Identification of Tumor Microenvironment-Related Prognostic Biomarkers for Ovarian Serous Cancer 3-Year Mortality Using Targeted Maximum Likelihood Estimation: A TCGA Data Mining Study.

Lu Wang,Fuzhong Xue,Xiaoru Sun,Chuandi Jin,Yue Fan

doi:10.3389/fgene.2021.625145

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.

Highlights

Ovarian cancer is one of the most common cancers and the leading cause of death of all gynecological cancers among women across the world (Bray et al, 2018)
285 Ovarian serous cancer (OSC) patients from the The Cancer Genome Atlas (TCGA) database were included for statistical analysis
Results from gene ontology (GO) enrichment analysis indicated that the differential expressed genes (DEGs) were mainly enriched for the immune-related GO terms, such as chemokine signaling pathway and immunoglobulin binding (Figure 2E)

Summary

Introduction

Ovarian cancer is one of the most common cancers and the leading cause of death of all gynecological cancers among women across the world (Bray et al, 2018). Ovarian serous cancer (OSC) is the most common histologic subtype, and it accounts for about 90% of all ovarian tumors (Bell et al, 2011). In the United States, approximately 1 in 78 women will develop ovarian cancer in their lifetime while the all-stage 5-year survival rate of ovarian cancers is only 47% (Torre et al, 2018). Over 70% of ovarian cancers are diagnosed at advanced stages (stage III or IV) and usually exhibit poor prognosis with a 5-year overall survival rate of ∼30% (Howlader et al, 2016; Torre et al, 2018). The identification of biomarkers with a poor short time prognosis remains significant. We focused on OSC 3-year mortality to evaluate short survival time-related biomarkers

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