Identification of Potential Crucial Genes and Key Pathways in Breast Cancer Using Bioinformatic Analysis

Background: The molecular mechanism of tumorigenesis remains to be fully understood in breast cancer. It is urgently required to identify genes that are associated with breast cancer development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets. Methods: Four datasets (GSE21422, GSE29431, GSE42568, and GSE61304) from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) dataset were used for the bioinformatic analysis. DEGs were identified using LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through FunRich. The protein-protein interaction (PPI) network of the DEGs was established through STRING (Search Tool for the Retrieval of Interacting Genes database) website, visualized by Cytoscape and further analyzed by Molecular Complex Detection (MCODE). UALCAN and Kaplan–Meier (KM) plotter were employed to analyze the expression levels and prognostic values of hub genes. The expression levels of the hub genes were also validated in clinical samples from breast cancer patients. In addition, the gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD). Results: In total, 203 up-regulated and 118 down-regulated DEGs were identified. Mitotic cell cycle and epithelial-to-mesenchymal transition pathway were the major enriched pathways for the up-regulated and down-regulated genes, respectively. The PPI network was constructed with 314 nodes and 1,810 interactions, and two significant modules are selected. The most significant enriched pathway in module 1 was the mitotic cell cycle. Moreover, six hub genes were selected and validated in clinical sample for further analysis owing to the high degree of connectivity, including CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK, and they were all correlated to worse overall survival (OS) in breast cancer. Conclusion: These results revealed that mitotic cell cycle and epithelial-to-mesenchymal transition pathway could be potential pathways accounting for the progression in breast cancer, and CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK may be potential crucial genes. Further, it could be utilized as new biomarkers for prognosis and potential new targets for drug synthesis of breast cancer.

[1]  Bioinformatics analysis reveals disturbance mechanism of MAPK signaling pathway and cell cycle in Glioblastoma multiforme. , 2014, Gene.

[2]  Qiang Liu,et al.  RRM1, TUBB3, TOP2A, CYP19A1, CYP2D6: Difference between mRNA and protein expression in predicting prognosis of breast cancer patients. , 2015, Oncology reports.

[3]  Cheng Wang,et al.  Identification of hub genes to regulate breast cancer metastasis to brain by bioinformatics analyses , 2018, Journal of cellular biochemistry.

[4]  H. Kocher,et al.  Pancreatic Cancer , 2019, Methods in Molecular Biology.

[5]  Gordon B Mills,et al.  Comprehensive Genomic Analysis Identifies Novel Subtypes and Targets of Triple-Negative Breast Cancer , 2014, Clinical Cancer Research.

[6]  T. Mak,et al.  Targeting Mitosis in Cancer: Emerging Strategies. , 2015, Molecular cell.

[7]  Yuan-Shan Zhu,et al.  H 19 lncRNA mediates 17 β-estradiol-induced cell proliferation in MCF-7 breast cancer cells , 2015 .

[8]  Yan Peng,et al.  H19 lncRNA mediates 17β-estradiol-induced cell proliferation in MCF-7 breast cancer cells. , 2015, Oncology reports.

[9]  Yuan-Yuan Pei,et al.  Kinesin family member 11 contributes to the progression and prognosis of human breast cancer , 2017, Oncology letters.

[10]  R. Schlegel,et al.  MELK is an oncogenic kinase essential for mitotic progression in basal-like breast cancer cells , 2014, eLife.

[11]  Davide Heller,et al.  STRING v10: protein–protein interaction networks, integrated over the tree of life , 2014, Nucleic Acids Res..

[12]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[13]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumours , 2013 .

[14]  A. Lánczky,et al.  miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients , 2016, Breast Cancer Research and Treatment.

[15]  Amy M. Sitapati,et al.  Breast Cancer, Version 4.2017, NCCN Clinical Practice Guidelines in Oncology. , 2018, Journal of the National Comprehensive Cancer Network : JNCCN.

[16]  J. Ji,et al.  CCNA2 Is a Prognostic Biomarker for ER+ Breast Cancer and Tamoxifen Resistance , 2014, PloS one.

[17]  X. Chen,et al.  Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. , 2011, The Journal of clinical investigation.

[18]  Wanqing Chen,et al.  Cancer statistics: updated cancer burden in China. , 2015, Chinese journal of cancer research = Chung-kuo yen cheng yen chiu.

[19]  Donghai Huang,et al.  Genome-wide analyses of long noncoding RNA expression profiles correlated with radioresistance in nasopharyngeal carcinoma via next-generation deep sequencing , 2016, BMC Cancer.

[20]  M. Barbacid,et al.  Cell cycle, CDKs and cancer: a changing paradigm , 2009, Nature Reviews Cancer.

[21]  Nathanael S Gray,et al.  MELK is not necessary for the proliferation of basal-like breast cancer cells , 2017, eLife.

[22]  C. Orengo,et al.  Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma , 2006, BMC Genomics.

[23]  Y. Miyoshi,et al.  Determination of the specific activity of CDK1 and CDK2 as a novel prognostic indicator for early breast cancer. , 2008, Annals of oncology : official journal of the European Society for Medical Oncology.

[24]  G. Evan,et al.  Proliferation, cell cycle and apoptosis in cancer , 2001, Nature.

[25]  Gary D. Bader,et al.  An automated method for finding molecular complexes in large protein interaction networks , 2003, BMC Bioinformatics.

[26]  Wei Zhang,et al.  Genome-scale analysis identifies GJB2 and ERO1LB as prognosis markers in patients with pancreatic cancer , 2017, Oncotarget.

[27]  Yueyin Pan,et al.  Novel key genes in triple‐negative breast cancer identified by weighted gene co‐expression network analysis , 2019, Journal of cellular biochemistry.

[28]  Chengqiong Mao,et al.  Triple negative breast cancer therapy with CDK1 siRNA delivered by cationic lipid assisted PEG-PLA nanoparticles. , 2014, Journal of controlled release : official journal of the Controlled Release Society.

[29]  C. Miller,et al.  Phosphatidylinositol 3-kinase pathway activation in breast cancer brain metastases , 2011, Breast Cancer Research.

[30]  Enhao Fang,et al.  Identification of breast cancer hub genes and analysis of prognostic values using integrated bioinformatics analysis. , 2017, Cancer biomarkers : section A of Disease markers.

[31]  Ping Chen,et al.  ISL1, a novel regulator of CCNB1, CCNB2 and c-MYC genes, promotes gastric cancer cell proliferation and tumor growth , 2016, Oncotarget.

[32]  Wei Zhang,et al.  COL3A1 and SNAP91: novel glioblastoma markers with diagnostic and prognostic value , 2016, Oncotarget.

[33]  Thomas C. Wiegers,et al.  Text Mining Effectively Scores and Ranks the Literature for Improving Chemical-Gene-Disease Curation at the Comparative Toxicogenomics Database , 2013, PloS one.

[34]  M. B. Davis,et al.  breast cancer cells , 2009 .

[35]  A. Lin,et al.  MELK expression correlates with tumor mitotic activity but is not required for cancer growth , 2018, eLife.

[36]  Pradip De,et al.  PI3K‐AKT‐mTOR inhibitors in breast cancers: From tumor cell signaling to clinical trials , 2017, Pharmacology & therapeutics.

[37]  Pedro Fonseca,et al.  A novel community driven software for functional enrichment analysis of extracellular vesicles data , 2017, Journal of extracellular vesicles.

[38]  Steven J. M. Jones,et al.  Comprehensive molecular portraits of human breast tumors , 2012, Nature.

[39]  Gang Yin,et al.  CCNA2 acts as a novel biomarker in regulating the growth and apoptosis of colorectal cancer , 2018, Cancer management and research.

[40]  Chad J. Creighton,et al.  UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses , 2017, Neoplasia.

[41]  W. Ghali,et al.  Kaplan-Meier survival analysis overestimates cumulative incidence of health-related events in competing risk settings: a meta-analysis. , 2018, Journal of clinical epidemiology.

[42]  Qiang Liu,et al.  RRM 1 , TUBB 3 , TOP 2 A , CYP 19 A 1 , CYP 2 D 6 : Difference between mRNA and protein expression in predicting prognosis of breast cancer patients , 2015 .

[43]  I. Andrulis,et al.  Topoisomerase II alpha and responsiveness of breast cancer to adjuvant chemotherapy. , 2009, Journal of the National Cancer Institute.

[44]  K. Friedrich,et al.  Impact of breast cancer subtypes and patterns of metastasis on outcome , 2015, Breast Cancer Research and Treatment.

[45]  A. Jemal,et al.  Cancer statistics, 2019 , 2019, CA: a cancer journal for clinicians.

[46]  Wenqing Li,et al.  CCNB1 is a prognostic biomarker for ER+ breast cancer. , 2014, Medical hypotheses.

[47]  Terukazu Nakamura,et al.  CDK1 and CDK2 activity is a strong predictor of renal cell carcinoma recurrence. , 2014, Urologic oncology.