Identification and Validation of Pyroptosis-Related lncRNA Signature and Its Correlation with Immune Landscape in Soft Tissue Sarcomas

Background: Pyroptosis is critically associated with cancer initiation and progression, which can be modulated by diverse long noncoding RNAs (lncRNAs). However, the roles of pyroptosis-related lncRNAs in soft tissue sarcomas (STS) are still largely unknown. Methods: Our study included a total of 259 STS patients extracted from The Cancer Genome Atlas Sarcoma (TCGA-SARC) dataset. Gene expression data fragments per kilobase of transcript per million mapped reads (FPKM) values were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) for the investigation of the expression pattern of pyroptosis-related lncRNAs. Unsupervised clustering based on pyroptosis-related lncRNAs was performed, and the associations of pyroptosis-related lncRNAs with clinical outcomes and immune microenvironment were investigated. Two risk signatures for overall survival (OS) and disease-free survival (DFS) were constructed and validated in independent cohorts. Results: A total of 166 pyroptosis-related lncRNAs were identified in STS. Patients were clustered into two subgroups by unsupervised clustering, and cluster 2 had better prognoses, higher immune scores, higher abundance of immune cells, and higher expression of some immune checkpoints. OS-and DFS-risk signatures based on 10 and 13 pyroptosis-related lncRNAs, respectively, with favorable discrimination were constructed and validated. High-risk patients had favorable prognoses, and receiver operating characteristic (ROC) curves showed that both risk signatures could function as excellent predictors for prognoses of STS patients. Besides, the OS-risk signature could also excellently predict the immune landscape of STS. Conclusion: In conclusion, our study revealed the clinical significance and critical roles of pyroptosis-related lncRNAs in STS, and constructed novel risk signatures based on pyrop-tosis-related lncRNAs that could effectively predict clinical outcomes and immune micro-environment in STS.