Analysis of the Prognosis Related Genes in HER2+ Breast Cancer Based on Weighted Gene Co-expression Network Analysis

Abstract

Background: Breast cancer is one of the malignant tumors that threaten women's health, with HER2+ breast cancer being more aggressive. In this study, bioinformatics methods were used to find potential key genes in HER2 + for diagnosis and treatment.Methods: Datasets of HER2+ breast cancer and normal tissue samples retrieved from TCGA databases were subjected to DEGs analysis using R software. Then WGCNA is constructed for DEGs. The key gene co-expression modules were then subjected to GO and KEGG pathway enrichment analyses, as well as construction of PPI networks using the STRING database for identifying key genes. Finally, key genes were further validated by survival analysis, protein expression, and COX regression models.Results: We identified 2063 DEGs and 4 gene co-expression modules. Functional enrichment analysis showed that these key co-expression modules were mainly associated with extracellular matrix organization, extracellular matrix structural constituent and neuroactive ligand−receptor interaction. PPI network visualization identified 100 key genes, 3 of which were not present in the other subtypes of breast cancer. UTS2 DRD4 and GLP1R are key genes specific to the HER2+ subtype. Survival analysis showed that UTS2 are prognosis-related key genes in HER2+ breast cancer. Finally, UTS2 combined with clinical data to construct Cox regression model.Conclusions: Combined with the two screening methods, 3 key genes closely related to HER2 + breast cancer were identified. UTS2 is a new potential key gene and may become a new therapeutic target for HER2 + breast cancer.

Identification of new co-diagnostic genes for sepsis and metabolic syndrome using single-cell data analysis and machine learning algorithms

“…More recently, bioinformatics has been widely applied to oncological and non-oncological diseases, including sepsis (Lai et al, 2020;Li Z. et al, 2021;Wu Z. et al, 2021). Previous studies have focused on differential genes in the blood of Sepsis patients and revealed the molecular pathways of differential genes (Chen et al, 2021). However, the common diagnostic genes of Sepsis and MetS and the shared metabolic pathways remain unclear.…”