A clinical prognostic model related to T cells based on machine learning for predicting the prognosis and immune response of ovarian cancer.

ABSTRACT:

Background

Ovarian cancer (OV) is regarded as one of the most lethal malignancies affecting the female reproductive system, with individuals diagnosed with OV often facing a dismal prognosis due to resistance to chemotherapy and the presence of an immunosuppressive environment. T cells serve as a crucial mediator for immune surveillance and cancer elimination. This study aims to analyze the mechanism of T cell-associated markers in OV and create a prognostic model for clinical use in enhancing outcomes for OV patients.

Methods

Based on the single-cell dataset GSE184880, this study used single-cell data analysis to identify characteristic T cell subsets. Analysis of high dimensional weighted gene co-expression network analysis (hdWGCNA) is utilized to identify crucial gene mo

SUBMITTER: Lin Q 

PROVIDER: S-EPMC11409031 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature