Screening prognostic markers for hepatocellular carcinoma based on pyroptosis-related lncRNA pairs

Abstract

Background Pyroptosis is closely related to cancer prognosis. In this study, we tried to construct an individualized prognostic risk model for hepatocellular carcinoma (HCC) based on within-sample relative expression orderings (REOs) of pyroptosis-related lncRNAs (PRlncRNAs). Methods RNA-seq data of 343 HCC samples derived from The Cancer Genome Atlas (TCGA) database were analyzed. PRlncRNAs were detected based on differentially expressed lncRNAs between sample groups clustered by 40 reported pyroptosis-related genes (PRGs). Univariate Cox regression was used to screen out prognosis-related PRlncRNA pairs. Then, based on REOs of prognosis-related PRlncRNA pairs, a risk model for HCC was constructed by combining LASSO and stepwise multivariate Cox regression analysis. Finally, a prognosis-related competing endogenous RNA (ceRNA) network was built based on information about lncRNA–miRNA–mRNA interactions derived from the miRNet and TargetScan databases. Results Hierarchical clustering of HCC patients according to the 40 PRGs identified two groups with a significant survival difference (Kaplan–Meier log-rank, p = 0.026). Between the two groups, 104 differentially expressed lncRNAs were identified (|log2(FC)|> 1 and FDR < 5%). Among them, 83 PRlncRNA pairs showed significant associations between their REOs within HCC samples and overall survival (Univariate Cox regression, p < 0.005). An optimal 11-PRlncRNA-pair prognostic risk model was constructed for HCC. The areas under the curves (AUCs) of time-dependent receiver operating characteristic (ROC) curves of the risk model for 1-, 3-, and 5-year survival were 0.737, 0.705, and 0.797 in the validation set, respectively. Gene Set Enrichment Analysis showed that inflammation-related interleukin signaling pathways were upregulated in the predicted high-risk group (p < 0.05). Tumor immune infiltration analysis revealed a higher abundance of regulatory T cells (Tregs) and M2 macrophages and a lower abundance of CD8 + T cells in the high-risk group, indicating that excessive pyroptosis might occur in high-risk patients. Finally, eleven lncRNA–miRNA–mRNA regulatory axes associated with pyroptosis were established. Conclusion Our risk model allowed us to determine the robustness of the REO-based PRlncRNA prognostic biomarkers in the stratification of HCC patients at high and low risk. The model is also helpful for understanding the molecular mechanisms between pyroptosis and HCC prognosis. High-risk patients may have excessive pyroptosis and thus be less sensitive to immune therapy.

A disulfidptosis-related lncRNAs signature in hepatocellular carcinoma: prognostic prediction, tumor immune microenvironment and drug susceptibility

“…Interestingly, the crucial role of RCD-related abnormally expressed lncRNAs in monitoring and promoting the development and progression of LIHC is drawing growing interest. Several RCD related lncRNA signatures to forecast prognosis and tumor progression in LIHC patients have been established, such as cuproptosis-related lncRNAs 20 – 22 , ferroptosis-related lncRNAs 23 – 25 , necroptosis-related lncRNAs 26 , 27 , pyroptosis-related lncRNAs 28 – 30 , and so on. For instance, Guo et al established a cuproptosis-prognostic signature for four cuproptosis-related lncRNAs (SNHG4, AC026412.3, AL590705.3, and CDKN2A-DT) that could predict prognosis and evaluate the efficacy of immunotherapy for LIHC 20 .…”

“…Over the past decade, the research on lncRNA-based clinical tools has expanded rapidly, including in cancer diagnostic and prognostic biomarkers, and therapeutic targets 19 . LncRNA-related signatures for prognostic prediction of LIHC have attracted more and more attention, such as cuproptosis-related lncRNAs 20 – 22 , ferroptosis-related lncRNAs 23 – 25 , necroptosis-related lncRNAs 26 , 27 , pyroptosis-related lncRNAs 28 – 30 , and so on. However, disulfidptosis-related lncRNAs (DRLs) as prognostic markers for LIHC have never been investigated.…”