A genomic and transcriptomic study toward breast cancer

Abstract

Background: Breast carcinoma is well recognized to be having the highest global occurrence rate among all cancers, being the leading cause of cancer mortality in females. The aim of this study was to elucidate breast cancer at the genomic and transcriptomic levels in different subtypes so that we can develop more personalized treatments and precision medicine to obtain better outcomes.Method: In this study, an expression profiling dataset downloaded from the Gene Expression Omnibus database, GSE45827, was re-analyzed to compare the expression profiles of breast cancer samples in the different subtypes. Using the GEO2R tool, different expression genes were identified. Using the STRING online tool, the protein–protein interaction networks were conducted. Using the Cytoscape software, we found modules, seed genes, and hub genes and performed pathway enrichment analysis. The Kaplan–Meier plotter was used to analyze the overall survival. MicroRNAs and transcription factors targeted different expression genes and were predicted by the Enrichr web server.Result: The analysis of these elements implied that the carcinogenesis and development of triple-negative breast cancer were the most important and complicated in breast carcinoma, occupying the most different expression genes, modules, seed genes, hub genes, and the most complex protein–protein interaction network and signal pathway. In addition, the luminal A subtype might occur in a completely different way from the other three subtypes as the pathways enriched in the luminal A subtype did not overlap with the others. We identified 16 hub genes that were related to good prognosis in triple-negative breast cancer. Moreover, SRSF1 was negatively correlated with overall survival in the Her2 subtype, while in the luminal A subtype, it showed the opposite relationship. Also, in the luminal B subtype, CCNB1 and KIF23 were associated with poor prognosis. Furthermore, new transcription factors and microRNAs were introduced to breast cancer which would shed light upon breast cancer in a new way and provide a novel therapeutic strategy.Conclusion: We preliminarily delved into the potentially comprehensive molecular mechanisms of breast cancer by creating a holistic view at the genomic and transcriptomic levels in different subtypes using computational tools. We also introduced new prognosis-related genes and novel therapeutic strategies and cast new light upon breast cancer.

Identification of prognostic biomarkers of invasive ductal carcinoma by an integrated bioinformatics approach

“…[53][54][55] Several studies have demonstrated that KIF23 plays a critical role in tumorigenesis and cancer progression through the disruption of normal cytokinesis and centrosome formation, resulting in cell division arrest or abnormalities that contribute to the formation of aneuploid cells that promote tumorigenesis. High levels of KIF23 in tumor-affected tissues ere associated with a poor prognosis and are correlated with TNM stage [56][57][58] and recurrence in numerous tumors, Table 2. Function of the ten dentified hub genes.…”

Delineating intra-tumoral heterogeneity and tumor evolution in breast cancer using precision-based approaches

“…For example, when gene expression in luminal A and luminal B tumors were compared with the designated PAM50 gene set, differences were observed in genes involved in the cell cycle and cell proliferation such as BIRC5, CCNB1, CDC20, CEP55, KIF2C, MELK, MKI67 and UBE2C (Tishchenko et al, 2016). Pathway enrichment shows that luminal A tumors amplify pathways associated with extracellular matrix organization and collagen formation, comtrasting to luminal B tumors that show enrichment of DNA repair pathways, similar to that of HER2-enriched tumors (Wang et al, 2022). Phenotypically, luminal A tumors expressing ER and PR have the lowest proliferation potential (Ki-67), and present with the best prognosis (Goldhirsch et al, 2013).…”

Transcriptomic analysis revealed potential regulatory biomarkers and repurposable drugs for breast cancer treatment

“…Several studies have been conducted and published in the last few years using public databases such as GREIN and Gene Expression Omnibus (GEO) to predict potential biomarkers. Liu et al conducted a study of 1203 BC samples from The Cancer Genome Atlas Database and identified 1317 differentially expressed genes, with 744 genes showing upregulation and 573 genes showing downregulation 9 . Besides, Wang et al also used one data set (GSE45827) from the GEO database and identified distinct expression genes in BC 10 .…”

“…Liu et al conducted a study of 1203 BC samples from The Cancer Genome Atlas Database and identified 1317 differentially expressed genes, with 744 genes showing upregulation and 573 genes showing downregulation. 9 Besides, Wang et al also used one data set (GSE45827) from the GEO database and identified distinct expression genes in BC. 10 Based on the examination of single transcriptome datasets, multiple studies have predicted different sets of hub genes for BC, 11 , 12 , 13 but none of them shared a single hub gene.…”