Identification of a six-gene signature to predict survival and immunotherapy effectiveness of gastric cancer.

ABSTRACT:

Background

Gastric cancer (GC) ranks as the fifth most prevalent malignancy and the second leading cause of oncologic mortality globally. Despite staging guidelines and standard treatment protocols, significant heterogeneity exists in patient survival and response to therapy for GC. Thus, an increasing number of research have examined prognostic models recently for screening high-risk GC patients.

Methods

We studied DEGs between GC tissues and adjacent non-tumor tissues in GEO and TCGA datasets. Then the candidate DEGs were further screened in TCGA cohort through univariate Cox regression analyses. Following this, LASSO regression was utilized to generate prognostic model of DEGs. We used the ROC curve, Kaplan-Meier curve, and risk score plot to evaluate the signature's perf

SUBMITTER: Wang Q 

PROVIDER: S-EPMC10316024 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature