Comparative Analysis of Gene Correlation Networks of Breast Cancer Patients Based on Mutations in TP53

Abstract

Breast cancer is one of the most prevalent cancers in females, with more than 450,000 deaths each year worldwide. Among the subtypes of breast cancer, basal-like breast cancer, also known as triple-negative breast cancer, shows the lowest survival rate and does not have effective treatments yet. Somatic mutations in the TP53 gene frequently occur across all breast cancer subtypes, but comparative analysis of gene correlations with respect to mutations in TP53 has not been done so far. The primary goal of this study is to identify gene correlations in two groups of breast cancer patients and to derive potential prognostic gene pairs for breast cancer. We partitioned breast cancer patients into two groups: one group with a mutated TP53 gene (mTP53) and the other with a wild-type TP53 gene (wtTP53). For every gene pair, we computed the hazard ratio using the Cox proportional hazard model and constructed gene correlation networks (GCNs) enriched with prognostic information. Our GCN is more informative than typical GCNs in the sense that it indicates the type of correlation between genes, the concordance index, and the prognostic type of a gene. Comparative analysis of correlation patterns and survival time of the two groups revealed several interesting findings. First, we found several new gene pairs with opposite correlations in the two GCNs and the difference in their correlation patterns was the most prominent in the basal-like subtype of breast cancer. Second, we obtained potential prognostic genes for breast cancer patients with a wild-type TP53 gene. From a comparative analysis of GCNs of mTP53 and wtTP53, we found several gene pairs that show significantly different correlation patterns in the basal-like breast cancer subtype and obtained prognostic genes for patients with a wild-type TP53 gene. The GCNs and prognostic genes identified in this study will be informative for the prognosis of survival and for selecting a drug target for breast cancer, in particular for basal-like breast cancer. To the best of our knowledge, this is the first attempt to construct GCNs for breast cancer patients with or without mutations in the TP53 gene and to find prognostic genes accordingly.

Elucidation of clinical implications Arising from circadian rhythm and insights into the tumor immune landscape in breast cancer

“…Multi-omics mutation analysis based on the CRRS model can identify specific genomic changes related to the circadian rhythm of BC, and could lead to more precise and individualized therapeutic strategies for patients with BC. Previous research has shown that somatic mutations are associated with poor outcomes in BC [ [53] , [54] , [55] , [56] ]. We investigated the differences in the distribution of somatic mutations between populations at different risk levels.…”

Research Progress of Natural Drugs for Breast Cancer in the Past 30 Years Based on Bibliometric and Knowledge Graph Analysis

“…After statistically scoring the information of breast cancer genes as new key pathogenic genes, these genes were recommended as potential targets for clinical therapy. The authors of [70] also conducted a study to identify gene correlations between two groups of breast cancer patients and to derive potential prognostic gene pairs for breast cancer.…”