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Wu J, Lin Z, Ji D, Li Z, Zhang H, Lu S, Wang S, Liu X, Ao L. Metabolism-Related Gene Pairs to Predict the Clinical Outcome and Molecular Characteristics of Early Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14163957. [PMID: 36010950 PMCID: PMC9406433 DOI: 10.3390/cancers14163957] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 08/13/2022] [Accepted: 08/13/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary After surgery, about 60–70% of early hepatocellular carcinoma patients suffer from relapse within 5 years, hindering long-term survival. Clinical and pathologic features cannot provide an accurate evaluation. We aimed to construct a stratification model from the metabolic aspect to predict the clinical outcome and reveal the molecular characteristics of different prognostic subgroups. An individualized metabolic signature of 10 gene pairs was developed from 250 early HCCs and validated in 311 samples from different datasets. The signature stratified early HCC cases one-by-one into two risk groups with different survival rates. The molecular characteristics of the two risk groups were analyzed by multi-omics data. The relationships with proliferation, immunity, and drug benefits were summarized. The signature was further validated in 47 institutional transcriptomic HCC samples and 101 public proteomic samples. Abstract Recurrence is the main factor affecting the prognosis of early hepatocellular carcinoma (HCC), which is not accurately evaluated by clinical indicators. The metabolic heterogeneity of HCC hints at the possibility of constructing a stratification model to predict the clinical outcome. On the basis of the relative expression orderings of 2939 metabolism-related genes, an individualized signature with 10 metabolism-related gene pairs (10-GPS) was developed from 250 early HCC samples in the discovery datasets, which stratified HCC patients into the high- and low-risk subgroups with significantly different survival rates. The 10-GPS was validated in 311 public transcriptomic samples from two independent validation datasets. A nomogram that included the 10-GPS, age, gender, and stage was constructed for eventual clinical evaluation. The low-risk group was characterized by lower proliferation, higher metabolism, increased activated immune microenvironment, and lower TIDE scores, suggesting a better response to immunotherapy. The high-risk group displayed hypomethylation, higher copy number alterations, mutations, and more overexpression of immune-checkpoint genes, which might jointly lead to poor outcomes. The prognostic accuracy of the 10-GPS was further validated in 47 institutional transcriptomic samples and 101 public proteomic samples. In conclusion, the 10-GPS is a robust predictor of the clinical outcome for early HCC patients and could help evaluate prognosis and characterize molecular heterogeneity.
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Affiliation(s)
- Junling Wu
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350025, China
| | - Zeman Lin
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350025, China
| | - Daihan Ji
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350025, China
| | - Zhenli Li
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
| | - Huarong Zhang
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350025, China
| | - Shuting Lu
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350025, China
| | - Shenglin Wang
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350025, China
| | - Xiaolong Liu
- The United Innovation of Mengchao Hepatobiliary Technology Key Laboratory of Fujian Province, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
- Correspondence: (L.A.); (X.L.); Tel.: +86-182-5905-6924 (L.A.); +86-133-1397-5783 (X.L.)
| | - Lu Ao
- Fujian Key Laboratory of Medical Bioinformatics, Department of Bioinformatics, School of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350025, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350122, China
- Correspondence: (L.A.); (X.L.); Tel.: +86-182-5905-6924 (L.A.); +86-133-1397-5783 (X.L.)
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2
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Ke X, Wu H, Chen YX, Guo Y, Yao S, Guo MR, Duan YY, Wang NN, Shi W, Wang C, Dong SS, Kang H, Dai Z, Yang TL. Individualized pathway activity algorithm identifies oncogenic pathways in pan-cancer analysis. EBioMedicine 2022; 79:104014. [PMID: 35487057 PMCID: PMC9117264 DOI: 10.1016/j.ebiom.2022.104014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/04/2022] [Accepted: 04/05/2022] [Indexed: 02/07/2023] Open
Abstract
Background Accumulative evidences have shown that dysregulation of biological pathways contributed to the initiation and progression of malignant tumours. Several methods for pathway activity measurement have been proposed, but they are restricted to making comparisons between groups or sensitive to experimental batch effects. Methods We introduced a novel method for individualized pathway activity measurement (IPAM) that is based on the ranking of gene expression levels in individual sample. Taking advantage of IPAM, we calculated the pathway activity of 318 pathways from KEGG database in the 10528 tumour/normal samples of 33 cancer types from TCGA to identify characteristic dysregulated pathways among different cancer types. Findings IPAM precisely quantified the level of activity of each pathway in pan-cancer analysis and exhibited better performance in cancer classification and prognosis prediction over five widely used tools. The average ROC-AUC of cancer diagnostic model using tumour-educated platelets (TEPs) reached 92.84%, suggesting the potential of our algorithm in early diagnosis of cancer. We identified several pathways significantly deregulated and associated with patient survival in a large fraction of cancer types, such as tyrosine metabolism, fatty acid degradation, cell cycle, p53 signalling pathway and DNA replication. We also confirmed the dominant role of metabolic pathways in cancer pathway dysregulation and identified the driving factors of specific pathway dysregulation, such as PPARA for branched-chain amino acid metabolism and NR1I2, NR1I3 for fatty acid metabolism. Interpretation Our study will provide novel clues for understanding the pathological mechanisms of cancer, ultimately paving the way for personalized medicine of cancer. Funding A full list of funding can be found in the Acknowledgements section.
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Affiliation(s)
- Xin Ke
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Hao Wu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Yi-Xiao Chen
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Shi Yao
- National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China
| | - Ming-Rui Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Yuan-Yuan Duan
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Nai-Ning Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Wei Shi
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Chen Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China
| | - Huafeng Kang
- Department of Oncology, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China
| | - Zhijun Dai
- Department of Breast Surgery, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, PR China
| | - Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China; National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China.
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3
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Li X, Li M, Xiang J, Zhao Z, Shang X. SEPA: Signalling entropy-based algorithm to evaluate personalized pathway activation for survival analysis on pan-cancer data. Bioinformatics 2022; 38:2536-2543. [PMID: 35199150 DOI: 10.1093/bioinformatics/btac122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 01/16/2022] [Accepted: 02/21/2022] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Biomarkers with prognostic ability and biological interpretability can be used to support decision-making in the survival analysis. Genes usually form functional modules to play synergistic roles, such as pathways. Predicting significant features from the functional level can effectively reduce the adverse effects of heterogeneity and obtain more reproducible and interpretable biomarkers. Personalized pathway activation inference can quantify the dysregulation of essential pathways involved in the initiation and progression of cancers, and can contribute to the development of personalized medical treatments. RESULTS In this study, we propose a novel method to evaluate personalized pathway activation based on signalling entropy for survival analysis (SEPA), which is a new attempt to introduce the information-theoretic entropy in generating pathway representation for each patient. SEPA effectively integrates pathway-level information into gene expression data, converting the high-dimensional gene expression data into the low-dimensional biological pathway activation scores. SEPA shows its classification power on the prognostic pan-cancer genomic data, and the potential pathway markers identified based on SEPA have statistical significance in the discrimination of high-risk and low-risk cohorts and are likely to be associated with the initiation and progress of cancers. The results show that SEPA scores can be used as an indicator to precisely distinguish cancer patients with different clinical outcomes, and identify important pathway features with strong discriminative power and biological interpretability. AVAILABILITY The MATLAB-package for SEPA is freely available from https://github.com/xingyili/SEPA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xingyi Li
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China
| | - Min Li
- School of Computer Science, Central South University, Changsha, Hunan, 410083, China
| | - Ju Xiang
- School of Computer Science, Central South University, Changsha, Hunan, 410083, China.,Neuroscience Research Center & Department of Basic Medical Sciences, Changsha Medical University, Changsha, Hunan, 410219, China
| | - Zhelin Zhao
- School of Software, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China
| | - Xuequn Shang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China
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4
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IndGOterm: a qualitative method for the identification of individually dysregulated GO terms in cancer. Brief Bioinform 2022; 23:6526723. [DOI: 10.1093/bib/bbac012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 12/24/2021] [Accepted: 01/08/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Individual pathway analysis can dissect heterogeneities among different cancer patients and provide efficient guidelines for individualized therapy. However, the existence of the batch effect brings extensive limitations for the application of many individual methods for pathway analysis. Previously, researchers proposed that methods based on within-sample relative expression ordering (REO) of the genes are notably insensitive to ‘batch effects’. In this article, we focus on the Gene Ontology (GO) database and propose an individual qualitative GO term analysis method (IndGOterm) based on the REO of genes. Compared with some current widely used single-sample enrichment analysis methods, such as ssGSEA and GSVA, IndGOterm has a predominance of ignoring the batch effects caused by diverse technologies. Through the survival and drug responses analysis, we found IndGOterm could capture more terms connected to cancer than other single-sample enrichment analysis methods. Furthermore, through the application of IndGOterm, we found some terms that present different dysregulation models that manifest heterogenetic in homologous patients. Collectively, these results attested that IndGOterm could capture useful information from patients and be a useful tool to reveal the intrinsic characteristic of cancer. An open-source R statistical analysis package ‘IndGOterm’ is available at https://github.com/robert19960424/IndGOterm.
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5
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Xu Y, Wang J, Li F, Zhang C, Zheng X, Cao Y, Shang D, Hu C, Xu Y, Mi W, Li X, Cao Y, Zhang Y. Identifying individualized risk subpathways reveals pan-cancer molecular classification based on multi-omics data. Comput Struct Biotechnol J 2022; 20:838-849. [PMID: 35222843 PMCID: PMC8842010 DOI: 10.1016/j.csbj.2022.01.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 01/18/2022] [Accepted: 01/18/2022] [Indexed: 11/24/2022] Open
Abstract
Cancer is a highly heterogeneous disease with different functional disorders among individuals. The initiation and progression of cancer is usually related to dysregulation of local regions within pathways. Identification of individualized risk pathways is crucial for revealing the mechanisms of tumorigenesis and heterogeneity. However, approach that focused on mining patient-specific risk subpathway regions is still lacking. Here, we developed an individualized cancer risk subpathway identification method that was referred as InCRiS by integrating multi-omics data. Then, the method was applied to nearly 3000 samples across 9 TCGA cancer types and its robustness and reliability were evaluated. Dissecting dysregulated subpathways in these tumor samples revealed several key regions which may play oncogenic roles in cancer. The construction of risk subpathway dysregulation profile of pan-cancers revealed 11 pan-cancer molecular classification (InCRiS subtypes) with significantly different clinical outcomes. Moreover, subpathway regions that tend to be disordered in individuals of specific subtypes were examined for understanding the pathogenesis of tumor and some key genes such as CTNNB1, EP300 and PRKDC were nominated in different subtypes. In summary, the proposed method and resulting data presented useful resources to study the mechanism of tumor heterogeneity and to discovery novel therapeutic targets for precise treatment of cancer.
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6
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Park KS, Kim SH, Oh JH, Kim SY. Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning. Brief Bioinform 2021; 22:bbaa336. [PMID: 33341874 PMCID: PMC8599295 DOI: 10.1093/bib/bbaa336] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 10/25/2020] [Accepted: 10/26/2020] [Indexed: 01/27/2023] Open
Abstract
Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, current diagnostic methods often subject patients to unnecessary surgical burden. In this study, we developed and validated an automated, highly accurate multi-study-derived diagnostic model for PTCs using personalized biological pathways coupled with a sophisticated machine learning algorithm. Surprisingly, the algorithm achieved near-perfect performance in discriminating PTCs from non-tumoral thyroid samples with an overall cross-study-validated area under the receiver operating characteristic curve (AUROC) of 0.999 (95% confidence interval [CI]: 0.995-1) and a Brier score of 0.013 on three independent development cohorts. In addition, the algorithm showed excellent generalizability and transferability on two large-scale external blind PTC cohorts consisting of The Cancer Genome Atlas (TCGA), which is the largest genomic PTC cohort studied to date, and the post-Chernobyl cohort, which includes PTCs reported after exposure to radiation from the Chernobyl accident. When applied to the TCGA cohort, the model yielded an AUROC of 0.969 (95% CI: 0.950-0.987) and a Brier score of 0.109. On the post-Chernobyl cohort, it yielded an AUROC of 0.962 (95% CI: 0.918-1) and a Brier score of 0.073. This algorithm also is robust against other various types of clinical scenarios, discriminating malignant from benign lesions as well as clinically aggressive thyroid cancer with poor prognosis from indolent ones. Furthermore, we discovered novel pathway alterations and prognostic signatures for PTC, which can provide directions for follow-up studies.
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Affiliation(s)
| | | | - Jung Hun Oh
- Department of Medical Physics at Memorial Sloan Kettering Cancer Center, USA
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7
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Malagoli Tagliazucchi G, Taccioli C. GMIEC: a shiny application for the identification of gene-targeted drugs for precision medicine. BMC Genomics 2020; 21:619. [PMID: 32912170 PMCID: PMC7488130 DOI: 10.1186/s12864-020-06996-y] [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: 04/15/2020] [Accepted: 08/17/2020] [Indexed: 11/28/2022] Open
Abstract
Abstract Background Precision medicine is a medical approach that takes into account individual genetic variability and often requires Next Generation Sequencing data in order to predict new treatments. Here we present GMIEC, Genomic Modules Identification et Characterization for genomics medicine, an application that is able to identify specific drugs at the level of single patient integrating multi-omics data such as RNA-sequencing, copy-number variation, methylation, Chromatin Immuno-Precipitation and Exome/Whole Genome sequencing. It is also possible to include clinical data related to each patient. GMIEC has been developed as a web-based R-Shiny platform and gives as output a table easy to use and explore. Results We present GMIEC, a Shiny application for genomics medicine. The tool allows the users the integration of two or more multiple omics datasets (e.g. gene-expression, copy-number), at sample level, to identify groups of genes that share common genomic and corresponding drugs. We demonstrate the characteristics of our application by using it to analyze a prostate cancer data set. Conclusions GMIEC provides a simple interface for genomics medicine. GMIEC was develop with Shiny to provide an application that does not require advanced programming skills. GMIEC consists of three sub-application for the analysis (GMIEC-AN), the visualization (GMIEC-VIS) and the exploration of results (GMIEC-RES). GMIEC is an open source software and is available at https://github.com/guidmt/GMIEC-shiny
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Affiliation(s)
- Guidantonio Malagoli Tagliazucchi
- Division of Cardiology, Azienda Ospedaliero-Universitaria di Parma, 43126, Parma, Italy.,Present address: Department of Genetics, Evolution and Environment, Darwin Building, Gower Street WC1E 6BT, London, UK
| | - Cristian Taccioli
- Department of Animal Medicine, Production and Health, University of Padova, 35020, Legnaro, PD, Italy.
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8
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Vitali F, Li Q, Schissler AG, Berghout J, Kenost C, Lussier YA. Developing a 'personalome' for precision medicine: emerging methods that compute interpretable effect sizes from single-subject transcriptomes. Brief Bioinform 2019; 20:789-805. [PMID: 29272327 PMCID: PMC6585155 DOI: 10.1093/bib/bbx149] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 10/06/2017] [Indexed: 12/13/2022] Open
Abstract
The development of computational methods capable of analyzing -omics data at the individual level is critical for the success of precision medicine. Although unprecedented opportunities now exist to gather data on an individual's -omics profile ('personalome'), interpreting and extracting meaningful information from single-subject -omics remain underdeveloped, particularly for quantitative non-sequence measurements, including complete transcriptome or proteome expression and metabolite abundance. Conventional bioinformatics approaches have largely been designed for making population-level inferences about 'average' disease processes; thus, they may not adequately capture and describe individual variability. Novel approaches intended to exploit a variety of -omics data are required for identifying individualized signals for meaningful interpretation. In this review-intended for biomedical researchers, computational biologists and bioinformaticians-we survey emerging computational and translational informatics methods capable of constructing a single subject's 'personalome' for predicting clinical outcomes or therapeutic responses, with an emphasis on methods that provide interpretable readouts. Key points: (i) the single-subject analytics of the transcriptome shows the greatest development to date and, (ii) the methods were all validated in simulations, cross-validations or independent retrospective data sets. This survey uncovers a growing field that offers numerous opportunities for the development of novel validation methods and opens the door for future studies focusing on the interpretation of comprehensive 'personalomes' through the integration of multiple -omics, providing valuable insights into individual patient outcomes and treatments.
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Affiliation(s)
| | - Qike Li
- BIO5 Institute, University of Arizona, Tucson, AZ, USA
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Yu H, Jiang W, Chen G, Yang F, Zhao X, Ji Z, Ni J, Fu Y, Chen F, Zhao B. Impact of Colon-Specific DNA Methylation-Regulated Gene Modules on Colorectal Cancer Patient Survival. Med Sci Monit 2019; 25:3549-3557. [PMID: 31083646 PMCID: PMC6528550 DOI: 10.12659/msm.916181] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Background Colorectal cancer (CRC) is the second most commonly diagnosed cancer in females and the third in males worldwide. Although existing evidence explained some critical functions of the single genetic abnormality in the pathogenesis of CRC, the function of interactors involved in the colon-specific regulatory network, especially DNA methylation regulated network is still poorly understood. Material/Methods In this work, matched gene expression and DNA methylation samples of CRC patients were retrieved. Differential gene expression and methylation analyses were performed. In addition, gene expression and DNA methylation were integrated into a colon-specific regulatory gene network, detecting the epigenetically regulated gene modules which drive CRC through an underlying epigenetic mechanism. Finally, the colon-specific DNA methylation-regulated gene modules were validated using an independent set of CRC patients. Results Differential gene expression analysis demonstrated the upregulation of the cell cycle and DNA replication and downregulation of cGMP-PKG signaling pathway and calcium signaling pathway in CRC. Differentially methylated regions (DMRs) showed the different levels of methylation in promoters, CpG islands, and genes in CRC. In addition, gene expression and DNA methylation were integrated into a colon-specific regulatory gene network, detecting 8 epigenetically regulated gene modules which drive CRC through an underlying epigenetic mechanism. Interestingly, 2 of the colon-specific DNA methylation-regulated gene modules showed a significant predictive ability for the survival of an independent set of CRC patients. Conclusions The results of this study could open a new era and aid the development of novel therapeutic targets for the treatment of CRC patient.
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Affiliation(s)
- Haitao Yu
- Department of Proctology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Wei Jiang
- Department of Gastroenterology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Gang Chen
- Department of Gastroenterology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Fan Yang
- Department of Proctology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Xingwang Zhao
- Department of Proctology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Zhiwu Ji
- Department of Proctology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Jian Ni
- Department of Proctology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Yan Fu
- Department of Proctology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Fujun Chen
- Department of Proctology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
| | - Bin Zhao
- Department of Proctology, The First Affiliated Hospital of Jiamusi Medical University, Jiamusi, Heilongjiang, China (mainland)
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Lim S, Lee S, Jung I, Rhee S, Kim S. Comprehensive and critical evaluation of individualized pathway activity measurement tools on pan-cancer data. Brief Bioinform 2018; 21:36-46. [PMID: 30462155 DOI: 10.1093/bib/bby097] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Revised: 08/20/2018] [Accepted: 09/09/2018] [Indexed: 12/11/2022] Open
Abstract
Motivation : Biological pathways are extensively used for the analysis of transcriptome data to characterize biological mechanisms underlying various phenotypes. There are a number of computational tools that summarize transcriptome data at the pathway level. However, there is no comparative study on how well these tools produce useful information at the cohort level, enabling comparison of many samples or patients. Results : In this study, we systematically compared and evaluated 13 different pathway activity inference tools based on 5 comparison criteria using pan-cancer data set. This study has two major contributions. First, our study provides a comprehensive survey on computational techniques used by existing pathway activity inference tools. The tools use different strategies and assume different requirements on data: input transformation, use of labels, necessity of cohort-level input data, use of gene relations and scoring metric. Second, we performed extensive evaluations on the performance of these tools. Because different tools use different methods to map samples to the pathway dimension, the tools are evaluated at the pathway level using five comparison criteria. Starting from measuring how well a tool maintains the characteristics of original gene expression values, robustness was also investigated by adding noise into gene expression data. Classification tasks on three clinical variables (tumor versus normal, survival and cancer subtypes) were performed to evaluate the utility of tools for their clinical applications. In addition, the inferred activity values were compared between the tools to see how similar they are along with the scoring schemes they use.
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Affiliation(s)
- Sangsoo Lim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
| | - Sangseon Lee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Korea
| | - Inuk Jung
- Bioinformatics Institute, Seoul National University, Seoul, Korea
| | - Sungmin Rhee
- Department of Computer Science and Engineering, Seoul National University, Seoul, Korea
| | - Sun Kim
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.,Department of Computer Science and Engineering, Seoul National University, Seoul, Korea.,Bioinformatics Institute, Seoul National University, Seoul, Korea
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Zhang CL, Xu YJ, Yang HX, Xu YQ, Shang DS, Wu T, Zhang YP, Li X. sPAGM: inferring subpathway activity by integrating gene and miRNA expression-robust functional signature identification for melanoma prognoses. Sci Rep 2017; 7:15322. [PMID: 29127397 PMCID: PMC5681640 DOI: 10.1038/s41598-017-15631-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2017] [Accepted: 10/30/2017] [Indexed: 02/07/2023] Open
Abstract
MicroRNAs (miRNAs) regulate biological pathways by inhibiting gene expression. However, most current analytical methods fail to consider miRNAs, when inferring functional or pathway activities. In this study, we developed a model called sPAGM to infer subpathway activities by integrating gene and miRNA expressions. In this model, we reconstructed subpathway graphs by embedding miRNA components, and characterized subpathway activity (sPA) scores by simultaneously considering the expression levels of miRNAs and genes. The results showed that the sPA scores could distinguish different samples across tumor types, as well as samples between tumor and normal conditions. Moreover, the sPAGM model displayed more specificities than the entire pathway-based analyses. This model was applied to melanoma tumors to perform a prognosis analysis, which identified a robust 55-subpathway signature. By using The Cancer Genome Atlas and independently verified data sets, the subpathway-based signature significantly predicted the patients’ prognoses, which were independent of clinical variables. In the prognostic performance comparison, the sPAGM model was superior to the gene-only and miRNA-only methods. Finally, we dissected the functional roles and interactions of components within the subpathway signature. Taken together, the sPAGM model provided a framework for inferring subpathway activities and identifying functional signatures for clinical applications.
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Affiliation(s)
- Chun-Long Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yan-Jun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Hai-Xiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Ying-Qi Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - De-Si Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Tan Wu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China
| | - Yun-Peng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
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12
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Ao L, Guo Y, Song X, Guan Q, Zheng W, Zhang J, Huang H, Zou Y, Guo Z, Wang X. Evaluating hepatocellular carcinoma cell lines for tumour samples using within-sample relative expression orderings of genes. Liver Int 2017; 37:1688-1696. [PMID: 28481424 DOI: 10.1111/liv.13467] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 04/28/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND & AIMS Concerns are raised about the representativeness of cell lines for tumours due to the culture environment and misidentification. Liver is a major metastatic destination of many cancers, which might further confuse the origin of hepatocellular carcinoma cell lines. Therefore, it is of crucial importance to understand how well they can represent hepatocellular carcinoma. METHODS The HCC-specific gene pairs with highly stable relative expression orderings in more than 99% of hepatocellular carcinoma but with reversed relative expression orderings in at least 99% of one of the six types of cancer, colorectal carcinoma, breast carcinoma, non-small-cell lung cancer, gastric carcinoma, pancreatic carcinoma and ovarian carcinoma, were identified. RESULTS With the simple majority rule, the HCC-specific relative expression orderings from comparisons with colorectal carcinoma and breast carcinoma could exactly discriminate primary hepatocellular carcinoma samples from both primary colorectal carcinoma and breast carcinoma samples. Especially, they correctly classified more than 90% of liver metastatic samples from colorectal carcinoma and breast carcinoma to their original tumours. Finally, using these HCC-specific relative expression orderings from comparisons with six cancer types, we identified eight of 24 hepatocellular carcinoma cell lines in the Cancer Cell Line Encyclopedia (Huh-7, Huh-1, HepG2, Hep3B, JHH-5, JHH-7, C3A and Alexander cells) that are highly representative of hepatocellular carcinoma. Evaluated with a REOs-based prognostic signature for hepatocellular carcinoma, all these eight cell lines showed the same metastatic properties of the high-risk metastatic hepatocellular carcinoma tissues. CONCLUSIONS Caution should be taken for using hepatocellular carcinoma cell lines. Our results should be helpful to select proper hepatocellular carcinoma cell lines for biological experiments.
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Affiliation(s)
- Lu Ao
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - You Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Department of Preventive Medicine, School of Basic Medicine Sciences, Gannan Medical University, Ganzhou, China
| | - Xuekun Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Qingzhou Guan
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Weicheng Zheng
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Jiahui Zhang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Haiyan Huang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Yi Zou
- Department of Automation and Key Laboratory of China MOE for System Control and Information Processing, Shanghai Jiao Tong University, Shanghai, China
| | - Zheng Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.,Fujian Key Laboratory of Tumour Microbiology, Fujian Medical University, Fuzhou, China
| | - Xianlong Wang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
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13
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Peng F, Wang R, Zhang Y, Zhao Z, Zhou W, Chang Z, Liang H, Zhao W, Qi L, Guo Z, Gu Y. Differential expression analysis at the individual level reveals a lncRNA prognostic signature for lung adenocarcinoma. Mol Cancer 2017; 16:98. [PMID: 28587642 PMCID: PMC5461634 DOI: 10.1186/s12943-017-0666-z] [Citation(s) in RCA: 94] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Accepted: 05/22/2017] [Indexed: 03/10/2023] Open
Abstract
BACKGROUND Deregulations of long non-coding RNAs (lncRNAs) have been implicated in cancer initiation and progression. Current methods can only capture differential expression of lncRNAs at the population level and ignore the heterogeneous expression of lncRNAs in individual patients. METHODS We propose a method (LncRIndiv) to identify differentially expressed (DE) lncRNAs in individual cancer patients by exploiting the disrupted ordering of expression levels of lncRNAs in each disease sample in comparison with stable normal ordering. LncRIndiv was applied to lncRNA expression profiles of lung adenocarcinoma (LUAD). Based on the expression profile of LUAD individual-level DE lncRNAs, we used a forward selection procedure to identify prognostic signature for stage I-II LUAD patients without adjuvant therapy. RESULTS In both simulated data and real pair-wise cancer and normal sample data, LncRIndiv method showed good performance. Based on the individual-level DE lncRNAs, we developed a robust prognostic signature consisting of two lncRNA (C1orf132 and TMPO-AS1) for stage I-II LUAD patients without adjuvant therapy (P = 3.06 × 10-6, log-rank test), which was confirmed in two independent datasets of GSE50081 (P = 1.82 × 10-2, log-rank test) and GSE31210 (P = 7.43 × 10-4, log-rank test) after adjusting other clinical factors such as smoking status and stages. Pathway analysis showed that TMPO-AS1 and C1orf132 could affect the prognosis of LUAD patients through regulating cell cycle and cell adhesion. CONCLUSIONS LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.
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Affiliation(s)
- Fuduan Peng
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Ruiping Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Yuanyuan Zhang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Zhangxiang Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, 150086, China
| | - Wenbin Zhou
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Zhiqiang Chang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Haihai Liang
- Department of Pharmacology, Harbin Medical University, Harbin, 150086, China
| | - Wenyuan Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Lishuang Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. .,Department of bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350001, China. .,Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, 350001, China.
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. .,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, 150086, China.
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14
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Schissler AG, Piegorsch WW, Lussier YA. Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation. Stat Methods Med Res 2017; 27:3797-3813. [PMID: 28552011 DOI: 10.1177/0962280217712271] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Modern precision medicine increasingly relies on molecular data analytics, wherein development of interpretable single-subject ("N-of-1") signals is a challenging goal. A previously developed global framework, N-of-1- pathways, employs single-subject gene expression data to identify differentially expressed gene set pathways in an individual patient. Unfortunately, the limited amount of data within the single-subject, N-of-1 setting makes construction of suitable statistical inferences for identifying differentially expressed gene set pathways difficult, especially when non-trivial inter-gene correlation is present. We propose a method that exploits external information on gene expression correlations to cluster positively co-expressed genes within pathways, then assesses differential expression across the clusters within a pathway. A simulation study illustrates that the cluster-based approach exhibits satisfactory false-positive error control and reasonable power to detect differentially expressed gene set pathways. An example with a single N-of-1 patient's triple negative breast cancer data illustrates use of the methodology.
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Affiliation(s)
- A Grant Schissler
- 1 Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, USA.,2 Center for Biomedical Informatics and Biostatistics (CB2), The University of Arizona, Tucson, AZ, USA.,3 BIO5 Institute, The University of Arizona, Tucson, AZ, USA.,4 Department of Medicine, The University of Arizona, Tucson, AZ, USA
| | - Walter W Piegorsch
- 1 Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, USA.,2 Center for Biomedical Informatics and Biostatistics (CB2), The University of Arizona, Tucson, AZ, USA.,3 BIO5 Institute, The University of Arizona, Tucson, AZ, USA.,5 Department of Mathematics, The University of Arizona, Tucson, AZ, USA
| | - Yves A Lussier
- 1 Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, USA.,2 Center for Biomedical Informatics and Biostatistics (CB2), The University of Arizona, Tucson, AZ, USA.,3 BIO5 Institute, The University of Arizona, Tucson, AZ, USA.,4 Department of Medicine, The University of Arizona, Tucson, AZ, USA
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15
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Hong G, Li H, Zhang J, Guan Q, Chen R, Guo Z. Identifying disease-associated pathways in one-phenotype data based on reversal gene expression orderings. Sci Rep 2017; 7:1348. [PMID: 28465555 PMCID: PMC5431047 DOI: 10.1038/s41598-017-01536-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 03/30/2017] [Indexed: 12/31/2022] Open
Abstract
Due to the invasiveness nature of tissue biopsy, it is common that investigators cannot collect sufficient normal controls for comparison with diseased samples. We developed a pathway enrichment tool, DRFunc, to detect significantly disease-disrupted pathways by incorporating normal controls from other experiments. The method was validated using both microarray and RNA-seq expression data for different cancers. The high concordant differentially ranked (DR) gene pairs were identified between cases and controls from different independent datasets. The DR gene pairs were used in the DRFunc algorithm to detect significantly disrupted pathways in one-phenotype expression data by combing controls from other studies. The DRFunc algorithm was exemplified by the detection of significant pathways in glioblastoma samples. The algorithm can also be used to detect altered pathways in the datasets with weak expression signals, as shown by the analysis on the expression data of chemotherapy-treated breast cancer samples.
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Affiliation(s)
- Guini Hong
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China.
| | - Hongdong Li
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China
| | - Jiahui Zhang
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China
| | - Qingzhou Guan
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China
| | - Rou Chen
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China
| | - Zheng Guo
- Department of Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350108, China.
- Fujian Key Laboratory of Tumor Microbiology, Fujian Medical University, Fuzhou, 350108, China.
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16
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Yan H, Guan Q, He J, Lin Y, Zhang J, Li H, Liu H, Gu Y, Guo Z, He F. Individualized analysis reveals CpG sites with methylation aberrations in almost all lung adenocarcinoma tissues. J Transl Med 2017; 15:26. [PMID: 28178989 PMCID: PMC5299650 DOI: 10.1186/s12967-017-1122-y] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 01/07/2017] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Due to the heterogeneity of cancer, identifying differentially methylated (DM) CpG sites between a set of cancer samples and a set of normal samples cannot tell us which patients have methylation aberrations in a particular DM CpG site. METHODS We firstly showed that the relative methylation-level orderings (RMOs) of CpG sites within individual normal lung tissues are highly stable but widely disrupted in lung adenocarcinoma tissues. This finding provides the basis of using the RankComp algorithm, previously developed for differential gene expression analysis at the individual level, to identify DM CpG sites in each cancer tissue compared with its own normal state. Briefly, through comparing with the highly stable normal RMOs predetermined in a large collection of samples for normal lung tissues, the algorithm finds those CpG sites whose hyper- or hypo-methylations may lead to the disrupted RMOs of CpG site pairs within a disease sample based on Fisher's exact test. RESULTS Evaluated in 59 lung adenocarcinoma tissues with paired adjacent normal tissues, RankComp reached an average precision of 94.26% for individual-level DM CpG sites. Then, after identifying DM CpG sites in each of the 539 lung adenocarcinoma samples from TCGA, we found five and 44 CpG sites hypermethylated and hypomethylated in above 90% of the disease samples, respectively. These findings were validated in 140 publicly available and eight additionally measured paired cancer-normal samples. Gene expression analysis revealed that four of the five genes, HOXA9, TAL1, ATP8A2, ENG and SPARCL1, each harboring one of the five frequently hypermethylated CpG sites within its promoters, were also frequently down-regulated in lung adenocarcinoma. CONCLUSIONS The common DNA methylation aberrations in lung adenocarcinoma tissues may be important for lung adenocarcinoma diagnosis and therapy.
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Affiliation(s)
- Haidan Yan
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China.,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Qingzhou Guan
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Jun He
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Yunqing Lin
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Juan Zhang
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Hongdong Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Huaping Liu
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Yunyan Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China
| | - Zheng Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, China. .,Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China.
| | - Fei He
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Epidemiology and Health Statistics, School of Public Health, Fujian Medical University, Fuzhou, 350001, China.
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17
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Guan Q, Chen R, Yan H, Cai H, Guo Y, Li M, Li X, Tong M, Ao L, Li H, Hong G, Guo Z. Differential expression analysis for individual cancer samples based on robust within-sample relative gene expression orderings across multiple profiling platforms. Oncotarget 2016; 7:68909-68920. [PMID: 27634898 PMCID: PMC5356599 DOI: 10.18632/oncotarget.11996] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2016] [Accepted: 08/09/2016] [Indexed: 11/25/2022] Open
Abstract
The highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue are widely reversed in the cancer condition. Based on this finding, we have recently proposed an algorithm named RankComp to detect differentially expressed genes (DEGs) for individual disease samples measured by a particular platform. In this paper, with 461 normal lung tissue samples separately measured by four commonly used platforms, we demonstrated that tens of millions of gene pairs with significantly stable REOs in normal lung tissue can be consistently detected in samples measured by different platforms. However, about 20% of stable REOs commonly detected by two different platforms (e.g., Affymetrix and Illumina platforms) showed inconsistent REO patterns due to the differences in probe design principles. Based on the significantly stable REOs (FDR<0.01) for normal lung tissue consistently detected by the four platforms, which tended to have large rank differences, RankComp detected averagely 1184, 1335 and 1116 DEGs per sample with averagely 96.51%, 95.95% and 94.78% precisions in three evaluation datasets with 25, 57 and 58 paired lung cancer and normal samples, respectively. Individualized pathway analysis revealed some common and subtype-specific functional mechanisms of lung cancer. Similar results were observed for colorectal cancer. In conclusion, based on the cross-platform significantly stable REOs for a particular normal tissue, differentially expressed genes and pathways in any disease sample measured by any of the platforms can be readily and accurately detected, which could be further exploited for dissecting the heterogeneity of cancer.
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Affiliation(s)
- Qingzhou Guan
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Rou Chen
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Haidan Yan
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Hao Cai
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - You Guo
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
- Department of Preventive Medicine, School of Basic Medicine Sciences, Gannan Medical University, Ganzhou, 341000, China
| | - Mengyao Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Xiangyu Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Mengsha Tong
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Lu Ao
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Hongdong Li
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Guini Hong
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
| | - Zheng Guo
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China
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18
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Peng F, Zhang Y, Wang R, Zhou W, Zhao Z, Liang H, Qi L, Zhao W, Wang H, Wang C, Guo Z, Gu Y. Identification of differentially expressed miRNAs in individual breast cancer patient and application in personalized medicine. Oncogenesis 2016; 5:e194. [PMID: 26878388 PMCID: PMC5154351 DOI: 10.1038/oncsis.2016.4] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2015] [Accepted: 12/31/2015] [Indexed: 12/19/2022] Open
Abstract
MicroRNAs (miRNAs) have key roles in breast cancer progression, and their expression levels are heterogeneous across individual breast cancer patients. Traditional methods aim to identify differentially expressed miRNAs in populations rather than in individuals and are affected by the expression intensities of miRNAs in different experimental batches or platforms. Thus it is urgent to conduct miRNA differential expression analysis at an individual level for further personalized medicine research. We proposed a straightforward method to determine the differential expression of each miRNA in an individual patient by utilizing the reversal expression order of miRNA pairs between two conditions (cancer and normal tissue). We applied our method to breast cancer miRNA expression profiles from The Cancer Genome Atlas and two other independent data sets. In total, 292 miRNAs were differentially expressed in individual breast cancer patients. Using the differential expression profile of miRNAs in individual patients, we found that the deregulations of miRNA tend to occur in specific breast cancer subtypes. We investigated the coordination effect between the miRNA and its target, based on the hypothesis that one gene function can be changed by copy number alterations of the corresponding gene or deregulation of the miRNA. We revealed that patients exhibiting an upregulation of hsa-miR-92b and patients with deletions of PTEN did not tend to overlap, and hsa-miR-92b and PTEN coordinately regulated the pathway of 'cell cycle' and so on. Moreover, we discovered a new prognostic signature, hsa-miR-29c, whose downregulation was associated with poor survival of breast cancer patients.
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Affiliation(s)
- F Peng
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Y Zhang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - R Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - W Zhou
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Z Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - H Liang
- Department of Pharmacology, Harbin Medical University, Harbin, China
| | - L Qi
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - W Zhao
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - H Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - C Wang
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Z Guo
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, China
| | - Y Gu
- Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
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19
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Liu C, Srihari S, Lal S, Gautier B, Simpson PT, Khanna KK, Ragan MA, Lê Cao KA. Personalised pathway analysis reveals association between DNA repair pathway dysregulation and chromosomal instability in sporadic breast cancer. Mol Oncol 2016; 10:179-93. [PMID: 26456802 PMCID: PMC5528935 DOI: 10.1016/j.molonc.2015.09.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 08/19/2015] [Accepted: 09/04/2015] [Indexed: 01/05/2023] Open
Abstract
The Homologous Recombination (HR) pathway is crucial for the repair of DNA double-strand breaks (DSBs) generated during DNA replication. Defects in HR repair have been linked to the initiation and development of a wide variety of human malignancies, and exploited in chemical, radiological and targeted therapies. In this study, we performed a personalised pathway analysis independently for four large sporadic breast cancer cohorts to investigate the status of HR pathway dysregulation in individual sporadic breast tumours, its association with HR repair deficiency and its impact on tumour characteristics. Specifically, we first manually curated a list of HR genes according to our recent review on this pathway (Liu et al., 2014), and then applied a personalised pathway analysis method named Pathifier (Drier et al., 2013) on the expression levels of the curated genes to obtain an HR score quantifying HR pathway dysregulation in individual tumours. Based on the score, we observed a great diversity in HR dysregulation between and within gene expression-based breast cancer subtypes, and by using two published HR-defect signatures, we found HR pathway dysregulation reflects HR repair deficiency. Furthermore, we identified a novel association between HR pathway dysregulation and chromosomal instability (CIN) in sporadic breast cancer. Although CIN has long been considered as a hallmark of most solid tumours, with recent extensive studies highlighting its importance in tumour evolution and drug resistance, the molecular basis of CIN in sporadic cancers remains poorly understood. Our results imply that HR pathway dysregulation might contribute to CIN in sporadic breast cancer.
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Affiliation(s)
- Chao Liu
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4067, Australia
| | - Sriganesh Srihari
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4067, Australia
| | - Samir Lal
- The University of Queensland, UQ Centre for Clinical Research, Herston, QLD 4029, Australia
| | - Benoît Gautier
- University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, QLD 4102, Australia
| | - Peter T Simpson
- The University of Queensland, UQ Centre for Clinical Research, Herston, QLD 4029, Australia; School of Medicine, The University of Queensland, Herston, QLD 4006, Australia
| | - Kum Kum Khanna
- QIMR-Berghofer Medical Research Institute, Herston, Brisbane, QLD 4029, Australia
| | - Mark A Ragan
- Institute for Molecular Bioscience, The University of Queensland, St. Lucia, QLD 4067, Australia.
| | - Kim-Anh Lê Cao
- University of Queensland Diamantina Institute, Translational Research Institute, Woolloongabba, QLD 4102, Australia.
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