Mathematical Biosciences and Engineering (Jan 2023)
Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer
Abstract
Background: Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer. Methods: Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed. Results: Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue. Conclusions: We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.
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