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Original Research

Using a Machine Learning Approach to Identify Key Biomarkers for Renal Clear Cell Carcinoma

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Pages 3541-3558 | Published online: 30 Mar 2022

References

  • Moch H, Cubilla AL, Humphrey PA, Reuter VE, Ulbright TM. The 2016 WHO classification of tumours of the urinary system and male genital organs-part a: renal, penile, and testicular tumours. Eur Urol. 2016;70(1):93–105. doi:10.1016/j.eururo.2016.02.029
  • Zhan C, Wang Z, Xu C, et al. Development and validation of a prognostic gene signature in clear cell renal cell carcinoma. Front Mol Biosci. 2021;8(45). doi:10.3389/fmolb.2021.609865
  • Li Y, Gong Y, Ning X, et al. Downregulation of CLDN7 due to promoter hypermethylation is associated with human clear cell renal cell carcinoma progression and poor prognosis. J Exp Clin Cancer Res. 2018;37(1):276. doi:10.1186/s13046-018-0924-y
  • Motzer RJ, Bander NH, Nanus DM. Renal-cell carcinoma. N Engl J Med. 1996;335(12):865–875. doi:10.1056/NEJM199609193351207
  • Liu H, Yang Y. Identification of mast cell-based molecular subtypes and a predictive signature in clear cell renal cell carcinoma. Front Mol Biosci. 2021;8(927). doi:10.3389/fmolb.2021.719982
  • Karakiewicz PI, Briganti A, Chun FK, et al. Multi-institutional validation of a new renal cancer-specific survival nomogram. J Clin Oncol. 2007;25(11):1316–1322. doi:10.1200/jco.2006.06.1218
  • Pantuck AJ, Zisman A, Belldegrun AS. The changing natural history of renal cell carcinoma. J Urol. 2001;166(5):1611–1623. doi:10.1016/S0022-5347(05)65640-6
  • Wood CG. Multimodal approaches in the management of locally advanced and metastatic renal cell carcinoma: combining surgery and systemic therapies to improve patient outcome. Clin Cancer Res. 2007;13(2 Pt 2):697s–702s. doi:10.1158/1078-0432.Ccr-06-2109
  • Muselaers CH, Boerman OC, Oosterwijk E, Langenhuijsen JF, Oyen WJ, Mulders PF. Indium-111-labeled girentuximab immunoSPECT as a diagnostic tool in clear cell renal cell carcinoma. Eur Urol. 2013;63(6):1101–1106. doi:10.1016/j.eururo.2013.02.022
  • Li F, Yang M, Li Y, et al. An improved clear cell renal cell carcinoma stage prediction model based on gene sets. BMC Bioinform. 2020;21(1):232. doi:10.1186/s12859-020-03543-0
  • Cai W, Li H, Zhang Y, Han G. Identification of key biomarkers and immune infiltration in the synovial tissue of osteoarthritis by bioinformatics analysis. PeerJ. 2020;8:e8390. doi:10.7717/peerj.8390
  • Huang Y, Zheng S, Wang R, Tang C, Zhu J, Li J. CCL5 and related genes might be the potential diagnostic biomarkers for the therapeutic strategies of rheumatoid arthritis. Clin Rheumatol. 2019;38(9):2629–2635. doi:10.1007/s10067-019-04533-1
  • Chen Y, Liao R, Yao Y, Wang Q, Fu L. Machine learning to identify immune-related biomarkers of rheumatoid arthritis based on WGCNA network. Clin Rheumatol. 2021. doi:10.1007/s10067-021-05960-9
  • Tibshirani R. Regression shrinkage and selection via the lasso. J Royal Stat Soc Series B. 1996;58(1):267–288.
  • Suzuki T, Kano S, Suzuki M, et al. Enhanced angiogenesis in salivary duct carcinoma ex-pleomorphic adenoma. Front Oncol. 2020;10:603717. doi:10.3389/fonc.2020.603717
  • Gutiérrez-Gómez L, Vohryzek J, Chiêm B, et al. Stable biomarker identification for predicting schizophrenia in the human connectome. Neuroimage Clin. 2020;27:102316. doi:10.1016/j.nicl.2020.102316
  • Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 2013;41(D1):D991–5. doi:10.1093/nar/gks1193
  • Gumz ML, Zou H, Kreinest PA, et al. Secreted frizzled-related protein 1 loss contributes to tumor phenotype of clear cell renal cell carcinoma. Clin Cancer Res. 2007;13(16):4740–4749. doi:10.1158/1078-0432.Ccr-07-0143
  • Lenburg ME, Liou LS, Gerry NP, Frampton GM, Cohen HT, Christman MF. Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of microarray data. BMC Cancer. 2003;3:31. doi:10.1186/1471-2407-3-31
  • von Roemeling CA, Radisky DC, Marlow LA, et al. Neuronal pentraxin 2 supports clear cell renal cell carcinoma by activating the AMPA-selective glutamate receptor-4. Cancer Res. 2014;74(17):4796–4810. doi:10.1158/0008-5472.Can-14-0210
  • Wozniak MB, Le Calvez-kelm F, Abedi-Ardekani B, et al. Integrative genome-wide gene expression profiling of clear cell renal cell carcinoma in Czech Republic and in the United States. PLoS One. 2013;8(3):e57886. doi:10.1371/journal.pone.0057886
  • Zhu YX, Huang JQ, Ming YY, Zhuang Z, Xia H. Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms. PLoS One. 2021;16(10):e0259475. doi:10.1371/journal.pone.0259475
  • Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. doi:10.1093/nar/gkv007
  • Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284–287. doi:10.1089/omi.2011.0118
  • Antonacci Y, Toppi J, Mattia D, Pietrabissa A, Astolfi L. Single-trial connectivity estimation through the least absolute shrinkage and selection operator. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:6422–6425. doi:10.1109/embc.2019.8857909
  • Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77. doi:10.1186/1471-2105-12-77
  • Sanz H, Valim C, Vegas E, Oller JM, Reverter F. SVM-RFE: selection and visualization of the most relevant features through non-linear kernels. BMC Bioinform. 2018;19(1). doi:10.1186/s12859-018-2451-4
  • Deng YJ, Ren E, Yuan WH, Zhang GZ, Xie QQ, Xie -Q-Q. GRB10 and E2F3 as diagnostic markers of osteoarthritis and their correlation with immune infiltration. Diagnostics. 2020;10(3):171. doi:10.3390/diagnostics10030171
  • Cao Y, Tang W, Tang W. Immune cell infiltration characteristics and related core genes in lupus nephritis: results from bioinformatic analysis. BMC Immunol. 2019;20. doi:10.1186/s12865-019-0316-x
  • Garza Z, Lenz M, Liebmann J, et al. Characterization of disease-specific cellular abundance profiles of chronic inflammatory skin conditions from deconvolution of biopsy samples. BMC Med Genomics. 2019;12:1–4.
  • Yang L, Shou YH, Yang YS, Xu JH. Elucidating the immune infiltration in acne and its comparison with rosacea by integrated bioinformatics analysis. PLoS One. 2021;16(3):e0248650. doi:10.1371/journal.pone.0248650
  • Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–457. doi:10.1038/nmeth.3337
  • Thorsson V, Gibbs DL, Brown SD, et al. The immune landscape of cancer. Immunity. 2018;48(4):812–830.e14. doi:10.1016/j.immuni.2018.03.023
  • Beck AH, Espinosa I, Edris B, et al. The macrophage colony-stimulating factor 1 response signature in breast carcinoma. Clin Cancer Res. 2009;15(3):778–787. doi:10.1158/1078-0432.Ccr-08-1283
  • Calabrò A, Beissbarth T, Kuner R, et al. Effects of infiltrating lymphocytes and estrogen receptor on gene expression and prognosis in breast cancer. Breast Cancer Res Treat. 2009;116(1):69–77. doi:10.1007/s10549-008-0105-3
  • Teschendorff AE, Gomez S, Arenas A, et al. Improved prognostic classification of breast cancer defined by antagonistic activation patterns of immune response pathway modules. BMC Cancer. 2010;10:604. doi:10.1186/1471-2407-10-604
  • Wolf DM, Lenburg ME, Yau C, Boudreau A, van ‘t Veer LJ. Gene co-expression modules as clinically relevant hallmarks of breast cancer diversity. PLoS One. 2014;9(2):e88309. doi:10.1371/journal.pone.0088309
  • Chang HY, Sneddon JB, Alizadeh AA, et al. Gene expression signature of fibroblast serum response predicts human cancer progression: similarities between tumors and wounds. PLoS Biol. 2004;2(2):E7. doi:10.1371/journal.pbio.0020007
  • Castiblanco-Valencia MM, Fraga TR, Pagotto AH, et al. Plasmin cleaves fibrinogen and the human complement proteins C3b and C5 in the presence of Leptospira interrogans proteins: a new role of LigA and LigB in invasion and complement immune evasion. Immunobiology. 2016;221(5):679–689. doi:10.1016/j.imbio.2016.01.001
  • Guglietta S, Rescigno M. Hypercoagulation and complement: connected players in tumor development and metastases. Semin Immunol. 2016;28(6):578–586. doi:10.1016/j.smim.2016.10.011
  • Singh S, Hassan D, Aldawsari HM, Molugulu N, Shukla R, Kesharwani P. Immune checkpoint inhibitors: a promising anticancer therapy. Drug Discov Today. 2020;25(1):223–229. doi:10.1016/j.drudis.2019.11.003
  • Li J, Wang G, Zhang W, et al.Graphene film-functionalized germanium as a chemically stable, electrically conductive, and biologically active substrate. J Mater Chem B Mater Biol Med. 2015;3:1544–1555.
  • Young-Jun P, Da-sol K, Yeonseok C. Future prospects of immune checkpoint blockade in cancer: from response prediction to overcoming resistance. Exp Mol Med. 2018;50(8):109.
  • Brahmer JR, Tykodi SS, Chow LQM, et al. Safety and activity of anti-PD-L1 antibody in patients with advanced cancer. J Urol. 2012;188(6):2148–2149. doi:10.1016/j.juro.2012.08.169
  • La Paglia L, Listì A, Caruso S, et al. Potential role of ANGPTL4 in the cross talk between metabolism and cancer through PPAR signaling pathway. PPAR Res. 2017;2017:8187235. doi:10.1155/2017/8187235
  • Oike Y, Yasunaga K, Ito Y, et al. Angiopoietin-related growth factor (AGF) promotes epidermal proliferation, remodeling, and regeneration. Proc Natl Acad Sci. 2003;100(16):9494–9499. doi:10.1073/pnas.1531901100
  • Oike Y, Ito Y, Maekawa H. Angiopoietin-related growth factor (AGF) promotes angiogenesis. Blood. 2004;103(10):3760–3765. doi:10.1182/blood-2003-04-1272
  • Wang Y, Lam KS, Lam JB, et al.Overexpression of angiopoietin-like protein 4 alters mitochondria activities and modulates methionine metabolic cycle in the liver tissues of db/db diabetic mice. Chin Biol Abstract. 2007;21(11):1.
  • Oike Y, Akao M, Yasunaga K, et al. Angiopoietin-related growth factor antagonizes obesity and insulin resistance. Nat Med. 2005;11(43):400–408. doi:10.1038/nm1214
  • Ge H, Cha J-Y, Gopal H, et al. Differential regulation and properties of angiopoietin-like proteins 3 and 4. J Lipid Res. 2005;46(7):1484–1490. doi:10.1194/jlr.M500005-JLR200
  • Xu A, Lam MC, Chan KW, et al. Angiopoietin-like protein 4 decreases blood glucose and improves glucose tolerance but induces hyperlipidemia and hepatic steatosis in mice. Proc Natl Acad Sci. 2005;102(17):6086–6091. doi:10.1073/pnas.0408452102
  • Li H, Ge C, Zhao F, et al.Hypoxia‐inducible factor 1 alpha–activated angiopoietin‐like protein 4 contributes to tumor metastasis via vascular cell adhesion molecule‐1/integrin β1 signaling in human hepatocellular carcinoma. Hepatology. 2011;54(3):910–919.
  • Kim SH, Park YY, Kim SW, Lee JS, Wang D, Dubois RN. ANGPTL4 induction by prostaglandin E2 under hypoxic conditions promotes colorectal cancer progression. Cancer Res. 2011;71(22):7010. doi:10.1158/0008-5472.CAN-11-1262
  • Zhang H, Wong C, Wei H, et al. HIF-1-dependent expression of angiopoietin-like 4 and L1CAM mediates vascular metastasis of hypoxic breast cancer cells to the lungs. Oncogene. 2012;31(14):1757–1770. doi:10.1038/onc.2011.365
  • Jan SL, Amy C, Cazes A, et al. Angiopoietin-like 4 is a proangiogenic factor produced during ischemia and in conventional renal cell carcinoma. Am J Pathol. 2003;162(5):1521–1528. doi:10.1016/S0002-9440(10)64285-X
  • Verine J, Lehmann-Che J, Soliman H, et al. Determination of Angptl4 mRNA as a diagnostic marker of primary and metastatic clear cell renal-cell carcinoma. PLoS One. 2010;5:e10421. doi:10.1371/journal.pone.0010421
  • Galaup A, Cazes A, Le jan S, et al. Angiopoietin-like 4 prevents metastasis through inhibition of vascular permeability and tumor cell motility and invasiveness. Proc Natl Acad Sci USA. 2006;103(49):18721–18726. doi:10.1073/pnas.0609025103
  • Cazes A, Galaup A, Chomel C. Extracellular matrix-bound angiopoietin-like 4 inhibits endothelial cell adhesion, migration, and sprouting and alters actin cytoskeleton. Circ Res. 2006;99(11):1207–1215. doi:10.1161/01.RES.0000250758.63358.91
  • Dong D, Jia L, Zhou Y, Ren L, Li J, Zhang J. Serum level of ANGPTL4 as a potential biomarker in renal cell carcinoma. Urol Oncol. 2017;35(5):279–285. doi:10.1016/j.urolonc.2016.12.017
  • Sarver AE, Sarver AL, Thayanithy V, Subramanian S. Identification, by systematic RNA sequencing, of novel candidate biomarkers and therapeutic targets in human soft tissue tumors. Lab Invest. 2015;95(9):1077–1088. doi:10.1038/labinvest.2015.80
  • Sato M, Mamada H, Anzai N, Shirasaka Y, Nakanishi T, Tamai I. Renal secretion of uric acid by organic anion transporter 2 (OAT2/SLC22A7) in human. Biol Pharm Bull. 2010;33(3):498–503. doi:10.1248/bpb.33.498
  • Srimaroeng C, Perry JL, Pritchard JB. Physiology, structure, and regulation of the cloned organic anion transporters. Xenobiotica. 2008;38(7–8):889–935. doi:10.1080/00498250801927435
  • Kang W, Zhang M, Wang Q, et al. The SLC family are candidate diagnostic and prognostic biomarkers in clear cell renal cell carcinoma. Biomed Res Int. 2020;2020:1932948. doi:10.1155/2020/1932948