Multifactor assessment of ovarian cancer reveals immunologically interpretable molecular subtypes with distinct prognoses - Guo, Yaping; Li, Siyu; Li, Chentan; Wang, Li; Ning, Wanshan (Frontiers in Immunology, 2023)
Ovarian cancer (OC) is a heterogeneous and malignant gynecological cancer, leading to the poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC, and develop the diagnostic model that can stratify OC patients, providing more clues for OC patients to access focused strategies. Gene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, ESTIMATE was applied.The cox analysis and the Log-rank test was used to screen OC risk factors. We adopted ConsensusClusterPlus to determine OC subtypes.Enrichment analysis based on KEGG and GO to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select feature gene and develop the diagnostic model. A ROC curve was depicted to evaluate model performance. 1273 survival-related genes (SRGs) were determined and used to clarify OC samples into different subtypes based on their molecular pattern. SRGs stratified OC patients into three subtypes, designated S-I (Immunoreactive and Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III has the worst prognosis and were enriched with OC patients at advanced stages. Meanwhile, functional analysis highlighted differences in biological pathways, genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, RBX1 were enriched in S-I, S-II combined multiple genes including genes associated with metabolism and transcription, while genes of S-III was involved in pathways reflecting malignancies, including kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC of 0.922 and 0.901. Furthermore, new dataset of independent cohort could be analyzed by this innovative pipeline and yield similar results. This study exploited an innovative approach to construct unexplored subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.