Construction and Validation of a Novel Pyroptosis-Related Four-lncRNA Prognostic Signature Related to Gastric Cancer and Immune Infiltration

Abstract

Increasing evidence has demonstrated that pyroptosis, a type of inflammatory programmed cell death, plays an important role in the pathogenesis and progression of gastric cancer. However, it remains unclear whether pyroptosis-related long non-coding RNAs (lncRNAs) can be used to predict the diagnosis and prognosis of gastric adenocarcinoma. This study aimed to evaluate and test the role of the lncRNA signature associated with pyroptosis as a prognostic tool for stomach adenocarcinoma (STAD) and to ascertain their immune value. Relative RNA-sequencing data were extracted from The Cancer Genome Atlas database (TCGA), and data preprocessing was performed for STAD. Pearson correlation analysis was used to determine whether lncRNAs were significantly correlated with pyroptosis based on 23 genes related to pyroptosis. Univariate Cox regression and least absolute shrinkage and selection operator(LASSO) analyses were both adopted to select features and establish the pyroptosis-related lncRNA (PRL) prognostic signature. Kaplan–Meier(KM) survival analysis of the different risk groups was conducted according to the risk scores. We further examined the functional enrichment, tumor microenvironment, and landscape of mutation status among the different risk groups, and these analyses further explained the reasons for the differences in the prediction as well as survival value of the different risk groups. Four lncRNAs, including HAND2-AS1, LINC01354, RP11-276H19.1, and PGM5-AS1, were involved in the PRL signature and used to split STAD patients into two risk groups. Overall survival time(OS) was significantly higher in the low-risk group than in the high-risk group in both the training and validation groups. Functional enrichment analysis was further employed to analyze differentially expressed genes in high- and low-risk groups to identify potential molecular functions and pathways associated with pyroptosis in the gastric cancer microenvironment. Protein-protein interaction (PPI) and Friends analysis identified hub genes that may play a key role in differentially expressed genes in high- and low-risk groups. In addition, there were remarkable discrepancies between the different risk groups in the tumor stage (P < 0.01) and histologic grade (P < 0.05). Furthermore, drug-susceptibility testing indicated potential sensitive chemotherapeutic drugs for each risk group. This study is the first to establish and validate STAD-associated PRLs that can effectively guide the prognosis and the immune microenvironment in STAD patients and provide evidence for the development of molecularly targeted therapies related to pyroptosis.

Constructing and Validating a Pyroptosis-Related Genes Prognostic Signature for Stomach Adenocarcinoma and Immune Infiltration: Potential Biomarkers for Predicting the Overall Survival

“…There have been a number of publications published in recent years that examine the association between pyroptosis and STAD [ 41 , 42 ]. However, when compared to other studies, the approach used in this study is novel.…”

Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer

“…As important immune regulators, lncRNAs might serve as potential biomarkers and therapeutic targets. Several previous studies have generated different lncRNA signatures to predict survival and immune features in GC (Liang et al, 2021;Ma et al, 2021;Xin et al, 2021;Nie et al, 2022;Wang et al, 2022), but these studies still have some issues that should be discussed. For example, the clinical data obtained from the databases should be preprocessed and standardized since some studies utilize TNM staging methods based on very old versions of the standards; patients with distant metastases should be excluded from the analysis since these data have a significant impact on the prognostic prediction.…”

Identification of cell senescence molecular subtypes in prediction of the prognosis and immunotherapy of hepatitis B virus-related hepatocellular carcinoma

“…First, we performed further filtering based on the 51 prognosis-related cellular senescence genes that were previously identified as significant. To formulate the CS score model and avoid an over-fitted scenario, the “glmnet” R package was employed for the lasso algorithm, and the following equation was implemented to determine the risk score for each patient ( 29 ). CS risk score=∑Gi∗Bi, where Gi is the expression level, and Bi is the coefficient.…”