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 22:13:906496.
doi: 10.3389/fgene.2022.906496. eCollection 2022.

Prognostic Signature and Tumor Immune Landscape of N7-Methylguanosine-Related lncRNAs in Hepatocellular Carcinoma

Wei Wei  1 , Chao Liu  2 , Meng Wang  1 , Wei Jiang  1 , Caihong Wang  3 , Shuqun Zhang  1
Affiliations

Prognostic Signature and Tumor Immune Landscape of N7-Methylguanosine-Related lncRNAs in Hepatocellular Carcinoma

Wei Wei et al. Front Genet. .

Abstract

Despite great advances in the treatment of liver hepatocellular carcinoma (LIHC), such as immunotherapy, the prognosis remains extremely poor, and there is an urgent need to develop novel diagnostic and prognostic markers. Recently, RNA methylation-related long non-coding RNAs (lncRNAs) have been demonstrated to be novel potential biomarkers for tumor diagnosis and prognosis as well as immunotherapy response, such as N6-methyladenine (m6A) and 5-methylcytosine (m5C). N7-Methylguanosine (m7G) is a widespread RNA modification in eukaryotes, but the relationship between m7G-related lncRNAs and prognosis of LIHC patients as well as tumor immunotherapy response is still unknown. In this study, based on the LIHC patients' clinical and transcriptomic data from TCGA database, a total of 992 m7G-related lncRNAs that co-expressed with 22 m7G regulatory genes were identified using Pearson correlation analysis. Univariate regression analysis was used to screen prognostic m7G-related lncRNAs, and the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to construct a 9-m7G-related-lncRNA risk model. The m7G-related lncRNA risk model was validated to exhibit good prognostic performance through Kaplan-Meier analysis and ROC analysis. Together with the clinicopathological features, the m7G-related lncRNA risk score was found to be an independent prognostic factor for LIHC. Furthermore, the high-risk group of LIHC patients was unveiled to have a higher tumor mutation burden (TMB), and their tumor microenvironment was more prone to the immunosuppressive state and exhibited a lower response rate to immunotherapy. In addition, 47 anti-cancer drugs were identified to exhibit a difference in drug sensitivity between the high-risk and low-risk groups. Taken together, the m7G-related lncRNA risk model might display potential value in predicting prognosis, immunotherapy response, and drug sensitivity in LIHC patients.

Keywords: LIHC; immune response; lncRNA; m7G methylation; prognostic model.

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
Detailed workflow of this study.
FIGURE 2
FIGURE 2
Identification of m7G-related lncRNAs in LIHC patients. (A) Heatmap showed the differences in the expression of m7G regulators between LIHC and normal groups. (B) Sankey diagram displayed the relationship between 22 m7G genes and 992 m7G-related lncRNAs.
FIGURE 3
FIGURE 3
Construction of a m7G-related lncRNA risk model for LIHC patients. (A) Forest plot showed 41 prognostic lncRNAs screened via univariate regression analysis. (B) LASSO regression of 20 m7G-related lncRNAs. (C) Cross-validation in LASSO regression. (D) Forest plot displayed 9 m7G-related lncRNAs selected by multivariate regression analysis. (E) PCA based on entire LIHC gene expression profiles in the two groups. (F) PCA based on 22 m7G regulator gene expressions in the two groups. (G) PCA based on 992 m7G-related lncRNA expressions in the two groups. (H) PCA based on 9 prognostic m7G-related lncRNA expressions in the two groups.
FIGURE 4
FIGURE 4
Prognostic value of the 9-m7G-related-lncRNA risk model between the two groups in the training set. (A) Distribution of the risk score (the x-axis represented the LIHC patients arranged according to the risk score, and the y-axis represented values of the risk score for each patient). (B) Scatter plot of survival status and risk score (the x-axis represented the LIHC patients arranged according to the risk score, and the y-axis represented the survival time of each patient). (C) Heatmap of the expression profile of the 9 m7G-related lncRNAs. (D) KM curves displayed the OS of LIHC patients between high- and low-risk groups. (E) ROC curves of the risk model of 1, 3, and 5 years for OS.
FIGURE 5
FIGURE 5
Prognostic value of the 9-m7G-related-lncRNA risk model between the two groups in the testing set. (A) Distribution of the risk score (the x-axis represented the LIHC patients arranged according to the risk score, and the y-axis represented values of the risk score for each patient). (B) Scatter plot of survival status and risk score (the x-axis represented the LIHC patients arranged according to the risk score, and the y-axis represented the survival time of each patient). (C) Heatmap of the expression profile of the 9 m7G-related lncRNAs. (D) KM curves displayed the OS of LIHC patients between high- and low-risk groups. (E) ROC curves of the risk model of 1, 3, and 5 years for OS.
FIGURE 6
FIGURE 6
Kaplan–Meier survival analysis stratified by age, gender, tumor grade, and stage between the high- and low-risk groups in the entire set. (A) Patients with age <65. (B) Patients with male gender. (C) Patients with tumor grade 1-2. (D) Patients with tumor stage Ⅰ–Ⅱ. (E) Patients with age ≥65. (F) Patients with female gender. (G) Patients with tumor grade 3-4. (H) Patients with tumor stage Ⅲ–Ⅳ.
FIGURE 7
FIGURE 7
Assessment of the independent prognostic factors and construction of a prognostic nomogram in the entire LIHC set. (A) Univariate Cox regression analysis of the clinical characteristics and risk score with the OS. (B) Multivariate Cox regression analysis of the clinical characteristics and risk score with the OS. (D) Nomogram predicting the probability of 1-, 2-, and 3-year OS. (C) ROC curves of the clinical characteristics and risk score. (E) Calibration plot of the nomogram predicting the probability of the 1-, 2-, and 3-year OS.
FIGURE 8
FIGURE 8
Evaluation of the tumor immune landscape and immunotherapy response based on the m7G-related lncRNA model in the entire LIHC set. (A,B) Waterfall plot displayed top 20 mutation genes’ information in the two risk groups. (C) KM survival analysis of OS stratified by tumor mutation burden and the m7G-related lncRNA model. (G) TIDE prediction difference between two risk score subgroups. (D) Significant KEGG pathways enriched in high-risk patients. (E) Difference in tumor infiltration immune cells based on ssGSEA scores between two risk groups. (F) Difference in immune-related functions based on ssGSEA scores between two risk score subgroups. (H) Expression of immune checkpoint blockade-related genes between the two risk groups.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Bradrick S. S., Gromeier M. (2009). Identification of Gemin5 as a Novel 7-Methylguanosine Cap-Binding Protein. PLoS One 4 (9), e7030. 10.1371/journal.pone.0007030 - DOI - PMC - PubMed
    1. Cai H., Zhang Y., Zhang H., Cui C., Li C., Lu S. (2020). Prognostic Role of Tumor Mutation Burden in Hepatocellular Carcinoma after Radical Hepatectomy. J. Surg. Oncol. 121 (6), 1007–1014. 10.1002/jso.25859 - DOI - PubMed
    1. Chen Z., Zhu W., Zhu S., Sun K., Liao J., Liu H., et al. (2021). METTL1 Promotes Hepatocarcinogenesis via m(7) G tRNA Modification-Dependent Translation Control. Clin. Transl. Med. 11 (12), e661. 10.1002/ctm2.661 - DOI - PMC - PubMed
    1. Chu J., Zhang W., Cencic R., O’Connor P. B. F., Robert F., Devine W. G., et al. (2020). Rocaglates Induce Gain-Of-Function Alterations to eIF4A and eIF4F. Cell Rep. 30 (8), 2481–2488. 10.1016/j.celrep.2020.02.002 - DOI - PMC - PubMed
    1. Dou Y., Kalmykova S., Pashkova M., Oghbaie M., Jiang H., Molloy K. R., et al. (2020). Affinity Proteomic Dissection of the Human Nuclear Cap-Binding Complex Interactome. Nucleic Acids Res. 48 (18), 10456–10469. 10.1093/nar/gkaa743 - DOI - PMC - PubMed

LinkOut - more resources