Exploring Breast Cancer-Associated Genes: A Comprehensive Analysis and Competitive Endogenous RNA Network Construction.

Breast cancer is a most common cancer that primarily affects women, in which cells become abnormal and multiply in an uncontrollable fashion. The etiology of these cancers is predominantly hereditary, with gene mutations and geographic indications being the predominant factors in most invasive breast cancer types. However, it is important to note that several other factors, including age, gender, ethnic background, and environmental influences, also contribute to the development of these diseases. Non-coding RNAs refer to a class of endogenous molecules that play a role in the development of various types of cancer. The objective of this research is to identify differentially expressed genes in cases of breast cancer. A series of analyses were conducted on the RNA-Seq data from the TCGA related to breast cancer. These analyses included both expression and survival studies. The objective of these analyses was to explore the gene expression of the samples and genes computationally through the use of R programming. The results obtained after each analysis were inferred both visually and logically. A total of 613 genes were identified as exhibiting differential expression among the samples, with 254 genes demonstrating increased expression and 359 genes exhibiting decreased expression. The differentially expressed genes obtained using the R package "TCGA Biolinks" were subsequently employed in the construction of the ceRNA network. A comprehensive analysis of the TCGA biolinks data set revealed the presence of aberrantly expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) in breast cancer samples. The analysis identified a total of 352, 183, and 254 cases, respectively, demonstrating significant disparities in gene expression patterns among different breast cancer samples. A study of 352 long non-coding ribonucleic acids (lncRNAs) revealed that two of these molecules, LINC00461 and MALAT1, exhibited particularly high levels of expression. These findings suggest that these two molecules may serve as more effective therapeutic biomarkers. Furthermore, the study identified a significant enrichment of microRNA target genes within the samples examined, suggesting a potential regulatory relationship between these molecules and their target genes. Consequently, this investigation has constructed competing endogenous RNA networks and has further elucidated the underlying biomarkers for breast cancer cohorts.