Integrated Expression Analysis May Support Serine/Threonine Kinases as Common Hub Genes in Breast Cancer.

Background: Breast cancer (BC) is the most common cancer affecting women worldwide. There is a strong need to identify molecular pathways that might represent effective therapeutic targets. Methods: We conducted a large-scale transcriptomic analysis using publicly available datasets from the NCBI GEO and TCGA databases. Microarray datasets (GSE161533, GSE162228, GSE70947, and GSE139038) and RNA-Seq data were analyzed to identify differentially expressed genes (DEGs) using cut-off criteria of adjusted P<0.05 and |log2FC|>1. Gene co-expression networks were constructed using Weighted Gene co-expression Network Analysis (WGCNA) in R (version 1.68), followed by hub gene identification with STRING and MCODE tools. Functional enrichment was further explored through Gene Ontology analysis. Results: Two regulatory modules enriched in cancer datasets were identified from both microarray and RNA-Seq analyses, corresponding to a network of 85 genes, compared to a distinct network of 474 genes enriched in control tissue samples. Further analyses to identify densely connected gene clusters within these networks revealed a cluster ``containing 29 cancer-related genes that included five hub gene candidates encoding serine/threonine kinase family proteins NimA-Related Protein: Kinase 2 (NEK2), Maternal Embryonic Leucine Zipper Kinase (MELK), Polo Like Kinase 1 (PLK1), Aurora Kinase B (AURKB), and Checkpoint Kinase 1 (CHEK1). Members of this family counter the expression of the tumor suppressor and cell cycle regulator Tumor Protein P53 (TP53), which is more highly expressed in healthy people. Moreover, all hub genes with higher transcript levels were associated with considerably poorer overall survival rates in BC patients. These results imply that these hub genes are relevant in terms of pathophysiology for the treatment of BC and deserve further attention. Kaplan-Meier survival analysis demonstrated that increased expression of all five genes was significantly associated with decreased survival (P<0.001). Hazard ratios (HRs) ranged from 1.41 to 1.77, indicating a substantial negative impact on patient survival for each gene. Conclusion: Survival analysis showed that tumors with higher expression levels of hub genes were associated with significantly shorter overall survival times among breast cancer patients. This finding suggests that these hub genes are highly relevant to BC pathophysiology and could be considered targets for monitoring.