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 Aug 8:13:866819.
doi: 10.3389/fgene.2022.866819. eCollection 2022.

Comprehensive analysis of 7-methylguanosine and immune microenvironment characteristics in clear cell renal cell carcinomas

Yu Xiao  1   2 , Junfeng Yang  1   2 , Maolin Yang  1   2 , Jinjun Len  1   2 , Yanhong Yu  1   2
Affiliations

Comprehensive analysis of 7-methylguanosine and immune microenvironment characteristics in clear cell renal cell carcinomas

Yu Xiao et al. Front Genet. .

Abstract

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. ccRCC has obvious immunological characteristics, and the infiltration of immune cells is related to the prognosis of ccRCC. The effect of immune checkpoint therapy is related to the dynamic changes of the tumor immune microenvironment (TIM). The 7-methylguanosine (m7G) is an additional mRNA modification ability besides m6A, which is closely related to the TIM and affects the occurrence and development of tumors. At present, the correlations between m7G and the immune microenvironment, treatment, and prognosis of ccRCC are not clear. As far as we know, there was no study on the relationship between m7G and the immune microenvironment and survival of clear cell renal cell carcinomas. A comprehensive analysis of the correlations between them and the construction of a prognosis model are helpful to improve the treatment strategy. Two different molecular subtypes were identified in 539 ccRCC samples by describing the differences of 29 m7G-related genes. It was found that the clinical features, TIM, and prognosis of ccRCC patients were correlated with the m7G-related genes. We found that there were significant differences in the expression of PD-1, CTLA4, and PD-L1 between high- and low-risk groups. To sum up, m7G-related genes play a potential role in the TIM, treatment, and prognosis of ccRCC. Our results provide new findings for ccRCC and help to improve the immunotherapy strategies and prognosis of patients.

Keywords: clear cell renal cell carcinomas; immune checkpoints; immunotherapy; m7G; tumor immune microenvironment.

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
Variation of m7G-related genes in ccRCC. (A) Genetic alteration on a query of m7G-related genes. (B) The frequency of CNV in m7G-related genes. (C) The location of the CNV alteration of the m7G-related genes changes on 23 chromosomes. (D) Gene expression of m7G-related genes in ccRCC and normal samples. *p < 0.05, **p < 0.01, ***p < 0.001; ccRCC, clear cell renal cell carcinoma.
FIGURE 2
FIGURE 2
Characteristics of m7G subtypes. (A) The correlations between m7G-related genes; the line indicates that the two were related, the red line represented the positive correlation, the blue line represented the negative correlation, and the size of the node represented the prognostic correlation. (B) M7G-related genes were divided into two subtypes. (C) PCA analysis showed that there were significant differences among different subtypes. (D) Survival analysis of different subtypes. (E) The expression of m7G-related genes in different subtypes and the clinical characteristics of the subtypes. PCA, principal components analysis.
FIGURE 3
FIGURE 3
M7G subtype enrichment analysis. (A) GSVA enrichment analysis of different subtypes, blue and red represented inhibition and activation pathways, respectively. (B) Difference in immune infiltration of m7G subtypes. (C,D) GO and KEGG enrichment analysis. GSVA, gene set variation analysis; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.
FIGURE 4
FIGURE 4
Characteristics of prognosis-related gene subtypes. (A) Prognosis-related genes were divided into three subtypes. (B) Survival analysis of prognosis-related gene subtypes. (C) The expression of prognosis-related gene subtypes and the clinical characteristics of the subtypes. (D) Correlations between prognosis-related gene subtypes and m7G-related genes. (E) The Sankey diagram in the process of constructing a prognostic model. *p < 0.05, **p < 0.01, ***p < 0.001.
FIGURE 5
FIGURE 5
Verification of prognostic model. (A) Differential analysis of risk scores for prognosis-related gene subtypes. (B) Differential analysis of risk scores for m7G subtypes. (C) Expression difference in m7G-related genes in high- and low-risk groups. (D) From left to right, the patients in the train group, the test group, and all patients analyzed for survival. (E) From left to right, the patients in the train group, the test group, and all patients analyzed for ROC curves. ROC, receiver operating characteristic.
FIGURE 6
FIGURE 6
Construction of nomogram, the correlations between the m7G score and patient survival status. (A) Nomogram predicted 1, 3, and 5-year overall survival of ccRCC patients. (B) Calibration curves of the nomogram for predicting of 1-, 3-, and 5-year overall survival of ccRCC patients. (C–E) Distribution of the m7G score among ccRCC patients, correlations between the m7G score and patient survival status, risk heat map of m7G score genes, from left to right were the patients in the training group, the patients in the test group and the patients in all groups.
FIGURE 7
FIGURE 7
The correlations between the m7G score and TIM. (A) Correlations between m7G score genes and abundance of immune cells. (B) Correlations between the m7G score and immune cell types. (C) The correlations between high- and low-risk group patients and TIM. TIM, tumor immune microenvironment.
FIGURE 8
FIGURE 8
M7G score and ccRCC clinicopathological features. (A) Correlation between the m7G score and ccRCC clinicopathological features. (B) Correlation between the TNM stage survival and the M7G score.
FIGURE 9
FIGURE 9
Mutation and immune checkpoint therapy. (A) Somatic mutation features of ccRCC patients in high- and low-risk groups, with colors representing different mutation types. (B,C) Correlations between the m7G score and tumor mutation burden. (D,E) Survival difference of patients with different TMB. (F) Correlations between the m7G score and RNAs. (G) Expression levels of PD-1, PD-L1 and CTLA4 in high- and low-risk groups. (H) Analysis of the m7G score in anti-PD-L1 and CTLA-4 immunotherapy.
See this image and copyright information in PMC

References

    1. Albiges L., Tannir N. M., Burotto M., McDermott D., Plimack E. R., Barthelemy P., et al. (2020). Nivolumab plus ipilimumab versus sunitinib for first-line treatment of advanced renal cell carcinoma: extended 4-year follow-up of the phase III CheckMate 214 trial. ESMO Open 5 (6), e001079. 10.1136/esmoopen-2020-001079 - DOI - PMC - PubMed
    1. Antonelli A., Minervini A., Sandri M., Bertini R., Bertolo R., Carini M., et al. (2018). Below safety limits, every unit of glomerular filtration rate counts: assessing the relationship between renal function and cancer-specific mortality in renal cell carcinoma. Eur. Urol. 74 (5), 661–667. 10.1016/j.eururo.2018.07.029 - DOI - PubMed
    1. Arbour K. C., Riely G. J. (2019). Systemic therapy for locally advanced and metastatic non-small cell lung cancer: a review. JAMA 322 (8), 764–774. 10.1001/jama.2019.11058 - DOI - PubMed
    1. Bedke J., Albiges L., Capitanio U., Giles R. H., Hora M., Lam T. B., et al. (2021). Updated European association of urology guidelines on renal cell carcinoma: nivolumab plus cabozantinib joins immune checkpoint inhibition combination therapies for treatment-naive metastatic clear-cell renal cell carcinoma. Eur. Urol. 79 (3), 339–342. 10.1016/j.eururo.2020.12.005 - DOI - PubMed
    1. Biswas S. K., Sica A., Lewis C. E. (2008). Plasticity of macrophage function during tumor progression: regulation by distinct molecular mechanisms. J. Immunol. 180 (4), 2011–2017. 10.4049/jimmunol.180.4.2011 - DOI - PubMed