Identification of CD8+ T Cell Related Biomarkers in Ovarian Cancer

Abstract

Background: Immunotherapy is a promising strategy for ovarian cancer (OC), and this study aims to identify biomarkers related to CD8+ T cell infiltration to further discover the potential therapeutic target.Methods: Three datasets with OC transcriptomic data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Two immunotherapy treated cohorts were obtained from the Single Cell Portal and Mariathasan’s study. The infiltration fraction of immune cells was quantified using three different algorithms, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), and microenvironment cell populations counter (MCPcounter), and single-sample GSEA (ssGSEA). Weighted gene co-expression network analysis (WGCNA) was applied to identify the co-expression modules and related genes. The nonnegative matrix factorization (NMF) method was proposed for sample classification. The mutation analysis was conducted using the “maftools” R package. Key molecular markers with implications for prognosis were screened by univariate COX regression analysis and K-M survival analysis, which were further determined by the receiver operating characteristic (ROC) curve.Results: A total of 313 candidate CD8+ T cell-related genes were identified by taking the intersection from the TCGA-OV and GSE140082 cohorts. The NMF clustering analysis suggested that patients in the TCGA-OV cohort were divided into two clusters and the Cluster 1 group showed a worse prognosis. In contrast, Cluster 2 had higher amounts of immune cell infiltration, elevated ssGSEA scores in immunotherapy, and a higher mutation burden. CSMD3, MACF1, PDE4DIP, and OBSCN were more frequently mutated in Cluster 1, while SYNE2 was more frequently mutated in Cluster 2. CD38 and CXCL13 were identified by univariate COX regression analysis and K-M survival analysis in the TCGA-OV cohort, which were further externally validated in GSE140082 and GSE32062. Of note, patients with lower CXCL13 expression showed a worse prognosis and the CR/PR group had a higher expression of CXCL13 in two immunotherapy treated cohorts.Conclusion: OC patients with different CD8+ T cell infiltration had distinct clinical prognoses. CXCL13 might be a potential therapeutic target for the treatment of OC.

Prognostic role of long non-coding RNA USP30-AS1 in ovarian cancer: insights into immune cell infiltration in the tumor microenvironment

“…Likewise, the existence of CD8 + T lymphocytes and B lymphocytes in TIME has been associated with better predictive outcomes in ovarian cancer. Our discoveries conform with these prior findings, implying that USP30-AS1 could have significant predictive consequences in ovarian cancer [39,40].…”

CT-based radiomics predicts CD38 expression: indirectly reflects clinical prognosis in epithelial ovarian cancer

“…CD38 could serve as a biomarker for re ecting prognosis and survival. Some previous research [14,31] and our study demonstrated the protective effect of high CD38 expression in improving the survival of EOC patients. Possible correlation with immune cell in ltration and signaling pathways have also been investigated, the CD38 high group exhibited increased in ltration of NK cells [32], γδT cells [33] with tumorkilling ability and immune regulation, as well as DC [34] that induce CD8 + T cell-mediated immune response [31,35].…”

“…Some previous research [14,31] and our study demonstrated the protective effect of high CD38 expression in improving the survival of EOC patients. Possible correlation with immune cell in ltration and signaling pathways have also been investigated, the CD38 high group exhibited increased in ltration of NK cells [32], γδT cells [33] with tumorkilling ability and immune regulation, as well as DC [34] that induce CD8 + T cell-mediated immune response [31,35]. The differences observed between the CD38 high and CD38 low groups primarily involve signaling pathways regulating cell proliferation, growth, differentiation, apoptosis, and tumor metabolism.…”

Identification of N1 methyladenosine‐related biomarker predicting overall survival outcomes and experimental verification in ovarian cancer

“…Li et al used weighted gene co-expression network analysis (WGCNA) and nonnegative matrix factorization (NMF) method sample classification to identify key biomarkers in CD8 T cell-related genes and construct prognostic features in OVCA. 12 Chen et al explored the underlying mechanisms of the tumorigenesis and progression based on genes associated with OVCA clinical features. 13 Considering the advantages of bioinformatics analysis recognizing markers in various tumors, in this study, according to the previous literature, 8,14,15 10 m1A genes were collected to cluster OVCA patients into two tumor subtypes to excavate pivotal m1A-related biomarkers.…”

“…Li et al used weighted gene co‐expression network analysis (WGCNA) and nonnegative matrix factorization (NMF) method sample classification to identify key biomarkers in CD8 T cell‐related genes and construct prognostic features in OVCA 12 . Chen et al explored the underlying mechanisms of the tumorigenesis and progression based on genes associated with OVCA clinical features 13 .…”