Pyroptosis-Related LncRNA Signature Predicts Prognosis and Is Associated With Immune Infiltration in Hepatocellular Carcinoma

Abstract

Pyroptosis is an inflammatory form of programmed cell death that is involved in various cancers, including hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) were recently verified as crucial mediators in the regulation of pyroptosis. However, the role of pyroptosis-related lncRNAs in HCC and their associations with prognosis have not been reported. In this study, we constructed a prognostic signature based on pyroptosis-related differentially expressed lncRNAs in HCC. A co-expression network of pyroptosis-related mRNAs-lncRNAs was constructed based on HCC data from The Cancer Genome Atlas. Cox regression analyses were performed to construct a pyroptosis-related lncRNA signature (PRlncSig) in a training cohort, which was subsequently validated in a testing cohort and a combination of the two cohorts. Kaplan-Meier analyses revealed that patients in the high-risk group had poorer survival times. Receiver operating characteristic curve and principal component analyses further verified the accuracy of the PRlncSig model. Besides, the external cohort validation confirmed the robustness of PRlncSig. Furthermore, a nomogram based on the PRlncSig score and clinical characteristics was established and shown to have robust prediction ability. In addition, gene set enrichment analysis revealed that the RNA degradation, the cell cycle, the WNT signaling pathway, and numerous immune processes were significantly enriched in the high-risk group compared to the low-risk group. Moreover, the immune cell subpopulations, the expression of immune checkpoint genes, and response to chemotherapy and immunotherapy differed significantly between the high- and low-risk groups. Finally, the expression levels of the five lncRNAs in the signature were validated by quantitative real-time PCR. In summary, our PRlncSig model shows significant predictive value with respect to prognosis of HCC patients and could provide clinical guidance for individualized immunotherapy.

Keywords: hepatocellular carcinoma; immune infiltration; immunotherapy; long non-coding RNA; pyroptosis.

Figure 1

Flowchart of the study process.

Figure 2

Functional enrichment analysis of differentially expressed pyroptosis-related genes. (A) Heatmap of expression of pyroptosis-related genes in HCC and adjacent tissues (blue: low expression level; dark orange: high expression level). (B) Enriched GO terms (up) and KEGG pathways (down) for differentially expressed pyroptosis-related genes. The outer circle shows a scatter diagram of the log fold change (FC) value allocated to each term. (C) Heatmap showing the relationships between differentially expressed pyroptosis-related genes and enriched KEGG pathways. Colors represent the logFC values of each gene in HCC compared with adjacent tissues.

Figure 3

Construction of prognostic risk signature for patients with HCC based on pyroptosis-related lncRNAs in the training set. (A) Forest plot of 46 pyroptosis-related lncRNAs associated with overall survival of HCC patients based on univariate Cox regression analysis. (B) Distribution plot of the partial likelihood deviation of the LASSO regression. Nine variables were retained when the partial likelihood deviation reached the minimum (log lambda = –3.1). (C) Distribution plot of the LASSO coefficient. (D) Multivariate Cox regression analysis identified five pyroptosis-related lncRNAs for the construction of a prognostic model. (E) Prognostic co-expression network of the five pyroptosis-related lncRNAs and mRNAs. (F) Sankey diagram of the relationship between lncRNAs and mRNAs. *P < 0.05.

Figure 4

Evaluation and validation of pyroptosis-related lncRNA signature for overall survival in patients with HCC in three datasets. Risk scores and expression profiles of five-lncRNA signature in the high- and low-risk groups showed in the training cohort (A), testing cohort (D), and entire cohort (G). Kaplan–Meier survival and ROC analyses in the training cohort (B, C), testing cohort (E, F), and entire cohort (H, I), respectively.

Figure 5

Correlations between risk score and different clinicopathological characteristics of HCC in the entire cohort. (A) Strip chart showing relationships between clinical characteristics and PRlncSig score. (B) ROC analysis of 3-year overall survival for multiple prognostic indicators of HCC samples. (C) Clinical stratification analysis of overall survival of patients with HCC in the high- and low-risk groups by age, gender, histological grade, and tumor stage. **P < 0.01, ***P < 0.001.

Figure 6

Construction and evaluation of the nomogram for clinicopathological characteristics and risk signature. (A) Nomogram combining PRlncSig and clinicopathological characteristics for predicting prognosis of HCC patients in the training cohort. ROC analysis of the predictions of 1-, 3-, and 5-year survival by the nomogram in the training cohort (B), testing cohort (C), and entire cohort (D). Calibration curve analysis of the nomogram for the probability of overall survival at 1, 3, and 5 years in the training cohort (E), testing cohort (F), and entire cohort (G). ***P < 0.001.

Figure 7

Functional GSEA analysis of PRlncSig and model comparisons. (A) Representative KEGG pathways significantly enriched in high-risk patients. (B) Immune-related signatures significantly enriched in high-risk patients. (C) ROC analysis of the prediction of 3-year survival by PRlncSig and four other signatures.

Figure 8

Immune infiltration analysis and prediction of immunotherapy and chemotherapy response. (A) Heatmap of all significantly differential immune responses between high- and low-risk groups based on TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL, and EPIC algorithms. Immune cell scores (B) and immune function scores (C) in high- and low-risk groups based on ssGSEA algorithm. (D) Comparison of stromal scores, immune scores, estimate scores, and tumor purity between HCC patients in high- and low-risk groups. (E) Expression levels of immune checkpoint genes in high- and low-risk groups. (F) Associations of PRlncSig score with pyroptosis-related lncRNAs and immune checkpoint genes. (G) Comparison of TIDE prediction scores (left) and response to immunotherapy (right) between the high- and low-risk groups in the TCGA_LIHC dataset. (H) Assessment of sensitivity to several chemotherapeutics (doxorubicin, gemcitabine, bleomycin, and paclitaxel in the high- and low-risk groups. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 9

Validation of the expression of five lncRNAs. (A) The expression level of five lncRNAs in HCC and adjacent tissues, as indicated by TCGA_LIHC dataset. (B) Five lncRNAs in HCC and paired adjacent tissues according to the TCGA_LIHC dataset. (C) The expression of five lncRNAs in twenty-four pairs of HCC tissues and adjacent tissues verified using qRT-PCR (n = 24). (D) Associations of pyroptosis-related gene, GSDME and five lncRNAs expression levels verified using qRT-PCR (n = 24). *P < 0.05, **P < 0.01, ***P < 0.001.