Abstract
BACKGROUND: Ovarian cancer (OC) is a significant health concern due to the complex nature of its causes, difficulties in early detection, and low 5-year survival rate. The function of mechanosensitive ion channel (MIC)-related prognostic gene signatures in OC is still not clearly defined. Our aim was to clarify the function of the MIC in OC. METHODS: We created OC subtypes and a prognostic model based on MICs to forecast patient outcomes using RNA sequencing and clinical data from The Cancer Genome Atlas (TCGA) database. RESULTS: In this study, the top 20 genes were identified based on their relevance scores and included PIEZO1, SCN5A, KCNQ1, CFTR, PIEZO2, KCNMA1, ASIC2, CACNA1C, ASIC3, SCN1A, TRPV4, TRPV1, KCNN4, SCNN1B, SCNN1A, CACNA1B, SCNN1G, TRPM7, KCNK2, and TRPA1. Patients were distinctly categorized into a high-risk group (cluster 1) and a low-risk group (cluster 2) based on genes related to MICs. Functional analysis revealed that the upregulated differentially expressed genes (DEGs) in cluster 1 were significantly enriched in pathways such as focal adhesion, axon guidance, proteoglycans in cancer, extracellular matrix (ECM)-receptor interaction, Wnt signaling pathway, Hippo signaling pathway, and thyroid hormone signaling pathway. Conversely, the downregulated DEGs in cluster 1 were predominantly enriched in pathways including oxidative phosphorylation, chemical carcinogenesis-reactive oxygen species, and nonalcoholic fatty liver disease. Gene Ontology (GO) analysis of the upregulated DEGs in cluster 1 indicated significant enrichment in biological pathways related to ECM organization, cell-substrate adhesion, and cell junction assembly. Conversely, the downregulated DEGs in cluster 1 were significantly enriched in pathways associated with oxidative phosphorylation, adenosine triphosphate metabolic processes, and cellular respiration. The estimation of immune scores revealed differences between the high- and low-risk groups. Using least absolute shrinkage and selection operator and Cox regression analyses, we identified a set of 20 genes linked to MICs in OC, from which three key genes-PIEZO1, CACNA1C, and TRPV4-were further selected. Single-cell RNA sequencing demonstrated that CACNA1C was expressed in fibroblasts and myofibroblasts, PIEZO1 was expressed across all five cell subtypes, and TRPV4 was expressed in fibroblasts and monocytes or macrophages. CONCLUSIONS: This study initially identified unique molecular subtypes and key genes for patients with OC from the novel angle of MICs.