Analysis and verification of N⁶-methyladenosine-modified genes as novel biomarkers for clear cell renal cell carcinoma

References

• Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30.
   PubMed Web of Science ®Google Scholar
• Yu W, Wang Y, Jiang Y, et al. Genetic analysis and clinicopathological features of ALK-rearranged renal cell carcinoma in a large series of resected Chinese renal cell carcinoma patients and literature review. Histopathology. 2017;71(1):53–62.
   PubMed Web of Science ®Google Scholar
• Sánchez-Gastaldo A, Kempf E, González Del Alba A, et al. Systemic treatment of renal cell cancer: a comprehensive review. Cancer Treat Rev. 2017;60:77–89.
   PubMed Web of Science ®Google Scholar
• Vera-Badillo FE, Templeton AJ, Duran I, et al. Systemic therapy for non-clear cell renal cell carcinomas: a systematic review and meta-analysis. Eur Urol. 2015;67(4):740–749.
   PubMed Web of Science ®Google Scholar
• Li Z, Peng Y, Li J, et al. N6-methyladenosine regulates glycolysis of cancer cells through PDK4. Nat Commun. 2020;11(1):2578.
   PubMed Web of Science ®Google Scholar
• Zhang X, Wang F, Wang Z, et al. ALKBH5 promotes the proliferation of renal cell carcinoma by regulating AURKB expression in an m6A-dependent manner. Ann Transl Med. 2020;8(10):646.
   PubMed Web of Science ®Google Scholar
• Zhou J, Wang J, Hong B, et al. Gene signatures and prognostic values of m6A regulators in clear cell renal cell carcinoma - a retrospective study using TCGA database. Aging (Albany NY). 2019;11(6):1633–1647.
   PubMedGoogle Scholar
• Zhang Q-J, Luan JC, Song LB, et al. m6A RNA methylation regulators correlate with malignant progression and have potential predictive values in clear cell renal cell carcinoma. Exp Cell Res. 2020;392(1):112015.
   PubMed Web of Science ®Google Scholar
• Wotschofsky Z, Gummlich L, Liep J, et al. Integrated microRNA and mRNA signature associated with the transition from the locally confined to the metastasized clear cell renal cell carcinoma exemplified by miR-146-5p. PloS One. 2016;11(2):e0148746.
   PubMed Web of Science ®Google Scholar
• Zheng Y, Nie P, Peng D, et al. m6AVar: a database of functional variants involved in m6A modification. Nucleic Acids Res. 2018;46(D1):D139–D145.
   PubMed Web of Science ®Google Scholar
• Bolstad BM, Irizarry RA, Astrand M, et al. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003;19(2):185–193.
   PubMed Web of Science ®Google Scholar
• Smyth GK. Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3.
   PubMedGoogle Scholar
• Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.
   PubMed Web of Science ®Google Scholar
• Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:Article17.
   PubMed Web of Science ®Google Scholar
• Chen B, Xie X, Lan F, et al. Identification of prognostic markers by weighted gene co-expression network analysis in non-small cell lung cancer. Bioengineered. 2021;12(1):4924–4935.
   PubMed Web of Science ®Google Scholar
• Li B, Pu K, Wu X. Identifying novel biomarkers in hepatocellular carcinoma by weighted gene co-expression network analysis. J Cell Biochem. 2019;120(7):11418–11431.
   PubMed Web of Science ®Google Scholar
• Wang Y, Chen L, Wang G, et al. Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis. J Cell Physiol. 2019;234(7):10225–10237.
   PubMed Web of Science ®Google Scholar
• Yu G, Wang L-G, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–287.
   PubMed Web of Science ®Google Scholar
• Jager KJ, van Dijk PC, Zoccali C, et al. The analysis of survival data: the Kaplan-Meier method. Kidney Int. 2008;74(5):560–565.
   PubMed Web of Science ®Google Scholar
• Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12:77.
   PubMed Web of Science ®Google Scholar
• Chiu RW, Murphy MF, Fidler C, et al. Determination of RhD zygosity: comparison of a double amplification refractory mutation system approach and a multiplex real-time quantitative PCR approach. Clin Chem. 2001;47(4):667–672.
   PubMed Web of Science ®Google Scholar
• Huang H, Weng H, Sun W, et al. Recognition of RNA N-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat Cell Biol. 2018;20(3):285–295.
   PubMed Web of Science ®Google Scholar
• Friendly M. Corrgrams: exploratory displays for correlation matrices. Am Stat. 2002;56(4):316–324.
   Web of Science ®Google Scholar
• Wang Y, Cong R, Liu S, et al. Decreased expression of METTL14 predicts poor prognosis and construction of a prognostic signature for clear cell renal cell carcinoma. Cancer Cell Int. 2021;21(1):46.
   PubMed Web of Science ®Google Scholar
• Sankin A, Hakimi AA, Mikkilineni N, et al. The impact of genetic heterogeneity on biomarker development in kidney cancer assessed by multiregional sampling. Cancer Med. 2014;3(6):1485–1492.
   PubMed Web of Science ®Google Scholar
• Zhao Y, Tao Z, Chen X. Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma. PeerJ. 2020;8:e8827.
   PubMed Web of Science ®Google Scholar
• Gao S, Niu S, Wang Y, et al. DMDRMR-mediated regulation of m6A-modified CDK4 by m6A reader IGF2BP3 drives ccRCC progression. Cancer Res. 2020;81(4):923–934.
   PubMed Web of Science ®Google Scholar
• Li X, Tang J, Huang W, et al. The M6A methyltransferase METTL3: acting as a tumor suppressor in renal cell carcinoma. Oncotarget. 2017;8(56):96103–96116.
   PubMedGoogle Scholar
• Weng H, Huang H, Chen J. RNA N-methyladenosine modification in normal and malignant hematopoiesis. Adv Exp Med Biol. 2019;1143:75–93.
   PubMed Web of Science ®Google Scholar
• Ma J-Z, Yang F, Zhou -C-C, et al. METTL14 suppresses the metastatic potential of hepatocellular carcinoma by modulating N6-methyladenosine-dependent primary MicroRNA processing. Hepatology. 2017;65(2):529–543.
   PubMed Web of Science ®Google Scholar
• Cui Q, Shi H, Ye P, et al. m6A RNA methylation regulates the self-renewal and tumorigenesis of glioblastoma stem cells. Cell Rep. 2017;18(11):2622–2634.
   PubMed Web of Science ®Google Scholar
• DeLuca JG, Moree B, Hickey JM, et al. hNuf2 inhibition blocks stable kinetochore-microtubule attachment and induces mitotic cell death in HeLa cells. J Cell Biol. 2002;159(4):549–555.
   PubMed Web of Science ®Google Scholar
• Wong CH, Lou UK, Li Y, et al. CircFOXK2 promotes growth and metastasis of pancreatic ductal adenocarcinoma by complexing with RNA-binding proteins and sponging MiR-942. Cancer Res. 2020;80(11):2138–2149.
   PubMed Web of Science ®Google Scholar
• Lim HH, Surana U. Tome-1, wee1, and the onset of mitosis: coupled destruction for timely entry. Mol Cell. 2003;11(4):845–846.
   PubMed Web of Science ®Google Scholar
• Zhang W, Lu Y, Li X, et al. CDCA3 promotes cell proliferation by activating the NF-κB/cyclin D1 signaling pathway in colorectal cancer. Biochem Biophys Res Commun. 2018;500(2):196–203.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Yin W, Cao W, et al. CDCA3 is a potential prognostic marker that promotes cell proliferation in gastric cancer. Oncol Rep. 2019;41(4):2471–2481.
   PubMed Web of Science ®Google Scholar
• Yang Z, Li C, Yan C, et al. KIF14 promotes tumor progression and metastasis and is an independent predictor of poor prognosis in human gastric cancer. Biochim Biophys Acta Mol Basis Dis. 2019;1865(1):181–192.
   PubMed Web of Science ®Google Scholar
• Singel SM, Cornelius C, Zaganjor E, et al. KIF14 promotes AKT phosphorylation and contributes to chemoresistance in triple-negative breast cancer. Neoplasia. 2014;16(3):247–256.
   PubMed Web of Science ®Google Scholar