An original aneuploidy-related gene model for predicting lung adenocarcinoma survival and guiding therapy

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Aneuploidy is a key feature of cancer, but its specific role and prognostic implications in lung adenocarcinoma (LUAD) are not fully understood.
• Identifying reliable biomarkers for LUAD patient stratification and treatment selection is crucial.

Purpose of the Study:

• To investigate the prognostic value of aneuploidy-related genes in LUAD.
• To develop a predictive model for survival and therapy response in LUAD patients based on aneuploidy-related gene signatures.

Main Methods:

• Utilized gene expression and copy number variation (CNV) data from TCGA and GEO databases.
• Performed molecular clustering, differential gene expression analysis, and LASSO/Cox regression to build an aneuploidy-related risk score (ARS) signature.
• Assessed tumor microenvironment and predicted drug sensitivity using bioinformatics tools.

Main Results:

• Stratified LUAD patients into three molecular clusters based on CNV, with one cluster showing significantly better survival and higher inflammatory infiltration.
• Developed a robust 6-gene ARS signature demonstrating effective prediction of patient survival.
• Identified potential drug sensitivities for different risk groups and created a nomogram for clinical treatment benefit prediction.

Conclusions:

• The established 6-gene aneuploidy-related signature serves as a valuable tool for predicting survival in LUAD patients.
• The ARS model and nomogram offer potential guidance for preoperative estimation and postoperative therapy decisions in LUAD management.