Glycosylation-related genes mediated prognostic signature contribute to prognostic prediction and treatment options in ovarian cancer: based on bulk and single‑cell RNA sequencing data.

ABSTRACT:

Background

Ovarian cancer (OC) is a complex disease with significant tumor heterogeneity with the worst prognosis and highest mortality among all gynecological cancers. Glycosylation is a specific post-translational modification that plays an important role in tumor progression, immune escape and metastatic spread. The aim of this work was to identify the major glycosylation-related genes (GRGs) in OC and construct an effective GRGs signature to predict prognosis and immunotherapy.

Methods

AUCell algorithm was used to identify glycosylation-related genes (GRGs) based on the scRNA-seq and bulk RNA-seq data. An effective GRGs signature was conducted using COX and LASSO regression algorithm. The texting dataset and clinical sample data were used to assessed the accuracy of GRGs

SUBMITTER: You Y 

PROVIDER: S-EPMC10865697 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature