Predictive value of m6A regulators in prognosis and immunotherapy response of clear cell renal cell carcinoma: a bioinformatics and radiomics analysis

Abstract

Aim: This study aimed to develop an m6A-related gene signature for predicting the prognosis of clear cell renal cell carcinoma (ccRCC) patients and explore its value in predicting the immunotherapy response.

Methods: In total, 530 ccRCC patients with gene expression data in the TCGA cohort were included and classified into the training (n = 371) and validation (n = 159) sets. Differential expression analyses of 23 m6A regulators between survivors and non-survivors were performed. Subsequently, an m6A-related gene signature was developed via LASSO Cox regression. All patients were categorized into two groups of m6A subtypes, i.e., low or high m6A score group. The Kaplan-Meier survival curves and Tumor Immune Dysfunction and Exclusion (TIDE) scores of the two m6A subtype groups were compared to measure the gene signature’s predictive value in prognosis and potential immunotherapy response, respectively.

Results: Eighteen m6A regulators were significantly differentially expressed between the survivors and non-survivors, and were also related to overall survival (OS). A gene signature containing five selected m6A methylation regulators (KIAA1429, METTL14, IGF2BP2, IGF2BP3, and SRSF2) was developed and showed favorable discrimination in the training (C-index 0.708) and validation (C-index 0.689) sets. Patients with low m6A scores had significantly better OS and lower TIDE scores than those with high m6A scores. Moreover, a robust MRI-based radiomic signature was developed to noninvasively predict the m6A subtype for each patient.

Conclusion: We demonstrated the prognostic value of five m6A regulators and constructed a gene signature for prognosis and immunotherapy response prediction among ccRCC patients. In addition, a radiomic signature was developed for noninvasive prediction of the m6A subtype. These findings may promote precision medicine and provide novel insights into the regulation of tumor immune microenvironment.

Keywords

Clear cell renal cell carcinoma / m6A / prognosis / immunotherapy response / tumor immune microenvironment / prediction model / radiomics

References

[1] Rini BI,Escudier B.Renal cell carcinoma.Lancet2009;373:1119-32

[2] Ljungberg B,Canfield S.EAU guidelines on renal cell carcinoma: 2014 update.Eur Urol2015;67:913-24

[3] Wolff I,Hoschke B.Do we need new high-risk criteria for surgically treated renal cancer patients to improve the outcome of future clinical trials in the adjuvant setting? Results of a comprehensive analysis based on the multicenter CORONA database.Eur J Surg Oncol2016;42:744-50

[4] Motzer RJ,McCann L,Choueiri TK.Overall survival in renal-cell carcinoma with pazopanib versus sunitinib.N Engl J Med2014;370:1769-70

[5] Aweys H,Sheriff M.Renal cell cancer - insights in drug resistance mechanisms.Anticancer Res2023;43:4781-92

[6] Hsieh JJ,Signoretti S.Renal cell carcinoma.Nat Rev Dis Primers2017;3:17009 PMCID:PMC5936048

[7] Choueiri TK.Systemic therapy for metastatic renal-cell carcinoma.N Engl J Med2017;376:354-66

[8] Motzer RJ,McDermott DF.Nivolumab versus everolimus in advanced renal-cell carcinoma.N Engl J Med2015;373:1803-13

[9] Brown JE,Gregory W.Temporary treatment cessation versus continuation of first-line tyrosine kinase inhibitor in patients with advanced clear cell renal cell carcinoma (STAR): an open-label, non-inferiority, randomised, controlled, phase 2/3 trial.Lancet Oncol2023;24:213-27

[10] Yang Y,Chen YS.Dynamic transcriptomic m⁶A decoration: writers, erasers, readers and functions in RNA metabolism.Cell Res2018;28:616-24 PMCID:PMC5993786

[11] Paramasivam A,Raghunandhakumar S.Implications of m6A modification in autoimmune disorders.Cell Mol Immunol2020;17:550-1 PMCID:PMC7192904

[12] Paramasivam A.Novel insights into m6A modification in circular RNA and implications for immunity.Cell Mol Immunol2020;17:668-9 PMCID:PMC7264242

[13] Paramasivam A,Raghunandhakumar S.N6-adenosine methylation (m6A): a promising new molecular target in hypertension and cardiovascular diseases.Hypertens Res2020;43:153-4

[14] Fu Y,Rechavi G.Gene expression regulation mediated through reversible m⁶A RNA methylation.Nat Rev Genet2014;15:293-306

[15] Tong J,Zhang T.m⁶A mRNA methylation sustains Treg suppressive functions.Cell Res2018;28:253-6 PMCID:PMC5799823

[16] Pinello N,Wong JJ.Aberrant expression of enzymes regulating m⁶A mRNA methylation: implication in cancer.Cancer Biol Med2018;15:323-34 PMCID:PMC6372906

[17] Guo L,Zhou C,Huang L.N6-methyladenosine RNA modification in the tumor immune microenvironment: novel implications for immunotherapy.Front Immunol2021;12:773570 PMCID:PMC8696183

[18] Wang P,Zheng L.Gene signatures and prognostic values of m6A regulators in hepatocellular carcinoma.Front Genet2020;11:540186 PMCID:PMC7567013

[19] Zhao R,Zhang S.The N⁶-methyladenosine-modified pseudogene HSPA7 correlates with the tumor microenvironment and predicts the response to immune checkpoint therapy in glioblastoma.Front Immunol2021;12:653711 PMCID:PMC8329659

[20] Xu W,Liu W.m⁶A regulator-mediated methylation modification model predicts prognosis, tumor microenvironment characterizations and response to immunotherapies of clear cell renal cell carcinoma.Front Oncol2021;11:709579 PMCID:PMC8290143

[21] Tibshirani R.Regression shrinkage and selection via the lasso: a retrospective.J R Stat Soc Series B2011;73:273-82

[22] Camp RL,Rimm DL.X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization.Clin Cancer Res2004;10:7252-9

[23] Jiang P,Pan D.Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response.Nat Med2018;24:1550-8 PMCID:PMC6487502

[24] Chen B,Liu CL,Alizadeh AA.Profiling tumor infiltrating immune cells with CIBERSORT. In: von Stechow L, editor. Cancer Systems Biology. New York: Springer; 2018. pp. 243-59. PMCID:PMC5895181

[25] Lambin P,Deist TM.Radiomics: the bridge between medical imaging and personalized medicine.Nat Rev Clin Oncol2017;14:749-62

[26] Zheng J,Batur J.A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning.Kidney Int2021;100:870-80

[27] Cai J,Shen J.A radiomics model for predicting the response to bevacizumab in brain necrosis after radiotherapy.Clin Cancer Res2020;26:5438-47

[28] Zheng J,Wu S.Development of a noninvasive tool to preoperatively evaluate the muscular invasiveness of bladder cancer using a radiomics approach.Cancer2019;125:4388-98

[29] van Griethuysen JJM,Parmar C.Computational radiomics system to decode the radiographic phenotype.Cancer Res2017;77:e104-7 PMCID:PMC5672828

[30] Cao G,Yin Z.Recent advances in dynamic m6A RNA modification.Open Biol2016;6:160003 PMCID:PMC4852458

[31] Wang X,Xue Y.Structural basis of N⁶-adenosine methylation by the METTL3-METTL14 complex.Nature2016;534:575-8

[32] Liu J,Han D.A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation.Nat Chem Biol2014;10:93-5 PMCID:PMC3911877

[33] Gundert L,von Hagen F.Systematic expression analysis of m⁶A RNA methyltransferases in clear cell renal cell carcinoma.BJUI Compass2021;2:402-11 PMCID:PMC8988738

[34] Xu T,Ruan H.METTL14 acts as a potential regulator of tumor immune and progression in clear cell renal cell carcinoma.Front Genet2021;12:609174 PMCID:PMC8194313

[35] Cui Q,Ye P.m⁶A RNA methylation regulates the self-renewal and tumorigenesis of glioblastoma stem cells.Cell Rep2017;18:2622-34 PMCID:PMC5479356

[36] Weng H,Wu H.METTL14 inhibits hematopoietic stem/progenitor differentiation and promotes leukemogenesis via mRNA m⁶A modification.Cell Stem Cell2018;22:191-205.e9 PMCID:PMC5860916

[37] Xie W,Guo J,Song X.Physiological functions of Wilms’ tumor 1-associating protein and its role in tumourigenesis.J Cell Biochem2019;120:10884-92

[38] Qu N,Zhang X.Multiple m⁶A RNA methylation modulators promote the malignant progression of hepatocellular carcinoma and affect its clinical prognosis.BMC Cancer2020;20:165 PMCID:PMC7047390

[39] Qian JY,Sun X.KIAA1429 acts as an oncogenic factor in breast cancer by regulating CDK1 in an N6-methyladenosine-independent manner.Oncogene2019;38:6123-41

[40] Li J,Liang C,Li P.The analysis of N6-methyladenosine regulators impacting the immune infiltration in clear cell renal cell carcinoma.Med Oncol2022;39:41

[41] Xing Q,Liu S,Wang Y.Six RNA binding proteins (RBPs) related prognostic model predicts overall survival for clear cell renal cell carcinoma and is associated with immune infiltration.Bosn J Basic Med Sci2022;22:435-52 PMCID:PMC9162755

[42] Xu X,Zong K,Gu Y.Up-regulation of IGF2BP2 by multiple mechanisms in pancreatic cancer promotes cancer proliferation by activating the PI3K/Akt signaling pathway.J Exp Clin Cancer Res2019;38:497 PMCID:PMC6921559

[43] Long JC.The SR protein family of splicing factors: master regulators of gene expression.Biochem J2009;417:15-27

[44] Merdzhanova G,De Seranno S.E2F1 controls alternative splicing pattern of genes involved in apoptosis through upregulation of the splicing factor SC35.Cell Death Differ2008;15:1815-23

[45] Haupt S,Goldberg Z.Apoptosis - the p53 network.J Cell Sci2003;116:4077-85

[46] Kędzierska H,Hoser G.Decreased expression of SRSF2 splicing factor inhibits apoptotic pathways in renal cancer.Int J Mol Sci2016;17:1598 PMCID:PMC5085631

[47] Garner E,Tschopp J,Raj K.Cells with defective p53-p21-pRb pathway are susceptible to apoptosis induced by p84N5 via caspase-6.Cancer Res2007;67:7631-7

[48] Mercer J,Stoneman V,Bennett MR.Endogenous p53 protects vascular smooth muscle cells from apoptosis and reduces atherosclerosis in ApoE knockout mice.Circ Res2005;96:667-74

[49] Amin AR,Gupta K.Restoration of p53 functions protects cells from concanavalin A-induced apoptosis.Mol Cancer Ther2010;9:471-9

[50] Haitel A,Baethge U,Susani M.mdm2 expression as a prognostic indicator in clear cell renal cell carcinoma: comparison with p53 overexpression and clinicopathological parameters.Clin Cancer Res2000;6:1840-4

[51] Lasorsa F,Milella M.Cancer stem cells in renal cell carcinoma: origins and biomarkers.Int J Mol Sci2023;24:13179 PMCID:PMC10487877

[52] Lucarelli G,Rutigliano M.MUC1 expression affects the immunoflogosis in renal cell carcinoma microenvironment through complement system activation and immune infiltrate modulation.Int J Mol Sci2023;24:4814 PMCID:PMC10003656

[53] Lucarelli G,Loizzo D.MUC1 tissue expression and its soluble form CA15-3 identify a clear cell renal cell carcinoma with distinct metabolic profile and poor clinical outcome.Int J Mol Sci2022;23:13968 PMCID:PMC9696833