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. 2022 Apr 8:13:856186.
doi: 10.3389/fimmu.2022.856186. eCollection 2022.

Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma

Jinfeng Zhu  1   2 , Qian Huang  3 , Sicheng Liu  1   2 , Xingyu Peng  1   2 , Ju Xue  4 , Tangbin Feng  5 , Wulang Huang  6 , Zhimeng Chen  1 , Kuiyuan Lai  1 , Yufei Ji  7 , Miaomiao Wang  8 , Rongfa Yuan  1
Affiliations

Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma

Jinfeng Zhu et al. Front Immunol. .

Abstract

Background: Genomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC.

Methods: Combining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program.

Results: Five GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC.

Conclusion: Our research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.

Keywords: genomic instability; hepatocellular carcinoma; immune infiltration; long non-coding RNAs; prognosis; signature; tumor immune activity.

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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
Flow Chart of This Study.
Figure 2
Figure 2
Screening and Validation of the GILncRNAs. (A) Genome instability-related lncRNAs (GILncRNAs) were filtered out through differential analysis of the expression levels between the genomic unstable (GU) and the genomic stable (GS) groups. Among them, 10 GILncRNAs with the most obvious up-regulation and 10 GILncRNAs with the most obvious down-regulation were presented in the form of heat maps. (B) All GILncRNA samples were divided into two clusters using the clustering algorithm: one containing the samples with a large number of gene mutations, the genomic unstable-like (GU-like) cluster, and another containing the samples with a small number of gene mutations, the genomic stable-like (GS-like) cluster. (C) Comparison of the somatic mutations in the GU-like and GS-like clusters. (D) Comparison of the NOD2 expression level in the GU-like and GS-like clusters.
Figure 3
Figure 3
Construction of GILncSig and Validation of its Prognostic Prediction Performance. (A) Prognosis-related GIlncRNAs from the training set, screened using univariate Cox regression analysis. (B–D) Differences in the overall survival(OS) of HCC patients between the high- and low-risk groups, and 1-,3-,5-year ROC curve of risk scores, in the training (B), testing (C), and TCGA sets (D). (E–G) Differences in the Disease-free survival (DFS) (E), Disease-specific survival (DSS) (F) and Progression-Free Survival (PFS) (G) of HCC patients between the high- and low-risk groups, and 1-,3-,5-year ROC curve of risk scores, in the TCGA set.
Figure 4
Figure 4
Analysis of Mutation Correlation. (A–C) Risk curve consisting of a heat map, mutation point plot, and gene expression plot in the training (A), testing (B), and TCGA sets (C). (D, F, H) Differences in the number of somatic mutations between the high- and low-risk groups in the training (D), testing (F), and TCGA sets (H). (E, G, I) Differences in the NOD2 expression level between the high- and low-risk groups in the training (E), testing (G), and TCGA sets (I). (J) Comparison of the proportion of the TP53 mutation status in the high- and low-risk groups in the training, testing, and TCGA sets. (K) Results of combined survival analysis of TP53 in the different gene states and different clusters.
Figure 5
Figure 5
Independent Prognostic Analysis of GILncSig. The Cox regression analysis of the risk score in the training set-OS (A), testing set-OS (B), TCGA set-OS (C), TCGA set-DFS (D), TCGA set-DSS (E) and TCGA set-PFS (F). Among them, blue represents univariate Cox regression analysis, and red represents multivariate Cox regression analysis.
Figure 6
Figure 6
Clinical Stratification Analysis, Performance Comparison and ICGC Cohort Validation of GILncSig. (A, B) Comparison of the OS between the high- and low-risk groups of patients who are <= 65 or > 65 years (A), with a grade of G1-2 or G3-4 (B). (C–E) Comparison of the area under ROC curve of the 1- (C), 2- (D), and 3-year (E) OS between GILncSig of this study and other prognostic signatures in the HCC patients. (F) Validation of the relationship between the expression of MIR210HG in GILncSig and OS in the ICGC database.
Figure 7
Figure 7
Adverse Effects of LUCTA1 on HCC in vitro. (A) ENCORI server analyzed the expression of the LUCAT1 gene in HCC. (B) Kaplan–Meier curve of the expression level of LUCAT1 on HCC patients using ENCORI. (C) qRT-PCR analysis of LUCAT1 mRNA levels in HCC tissues and corresponding adjacent tissues (n=37). (D) The efficiency of knockdown of LUCAT1 expression in MHCC97H cells was verified by qRT-PCR. (E) After LUCAT1 silencing, the cell viability of MHCC97H was significantly inhibited by the CCK- 8 assay. (F) Compared with the control group, the proliferation rate of MHCC97H cells was significantly inhibited after LUCAT1 silencing by EdU staining. (G) Transwell experiments showed that the migratory ability of MHCC97H was inhibited after LCUAT1 silencing. (H) Wound healing array showed that LUCAT1-downregulated MHCC97H cells exhibited significantly delayed wound healing compared with controls. Scale bar: 50μm, *p <0.05, **p <0.01, ***p <0.001.
Figure 8
Figure 8
Exploration of the Possible Functions and Pathways of GILncSig. (A) LncRNA-mRNA co-expression network diagram. (B) Analysis of the Cellular Component (CC) terms of Gene Ontology (GO) enrichment demonstrated the possible function of the genome instability-related lncRNA signature (GILncSig). (C) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed the possible pathways associated with genomic instability (GI).
Figure 9
Figure 9
Assessment of Tumor Microenvironment, Immune Cell Infiltration and Immune Checkpoint Genes in Different Groups. (A-C) Comparison of ESTIMATE score (A), stromal score (B), and immune score (C) between the high- and low-risk groups. (D) Differences in the infiltration of immune cells between the high- and low-risk groups. (E) Differential expression of immune checkpoint genes between the high- and low-risk groups. *p<0.05, **p<0.01, ***p<0.001; ns, not statistically different.
Figure 10
Figure 10
GILncSig predicts chemotherapy and immunotherapy response. (A–G) The signature showed high-risk scores were associated with a lower IC50 for chemotherapeutics such as (A) bortezomib, (B) gemcitabine, (C) imatinib, and (D) paclitaxel, whereas they were related to a higher IC50 for (E) axitinib, (F) docetaxel, and (G) lapatinib. (H) Differences in TIDE score between high- and low-risk groups. (I) Different expression of LINC00221 predicts response to anti-PD-1 immune checkpoint inhibition therapy in HCC patients. (J) Effects of with or without inhibition of LUCAT1 expression on PD-L1 protein expression levels by western blotting. **p<0.01,***p<0.001.
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