Integrated analysis of prognostic immune-related genes in the tumor microenvironment of ovarian cancer.

BACKGROUND

Ovarian cancer (OC) is a major cause of most gynecological cancer deaths, and the rates of incidence and mortality are increasing worldwide. However, factors in the tumor microenvironment (TME) related to OC and certain prognostic markers of OC are still unknown. We aimed to identify biomarkers connected to prognostic immunity based on clinical patients' data from The Cancer Genome Atlas (TCGA).

METHODS

We used the ESTIMATE algorithm to compute the immune and matrix scores of OC patients from TCGA. Next, differentially expressed genes (DEGs) according to the immune and matrix scores were obtained. Subsequently, genes (, , , and ) connected with prognostic immunity were determined. Moreover, functional enrichment analysis and the protein-protein interaction network showed that these genes were enriched in many biological processes related to immune function. The Tumor Immune Estimation Resource (TIMER) algorithm was also used to analyze the immune prognostic genes according to six immuno-infiltrating cells.

RESULTS

According to high/low immune-scores and matrix-score groups, 682 common genes were identified, within 420 upregulated genes and 262 downregulated genes. Gene ontology (GO) analysis of biological process primarily enriched in T cell activation, regulation of lymphocyte activation and lymphocyte differentiation. OS analysis showed 45 genes (6.6%) were relevant in the final results. The Kaplan-Meier plotter database verified the top 10 genes related to prognosis, but only , , and were related to overall survival (OS).

CONCLUSIONS

, , and may influence prognosis via their effects on the infiltration of immune cells and therefore represent potential targets for OC immunotherapy.