Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Gastric Cancer

SIMPLE SUMMARY: In this study, we aimed to determine the correlation between pyroptosis-related lncRNAs and gastric cancer prognoses. A novel predictive signature including six pyroptosis-related lncRNAs was established for the purposes of gastric cancer and immune status prognoses, which were achieved by using bioinformatics tools. After multiple validations, we confirmed that this signature possessed a good predictive performance. We found that high risk was associated with increased immune cell infiltration, increased immune function scores, and up-regulated expressions of immune checkpoints; in other words, the high-risk gastric cancer patients were more likely to benefit from the combination of immunotherapy and chemotherapy. Then, we performed quantitative reverse transcription polymerase chain reactions in order to verify the risk model. Further, the results indicated that pyroptosis-related genes play a crucial role in tumor progression and prognosis. In summary, the six pyroptosis-related lncRNAs in this study can be used as novel biomarkers for the prognosis and treatment of gastric cancer. ABSTRACT: Gastric cancer (GC) is a malignant tumor with a low survival rate, high recurrence rate, and poor prognosis. With respect to this, pyroptosis is a type of programmed cell death that can affect the occurrence and development of tumors. Indeed, long non-coding RNAs (lncRNAs) were broadly applied for the purposes of early diagnosis, treatment, and prognostic analysis in regard to cancer. Based on the association of these three purposes, we developed a novel prognosis risk model based on pyroptosis-related lncRNAs (PRlncRNAs) for GC. The PRlncRNAs were obtained via univariate and multivariate Cox regression in order to build the predictive signatures. The Kaplan–Meier and gene set enrichment analysis (GSEA) methods were used to evaluate the overall survival (OS) and functional differences between the high- and low-risk groups. Moreover, the correlation of the signatures with immune cell infiltration was determined through single-sample gene set enrichment analysis (ssGSEA). Finally, we analyzed this correlation with the treatment responses in the GC patients; then, we performed quantitative reverse transcription polymerase chain reactions (qRT-PCRs) in order to verify the risk model. The high-risk group received a worse performance in terms of prognosis and OS when compared to the low-risk group. With respect to this, the area under the receiver operating characteristic curve (ROC) was found to be 0.808. Through conducting the GSEA, it was found that the high-risk groups possessed a significant enrichment in terms of tumor–immunity pathways. Furthermore, the ssGSEA revealed that the predictive features possessed strong associations with immune cell infiltration in regard to GC. In addition, we highlighted that anti-immune checkpoint therapy, combined with conventional chemotherapy drugs, may be more suitable for high-risk patients. The expression levels of LINC01315, AP003392.1, AP000695.2, and HAGLR were significantly different between the GC cell lines and the normal cell lines. As such, the six PRlncRNAs could be regarded as important prognostic biomarkers for the purposes of subsequent diagnoses, treatments, prognostic predictions, and the mechanism research of GC.