Identification and validation of a novel cellular senescence-related lncRNA prognostic signature for predicting immunotherapy response in stomach adenocarcinoma

Abstract

Background: Cellular senescence is a novel hallmark of cancer associated with patient outcomes and tumor immunotherapy. However, the value of cellular senescence-related long non-coding RNAs (lncRNAs) in predicting prognosis and immunotherapy response for stomach adenocarcinoma (STAD) patients needs further investigation.Methods: The transcriptome and corresponding clinical information of STAD and cellular senescence-related genes were, respectively, downloaded from the Cancer Genome Atlas (TCGA) and CellAge databases. Differential expression analysis and coexpression analysis were performed to obtain cellular senescence-related lncRNAs. Univariate regression analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis were conducted to establish the cellular senescence-related lncRNA prognostic signature (CSLPS). Next, the survival curve, ROC curve, and nomogram were developed to assess the capacity of predictive models. Moreover, principal component analysis (PCA), gene set enrichment analysis (GSEA), tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE) score analysis were performed between high- and low-risk groups.Results: A novel CSLPS involving fifteen lncRNAs (REPIN1-AS1, AL355574.1, AC104695.3, AL033527.2, AC083902.1, TYMSOS, LINC00460, AC005165.1, AL136115.1, AC007405.2, AL391152.1, SCAT1, AC129507.1, AL121748.1, and ADAMTS9-AS1) was developed. According to the nomogram, the risk model based on the CSLPS was an independent prognostic factor and could predict 1-, 3-, and 5-year overall survival for STAD patients. GSEA suggested that the high-risk group was mainly associated with Toll-like receptor, JAK/STAT, NOD-like receptor, and chemokine signaling pathways. Further analysis revealed that STAD patients in the low-risk group with better clinical outcomes had a higher TMB, higher proportion of high microsatellite instability (MSI-H), better immune infiltration, and lower TIDE scores.Conclusion: A fifteen-CSlncRNA prognostic signature could predict survival outcomes, and patients in the low-risk group may be more sensitive to immunotherapy.

Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer

“…We analyzed the differences in the expression of critical immune checkpoints such as PD-1, PD-L1, and CTLA-4 between the two subgroups. Subsequently, we calculated the infiltration level of 22 immune cells in each sample using the CIBERSORT algorithm (Newman et al, 2015) and analyzed the abundance of immune cell infiltrates between the two subgroups using the single sample gene set enrichment analysis (ssGSEA) algorithm (Zeng et al, 2022). In addition, gene set variation analysis (GSVA) was performed with the hallmark gene set (h.all.v7.5.1.symbols) to investigate the differences in TGF-β subgroups in signaling pathways (Hänzelmann et al, 2013).…”

“…Patients were divided into high-and low-risk groups based on the median risk score. Furthermore, we analyzed the relationship between the TGF-β cluster, gene cluster, risk score, and survival status using the R package ggalluvial and the differences in risk scores between distinct subgroups (Zeng et al, 2022). In the training and validation sets, we performed Kaplan-Meier survival analysis with the R package survminer and survival (Wang et al, 2020) and ROC curve analysis with the R package timeROC (Zeng et al, 2022), respectively.…”

“…Age, gender, tumor size (T), lymph node metastasis (N), and risk score were included for analysis. In addition, we constructed a nomogram integrated the risk score and clinicopathological factors to predict the survival of gastric cancer patients at 1-, 3-, and 5-year using R package rms in the training set, testing set, and entire set, respectively (Zeng et al, 2022).…”

“…To explore the differences in the tumor immune microenvironment between high-and low-risk groups of gastric cancer patients based on the risk model, we first analyzed the stromal score, immune score, and ESTIMATE score between the two groups using the ESTIMATE algorithm. Then, we analyzed the Spearman correlation between the risk score and immune cells using seven methods, including the XCELL, TIMER, QUANTISEQ, MCPCOUNTER, EPIC, CIBERSORT-ABS, and CIBERSORT algorithms (Zeng et al, 2022). We further analyzed the Spearman correlation between the expression of 5 genes in the model and immune cells.…”

“…TMB (Rizzo et al, 2021), MSS (Rizzo et al, 2021), IPS (Wu et al, 2021), and TIDE (Zeng et al, 2022) scores were considered markers to predict immunotherapy response. First, we downloaded the mutation data of gastric cancer patients in MAF format from the TCGA database and annotated them using the R package maftools (Mayakonda et al, 2018), and subsequently analyzed the correlation between the risk score and TMB as well as the mutated genes common to patients in highand low-risk groups.…”