Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in...

Area of Science:

• Oncology
• Cell Death Mechanisms
• Immunotherapy

Background:

• Disulfidptosis is a cell death mechanism linked to cellular damage.
• Understanding disulfidptosis in lung adenocarcinoma (LUAD) offers new insights into tumor dynamics and treatment strategies.

Purpose of the Study:

• To investigate the impact of disulfidptosis on the tumor immune microenvironment in LUAD.
• To develop a prognostic model for predicting immunotherapy response in LUAD patients based on disulfidptosis-related genes.

Main Methods:

• Pan-cancer transcriptomics analysis of disulfidptosis-related genes.
• Multi-omics data analysis of LUAD cohorts from TCGA.
• Machine learning model construction for prognosis and immunotherapy prediction.
• Single-cell transcriptome analysis to assess tumor microenvironment impact.
• In vitro validation of gene functions.

Main Results:

• Disulfidptosis genes show significant expression and prognostic value in cancers, including LUAD.
• Two distinct disulfidptosis subtypes identified in LUAD with different prognoses.
• A robust Disulfidptosis Risk Score (DSRS) model developed; lower scores predict better immunotherapy response and survival.
• NAPSA identified as a key gene inhibiting LUAD cell proliferation and migration.

Conclusions:

• An innovative disulfidptosis-based prognostic model for LUAD patients has been developed.
• The model accurately predicts survival and therapeutic outcomes, identifying high-risk populations with immunosuppression.
• NAPSA shows potential as a therapeutic target to inhibit LUAD cell proliferation and invasion.