79
Views
0
CrossRef citations to date
0
Altmetric
ORIGINAL RESEARCH

Identification of Shared Signature Genes and Immune Microenvironment Subtypes for Heart Failure and Chronic Kidney Disease Based on Machine Learning

, , , &
Pages 1873-1895 | Received 01 Dec 2023, Accepted 19 Mar 2024, Published online: 21 Mar 2024

References

  • Isakova T, Nickolas TL, Denburg M, et al. KDOQI US Commentary on the 2017 KDIGO Clinical Practice Guideline Update for the Diagnosis, Evaluation, Prevention, and Treatment of Chronic Kidney Disease-Mineral and Bone Disorder (CKD-MBD). Am J Kidney Dis. 2017;70(6):737–751. doi:10.1053/j.ajkd.2017.07.019
  • Porter AC, Lash JP, Xie D, et al. Predictors and Outcomes of Health-Related Quality of Life in Adults with CKD. Clin J Am Soc Nephrol. 2016;11(7):1154–1162. doi:10.2215/CJN.09990915
  • Gansevoort RT, Correa-Rotter R, Hemmelgarn BR, et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet. 2013;382(9889):339–352. doi:10.1016/S0140-6736(13)60595-4
  • Sud M, Tangri N, Pintilie M, Levey AS, Naimark D. Risk of end-stage renal disease and death after cardiovascular events in chronic kidney disease. Circulation. 2014;130(6):458–465. doi:10.1161/CIRCULATIONAHA.113.007106
  • Schefold JC, Filippatos G, Hasenfuss G, Anker SD, von Haehling S. Heart failure and kidney dysfunction: epidemiology, mechanisms and management. Nat Rev Nephrol. 2016;12(10):610–623. doi:10.1038/nrneph.2016.113
  • Rahman M, Xie D, Feldman HI, et al. Association between chronic kidney disease progression and cardiovascular disease: results from the CRIC Study. Am J Nephrol. 2014;40(5):399–407. doi:10.1159/000368915
  • Bang C, Antoniades C, Antonopoulos AS, et al. Intercellular communication lessons in heart failure. Eur J Heart Fail. 2015;17(11):1091–1103. doi:10.1002/ejhf.399
  • Obi Y, Kim T, Kovesdy CP, Amin AN, Kalantar-Zadeh K. Current and Potential Therapeutic Strategies for Hemodynamic Cardiorenal Syndrome. Cardiorenal Med. 2016;6(2):83–98. doi:10.1159/000441283
  • Rajapakse NW, Nanayakkara S, Kaye DM. Pathogenesis and treatment of the cardiorenal syndrome: implications of L-arginine-nitric oxide pathway impairment. Pharmacol Ther. 2015;154:1–12. doi:10.1016/j.pharmthera.2015.05.011
  • Giam B, Kaye DM, Rajapakse NW. Role of Renal Oxidative Stress in the Pathogenesis of the Cardiorenal Syndrome. Heart Lung Circ. 2016;25(8):874–880. doi:10.1016/j.hlc.2016.02.022
  • Li X, Hassoun HT, Santora R, Rabb H. Organ crosstalk: the role of the kidney. Curr Opin Crit Care. 2009;15(6):481–487. doi:10.1097/MCC.0b013e328332f69e
  • Rosner MH, Ronco C, Okusa MD. The role of inflammation in the cardio-renal syndrome: a focus on cytokines and inflammatory mediators. Semin Nephrol. 2012;32(1):70–78. doi:10.1016/j.semnephrol.2011.11.010
  • Vinod P, Krishnappa V, Chauvin AM, Khare A, Raina R. Cardiorenal Syndrome: role of Arginine Vasopressin and Vaptans in Heart Failure. Cardiol Res. 2017;8(3):87–95. doi:10.14740/cr553w
  • White LE, Hassoun HT. Inflammatory Mechanisms of Organ Crosstalk during Ischemic Acute Kidney Injury. Int J Nephrol. 2012;2012:505197. doi:10.4061/2012/505197
  • Murakami M, Hirano T. The pathological and physiological roles of IL-6 amplifier activation. Int J Biol Sci. 2012;8(9):1267–1280. doi:10.7150/ijbs.4828
  • Tornatore L, Thotakura AK, Bennett J, Moretti M, Franzoso G. The nuclear factor kappa B signaling pathway: integrating metabolism with inflammation. Trends Cell Biol. 2012;22(11):557–566. doi:10.1016/j.tcb.2012.08.001
  • Kingma JG, Simard D, Rouleau JR, Drolet B, Simard C. The Physiopathology of Cardiorenal Syndrome: a Review of the Potential Contributions of Inflammation. J Cardiovasc Dev Dis. 2017;4(4). doi:10.3390/jcdd4040021
  • Barrett T, Wilhite SE, Ledoux P, et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res. 2013;41(Database issue):D991–5. doi:10.1093/nar/gks1193
  • Liu Y, Morley M, Brandimarto J, et al. RNA-Seq identifies novel myocardial gene expression signatures of heart failure. Genomics. 2015;105(2):83–89. doi:10.1016/j.ygeno.2014.12.002
  • Nakagawa S, Nishihara K, Miyata H, et al. Molecular Markers of Tubulointerstitial Fibrosis and Tubular Cell Damage in Patients with Chronic Kidney Disease. PLoS One. 2015;10(8):e0136994. doi:10.1371/journal.pone.0136994
  • Greco S, Fasanaro P, Castelvecchio S, et al. MicroRNA dysregulation in diabetic ischemic heart failure patients. Diabetes. 2012;61(6):1633–1641. doi:10.2337/db11-0952
  • Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29. doi:10.1186/gb-2014-15-2-r29
  • 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
  • Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinf. 2013;14(1):7. doi:10.1186/1471-2105-14-7
  • Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–15550. doi:10.1073/pnas.0506580102
  • Pudjihartono N, Fadason T, Kempa-Liehr AW, O’Sullivan JM. A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction. Front Bioinform. 2022;2:927312. doi:10.3389/fbinf.2022.927312
  • Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinf. 2008;9(1):559. doi:10.1186/1471-2105-9-559
  • Kitai T, Kirsop J, Tang WH. Exploring the Microbiome in Heart Failure. Curr Heart Fail Rep. 2016;13(2):103–109. doi:10.1007/s11897-016-0285-9
  • Colombo PC, Ganda A, Lin J, et al. Inflammatory activation: cardiac, renal, and cardio-renal interactions in patients with the cardiorenal syndrome. Heart Fail Rev. 2012;17(2):177–190. doi:10.1007/s10741-011-9261-3
  • Pastori S, Virzì GM, Brocca A, et al. Cardiorenal syndrome type 1: a defective regulation of monocyte apoptosis induced by proinflammatory and proapoptotic factors. Cardiorenal Med. 2015;5(2):105–115. doi:10.1159/000371898
  • Hayıroğlu M, Altay S. The Role of Artificial Intelligence in Coronary Artery Disease and Atrial Fibrillation. Balkan Med J. 2023;40(3):151–152. doi:10.4274/balkanmedj.galenos.2023.06042023
  • Yilmaz A, Hayıroğlu M, Salturk S, et al. Machine Learning Approach on High Risk Treadmill Exercise Test to Predict Obstructive Coronary Artery Disease by using P, QRS, and T waves’ Features. Curr Probl Cardiol. 2023;48(2):101482. doi:10.1016/j.cpcardiol.2022.101482
  • Frank D, Mendelsohn CL, Ciccone E, Svensson K, Ohlsson R, Tycko B. A novel pleckstrin homology-related gene family defined by Ipl/Tssc3, TDAG51, and Tih1: tissue-specific expression, chromosomal location, and parental imprinting. Mamm Genome. 1999;10(12):1150–1159. doi:10.1007/s003359901182
  • Park CG, Lee SY, Kandala G, Lee SY, Choi Y. A novel gene product that couples TCR signaling to Fas(CD95) expression in activation-induced cell death. Immunity. 1996;4(6):583–591. doi:10.1016/S1074-7613(00)80484-7
  • Hossain GS, van Thienen JV, Werstuck GH, et al. TDAG51 is induced by homocysteine, promotes detachment-mediated programmed cell death, and contributes to the development of atherosclerosis in hyperhomocysteinemia. J Biol Chem. 2003;278(32):30317–30327. doi:10.1074/jbc.M212897200
  • Wang J, Wang F, Zhu J, Song M, An J, Li W. Transcriptome Profiling Reveals PHLDA1 as a Novel Molecular Marker for Ischemic Cardiomyopathy. J Mol Neurosci. 2018;65(1):102–109. doi:10.1007/s12031-018-1066-6
  • Liu L, Huang J, Liu Y, et al. Multiomics Analysis of Transcriptome, Epigenome, and Genome Uncovers Putative Mechanisms for Dilated Cardiomyopathy. Biomed Res Int. 2021;2021:6653802. doi:10.1155/2021/6653802
  • Guo Y, Jia P, Chen Y, et al. PHLDA1 is a new therapeutic target of oxidative stress and ischemia reperfusion-induced myocardial injury. Life Sci. 2020;245:117347. doi:10.1016/j.lfs.2020.117347
  • Carlisle RE, Mohammed-Ali Z, Lu C, et al. TDAG51 induces renal interstitial fibrosis through modulation of TGF-β receptor 1 in chronic kidney disease. Cell Death Dis. 2021;12(10):921. doi:10.1038/s41419-021-04197-3
  • Han C, Yan P, He T, et al. PHLDA1 promotes microglia-mediated neuroinflammation via regulating K63-linked ubiquitination of TRAF6. Brain Behav Immun. 2020;88:640–653. doi:10.1016/j.bbi.2020.04.064
  • Morth JP, Pedersen BP, Buch-Pedersen MJ, et al. A structural overview of the plasma membrane Na+, K+-ATPase and H+-ATPase ion pumps. Nat Rev Mol Cell Biol. 2011;12(1):60–70. doi:10.1038/nrm3031
  • Khalaf FK, Tassavvor I, Mohamed A, et al. Epithelial and Endothelial Adhesion of Immune Cells Is Enhanced by Cardiotonic Steroid Signaling Through Na(+)/K(+)-ATPase-α-1. J Am Heart Assoc. 2020;9(3):e013933. doi:10.1161/JAHA.119.013933
  • Farbehi N, Patrick R, Dorison A, et al. Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury. Elife. 2019;8.
  • Stawowczyk M, Van Scoy S, Kumar KP, Reich NC. The interferon stimulated gene 54 promotes apoptosis. J Biol Chem. 2011;286(9):7257–7266. doi:10.1074/jbc.M110.207068
  • Liu L, Liu H, Zhou Y, et al. HLTF suppresses the migration and invasion of colorectal cancer cells via TGF‑β/SMAD signaling in vitro. Int J Oncol. 2018;53(6):2780–2788. doi:10.3892/ijo.2018.4591
  • Piao S, Ojha R, Rebecca VW, et al. ALDH1A1 and HLTF modulate the activity of lysosomal autophagy inhibitors in cancer cells. Autophagy. 2017;13(12):2056–2071. doi:10.1080/15548627.2017.1377377
  • Debauve G, Capouillez A, Belayew A, Saussez S. The helicase-like transcription factor and its implication in cancer progression. Cell Mol Life Sci. 2008;65(4):591–604. doi:10.1007/s00018-007-7392-4
  • Kang YA, Paik H, Zhang SY, et al. Secretory MPP3 reinforce myeloid differentiation trajectory and amplify myeloid cell production. J Exp Med. 2023;220(8). doi:10.1084/jem.20230088
  • Ngkelo A, Richart A, Kirk JA, et al. Mast cells regulate myofilament calcium sensitization and heart function after myocardial infarction. J Exp Med. 2016;213(7):1353–1374. doi:10.1084/jem.20160081
  • Laroumanie F, Douin-Echinard V, Pozzo J, et al. CD4+ T cells promote the transition from hypertrophy to heart failure during chronic pressure overload. Circulation. 2014;129(21):2111–2124. doi:10.1161/CIRCULATIONAHA.113.007101
  • Lee H, Fessler MB, Qu P, Heymann J, Kopp JB. Macrophage polarization in innate immune responses contributing to pathogenesis of chronic kidney disease. BMC Nephrol. 2020;21(1):270. doi:10.1186/s12882-020-01921-7
  • Han Y, Ma FY, Tesch GH, Manthey CL, Nikolic-Paterson DJ. Role of macrophages in the fibrotic phase of rat crescentic glomerulonephritis. Am J Physiol Renal Physiol. 2013;304(8):F1043–53. doi:10.1152/ajprenal.00389.2012
  • Anders HJ, Ryu M. Renal microenvironments and macrophage phenotypes determine progression or resolution of renal inflammation and fibrosis. Kidney Int. 2011;80(9):915–925. doi:10.1038/ki.2011.217
  • Zhou X, Yao Q, Sun X, et al. Slit2 ameliorates renal inflammation and fibrosis after hypoxia-and lipopolysaccharide-induced epithelial cells injury in vitro. Exp Cell Res. 2017;352(1):123–129. doi:10.1016/j.yexcr.2017.02.001
  • Liu Y, Yin Z, Xu X, et al. Crosstalk between the activated Slit2-Robo1 pathway and TGF-β1 signalling promotes cardiac fibrosis. ESC Heart Fail. 2021;8(1):447–460. doi:10.1002/ehf2.13095
  • Li L, Galichon P, Xiao X, et al. Orphan nuclear receptor COUP-TFII enhances myofibroblast glycolysis leading to kidney fibrosis. EMBO Rep. 2021;22(6):e51169. doi:10.15252/embr.202051169
  • Dougherty EJ, Chen LY, Awad KS, et al. Inflammation and DKK1-induced AKT activation contribute to endothelial dysfunction following NR2F2 loss. Am J Physiol Lung Cell Mol Physiol. 2023;324(6):L783–L798. doi:10.1152/ajplung.00171.2022
  • Floege J, Eitner F, Alpers CE. A new look at platelet-derived growth factor in renal disease. J Am Soc Nephrol. 2008;19(1):12–23. doi:10.1681/ASN.2007050532
  • Gallini R, Lindblom P, Bondjers C, Betsholtz C, Andrae J. PDGF-A and PDGF-B induces cardiac fibrosis in transgenic mice. Exp Cell Res. 2016;349(2):282–290. doi:10.1016/j.yexcr.2016.10.022