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Jia P, Yang X, Yang X, Wang T, Xu Y, Ye K. MSIsensor-RNA: Microsatellite Instability Detection for Bulk and Single-cell Gene Expression Data. GENOMICS, PROTEOMICS & BIOINFORMATICS 2024; 22:qzae004. [PMID: 39341794 DOI: 10.1093/gpbjnl/qzae004] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 10/17/2023] [Accepted: 11/13/2023] [Indexed: 10/01/2024]
Abstract
Microsatellite instability (MSI) is an indispensable biomarker in cancer immunotherapy. Currently, MSI scoring methods by high-throughput omics methods have gained popularity and demonstrated better performance than the gold standard method for MSI detection. However, the MSI detection method on expression data, especially single-cell expression data, is still lacking, limiting the scope of clinical application and prohibiting the investigation of MSI at a single-cell level. Herein, we developed MSIsensor-RNA, an accurate, robust, adaptable, and standalone software to detect MSI status based on expression values of MSI-associated genes. We demonstrated the favorable performance and promise of MSIsensor-RNA in both bulk and single-cell gene expression data in multiplatform technologies including RNA sequencing (RNA-seq), microarray, and single-cell RNA-seq. MSIsensor-RNA is a versatile, efficient, and robust method for MSI status detection from both bulk and single-cell gene expression data in clinical studies and applications. MSIsensor-RNA is available at https://github.com/xjtu-omics/msisensor-rna.
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Affiliation(s)
- Peng Jia
- Department of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xuanhao Yang
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Xiaofei Yang
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- School of Computer Science and Technology, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Tingjie Wang
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Yu Xu
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
| | - Kai Ye
- Department of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
- MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
- Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
- School of Life Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
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2
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Choi JW, Lee JO, Lee S. Detecting microsatellite instability by length comparison of microsatellites in the 3' untranslated region with RNA-seq. Brief Bioinform 2024; 25:bbae423. [PMID: 39210504 PMCID: PMC11361843 DOI: 10.1093/bib/bbae423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 07/30/2024] [Accepted: 08/09/2024] [Indexed: 09/04/2024] Open
Abstract
Microsatellite instability (MSI), a phenomenon caused by deoxyribonucleic acid (DNA) mismatch repair system deficiencies, is an important biomarker in cancer research and clinical diagnostics. MSI detection often involves next-generation sequencing data, with many studies focusing on DNA. Here, we introduce a novel approach by measuring microsatellite lengths directly from ribonucleic acid sequencing (RNA-seq) data and comparing its distribution to detect MSI. Our findings reveal distinct instability patterns between MSI-high (MSI-H) and microsatellite stable samples, indicating the efficacy of RNA-based MSI detection. Additionally, microsatellites in the 3'-untranslated regions showed the greatest predictive value for MSI detection. Notably, this efficacy extends to detecting MSI-H samples even in tumors not commonly associated with MSI. Our approach highlights the utility of RNA-seq data in MSI detection, facilitating more precise diagnostics through the integration of various biological data.
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Affiliation(s)
- Jin-Wook Choi
- Department of Health Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 08826 Seoul, Republic of Korea
| | - Jin-Ok Lee
- Department of Health Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 08826 Seoul, Republic of Korea
| | - Sejoon Lee
- Department of Health Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, 08826 Seoul, Republic of Korea
- Department of Pathology and Translational Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, 82 Gumi-ro 173beon-gil, Bundang-gu, 13620 Seongnam, Republic of Korea
- Precision Medicine Center, Seoul National University Bundang Hospital, 82 Gumi-ro, Bundang-gu, 13620 Seongnam, Republic of Korea
- Department of Genomic Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro, Bundang-gu, 13620 Seongnam, Republic of Korea
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3
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Santamarina-García M, Brea-Iglesias J, Bramsen JB, Fuentes-Losada M, Caneiro-Gómez FJ, Vázquez-Bueno JÁ, Lázare-Iglesias H, Fernández-Díaz N, Sánchez-Rivadulla L, Betancor YZ, Ferreiro-Pantín M, Conesa-Zamora P, Antúnez-López JR, Kawazu M, Esteller M, Andersen CL, Tubio JMC, López-López R, Ruiz-Bañobre J. MSIMEP: Predicting microsatellite instability from microarray DNA methylation tumor profiles. iScience 2023; 26:106127. [PMID: 36879816 PMCID: PMC9984554 DOI: 10.1016/j.isci.2023.106127] [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: 08/20/2022] [Revised: 12/15/2022] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
Deficiency in DNA MMR activity results in tumors with a hypermutator phenotype, termed microsatellite instability (MSI). Beyond its utility in Lynch syndrome screening algorithms, today MSI has gained importance as predictive biomarker for various anti-PD-1 therapies across many different tumor types. Over the past years, many computational methods have emerged to infer MSI using either DNA- or RNA-based approaches. Considering this together with the fact that MSI-high tumors frequently exhibit a hypermethylated phenotype, herein we developed and validated MSIMEP, a computational tool for predicting MSI status from microarray DNA methylation tumor profiles of colorectal cancer samples. We demonstrated that MSIMEP optimized and reduced models have high performance in predicting MSI in different colorectal cancer cohorts. Moreover, we tested its consistency in other tumor types with high prevalence of MSI such as gastric and endometrial cancers. Finally, we demonstrated better performance of both MSIMEP models vis-à-vis a MLH1 promoter methylation-based one in colorectal cancer.
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Affiliation(s)
- Martín Santamarina-García
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Jenifer Brea-Iglesias
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Translational Oncology Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO, Álvaro Cunqueiro Hospital, 36213 Vigo, Spain
| | | | - Mar Fuentes-Losada
- Department of Medical Oncology, University Clinical Hospital of Santiago de Compostela (SERGAS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Francisco Javier Caneiro-Gómez
- Department of Pathology, University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | | | - Héctor Lázare-Iglesias
- Department of Pathology, University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Natalia Fernández-Díaz
- Department of Medical Oncology, University Clinical Hospital of Santiago de Compostela (SERGAS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Laura Sánchez-Rivadulla
- Department of Gynaecology and Obstetrics, Complejo Hospitalario Universitario de Ferrol, 15405 Ferrol, Spain
| | - Yoel Z Betancor
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Miriam Ferreiro-Pantín
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Pablo Conesa-Zamora
- Department of Clinical Analysis, Santa Lucía University Hospital, 30202 Cartagena, Spain
| | - José Ramón Antúnez-López
- Department of Pathology, University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Masahito Kawazu
- Chiba Cancer Center, Research Institute, 260-0801 Chiba, Japan.,Division of Cellular Signaling, National Cancer Center Research Institute, 104-0045 Tokyo, Japan
| | - Manel Esteller
- Josep Carreras Leukaemia Research Institute (IJC), 08916 Badalona, Barcelona, Spain.,Institucio Catalana de Recerca i Estudis Avançats (ICREA), 08010 Barcelona, Spain.,Physiological Sciences Department, School of Medicine and Health Sciences, University of Barcelona (UB), 08907 Barcelona, Spain.,Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029 Madrid, Spain
| | | | - Jose M C Tubio
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain
| | - Rafael López-López
- Department of Medical Oncology, University Clinical Hospital of Santiago de Compostela (SERGAS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029 Madrid, Spain
| | - Juan Ruiz-Bañobre
- Genomes and Disease, Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Department of Medical Oncology, University Clinical Hospital of Santiago de Compostela (SERGAS), University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago de Compostela, University of Santiago de Compostela (USC), 15706 Santiago de Compostela, Spain.,Centro de Investigación Biomédica en Red Cáncer (CIBERONC), 28029 Madrid, Spain
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4
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Pačínková A, Popovici V. Using empirical biological knowledge to infer regulatory networks from multi-omics data. BMC Bioinformatics 2022; 23:351. [PMID: 35996085 PMCID: PMC9396869 DOI: 10.1186/s12859-022-04891-9] [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: 01/24/2022] [Accepted: 08/08/2022] [Indexed: 12/13/2022] Open
Abstract
Background Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. Results We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. Conclusions We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04891-9.
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Affiliation(s)
- Anna Pačínková
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic. .,Faculty of Informatics, Masaryk University, Botanicka 68a, Brno, Czech Republic.
| | - Vlad Popovici
- RECETOX, Faculty of Science, Masaryk University, Kotlarska 2, Brno, Czech Republic
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5
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Seo MK, Kang H, Kim S. Tumor microenvironment-aware, single-transcriptome prediction of microsatellite instability in colorectal cancer using meta-analysis. Sci Rep 2022; 12:6283. [PMID: 35428835 PMCID: PMC9012745 DOI: 10.1038/s41598-022-10182-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 03/28/2022] [Indexed: 01/27/2023] Open
Abstract
Detecting microsatellite instability (MSI) in colorectal cancers (CRCs) is essential because it is the determinant of treatment strategies, including immunotherapy and chemotherapy. Yet, no attempt has been made to exploit transcriptomic profile and tumor microenvironment (TME) of it to unveil MSI status in CRC. Hence, we developed a novel TME-aware, single-transcriptome predictor of MSI for CRC, called MAP (Microsatellite instability Absolute single sample Predictor). MAP was developed utilizing recursive feature elimination-random forest with 466 CRC samples from The Cancer Genome Atlas, and its performance was validated in independent cohorts, including 1118 samples. MAP showed robustness and predictive power in predicting MSI status in CRC. Additional advantages for MAP were demonstrated through comparative analysis with existing MSI classifier and other cancer types. Our novel approach will provide access to untouched vast amounts of publicly available transcriptomic data and widen the door for MSI CRC research and be useful for gaining insights to help with translational medicine.
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Affiliation(s)
- Mi-Kyoung Seo
- Department of Biomedical Systems Informatics, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, 03722, South Korea
| | - Hyundeok Kang
- Department of Biomedical Systems Informatics, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, 03722, South Korea
| | - Sangwoo Kim
- Department of Biomedical Systems Informatics, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, 03722, South Korea.
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6
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Sorokin M, Rabushko E, Efimov V, Poddubskaya E, Sekacheva M, Simonov A, Nikitin D, Drobyshev A, Suntsova M, Buzdin A. Experimental and Meta-Analytic Validation of RNA Sequencing Signatures for Predicting Status of Microsatellite Instability. Front Mol Biosci 2021; 8:737821. [PMID: 34888350 PMCID: PMC8650122 DOI: 10.3389/fmolb.2021.737821] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 10/19/2021] [Indexed: 01/16/2023] Open
Abstract
Microsatellite instability (MSI) is an important diagnostic and prognostic cancer biomarker. In colorectal, cervical, ovarian, and gastric cancers, it can guide the prescription of chemotherapy and immunotherapy. In laboratory diagnostics of susceptible tumors, MSI is routinely detected by the size of marker polymerase chain reaction products encompassing frequent microsatellite expansion regions. Alternatively, MSI status is screened indirectly by immunohistochemical interrogation of microsatellite binding proteins. RNA sequencing (RNAseq) profiling is an emerging source of data for a wide spectrum of cancer biomarkers. Recently, three RNAseq-based gene signatures were deduced for establishing MSI status in tumor samples. They had 25, 15, and 14 gene products with only one common gene. However, they were developed and tested on the incomplete literature of The Cancer Genome Atlas (TCGA) sampling and never validated experimentally on independent RNAseq samples. In this study, we, for the first time, systematically validated these three RNAseq MSI signatures on the literature colorectal cancer (CRC) (n = 619), endometrial carcinoma (n = 533), gastric cancer (n = 380), uterine carcinosarcoma (n = 55), and esophageal cancer (n = 83) samples and on the set of experimental CRC RNAseq samples (n = 23) for tumors with known MSI status. We found that all three signatures performed well with area under the curve (AUC) ranges of 0.94-1 for the experimental CRCs and 0.94-1 for the TCGA CRC, esophageal cancer, and uterine carcinosarcoma samples. However, for the TCGA endometrial carcinoma and gastric cancer samples, only two signatures were effective with AUC 0.91-0.97, whereas the third signature showed a significantly lower AUC of 0.69-0.88. Software for calculating these MSI signatures using RNAseq data is included.
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Affiliation(s)
- Maksim Sorokin
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- OmicsWay Corp., Walnut, CA, United States
| | - Elizaveta Rabushko
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
- Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Victor Efimov
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
- Oncobox Ltd., Moscow, Russia
| | - Elena Poddubskaya
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Marina Sekacheva
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Alexander Simonov
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
- Oncobox Ltd., Moscow, Russia
| | - Daniil Nikitin
- Oncobox Ltd., Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
| | - Aleksey Drobyshev
- Laboratory For Clinical and Genomic Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia
| | - Maria Suntsova
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Anton Buzdin
- Moscow Institute of Physics and Technology, Dolgoprudny, Russia
- OmicsWay Corp., Walnut, CA, United States
- World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov First Moscow State Medical University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Moscow, Russia
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7
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Multi-omics characterization and validation of MSI-related molecular features across multiple malignancies. Life Sci 2021; 270:119081. [PMID: 33516699 DOI: 10.1016/j.lfs.2021.119081] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/09/2021] [Accepted: 01/10/2021] [Indexed: 01/17/2023]
Abstract
HEADINGS AIMS To establish a microsatellite instability (MSI) predictive model in pan-cancer and compare the multi-omics characterization of MSI-related molecular features. MATERIALS AND METHODS We established a 15-gene signature for predicting MSI status and performed a systematic assessment of MSI-related molecular features including gene and miRNA expression, DNA methylation, and somatic mutation, in approximately 10,000 patients across 30 cancer types from The Cancer Genome Atlas, Gene Expression Omnibus database, and our institution. Then we identified common MSI-associated dysregulated molecular features across six cancers and explored their mutual interfering relationships and the drug sensitivity. KEY FINDINGS we demonstrated the model's high prediction performance and found the samples with high-MSI were mainly distributed in six cancers: BRCA, COAD, LUAD, LIHC, STAD, and UCEC. We found RPL22L1 was up-regulated in the high-MSI group of 5/6 cancer types. CYP27A1 and RAI2 were down-regulated in 4/6 cancer types. More than 20 miRNAs and 39 DMGs were found up-regulated in MSI-H at least three cancers. We discovered some drugs, including OSI-027 and AZD8055 had a higher sensitivity in the high MSI-score group. Functional enrichment analysis revealed the correlation between MSI score and APM score, HLA score, or glycolysis score. The complicated regulatory mechanism of tumor MSI status in multiple dimensions was explored by an integrated analysis of the correlations among MSI-related genes, miRNAs, methylation, and drug response data. SIGNIFICANCE Our pan-cancer study provides a valuable predictive model and a comprehensive atlas of tumor MSI, which may guide more precise and personalized therapeutic strategies for tumor patients.
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Novel Epigenetic Eight-Gene Signature Predictive of Poor Prognosis and MSI-Like Phenotype in Human Metastatic Colorectal Carcinomas. Cancers (Basel) 2021; 13:cancers13010158. [PMID: 33466447 PMCID: PMC7796477 DOI: 10.3390/cancers13010158] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 12/30/2020] [Indexed: 12/13/2022] Open
Abstract
Simple Summary The global methylation profile of two human metastatic colorectal carcinoma subgroups with significantly different outcomes (primary-resistant versus drug-sensitive tumors) was analyzed and compared with the gene expression and methylation data from The Cancer Genome Atlas COlon ADenocarcinoma (TCGA COAD) metastatic colorectal carcinoma dataset with the aim to identify a prognostic signature of functionally methylated genes. A novel epigenetic eight-gene signature, with hypermethylation of the promoter regions, was identified and validated for its capacity to predict poor outcome, which had a CpG-island methylator phenotype (CIMP)-high status and microsatellite instability (MSI)-like phenotype. Abstract Epigenetics is involved in tumor progression and drug resistance in human colorectal carcinoma (CRC). This study addressed the hypothesis that the DNA methylation profiling may predict the clinical behavior of metastatic CRCs (mCRCs). The global methylation profile of two human mCRC subgroups with significantly different outcome was analyzed and compared with gene expression and methylation data from The Cancer Genome Atlas COlon ADenocarcinoma (TCGA COAD) and the NCBI GENE expression Omnibus repository (GEO) GSE48684 mCRCs datasets to identify a prognostic signature of functionally methylated genes. A novel epigenetic signature of eight hypermethylated genes was characterized that was able to identify mCRCs with poor prognosis, which had a CpG-island methylator phenotype (CIMP)-high and microsatellite instability (MSI)-like phenotype. Interestingly, methylation events were enriched in genes located on the q-arm of chromosomes 13 and 20, two chromosomal regions with gain/loss alterations associated with adenoma-to-carcinoma progression. Finally, the expression of the eight-genes signature and MSI-enriching genes was confirmed in oxaliplatin- and irinotecan-resistant CRC cell lines. These data reveal that the hypermethylation of specific genes may provide prognostic information that is able to identify a subgroup of mCRCs with poor prognosis.
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Li L, Feng Q, Wang X. PreMSIm: An R package for predicting microsatellite instability from the expression profiling of a gene panel in cancer. Comput Struct Biotechnol J 2020; 18:668-675. [PMID: 32257050 PMCID: PMC7113609 DOI: 10.1016/j.csbj.2020.03.007] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/06/2020] [Accepted: 03/08/2020] [Indexed: 01/10/2023] Open
Abstract
Microsatellite instability (MSI) is a genomic property of the cancers with defective DNA mismatch repair and is a useful marker for cancer diagnosis and treatment in diverse cancer types. In particular, MSI has been associated with the active immune checkpoint blockade therapy response in cancer. Most of computational methods for predicting MSI are based on DNA sequencing data and a few are based on mRNA expression data. Using the RNA-Seq pan-cancer datasets for three cancer cohorts (colon, gastric, and endometrial cancers) from The Cancer Genome Atlas (TCGA) program, we developed an algorithm (PreMSIm) for predicting MSI from the expression profiling of a 15-gene panel in cancer. We demonstrated that PreMSIm had high prediction performance in predicting MSI in most cases using both RNA-Seq and microarray gene expression datasets. Moreover, PreMSIm displayed superior or comparable performance versus other DNA or mRNA-based methods. We conclude that PreMSIm has the potential to provide an alternative approach for identifying MSI in cancer.
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Key Words
- ACC, adrenocortical carcinoma
- AUC, area under the curve
- Algorithm
- BLCA, bladder urothelial carcinoma
- BRCA, breast invasive carcinoma
- CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma
- CHOL, cholangiocarcinoma
- COAD, colon adenocarcinoma
- CV, cross validation
- Cancer
- Classification
- DLBC, lymphoid neoplasm diffuse large B-cell lymphoma
- ESCA, esophageal carcinoma
- GBM, glioblastoma multiforme
- GEO, Gene Expression Omnibus
- GO, gene ontology
- Gene expression profiling
- HNSC, head and neck squamous cell carcinoma
- KICH, kidney chromophobe
- KIRC, kidney renal clear cell carcinoma
- KIRP, kidney renal papillary cell carcinoma
- LAML, acute myeloid leukemia
- LGG, brain lower grade glioma
- LIHC, liver hepatocellular carcinoma
- LUAD, lung adenocarcinoma
- LUSC, lung squamous cell carcinoma
- MESO, mesothelioma
- MSI, microsatellite instability
- MSS, microsatellite stability
- Machine learning
- Microsatellite instability
- OV, ovarian serous cystadenocarcinoma
- PAAD, pancreatic adenocarcinoma
- PCPG, pheochromocytoma and paraganglioma
- PPI, protein-protein interaction
- PRAD, prostate adenocarcinoma
- READ, rectum adenocarcinoma
- RF, random forest
- ROC, receiver operating characteristic
- SARC, sarcoma
- SKCM, skin cutaneous melanoma
- STAD, stomach adenocarcinoma
- SVM, support vector machine
- TCGA, The Cancer Genome Atlas
- TGCT, testicular germ cell tumors
- THCA, thyroid carcinoma
- THYM, thymoma
- UCEC, uterine corpus endometrial carcinoma
- UCS, uterine carcinosarcoma
- UVM, uveal melanoma
- XGBoost, extreme gradient boosting
- k-NN, k-nearest neighbor
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Affiliation(s)
- Lin Li
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Big Data Research Institute, China Pharmaceutical University, Nanjing 211198, China
| | - Qiushi Feng
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Big Data Research Institute, China Pharmaceutical University, Nanjing 211198, China
| | - Xiaosheng Wang
- Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing 211198, China.,Big Data Research Institute, China Pharmaceutical University, Nanjing 211198, China
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