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 apoptosisregulated 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.Breast cancer (BC) is the most common malignant tumor in women and the biggest threat to the health of wome...

Thymoquinone Inhibition of Chemokines in TNF-α-Induced Inflammatory and Metastatic Effects in Triple-Negative Breast Cancer Cells

“…In particular, CCL2 and CCL3 have been found to have direct protumor effects by promoting tumor invasion and proliferation, survival, motility, and stemness and preventing apoptosis [ 30 , 31 ]. TNBC can develop from the inability of proapoptotic proteins to function properly or from the overabundance of antiapoptotic proteins [ 32 ]. Considering what we have discovered, TQ may effectively reduce protumor chemokines and inhibit inflammation-driven TNBC cell proliferation.…”

Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents

“…By analyzing molecular profiles of biosimilar candidates and reference molecules, bioinformatics can uncover similarities and differences that contribute to biosimilarity assessment [97]. Furthermore, bioinformatics aids in the identification of potential therapeutic targets by integrating data from various sources, including gene expression [98], protein-protein interactions [99], and pathway analysis [100].…”