Comprehensive bioinformatic analysis constructs a CXCL model for predicting survival and immunotherapy effectiveness in ovarian cancer - Li, Shuang; Zou, Dawei; Liu, Zhaoqian (Frontiers in Pharmacology, 2023)
Ovarian cancer (OC) patients face limited benefits from immunotherapy, highlighting the need for reliable biomarkers to predict the effectiveness of these treatments. The C-X-C motif chemokine ligands (CXCLs) have been shown to be associated with survival outcomes and immunotherapy efficacy in cancer patients. In this study, we aimed to evaluate the predictive value of 16 CXCLs in OC patients. We analyzed RNA-seq data from The Cancer Genome Atlas (TCGA), GSE140082, and GTEX and found that most CXCL family genes were overexpressed in OC tissues compared to normal tissues. We performed the consensus cluster analysis to construct three CXCL and three gene clusters and investigated their biological pathway alterations and immune infiltration patterns. Our results showed that the CXCL and gene subtypes could effectively predict the survival and immune cell infiltration level for OC patients. Additionally, we developed a CXCL score model through principal component analysis. We also evaluated the immunotherapy effectiveness of the CXCL score model by assessing tumor microenvironment cell infiltration, tumor mutational burden estimation, and immunophenoscore analysis (IPS). Our findings indicate that patients with high CXCL scores had significantly better survival outcomes, higher levels of immune cell infiltration, higher IPS, and higher expression of PD-L1/CTLA4 than those with low CXCL scores. In conclusion, the CXCL score has the potential to be a promising biomarker to guide immunotherapy in individual OC patients and predict their clinical outcomes and immunotherapy responses.