Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Sep;11(9):3175-3186.
doi: 10.21037/tcr-22-366.

A newly identified pyroptosis-related gene signature for predicting prognosis of patients with hepatocellular cancer

Qinyan Shen  1 , Yangping Jiang  2 , Xihong Hu  2 , Zhenwu Du  1
Affiliations

A newly identified pyroptosis-related gene signature for predicting prognosis of patients with hepatocellular cancer

Qinyan Shen et al. Transl Cancer Res. 2022 Sep.

Abstract

Background: Hepatocellular carcinoma (HCC) is a very heterogeneous illness, making prognosis prediction a huge problem. Pyroptosis, which has recently been shown to be an inflammatory type of programmed cell death, is involved in HCC. Nevertheless, the role of pyroptosis-related genes in HCC has not been fully elucidated. Thus, this study aimed to construct a prognostic signature based on pyroptosis-related genes for HCC.

Methods: The messenger RNA expression patterns of HCC patients, as well as the accompanying clinical information, were retrieved from The Cancer Genome Atlas (TCGA) database for this research. After differentially expressed pyroptosis-related Gene in tumor and normal groups were identified, Cox regression analyses were performed to construct a prognostic signature which was then assessed through independent prognostic analysis.

Results: A signature consisting of four genes (CASP8, GSDME, NOD2, and PLCG1) was constructed to predict overall survival (OS) for HCC. The signature was identified to be independent by the cox regression analysis and obtained the largest area under the receiver operating characteristic (ROC) curve (AUC) was 0.691, 0.628, and 0.632 for survival at 1, 2, and 3 years, respectively.

Conclusions: We discovered that the levels of pyroptosis-related genes expression differed across HCC patients and were associated with both survival and prognosis. This suggested that targeting pyroptosis as a treatment strategy for HCC may be a viable option.

Keywords: Hepatocellular carcinoma (HCC); gene signature; overall survival (OS); pyroptosis.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-366/coif). The authors have no conflicts of interest to declare.

Figures

Figure 1
Figure 1
The expression of 33 pyroptosis-related genes and the relationships between DEGs. (A) A heatmap of pyroptosis-related genes compared between the normal (brilliant blue) and the tumor tissues (red) is shown. P values are presented as **P<0.01, ***P<0.001. (B) The correlation network of DEGs is represented by a red line for a positive correlation or a blue line for a negative correlation. The darker the color, the stronger the correlation. N, normal tissue; T, tumor tissue; DEGs, differentially expressed genes.
Figure 2
Figure 2
Survival analysis and gene selection in the anticipating of prognosis of patients with HCC. (A) Hazard ratios for survival-associated pyroptosis-related genes in HCC were determined using forest plots. (B) A LASSO Cox regression model was utilized to plot partial likelihood deviance against log (λ). (C) The λ parameter represents the coefficients of special characteristics. Each line represents a gene (line 1: NOD1, line 2: CASP8, line 3: GSDME, line 4: CASP3, line 5: NOD2, line 6: PLCG1). HCC, hepatocellular carcinoma; LASSO, least absolute shrinkage and selection operator.
Figure 3
Figure 3
Prognostic evaluation of the four-gene signature model in the TCGA cohort. (A) The risk score distribution and median values in the TCGA cohort (the dashed lines on the left portion are the populations at low risk while those on the right portion are the populations at high risk). (B) PCA plot for HCC on the basis of the risk score. (C) Each patient’s survival status (the dashed lines on the left portion are the populations at low risk while those on the right portion are the populations at high risk). (D) Kaplan-Meier graphs illustrating the OS of patients classified as high- or low-risk groups. (E) ROC curves validated the predictive effectiveness of the risk score. PCA, principal component analysis; AUC, area under the ROC curve; ROC, receiver operating characteristic; TCGA, The Cancer Genome Atlas; HCC, hepatocellular carcinoma; OS, overall survival.
Figure 4
Figure 4
Analyses of the risk score utilizing multivariate and univariate Cox regression. (A) The forest plot shows the results of the univariate Cox regression analysis in HCC. (B) The forest plot shows the results of a multivariate Cox regression evaluation in HCC (P<0.05). T, tumor; M, metastasis; N, node; HCC, hepatocellular carcinoma.
Figure 5
Figure 5
Functional analysis based on the DEGs. (A) Bubble graph for GO enrichment. (B) Bubble graph for KEGG pathways. (The bigger bubble means the more genes enriched, and the increasing depth of red means the differences were more obvious; BP, biological process; CC, cellular component; MF, molecular function; DEGs, differentially expressed genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
Figure 6
Figure 6
Comparison of the ssGSEA scores for immune cells and immune pathways. (A) Comparison of the enrichment scores of 16 types of immune cells between low- (green box) and high-risk (red box) group. (B) Comparison of the enrichment scores of 13 immune-related pathways between low- (blue box) and high-risk (red box) group. P values were showed as: ns, not significant; *P<0.05; **P<0.01; ***P<0.001. aDCs, activated dendritic cells; DCs, dendritic cells; iDCs, immature dendritic cells; NK, natural killer; pDCs, plasmacytoid dendritic cells; Tfh, T follicular helper cell; Th1, T-helper 1; Th2, T-helper 2; TIL, tumor-infiltrating lymphocyte; APC, antigen presenting cell; CCR, C-C chemokine receptor; HLA, human leukocyte antigen; MHC, major histocompatibility complex; IFN, interferon; ssGSEA, single-sample gene set enrichment analysis.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Liu JKH, Irvine AF, Jones RL, et al. Immunotherapies for hepatocellular carcinoma. Cancer Med 2022;11:571-91. 10.1002/cam4.4468 - DOI - PMC - PubMed
    1. Hou Z, Liu J, Jin Z, et al. Use of chemotherapy to treat hepatocellular carcinoma. Biosci Trends 2022;16:31-45. 10.5582/bst.2022.01044 - DOI - PubMed
    1. Wang W, Hao LP, Song H, et al. The Potential Roles of Exosomal Non-Coding RNAs in Hepatocellular Carcinoma. Front Oncol 2022;12:790916. 10.3389/fonc.2022.790916 - DOI - PMC - PubMed
    1. Chidambaranathan-Reghupaty S, Fisher PB, Sarkar D. Hepatocellular carcinoma (HCC): Epidemiology, etiology and molecular classification. Adv Cancer Res 2021;149:1-61. 10.1016/bs.acr.2020.10.001 - DOI - PMC - PubMed
    1. Jemal A, Ward EM, Johnson CJ, et al. Annual Report to the Nation on the Status of Cancer, 1975-2014, Featuring Survival. J Natl Cancer Inst 2017. 10.1093/jnci/djx030 - DOI - PMC - PubMed