Abstract
Ovarian serous cancer (OSC) is one of the leading causes of death across the world. The role of the tumor microenvironment (TME) in OSC has received increasing attention. Targeted maximum likelihood estimation (TMLE) is developed under a counterfactual framework to produce effect estimation for both the population level and individual level. In this study, we aim to identify TME-related genes and using the TMLE method to estimate their effects on the 3-year mortality of OSC. In total, 285 OSC patients from the 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 the average effect (AE), individual effect (IE), and marginal odds ratio (MOR). The validation was performed using three datasets from Gene Expression Omnibus (GEO) database. Additionally, 355 DEGs were selected after differential analysis, and 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. AEs were estimated to provide averaged effects based on the population level, while IEs were estimated to provide individualized effects and may be helpful for precision medicine.
Keywords:
gene expression; ovarian cancer; prognostic biomarker; targeted maximum likelihood estimation; tumor microenvironment.