The Role of Apoptotic Genes and Protein-Protein Interactions in Triple-negative Breast Cancer

Abstract Background/Aim: Compared to other breast cancer types, triple-negative breast cancer (TNBC) has historically had few treatment alternatives. Therefore, exploring and pinpointing potentially implicated genes could be used for treating and managing TNBC. By doing this, we will provide essential data to comprehend how the genes are involved in the apoptotic pathways of the cancer cells to identify potential therapeutic targets. Analysis of a single genetic alteration may not reveal the pathogenicity driving TNBC due to the high genomic complexity and heterogeneity of TNBC. Therefore, searching through a large variety of gene interactions enabled the identification of molecular therapeutic genes. Materials and Methods: This study used integrated bioinformatics methods such as UALCAN, TNM plotter, PANTHER, GO-KEEG and PPIs to assess the gene expression, protein-protein interaction (PPI), and transcription factor interaction of apoptosis-regulated genes. Results: Compared to normal breast tissue, gene expressions of BNIP3, TNFRSF10B, MCL1, and CASP4 were downregulated in UALCAN. At the same time, BIK, AKT1, BAD, FADD, DIABLO, and CASP9 was down-regulated in bc-GeneExMiner v4.5 mRNA expression (BCGM) databases. Based on GO term enrichment analysis, the cellular process (GO:0009987), which has about 21 apoptosis-regulated genes, is the top category in the biological processes (BP), followed by biological regulation (GO:0065007). We identified 29 differentially regulated pathways, including the p53 pathway, angiogenesis, apoptosis signaling pathway, and the Alzheimer’s disease presenilin pathway. We examined the PPIs between the genes that regulate apoptosis; CASP3 and CASP9 interact with FADD, MCL1, TNF, TNFRSRF10A, and TNFRSF10; additionally, CASP3 significantly forms PPIs with CASP9, DFFA, and TP53, and CASP9 with DIABLO. In the top 10 transcription factors, the androgen receptor (AR) interacts with five apoptosis-regulated genes (p<0.0001; q<0.01), followed by retinoic acid receptor alpha (RARA) (p<0.0001; q<0.01) and ring finger protein (RNF2) (p<0.0001; q<0.01). Overall, the gene expression profile, PPIs, and the apoptosis-TF interaction findings suggest that the 27 apoptosis-regulated genes might be used as promising targets in treating and managing TNBC. Furthermore, from a total of 27 key genes, CASP2, CASP3, DAPK1, TNF, TRAF2, and TRAF3 were significantly correlated with poor overall survival in TNBC (p-value <0.05); they could play important roles in the progression of TNBC and provide attractive therapeutic targets that may offer new candidate molecules for targeted therapy. Conclusion: Our findings demonstrate that CASP2, CASP3, DAPK1, TNF, TRAF2, and TRAF3 were substantially associated with the overall survival rate (OS) difference of TNBC patients out of a total of 27 specific genes used in this study, which may play crucial roles in the development of TNBC and offer promising therapeutic interventions.

[1]  E. Taka,et al.  Anticancer Effects of Thymoquinone through the Antioxidant Activity, Upregulation of Nrf2, and Downregulation of PD-L1 in Triple-Negative Breast Cancer Cells , 2022, Nutrients.

[2]  Xinghai Li,et al.  HC-1119, a deuterated Enzalutamide, inhibits Migration, Invasion and Metastasis of the AR-positive triple-negative breast Cancer cells , 2022, Molecular Biology Reports.

[3]  Jun Lu,et al.  A Metabolic Gene Signature to Predict Breast Cancer Prognosis , 2022, Frontiers in Molecular Biosciences.

[4]  K. Soliman,et al.  Thymoquinone Anticancer Effects Through the Upregulation of NRF2 and the Downregulation of PD-L1 in MDA-MB-231 Triple-Negative Breast Cancer Cells. , 2022, The FASEB Journal.

[5]  E. Taka,et al.  Thymoquinone Alterations of the Apoptotic Gene Expressions and Cell Cycle Arrest in Genetically Distinct Triple-Negative Breast Cancer Cells , 2022, Nutrients.

[6]  C. Tu,et al.  Snail Mucus Enhances Chemosensitivity of Triple-negative Breast Cancer Via Activation of the Fas Pathway , 2022, AntiCancer Research.

[7]  Zhaohui S. Qin,et al.  UALCAN: An update to the integrated cancer data analysis platform , 2022, Neoplasia.

[8]  E. Mazzio,et al.  Therapeutic Potential of Thymoquinone in Triple-Negative Breast Cancer Prevention and Progression through the Modulation of the Tumor Microenvironment , 2021, Nutrients.

[9]  Jun Wu,et al.  Overexpression of IRF3 Predicts Poor Prognosis in Clear Cell Renal Cell Carcinoma , 2021, International journal of general medicine.

[10]  B. Győrffy,et al.  Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer , 2021, Computational and structural biotechnology journal.

[11]  L. Moro,et al.  Programmed Cell Death in Health and Disease , 2021, Cells.

[12]  M. Kim,et al.  NR4A1 Regulates Tamoxifen Resistance by Suppressing ERK Signaling in ER-Positive Breast Cancer , 2021, Cells.

[13]  Zhijie Liang,et al.  Resveratrol Mediates the Apoptosis of Triple Negative Breast Cancer Cells by Reducing POLD1 Expression , 2021, Frontiers in Oncology.

[14]  Fadoua Ben Azzouz,et al.  bc-GenExMiner 4.5: new mining module computes breast cancer differential gene expression analyses , 2021, Database J. Biol. Databases Curation.

[15]  Anushya Muruganujan,et al.  PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API , 2020, Nucleic Acids Res..

[16]  B. Győrffy,et al.  Pancancer survival analysis of cancer hallmark genes , 2020, Scientific Reports.

[17]  K. Shen,et al.  Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods , 2020, PloS one.

[18]  K. Soliman,et al.  Gene Expression Alterations Associated with Oleuropein-Induced Antiproliferative Effects and S-Phase Cell Cycle Arrest in Triple-Negative Breast Cancer Cells , 2020, Nutrients.

[19]  B. Győrffy,et al.  TNMplot.com: A Web Tool for the Comparison of Gene Expression in Normal, Tumor and Metastatic Tissues , 2020, bioRxiv.

[20]  Geoffroy Andrieux,et al.  Integration of Online Omics-Data Resources for Cancer Research , 2020, Frontiers in Genetics.

[21]  V. Di Lauro,et al.  Biomarkers in Triple-Negative Breast Cancer: State-of-the-Art and Future Perspectives , 2020, International journal of molecular sciences.

[22]  X. Bian,et al.  Triple-negative breast cancer molecular subtyping and treatment progress , 2020, Breast Cancer Research.

[23]  K. Soliman,et al.  Rosmarinic Acid‐Induced Apoptosis and Cell Cycle Arrest in Triple‐Negative Breast Cancer Cells , 2020, The FASEB Journal.

[24]  U. Testa,et al.  Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments , 2020, Medical sciences.

[25]  Nektarios Tavernarakis,et al.  UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network , 2020, NAR genomics and bioinformatics.

[26]  Carlina Duan.  Programmed , 2020, Pleiades: Literature in Context.

[27]  Sarah K. Wooller,et al.  Defining Signatures of Arm-Wise Copy Number Change and Their Associated Drivers in Kidney Cancers , 2019, International journal of molecular sciences.

[28]  K. Soliman,et al.  The Anticancer and Antioxidant Effects of Muscadine Grape Extracts on Racially Different Triple-negative Breast Cancer Cells , 2019, AntiCancer Research.

[29]  K. Soliman,et al.  Effects of gossypol on apoptosis-related gene expression in racially distinct triple-negative breast cancer cells , 2019, Oncology reports.

[30]  P. Vandenabeele,et al.  The molecular machinery of regulated cell death , 2019, Cell Research.

[31]  H. Salehiniya,et al.  Epidemiological characteristics of and risk factors for breast cancer in the world , 2019, Breast cancer.

[32]  Damian Szklarczyk,et al.  STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets , 2018, Nucleic Acids Res..

[33]  Raymond M. Moore,et al.  Triple-Negative Breast Cancer Risk Genes Identified by Multigene Hereditary Cancer Panel Testing , 2018, Journal of the National Cancer Institute.

[34]  G. Calaf,et al.  Curcumin and paclitaxel induce cell death in breast cancer cell lines. , 2018, Oncology reports.

[35]  Bo Liu,et al.  Breast cancer development and progression: Risk factors, cancer stem cells, signaling pathways, genomics, and molecular pathogenesis , 2018, Genes & diseases.

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

[37]  A. Aranda,et al.  Pro-apoptotic signaling induced by Retinoic acid and dsRNA is under the control of Interferon Regulatory Factor-3 in breast cancer cells , 2017, Apoptosis.

[38]  Nichollas E. Scott,et al.  Interactome disassembly during apoptosis occurs independent of caspase cleavage , 2017, Molecular systems biology.

[39]  He Wang,et al.  Knockdown of RNF2 induces cell cycle arrest and apoptosis in prostate cancer cells through the upregulation of TXNIP , 2016, Oncotarget.

[40]  G. Calaf,et al.  Effect of curcumin and paclitaxel on breast carcinogenesis. , 2016, International journal of oncology.

[41]  J. Balko,et al.  Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease , 2016, Nature Reviews Clinical Oncology.

[42]  Wei Huang,et al.  SREBP-1 is an independent prognostic marker and promotes invasion and migration in breast cancer , 2016, Oncology letters.

[43]  Andrew D. Rouillard,et al.  Enrichr: a comprehensive gene set enrichment analysis web server 2016 update , 2016, Nucleic Acids Res..

[44]  F. Zhou,et al.  Overexpression of RNF2 Is an Independent Predictor of Outcome in Patients with Urothelial Carcinoma of the Bladder Undergoing Radical Cystectomy , 2016, Scientific Reports.

[45]  R. K. Brojen Singh,et al.  Apoptosis regulatory protein-protein interaction demonstrates hierarchical scale-free fractal network , 2015, Briefings Bioinform..

[46]  I. Ben-Sahra,et al.  Oncogenic PI3K and K-Ras stimulate de novo lipid synthesis through mTORC1 and SREBP , 2015, Oncogene.

[47]  M. Francois,et al.  Pharmacological manipulation of transcription factor protein-protein interactions: opportunities and obstacles , 2015, Cell Regeneration.

[48]  A. Filippini,et al.  TLR3 engagement induces IRF-3-dependent apoptosis in androgen-sensitive prostate cancer cells and inhibits tumour growth in vivo , 2014, Journal of cellular and molecular medicine.

[49]  Xiaoli Xie,et al.  KEGG-PATH: Kyoto encyclopedia of genes and genomes-based pathway analysis using a path analysis model. , 2014, Molecular bioSystems.

[50]  C. Peng,et al.  H2AK119Ub1 and H3K27Me3 in molecular staging for survival prediction of patients with pancreatic ductal adenocarcinoma , 2014, Oncotarget.

[51]  P Manimaran,et al.  The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach. , 2014, Molecular bioSystems.

[52]  Yanhui Hu,et al.  Integrating protein-protein interaction networks with phenotypes reveals signs of interactions , 2013, Nature Methods.

[53]  Weiya Ma,et al.  Knockdown of RNF2 induces apoptosis by regulating MDM2 and p53 stability , 2013, Oncogene.

[54]  Bhaskara Rao Siddani,et al.  Candidate Gene Identification for Systemic Lupus Erythematosus Using Network Centrality Measures and Gene Ontology , 2013, PloS one.

[55]  Edward Y. Chen,et al.  Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool , 2013, BMC Bioinformatics.

[56]  A. Koromilas,et al.  The tumor suppressor function of STAT1 in breast cancer , 2013, JAK-STAT.

[57]  Wilfried Gouraud,et al.  bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses , 2013, Database J. Biol. Databases Curation.

[58]  Damian Szklarczyk,et al.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..

[59]  Anushya Muruganujan,et al.  PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees , 2012, Nucleic Acids Res..

[60]  Xuetao Wang,et al.  Transcriptome Profiling of the Cancer, Adjacent Non-Tumor and Distant Normal Tissues from a Colorectal Cancer Patient by Deep Sequencing , 2012, PloS one.

[61]  K. Chou,et al.  Identification of Colorectal Cancer Related Genes with mRMR and Shortest Path in Protein-Protein Interaction Network , 2012, PloS one.

[62]  M. Campone,et al.  bc-GenExMiner: an easy-to-use online platform for gene prognostic analyses in breast cancer , 2012, Breast Cancer Research and Treatment.

[63]  L. Chung,et al.  Activation of Androgen Receptor, Lipogenesis, and Oxidative Stress Converged by SREBP-1 Is Responsible for Regulating Growth and Progression of Prostate Cancer Cells , 2011, Molecular Cancer Research.

[64]  F. Khuri,et al.  Retinoic acid enhances TRAIL-induced apoptosis in cancer cells by upregulating TRAIL receptor 1 expression. , 2011, Cancer research.

[65]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[66]  Gary D. Bader,et al.  The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function , 2010, Nucleic Acids Res..

[67]  S. Horvath,et al.  EGFR Signaling Through an Akt-SREBP-1–Dependent, Rapamycin-Resistant Pathway Sensitizes Glioblastomas to Antilipogenic Therapy , 2009, Science Signaling.

[68]  A. Jimeno,et al.  BRCA in breast cancer: from risk assessment to therapeutic prediction. , 2009, Drug News and Perspectives.

[69]  V. Hower,et al.  A systems biology view of cancer. , 2009, Biochimica et biophysica acta.

[70]  Mauno Vihinen,et al.  Identification of candidate disease genes by integrating Gene Ontologies and protein-interaction networks: case study of primary immunodeficiencies , 2008, Nucleic acids research.

[71]  Huaiyu Mi,et al.  PANTHER pathway: an ontology-based pathway database coupled with data analysis tools. , 2009, Methods in molecular biology.

[72]  Xia Li,et al.  KLF6 Induces Apoptosis in Prostate Cancer Cells through Up-regulation of ATF3* , 2008, Journal of Biological Chemistry.

[73]  Jeroen de Ridder,et al.  Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data , 2008, Nucleic acids research.

[74]  J. Cigudosa,et al.  Variability in the expression of polycomb proteins in different normal and tumoral tissues. A pilot study using tissue microarrays , 2006, Modern Pathology.

[75]  B. Snel,et al.  Predicting disease genes using protein–protein interactions , 2006, Journal of Medical Genetics.

[76]  Chi-Ping Day,et al.  Cancer-specific gene therapy. , 2005, Advances in genetics.

[77]  P. Johnston,et al.  STAT1: a modulator of chemotherapy-induced apoptosis. , 2004, Cancer research.

[78]  A. Barabasi,et al.  Network biology: understanding the cell's functional organization , 2004, Nature Reviews Genetics.

[79]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[80]  Michèl Schummer,et al.  The HE4 (WFDC2) protein is a biomarker for ovarian carcinoma. , 2003, Cancer research.

[81]  G. Bubley,et al.  Biology of prostate-specific antigen. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[82]  Ziwei Huang,et al.  The chemical biology of apoptosis. Exploring protein-protein interactions and the life and death of cells with small molecules. , 2002, Chemistry & biology.

[83]  A. Dalgleish,et al.  Retinoids cause apoptosis in pancreatic cancer cells via activation of RAR-γ and altered expression of Bcl-2/Bax , 2002, British Journal of Cancer.

[84]  S. Fuqua,et al.  Androgen Receptor Acetylation Governs trans Activation and MEKK1-Induced Apoptosis without Affecting In Vitro Sumoylation and trans-Repression Function , 2002, Molecular and Cellular Biology.

[85]  I. Leav,et al.  Prostatic intraepithelial neoplasia in mice expressing an androgen receptor transgene in prostate epithelium , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[86]  J. Gray,et al.  Genome changes and gene expression in human solid tumors. , 2000, Carcinogenesis.

[87]  M. T. Abreu-Martin,et al.  Mitogen-Activated Protein Kinase Kinase Kinase 1 Activates Androgen Receptor-Dependent Transcription and Apoptosis in Prostate Cancer , 1999, Molecular and Cellular Biology.

[88]  D. Scheinberg,et al.  Modulation of p53, WAF1/p21 and BCL-2 expression during retinoic acid-induced differentiation of NB4 promyelocytic cells. , 1997, Leukemia research : a Forum for Studies on Leukemia and Normal Hemopoiesis.

[89]  V. Seewaldt,et al.  Expression of retinoic acid receptor beta mediates retinoic acid-induced growth arrest and apoptosis in breast cancer cells. , 1995, Cell growth & differentiation : the molecular biology journal of the American Association for Cancer Research.