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. 2021 Sep 15:11:706915.
doi: 10.3389/fonc.2021.706915. eCollection 2021.

A Novel Nine-lncRNA Risk Signature Correlates With Immunotherapy in Hepatocellular Carcinoma

Ye Nie  1   2 , Jianhui Li  1   2 , Wenlong Wu  2 , Dongnan Guo  3 , Xinjun Lei  1   2 , Tianchen Zhang  1   2 , Yanfang Wang  1   2 , Zhenzhen Mao  1   2 , Xuan Zhang  2 , Wenjie Song  2
Affiliations

A Novel Nine-lncRNA Risk Signature Correlates With Immunotherapy in Hepatocellular Carcinoma

Ye Nie et al. Front Oncol. .

Abstract

Background: Hepatocellular carcinoma is one of the most common malignant tumors with a very high mortality rate. The emergence of immunotherapy has brought hope for the cure of hepatocellular carcinoma. Only a small number of patients respond to immune checkpoint inhibitors, and ferroptosis and tertiary lymphoid structure contribute to the increased response rate of immune checkpoint inhibitors; thus, we first need to identify those who are sensitive to immunotherapy and then develop different methods to improve sensitivity for different groups.

Methods: The sequencing data of hepatocellular carcinoma from The Cancer Genome Atlas and Gene Expression Omnibus was downloaded to identify the immune-related long non-coding RNAs (lncRNAs). LncRNAs related to survival data were screened out, and a risk signature was established using Cox proportional hazard regression model. R software was used to calculate the riskScore of each patient, and the patients were divided into high- and low-risk groups. The prognostic value of riskScore and its application in clinical chemotherapeutic drugs were confirmed. The relationship between riskScore and immune checkpoint genes, ferroptosis genes, and genes related to tertiary lymphoid structure formation was analyzed by Spearman method. TIMER, CIBERSORT, ssGSEA, and ImmuCellAI were used to evaluate the relative number of lymphocytes in tumor. The Wilcoxon signed-rank test confirmed differences in immunophenoscore between the high- and low-risk groups.

Results: Data analysis revealed that our signature could well predict the 1-, 2-, 3-, and 5-year survival rates of hepatocellular carcinoma and to predict susceptible populations with Sorafenib. The risk signature were significantly correlated with immune checkpoint genes, ferroptosis genes, and tertiary lymphoid structure-forming genes, and predicted tumor-infiltrating lymphocyte status. There was a significant difference in IPS scores between the low-risk group and the high-risk group, while the low-risk group had higher scores.

Conclusion: The riskScore obtained from an immune-related lncRNA signature could successfully predict the survival time and reflect the efficacy of immune checkpoint inhibitors. More importantly, it is possible to select different treatments for different hepatocellular carcinoma patients that increase the response rate of immune checkpoint inhibitors and will help improve the individualized treatment of hepatocellular carcinoma.

Keywords: ferroptosis; hepatocellular carcinoma; immunotherapy response; prognosis; tertiary lymphoid structure; tumor-infiltrating lymphocytes.

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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
(A) The lncRNAs used to build the signature and the ability of the signature to predict the survival time. (B) LncRNAs associated with survival time. (C) The predictive ability of the signature for 1, 2, 3, and 5 years.
Figure 2
Figure 2
The signature of hepatocellular carcinoma (HCC) from the training set. (A) Kaplan–Meier survival curves, (B) multi-index ROC curves, (C) risk curves, (D) survival status distribution map, and (E) heatmap of the risk gene expression profiles.
Figure 3
Figure 3
The signature of HCC from the testing set. (A) Kaplan–Meier survival curves, (B) multi-index ROC curves, (C) risk curves, (D) survival status distribution map, and (E) heatmap of the risk genes expression profiles.
Figure 4
Figure 4
The signature of HCC from the external validation set. (A) Kaplan–Meier survival curves, (B) multi-index ROC curves, (C) risk curves, (D) survival status distribution map, and (E) heatmap of the risk genes expression profiles.
Figure 5
Figure 5
(A) Survival curve of early patients and (B) survival curve of advanced patients.
Figure 6
Figure 6
Clinical Evaluation by the Signature. A strip chart (A) along with the scatter diagram showed that survival status (B), gender (C), tumor grade (D), clinical stage (E), and T stage (F) were significantly associated with the riskScore (P <0.001 = ***, P <0.01 = **, and P <0.05 = *).
Figure 7
Figure 7
Protein–protein interaction networks and GSEA indicates the enrichment of significant pathways. (A) Protein–protein interaction networks. Each dot represents a protein molecule, and the connection between the dots means that the two molecules interact with each other. (B) Significant immune-related pathways enriched by GSEA.
Figure 8
Figure 8
Enrichment analysis. (A, B) The significantly enriched GO terms and (C, D) KEGG pathways. The abscissa indicates the number and ratio of genes enriched in the pathway.
Figure 9
Figure 9
The signature can be used as a potential indicator to predict the sensitivity to sorafenib, and the sorafenib IC50 was higher in the high-risk group.
Figure 10
Figure 10
The signature was used to evaluate tumor-infiltrating lymphocytes, immunophenoscore (IPS), and immune checkpoint genes. (A) Spearman correlation analysis was used to calculate the correlation between the riskScore and the number of tumor-infiltrating lymphocytes. (B) Correlation heatmap of 22 tumor-infiltrating lymphocytes. (C) Analysis of the correlation between the riskScore and immune checkpoint genes. (D) The IPS, IPS-PD1 blocker, IPS–CTLA4 blocker, and IPS–PD1–CTLA4 blocker values are higher in the low risk group. (P <0.001 = *** and P <0.01 = **).
Figure 11
Figure 11
(A) Correlation between signature and genes related to tertiary lymphoid structure formation was calculated by Spearman. (B) Correlation between riskScore and ferroptosis-related genes was calculated by Spearman. (C) Ferroptosis-related gene univariate Cox regression analysis.
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