Integrated Analysis of Multi-Omics Alteration, Immune Profile, and Pharmacological Landscape of Pyroptosis-Derived lncRNA Pairs in Gastric Cancer

Abstract

Background: Recent evidence demonstrates that pyroptosis-derived long non-coding RNAs (lncRNAs) have profound impacts on the initiation, progression, and microenvironment of tumors. However, the roles of pyroptosis-derived lncRNAs (PDLs) in gastric cancer (GC) remain elusive.Methods: We comprehensively analyzed the multi-omics data of 839 GC patients from three independent cohorts. The previous gene set enrichment analysis embedding algorithm was utilized to identify PDLs. A gene pair pipeline was developed to facilitate clinical translation via qualitative relative expression orders. The LASSO algorithm was used to construct and validate a pyroptosis-derived lncRNA pair prognostics signature (PLPPS). The associations between PLPPS and multi-omics alteration, immune profile, and pharmacological landscape were further investigated.Results: A total of 350 PDLs and 61,075 PDL pairs in the training set were generated. Cox regression revealed 15 PDL pairs associated with overall survival, which were utilized to construct the PLPPS model via the LASSO algorithm. The high-risk group demonstrated adverse prognosis relative to the low-risk group. Remarkably, genomic analysis suggested that the lower tumor mutation burden and gene mutation frequency (e.g., TTN, MUC16, and LRP1B) were found in the high-risk group patients. The copy number variants were not significantly different between the two groups. Additionally, the high-risk group possessed lower immune cell infiltration abundance and might be resistant to a few chemotherapeutic drugs (including cisplatin, paclitaxel, and gemcitabine).Conclusion: PDLs were closely implicated in the biological process and prognosis of GC, and our PLPPS model could serve as a promising tool to advance prognostic management and personalized treatment of GC patients.

A novel pyroptosis-regulated gene signature for predicting prognosis and immunotherapy response in hepatocellular carcinoma

“…Figures 9A,B show that there were no significant differences between age groups (≤65 and >65 years old) and gender. A previous study reported that the collagen proportional area (CPA) measurement may be a useful prognostic indicator for HCC ( Guo et al, 2022 ). Our study demonstrated that there were statistically significant differences among all CPA stages ( Figure 9C , C1: 0%–5%, C2: 5%–10%, C3: 10%–20%, C4: >20%).…”

“…To determine the proportions and differences in immune cells in different risk groups, we used ssGSEA in the R package GSVA to evaluate the infiltration level of 28 types of immune cells. The detailed method has been described in previous studies ( Liu et al, 2021 ; Guo et al, 2022 ). The Wilcoxon test was used to compare the differences between the high- and low-risk groups.…”

“…Based on a risk model consist of 15 pyroptosis-related lncRNAs, researchers found that the high-risk group displayed higher immune infiltration fraction and activity in glioblastoma ( Xing et al, 2022 ). In a pyroptosis-derived lncRNA pair prognostics signature in gastric cancer, the high-risk group possessed lower levels of macrophage, monocytes, myeloid dendritic cells, endothelial cells, and cancer-associated fibroblasts compared with the low-risk group ( Guo et al, 2022 ). A lncRNA signature encompassing 14 pyroptosis-related lncRNAs revealed that the high-risk group characterized an immunologically “cold” profile based on the immune cell infiltration landscape in head and neck squamous cell carcinoma ( Zhu et al, 2021 ).…”

Risk Model and Immune Signature of m7G-Related lncRNA Based on Lung Adenocarcinoma

“…Immune cell bubble plots showed that low-risk groups had higher levels of immune infiltration. It has been reported that the high immune infiltration state tends to have a better immunotherapeutic effect ( Guo et al, 2022 ; Luo et al, 2022 ). Interestingly, immune scores and immune checkpoint expression levels were also higher in the low-risk group, which is consistent with the results of Yu et al (2021) .…”

A novel necroptosis-related LncRNA signature for prediction of prognosis and therapeutic responses of head and neck squamous cell carcinoma

“…KM and ROC curves indicate that NLPS can accurately predict the prognosis of patients in different centers, which suggests that NLPS has clinical generalizability. Prognostic differences between patients were often due to their underlying molecular mechanisms and characteristics ( Guo et al, 2022 ). Based on the GSEA and GSVA algorithms, we further explore the biological characteristics of patients in the high- and low-risk groups.…”