Integrated Analysis of Multi-Omics Alteration, Immune Profile, and Pharmacological Landscape of Pyroptosis-Derived lncRNA Pairs in Gastric Cancer - Guo, Chunguang; Liu, Zaoqu; Yu, Yin; Liu, Shirui; Ma, Ke; Ge, Xiaoyong; Xing, Zhe; Lu, Taoyuan; Weng, Siyuan; Wang, Libo; Liu, Long; Hua, Zhaohui; et al. (Frontiers in Cell and Developmental Biology, 2022)
Background: Recent evidence demonstrate that pyroptosis derived long noncoding RNAs (lncRNAs) have profound impacts on the initiation, progression, and microenvironment of tumors. However, the roles of pyroptosis-derived lncRNA (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 performed 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 61075 PDL pairs in the training set were generated. Cox regression revealed 15 PDLs 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 shown 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 in GC, and our PLPPS model could serve as a promising tool to advance prognostic management and personalized treatment of GC patients.