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 Jul 25:13:900713.
doi: 10.3389/fgene.2022.900713. eCollection 2022.

Establishment of a Necroptosis-Related Prognostic Signature to Reveal Immune Infiltration and Predict Drug Sensitivity in Hepatocellular Carcinoma

Huili Ren  1 , Jianglin Zheng  2 , Qi Cheng  3 , Xiaoyan Yang  1   4 , Qin Fu  1   4
Affiliations

Establishment of a Necroptosis-Related Prognostic Signature to Reveal Immune Infiltration and Predict Drug Sensitivity in Hepatocellular Carcinoma

Huili Ren et al. Front Genet. .

Abstract

Background: Hepatocellular carcinoma (HCC) is a common type of primary liver cancer and has a poor prognosis. In recent times, necroptosis has been reported to be involved in the progression of multiple cancers. However, the role of necroptosis in HCC prognosis remains elusive. Methods: The RNA-seq data and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Differentially expressed genes (DEGs) and prognosis-related genes were explored, and the nonnegative matrix factorization (NMF) clustering algorithm was applied to divide HCC patients into different subtypes. Based on the prognosis-related DEGs, univariate Cox and LASSO Cox regression analyses were used to construct a necroptosis-related prognostic model. The relationship between the prognostic model and immune cell infiltration, tumor mutational burden (TMB), and drug response were explored. Results: In this study, 13 prognosis-related DEGs were confirmed from 18 DEGs and 24 prognostic-related genes. Based on the prognosis-related DEGs, patients in the TCGA cohort were clustered into three subtypes by the NMF algorithm, and patients in C3 had better survival. A necroptosis-related prognostic model was established according to LASSO analysis, and HCC patients in TCGA and ICGC were divided into high- and low-risk groups. Kaplan-Meier (K-M) survival analysis revealed that patients in the high-risk group had a shorter survival time compared to those in the low-risk group. Using univariate and multivariate Cox analyses, the prognostic model was identified as an independent prognostic factor and had better survival predictive ability in HCC patients compared with other clinical biomarkers. Furthermore, the results revealed that the high-risk patients had higher stromal, immune, and ESTIMATE scores; higher TP53 mutation rate; higher TMB; and lower tumor purities compared to those in the low-risk group. In addition, there were significant differences in predicting the drug response between the high- and low-risk groups. The protein and mRNA levels of these prognostic genes were upregulated in HCC tissues compared to normal liver tissues. Conclusion: We established a necroptosis-related prognostic signature that may provide guidance for individualized drug therapy in HCC patients; however, further experimentation is needed to validate our results.

Keywords: chemosensitivity; hepatocellular carcinoma; immune microenvironment; necroptosis; prognostic.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart showing the scheme of the study.
FIGURE 2
FIGURE 2
Identification of prognosis-related genes in hepatocellular carcinoma (HCC) patients. Volcano plot (A) and heatmap (B) of 18 differentially expressed necroptosis-related genes (NRGs). (C) Univariate Cox regression analysis of prognosis-related genes. (D) Venn plot showing the 13 intersection genes. (E) Protein–protein interaction network of the interactions among intersection genes.
FIGURE 3
FIGURE 3
Screening of molecular subgroups through the nonnegative matrix factorization (NMF) cluster. (A) Consensus map of NMF clustering. The Kaplan–Meier (K–M) analysis of (B) overall survival and (C) progression-free survival for patients in different clusters. (D) Heatmap of the NRG expression of the molecular subtypes. (E) Heatmap showed the ssGSEA Z-scores of 50 hallmarks among the three clusters.
FIGURE 4
FIGURE 4
Construction of the necroptosis gene-based prognostic model in the Cancer Genome Atlas (TCGA) training cohort. The association between log(lamba) and coefficients of genes (A) and deviance (B). (C) Univariate Cox regression analysis was used to construct a prognostic model. (D) Distribution of the risk scores, survival status, and expression of the four necroptosis-related risk genes in the training cohort. (E) The K–M curve of the low- and high-risk groups based on the seven-necroptosis-related gene profile. (F) Time-dependent ROC analysis for the 1-, 2-, and 3-year OS of the prognosis. PCA (G) and t-SNE (H) analysis of the TCGA cohort based on the risk score.
FIGURE 5
FIGURE 5
Risk score is an independent prognostic factor for HCC. (A,C) Univariate and (B,D) multivariate Cox analyses of the risk score and clinical variables in the TCGA and International Cancer Genome Consortium (ICGC) cohort. (E) ROC curve analysis of the risk score and clinicopathological characteristics in predicting 1-, 3-, and 5-year survival rates.
FIGURE 6
FIGURE 6
The relationship between the risk score and clinical factors. (A) The overview of the correspondence between necroptosis-related prognostic and other features of HCC patients. The proportion of patients with alive and death (B), G1–G2 and G3–G4 (C), stage I–II and III–IV (D), and female and male (E).
FIGURE 7
FIGURE 7
Functional enrichment analysis. (A,B) The top 10 biological process (BP) terms, CC terms, and MF terms of gene ontology analysis. (C,D) Kyoto Encyclopedia of Genes and Genomes enrichment analysis indicating related genes and pathways.
FIGURE 8
FIGURE 8
Correlation of immune cell infiltration and tumor mutational burden with prognostic signature. (A) A detailed Spearman correlation analysis was also performed using different algorithms. (B) The heatmap revealed the immune-related genes between the high- and low-risk groups. The activity differences of immune cells (C) and immune function (D) between the high- and low-risk groups. The immune score (E), stromal score (F), ESTIMATE score (G), and tumor purity (H) were compared in the high- and low-risk groups.
FIGURE 9
FIGURE 9
Correlation analysis between the estimated IC50 values of chemotherapy drugs and the risk score in HCC patients from the TCGA database. (A) Regorafenib, (B) Cisplatin, (C) Tipifarnib, (D) Atezolizumab, (E) Gefitinib, (F) Sorafenib, (G) Erlotinib, (H) Axitinib, (I) Bevacizumab.
FIGURE 10
FIGURE 10
Expression of the independent prognostic genes. (A) Immunohistochemistry of TRAF2, SQSTM1, CDKN2A, PLK1, and HSP90AA1 in the normal and tumor groups from the Human Protein Atlas database. (B) The mRNA levels of TRAF2, SQSTM1, CDKN2A, PLK1, MYCN, HSP90AA1, and TNFRSF21 were measured by RT-PCR.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Abat D., Demirhan O., Inandiklioglu N., Tunc E., Erdogan S., Tastemir D., et al. (2014). Genetic Alterations of Chromosomes, P53 and P16 Genes in Low- and High-Grade Bladder Cancer. Oncol. Lett. 8 (1), 25–32. 10.3892/ol.2014.2108 - DOI - PMC - PubMed
    1. Arvanitis C., Felsher D. W. (2006). Conditional Transgenic Models Define How MYC Initiates and Maintains Tumorigenesis. Seminars Cancer Biol. 16 (4), 313–317. 10.1016/j.semcancer.2006.07.012 - DOI - PubMed
    1. Borghi A., Verstrepen L., Beyaert R. (2016). TRAF2 Multitasking in TNF Receptor-Induced Signaling to NF-κB, MAP Kinases and Cell Death. Biochem. Pharmacol. 116, 1–10. 10.1016/j.bcp.2016.03.009 - DOI - PubMed
    1. Breyer J., Wirtz R. M., Erben P., Worst T. S., Stoehr R., Eckstein M., et al. (2018). High CDKN2A/p16 and Low FGFR3 Expression Predict Progressive Potential of Stage pT1 Urothelial Bladder Carcinoma. Clin. Genitourin. Cancer 16 (4), 248–256. 10.1016/j.clgc.2018.01.009 - DOI - PubMed
    1. Brodeur G. M., Seeger R. C., Schwab M., Varmus H. E., Bishop J. M. (1984). Amplification of N- Myc in Untreated Human Neuroblastomas Correlates with Advanced Disease Stage. Science 224 (4653), 1121–1124. 10.1126/science.6719137 - DOI - PubMed