Exploration of the Immunotyping Landscape and Immune Infiltration-Related Prognostic Markers in Ovarian Cancer Patients

Abstract

BackgroundIncreasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.MethodsThe ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.ResultsThree immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients’ risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.ConclusionsOur study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.

CCL20 and CD8A as potential diagnostic biomarkers for HBV-induced liver fibrosis in chronic hepatitis B

“…CD8A , as one of the immune cell marker genes, is mainly involved in cell-mediated immune defense and T-cell development [ 47 , 48 ]. Also, it can serve as a diagnostic and progressive biomarker for many diseases, including tumors and inflammatory diseases [ [49] , [50] , [51] ].…”

Epitranscriptomic RNA Modification in Non-apoptotic Forms of Regulated Cell Death

“…LASSO is a regression analysis approach that utilizes regularization for improving the predictive accuracy (Xing et al, 2022). SVM-RFE is a reliable feature selection approach that determines the optimal variables through removing the feature vectors produced by SVM (Zhao et al, 2020).…”

“…A NRRS model containing 12 genes (NACA2, DOCK11, EPB41L3, SCN1B, KRT18, THEMIS2, PLEKHF1, HMGN3, WAR3, HLA_DOB, FBXO16, PLA2G2D) was constructed. Among all 12 prognostic related genes, 8 genes (EPB41L3, SCN1B, KRT18, THEMIS2, PLEKHF1, HLA_DOB, FBXO16, PLA2G2D) (Dafou et al, 2010;Trisdale et al, 2016;Brummelhuis et al, 2021;Ji et al, 2021;Huang et al, 2022;Zhao et al, 2022) have been reported to be involved in tumorigenesis of OC or to be important predictors of overall survival. This implies that our bioinformatics analyses using cohorts have prognostic value.…”

Identification of necroptosis subtypes and development of necroptosis-related risk score model for in ovarian cancer

“…A NRRS model containing 12 genes (NACA2, DOCK11, EPB41L3, SCN1B, KRT18, THEMIS2, PLEKHF1, HMGN3, WAR3, HLA_DOB, FBXO16, PLA2G2D) was constructed. Among all 12 prognostic related genes, 8 genes (EPB41L3, SCN1B, KRT18, THEMIS2, PLEKHF1, HLA_DOB, FBXO16, PLA2G2D) ( Dafou et al, 2010 ; Trisdale et al, 2016 ; Li et al, 2020 ; Brummelhuis et al, 2021 ; Ji et al, 2021 ; Zheng et al, 2021 ; Huang et al, 2022 ; Zhao et al, 2022 ) have been reported to be involved in tumorigenesis of OC or to be important predictors of overall survival. This implies that our bioinformatics analyses using cohorts have prognostic value.…”