Work

A novel prognostic model in ovarian cancer based on the Nectin family and Necl-like molecules related transcriptomics

Year: 2025

Type: article

Abstract: BACKGROUND: Ovarian cancer (OC) is the deadliest malignant tumor among gynecological tumors. The current treatment measures for OC remain not optimistic, so it is important to determine reliable prognostic biomarkers to prolong OC patients' survival. METHODS: The OC-related transcriptome data were downloaded from the University of California Santa Cruz (UCSC) and we obtained the Differentially Expressed Genes (DEGs) between OC and normal samples. The Nectins and Nectin-like (Necl) scores of OC patients were calculated by single-sample Gene Set Enrichment Analysis (ssGSEA), the correlation between these scores and the prognosis of OC patients was explored using the Kaplan-Meier survival curve. DEGs were overlapped with Nectins and Necls-related genes selected by Weighted Gene Co-expression Network Analysis (WGCNA) to obtain the differentially expressed Nectins and Necls-related genes (DENNGs). Next, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were carried out on DENNGs. The Protein-Protein Interaction (PPI) network of DENNGs was constructed and hub genes were screened. In addition, univariate and multivariate Cox regression analysis were used to obtain the prognosis-related genes of OC patients and construct a prognostic model. Gene Set Variation Analysis (GSVA) was performed on the genes of high and low risk groups. SsGSEA algorithm was used to calculate the immune cell scores and the correlation between different immune cells and prognosis-related genes was explored. RESULTS: We obtained 583 DENNGs by crossing 6778 DEGs in OC and 584 Nectin- and Necl-related genes. The scores of Nectins and Necls in the OC group increased significantly, together with the poor prognosis. KEGG and GO analyses showed that DENNGs were mainly associated with cell proliferation, aging, canceration, and virus infection. Univariate and multivariate Cox analyses screened six prognosis-related genes (PTTG1, MELK, CENPF, PLK1, KIF20A, TOP2A) and modeled prognosis risks. Furthermore, a nomogram that integrated the risk model and patient age accurately predicted OC prognosis. The results of GSVA showed that TGF-β-mediated epithelial-mesenchymal transition, tumor cell invasion and metastasis were activated in the high-risk group, while pathways related to tumor cell invasiveness, such as hedgehog (Hh) mediated by hypoxia, were activated in the low-risk group. Finally, we found five immune cells (activated CD4+ T cells, central memory CD4+ T cells, central memory CD8+ T cells, T follicular helper cells (tTFH), and type II helper T cells(Th2)) different between OC and normal samples, and prognosis-related genes were positively correlated with activated CD4+ cells and Th2 of immune cells but negatively correlated with tTFH. CONCLUSION: We identified six prognosis-related genes and constructed a prognostic model, providing a theoretical basis for the clinical prognosis prediction and treatment for OC patients.

Source: SLAS TECHNOLOGY

DOI: 10.1016/j.slast.2025.100385