Construction of a disulfidptosis-associated lncRNAs risk model to predict prognosis and immuno-infiltration analysis of lung adenocarcinoma.

Objective: To develop a risk model based on LncRNAs associated with disulfidptosis to forecast the prognosis and assess immune infiltration of Lung adenocarcinoma (LUAD).

Methods: This study employed a bioinformatics approach. The study was conducted from March 29, 2023 and concluded on July 1, 2023 at Guangzhou University of Chinese Medicine, Guangzhou, China. Transcriptomic data specific to LUAD were collected from TCGA database. Disulfidptosis-related LncRNAs were preliminarily screened using co-expression analysis, followed by screening using Lasso regression and Cox regression to identify LncRNAs. Subsequently, prognostic prediction models were constructed. To assess the model, survival analysis, subject operating characteristic curves, and calibration curves were employed. To evaluate the tumor microenvironment, the "estimate" package was used, while the "ggpubr" package was utilized to visualize the variations. Additionally, we employed CIBERSORT to examine immune cell infiltration abundance.

Results: A prognostic prediction model was constructed using five LncRNAs. The high-risk group displayed a shorter overall survival and progression-free survival (P<0.05). The concordance index was calculated as 0.704 (95.

Ci: 0.654-0.754). GSEA analysis reveals that high risk group is associated with the cell cycle pathway and steroid hormone biosynthesis pathway, while the low-risk group is associated with hematopoietic cell pathway and allograft rejection pathway. Immune cell infiltration analysis indicated associations between the prognostic model and activated T cells CD4 memory, T cells CD8, etc.

Conclusions: The risk model of Disulfidptosis-related LncRNAs can predict the prognosis of LUAD and evaluate the immune infiltration, providing a new direction for the treatment of LUAD.