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. 2022 Jul 5:9:905897.
doi: 10.3389/fsurg.2022.905897. eCollection 2022.

A Novel Prognostic Signature Associated with Immunotherapeutic Response for Hepatocellular Carcinoma

Xinmin Jin  1 , Jinhuan Wang  2
Affiliations

A Novel Prognostic Signature Associated with Immunotherapeutic Response for Hepatocellular Carcinoma

Xinmin Jin et al. Front Surg. .

Abstract

Background: Although accumulating literature has validated that necroptosis plays a prominent role in the tumorigenesis and progression of various malignant cancer, its mechanism in hepatocellular carcinoma (HCC) is poorly understood. Therefore, in the present study, we want to study the impact of necroptosis-related genes on the prognosis and microenvironment-infiltrating immunocytes and the effect of immunotherapy on patients with HCC.

Methods: The necroptosis-related genes were obtained by reviewing the available published literature; we then evaluated the effects of the prognostic genes on the relative abundance of microenvironment infiltrated immunocytes. After construction of the Risk Score Signature, we evaluated the prognostic value and the effects on immune cells infiltrating the tumor microenvironment (TME). Combining the available data on immunotherapy, we also investigated the impact on anti-PD-L1-based immunotherapy.

Results: A comprehensive study of the published literature confirmed that 22 genes are related to necroptosis. Among them, 10 genes were related to the prognosis of the HCC cohort in The Cancer Genome Atlas (TCGA) and had a multifaceted influence on TME. We obtained the Risk Score Signature by Lasso regression. Furthermore, we also corroborated the correlation between the Risk Score Signature and tumor-infiltrating immune cells in the TME. Next, in the study of the correlation between the Signature and immunotherapy, we found that the Signature was significantly correlated with the reactivity of anti-PD-L1 immunotherapy. We also confirmed that the Risk Score Signature is a reliable and efficient independent prognostic marker of HCC.

Conclusion: We established a novel and effective prognostic model for patients with HCC, which is markedly related to the TME and immune infiltration in HCC and can also predict immunotherapeutic response and prognosis.

Keywords: hepatocellular carcinoma; immune infiltration; immunotherapy; necroptosis; prognostic signature.

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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
Necroptosis pathway (A) and its role in cancer immunity (B)
Figure 2
Figure 2
Expression and genetic mutation of necroptosis-related genes in HCC. (A and B) Expression of necroptosis-related genes in HCC. (C and D) Incidence of necroptosis-related gene mutations and their categorization in HCC.
Figure 3
Figure 3
Univariate Cox analysis, correlation analysis, and function enrichment analysis of necroptosis-related genes. (A) Forest map showing 18 necroptosis-related genes for HCC identified by univariate Cox analysis. (B) Correlation visualization matrix displaying the pairwise correlation between the 10 necroptosis-related genes. (C and D) Function enrichment analysis of necroptosis-related genes.
Figure 4
Figure 4
Characterization of TME immune cell infiltration. (A) Correlation between prognostic necroptosis-related genes and 28 TME-infiltrating cell types. (B) Relationship between prognostic necroptosis genes and immune checkpoint molecules.
Figure 5
Figure 5
(A and B) Coefficient and partial likelihood deviance of the Risk Score Signature.
Figure 6
Figure 6
Prognostic value of the Risk Score Signature. Forest plot showing that the Risk Score Signature was an independent prognostic biomarker.
Figure 7
Figure 7
Determination of cutoff.
Figure 8
Figure 8
Risk score analysis, prognostic performance, and survival analysis of the Risk Score Signature in the TCGA cohort. (A) Risk score and survival time distribution of patients and gene expression of necroptosis-related genes in the Risk Score Signature. (B and C) Overall survival curve and ROC curve of the Signature. (D) Key molecules expressed in the low- and high-risk groups.
Figure 9
Figure 9
Risk score analysis, prognostic performance, and survival analysis of the Risk Score Signature in the ICGC cohort. (A) Risk score and survival time distribution of patients as well as gene expression of necroptosis-related genes in Risk Score Signature. (B and C) Signature's overall survival and ROC curves. (D) Expression of key molecules in the low- and high-risk groups.
Figure 10
Figure 10
Establishment and evaluation of Nomograms. (A) 1-, 3-, and 5-year Nomograms for predicting OS of HCC. (B) Nomogram's calibration curves for predicting 1-, 3-, and 5-year OS in the TCGA cohort.
Figure 11
Figure 11
Role of the Risk Score Signature in the TME cell infiltration and immune checkpoint molecule. (A) Relationship between the Risk Score Signature and the infiltration of 28 TME cells. (B) Correlation between the Risk Score Signature and immune checkpoint molecule.
Figure 12
Figure 12
Distribution of key genes constituting the Risk Score Signature at the single-cell level. (A) Immune cells clustered in GSE140228. (B) Violin map and heat map of the expression of key genes in clustered immune cells. Umap map of the expression of PGAM5 (C), ADH2 (D), EZH2 (E), and CXCL1 (F) in clustered immune cells.
Figure 13
Figure 13
Role of the Risk Score Signature in immunotherapeutic responses. (A) Determination of cutoff. (B) Differences in 28 TME infiltration cells between low- and high-risk groups. (C) Relationship between the Risk Score Signature and anti-PD-L1 clinical response. (D) Proportion of patients responding to anti-PD-L1 therapy in low- or high-risk groups.
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