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

Apr 30, 2024Current medicinal chemistry

Using Multiple Biological Data and Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma Based on Disulfide-Related Cell Death Patterns

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Abstract

Genes related to disulfidptosis show high expression and significant prognostic value in lung adenocarcinoma (LUAD).

Two distinct disulfidptosis subtypes with different prognoses and molecular characteristics were identified in LUAD.
A robust prognostic model based on disulfidptosis genes predicts that a lower risk score is associated with a higher response rate to immunotherapy.
The model also correlates better patient prognosis with lower risk scores.
The gene NAPSA was found to inhibit the proliferation and migration of LUAD cells.
The research highlights the potential of disulfidptosis genes in predicting survival rates and therapeutic outcomes for LUAD patients.

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BACKGROUND: Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune microenvironment and immunotherapy in lung adenocarcinoma (LUAD).

METHODS: We initially utilized pan-cancer transcriptomics to explore the expression, prognosis, and mutation status of genes related to disulfidptosis. Using the LUAD multi- -omics cohorts in the TCGA database, we explore the molecular characteristics of subtypes related to disulfidptosis. Employing various machine learning algorithms, we construct a robust prognostic model to predict immune therapy responses and explore the model's impact on the tumor microenvironment through single-cell transcriptome data. Finally, the biological functions of genes related to the prognostic model are verified through laboratory experiments.

RESULTS: Genes related to disulfidptosis exhibit high expression and significant prognostic value in various cancers, including LUAD. Two disulfidptosis subtypes with distinct prognoses and molecular characteristics have been identified, leading to the development of a robust DSRS prognostic model, where a lower risk score correlates with a higher response rate to immunotherapy and a better patient prognosis. NAPSA, a critical gene in the risk model, was found to inhibit the proliferation and migration of LUAD cells.

CONCLUSION: Our research introduces an innovative prognostic risk model predicated upon disulfidptosis genes for patients afflicted with Lung Adenocarcinoma (LUAD). This model proficiently forecasts the survival rates and therapeutic outcomes for LUAD patients, thereby delineating the high-risk population with distinctive immune cell infiltration and a state of immunosuppression. Furthermore, NAPSA can inhibit the proliferation and invasion capabilities of LUAD cells, thereby identifying new molecules for clinical targeted therapy.

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

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