Literature DB >> 31402953

Bioinformatics analysis identifies potential chemoresistance-associated genes across multiple types of cancer.

Jingsheng Yuan1, Lulu Tan1, Zhijie Yin1, Kaixiong Tao1, Guobing Wang1, Wenjia Shi2, Jinbo Gao1.   

Abstract

Despite the fact that studies have revealed mechanisms underlying tumor chemoresistance, the functions of numerous potential chemoresistance-associated genes have yet to be elucidated. A bioinformatics analysis was conducted to screen differentially expressed genes (DEGs) across four types of chemoresistant tumors and functional enrichment analysis was used to examine the biological significance of these genes. Furthermore, a gene network was constructed using weighted gene co-expression network analysis to identify hub genes. A total of 6,015, 2,074, 2,141 and 954 differentially expressed genes were identified in estrogen receptor-negative breast cancer, ovarian cancer, rectal cancer and gastric cancer, respectively; however, only five of these DEGs were dysregulated in all four types of cancer. Functional enrichment analysis of the DEGs suggested that genomic stability and immune response are crucial determinants of tumor chemoresistance. In addition, 14, 8, 6 and 1 co-expressed gene modules were identified in estrogen receptor-negative breast cancer, ovarian cancer, rectal cancer and gastric cancer, respectively, and protein-protein interaction networks were created. The analysis identified only calcium-calmodulin-dependent protein kinase kinase 2, erythropoietin receptor, mitochondrial poly(A) RNA polymerase, α-parvin, and zinc finger and BTB domain-containing protein 44 to be dysregulated in all four cancer types, indicating varying mechanisms of chemoresistance in different tumor types. Furthermore, our analysis suggests that type I collagen α1, fibroblast growth factor 14 and major histocompatibility complex, class II, DR β1 potentially serve key roles in the development of chemoresistance. In conclusion, the present study proposes a simple and effective strategy for identifying genes involved in chemoresistance and predicting their potential functional roles, which may guide subsequent experimental studies.

Entities:  

Keywords:  bioinformatics; cancer; chemoresistance; network-based screening; weighted gene co-expression network analysis

Year:  2019        PMID: 31402953      PMCID: PMC6676748          DOI: 10.3892/ol.2019.10533

Source DB:  PubMed          Journal:  Oncol Lett        ISSN: 1792-1074            Impact factor:   2.967


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Review 1.  Network biology: understanding the cell's functional organization.

Authors:  Albert-László Barabási; Zoltán N Oltvai
Journal:  Nat Rev Genet       Date:  2004-02       Impact factor: 53.242

2.  An integrative genomics approach to infer causal associations between gene expression and disease.

Authors:  Eric E Schadt; John Lamb; Xia Yang; Jun Zhu; Steve Edwards; Debraj Guhathakurta; Solveig K Sieberts; Stephanie Monks; Marc Reitman; Chunsheng Zhang; Pek Yee Lum; Amy Leonardson; Rolf Thieringer; Joseph M Metzger; Liming Yang; John Castle; Haoyuan Zhu; Shera F Kash; Thomas A Drake; Alan Sachs; Aldons J Lusis
Journal:  Nat Genet       Date:  2005-06-19       Impact factor: 38.330

3.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.

Authors:  Da Wei Huang; Brad T Sherman; Richard A Lempicki
Journal:  Nat Protoc       Date:  2009       Impact factor: 13.491

4.  Uncovering regulatory pathways that affect hematopoietic stem cell function using 'genetical genomics'.

Authors:  Leonid Bystrykh; Ellen Weersing; Bert Dontje; Sue Sutton; Mathew T Pletcher; Tim Wiltshire; Andrew I Su; Edo Vellenga; Jintao Wang; Kenneth F Manly; Lu Lu; Elissa J Chesler; Rudi Alberts; Ritsert C Jansen; Robert W Williams; Michael P Cooke; Gerald de Haan
Journal:  Nat Genet       Date:  2005-02-13       Impact factor: 38.330

Review 5.  Brain and cancer: the protective role of erythropoietin.

Authors:  Michele Buemi; Chiara Caccamo; Lorena Nostro; Emanuela Cavallaro; Fulvio Floccari; Giovanni Grasso
Journal:  Med Res Rev       Date:  2005-03       Impact factor: 12.944

6.  Variations in DNA elucidate molecular networks that cause disease.

Authors:  Yanqing Chen; Jun Zhu; Pek Yee Lum; Xia Yang; Shirly Pinto; Douglas J MacNeil; Chunsheng Zhang; John Lamb; Stephen Edwards; Solveig K Sieberts; Amy Leonardson; Lawrence W Castellini; Susanna Wang; Marie-France Champy; Bin Zhang; Valur Emilsson; Sudheer Doss; Anatole Ghazalpour; Steve Horvath; Thomas A Drake; Aldons J Lusis; Eric E Schadt
Journal:  Nature       Date:  2008-03-16       Impact factor: 49.962

Review 7.  The parvins.

Authors:  J L Sepulveda; C Wu
Journal:  Cell Mol Life Sci       Date:  2006-01       Impact factor: 9.261

8.  Functional and topological characterization of protein interaction networks.

Authors:  Soon-Hyung Yook; Zoltán N Oltvai; Albert-László Barabási
Journal:  Proteomics       Date:  2004-04       Impact factor: 3.984

9.  WGCNA: an R package for weighted correlation network analysis.

Authors:  Peter Langfelder; Steve Horvath
Journal:  BMC Bioinformatics       Date:  2008-12-29       Impact factor: 3.169

10.  Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.

Authors:  Da Wei Huang; Brad T Sherman; Richard A Lempicki
Journal:  Nucleic Acids Res       Date:  2008-11-25       Impact factor: 16.971

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