Improving ovarian cancer treatment decision using a novel risk predictive tool.

ABSTRACT:

Background

As a major component of the tumor tissue, the tumor microenvironment (TME) has been proven to associate with tumor progression and immunotherapy. Ovarian cancer accounts for the highest mortality rate among gynecologic malignancies. Its clinical treatment decision is highly correlated with the prognosis, underscoring the need to evaluate the prognosis and choose the proper clinical treatment through TME information.

Method

This study constructs a score with TME information obtained by the CIBERSORT algorithm, which classifies the patients into high and low TMEscore groups with quantified TME infiltration patterns through the PCA algorithm. TMEscore was constructed by TCGA cohort and validated in GEO cohort. Univariate and multivariate Cox proportional hazards mode

SUBMITTER: Xu Z 

PROVIDER: S-EPMC9085236 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature