A Comprehensive Analysis of Pyroptosis-Related lncRNAs Signature Associated With Prognosis and Tumor Immune Microenvironment of Pancreatic Adenocarcinoma

• DOI: 10.3389/fgene.2022.899496
• Publication Date: 2022-07-06T04:46:46Z
• Authors: Kai Zhao; Xiangyu Li; Yuanxin Shi; Yun Lu; Peng Qiu; Zhengdong Deng; Wei Yao; Jianming Wang
• Keywords: Pyroptosis

Background: Globally, pancreatic adenocarcinoma (PAAD) is a common and highly devastating gastrointestinal malignancy that seriously threatens human health. Pyroptosis refers to an emerging form of programmed cell death has been discovered in recent years, studies have demonstrated long non-coding RNA (lncRNA) may act as moderator the pyroptosis process cancer cells. However, relevant explorations about lncRNAs are still insufficient PAAD. Therefore, our research designed make comprehensive analysis potential values pyroptosis-related Methods: By integrating RNA-sequencing, somatic mutation, copy number variation (CNV) datasets, well clinicopathological features, we established validated risk signature based on lncRNAs, comprehensively analyzed its clinical significance connection with tumor immune microenvironment (TIME). Consequences: The genetic landscape displayed mutations were rare while CNV changes general mainly concentrated amplification these 52 genes. Subsequently, consisting 10 (TRAF3IP2-AS1, LINC00519, LINC01133, LINC02251, AC005332.6, AL590787.1, AC090114.2, TRPC7-AS1, MIR223HG, MIR3142HG) was constructed patients divided into different subgroups according median score; high-risk scores presented worse outcomes compared those low-risk training, testing, entire cohorts. Furthermore, at possessed higher infiltration abundance cells patients, which consistent expression levels between high/low-risk groups. Drug sensitivity showed related anti-cancer agents like AICAR Axitinib, whereas connected certain drugs such AUY922. These results could be used for prognosis prediction; additionally, it also TIME might indicator instruct immunotherapeutic strategies. Conclusion: This work explored model by prediction internal link expected assist diagnosis, prognostic assessment, management