Establishment of a novel glycolysis-related prognostic gene signature for ovarian cancer and its relationships with immune infiltration of the tumor microenvironment.

BACKGROUND: Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. In this study, we aimed to construct a glycolysis-related prognostic model for ovarian cancer and analyze its relationship with the tumor microenvironment's immune cell infiltration. METHODS: We obtained six glycolysis-related gene sets for gene set enrichment analysis (GSEA). Ovarian cancer data from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets were divided into two groups after removing batch effects. We compared the tumor environments' immune components in high-risk and low-risk groups and analyzed the correlation between glycolysis- and immune-related genes. Then, we generated and validated a predictive model for the prognosis of ovarian cancer using the glycolysis-related genes. RESULTS: Overall, 27/329 glycolytic genes were associated with survival in ovarian cancer, 8 of which showed predictive value. The tumor cell components in the tumor microenvironment did not differ between the high-risk and low-risk groups; however, the immune score differed significantly between groups. In total, 13/24 immune cell types differed between groups, including 10 T cell types and three other immune cell types. Eight glycolysis-related prognostic genes were related to the expression of multiple immune-related genes at varying degrees, suggesting a relationship between glycolysis and immune response. CONCLUSIONS: We identified eight glycolysis-related prognostic genes that effectively predicted survival in ovarian cancer. To a certain extent, the newly identified gene signature was related to the tumor microenvironment, especially immune cell infiltration and immune-related gene expression. These findings provide potential biomarkers and therapeutic targets for ovarian cancer.

RNA sequencing-based long non-coding RNA analysis and immunoassay in ovarian endometriosis.

PROBLEM: The mechanism underlying endometriosis is currently unknown. However, studies have indicated that immunity plays an important role in endometriosis occurrence and development. Long non-coding RNAs (lncRNAs) do not encode proteins but participate in a variety of biological processes via different mechanisms. This study investigated differences in immune cells and immune-related lncRNAs via high-throughput RNA sequencing (RNA-seq) analysis of ectopic and eutopic endometria with endometriosis. METHOD OF STUDY: RNA-seq was performed in six pairs of ectopic and eutopic endometria samples, and real-time quantitative polymerase chain reaction was used to verify the results of RNA-seq for 30 pairs of samples. Different immune cell types were identified based on the RNA-seq results, using ImmuCellAI. Immune-related lncRNAs were obtained by analyzing immune-related genes from the ImmPort Database and RNA-seq results. RESULTS: A total of 952 differentially expressed lncRNAs were identified, of which 446 were immune-related. The ectopic and eutopic endometrium could easily be distinguished in the principal component analysis of immune-related lncRNAs. Analysis of 24 immune cell types revealed the differential abundance of 13 types. Sixty immune-related mRNAs were associated with the top 20 dysregulated immune-related lncRNAs, 11 of which were transcripts of immune cell marker genes. CONCLUSIONS: Our data indicated that a variety of dysregulated lncRNAs were associated with immunity, and these may provide a basis for future immune-related endometriosis research.