Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

Abstract

BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and ResultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.

Clinical Characteristics in the Prediction of Posttreatment Survival of Patients with Ovarian Cancer

“…There have been reports of various biological prognostic biomarkers for ovarian cancer [ 3 – 7 ]. Interestingly, Yang et al showed that some clinical variables were good predictors [ 8 ].…”

Establishment and validation of a DNA damage repair gene based model for ovarian cancer typing and prognosis

“…Although the study screening gene pathways were very different, some of our constructed risk model genes were also screened as prognostic predictors of ovarian cancer in other ovarian cancer risk model studies, which further side validating the high accuracy of our constructed model: Fc Gamma Binding Protein (FCGBP) [18],Calcium Voltage-Gated Channel Subunit Alpha1 C (CACNA1C) [19],Ubiquitin D(UBD) [20,21],Peptidase Inhibitor 3(PI3) [22,23],Interferon Stimulated Exonuclease Gene 20 (ISG20) [24][25][26],A-Kinase Anchoring Protein 12(AKAP122) [26,27],Ribosomal Protein S6 Kinase A2(RPS6KA2)[28],C-X-C Motif Chemokine Ligand 11(CXCL11) [21,[29][30][31][32][33][34],Transient Receptor Potential Cation Channel Subfamily V Member 4(TRPV4) [35],Cholesterol 25-Hydroxylase(CH25H) [23,36],Solute Carrier Family 4 Member 8(SLC4A8) [37] However, the following genes have not been identi ed in ovarian cancer studies: Translocase Of Outer Mitochondrial Membrane 20 Like (TOMM20L), C-C Motif Chemokine Receptor 7(CCR7), Phosphatidylinositol Glycan Anchor Biosynthesis Class S(PIGS), Syntaxin 18(STX18), Carboxymethylenebutenolidase Homolog (CMBL)have not been found to be studied in ovarian cancer. This is the rst study to type ovarian cancer based on DDR-related genes and to investigate the prognostic relevance of genes that differ between types in ovarian cancer.…”

Necroptosis-Related LncRNAs Signature and Subtypes for Predicting Prognosis and Revealing the Immune Microenvironment in Breast Cancer

“…Via the “pRRophetic” package, we calculated the half-maximum inhibitory concentration (IC 50 ) to evaluate the difference in drug response between different groups in the Genomics of Drug Sensitivity in Cancer (GDSC) database ( 25 , 26 ) using Ridge’s regression, along with 10-fold cross-validation for the purpose of improving the accuracy of the prediction ( 27 , 28 ).…”