Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study - Wang, Lu; Sun, Xiaoru; Jin, Chuandi; Fan, Yue; Xue, Fuzhong (Frontiers in Genetics, 2021)
Ovarian serous cancer (OSC) is one of the leading causes of death across the world. The role of tumor microenvironment (TME) in OSC has received increasing attention. Targeted maximum likelihood estimation (TMLE) is developed under counterfactual framework to produce effects estimation for both population level and individual level. In this study, we aim to identify TME-related genes and using TMLE method to estimate their effects on 3-year mortality of OSC. 285 OSC patients from TCGA database constituted the studying population. ESTIMATE algorithm was implemented to evaluate immune and stromal components in TME. Differential analysis between high-score and low-score groups regarding ImmuneScore and StromalScore was performed to select shared differential expressed genes (DEGs). Univariate logistic regression analysis was followed to evaluate associations between DEGs and clinical pathologic factors with 3-year mortality. TMLE analysis was conducted to estimate average effect (AE), individual effect (IE) and marginal odds ratio (MOR). The validation was performed using three datasets from Gene Expression Omnibus (GEO) database. 355 DEGs were selected after differential analysis, 12 genes from DEGs were significant after univariate logistic regression. Four genes remained significant after TMLE analysis. In specific, ARID3C and FREM2 were negatively correlated with OSC 3-year mortality. CROCC2 and PTF1A were positively correlated with OSC 3-year mortality. Combining of ESTIMATE algorithm and TMLE algorithm, we identified four TME-related genes in OSC. AE were estimated to provide averaged effects from population level, while IE were estimated to provide individualized effects and may be helpful for precision medicine.