Identification of a novel inflammatory-related gene signature to evaluate the prognosis of gastric cancer patients

Area of Science:

• Oncology
• Cancer Genomics
• Inflammation and Cancer

Background:

• Gastric cancer (GC) is a complex and aggressive malignancy with variable prognosis.
• Inflammation is a key factor in GC development and progression, impacting patient outcomes.
• Identifying reliable prognostic biomarkers for GC is crucial for effective treatment strategies.

Purpose of the Study:

• To develop and validate an inflammatory-related gene signature for predicting gastric cancer patient prognosis.
• To assess the signature's ability to stratify patients and guide personalized treatment decisions.

Main Methods:

• Utilized Gene Expression Omnibus (GEO) datasets (GSE66229 and GSE26253) for GC patients.
• Constructed a prognostic model using least absolute shrinkage and selection operator (LASSO) Cox regression with inflammatory genes.
• Validated the model through univariate/multivariate Cox analyses, nomogram establishment, and decision curve analysis.

Main Results:

• A three-gene prognostic signature (MRPS17, GUF1, PDK4) was developed, identifying high-risk patients with poorer prognoses.
• The risk score independently predicted GC prognosis, outperforming traditional staging.
• Identified distinct inflammatory subtypes and linked the risk score to differential drug sensitivity.

Conclusions:

• A novel three-gene prognostic signature for gastric cancer was established.
• This signature aids in predicting prognosis and personalizing treatment for GC patients.
• The findings highlight the role of inflammation in GC and its potential for therapeutic targeting.