Construction and validation of an m6A RNA methylation regulator prognostic model for early‑stage clear cell renal cell carcinoma

Wang,Zhan; Zhang,Mingxin; Seery,Samuel; Zheng,Guoyang; Wang,Wenda; Zhao,Yang; Wang,Xu; Zhang,Yushi

doi:10.3892/ol.2022.13370

Construction and validation of an m6A RNA methylation regulator prognostic model for early‑stage clear cell renal cell carcinoma

Authors:
- Zhan Wang
- Mingxin Zhang
- Samuel Seery
- Guoyang Zheng
- Wenda Wang
- Yang Zhao
- Xu Wang
- Yushi Zhang
View Affiliations

Affiliations: Department of Urology, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, P.R. China, Department of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong 266003, P.R. China, School of Humanities and Social Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, P.R. China
Published online on: June 10, 2022 https://doi.org/10.3892/ol.2022.13370
Article Number: 250
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

N6‑methyladenosine (m6A) is the most common type of RNA methylation and is considered to participate in various biological and pathological processes, specifically in the regulation of tumorigenesis and metastasis. However, the exact prognostic role of m6A methylation regulators in early‑stage clear cell renal cell carcinoma (ccRCC) is currently unknown. In the present study, a prognostic model consisting of m6A RNA methylation regulators in early stage ccRCC was constructed and the reliability of the signature was assessed by proteomics and immunohistochemistry. Additionally, the relationship between the prognostic model and tumor infiltrating immune cells within the tumor microenvironment was investigated. Gene mutation and RNA sequencing data of 19 m6A methylation regulators for early‑stage ccRCC patients were extracted from The Cancer Genome Atlas (TCGA) database with the corresponding clinical information. Univariate and multivariate Cox regression analysis were applied to construct a prognostic model and the proteomic data as well as immunohistochemistry were used to validate the result. The correlations between the prognostic model and tumor infiltrating immune cells were assessed using Spearman's rank correlation analysis. A total of 192 early stage ccRCC gene mutation data as well as 261 RNA sequencing data with relative clinical data were extracted from the TCGA. The overall mutation frequency of the 19 m6A RNA methylation regulators was relatively low with 4.69%. The transcriptome data revealed that 11 genes were differentially expressed between cancer tissues and relatively normal tissues. Survival analysis highlighted four specific genes as having a significant influence on overall survival. An established model with four genes demonstrated the best predictability for early‑stage ccRCC. After integrating clinical characteristics into the multivariate analysis, the model remained effective at predicting ccRCC prognosis. Spearman's rank analysis suggested several tumor infiltrating immune cells such as dendric cells, CD4⁺ cells, CD8⁺ T cells and macrophages were significantly correlated with the model. Proteomic data analysis as well as immunohistochemistry from the Human Protein Atlas showed that all the genes used to construct the model were differentially expressed between ccRCC and normal tissues. In conclusion, a novel m6A methylation regulators‑based prognostic signature was established and validated with proteomics and immunohistochemistry. In addition, the model was significantly correlated with multiple infiltrating immune cells in tumor microenvironment.

View Figures

View References

1	Siegel RL, Miller KD, Fuchs HE and Jemal A: Cancer statistics, 2021. CA Cancer J Clin. 71:7–33. 2021. View Article : Google Scholar : PubMed/NCBI
2	Capitanio U, Bensalah K, Bex A, Boorjian SA, Bray F, Coleman J, Gore JL, Sun M, Wood C and Russo P: Epidemiology of renal cell carcinoma. Eur Urol. 75:74–84. 2019. View Article : Google Scholar : PubMed/NCBI
3	Obeng RC, Arnold RS, Ogan K, Master VA, Pattaras JG, Petros JA and Osunkoya AO: Molecular characteristics and markers of advanced clear cell renal cell carcinoma: Pitfalls due to intratumoral heterogeneity and identification of genetic alterations associated with metastasis. Int J Urol. 27:790–797. 2020. View Article : Google Scholar : PubMed/NCBI
4	Downs TM, Schultzel M, Shi H, Sanders C, Tahir Z and Sadler GR: Renal cell carcinoma: Risk assessment and prognostic factors for newly diagnosed patients. Crit Rev Oncol Hematol. 70:59–70. 2009. View Article : Google Scholar : PubMed/NCBI
5	Motzer RJ, Jonasch E, Agarwal N, Alva A, Baine M, Beckermann K, Carlo MI, Choueiri TK, Costello BA, Derweesh IA, et al: Kidney cancer, version 3.2022, nccn clinical practice guidelines in oncology. J Natl Compr Canc Netw. 20:71–90. 2022. View Article : Google Scholar : PubMed/NCBI
6	Li N, Kang Y, Wang L, Huff S, Tang R, Hui H, Agrawal K, Gonzalez GM, Wang Y, Patel SP and Rana TM: ALKBH5 regulates anti-PD-1 therapy response by modulating lactate and suppressive immune cell accumulation in tumor microenvironment. Proc Natl Acad Sci USA. 117:20159–20170. 2020. View Article : Google Scholar : PubMed/NCBI
7	Wu H, Xu Z, Wang Z, Ren Z, Li L and Ruan Y: Exosomes from dendritic cells with Mettl3 gene knockdown prevent immune rejection in a mouse cardiac allograft model. Immunogenetics. 72:423–430. 2020. View Article : Google Scholar : PubMed/NCBI
8	Wang L, Hui H, Agrawal K, Kang Y, Li N, Tang R, Yuan J and Rana TM: m⁶A RNA methyltransferases METTL3/14 regulate immune responses to anti-PD-1 therapy. EMBO J. 39:e1045142020. View Article : Google Scholar : PubMed/NCBI
9	Liu X, Wang P, Teng X, Zhang Z and Song S: Comprehensive analysis of expression regulation for RNA m6A regulators with clinical significance in human cancers. Front Oncol. 11:6243952021. View Article : Google Scholar : PubMed/NCBI
10	Fang J, Hu M, Sun Y, Zhou S and Li H: Expression profile analysis of m6A RNA methylation regulators indicates they are immune signature associated and can predict survival in kidney renal cell carcinoma. DNA Cell Biol. 39:2194–2211. 2020. View Article : Google Scholar
11	Wang J, Zhang C, He W and Gou X: Effect of m⁶A RNA methylation regulators on malignant progression and prognosis in renal clear cell carcinoma. Front Oncol. 10:32020. View Article : Google Scholar : PubMed/NCBI
12	Mayakonda A, Lin DC, Assenov Y, Plass C and Koeffler HP: Maftools: Efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 28:1747–1756. 2018. View Article : Google Scholar : PubMed/NCBI
13	Love MI, Huber W and Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:5502014. View Article : Google Scholar : PubMed/NCBI
14	Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC and Müller M: pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 12:772011. View Article : Google Scholar : PubMed/NCBI
15	Kassambara A, Kosinski M and Biecek P: Survminer: Drawing survival curves using ‘ggplot2’. R package version 0.4.9. 2021.https://CRAN.R-project.org/package=survminer
16	Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson A, Kampf C, Sjöstedt E, Asplund A, et al: Tissue-based map of the human proteome. Science. 347:12604192015. View Article : Google Scholar : PubMed/NCBI
17	Harrell FE Jr: Rms: Regression modeling strategies. R package version 6.2-0. 2021.https://cran.r-project.org/web/packages/rms/index.html
18	Yoshihara K, Kim H and Verhaak RG: Estimate: Estimate of stromal and immune cells in malignant tumor tissues from expression data. R package version 1.0.13/r21. 2016.https://r-forge.r-project.org/projects/estimate/
19	Shen C, Xuan B, Yan T, Ma Y, Xu P, Tian X, Zhang X, Cao Y, Ma D, Zhu X, et al: m⁶A-dependent glycolysis enhances colorectal cancer progression. Mol Cancer. 19:722020. View Article : Google Scholar : PubMed/NCBI
20	Wang T, Kong S, Tao M and Ju S: The potential role of RNA N6-methyladenosine in cancer progression. Mol Cancer. 19:882020. View Article : Google Scholar : PubMed/NCBI
21	Wojciechowski P, Lipowska A, Rys P, Ewens KG, Franks S, Tan S, Lerchbaum E, Vcelak J, Attaoua R, Straczkowski M, et al: Impact of FTO genotypes on BMI and weight in polycystic ovary syndrome: A systematic review and meta-analysis. Diabetologia. 55:2636–2645. 2012. View Article : Google Scholar : PubMed/NCBI
22	Zarza-Rebollo JA, Molina E and Rivera M: The role of the FTO gene in the relationship between depression and obesity. A systematic review. Neurosci Biobehav Rev. 127:630–637. 2021. View Article : Google Scholar : PubMed/NCBI
23	Qiu Y, Wang X, Fan Z, Zhan S, Jiang X and Huang J: Integrated analysis on the N6-methyladenosine-related long noncoding RNAs prognostic signature, immune checkpoints, and immune cell infiltration in clear cell renal cell carcinoma. Immun Inflamm Dis. 9:1596–1612. 2021. View Article : Google Scholar : PubMed/NCBI
24	Xu T, Gao S, Ruan H, Liu J, Liu Y, Liu D, Tong J, Shi J, Yang H, Chen K and Zhang X: METTL14 Acts as a potential regulator of tumor immune and progression in clear cell renal cell carcinoma. Front Genet. 12:6091742021. View Article : Google Scholar : PubMed/NCBI
25	Mu Z, Dong D, Sun M, Li L, Wei N and Hu B: Prognostic value of YTHDF2 in clear cell renal cell carcinoma. Front Oncol. 10:15662020. View Article : Google Scholar : PubMed/NCBI
26	Lan Q, Liu PY, Haase J, Bell JL, Hüttelmaier S and Liu T: The critical role of RNA m⁶A methylation in cancer. Cancer Res. 79:1285–1292. 2019. View Article : Google Scholar : PubMed/NCBI
27	Zhang C, Huang S, Zhuang H, Ruan S, Zhou Z, Huang K, Ji F, Ma Z, Hou B and He X: YTHDF2 promotes the liver cancer stem cell phenotype and cancer metastasis by regulating OCT4 expression via m6A RNA methylation. Oncogene. 39:4507–4518. 2020. View Article : Google Scholar : PubMed/NCBI
28	Hou J, Zhang H, Liu J, Zhao Z, Wang J, Lu Z, Hu B, Zhou J, Zhao Z, Feng M, et al: YTHDF2 reduction fuels inflammation and vascular abnormalization in hepatocellular carcinoma. Mol Cancer. 18:1632019. View Article : Google Scholar : PubMed/NCBI
29	Fang R, Chen X, Zhang S, Shi H, Ye Y, Shi H, Zou Z, Li P, Guo Q, Ma L, et al: EGFR/SRC/ERK-stabilized YTHDF2 promotes cholesterol dysregulation and invasive growth of glioblastoma. Nat Commun. 12:1772021. View Article : Google Scholar : PubMed/NCBI
30	Xie H, Li J, Ying Y, Yan H, Jin K, Ma X, He L, Xu X, Liu B, Wang X, et al: METTL3/YTHDF2 m(6)A axis promotes tumorigenesis by degrading SETD7 and KLF4 mRNAs in bladder cancer. J Cell Mol Med. 24:4092–4104. 2020. View Article : Google Scholar : PubMed/NCBI
31	Edens BM, Vissers C, Su J, Arumugam S, Xu Z, Shi H, Miller N, Ringeling FR, Ming GL, He C and Song H: FMRP modulates neural differentiation through m(6)a-dependent mRNA nuclear export. Cell Rep. 28:845–854.e845. 2019. View Article : Google Scholar : PubMed/NCBI
32	Hsu PJ, Shi H, Zhu AC, Lu Z, Miller N, Edens BM, Ma YC and He C: The RNA-binding protein FMRP facilitates the nuclear export of N⁶-methyladenosine-containing mRNAs. J Biol Chem. 294:19889–19895. 2019. View Article : Google Scholar : PubMed/NCBI
33	Zhang F, Kang Y, Wang M, Li Y, Xu T, Yang W, Song H, Wu H, Shu Q and Jin P: Fragile X mental retardation protein modulates the stability of its m6A-marked messenger RNA targets. Hum Mol Genet. 27:3936–3950. 2018.PubMed/NCBI
34	Liu L, Wang Y, Wu J, Liu J, Qin Z and Fan H: N ⁶-Methyladenosine: A potential breakthrough for human cancer. Mol Ther Nucleic Acids. 19:804–813. 2020. View Article : Google Scholar : PubMed/NCBI
35	Huang H, Weng H, Zhou K, Wu T, Zhao BS, Sun M, Chen Z, Deng X, Xiao G, Auer F, et al: Histone H3 trimethylation at lysine 36 guides m⁶A RNA modification co-transcriptionally. Nature. 567:414–419. 2019. View Article : Google Scholar : PubMed/NCBI
36	Zhang C, Chen L, Liu Y, Huang J, Liu A, Xu Y, Shen Y, He H and Xu D: Downregulated METTL14 accumulates BPTF that reinforces super-enhancers and distal lung metastasis via glycolytic reprogramming in renal cell carcinoma. Theranostics. 11:3676–3693. 2021. View Article : Google Scholar : PubMed/NCBI
37	Yang F, Hu A, Li D, Wang J, Guo Y, Liu Y, Li H, Chen Y, Wang X, Huang K, et al: Circ-HuR suppresses HuR expression and gastric cancer progression by inhibiting CNBP transactivation. Mol Cancer. 18:1582019. View Article : Google Scholar : PubMed/NCBI
38	Ronkainen H, Vaarala MH, Hirvikoski P and Ristimäki A: HuR expression is a marker of poor prognosis in renal cell carcinoma. Tumour Biol. 32:481–487. 2011. View Article : Google Scholar : PubMed/NCBI
39	Danilin S, Sourbier C, Thomas L, Rothhut S, Lindner V, Helwig JJ, Jacqmin D, Lang H and Massfelder T: Von hippel-lindau tumor suppressor gene-dependent mRNA stabilization of the survival factor parathyroid hormone-related protein in human renal cell carcinoma by the RNA-binding protein HuR. Carcinogenesis. 30:387–396. 2009. View Article : Google Scholar : PubMed/NCBI
40	Danilin S, Sourbier C, Thomas L, Lindner V, Rothhut S, Dormoy V, Helwig JJ, Jacqmin D, Lang H and Massfelder T: Role of the RNA-binding protein HuR in human renal cell carcinoma. Carcinogenesis. 31:1018–1026. 2010. View Article : Google Scholar : PubMed/NCBI