Abstract
Background: Breast cancer (BRCA) is the most frequent malignancy. Identification of potential biomarkers could help to better understand and combat the disease at early stages.Methods: We selected the overlapping genes of differential expressed genes and genes in BRCA-highly correlated modules by Weighted Gene Co-Expression Network Analysis (WGCNA) in TCGA and GEO data and performed KEGG and GO enrichment. PPARG was achieved from Protein-Protein Interaction (PPI) network analysis and prognostic analysis. TIMER, UALCAN, GEO, TCGA, and western blot analysis were used to validate the expression of PPARG in BRCA. PPARG was further analyzed by DNA methylation, immune parameters, and tumor mutation burden.Results: Among 381 overlapping genes, the lipid metabolic process was identified as highly enriched pathways in BRCA by TCGA and GEO data. When the prognostic analysis of 10 core genes by PPI network was performed, results revealed that high expression of PPARG was significantly correlated to a better prognosis. PPARG was lesser expression in BRCA according to TIMER, UALCAN, GEO, TCGA, and western blot in both mRNA level and protein level. PPARG had several high DNA methylation level sites and the methylation level is negatively correlated to expression. PPARG is also correlated to TNM stages, tumor microenvironment, and tumor burden.Conclusions: Findings of our study identified the PPARG as a potential biomarker by confirming its low expression in BRCA and its correlation to prognosis. Moreover, its correlation to DNA methylation and tumor microenvironment may guide new therapeutic strategies for BRCA patients.
Highlights
Breast cancer (BRCA) is one of the most common, complex and aggressive malignant tumors among females and it is the 2nd most occurring and 5th major cause of cancer related deaths in women all over the world (Kotsopoulos, 2018; Guo et al, 2020; t’Kint de Roodenbeke et al, 2020; Yu et al, 2021)
1https://portal.gdc.cancer.gov 2https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42568 and GEO data were analyzed for significant difference genes, and the screening conditions were based on false discovery rate (FDR) < 0.05 and | log2 fold change (FC)| ≥ 1
We first investigated the genes related to BRCA by conducting a differential gene (DEG) analysis on the TCGA data (Figure 1A) and GEO data (Figure 1D), and we identified the modules related to BRCA in the TCGA data (Figures 1B,C) and GEO data (Figures 1E,F) through the weighted gene co-expression network analysis (WGCNA) algorithm
Summary
Breast cancer (BRCA) is one of the most common, complex and aggressive malignant tumors among females and it is the 2nd most occurring and 5th major cause of cancer related deaths in women all over the world (Kotsopoulos, 2018; Guo et al, 2020; t’Kint de Roodenbeke et al, 2020; Yu et al, 2021). Despite the huge advances in early screening and various therapies for BRCA patients in the last decades, their prognosis remains poor (Lang et al, 2017; Guney Eskiler et al, 2018; Mylavarapu et al, 2018). There is an urgent need to explore new and effective biomarkers and therapeutic targets to improve the prognosis and life quality of BRCA patients. The main role of PPAR-gamma is to regulate adipocyte differentiation, dysregulation of PPAR-gamma has been the cause for numerous deadly diseases like diabetes and cancer. PPARG possess high affinity with the thiazolidinediones (TZDs) class of antidiabetic drugs, supporting its potential role for cancer therapy (Liu et al, 2019; Wu et al, 2019; Shi et al, 2020). Identification of potential biomarkers could help to better understand and combat the disease at early stages
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