1
|
Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies. Mol Biotechnol 2024:10.1007/s12033-024-01133-6. [PMID: 38565775 DOI: 10.1007/s12033-024-01133-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Accepted: 02/27/2024] [Indexed: 04/04/2024]
Abstract
In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
Collapse
Affiliation(s)
- Arnab Mukherjee
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Suzanna Abraham
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - Akshita Singh
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - S Balaji
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India
| | - K S Mukunthan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India.
| |
Collapse
|
2
|
Alanazi SA, Alshammari N, Alruwaili M, Junaid K, Abid MR, Ahmad F. Integrative analysis of RNA expression data unveils distinct cancer types through machine learning techniques. Saudi J Biol Sci 2024; 31:103918. [PMID: 38283772 PMCID: PMC10821588 DOI: 10.1016/j.sjbs.2023.103918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 12/15/2023] [Accepted: 12/22/2023] [Indexed: 01/30/2024] Open
Abstract
Cancer is a highly complex and heterogeneous disease. Traditional methods of cancer classification based on histopathology have limitations in guiding personalized prognosis and therapy. Gene expression profiling provides a powerful approach to unraveling molecular intricacies and better-stratifying cancer subtypes. In this study, we performed an integrative analysis of RNA sequencing data from five cancer types - BRCA, KIRC, COAD, LUAD, and PRAD. A machine learning workflow consisting of dataset identification, normalization, feature selection, dimensionality reduction, clustering, and classification was implemented. The k-means algorithm was applied to categorize samples into distinct clusters based solely on gene expression patterns. Five unique clusters emerged from the unsupervised machine learning based analysis, significantly correlating with the known cancer types. BRCA aligned predominantly with one cluster, while COAD spanned three clusters. KIRC was represented within two main clusters. LUAD is associated strongly with a single cluster and PRAD with another cluster. This demonstrates the ability of machine learning approaches to unravel complex signatures within transcriptomic profiles that can delineate cancer subtypes. The proposed study highlights the potential of integrative analytics to derive meaningful biological insights from high-dimensional omics datasets. Molecular subtyping through machine learning clustering enhances our understanding of the intrinsic heterogeneities and pathways dysregulated in different cancers. Overall, this study exemplifies a powerful computational framework to classify gene expressions of patients having different types of cancers and guide personalized therapeutic decisions. Finally, Wide Neural Network demonstrates a significantly higher accuracy, achieving 99.834% on the validation set and an even more impressive 99.995% on the test set.
Collapse
Affiliation(s)
- Saad Awadh Alanazi
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf 72341, Saudi Arabia
| | - Nasser Alshammari
- Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka, Aljouf 72341, Saudi Arabia
| | - Maddalah Alruwaili
- Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka 72341, Saudi Arabia
| | - Kashaf Junaid
- School of Biological and Behavioural Sciences, Queen Mary University of London, London E1 4NS, United Kingdom
| | - Muhammad Rizwan Abid
- Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, United States
| | - Fahad Ahmad
- Department of Basic Sciences, Common First Year, Jouf University, Sakaka 72341, Saudi Arabia
| |
Collapse
|
3
|
M T, Koti MS, B A N, V G, K P S, Mathivanan SK, Dalu GT. Lung cancer diagnosis based on weighted convolutional neural network using gene data expression. Sci Rep 2024; 14:3656. [PMID: 38351141 PMCID: PMC10864291 DOI: 10.1038/s41598-024-54124-7] [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: 10/06/2023] [Accepted: 02/08/2024] [Indexed: 02/16/2024] Open
Abstract
Lung cancer is thought to be a genetic disease with a variety of unknown origins. Globocan2020 report tells in 2020 new cancer cases identified was 19.3 million and nearly 10.0 million died owed to cancer. GLOBOCAN envisages that the cancer cases will raised to 28.4 million in 2040. This charge is superior to the combined rates of the former generally prevalent malignancies, like breast, colorectal, and prostate cancers. For attribute selection in previous work, the information gain model was applied. Then, for lung cancer prediction, multilayer perceptron, random subspace, and sequential minimal optimization (SMO) are used. However, the total number of parameters in a multilayer perceptron can become extremely large. This is inefficient because of the duplication in such high dimensions, and SMO can become ineffective due to its calculating method and maintaining a single threshold value for prediction. To avoid these difficulties, our research presented a novel technique including Z-score normalization, levy flight cuckoo search optimization, and a weighted convolutional neural network for predicting lung cancer. This result findings show that the proposed technique is effective in precision, recall, and accuracy for the Kent Ridge Bio-Medical Dataset Repository.
Collapse
Affiliation(s)
- Thangamani M
- Department of Computer Science and Engineering, Hindusthan Institute of Technology, Valley Campus, Pollachi Highway, Othakkalmandapam (Post), Coimbatore, Tamil Nadu, 641032, India
| | - Manjula Sanjay Koti
- Department of Master of Computer Applications, Dayananda Sagar Academy of Technology and Management, Bangalore, Karnataka, 560082, India
| | - Nagashree B A
- Department of Computer Science, School of Computing, Amrita Vishwa Vidyapeetham, Mysuru, 570026, India
| | - Geetha V
- Department of Computer Science, School of Applied Sciences, REVA University, Bangalore, 560064, India
| | - Shreyas K P
- Department of Computer Science and Applications, School of Computer Science and Applications, REVA University, Bangalore, 560064, India
| | | | - Gemmachis Teshite Dalu
- Department of Software Engineering, College of Computing and Informatics, Haramaya University, POB 138, Dire Dawa, Ethiopia.
| |
Collapse
|
4
|
Çalışkan M, Tazaki K. AI/ML advances in non-small cell lung cancer biomarker discovery. Front Oncol 2023; 13:1260374. [PMID: 38148837 PMCID: PMC10750392 DOI: 10.3389/fonc.2023.1260374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 11/16/2023] [Indexed: 12/28/2023] Open
Abstract
Lung cancer is the leading cause of cancer deaths among both men and women, representing approximately 25% of cancer fatalities each year. The treatment landscape for non-small cell lung cancer (NSCLC) is rapidly evolving due to the progress made in biomarker-driven targeted therapies. While advancements in targeted treatments have improved survival rates for NSCLC patients with actionable biomarkers, long-term survival remains low, with an overall 5-year relative survival rate below 20%. Artificial intelligence/machine learning (AI/ML) algorithms have shown promise in biomarker discovery, yet NSCLC-specific studies capturing the clinical challenges targeted and emerging patterns identified using AI/ML approaches are lacking. Here, we employed a text-mining approach and identified 215 studies that reported potential biomarkers of NSCLC using AI/ML algorithms. We catalogued these studies with respect to BEST (Biomarkers, EndpointS, and other Tools) biomarker sub-types and summarized emerging patterns and trends in AI/ML-driven NSCLC biomarker discovery. We anticipate that our comprehensive review will contribute to the current understanding of AI/ML advances in NSCLC biomarker research and provide an important catalogue that may facilitate clinical adoption of AI/ML-derived biomarkers.
Collapse
Affiliation(s)
- Minal Çalışkan
- Translational Science Department, Precision Medicine Function, Daiichi Sankyo, Inc., Basking Ridge, NJ, United States
| | - Koichi Tazaki
- Translational Science Department I, Precision Medicine Function, Daiichi Sankyo, Tokyo, Japan
| |
Collapse
|
5
|
Kishore A, Venkataramana L, Prasad DVV, Mohan A, Jha B. Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture. Med Biol Eng Comput 2023; 61:2895-2919. [PMID: 37530887 DOI: 10.1007/s11517-023-02892-1] [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: 12/06/2022] [Accepted: 07/19/2023] [Indexed: 08/03/2023]
Abstract
Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data-based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages - stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 - minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 - mRMR, mutual information (MI) and CFS, and Pipeline 3 - mRMR and support vector machine-recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.
Collapse
Affiliation(s)
- Akash Kishore
- Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India
| | - Lokeswari Venkataramana
- Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India.
| | - D Venkata Vara Prasad
- Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India
| | - Akshaya Mohan
- Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India
| | - Bhavya Jha
- Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India
| |
Collapse
|
6
|
Mohamed TIA, Ezugwu AE, Fonou-Dombeu JV, Ikotun AM, Mohammed M. A bio-inspired convolution neural network architecture for automatic breast cancer detection and classification using RNA-Seq gene expression data. Sci Rep 2023; 13:14644. [PMID: 37670037 PMCID: PMC10480180 DOI: 10.1038/s41598-023-41731-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 08/30/2023] [Indexed: 09/07/2023] Open
Abstract
Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy.
Collapse
Affiliation(s)
- Tehnan I A Mohamed
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa.
| | - Absalom E Ezugwu
- Unit for Data Science and Computing, North-West University, Potchefstroom, South Africa.
| | - Jean Vincent Fonou-Dombeu
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| | - Abiodun M Ikotun
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| | - Mohanad Mohammed
- School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa
| |
Collapse
|
7
|
Yazlovitskaya EM, Graham TR. Type IV P-Type ATPases: Recent Updates in Cancer Development, Progression, and Treatment. Cancers (Basel) 2023; 15:4327. [PMID: 37686603 PMCID: PMC10486736 DOI: 10.3390/cancers15174327] [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: 07/11/2023] [Revised: 08/15/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
Adaptations of cancer cells for survival are remarkable. One of the most significant properties of cancer cells to prevent the immune system response and resist chemotherapy is the altered lipid metabolism and resulting irregular cell membrane composition. The phospholipid distribution in the plasma membrane of normal animal cells is distinctly asymmetric. Lipid flippases are a family of enzymes regulating membrane asymmetry, and the main class of flippases are type IV P-type ATPases (P4-ATPases). Alteration in the function of flippases results in changes to membrane organization. For some lipids, such as phosphatidylserine, the changes are so drastic that they are considered cancer biomarkers. This review will analyze and discuss recent publications highlighting the role that P4-ATPases play in the development and progression of various cancer types, as well as prospects of targeting P4-ATPases for anti-cancer treatment.
Collapse
Affiliation(s)
| | - Todd R. Graham
- Department of Biological Sciences, Vanderbilt University, Nashville, TN 37232, USA
| |
Collapse
|
8
|
Paylar B, Längkvist M, Jass J, Olsson PE. Utilization of Computer Classification Methods for Exposure Prediction and Gene Selection in Daphnia magna Toxicogenomics. BIOLOGY 2023; 12:biology12050692. [PMID: 37237504 DOI: 10.3390/biology12050692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/02/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023]
Abstract
Zinc (Zn) is an essential element that influences many cellular functions. Depending on bioavailability, Zn can cause both deficiency and toxicity. Zn bioavailability is influenced by water hardness. Therefore, water quality analysis for health-risk assessment should consider both Zn concentration and water hardness. However, exposure media selection for traditional toxicology tests are set to defined hardness levels and do not represent the diverse water chemistry compositions observed in nature. Moreover, these tests commonly use whole organism endpoints, such as survival and reproduction, which require high numbers of test animals and are labor intensive. Gene expression stands out as a promising alternative to provide insight into molecular events that can be used for risk assessment. In this work, we apply machine learning techniques to classify the Zn concentrations and water hardness from Daphnia magna gene expression by using quantitative PCR. A method for gene ranking was explored using techniques from game theory, namely, Shapley values. The results show that standard machine learning classifiers can classify both Zn concentration and water hardness simultaneously, and that Shapley values are a versatile and useful alternative for gene ranking that can provide insight about the importance of individual genes.
Collapse
Affiliation(s)
- Berkay Paylar
- The Life Science Center-Biology, School of Science and Technology, Örebro University, SE-701 82 Örebro, Sweden
| | - Martin Längkvist
- Center for Applied Autonomous Sensor Systems, Örebro University, SE-701 82 Örebro, Sweden
| | - Jana Jass
- The Life Science Center-Biology, School of Science and Technology, Örebro University, SE-701 82 Örebro, Sweden
| | - Per-Erik Olsson
- The Life Science Center-Biology, School of Science and Technology, Örebro University, SE-701 82 Örebro, Sweden
| |
Collapse
|
9
|
Alharbi F, Vakanski A. Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review. Bioengineering (Basel) 2023; 10:bioengineering10020173. [PMID: 36829667 PMCID: PMC9952758 DOI: 10.3390/bioengineering10020173] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 01/24/2023] [Accepted: 01/26/2023] [Indexed: 01/31/2023] Open
Abstract
Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.
Collapse
|
10
|
Favreau E, Geist KS, Wyatt CDR, Toth AL, Sumner S, Rehan SM. Co-expression Gene Networks and Machine-learning Algorithms Unveil a Core Genetic Toolkit for Reproductive Division of Labour in Rudimentary Insect Societies. Genome Biol Evol 2022; 15:6926469. [PMID: 36527688 PMCID: PMC9830183 DOI: 10.1093/gbe/evac174] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 12/06/2022] [Accepted: 12/10/2022] [Indexed: 12/23/2022] Open
Abstract
The evolution of eusociality requires that individuals forgo some or all their own reproduction to assist the reproduction of others in their group, such as a primary egg-laying queen. A major open question is how genes and genetic pathways sculpt the evolution of eusociality, especially in rudimentary forms of sociality-those with smaller cooperative nests when compared with species such as honeybees that possess large societies. We lack comprehensive comparative studies examining shared patterns and processes across multiple social lineages. Here we examine the mechanisms of molecular convergence across two lineages of bees and wasps exhibiting such rudimentary societies. These societies consist of few individuals and their life histories range from facultative to obligately social. Using six species across four independent origins of sociality, we conduct a comparative meta-analysis of publicly available transcriptomes. Standard methods detected little similarity in patterns of differential gene expression in brain transcriptomes among reproductive and non-reproductive individuals across species. By contrast, both supervised machine learning and consensus co-expression network approaches uncovered sets of genes with conserved expression patterns among reproductive and non-reproductive phenotypes across species. These sets overlap substantially, and may comprise a shared genetic "toolkit" for sociality across the distantly related taxa of bees and wasps and independently evolved lineages of sociality. We also found many lineage-specific genes and co-expression modules associated with social phenotypes and possible signatures of shared life-history traits. These results reveal how taxon-specific molecular mechanisms complement a core toolkit of molecular processes in sculpting traits related to the evolution of eusociality.
Collapse
Affiliation(s)
| | | | - Christopher D R Wyatt
- Department of Genetics, Environment, Evolution, University College London, London WC1E 6BT, United Kingdom
| | | | | | | |
Collapse
|
11
|
Sui JSY, Martin P, Keogh A, Murchan P, Ryan L, Nicholson S, Cuffe S, Broin PÓ, Finn SP, Fitzmaurice GJ, Ryan R, Young V, Gray SG. Altered expression of ACOX2 in non-small cell lung cancer. BMC Pulm Med 2022; 22:321. [PMID: 35999530 PMCID: PMC9396774 DOI: 10.1186/s12890-022-02115-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 08/16/2022] [Indexed: 12/24/2022] Open
Abstract
Peroxisomes are organelles that play essential roles in many metabolic processes, but also play roles in innate immunity, signal transduction, aging and cancer. One of the main functions of peroxisomes is the processing of very-long chain fatty acids into metabolites that can be directed to the mitochondria. One key family of enzymes in this process are the peroxisomal acyl-CoA oxidases (ACOX1, ACOX2 and ACOX3), the expression of which has been shown to be dysregulated in some cancers. Very little is however known about the expression of this family of oxidases in non-small cell lung cancer (NSCLC). ACOX2 has however been suggested to be elevated at the mRNA level in over 10% of NSCLC, and in the present study using both standard and bioinformatics approaches we show that expression of ACOX2 is significantly altered in NSCLC. ACOX2 mRNA expression is linked to a number of mutated genes, and associations between ACOX2 expression and tumour mutational burden and immune cell infiltration were explored. Links between ACOX2 expression and candidate therapies for oncogenic driver mutations such as KRAS were also identified. Furthermore, levels of acyl-CoA oxidases and other associated peroxisomal genes were explored to identify further links between the peroxisomal pathway and NSCLC. The results of this biomarker driven study suggest that ACOX2 may have potential clinical utility in the diagnosis, prognosis and stratification of patients into various therapeutically targetable options.
Collapse
Affiliation(s)
- Jane S Y Sui
- Thoracic Oncology Research Group, Laboratory Medicine and Molecular Pathology, Central Pathology Laboratory, St. James's Hospital, Dublin, D08RX0X, Ireland.,Department of Medicine, Thoracic Oncology Service, Memorial Sloan Kettering Cancer Center, New York, USA
| | - Petra Martin
- Thoracic Oncology Research Group, Laboratory Medicine and Molecular Pathology, Central Pathology Laboratory, St. James's Hospital, Dublin, D08RX0X, Ireland.,Midland Regional Hospital Tullamore, Tullamore, Ireland
| | - Anna Keogh
- Thoracic Oncology Research Group, Laboratory Medicine and Molecular Pathology, Central Pathology Laboratory, St. James's Hospital, Dublin, D08RX0X, Ireland
| | - Pierre Murchan
- Department of Histopathology and Morbid Anatomy, Trinity College Dublin, Dublin, Ireland.,School of Mathematics, Statistics, and Applied Mathematics, National University of Ireland Galway, Galway, Ireland
| | - Lisa Ryan
- Department of Histopathology, Labmed Directorate, St. James's Hospital, Dublin, Ireland
| | - Siobhan Nicholson
- Department of Histopathology, Labmed Directorate, St. James's Hospital, Dublin, Ireland
| | - Sinead Cuffe
- HOPE Directorate, St James's Hospital, Dublin, Ireland
| | - Pilib Ó Broin
- School of Mathematics, Statistics, and Applied Mathematics, National University of Ireland Galway, Galway, Ireland
| | - Stephen P Finn
- Thoracic Oncology Research Group, Laboratory Medicine and Molecular Pathology, Central Pathology Laboratory, St. James's Hospital, Dublin, D08RX0X, Ireland.,Department of Histopathology and Morbid Anatomy, Trinity College Dublin, Dublin, Ireland.,Department of Histopathology, Labmed Directorate, St. James's Hospital, Dublin, Ireland.,Cancer Molecular Diagnostics, Labmed Directorate, St. James's Hospital, Dublin, Ireland
| | - Gerard J Fitzmaurice
- Surgery, Anaesthesia and Critical Care Directorate, St James's Hospital, Dublin, Ireland
| | - Ronan Ryan
- Surgery, Anaesthesia and Critical Care Directorate, St James's Hospital, Dublin, Ireland
| | - Vincent Young
- Surgery, Anaesthesia and Critical Care Directorate, St James's Hospital, Dublin, Ireland
| | - Steven G Gray
- Thoracic Oncology Research Group, Laboratory Medicine and Molecular Pathology, Central Pathology Laboratory, St. James's Hospital, Dublin, D08RX0X, Ireland. .,Department of Clinical Medicine, Trinity College Dublin, Dublin, Ireland. .,School of Biological Sciences, Technological University Dublin, Dublin, Ireland.
| |
Collapse
|
12
|
MotieGhader H, Tabrizi-Nezhadi P, Deldar Abad Paskeh M, Baradaran B, Mokhtarzadeh A, Hashemi M, Lanjanian H, Jazayeri SM, Maleki M, Khodadadi E, Nematzadeh S, Kiani F, Maghsoudloo M, Masoudi-Nejad A. Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis. Sci Rep 2022; 12:9417. [PMID: 35676421 PMCID: PMC9177601 DOI: 10.1038/s41598-022-13719-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 05/16/2022] [Indexed: 12/14/2022] Open
Abstract
Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.
Collapse
|
13
|
Park H, Yamaguchi R, Imoto S, Miyano S. Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks. PLoS One 2022; 17:e0261630. [PMID: 35584089 PMCID: PMC9116684 DOI: 10.1371/journal.pone.0261630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Accepted: 12/06/2021] [Indexed: 12/03/2022] Open
Abstract
In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.
Collapse
Affiliation(s)
- Heewon Park
- M&D Data Science Center, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
- * E-mail:
| | - Rui Yamaguchi
- Division of Cancer Systems Biology, Aichi Cancer Center Research Institute, Chikusa-ku, Nagoya, Aichi, Japan
- Division of Cancer Informatics, Nagoya University Graduate School of Medicine, Showa-ku, Nagoya, Aichi, Japan
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Seiya Imoto
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| | - Satoru Miyano
- M&D Data Science Center, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo, Japan
- Human Genome Center, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo, Japan
| |
Collapse
|
14
|
Lu Q, Chen F, Li Q, Chen L, Tong L, Tian G, Zhou X. A Machine Learning Method to Trace Cancer Primary Lesion Using Microarray-Based Gene Expression Data. Front Oncol 2022; 12:832567. [PMID: 35530331 PMCID: PMC9071249 DOI: 10.3389/fonc.2022.832567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 03/21/2022] [Indexed: 11/17/2022] Open
Abstract
Cancer of unknown primary site (CUP) is a heterogeneous group of cancers whose tissue of origin remains unknown after detailed investigation by conventional clinical methods. The number of CUP accounts for roughly 3%–5% of all human malignancies. CUP patients are usually treated with broad-spectrum chemotherapy, which often leads to a poor prognosis. Recent studies suggest that the treatment targeting the primary lesion of CUP will significantly improve the prognosis of the patient. Therefore, it is urgent to develop an efficient method to accurately detect tissue of origin of CUP in clinical cancer research. In this work, we developed a novel framework that uses Extreme Gradient Boosting (XGBoost) to trace the primary site of CUP based on microarray-based gene expression data. First, we downloaded the microarray-based gene expression profiles of 59,385 genes for 57,08 samples from The Cancer Genome Atlas (TCGA) and 6,364 genes for 3,101 samples from the Gene Expression Omnibus (GEO). Both data were divided into training and independent testing data with a ratio of 4:1. Then, we obtained in the training data 200 and 290 genes from TCGA and the GEO datasets, respectively, to train XGBoost models for the identification of the primary site of CUP. The overall 5-fold cross-validation accuracies of our methods were 96.9% and 95.3% on TCGA and GEO training datasets, respectively. Meanwhile, the macro-precision for the independent dataset reached 96.75% and 98.8% on, respectively, TCGA and GEO. Experimental results demonstrated that the XGBoost framework not only can reduce the cost of clinical cancer traceability but also has high efficiency, which might be useful in clinical usage.
Collapse
Affiliation(s)
- Qingfeng Lu
- Oncology Department, Daqing Oilfield General Hospital, Daqing, China
| | - Fengxia Chen
- Department of Thoracic Surgery, Hainan General Hospital, Haikou, China
| | - Qianyue Li
- Department of R&D, Geneis (Beijing) Co., Ltd., Beijing, China
| | - Lihong Chen
- Department of Emergency, Qingdao Eighth People's Hospital, Qingdao, China
| | - Ling Tong
- Department of Pathology, Chifeng Municipal Hospital, Chifeng Clinical Medical School of Inner Mongolia Medical University, Chifeng, China
| | - Geng Tian
- Department of R&D, Geneis (Beijing) Co., Ltd., Beijing, China
| | - Xiaohong Zhou
- Second Division of Cancer, Jiamusi Cancer Hospital, Jiamusi, China
| |
Collapse
|
15
|
Liu Z, Wang R, Zhang W. Improving the generalization of unsupervised feature learning by using data from different sources on gene expression data for cancer diagnosis. Med Biol Eng Comput 2022; 60:1055-1073. [DOI: 10.1007/s11517-022-02522-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 01/30/2022] [Indexed: 10/19/2022]
|
16
|
Feng CH, Disis ML, Cheng C, Zhang L. Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models. J Transl Med 2022; 102:236-244. [PMID: 34537824 DOI: 10.1038/s41374-021-00662-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/10/2021] [Accepted: 08/12/2021] [Indexed: 11/09/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide, and a leading cause of cancer deaths. Better classifying multicategory outcomes of CRC with clinical and omic data may help adjust treatment regimens based on individual's risk. Here, we selected the features that were useful for classifying four-category survival outcome of CRC using the clinical and transcriptomic data, or clinical, transcriptomic, microsatellite instability and selected oncogenic-driver data (all data) of TCGA. We also optimized multimetric feature selection to develop the best multinomial logistic regression (MLR) and random forest (RF) models that had the highest accuracy, precision, recall and F1 score, respectively. We identified 2073 differentially expressed genes of the TCGA RNASeq dataset. MLR overall outperformed RF in the multimetric feature selection. In both RF and MLR models, precision, recall and F1 score increased as the feature number increased and peaked at the feature number of 600-1000, while the models' accuracy remained stable. The best model was the MLR one with 825 features based on sum of squared coefficients using all data, and attained the best accuracy of 0.855, F1 of 0.738 and precision of 0.832, which were higher than those using clinical and transcriptomic data. The top-ranked features in the MLR model of the best performance using clinical and transcriptomic data were different from those using all data. However, pathologic staging, HBS1L, TSPYL4, and TP53TG3B were the overlapping top-20 ranked features in the best models using clinical and transcriptomic, or all data. Thus, we developed a multimetric feature-selection based MLR model that outperformed RF models in classifying four-category outcome of CRC patients. Interestingly, adding microsatellite instability and oncogenic-driver data to clinical and transcriptomic data improved models' performances. Precision and recall of tuned algorithms may change significantly as the feature number changes, but accuracy appears not sensitive to these changes.
Collapse
Affiliation(s)
| | - Mary L Disis
- UW Medicine Cancer Vaccine Institute, University of Washington, Seattle, WA, USA
| | - Chao Cheng
- Department of Medicine, Section of Epidemiology and Population Sciences, Baylor College of Medicine, Houston, TX, USA.,Department of Medicine, Baylor College of Medicine, Houston, TX, USA.,Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Lanjing Zhang
- Department of Biological Sciences, Rutgers University, Newark, NJ, USA. .,Department of Pathology, Princeton Medical Center, Plainsboro, NJ, USA. .,Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA. .,Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA.
| |
Collapse
|
17
|
Thalor A, Kumar Joon H, Singh G, Roy S, Gupta D. Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer. Comput Struct Biotechnol J 2022; 20:1618-1631. [PMID: 35465161 PMCID: PMC9014315 DOI: 10.1016/j.csbj.2022.03.019] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 03/19/2022] [Accepted: 03/21/2022] [Indexed: 12/12/2022] Open
Abstract
Tumor heterogeneity and the unclear metastasis mechanisms are the leading cause for the unavailability of effective targeted therapy for Triple-negative breast cancer (TNBC), a breast cancer (BrCa) subtype characterized by high mortality and high frequency of distant metastasis cases. The identification of prognostic biomarker can improve prognosis and personalized treatment regimes. Herein, we collected gene expression datasets representing TNBC and Non-TNBC BrCa. From the complete dataset, a subset reflecting solely known cancer driver genes was also constructed. Recursive Feature Elimination (RFE) was employed to identify top 20, 25, 30, 35, 40, 45, and 50 gene signatures that differentiate TNBC from the other BrCa subtypes. Five machine learning algorithms were employed on these selected features and on the basis of model performance evaluation, it was found that for the complete and driver dataset, XGBoost performs the best for a subset of 25 and 20 genes, respectively. Out of these 45 genes from the two datasets, 34 genes were found to be differentially regulated. The Kaplan-Meier (KM) analysis for Distant Metastasis Free Survival (DMFS) of these 34 differentially regulated genes revealed four genes, out of which two are novel that could be potential prognostic genes (POU2AF1 and S100B). Finally, interactome and pathway enrichment analyses were carried out to investigate the functional role of the identified potential prognostic genes in TNBC. These genes are associated with MAPK, PI3-AkT, Wnt, TGF-β, and other signal transduction pathways, pivotal in metastasis cascade. These gene signatures can provide novel molecular-level insights into metastasis.
Collapse
Affiliation(s)
- Anamika Thalor
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Hemant Kumar Joon
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
- Regional Centre for Biotechnology, Faridabad 121001, Haryana, India
| | - Gagandeep Singh
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Shikha Roy
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, Aruna Asaf Ali Marg, New Delhi 110067, India
- Corresponding author at: Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology, India.
| |
Collapse
|
18
|
Bajo-Morales J, Galvez JM, Prieto-Prieto JC, Herrera LJ, Rojas I, Castillo-Secilla D. Heterogeneous Gene Expression Cross-Evaluation of Robust Biomarkers
Using Machine Learning Techniques Applied to Lung Cancer. Curr Bioinform 2022. [DOI: 10.2174/1574893616666211005114934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Background:
Nowadays, gene expression analysis is one of the most promising pillars for
understanding and uncovering the mechanisms underlying the development and spread of cancer. In this
sense, Next Generation Sequencing technologies, such as RNA-Seq, are currently leading the market
due to their precision and cost. Nevertheless, there is still an enormous amount of non-analyzed data obtained
from older technologies, such as Microarray, which could still be useful to extract relevant
knowledge.
Methods:
Throughout this research, a complete machine learning methodology to cross-evaluate the
compatibility between both RNA-Seq and Microarray sequencing technologies is described and implemented.
In order to show a real application of the designed pipeline, a lung cancer case study is addressed
by considering two detected subtypes: adenocarcinoma and squamous cell carcinoma. Transcriptomic
datasets considered for our study have been obtained from the public repositories
NCBI/GEO, ArrayExpress and GDC-Portal. From them, several gene experiments have been carried
out with the aim of finding gene signatures for these lung cancer subtypes, linked to both transcriptomic
technologies. With these DEGs selected, intelligent predictive models capable of classifying new samples
belonging to these cancer subtypes have been developed.
Results:
The predictive models built using one technology are capable of discerning samples from a different
technology. The classification results are evaluated in terms of accuracy, F1-score and ROC
curves along with AUC. Finally, the biological information of the gene sets obtained and their relationship
with lung cancer are reviewed, encountering strong biological evidence linking them to the disease.
Conclusion:
Our method has the capability of finding strong gene signatures which are also independent
of the transcriptomic technology used to develop the analysis. In addition, our article highlights the
potential of using heterogeneous transcriptomic data to increase the amount of samples for the studies,
increasing the statistical significance of the results.
Collapse
Affiliation(s)
- Javier Bajo-Morales
- Department of Computer Architecture and Technology, University of Granada, C.I.T.I.C., Periodista Rafael Gómez
Montero, 2, 18014, Granada, Spain
| | - Juan Manuel Galvez
- Department of Computer Architecture and Technology, University of Granada, C.I.T.I.C., Periodista Rafael Gómez
Montero, 2, 18014, Granada, Spain
| | - Juan Carlos Prieto-Prieto
- Nuclear Medicine Department, IMIBIC, University Hospital Reina Sofia, Menéndez
Pidal Avenue, 14004, Córdoba, Spain
| | - Luis Javier Herrera
- Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18014, Granada,Spain
| | - Ignacio Rojas
- Department of Computer Architecture and Technology, University of Granada, C.I.T.I.C., Periodista Rafael Gómez
Montero, 2, 18014, Granada, Spain
| | - Daniel Castillo-Secilla
- Department of Computer Architecture and Technology, University of Granada. C.I.T.I.C., Periodista Rafael Gómez Montero, 2, 18014, Granada,Spain
| |
Collapse
|
19
|
Li Y, Chen D, Wu X, Yang W, Chen Y. A narrative review of artificial intelligence-assisted histopathologic diagnosis and decision-making for non-small cell lung cancer: achievements and limitations. J Thorac Dis 2022; 13:7006-7020. [PMID: 35070383 PMCID: PMC8743410 DOI: 10.21037/jtd-21-806] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 12/01/2021] [Indexed: 12/12/2022]
Abstract
Objective To summarize the current evidence regarding the applications, workflow, and limitations of artificial intelligence (AI) in the management of patients pathologically-diagnosed with lung cancer. Background Lung cancer is one of the most common cancers and the leading cause of cancer-related deaths worldwide. AI technologies have been applied to daily medical workflow and have achieved an excellent performance in predicting histopathologic subtypes, analyzing gene mutation profiles, and assisting in clinical decision-making for lung cancer treatment. More advanced deep learning for classifying pathologic images with minimal human interactions has been developed in addition to the conventional machine learning scheme. Methods Studies were identified by searching databases, including PubMed, EMBASE, Web of Science, and Cochrane Library, up to February 2021 without language restrictions. Conclusions A number of studies have evaluated AI pipelines and confirmed that AI is robust and efficacious in lung cancer diagnosis and decision-making, demonstrating that AI models are a useful tool for assisting oncologists in health management. Although several limitations that pose an obstacle for the widespread use of AI schemes persist, the unceasing refinement of AI techniques is poised to overcome such problems. Thus, AI technology is a promising tool for use in diagnosing and managing lung cancer.
Collapse
Affiliation(s)
- Yongzhong Li
- Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Donglai Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, School of Medicine, Shanghai, China
| | - Xuejie Wu
- Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Wentao Yang
- Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yongbing Chen
- Department of Thoracic Surgery, the Second Affiliated Hospital of Soochow University, Suzhou, China
| |
Collapse
|
20
|
Liang ZQ, Zhong LY, Li J, Shen JH, Tu XY, Zhong ZH, Zeng JJ, Chen JH, Wei ZX, Dang YW, Huang SN, Chen G. Clinicopathological significance and underlying molecular mechanism of downregulation of basonuclin 1 expression in ovarian carcinoma. Exp Biol Med (Maywood) 2022; 247:106-119. [PMID: 34644201 PMCID: PMC8777474 DOI: 10.1177/15353702211052036] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
In this study, we aim to identify the clinical significance of basonuclin 1 (BNC1) expression in ovarian carcinoma (OV) and to explore its latent mechanisms. Via integrating in-house tissue microarrays, gene chips, and RNA-sequencing data, we explored the expression and clinical value of BNC1 in OV. Immunohistochemical staining was utilized to confirm the protein expression status of BNC1. A combined SMD of -2.339 (95% CI: -3.649 to -1.028, P < 0.001) identified that BNC1 was downregulated based on 1346 samples, and the sROC (AUC = 0.93) showed a favorable discriminatory ability of BNC1 in OV patients. We used univariate and multivariate Cox regulation to evaluate the prognostic role of BNC1 for OV patients, and a combined hazard ratio of 0.717 (95% CI: 0.445-0.989, P < 0.001) revealed that BNC1 was a protective factor for OV. Furthermore, the fraction of infiltrating naive B cells, memory B cells, and other immune cells showed statistical differences between the high- and low-BNC1 expression groups through cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Enrichment analysis showed that BNC1 may have a relationship with immune-related items in OV. By predicting the potential regulatory transcription factors (TFs) of BNC1, friend leukemia virus integration 1 (FLI1) may be a potential upstream TF of BNC1. Corporately, a decreasing trend of BNC1 may serve as a tumor suppressor and prognostic biomarker in OV patients. Moreover, BNC1 may take part in immune-related pathways and influence the fraction of tumor-infiltrating immune cells.
Collapse
Affiliation(s)
- Zi-Qian Liang
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Lu-Yang Zhong
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Jie Li
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Jin-Hai Shen
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Xin-Yue Tu
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Zheng-Hong Zhong
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Jing-Jing Zeng
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Jun-Hong Chen
- Department of Pathology, Maternal and Child Health Hospital of
Guangxi Zhuang Autonomous Region, Nanning 530003, P. R. China
| | - Zhu-Xin Wei
- Department of Radiotherapy, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Yi-Wu Dang
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
| | - Su-Ning Huang
- Department of Radiotherapy, Guangxi Medical University Cancer
Hospital, Nanning 530021, P.R. China
| | - Gang Chen
- Department of Pathology, The First Affiliated Hospital of
Guangxi Medical University, Nanning 530021, P. R. China
- Gang Chen.
| |
Collapse
|
21
|
Luo J, Du X. A promising prognostic signature for lung adenocarcinoma (LUAD) patients basing on 6 hypoxia-related genes. Medicine (Baltimore) 2021; 100:e28237. [PMID: 34918689 PMCID: PMC8677978 DOI: 10.1097/md.0000000000028237] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 11/24/2021] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Hypoxia signaling plays a critical role in the development of lung adenocarcinoma (LUAD). We herein aimed to explore the prognostic value of hypoxia-related genes and construct the hypoxia-related prognostic signature for LUAD patients. METHODS A total of 26 hypoxia-related genes were collected. Five hundred thirteen and 246 LUAD samples were obtained from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Univariate Cox regression and LASSO Cox regression analyses were conducted to screen the hypoxia-related genes associated with the prognosis of LUAD patients, which would be used for constructing prognosis predictive model for LUAD patients. Multivariate Cox regression analysis was done to determine the independent prognostic factors. The Nomogram model was constructed to predict the prognosis of LUAD patients. RESULTS Based on 26 hypoxia-related genes, LUAD samples could be divided into 4 clusters with different prognoses. Among which, 6 genes were included to construct the Risk Score and the LUAD patients with higher Risk Score had worse prognosis. Besides, the Nomogram based on all the independent risk factors could relatively reliably predict the survival probability. And 9 types of immune cells' infiltration was significantly differential between high and low risk LUAD patients. CONCLUSION The Risk Score model based on the 6 crucial hypoxia-related genes could relatively reliably predict the prognosis of LUAD patients.
Collapse
|
22
|
Zong Y, Li X. Identification of Causal Genes of COVID-19 Using the SMR Method. Front Genet 2021; 12:690349. [PMID: 34290742 PMCID: PMC8287881 DOI: 10.3389/fgene.2021.690349] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 05/07/2021] [Indexed: 01/03/2023] Open
Abstract
Since the first report of COVID-19 in December 2019, more than 100 million people have been infected with SARS-CoV-2. Despite ongoing research, there is still limited knowledge about the genetic causes of COVID-19. To resolve this problem, we applied the SMR method to analyze the genes involved in COVID-19 pathogenesis by the integration of multiple omics data. Here, we assessed the SNPs associated with COVID-19 risk from the GWAS data of Spanish and Italian patients and lung eQTL data from the GTEx project. Then, GWAS and eQTL data were integrated by summary-data-based (SMR) methods using SNPs as instrumental variables (IVs). As a result, six protein-coding and five non-protein-coding genes regulated by nine SNPs were identified as significant risk factors for COVID-19. Functional analysis of these genes showed that UQCRH participates in cardiac muscle contraction, PPA2 is closely related to sudden cardiac failure (SCD), and OGT, as the interacting gene partner of PANO1, is associated with neurological disease. Observational studies show that myocardial damage, SCD, and neurological disease often occur in COVID-19 patients. Thus, our findings provide a potential molecular mechanism for understanding the complications of COVID-19.
Collapse
Affiliation(s)
- Yan Zong
- Department of Infectious Diseases, Yiwu Central Hospital, Jinhua, China
| | - Xiaofei Li
- Department of Infectious Diseases, Yiwu Central Hospital, Jinhua, China
| |
Collapse
|
23
|
An Ensemble Learning-Based Method for Inferring Drug-Target Interactions Combining Protein Sequences and Drug Fingerprints. BIOMED RESEARCH INTERNATIONAL 2021; 2021:9933873. [PMID: 33987446 PMCID: PMC8093043 DOI: 10.1155/2021/9933873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/14/2021] [Accepted: 04/16/2021] [Indexed: 11/24/2022]
Abstract
Identifying the interactions of the drug-target is central to the cognate areas including drug discovery and drug reposition. Although the high-throughput biotechnologies have made tremendous progress, the indispensable clinical trials remain to be expensive, laborious, and intricate. Therefore, a convenient and reliable computer-aided method has become the focus on inferring drug-target interactions (DTIs). In this research, we propose a novel computational model integrating a pyramid histogram of oriented gradients (PHOG), Position-Specific Scoring Matrix (PSSM), and rotation forest (RF) classifier for identifying DTIs. Specifically, protein primary sequences are first converted into PSSMs to describe the potential biological evolution information. After that, PHOG is employed to mine the highly representative features of PSSM from multiple pyramid levels, and the complete describers of drug-target pairs are generated by combining the molecular substructure fingerprints and PHOG features. Finally, we feed the complete describers into the RF classifier for effective prediction. The experiments of 5-fold Cross-Validations (CV) yield mean accuracies of 88.96%, 86.37%, 82.88%, and 76.92% on four golden standard data sets (enzyme, ion channel, G protein-coupled receptors (GPCRs), and nuclear receptor, respectively). Moreover, the paper also conducts the state-of-art light gradient boosting machine (LGBM) and support vector machine (SVM) to further verify the performance of the proposed model. The experimental outcomes substantiate that the established model is feasible and reliable to predict DTIs. There is an excellent prospect that our model is capable of predicting DTIs as an efficient tool on a large scale.
Collapse
|
24
|
LC-MS/MS-Based Quantitative Proteomics Analysis of Different Stages of Non-Small-Cell Lung Cancer. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5561569. [PMID: 33728331 PMCID: PMC7937045 DOI: 10.1155/2021/5561569] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 01/25/2021] [Accepted: 02/07/2021] [Indexed: 12/11/2022]
Abstract
Lung cancer has a higher incidence rate and mortality rate than all other cancers. Early diagnosis and treatment of lung cancer remain a major challenge, and the 5-year survival rate of its patients is only 15%. Basic and clinical research, especially the discovery of biomarkers, is crucial for improving the diagnosis and treatment of lung cancer patients. To identify novel biomarkers for lung cancer, we used the iTRAQ8-plex labeling technology combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to analyze the serum and urine of patients with different stages of lung adenocarcinoma and healthy individuals. A total of 441 proteins were identified in the serum, and 1,161 proteins were identified in the urine. The levels of elongation factor 1-alpha 2, proteasome subunit alpha type, and spermatogenesis-associated protein increased significantly in the serum of patients with lung cancer compared with those in healthy controls. The levels of transmembrane protein 143, cadherin 5, fibronectin 1, and collectin-11 decreased significantly in the serum of patients with metastases compared with those of nonmetastatic lung cancer patients. In the urine of stage III and IV lung cancer patients, the prostate-specific antigen and prostatic acid phosphatase decreased significantly, whereas neutrophil defensin 1 increased significantly. The results of LC-MS/MS were confirmed by enzyme-linked immunosorbent assay (ELISA) for transmembrane protein 143, cadherin 5, fibronectin 1, and collectin-11 in the serum. These proteins may be a potential early diagnosis and metastasis biomarkers for lung adenocarcinoma. Furthermore, the relative content of these markers in the serum and urine could be used to determine the progression of lung adenocarcinoma and achieve accurate staging and diagnosis.
Collapse
|
25
|
Chen S, Zhou W, Tu J, Li J, Wang B, Mo X, Tian G, Lv K, Huang Z. A Novel XGBoost Method to Infer the Primary Lesion of 20 Solid Tumor Types From Gene Expression Data. Front Genet 2021; 12:632761. [PMID: 33613644 PMCID: PMC7886791 DOI: 10.3389/fgene.2021.632761] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 01/06/2021] [Indexed: 11/19/2022] Open
Abstract
Purpose Establish a suitable machine learning model to identify its primary lesions for primary metastatic tumors in an integrated learning approach, making it more accurate to improve primary lesions’ diagnostic efficiency. Methods After deleting the features whose expression level is lower than the threshold, we use two methods to perform feature selection and use XGBoost for classification. After the optimal model is selected through 10-fold cross-validation, it is verified on an independent test set. Results Selecting features with around 800 genes for training, the R2-score of a 10-fold CV of training data can reach 96.38%, and the R2-score of test data can reach 83.3%. Conclusion These findings suggest that by combining tumor data with machine learning methods, each cancer has its corresponding classification accuracy, which can be used to predict primary metastatic tumors’ location. The machine-learning-based method can be used as an orthogonal diagnostic method to judge the machine learning model processing and clinical actual pathological conditions.
Collapse
Affiliation(s)
- Sijie Chen
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Wenjing Zhou
- Department of Oncology, Hiser Medical Center of Qingdao, Qingdao, China
| | - Jinghui Tu
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Jian Li
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Bo Wang
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Xiaofei Mo
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Geng Tian
- Qingdao Geneis Institute of Big Data Mining and Precision Medicine, Qingdao, China.,Geneis Beijing Co., Ltd., Beijing, China
| | - Kebo Lv
- Department of Mathematics, Ocean University of China, Qingdao, China
| | - Zhijian Huang
- Department of Breast Surgical Oncology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, China
| |
Collapse
|
26
|
Nersesian S, Schwartz SL, Grantham SR, MacLean LK, Lee SN, Pugh-Toole M, Boudreau JE. NK cell infiltration is associated with improved overall survival in solid cancers: A systematic review and meta-analysis. Transl Oncol 2020; 14:100930. [PMID: 33186888 PMCID: PMC7670197 DOI: 10.1016/j.tranon.2020.100930] [Citation(s) in RCA: 85] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 10/20/2020] [Accepted: 10/23/2020] [Indexed: 01/03/2023] Open
Abstract
The immune landscape of a tumor is highly connected to patient prognosis and response to treatment, but little is known about how natural killer (NK) cells predict overall survival (OS) among patients with solid tumors. We present the first meta-analysis on NK cell infiltration into solid tumors as a prognostic indicator for OS, considering cancer types independently, and together. Samples were collected from 1973 to 2016 with results published between 1989 and 2020. From 53 studies, we found that NK cell infiltration corresponds with decreased risk of death (HR=0.34, 95% CI: 0.26-0.46; p<0.0001). Among studies that investigated the prognostic potential of NK cells in specific regions of the tumor, intraepithelial infiltration was better predictive of OS than NK infiltration in the tumor-adjacent stroma. Generally, NK cell infiltration is lower in advanced-stage and lower-grade tumors; nevertheless, it remains prognostically beneficial. This meta-analysis highlights an important prognostic role of NK cells in solid tumors, but exposes that few studies have considered the contributions of NK cells. Toward NK cell-based immunotherapies, it will be important to understand the conditions under which NK cells can be effective agents of tumor control.
Collapse
Affiliation(s)
- Sarah Nersesian
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Sarah L Schwartz
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Stephanie R Grantham
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Leah K MacLean
- Department of Pathology, Dalhousie University, Halifax, NS, Canada
| | - Stacey N Lee
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Morgan Pugh-Toole
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada
| | - Jeanette E Boudreau
- Department of Microbiology and Immunology, Dalhousie University, Halifax, NS, Canada; Department of Pathology, Dalhousie University, Halifax, NS, Canada.
| |
Collapse
|