PDF
(4094KB)
Abstract
Background: Renal cell carcinoma (RCC) is among the top adult cancers worldwide, with a challenging management due to lack of early diagnosis, therapy resistance, and diverse molecular background. Aberrant DNA methylation has been associated with RCC development due to transcription deregulation. We discovered potential DNA methylation-based biomarkers for stage I RCC in Caucasian population from The Cancer Genome Atlas (TCGA) database.
Methods: Patients’ clinical, methylation beta-value, and mRNA expression data were retrieved. Differential methylation and expression analysis were conducted to obtain differentially methylated CpG-gene pairs. Inversely correlated CpG-gene pairs between their expression and methylation levels were selected using Pearson’s correlation test and then screened for any recorded somatic mutations. Their biomarker capacities were analyzed using the Kaplan-Meier and receiver operating characteristic analysis, followed by protein network and functional enrichment analysis.
Results: We obtained differentially methylated CpGs in clear cell (KIRC) and papillary RCC (KIRP) but not chromophobe RCC (KICH). Six inversely correlated CpG-gene pairs with no reported cancer-associated mutations were selected. Prognostic values were found in ATXN1 and RFTN1 for KIRC, along with GRAMD1B and TM4SF19 for KIRP, while diagnostic values were found in VIM and RFTN1 for KIRC, along with TNFAIP6 and TM4SF19 for KIRP. Both subtypes showed enrichment of immune and metabolism-related pathways.
Conclusion: We discovered novel potential DNA methylation-based prognostic and diagnostic markers for early-stage RCC in Caucasian population. Validation by wet laboratory analysis and adjustments for confounding variables might be needed, considering our study limitation to specific race.
Graphical abstract
Keywords
biomarker
/
chromophobe renal cell carcinoma
/
clear cell renal cell carcinoma
/
DNA methylation
/
papillary renal cell carcinoma
/
TCGA
Cite this article
Download citation ▾
Alvaro Filbert Liko, Edward Ciputra, Nathaniel Alvin Sanjaya, Priskila Cherisca Thenaka, David Agustriawan.
DNA methylation profiling reveals new potential subtype-specific gene markers for early-stage renal cell carcinoma in Caucasian population.
Quant. Biol., 2022, 10(1): 79-93 DOI:10.15302/J-QB-021-0279
| [1] |
Capitanio,U., Bensalah,K., Bex,A., Boorjian,S. A., Bray,F., Coleman,J., Gore,J. L., Sun,M., Wood,C. ( 2019). Epidemiology of renal cell carcinoma. Eur. Urol., 75 : 74– 84
|
| [2] |
Lasseigne,B. N. Brooks,J. ( 2018). The role of DNA methylation in renal cell carcinoma. Mol. Diagn. Ther., 22 : 431– 442
|
| [3] |
Tabibu,S., Vinod,P. K. Jawahar,C. ( 2019). Pan-renal cell carcinoma classification and survival prediction from histopathology images using deep learning. Sci. Rep., 9 : 10509
|
| [4] |
Shuch,B., Amin,A., Armstrong,A. J., Eble,J. N., Ficarra,V., Lopez-Beltran,A., Martignoni,G., Rini,B. I. ( 2015). Understanding pathologic variants of renal cell carcinoma: distilling therapeutic opportunities from biologic complexity. Eur. Urol., 67 : 85– 97
|
| [5] |
nn,E. A., Landfors,M., Haider,Z., hn,L., Ljungberg,B., Roos,G. ( 2019). DNA methylation associates with survival in non-metastatic clear cell renal cell carcinoma. BMC Cancer, 19 : 65
|
| [6] |
Maher,E. ( 2018). Hereditary renal cell carcinoma syndromes: diagnosis, surveillance and management. World J. Urol., 36 : 1891– 1898
|
| [7] |
Sharma,S., Kelly,T. K. Jones,P. ( 2010). Epigenetics in cancer. Carcinogenesis, 31 : 27– 36
|
| [8] |
Ricketts,C. J., De Cubas,A. A., Fan,H., Smith,C. C., Lang,M., Reznik,E., Bowlby,R., Gibb,E. A., Akbani,R., Beroukhim,R. . ( 2018). The Cancer Genome Atlas comprehensive molecular characterization of renal cell carcinoma. Cell Rep., 23 : 313– 326.e5
|
| [9] |
Chen,F., Zhang,Y., lu,Y., Ciriello,G., Yang,L., Reznik,E., Shuch,B., Micevic,G., De Velasco,G., Shinbrot,E. . ( 2016). Multilevel genomics-based taxonomy of renal cell carcinoma. Cell Rep., 14 : 2476– 2489
|
| [10] |
Chen,W., Zhuang,J., Wang,P. P., Jiang,J., Lin,C., Zeng,P., Liang,Y., Zhang,X., Dai,Y. ( 2019). DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups. Cancer Cell Int., 19 : 185
|
| [11] |
Nguyen,D. P., Vertosick,E. A., Corradi,R. B., Vilaseca,A., Benfante,N. E., Touijer,K. A., Sjoberg,D. D. ( 2016). Histological subtype of renal cell carcinoma significantly affects survival in the era of partial nephrectomy. Urol. Oncol., 34 : 259.e1– 259.e8
|
| [12] |
Peters,I., Merseburger,A., Tezval,H., Lafos,M., Tabrizi,P., Mazdak,M., Wolters,M., Kuczyk,M. A., Serth,J. von Klot,C. ( 2020). The prognostic value of DNA methylation markers in renal cell cancer: A systematic review. Kidney Cancer, 4 : 3– 13
|
| [13] |
Lommen,K., Vaes,N., Aarts,M. J., van Roermund,J. G., Schouten,L. J., Oosterwijk,E., Melotte,V., Tjan-Heijnen,V. C., van Engeland,M. Smits,K. ( 2021). Diagnostic DNA methylation biomarkers for renal cell carcinoma: A systematic review. Eur. Urol. Oncol., 4 : 215– 226
|
| [14] |
Cairns,P. ( 2010). Renal cell carcinoma. Cancer Biomark., 9 : 461– 473
|
| [15] |
Lipworth,L., McLaughlin,J. K., Tarone,R. E. Blot,W. ( 2011). Renal cancer paradox: higher incidence but not higher mortality among African-Americans. Eur. J. Cancer Prev., 20 : 331– 333
|
| [16] |
Malouf,G. G., Su,X., Zhang,J., Creighton,C. J., Ho,T. H., Lu,Y., Raynal,N. J. M., Karam,J. A., Tamboli,P., Allanick,F. . ( 2016). DNA methylation signature reveals cell ontogeny of renal cell carcinomas. Clin. Cancer Res., 22 : 6236– 6246
|
| [17] |
nn,E. A., Degerman,S., hn,L., Landfors,M., Ljungberg,B. ( 2016). DNA methylation status defines clinicopathological parameters including survival for patients with clear cell renal cell carcinoma (ccRCC). Tumour Biol., 37 : 10219– 10228
|
| [18] |
Spainhour,J. C., Lim,H. S., Yi,S. V. ( 2019). Correlation patterns between DNA methylation and gene expression in The Cancer Genome Atlas. Cancer Inform., 18 : 1176935119828776
|
| [19] |
Li,M., Zou,D., Li,Z., Gao,R., Sang,J., Zhang,Y., Li,R., Xia,L., Zhang,T., Niu,G. . ( 2019). EWAS Atlas: A curated knowledgebase of epigenome-wide association studies. Nucleic Acids Res., 47 : D983– D988
|
| [20] |
Jeziorska,D. M., Murray,R. J. S., De Gobbi,M., Gaentzsch,R., Garrick,D., Ayyub,H., Chen,T., Li,E., Telenius,J., Lynch,M. . ( 2017). DNA methylation of intragenic CpG islands depends on their transcriptional activity during differentiation and disease. Proc. Natl. Acad. Sci. USA, 114 : E7526– E7535
|
| [21] |
Deaton,A. M. ( 2011). CpG islands and the regulation of transcription. Genes Dev., 25 : 1010– 1022
|
| [22] |
Jia,P. ( 2017). Impacts of somatic mutations on gene expression: an association perspective. Brief Bioinform., 18 : 413– 425
|
| [23] |
Strimbu,K. Tavel,J. ( 2010). What are biomarkers?. Curr. Opin. HIV AIDS, 5 : 463– 466
|
| [24] |
Zhang,S., Zhang,E., Long,J., Hu,Z., Peng,J., Liu,L., Tang,F., Li,L., Ouyang,Y. ( 2019). Immune infiltration in renal cell carcinoma. Cancer Sci., 110 : 1564– 1572
|
| [25] |
Muglia,V. F. ( 2015). Renal cell carcinoma: histological classification and correlation with imaging findings. Radiol. Bras., 48 : 166– 174
|
| [26] |
nchez,I., ( 2016). Ataxin-1 regulates the cerebellar bioenergetics proteome through the GSK3β-mTOR pathway which is altered in Spinocerebellar ataxia type 1 (SCA1). Hum. Mol. Genet., 25 : 4021– 4040
|
| [27] |
Williams,A. A., Higgins,J. P., Zhao,H., Ljunberg,B. Brooks,J. ( 2009). CD 9 and vimentin distinguish clear cell from chromophobe renal cell carcinoma. BMC Clin. Pathol., 9 : 9
|
| [28] |
Chen,H. K., Fernandez-Funez,P., Acevedo,S. F., Lam,Y. C., Kaytor,M. D., Fernandez,M. H., Aitken,A., Skoulakis,E. M. C., Orr,H. T., Botas,J. . ( 2003). Interaction of Akt-phosphorylated ataxin-1 with 14-3-3 mediates neurodegeneration in spinocerebellar ataxia type 1. Cell, 113 : 457– 468
|
| [29] |
Svandova,E., Lesot,H., Vanden Berghe,T., Tucker,A. S., Sharpe,P. T., Vandenabeele,P. ( 2014). Non-apoptotic functions of caspase-7 during osteogenesis. Cell Death Dis., 5 : e1366
|
| [30] |
Vilella-Arias,S. A., Rocha,R. M., da Costa,W. H., Zequi,S. C., es,G. C., Verjovski-Almeida,S., Soares,F. A. Reis,E. ( 2013). Loss of caspase 7 expression is associated with poor prognosis in renal cell carcinoma clear cell subtype. Urology, 82 : 974.e1– 974.e7
|
| [31] |
Yang,C., Kaushal,V., Haun,R. S., Seth,R., Shah,S. V. Kaushal,G. ( 2008). Transcriptional activation of caspase-6 and -7 genes by cisplatin-induced p53 and its functional significance in cisplatin nephrotoxicity. Cell Death Differ., 15 : 530– 544
|
| [32] |
Kang,J. H., Lee,J. S., Hong,D., Lee,S. H., Kim,N., Lee,W. K., Sung,T. W., Gong,Y. D. Kim,S. ( 2016). Renal cell carcinoma escapes death by p53 depletion through transglutaminase 2-chaperoned autophagy. Cell Death Dis., 7 : e2163
|
| [33] |
Ke,W., Lu,Z. N., Deng,X. R., Li,W. L., Du,M., Yang,H. Liu,Y. ( 2017). MicroRNA-18a targets ATXN1 to alleviate injury induced by permanent middle cerebral artery occlusion in mice. Int. J. Clin. Exp. Med., 10 : 965– 971
|
| [34] |
Kang,A. R., An,H. T., Ko,J. ( 2017). Ataxin-1 regulates epithelial-mesenchymal transition of cervical cancer cells. Oncotarget, 8 : 18248– 18259
|
| [35] |
Kang,A. R., An,H. T., Ko,J., Choi,E. J. ( 2017). Ataxin-1 is involved in tumorigenesis of cervical cancer cells via the EGFR-RAS-MAPK signaling pathway. Oncotarget, 8 : 94606– 94618
|
| [36] |
Staubach,S. Hanisch,F. ( 2011). Lipid rafts: signaling and sorting platforms of cells and their roles in cancer. Expert Rev. Proteomics, 8 : 263– 277
|
| [37] |
Watanabe,A., Tatematsu,M., Saeki,K., Shibata,S., Shime,H., Yoshimura,A., Obuse,C., Seya,T. ( 2011). Raftlin is involved in the nucleocapture complex to induce poly(I: C)-mediated TLR3 activation. J. Biol. Chem., 286 : 10702– 10711
|
| [38] |
Bayliss,A. L., Sundararaman,A., Granet,C. ( 2020). Raftlin is recruited by neuropilin-1 to the activated VEGFR2 complex to control proangiogenic signaling. Angiogenesis, 23 : 371– 383
|
| [39] |
WuG., WangQ., XuY., LiJ., ZhangH., QiG. Xia Q. ( 2019) Targeting the transcription factor receptor LXR to treat clear cell renal cell carcinoma: agonist or inverse agonist? Cell Death Dis., 10, 416
|
| [40] |
Yuan,T., Hong,S., Yao,Y. ( 2007). Glut-4 is translocated to both caveolae and non-caveolar lipid rafts, but is partially internalized through caveolae in insulin-stimulated adipocytes. Cell Res., 17 : 772– 782
|
| [41] |
Unbekandt,M. Olson,M. ( 2014). The actin-myosin regulatory MRCK kinases: regulation, biological functions and associations with human cancer. J. Mol. Med. (Berl. ), 92 : 217– 225
|
| [42] |
Arias-Romero,L. E. ( 2013). Targeting Cdc42 in cancer. Expert Opin. Ther. Targets, 17 : 1263– 1273
|
| [43] |
Uhlen,M., Oksvold,P., Fagerberg,L., Lundberg,E., Jonasson,K., Forsberg,M., Zwahlen,M., Kampf,C., Wester,K., Hober,S. . ( 2010). Towards a knowledge-based Human Protein Atlas. Nat. Biotechnol., 28 : 1248– 1250
|
| [44] |
Uhlen,M., Zhang,C., Lee,S., stedt,E., Fagerberg,L., Bidkhori,G., Benfeitas,R., Arif,M., Liu,Z., Edfors,F. . ( 2017). A pathology atlas of the human cancer transcriptome. Science, 357 : eaan2507
|
| [45] |
HumanProtein Atlas. Expression of CDC42BPG in cancer. Retrieved in 30 June 2020. Data available from the website of The Human Protein Atlas
|
| [46] |
Velikkakath,A. K., Nishimura,T., Oita,E., Ishihara,N. ( 2012). Mammalian Atg2 proteins are essential for autophagosome formation and important for regulation of size and distribution of lipid droplets. Mol. Biol. Cell, 23 : 896– 909
|
| [47] |
Kusama,Y., Sato,K., Kimura,N., Mitamura,J., Ohdaira,H. ( 2009). Comprehensive analysis of expression pattern and promoter regulation of human autophagy-related genes. Apoptosis, 14 : 1165– 1175
|
| [48] |
Yu,B., Zhang,J., Sun,Z., Cao,P., Zheng,X., Gao,Z., Cao,H., Zhang,F. ( 2021). Interferon-inducible protein 16 may be a biomarker and prognostic factor in renal cell carcinoma by bioinformatics analysis. Medicine (Baltimore), 100 : e24257
|
| [49] |
Wen,L., Guo,L., Zhang,W., Li,Y., Jiang,W., Di,X., Ma,J., Feng,L., Zhang,K. ( 2019). Cooperation between the inflammation and coagulation systems promotes the survival of circulating tumor cells in renal cell carcinoma patients. Front. Oncol., 9 : 504
|
| [50] |
Guo,F. ( 2020). Tumor necrosis factor alpha-induced proteins in malignant tumors: progress and prospects. OncoTargets Ther., 13 : 3303– 3318
|
| [51] |
Chan,T. C., Li,C. F., Ke,H. L., Wei,Y. C., Shiue,Y. L., Li,C. C., Yeh,H. C., Lee,H. Y., Huang,S. K., Wu,W. J. . ( 2019). High TNFAIP6 level is associated with poor prognosis of urothelial carcinomas. Urol. Oncol., 37 : 293.e11– 293.e24
|
| [52] |
Offenberg,H., nner,N., Mansilla,F., rntoft Torben,F. ( 2008). TIMP-1 expression in human colorectal cancer is associated with TGF-B1, LOXL2, INHBA1, TNF-AIP6 and TIMP-2 transcript profiles. Mol. Oncol., 2 : 233– 240
|
| [53] |
Zhang,X., Xue,J., Yang,H., Zhou,T. ( 2021). TNFAIP6 promotes invasion and metastasis of gastric cancer and indicates poor prognosis of patients. Tissue Cell, 68 : 101455
|
| [54] |
Shin,S. Jang,H. Xu,R., Won,J. ( 2020). Active PLK1-driven metastasis is amplified by TGF-β signaling that forms a positive feedback loop in non-small cell lung cancer. Oncogene, 39 : 767– 785
|
| [55] |
Bommaya,G., Meran,S., Krupa,A., Phillips,A. O. ( 2011). Tumour necrosis factor-stimulated gene (TSG)-6 controls epithelial-mesenchymal transition of proximal tubular epithelial cells. Int. J. Biochem. Cell Biol., 43 : 1739– 1746
|
| [56] |
Pasquier,J., Abu-Kaoud,N., Al Thani,H. ( 2015). Epithelial to mesenchymal transition in a clinical perspective. J. Oncol., 2015 : 792182
|
| [57] |
Peng,Q., Zhao,L., Hou,Y., Sun,Y., Wang,L., Luo,H., Peng,H. ( 2013). Biological characteristics and genetic heterogeneity between carcinoma-associated fibroblasts and their paired normal fibroblasts in human breast cancer. PLoS One, 8 : e60321
|
| [58] |
Fu,F., Yang,X., Zheng,M., Zhao,Q., Zhang,K., Li,Z., Zhang,H. ( 2020). Role of transmembrane 4 L six family 1 in the development and progression of cancer. Front. Mol. Biosci., 7 : 202
|
| [59] |
Chung,C. T., Yeh,K. C., Lee,C. H., Chen,Y. Y., Ho,P. J., Chang,K. Y., Chen,C. H., Lai,Y. K. Chen,C. ( 2020). Molecular profiling of afatinib-resistant non-small cell lung cancer cells in vivo derived from mice. Pharmacol. Res., 161 : 105183
|
| [60] |
Scherer,A., nther,O. P., Balshaw,R. F., Hollander,Z., Wilson-McManus,J., Ng,R., McMaster,W. R., McManus,B. M. Keown,P. ( 2013). Alteration of human blood cell transcriptome in uremia. BMC Med. Genomics, 28 : 23
|
| [61] |
Liao,L. M., Schwartz,K., Pollak,M., Graubard,B. I., Li,Z., Ruterbusch,J., Rothman,N., Davis,F., Wacholder,S., Colt,J. . ( 2013). Serum leptin and adiponectin levels and risk of renal cell carcinoma. Obesity (Silver Spring), 21 : 1478– 1485
|
| [62] |
YeoX.. ( 2018) Investigation and validation of the functional role of sMEK1 acetylation in DNA damage repair and HDACi-mediated radiosensitisation. Master’s thesis. University of Oxford
|
| [63] |
Wenta,T., Rychlowski,M., Jarzab,M. ( 2019). HtrA4 protease promotes chemotherapeutic-dependent cancer cell death. Cells, 8 : 1112
|
| [64] |
Singh,H., Zhao,M., Chen,Q., Wang,Y., Li,Y., u-Lino,T. J., Tong,S. ( 2015). Human HtrA4 expression is restricted to the placenta, is significantly up-regulated in early-onset preeclampsia, and high levels of HtrA4 cause endothelial dysfunction. J. Clin. Endocrinol. Metab., 100 : E936– E945
|
| [65] |
Ding,L., Li,L. M., Hu,B., Wang,J. L., Lu,Y. B., Zhang,R. Y., He,X., Shi,C., Wu,L. M., Wu,C. M. . ( 2020). TM4SF19 aggravates LPS-induced attenuation of vascular endothelial cell adherens junctions by suppressing VE-cadherin expression. Biochem. Biophys. Res. Commun., 533 : 1204– 1211
|
| [66] |
Khanna,P., Chua,P. J., Wong,B. S. E., Yin,C., Thike,A. A., Wan,W. K., Tan,P. H. Baeg,G. ( 2017). GRAM domain-containing protein 1B (GRAMD1B), a novel component of the JAK/STAT signaling pathway, functions in gastric carcinogenesis. Oncotarget, 8 : 115370– 115383
|
| [67] |
Khanna,P., Lee,J. S., Sereemaspun,A., Lee,H. Baeg,G. ( 2018). GRAMD1B regulates cell migration in breast cancer cells through JAK/STAT and Akt signaling. Sci. Rep., 8 : 9511
|
| [68] |
Wu,S. Y., Yang,X., Gharpure,K. M., Hatakeyama,H., Egli,M., McGuire,M. H., Nagaraja,A. S., Miyake,T. M., Rupaimoole,R., Pecot,C. V. . ( 2014). 2′-OMe-phosphorodithioate-modified siRNAs show increased loading into the RISC complex and enhanced anti-tumour activity. Nat. Commun., 5 : 3459
|
| [69] |
Lv,T., Lv,H., Fei,J., Xie,Y., Lian,D., Hu,J., Tang,L., Shi,X., Wang,J., Zhang,S. . ( 2020). p53-R273H promotes cancer cell migration via upregulation of neuraminidase-1. J. Cancer, 11 : 6874– 6882
|
| [70] |
Hajian-Tilaki, ( 2013). Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med, 4 : 627– 635
|
| [71] |
Jung,M., Ramankulov,A., Roigas,J., Johannsen,M., Ringsdorf,M., Kristiansen,G. ( 2007). In search of suitable reference genes for gene expression studies of human renal cell carcinoma by real-time PCR. BMC Mol. Biol., 8 : 47
|
| [72] |
Wong,C. S., Chen,T. T., Chang,W. P., Wong,H. S., Wu,M. Y., Adikusuma,W., Lin,Y. F. Chang,W. ( 2020). Prognostic effect of comorbid disease and immune gene expression on mortality in kidney cancer-a population based study. Cancers (Basel), 12 : 1654
|
| [73] |
Wei,L., Jin,Z., Yang,S., Xu,Y., Zhu,Y. ( 2018). TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data. Bioinformatics, 34 : 1615– 1617
|
| [74] |
Colaprico,A., Silva,T. C., Olsen,C., Garofano,L., Cava,C., Garolini,D., Sabedot,T. S., Malta,T. M., Pagnotta,S. M., Castiglioni,I. . ( 2016). TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res., 44 : e71
|
| [75] |
Robinson,M. D., McCarthy,D. J. Smyth,G. ( 2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26 : 139– 140
|
| [76] |
Sondka,Z., Bamford,S., Cole,C. G., Ward,S. A., Dunham,I. Forbes,S. ( 2018). The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer, 18 : 696– 705
|
| [77] |
In,J. Lee,D. ( 2019). Survival analysis: part II − applied clinical data analysis. Korean J. Anesthesiol., 72 : 441– 457
|
| [78] |
Robin,X., Turck,N., Hainard,A., Tiberti,N., Lisacek,F., Sanchez,J. C. ( 2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 12 : 77
|
| [79] |
Yang,S. ( 2017). The receiver operating characteristic (ROC) curve. Chronicles, 5 : 34– 36
|
| [80] |
Szklarczyk,D., Gable,A. L., Lyon,D., Junge,A., Wyder,S., Huerta-Cepas,J., Simonovic,M., Doncheva,N. T., Morris,J. H., Bork,P. . ( 2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res., 47 : D607– D613
|
| [81] |
Zhang,B., Wu,Q., Wang,Z., Xu,R., Hu,X., Sun,Y., Wang,Q., Ju,F., Ren,S., Zhang,C. . ( 2019). The promising novel biomarkers and candidate small molecule drugs in kidney renal clear cell carcinoma: Evidence from bioinformatics analysis of high-throughput data. Mol. Genet. Genomic Med., 7 : e607
|
RIGHTS & PERMISSIONS
The Author(s) 2021. Published by Higher Education Press.