug target proteins. To the best of our knowledge, this is one of the first attempts to employ deep learning approach for prediction of HDI prediction. Amino acid composition and pseudo amino acid composition were applied to represent target information using 30 physicochemical and conformational properties of the proteins. To handle the imbalance problem in the data, we applied synthetic minority over-sampling technique technique. Additionally, we constructed novel datasets for HDI prediction and the risk level of their interaction. HormoNet achieved high performance on our constructed hormone-drug benchmark datasets. The results provide insights into the understanding of the relationship between hormone and a drug, and indicate the potential benefit of reducing risk levels of interactions in designing more effective therapies for patients in drug treatments. Our benchmark datasets and the source codes for HormoNet are available in: https://github.com/EmamiNeda/HormoNet .
Collapse
Affiliation(s)
- Neda Emami
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran.
| | - Reza Ferdousi
- Department of Health Information Technology, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
| |
Collapse
|
30
|
Zhang X, Meng X, Wang P, Luan C, Wang H. Bioinformatics analysis for the identification of Sprouty-related EVH1 domain-containing protein 3 expression and its clinical significance in thyroid carcinoma. Sci Rep 2024; 14:4549. [PMID: 38402263 PMCID: PMC10894204 DOI: 10.1038/s41598-024-55187-2] [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: 12/12/2023] [Accepted: 02/21/2024] [Indexed: 02/26/2024] Open
Abstract
The poorly differentiated thyroid carcinoma (THCA) subtype is associated with an aggressive disease course, a less favorable overall prognosis, and an increased risk of distant organ metastasis. In this study, our objective was to explore the potential utility of the Sprouty-related EVH1 domain-containing protein 3 (SPRED3) as a biomarker for early diagnosis and prognosis in THCA patients. The differentially expressed prognostic-related genes associated with THCA were identified by querying The Cancer Genome Atlas (TCGA) database. The difference in the expression of the SPRED3 gene between thyroid carcinoma (THCA) tissues and normal tissues was analyzed using data from The Cancer Genome Atlas (TCGA) and further validated through immunohistochemistry. Univariate and multivariate Cox regression models were used, along with clinical information from THCA patients, to analyze the prognostic value of the SPRED3 gene in THCA patients. Functional enrichment analysis was subsequently performed to elucidate the molecular mechanisms underlying the regulatory effects of the SPRED3 gene on thyroid carcinoma. Additionally, we calculated the percentage of infiltrating immune cells in THCA patients and evaluated their correlation with SPRED3 gene expression. Compared with those in noncancerous thyroid tissue, the gene and protein expression levels of SPRED3 were found to be elevated in thyroid carcinoma tissues. Furthermore, the expression of SPRED3 in thyroid carcinoma exhibited significant correlations with tumor location, histological grade, pathological stage, and tumor node metastasis classification (TNM) stage. Univariate and multivariate Cox proportional hazards (Cox) regression analyses demonstrated that SPRED3 could serve as an independent prognostic factor for predicting the overall survival of THCA patients. The results of functional enrichment analysis suggested the potential involvement of SPRED3 in the regulation of extracellular matrix organization, epidermal development, signaling receptor activator activity, skin development, receptor ligand activity, glycosaminoglycan binding, neuroactive ligand‒receptor interaction, the IL-17 signaling pathway, and the PI3K-Akt signaling pathway. Additionally, there were significant correlations between the expression level of the SPRED3 gene and the infiltration of various immune cells (eosinophils, central memory T cells, neutrophils, macrophages, and NK cells) within the thyroid tumor microenvironment. SPRED3 can be used as a prognostic biomarker in patients with THCA could potentially be therapeutic target for THCA.
Collapse
Affiliation(s)
- Xiaowei Zhang
- Department of Orthopedics, Zibo Central Hospital, No 54, Gong Qing Tuan Xi Road, Zibo, 255036, People's Republic of China
| | - Xiangwei Meng
- Department of Drug Clinical Trials, Zibo Central Hospital, Zibo, People's Republic of China
| | - Pengyun Wang
- Department of Orthopedics, Zibo Central Hospital, No 54, Gong Qing Tuan Xi Road, Zibo, 255036, People's Republic of China
| | - Chong Luan
- Department of Orthopedics, Zibo Central Hospital, No 54, Gong Qing Tuan Xi Road, Zibo, 255036, People's Republic of China.
| | - Haiming Wang
- Department of thyroid and breast surgery, Zibo Municipal Hospital, Zibo, 255400, People's Republic of China.
| |
Collapse
|
31
|
Shoombuatong W, Homdee N, Schaduangrat N, Chumnanpuen P. Leveraging a meta-learning approach to advance the accuracy of Na v blocking peptides prediction. Sci Rep 2024; 14:4463. [PMID: 38396246 PMCID: PMC10891130 DOI: 10.1038/s41598-024-55160-z] [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: 12/28/2023] [Accepted: 02/21/2024] [Indexed: 02/25/2024] Open
Abstract
The voltage-gated sodium (Nav) channel is a crucial molecular component responsible for initiating and propagating action potentials. While the α subunit, forming the channel pore, plays a central role in this function, the complete physiological function of Nav channels relies on crucial interactions between the α subunit and auxiliary proteins, known as protein-protein interactions (PPI). Nav blocking peptides (NaBPs) have been recognized as a promising and alternative therapeutic agent for pain and itch. Although traditional experimental methods can precisely determine the effect and activity of NaBPs, they remain time-consuming and costly. Hence, machine learning (ML)-based methods that are capable of accurately contributing in silico prediction of NaBPs are highly desirable. In this study, we develop an innovative meta-learning-based NaBP prediction method (MetaNaBP). MetaNaBP generates new feature representations by employing a wide range of sequence-based feature descriptors that cover multiple perspectives, in combination with powerful ML algorithms. Then, these feature representations were optimized to identify informative features using a two-step feature selection method. Finally, the selected informative features were applied to develop the final meta-predictor. To the best of our knowledge, MetaNaBP is the first meta-predictor for NaBP prediction. Experimental results demonstrated that MetaNaBP achieved an accuracy of 0.948 and a Matthews correlation coefficient of 0.898 over the independent test dataset, which were 5.79% and 11.76% higher than the existing method. In addition, the discriminative power of our feature representations surpassed that of conventional feature descriptors over both the training and independent test datasets. We anticipate that MetaNaBP will be exploited for the large-scale prediction and analysis of NaBPs to narrow down the potential NaBPs.
Collapse
Affiliation(s)
- Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand.
| | - Nutta Homdee
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Nalini Schaduangrat
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| | - Pramote Chumnanpuen
- Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, 10900, Thailand
- Omics Center for Agriculture, Bioresources, Food, and Health, Kasetsart University (OmiKU), Bangkok, 10900, Thailand
| |
Collapse
|
32
|
Qu H, Liang Y, Guo Q, Lu L, Yang Y, Xu W, Zhang Y, Qin Y. Identifying CTH and MAP1LC3B as ferroptosis biomarkers for prognostic indication in gastric cancer decoding. Sci Rep 2024; 14:4352. [PMID: 38388661 PMCID: PMC10883967 DOI: 10.1038/s41598-024-54837-9] [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: 11/10/2023] [Accepted: 02/17/2024] [Indexed: 02/24/2024] Open
Abstract
Gastric cancer (GC), known for its high incidence and poor prognosis, urgently necessitates the identification of reliable prognostic biomarkers to enhance patient outcomes. We scrutinized data from 375 GC patients alongside 32 non-cancer controls, sourced from the TCGA database. A univariate Cox Proportional Hazards Model (COX) regression was employed to evaluate expressions of ferroptosis-related genes. This was followed by the application of Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate COX regression for the development of prognostic models. The composition of immune cell subtypes was quantified utilizing CIBERSORT, with their distribution in GC versus control samples being comparatively analyzed. Furthermore, the correlation between the expressions of Cystathionine Gamma-Lyase (CTH) and Microtubule Associated Protein 1 Light Chain 3 Beta (MAP1LC3B) and the abundance of immune cell subtypes was explored. Our bioinformatics findings underwent validation through immunohistochemical analysis. Our prognostic models integrated CTH and MAP1LC3B. Survival analysis indicated that patients categorized as high-risk, as defined by the model, exhibited significantly lower survival rates compared to their low-risk counterparts. Notably, CTH expression inversely correlated with monocyte levels, while MAP1LC3B expression showed an inverse relationship with the abundance of M2 macrophages. Immunohistochemical validation corroborated lower expressions of CTH and MAP1LC3B in GC tissues relative to control samples, in concordance with our bioinformatics predictions. Our study suggests that the dysregulation of CTH, MAP1LC3B, and the accompanying monocyte-macrophage dynamics could be pivotal in the prognosis of GC. These elements present potential targets for prognostic assessment and therapeutic intervention.
Collapse
Affiliation(s)
- Haishun Qu
- Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yunxiao Liang
- Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Quan Guo
- Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Ling Lu
- Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yanwei Yang
- Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Weicheng Xu
- Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yitian Zhang
- Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Yijue Qin
- Guangxi Academy of Medical Sciences, People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, China.
| |
Collapse
|
33
|
Liu L, Wei Y, Tan Z, Zhang Q, Sun J, Zhao Q. Predicting circRNA-RBP Binding Sites Using a Hybrid Deep Neural Network. Interdiscip Sci 2024:10.1007/s12539-024-00616-z. [PMID: 38381315 DOI: 10.1007/s12539-024-00616-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/26/2024] [Accepted: 01/29/2024] [Indexed: 02/22/2024]
Abstract
Circular RNAs (circRNAs) are non-coding RNAs generated by reverse splicing. They are involved in biological process and human diseases by interacting with specific RNA-binding proteins (RBPs). Due to traditional biological experiments being costly, computational methods have been proposed to predict the circRNA-RBP interaction. However, these methods have problems of single feature extraction. Therefore, we propose a novel model called circ-FHN, which utilizes only circRNA sequences to predict circRNA-RBP interactions. The circ-FHN approach involves feature coding and a hybrid deep learning model. Feature coding takes into account the physicochemical properties of circRNA sequences and employs four coding methods to extract sequence features. The hybrid deep structure comprises a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU). The CNN learns high-level abstract features, while the BiGRU captures long-term dependencies in the sequence. To assess the effectiveness of circ-FHN, we compared it to other computational methods on 16 datasets and conducted ablation experiments. Additionally, we conducted motif analysis. The results demonstrate that circ-FHN exhibits exceptional performance and surpasses other methods. circ-FHN is freely available at https://github.com/zhaoqi106/circ-FHN .
Collapse
Affiliation(s)
- Liwei Liu
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
- Key Laboratory of Computational Science and Application of Hainan Province, Hainan Normal University, Haikou, 571158, China
| | - Yixin Wei
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
| | - Zhebin Tan
- College of Software, Dalian Jiaotong University, Dalian, 116028, China
| | - Qi Zhang
- College of Science, Dalian Jiaotong University, Dalian, 116028, China
| | - Jianqiang Sun
- School of Information Science and Engineering, Linyi University, Linyi, 276000, China.
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
| |
Collapse
|
34
|
Ge W, Yuan G, Wang D, Dong L. Exploring the therapeutic mechanisms and prognostic targets of Biochanin A in glioblastoma via integrated computational analysis and in vitro experiments. Sci Rep 2024; 14:3783. [PMID: 38360888 PMCID: PMC10869694 DOI: 10.1038/s41598-024-53442-0] [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: 10/13/2023] [Accepted: 01/31/2024] [Indexed: 02/17/2024] Open
Abstract
Glioblastoma (GBM) is the most aggressive brain tumor and is characterized by a poor prognosis and high recurrence and mortality rates. Biochanin A (BCA) exhibits promising clinical anti-tumor effects. In this study, we aimed to explore the pharmacological mechanisms by which BCA acts against GBM. Network pharmacology was employed to identify overlapping target genes between BCA and GBM. Differentially expressed genes from the Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database were visualized using VolcaNose. Interactions among these overlapping genes were analyzed using the Search Tool for the Retrieval of Interacting Genes/Proteins database. Protein-protein interaction networks were constructed using Cytoscape 3.8.1. The Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology enrichment analyses were conducted using the Database for Annotation, Visualization, and Integrated Discovery. Survival analyses for these genes were performed using the GEPIA2 database. The Chinese Glioma Genome Atlas database was used to study the correlations between key prognostic genes. Molecular docking was confirmed using the DockThor database and visualized with PyMol software. Cell viability was assessed via the CCK-8 assay, apoptosis and the cell cycle stages were examined using flow cytometry, and protein expression was detected using western blotting. In all, 63 genes were initially identified as potential targets for BCA in treating GBM. Enrichment analysis suggested that the pharmacological mechanisms of BCA primarily involved cell cycle inhibition, induction of cell apoptosis, and immune regulation. Based on these findings, AKT1, EGFR, CASP3, and MMP9 were preliminarily predicted as key prognostic target genes for BCA in GBM treatment. Furthermore, molecular docking analysis suggested stable binding of BCA to the target protein. In vitro experiments revealed the efficacy of BCA in inhibiting GBM, with an IC50 value of 98.37 ± 2.21 μM. BCA inhibited cell proliferation, induced cell apoptosis, and arrested the cell cycle of GBM cells. Furthermore, the anti-tumor effects of BCA on U251 cells were linked to the regulation of the target protein. We utilized integrated bioinformatics analyses to predict targets and confirmed through experiments that BCA possesses remarkable anti-tumor activities. We present a novel approach for multi-target treatment of GBM using BCA.
Collapse
Affiliation(s)
- Wanwen Ge
- Lanzhou University Second Hospital, Lanzhou, 730030, China
| | - Guoqiang Yuan
- Lanzhou University Second Hospital, Lanzhou, 730030, China
| | - Dongping Wang
- Gansu Provincial Hospital, Lanzhou, 730000, China.
- Gansu University of Chinese Medicine, Lanzhou, 730000, China.
| | - Li Dong
- Gansu Provincial Hospital, Lanzhou, 730000, China.
| |
Collapse
|
35
|
Ge X, Lei S, Wang P, Wang W, Wang W. The metabolism-related lncRNA signature predicts the prognosis of breast cancer patients. Sci Rep 2024; 14:3500. [PMID: 38347041 PMCID: PMC10861477 DOI: 10.1038/s41598-024-53716-7] [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: 01/03/2024] [Accepted: 02/04/2024] [Indexed: 02/15/2024] Open
Abstract
Long non-coding RNAs (lncRNAs) involved in metabolism are recognized as significant factors in breast cancer (BC) progression. We constructed a novel prognostic signature for BC using metabolism-related lncRNAs and investigated their underlying mechanisms. The training and validation cohorts were established from BC patients acquired from two public sources: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The prognostic signature of metabolism-related lncRNAs was constructed using the least absolute shrinkage and selection operator (LASSO) cox regression analysis. We developed and validated a new prognostic risk model for BC using the signature of metabolism-related lncRNAs (SIRLNT, SIAH2-AS1, MIR205HG, USP30-AS1, MIR200CHG, TFAP2A-AS1, AP005131.2, AL031316.1, C6orf99). The risk score obtained from this signature was proven to be an independent prognostic factor for BC patients, resulting in a poor overall survival (OS) for individuals in the high-risk group. The area under the curve (AUC) for OS at three and five years were 0.67 and 0.65 in the TCGA cohort, and 0.697 and 0.68 in the GEO validation cohort, respectively. The prognostic signature demonstrated a robust association with the immunological state of BC patients. Conventional chemotherapeutics, such as docetaxel and paclitaxel, showed greater efficacy in BC patients classified as high-risk. A nomogram with a c-index of 0.764 was developed to forecast the survival time of BC patients, considering their risk score and age. The silencing of C6orf99 markedly decreased the proliferation, migration, and invasion capacities in MCF-7 cells. Our study identified a signature of metabolism-related lncRNAs that predicts outcomes in BC patients and could assist in tailoring personalized prevention and treatment plans.
Collapse
Affiliation(s)
- Xin Ge
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi District, Zhengzhou, 450052, China
| | - Shu Lei
- Department of Gynecology and Obstetrics, The Third Affiliated Hospital of Zhengzhou University, No.3 Kangfu Middle Street, Erqi District, Zhengzhou, 450052, China
| | - Panliang Wang
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi District, Zhengzhou, 450052, China
| | - Wenkang Wang
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi District, Zhengzhou, 450052, China
| | - Wendong Wang
- Department of Breast Surgery, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Erqi District, Zhengzhou, 450052, China.
| |
Collapse
|
36
|
Mahmud SMH, Goh KOM, Hosen MF, Nandi D, Shoombuatong W. Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features. Sci Rep 2024; 14:2961. [PMID: 38316843 PMCID: PMC10844231 DOI: 10.1038/s41598-024-52653-9] [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: 11/25/2023] [Accepted: 01/22/2024] [Indexed: 02/07/2024] Open
Abstract
DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
Collapse
Affiliation(s)
- S M Hasan Mahmud
- Department of Computer Science, American International University-Bangladesh (AIUB), Kuratoli, Dhaka, 1229, Bangladesh.
- Centre for Advanced Machine Learning and Applications (CAMLAs), Dhaka, 1229, Bangladesh.
| | - Kah Ong Michael Goh
- Faculty of Information Science & Technology (FIST), Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia.
| | - Md Faruk Hosen
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Santosh, Tangail, 1902, Bangladesh
| | - Dip Nandi
- Department of Computer Science, American International University-Bangladesh (AIUB), Kuratoli, Dhaka, 1229, Bangladesh
| | - Watshara Shoombuatong
- Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, 10700, Thailand
| |
Collapse
|
37
|
Song S, Yu J. Identification of the shared genes in type 2 diabetes mellitus and osteoarthritis and the role of quercetin. J Cell Mol Med 2024; 28:e18127. [PMID: 38332532 PMCID: PMC10853600 DOI: 10.1111/jcmm.18127] [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: 10/22/2023] [Revised: 12/28/2023] [Accepted: 01/09/2024] [Indexed: 02/10/2024] Open
Abstract
This study investigated the underlying comorbidity mechanism between type 2 diabetes mellitus (T2DM) and osteoarthritis (OA), while also assessing the therapeutic potential of quercetin for early intervention and treatment of these two diseases. The shared genes were obtained through GEO2R, limma and weighted gene co-expression network analysis (WGCNA), and validated using clinical databases and the area under the curves (ROC). Functional enrichment analysis was conducted to elucidate the underlying mechanisms of comorbidity between T2DM and OA. The infiltration of immune cells was analysed using the CIBERSORT algorithm in conjunction with ESTIMATE algorithm. Subsequently, transcriptional regulation analysis, potential chemical prediction, gene-disease association, relationships between the shared genes and ferroptosis as well as immunity-related genes were investigated along with molecular docking. We identified the 12 shared genes (EPHA3, RASIP1, PENK, LRRC17, CEBPB, EFEMP2, UBAP1, PPP1R15A, SPEN, MAFF, GADD45B and KLF4) across the four datasets. Our predictions suggested that targeting these shared genes could potentially serve as therapeutic interventions for both T2DM and OA. Specifically, they are involved in key signalling pathways such as p53, IL-17, NF-kB and MAPK signalling pathways. Furthermore, the regulation of ferroptosis and immunity appears to be interconnected in both diseases. Notably, in this context quercetin emerges as a promising drug candidate for treating T2DM and OA by specifically targeting the shared genes. We conducted a bioinformatics analysis to identify potential therapeutic targets, mechanisms and drugs for T2DM and OA, thereby offering novel insights into molecular therapy for these two diseases.
Collapse
Affiliation(s)
- Siyuan Song
- Affiliated Hospital of Nanjing University of Chinese MedicineNanjingJiangsuChina
- Nanjing University of Chinese MedicineNanjingJiangsuChina
- Department of Endocrinology, Jiangsu Province Hospital of Chinese MedicineAffiliated Hospital of Nanjing University of Chinese MedicineNanjingJiangsuChina
| | - Jiangyi Yu
- Affiliated Hospital of Nanjing University of Chinese MedicineNanjingJiangsuChina
- Nanjing University of Chinese MedicineNanjingJiangsuChina
- Department of Endocrinology, Jiangsu Province Hospital of Chinese MedicineAffiliated Hospital of Nanjing University of Chinese MedicineNanjingJiangsuChina
| |
Collapse
|
38
|
Shi H, Yuan X, Yang X, Huang R, Fan W, Liu G. A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration. BMC Genomics 2024; 25:125. [PMID: 38287255 PMCID: PMC10826017 DOI: 10.1186/s12864-024-10038-2] [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: 11/07/2023] [Accepted: 01/22/2024] [Indexed: 01/31/2024] Open
Abstract
BACKGROUND Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis. METHODS We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset. RESULTS In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929). CONCLUSION This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.
Collapse
Affiliation(s)
- Hongshuo Shi
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xin Yuan
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Xiao Yang
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Renyan Huang
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Weijing Fan
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Guobin Liu
- Department of Peripheral Vascular Surgery, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
- Guangming Traditional Chinese Medicine Hospital Pudong New Area, Shanghai, China.
| |
Collapse
|
39
|
Qiu Z, Qiao Y, Shi W, Liu X. A robust framework for enhancing cardiovascular disease risk prediction using an optimized category boosting model. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:2943-2969. [PMID: 38454714 DOI: 10.3934/mbe.2024131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Cardiovascular disease (CVD) is a leading cause of mortality worldwide, and it is of utmost importance to accurately assess the risk of cardiovascular disease for prevention and intervention purposes. In recent years, machine learning has shown significant advancements in the field of cardiovascular disease risk prediction. In this context, we propose a novel framework known as CVD-OCSCatBoost, designed for the precise prediction of cardiovascular disease risk and the assessment of various risk factors. The framework utilizes Lasso regression for feature selection and incorporates an optimized category-boosting tree (CatBoost) model. Furthermore, we propose the opposition-based learning cuckoo search (OCS) algorithm. By integrating OCS with the CatBoost model, our objective is to develop OCSCatBoost, an enhanced classifier offering improved accuracy and efficiency in predicting CVD. Extensive comparisons with popular algorithms like the particle swarm optimization (PSO) algorithm, the seagull optimization algorithm (SOA), the cuckoo search algorithm (CS), K-nearest-neighbor classification, decision tree, logistic regression, grid-search support vector machine (SVM), grid-search XGBoost, default CatBoost, and grid-search CatBoost validate the efficacy of the OCSCatBoost algorithm. The experimental results demonstrate that the OCSCatBoost model achieves superior performance compared to other models, with overall accuracy, recall, and AUC values of 73.67%, 72.17%, and 0.8024, respectively. These outcomes highlight the potential of CVD-OCSCatBoost for improving cardiovascular disease risk prediction.
Collapse
Affiliation(s)
- Zhaobin Qiu
- School of Mathematics and Information Sciences, North Minzu University, Yinchuan, China
| | - Ying Qiao
- School of Mathematics and Information Sciences, North Minzu University, Yinchuan, China
- Ningxia Collaborative Innovation Center for Scientific Computing and Intelligent Information Processing, North Minzu University, Yinchuan, China
| | - Wanyuan Shi
- School of Mathematics and Information Sciences, North Minzu University, Yinchuan, China
| | - Xiaoqian Liu
- School of Mathematics and Information Sciences, North Minzu University, Yinchuan, China
| |
Collapse
|
40
|
Guo J. Improving structure-based protein-ligand affinity prediction by graph representation learning and ensemble learning. PLoS One 2024; 19:e0296676. [PMID: 38232063 DOI: 10.1371/journal.pone.0296676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 12/15/2023] [Indexed: 01/19/2024] Open
Abstract
Predicting protein-ligand binding affinity presents a viable solution for accelerating the discovery of new lead compounds. The recent widespread application of machine learning approaches, especially graph neural networks, has brought new advancements in this field. However, some existing structure-based methods treat protein macromolecules and ligand small molecules in the same way and ignore the data heterogeneity, potentially leading to incomplete exploration of the biochemical information of ligands. In this work, we propose LGN, a graph neural network-based fusion model with extra ligand feature extraction to effectively capture local features and global features within the protein-ligand complex, and make use of interaction fingerprints. By combining the ligand-based features and interaction fingerprints, LGN achieves Pearson correlation coefficients of up to 0.842 on the PDBbind 2016 core set, compared to 0.807 when using the features of complex graphs alone. Finally, we verify the rationalization and generalization of our model through comprehensive experiments. We also compare our model with state-of-the-art baseline methods, which validates the superiority of our model. To reduce the impact of data similarity, we increase the robustness of the model by incorporating ensemble learning.
Collapse
Affiliation(s)
- Jia Guo
- Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Beijing, P.R. China
- Chongqing School, University of Chinese Academy of Sciences, Chongqing, China
| |
Collapse
|
41
|
Gao Y, Li W, Guo H, Hao Y, Lu L, Li J, Piao S. Construction of an abnormal glycosylation risk model and its application in predicting the prognosis of patients with head and neck cancer. Sci Rep 2024; 14:1310. [PMID: 38225277 PMCID: PMC10789784 DOI: 10.1038/s41598-023-50092-6] [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: 10/31/2023] [Accepted: 12/15/2023] [Indexed: 01/17/2024] Open
Abstract
Head and neck squamous cell carcinoma (HNSCC) is the most common malignant tumor of the head and neck, and the incidence rate is increasing year by year. Protein post-translational modification, recognized as a pivotal and extensive form of protein modification, has been established to possess a profound association with tumor occurrence and progression. This study employed bioinformatics analysis utilizing transcriptome sequencing data, patient survival data, and clinical data from HNSCC to establish predictive markers of genes associated with glycosylation as prognostic risk markers. The R procedure WGCNA was employed to construct a gene co-expression network using the gene expression profile and clinical characteristics of HNSCC samples. Multiple Cox Proportional Hazards Regression Model (Cox regression) and LASSO analysis were conducted to identify the key genes exhibiting the strongest association with prognosis. A risk score, known as the glycosylation-related genes risk score (GLRS), was subsequently formulated utilizing the aforementioned core genes. This scoring system facilitated the classification of samples into high-risk and low-risk categories, thereby enabling the prediction of patient prognosis. The association between GLRS and clinical variables was examined through both univariate and multivariate Cox regression analysis. The validation of six core genes was accomplished using quantitative real-time polymerase chain reaction (qRT-PCR). The findings demonstrated noteworthy variations in risk scores among subgroups, thereby affirming the efficacy of GLRS in prognosticating patient outcomes. Furthermore, a correlation has been observed between the risk-scoring model and immune infiltration. Moreover, significant disparities exist in the expression levels of diverse immune checkpoints, epithelial-mesenchymal transition genes, and angiogenic factors between the high and low-risk groups.
Collapse
Affiliation(s)
- Yihan Gao
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, China
- School of Stomatology, Harbin Medical University, Harbin, 150000, China
| | - Wenjing Li
- College of Animal Science, Zhejiang University, Hangzhou, 310058, China
| | - Haobing Guo
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, China
- School of Stomatology, Harbin Medical University, Harbin, 150000, China
| | - Yacui Hao
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, China
- School of Stomatology, Harbin Medical University, Harbin, 150000, China
| | - Lili Lu
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, China
- School of Stomatology, Harbin Medical University, Harbin, 150000, China
| | - Jichen Li
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, China.
- School of Stomatology, Harbin Medical University, Harbin, 150000, China.
| | - Songlin Piao
- Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, China.
- School of Stomatology, Harbin Medical University, Harbin, 150000, China.
| |
Collapse
|
42
|
Zhang Y, Cai G, Li X, Chen M. GCN-Based Heterogeneous Complex Feature Learning to Enhance Predictability for LncRNA-Disease Associations. ACS OMEGA 2024; 9:1472-1484. [PMID: 38222651 PMCID: PMC10785310 DOI: 10.1021/acsomega.3c07923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 01/16/2024]
Abstract
Using computational models to predict potential lncRNA-disease associations (LDAs) has emerged as an effective supplement to bioexperiments for exploring the pathogenesis of diseases. However, current computational models still face limitations in their ability to learn the complex features of bionetworks. In this study, HGCNLDA, a model which combines graph convolutional network (GCN)-based aggregation, heterogeneous information fusion, and a bilinear-decoder to infer LDAs was proposed. Recognizing the need to extract essential features during data processing, our HGCNLDA explored four key steps for uncovering interaction patterns within the bionetwork: (1) a novel type of tripartite heterogeneous network, known as the lncRNA-disease-miRNA network (LDMN), was constructed using computed similarities and known associations. (2) Homogeneous and heterogeneous features of nodes were extracted from domains within the LDMN by a GCN-based encoder. (3) Feature fusions, including bipolymerization operations and attention mechanism, were employed to capture a more accurate and comprehensive representation of nodes. (4) Bilinear-decoder was used to rebuild the edge type (or rating type) for a specific node pair, resulting in the predicted association score. Through a 5-fold cross-validation on two data sets, namely, data set1 and data set2, our HGCNLDA consistently demonstrated superior performance compared to five related models. It almost achieved the highest AUROC and AUPR values on both data sets, especially on data set2 where the results obtained were more challenging and objective. Case studies involving three real cancer scenarios further validated the practicality of HGCNLDA in identifying potential LDAs in real-world contexts. The source code and data for this study are available at https://github.com/zywait/HGCNLDA.
Collapse
Affiliation(s)
- Yi Zhang
- Guilin
University of Technology, Guilin 541004, China
- Guangxi Key Laboratory of Embedded Technology
and Intelligent System, Guilin University
of Technology, Guilin 541004, China
| | - Gangsheng Cai
- Guilin
University of Technology, Guilin 541004, China
- Guangxi Key Laboratory of Embedded Technology
and Intelligent System, Guilin University
of Technology, Guilin 541004, China
| | - Xin Li
- Guilin
University of Technology, Guilin 541004, China
- Guangxi Key Laboratory of Embedded Technology
and Intelligent System, Guilin University
of Technology, Guilin 541004, China
| | - Min Chen
- School
of Computer Science and Technology, Hunan
Institute of Technology, Hengyang 421010, China
| |
Collapse
|
43
|
Zhao H, Zhu B, Jiang T, Cui Z, Wu H. Identification of DNA-protein binding residues through integration of Transformer encoder and Bi-directional Long Short-Term Memory. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:170-185. [PMID: 38303418 DOI: 10.3934/mbe.2024008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
DNA-protein binding is crucial for the normal development and function of organisms. The significance of accurately identifying DNA-protein binding sites lies in its role in disease prevention and the development of innovative approaches to disease treatment. In the present study, we introduce a precise and robust identifier for DNA-protein binding residues. In the context of protein representation, we combine the evolutionary information of the protein, represented by its position-specific scoring matrix, with the spatial information of the protein's secondary structure, enriching the overall informational content. This approach initially employs a combination of Bi-directional Long Short-Term Memory and Transformer encoder to jointly extract the interdependencies among residues within the protein sequence. Subsequently, convolutional operations are applied to the resulting feature matrix to capture local features of the residues. Experimental results on the benchmark dataset demonstrate that our method exhibits a higher level of competitiveness when compared to contemporary classifiers. Specifically, our method achieved an MCC of 0.349, SP of 96.50%, SN of 44.03% and ACC of 94.59% on the PDNA-41 dataset.
Collapse
Affiliation(s)
- Haipeng Zhao
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Baozhong Zhu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | | | - Zhiming Cui
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| | - Hongjie Wu
- School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China
| |
Collapse
|
44
|
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: 9] [Impact Index Per Article: 9.0] [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.
Collapse
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.
| |
Collapse
|
45
|
Gopalakrishnan S, Venkatraman S. Prediction of influential proteins and enzymes of certain diseases using a directed unimodular hypergraph. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:325-345. [PMID: 38303425 DOI: 10.3934/mbe.2024015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Protein-protein interaction (PPI) analysis based on mathematical modeling is an efficient means of identifying hub proteins, corresponding enzymes and many underlying structures. In this paper, a method for the analysis of PPI is introduced and used to analyze protein interactions of diseases such as Parkinson's, COVID-19 and diabetes melitus. A directed hypergraph is used to represent PPI interactions. A novel directed hypergraph depth-first search algorithm is introduced to find the longest paths. The minor hypergraph reduces the dimension of the directed hypergraph, representing the longest paths and results in the unimodular hypergraph. The property of unimodular hypergraph clusters influential proteins and enzymes that are related thereby providing potential avenues for disease treatment.
Collapse
Affiliation(s)
- Sathyanarayanan Gopalakrishnan
- Department of Mathematics, Srinivasa Ramanujan Centre, School of Arts, Sciences, Humanities and Education, SASTRA Deemed University, Thanjavur, India
| | - Swaminathan Venkatraman
- Department of Mathematics, School of Arts, Sciences, Humanities and Education, SASTRA Deemed University, Thanjavur, India
| |
Collapse
|
46
|
Xiao K, Ullah I, Yang F, Wang J, Hou C, Liu Y, Li X. Comprehensive bioinformatics analysis of FXR1 across pan-cancer: Unraveling its diagnostic, prognostic, and immunological significance. Medicine (Baltimore) 2023; 102:e36456. [PMID: 38050239 PMCID: PMC10695598 DOI: 10.1097/md.0000000000036456] [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] [Accepted: 11/13/2023] [Indexed: 12/06/2023] Open
Abstract
Fragile X-related protein 1 (FXR1) is an RNA-binding protein that belongs to the fragile X-related (FXR) family. Studies have shown that FXR1 plays an important role in cancer cell proliferation, invasion and migration and is differentially expressed in cancers. This study aimed to gain a comprehensive and systematic understanding of the analysis of FXR1's role in cancers. This would lead to a better understanding of how it contributes to the development and progression of various malignancies. this study conducted through The Cancer Genome Atlas (TCGA), GTEx, cBioPortal, TISIDB, GEPIA2 and HPA databases to investigated FXR1's role in cancers. For data analysis, various software platforms and web platforms were used, such as R, Cytoscape, hiplot plateform. A significant difference in FXR1 expression was observed across molecular and immune subtypes and across types of cancer. FXR1 expression correlates with disease-specific survival (DSS), and overall survival (OS) in several cancer pathways, further in progression-free interval (PFI) in most cancers. Additionally, FXR1 showed a correlation with genetic markers of immunomodulators in different cancer types. Our study provides insights into the role of FXR1 in promoting, inhibiting, and treating diverse cancers. FXR1 has the potential to serve as a diagnostic and prognostic biomarker for cancer, with therapeutic value in immune-based, targeted, or cytotoxic treatments. Further clinical validation and exploration of FXR1 in cancer treatment is necessary.
Collapse
Affiliation(s)
- Keyuan Xiao
- Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Ihsan Ullah
- National Chinmedomics Research Center, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Fan Yang
- Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Jiao Wang
- Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Chunxia Hou
- Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, China
| | - Yuqiang Liu
- National Chinmedomics Research Center, Heilongjiang University of Chinese Medicine, Harbin, China
| | - Xinghua Li
- Changzhi People’s Hospital Affiliated to Changzhi Medical College, Changzhi, China
| |
Collapse
|