Construction and Validation of a Novel Disulfidptosis-Related lncRNA Signature to Predict Immune Landscape and Precise Treatment in Stomach Adenocarcinoma

Background: Long non-coding RNAs (lncRNAs) are crucial factors affecting the occurrence, progression and prognosis of gastric cancer. Disulfidptosis, a type of programmed cell death, results from abnormal accumulation of disulfide bonds in high-SLC7A11 cells. Research have demonstrated that upregulated SLC7A11 is common in human cancers, but the effect of disulfidptosis on gastric cancer remains unclear. It is of great significance to recognize disulfidptosis -related lncRNAs (drlncRNAs) and develop a risk signature with prognostic value in gastric cancer. Methods: The transcriptome data and clinical information of gastric cancer patients were obtained from TCGA (the Cancer Genome Atlas) database. A 3 drlncRNAs risk model was built by three common regression analysis methods. Then we used ROC curves, independent prognostic analysis and other methods to assess the accuracy of the model. Furthermore, GO and KEGG enrichment analysis, immune infiltration analysis and drug sensitivity prediction were also performed in this study. Finally, TMB, MSI, and TIDE analyses were conducted to further explore the difference of high- and low- risk score group in immunotherapy response. Results: We constructed a prognostic model composed of 3 drlncRNAs (AC107021.2, AC016394.2 and AC129507.1). Univariate and multivariate Cox regression proved that the model was able to predict the prognosis of GC patients independently. GO and KEGG analyses suggested that the high-risk group mainly enriched in sulfur compound binding, canonical WNT signaling pathway, cell-substrate adherens junction, cAMP signaling pathway and so on. TME analysis indicated that ImmuneScores, StromalScores, and ESTIMATEScores were higher in the high-risk group. Meanwhile, the high-risk group showed higher levels of immune cell infiltration, while the low-risk group exhibited higher expression levels of immune checkpoints. Our research further revealed that, compared to patients in the high-risk group, patients in the low-risk group had higher tumor mutation burden, a higher proportion of MSI-H, and lower TIDE scores. Finally, gemcitabine, ABT.888 (veliparib) and other sensitive drugs were confirmed to be more effective in low-risk groups. Conclusion: The risk model we constructed can independently predict prognosis and provide precise and individual clinical treatment guidance for patients with GC.