2024 World Conference on Lung Cancer (WCLC) - ePosters-EP.06B.11 Construction of a Prognostic Model for Lung Adenocarcinoma Based on Disulfidptosis-related LncRNAs

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This study focuses on creating a prognostic model for lung adenocarcinoma (LUAD) using long noncoding RNAs associated with disulfidptosis, a novel cell death mechanism triggered by disulfide stress under glucose starvation, resulting from accumulated intracellular cystine. LUAD, being the most common form of lung cancer, necessitates effective models for prognosis and treatment strategies due to its poor outcome.<br /><br />The researchers utilized RNA-Seq data and clinical information from LUAD patients, developing a prognostic model through LASSO-COX regression that stratifies patients into high- and low-risk categories. The model's reliability was affirmed using ROC curves, C-index, and Kaplan-Meier analysis, while tools like GSEA and single-sample GSEA were employed to explore pathway enrigemens, immune functions, and treatment responses in differing risk groups.<br /><br />Through analysis of the TCGA database, the study identified 149 differential genes among LUAD and adjacent normal tissues, highlighting co-expression and a PPI network revealing 10 hub genes vital for LUAD development. Functional enrichment analysis of these differential genes via GO and KEGG underscored their roles in pathways influencing tumor proliferation, migration, invasion, and the tumor immune microenvironment, suggesting DRlncRNAs may drive tumor progression and affect prognosis.<br /><br />An 8-lncRNA prognostic model was subsequently developed, reporting superior accuracy, sensitivity, and specificity in predicting patient outcomes compared to other clinical and pathological factors. Furthermore, the study’s analysis indicated that high-risk patients, defined by the expression of DRlncRNAs, exhibit greater immune suppression, higher gene mutation rates, and worse prognoses, providing strategies to refine prognosis and tailor immunotherapy approaches for LUAD patients.<br /><br />In conclusion, the study presents a significant advancement in LUAD management, offering a novel gene-based prognostic tool that personalizes and improves outcome predictions, enhancing clinical application strategies surrounding disulfidptosis.

Asset Subtitle

Man Sun

Meta Tag

Speaker Man Sun

Topic Pathology and Biomarkers

Keywords

lung adenocarcinoma

prognostic model

long noncoding RNAs

disulfidptosis

LASSO-COX regression

RNA-Seq data

TCGA database

immune microenvironment

8-lncRNA model

immunotherapy