Login Register Cart 0
Generic placeholder image

Current Genomics

Editor-in-Chief

ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Identification of Potential Genes and Critical Pathways in Postoperative Recurrence of Crohn’s Disease by Machine Learning And WGCNA Network Analysis

Author(s): Aruna Rajalingam, Kanagaraj Sekar and Anjali Ganjiwale*

Volume 24, Issue 2, 2023

Published on: 13 June, 2023

Page: [84 - 99] Pages: 16

DOI: 10.2174/1389202924666230601122334

Price: $65

Abstract

Background: Crohn's disease (CD) is a chronic idiopathic inflammatory bowel disease affecting the entire gastrointestinal tract from the mouth to the anus. These patients often experience a period of symptomatic relapse and remission. A 20 - 30% symptomatic recurrence rate is reported in the first year after surgery, with a 10% increase each subsequent year. Thus, surgery is done only to relieve symptoms and not for the complete cure of the disease. The determinants and the genetic factors of this disease recurrence are also not well-defined. Therefore, enhanced diagnostic efficiency and prognostic outcome are critical for confronting CD recurrence.

Methods: We analysed ileal mucosa samples collected from neo-terminal ileum six months after surgery (M6=121 samples) from Crohn's disease dataset (GSE186582). The primary aim of this study is to identify the potential genes and critical pathways in post-operative recurrence of Crohn’s disease. We combined the differential gene expression analysis with Recursive feature elimination (RFE), a machine learning approach to get five critical genes for the postoperative recurrence of Crohn's disease. The features (genes) selected by different methods were validated using five binary classifiers for recurrence and remission samples: Logistic Regression (LR), Decision tree classifier (DT), Support Vector Machine (SVM), Random Forest classifier (RF), and K-nearest neighbor (KNN) with 10-fold cross-validation. We also performed weighted gene co-expression network analysis (WGCNA) to select specific modules and feature genes associated with Crohn's disease postoperative recurrence, smoking, and biological sex. Combined with other biological interpretations, including Gene Ontology (GO) analysis, pathway enrichment, and protein-protein interaction (PPI) network analysis, our current study sheds light on the indepth research of CD diagnosis and prognosis in postoperative recurrence.

Results: PLOD2, ZNF165, BOK, CX3CR1, and ARMCX4, are the important genes identified from the machine learning approach. These genes are reported to be involved in the viral protein interaction with cytokine and cytokine receptors, lysine degradation, and apoptosis. They are also linked with various cellular and molecular functions such as Peptidyl-lysine hydroxylation, Central nervous system maturation, G protein-coupled chemoattractant receptor activity, BCL-2 homology (BH) domain binding, Gliogenesis and negative regulation of mitochondrial depolarization. WGCNA identified a gene co-expression module that was primarily involved in mitochondrial translational elongation, mitochondrial translational termination, mitochondrial translation, mitochondrial respiratory chain complex, mRNA splicing via spliceosome pathways, etc.; Both the analysis result emphasizes that the mitochondrial depolarization pathway is linked with CD recurrence leading to oxidative stress in promoting inflammation in CD patients.

Conclusion: These key genes serve as the novel diagnostic biomarker for the postoperative recurrence of Crohn’s disease. Thus, among other treatment options present until now, these biomarkers would provide success in both diagnosis and prognosis, aiming for a long-lasting remission to prevent further complications in CD.

Keywords: Crohn's disease, postoperative recurrence, protein-protein interaction (PPI) network, diagnostic biomarker, remission, diagnosis.

« Previous
Graphical Abstract

[1]
Loftus EV Jr, Silverstein MD, Sandborn WJ, Tremaine WJ, Harmsen WS, Zinsmeister AR. Crohn’s disease in Olmsted County, Minnesota, 1940-1993: incidence, prevalence, and survival. Gastroenterology 1998; 114(6): 1161-8.
[http://dx.doi.org/10.1016/s0016-5085(98)70421-4]
[2]
Feuerstein JD, Cheifetz AS. Crohn Disease: Epidemiology, Diagnosis, and Management. Mayo Clin Proc 2017; 92(7): 1088-103.
[http://dx.doi.org/10.1016/j.mayocp.2017.04.010]
[3]
Molodecky NA, Soon IS, Rabi DM, Ghali WA, Ferris M, Chernoff G, et al. Increasing incidence and prevalence of the inflammatory bowel diseases with time, based on systematic review. Gastroenterology 2012; 142(1): 46-54.e42.
[http://dx.doi.org/10.1053/j.gastro201110001]
[4]
Ng SC, Bernstein CN, Vatn MH, Lakatos PL, Loftus EV Jr, Tysk C, et al. Epidemiology and Natural History Task Force of the International Organization of Inflammatory Bowel Disease (IOIBD). Geographical variability and environmental risk factors in inflammatory bowel disease. Gut 2013; 62(4): 630-49.
[http://dx.doi.org/10.1136/gutjnl-2012-303661]
[5]
Loftus EV Jr. Clinical epidemiology of inflammatory bowel disease: Incidence, prevalence, and environmental influences. Gastroenterology 2004; 126(6): 1504-17.
[http://dx.doi.org/10.1053/j.gastro.2004.01.063]
[6]
Cheifetz AS. Management of active Crohn disease. JAMA 2013; 309(20): 2150-8.
[http://dx.doi.org/10.1001/jama.2013.4466]
[7]
Torres J, Mehandru S, Colombel JF, Peyrin-Biroulet L. Crohn’s disease. Lancet 2017; 389(10080): 1741-55.
[http://dx.doi.org/10.1016/S0140-6736(16)31711-1]
[8]
Olaison G, Sjödahl R, Tagesson C. Glucocorticoid treatment in ileal Crohn’s disease: relief of symptoms but not of endoscopically viewed inflammation. Gut 1990; 31(3): 325-8.
[http://dx.doi.org/10.1136/gut.31.3.325]
[9]
Michelassi F, Balestracci T, Chappell R, Block GE. Primary and recurrent Crohn's disease. Experience with 1379 patients. AnnSurg 1991; 214(3): 238-40.
[http://dx.doi.org/10.1097/00000658-199109000-00006]
[10]
Sakibuzzaman M, Moosa SA, Akhter M, Trisha IH, Talib KA. Identifying the Neurogenetic Framework of Crohn’s Disease Through Investigative Analysis of the Nucleotide-binding Oligomerization Domain-containing Protein 2 Gene Mutation. Cureus 2019; 11(9): e5680.
[http://dx.doi.org/10.7759/cureus.5680]
[11]
Borley NR, Mortensen NJ, Jewell DP. Preventing postoperative recurrence of Crohn’s disease. Br J Surg 1997; 84(11): 1493-502.
[PMID: 9393267]
[12]
Sachar DB, Wolfson DM, Greenstein AJ, Goldberg J, Styczynski R, Janowitz HD. Risk factors for postoperative recurrence of Crohn’s disease. Gastroenterology 1983; 85(4): 917-21.
[PMID: 6884714]
[13]
Ellis L, Calhoun P, Kaiser DL, Rudolf LE, Hanks JB. Postoperative recurrence in Crohn’s disease. The effect of the initial length of bowel resection and operative procedure. Ann Surg 1984; 199(3): 340-7.
[http://dx.doi.org/10.1097/00000658-198403000-00015]
[14]
Shivananda S, Hordijk ML, Pena AS, Mayberry JF. Crohn’s disease: risk of recurrence and reoperation in a defined population. Gut 1989; 30(7): 990-5.
[http://dx.doi.org/10.1136/gut.30.7.990]
[15]
Wettergren A, Christiansen J. Risk of recurrence and reoperation after resection for ileocolic Crohn’s disease. Scand J Gastroenterol 1991; 26(12): 1319-22.
[http://dx.doi.org/10.3109/00365529108998629]
[16]
Cottone M, Rosselli M, Orlando A, et al. Smoking habits and recurrence in Crohn’s disease. Gastroenterology 1994; 106(3): 643-8.
[http://dx.doi.org/10.1016/0016-5085(94)90697-1]
[17]
Post S, Herfarth C, Böhm E, et al. The impact of disease pattern, surgical management, and individual surgeons on the risk for relaparotomy for recurrent Crohn’s disease. Ann Surg 1996; 223(3): 253-60.
[http://dx.doi.org/10.1097/00000658-199603000-00005]
[18]
Anseline PF, Wlodarczyk J, Murugasu R. Presence of granulomas is associated with recurrence after surgery for Crohn’s disease: experience of a surgical unit. Br J Surg 1997; 84(1): 78-82.
[PMID: 9043461]
[19]
Bernell O, Lapidus A, Hellers G. Risk factors for surgery and postoperative recurrence in Crohn’s disease. Ann Surg 2000; 231(1): 38-45.
[http://dx.doi.org/10.1097/00000658-200001000-00006]
[20]
Yamamoto T. Factors affecting recurrence after surgery for Crohn’s disease. World J Gastroenterol 2005; 11(26): 3971-9.
[http://dx.doi.org/10.3748/wjg.v11.i26.3971]
[21]
Ashton JJ, Seaby EG, Beattie RM, Ennis S. NOD2 in Crohn’s disease- unfinished business. J Crohn’s Colitis 2022; •••: jjac124.
[http://dx.doi.org/10.1093/ecco-jcc/jjac124]
[22]
Netea MG, Ferwerda G, de Jong DJ, Werts C, Boneca IG, Jéhanno M, et al. The frameshift mutation in Nod2 results in unresponsiveness not only to Nod2- but also Nod1-activating peptidoglycan agonists. J Biol Chem 2005; 280(43): 35859-67.
[http://dx.doi.org/10.1074/jbc.M504924200]
[23]
Solon JG, Burke JP, Walsh SR, Coffey JC. The effect of NOD2 polymorphism on postsurgical recurrence in Crohn’s disease: a systematic review and meta-analysis of available literature. Inflamm Bowel Dis 2013; 19(5): 1099-105.
[http://dx.doi.org/10.1097/MIB.0b013e3182813391]
[24]
Fowler SA, Ananthakrishnan AN, Gardet A, et al. SMAD3 gene variant is a risk factor for recurrent surgery in patients with Crohn’s disease. J Crohn’s Colitis 2014; 8(8): 845-51.
[http://dx.doi.org/10.1016/j.crohns.2014.01.003]
[25]
Laffin MR, Fedorak RN, Wine E, Dicken B, Madsen KL. A BACH2 Gene Variant Is Associated with Postoperative Recurrence of Crohn’s Disease. J Am Coll Surg 2018; 226(5): 902-8.
[http://dx.doi.org/10.1016/j.jamcollsurg.2018.01.052]
[26]
Razmara M, Srinivasula SM, Wang L, et al. CARD-8 protein, a new CARD family member that regulates caspase-1 activation and apoptosis. J Biol Chem 2002; 277(16): 13952-8.
[http://dx.doi.org/10.1074/jbc.M107811200]
[27]
Germain A, Guéant RM, Chamaillard M, Bresler L, Guéant JL, Peyrin-Biroulet L. CARD8 gene variant is a risk factor for recurrent surgery in patients with Crohn’s disease. Dig Liver Dis 2015; 47(11): 938-42.
[http://dx.doi.org/10.1016/j.dld.2015.07.013]
[28]
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9: 559.
[http://dx.doi.org/10.1186/1471-2105-9-559]
[29]
Shi H, Sun S, Zhou X, He Y, Peng Q. GBP4 is an immune-related biomarker for patients with ileocolonic Crohn’s disease by comprehensive analysis. Eur J Inflamm 2022; •••: 20.
[http://dx.doi.org/10.1177/1721727X221116743]
[30]
Jamshidi A, Pelletier JP, Martel-Pelletier J. Machine-learning-based patient-specific prediction models for knee osteoarthritis. Nat Rev Rheumatol 2019; 15(1): 49-60.
[http://dx.doi.org/10.1038/s41584-018-0130-5]
[31]
Emig D, Salomonis N, Baumbach J, Lengauer T, Conklin BR, Albrecht M. AltAnalyze and DomainGraph: analyzing and visualizing exon expression data. Nucleic Acids Res 2010; 38: w755-62.
[http://dx.doi.org/10.1093/nar/gkq405]
[32]
Irizarry RA, Hobbs B, Collin F, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 2003; 4(2): 249-64.
[http://dx.doi.org/10.1093/biostatistics/4.2.249]
[33]
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 1995; 57(1): 289-300.
[http://dx.doi.org/10.1111/j.2517-6161.1995.tb02031.x]
[34]
Hassan CA, Khan MS, Shah MA. Comparison of machine learning algorithms in data classification. IEEE 2018; 8748995.
[http://dx.doi.org/10.23919/IConAC.2018.8748995]
[35]
Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011; 12: 2825-30.
[36]
Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn 2002; 46(1): 389-422.
[http://dx.doi.org/10.1023/A:1012487302797]
[37]
Díaz-Uriarte R, Alvarez de Andrés S. Gene selection and classification of microarray data using random forest. BMC Bioinformatics 2006; 7: 3.
[http://dx.doi.org/10.1186/1471-2105-7-3]
[38]
Karthik KV, Rajalingam A, Shivashankar M, Ganjiwale A. Recursive Feature Elimination-based Biomarker Identification for Open Neural Tube Defects. Curr Genomics 2022; 23(3): 195-206.
[http://dx.doi.org/10.2174/1389202923666220511162038]
[39]
Baldi P, Brunak S, Chauvin Y, Andersen CA, Nielsen H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000; 16(5): 412-24.
[http://dx.doi.org/10.1093/bioinformatics/16.5.412]
[40]
Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit 1997; 30(7): 1145-59.
[http://dx.doi.org/10.1016/S0031-3203(96)00142-2]
[41]
Abbas M, El-Manzalawy Y. Machine learning based refined differential gene expression analysis of pediatric sepsis. BMC Med Genomics 2020; 13(1): 122.
[http://dx.doi.org/10.1186/s12920-020-00771-4]
[42]
Sherman BT, Hao M, Qiu J, et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res 2022; 50(W1): W216–21.
[http://dx.doi.org/10.1093/nar/gkac194]
[43]
Ge SX, Jung D, Yao R. ShinyGO: a graphical gene-set enrichment tool for animals and plants. Bioinformatics 2020; 36(8): 2628-9.
[http://dx.doi.org/10.1093/bioinformatics/btz931]
[44]
Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res 2010; 38: w214-20.
[http://dx.doi.org/10.1093/nar/gkq537]
[45]
Davis AP, Grondin CJ, Johnson RJ, Sciaky D, Wiegers J, Wiegers TC, et al. Comparative toxicogenomics database (CTD):update 2021. Nucleic Acids Res 2020; 49(dl): d1138-43.
[http://dx.doi.org/10.1093/nar/gkaa891]
[46]
Sugimoto K. Role of STAT3 in inflammatory bowel disease. World J Gastroenterol 2008; 14(33): 5110-4.
[http://dx.doi.org/10.3748/wjg.14.5110]
[47]
Ngollo M, Perez K, Hammoudi N, et al. Identification of Gene Expression Profiles Associated with an Increased Risk of Postoperative Recurrence in Crohn’s Disease. J Crohn’s Colitis 2022; 16(8): 1269-80.
[http://dx.doi.org/10.1093/ecco-jcc/jjac021]
[48]
Yu H, Pardoll D, Jove R. STATs in cancer inflammation and immunity: a leading role for STAT3. Nat Rev Cancer 2009; 9(11): 798-809.
[http://dx.doi.org/10.1038/nrc2734]
[49]
Takatsu K, Nakajima H. IL-5 and eosinophilia. Curr Opin Immunol 2008; 20(3): 288-94.
[http://dx.doi.org/10.1016/j.coi.2008.04.001]
[50]
Huo Y, Cao K, Kou B, et al. Tumor Suppressor p53-Binding Protein 2 (TP53BP2): Roles in suppressing tumorigenesis and therapeutic opportunities. Genes Dis 2022.
[http://dx.doi.org/10.1016/j.gendis.2022.08.014]
[51]
Pong Ng H, Kim GD, Ricky Chan E, Dunwoodie SL, Mahabeleshwar GH. CITED2 limits pathogenic inflammatory gene programs in myeloid cells. FASEB J 2020; 34(9): 12100-13.
[http://dx.doi.org/10.1096/fj.202000864R]
[52]
Garmendia I, Redin E, Montuenga LM, Calvo A. YES1: A Novel Therapeutic Target and Biomarker in Cancer. Mol Cancer Ther 2022; 21(9): 1371-80.
[http://dx.doi.org/10.1158/1535-7163.MCT-21-0958]
[53]
Arafeh R, Qutob N, Emmanuel R, Keren-Paz A, Madore J, Elkahloun A, et al. Recurrent inactivating RASA2 mutations in melanoma. Nat Genet 2015; 47(12): 1408-10.
[http://dx.doi.org/10.1038/ng.3427]
[54]
Awada Z, Nasr R, Akika R, et al. DNA methylome-wide alterations associated with estrogen receptor-dependent effects of bisphenols in breast cancer. Clin Epigenetics 2019; 11(1): 138.
[http://dx.doi.org/10.1186/s13148-019-0725-y]
[55]
Tryndyak V, Kindrat I, Dreval K, Churchwell MI, Beland FA, Pogribny IP. Churchwell MI, Beland FA,Pogribny IP (2018) Effect of aflatoxin B1, benzo[a]pyrene, andmethapyrilene on transcriptomic and epigenetic alterations in human liver HepaRG cells. Food Chem Toxicol 2018; 121: 214-23.
[http://dx.doi.org/10.1016/j.fct.2018.08.034]
[56]
van Breda SGJ, Claessen SMH, van Herwijnen M, et al. Integrative omics data analyses of repeated dose toxicity of valproic acid in vitro reveal new mechanisms of steatosis induction. Toxicology 2018; 393: 160-70.
[http://dx.doi.org/10.1016/j.tox.2017.11.013]
[57]
Jiang CL, He SW, Zhang YD, et al. Air pollution and DNA methylation alterations in lung cancer: A systematic and comparative study. Oncotarget Jan 2017; 3(8): 1369-91.
[http://dx.doi.org/10.18632/oncotarget.13622]
[58]
Eckstein M, Rea M, Fondufe-Mittendorf YN. Microarray dataset of transient and permanent DNA methylation changes in HeLa cells undergoing inorganic arsenic-mediated epithelial-to-mesenchymal transition. Data Brief 2017; 13: 6-9.
[http://dx.doi.org/10.1016/j.dib.2017.05.002]
[59]
Gulec C, Coban N, Ozsait-Selcuk B, Sirma-Ekmekci S, Yildirim O, Erginel-Unaltuna N. Identification of potential targetgenes of ROR-alpha in THP1 and HUVEC cell lines Exp CellRes 2017; 353(1): 5-15.
[http://dx.doi.org/10.1016/j.yexcr.2017.02.028]
[60]
Ausman J, Abbade J, Ermini L, et al. Ceramide-induced BOK promotes mitochondrial fission in preeclampsia. Cell Death Dis 2018; 9(3): 298.
[http://dx.doi.org/10.1038/s41419-018-0360-0]
[61]
Wu S, Zhou F, Zhang Z, Xing D. Mitochondrial oxidative stress causes mitochondrial fragmentation via differential modulation of mitochondrial fission-fusion proteins. FEBS J 2011; 278(6): 941-54.
[http://dx.doi.org/10.1111/j.1742-4658.2011.08010.x]
[62]
Wan Y, Yang L, Jiang S, Qian D, Duan J. Excessive Apoptosis in Ulcerative Colitis: Crosstalk Between Apoptosis, ROS, ER Stress, and Intestinal Homeostasis. Inflamm Bowel Dis 2022; 28(4): 639-48.
[http://dx.doi.org/10.1093/ibd/izab277]
[63]
Hu W, Fang T, Zhou M. Identification of hub genes and immune infiltration in ulcerative colitis using bioinformatics. Sci Rep 2023; 13(1): 6036.
[http://dx.doi.org/10.1038/s41598-023-33292-y]
[64]
Shao J, Li Z, Gao Y, et al. Construction of a “Bacteria-Metabolites” Co-Expression Network to Clarify the Anti-Ulcerative Colitis Effect of Flavonoids of Sophora flavescens Aiton by Regulating the “Host-Microbe” Interaction. Front Pharmacol 2021; 12: 710052.
[http://dx.doi.org/10.3389/fphar.2021.710052]
[65]
Helbig KL, Nothnagel M, Hampe J, et al. A case-only study of gene-environment interaction between genetic susceptibility variants in NOD2 and cigarette smoking in Crohn’s disease aetiology. BMC Med Genet 2012; 13: 14.
[http://dx.doi.org/10.1186/1471-2350-13-14]
[66]
Li JH, Wu N, Yang HM, Tang HB, Bao DP, Ji JM. Interaction between STAT3 gene polymorphisms and smoking on Crohn’s disease susceptibility: a case-control study in a Chinese Han population. Inflamm Res 2016; 65(7): 573-8.
[http://dx.doi.org/10.1007/s00011-016-0941-2]
[67]
Hammoudi N, Cazals-Hatem D, Auzolle C, Gardair C, Ngollo M, Bottois H, et al. Association Between Microscopic Lesions at Ileal Resection Margin and Recurrence After Surgery in Patients With Crohn's Disease. Clin Gastroenterol Hepatol 2020; 18(1): 141-9.
[http://dx.doi.org/10.1016/j.cgh.2019.04.045]
[68]
Allez M, Auzolle C, Ngollo M, Bottois H, Chardiny V, Corraliza AM, et al. cell clonal expansions in ileal Crohn's disease are associated with smoking behaviour and postoperative recurrence. Gut 2019; 68(11): 1961-70.
[http://dx.doi.org/10.1136/gutjnl-2018-317878]
[69]
Yamauchi M, Terajima M, Shiiba M. Lysine Hydroxylation and Cross-Linking of Collagen. Methods Mol Biol 2019; 1934: 309-24.
[http://dx.doi.org/10.1007/978-1-4939-9055-9_19]
[70]
Spinelli A, Correale C, Szabo H, Montorsi M. Intestinal fibrosis in Crohn’s disease: medical treatment or surgery? Curr Drug Targets 2010; 11(2): 242-8.
[http://dx.doi.org/10.2174/138945010790309984]
[71]
van Haaften WT, Blokzijl T, Hofker HS, et al. Intestinal stenosis in Crohn’s disease shows a generalized upregulation of genes involved in collagen metabolism and recognition that could serve as novel anti-fibrotic drug targets. Therap Adv Gastroenterol 2020; 13: 1756284820952578.
[http://dx.doi.org/10.1177/1756284820952578]
[72]
van der Slot AJ, Zuurmond AM, Bardoel AF, Wijmenga C, Pruijs HE, Sillence DO. Identification of PLOD2 as telopeptide lysyl hydroxylase, an important enzyme in fibrosis. J Biol Chem 2003; 278(42): 40967-72.
[http://dx.doi.org/10.1074/jbc.M307380200]
[73]
Wang Y, Wen H, Fu J, Cai L, Li PL, Zhao CL, et al. Hepatocyte TNF Receptor-Associated Factor 6 Aggravates Hepatic Inflammation and Fibrosis by Promoting Lysine 6-Linked Polyubiquitination of Apoptosis Signal-Regulating Kinase 1. Hepatology 2020; 71(1): 93-111.
[74]
Gil J, Ramírez-Torres A, Encarnación-Guevara S. Lysine acetylation and cancer: A proteomics perspective. J Proteomics 2017; 150: 297-309.
[http://dx.doi.org/10.1016/j.jprot.2016.10.003]
[75]
Li G, Wang X, Liu G. PLOD2 Is a Potent Prognostic Marker and Associates with Immune Infiltration in Cervical Cancer. Biomed Res Int 2021; 5512340.
[http://dx.doi.org/10.1155/2021/5512340]
[76]
Rungoe C, Simonsen J, Riis L, Frisch M, Langholz E, Jess T. Inflammatory bowel disease and cervical neoplasia: a population-based nationwide cohort study. Clin Gastroenterol Hepatol 2015; 13(4): 693-700.
[http://dx.doi.org/10.1016/j.cgh.2014.07.036]
[77]
Wang Z, Fan G, Zhu H, Yu L, She D. PLOD2 high expression associates with immune infiltration and facilitates cancer progression in osteosarcoma Front Oncol 2022; 12: 980390.
[http://dx.doi.org/10.3389/fonc.2022.980390]
[78]
Du W, Liu N, Zhang Y, et al. PLOD2 promotes aerobic glycolysis and cell progression in colorectal cancer by upregulating HK2. Biochem Cell Biol 2020; 98(3): 386-95.
[http://dx.doi.org/10.1139/bcb-2019-0256]
[79]
Renthal W, Nestler EJ. Histone acetylation in drug addiction. Semin Cell Dev Biol 2009; 20(4): 387-94.
[http://dx.doi.org/10.1016/j.semcdb.2009.01.005]
[80]
Tsaprouni LG, Ito K, Powell JJ, Adcock IM, Punchard N. Differential patterns of histone acetylation in inflammatory bowel diseases. J Inflamm (Lond) 2011; 8(1): 1.
[http://dx.doi.org/10.1186/1476-9255-8-1]
[81]
Drazic A, Myklebust LM, Ree R, Arnesen T. The world of protein acetylation. Biochim Biophys Acta 2016; 1864(10): 1372-401.
[http://dx.doi.org/10.1016/j.bbapap.2016.06.007]
[82]
Tirosvoutis KN, Divane A, Jones M, Affara NA. Characterization of a novel zinc finger gene (ZNF165) mapping to 6p21 that is expressed specifically in testis. Genomics 1995; 28(3): 485-90.
[http://dx.doi.org/10.1006/geno.1995.1178]
[83]
Choi PM, Zelig MP. Similarity of colorectal cancer in Crohn’s disease and ulcerative colitis: implications for carcinogenesis and prevention. Gut 1994; 35(7): 950-4.
[http://dx.doi.org/10.1136/gut.35.7.950]
[84]
Miguel JC, Maxwell AA, Hsieh JJ, et al. Epidermal growth factor suppresses intestinal epithelial cell shedding through a MAPK-dependent pathway. J Cell Sci 2017; 130(1): 90-6.
[http://dx.doi.org/10.1242/jcs.182584]
[85]
Dong XY, Yang XA, Wang YD, Chen WF. Zinc-finger protein ZNF165 is a novel cancer-testis antigen capable of eliciting antibody response in hepatocellular carcinoma patients. Br J Cancer 2004; 91(8): 1566-70.
[http://dx.doi.org/10.1038/sj.bjc.6602138]
[86]
Singh PK, Srivastava AK, Dalela D, Rath SK, Goel MM, Bhatt ML. Frequent expression of zinc-finger protein ZNF165 in human urinary bladder transitional cell carcinoma. Immunobiology 2015; 220(1): 68-73.
[http://dx.doi.org/10.1016/j.imbio.2014.08.018]
[87]
Maxfield KE, Taus PJ, Corcoran K, et al. Comprehensive functional characterization of cancer-testis antigens defines obligate participation in multiple hallmarks of cancer. Nat Commun 2015; 6: 8840.
[http://dx.doi.org/10.1038/ncomms9840]
[88]
Li F, Cao Y, Townsend CM Jr, Ko TC. TGF-beta signaling in colon cancer cells. World J Surg 2005; 29(3): 306-11.
[http://dx.doi.org/10.1007/s00268-004-7813-6]
[89]
Jääskeläinen M, Nieminen A, Pökkylä RM, et al. Regulation of cell death in human fetal and adult ovaries--role of Bok and Bcl-X(L). Mol Cell Endocrinol 2010; 330(1-2): 17-24.
[http://dx.doi.org/10.1016/j.mce.2010.07.020]
[90]
Yakovlev AG, Di Giovanni S, Wang G, Liu W, Stoica B, Faden AI. BOK and NOXA are essential mediators of p53-dependent apoptosis. J Biol Chem 2004; 279(27): 28367-74.
[http://dx.doi.org/10.1074/jbc.M313526200]
[91]
Einsele-Scholz S, Malmsheimer S, Bertram K, et al. Bok is a genuine multi-BH-domain protein that triggers apoptosis in the absence of Bax and Bak. J Cell Sci 2016; 129(11): 2213-23.
[http://dx.doi.org/10.1242/jcs.181727]
[92]
Adams JM, Cory S. The BCL-2 arbiters of apoptosis and their growing role as cancer targets. Cell Death Differ 2018; 25(1): 27-36.
[http://dx.doi.org/10.1038/cdd.2017.161]
[93]
Thompson CB. Apoptosis in the pathogenesis and treatment of disease. Science 1995; 267(5203): 1456-62.
[http://dx.doi.org/10.1126/science.7878464]
[94]
Doering J, Begue B, Lentze MJ, et al. Induction of T lymphocyte apoptosis by sulphasalazine in patients with Crohn’s disease. Gut 2004; 53(11): 1632-8.
[http://dx.doi.org/10.1136/gut.2003.037911]
[95]
Liu Z, Yadav PK, Xu X, et al. The increased expression of IL-23 in inflammatory bowel disease promotes intraepithelial and lamina propria lymphocyte inflammatory responses and cytotoxicity. J Leukoc Biol 2011; 89(4): 597-606.
[http://dx.doi.org/10.1189/jlb.0810456]
[96]
Hsu SY, Kaipia A, McGee E, Lomeli M, Hsueh AJ. Bok is a pro-apoptotic Bcl-2 protein with restricted expression in reproductive tissues and heterodimerizes with selective anti-apoptotic Bcl-2 family members. Proc Natl Acad Sci USA 1997; 94(23): 12401-6.
[http://dx.doi.org/10.1073/pnas.94.23.12401]
[97]
Srivastava R, Cao Z, Nedeva C, et al. BCL-2 family protein BOK is a positive regulator of uridine metabolism in mammals. Proc Natl Acad Sci USA 2019; 116(31): 15469-74.
[http://dx.doi.org/10.1073/pnas.1904523116]
[98]
Carberry S, D’Orsi B, Monsefi N, et al. The BAX/BAK-like protein BOK is a prognostic marker in colorectal cancer. Cell Death Dis 2018; 9(2): 125.
[http://dx.doi.org/10.1038/s41419-017-0140-2]
[99]
Hutfless S, Fireman B, Kane S, Herrinton LJ. Screening differences and risk of cervical cancer in inflammatory bowel disease. Aliment Pharmacol Ther 2008; 28(5): 598-605.
[http://dx.doi.org/10.1111/j.1365-2036.2008.03766.x]
[100]
Chang YS, Huang HD, Yeh KT, Chang JG. Identification of novel mutations in endometrial cancer patients by whole-exome sequencing. Int J Oncol 2017; 50(5): 1778-84.
[http://dx.doi.org/10.3892/ijo.2017.3919]
[101]
Fujita M, Takada YK, Takada Y. Integrins αvβ3 and α4β1 act as coreceptors for fractalkine, and the integrin-binding defective mutant of fractalkine is an antagonist of CX3CR1. J Immunol 2012; 189(12): 5809-19.
[http://dx.doi.org/10.4049/jimmunol.1200889]
[102]
Nishimura M, Umehara H, Nakayama T, et al. Dual functions of fractalkine/CX3C ligand 1 in trafficking of perforin+/granzyme B+ cytotoxic effector lymphocytes that are defined by CX3CR1 expression. J Immunol 2002; 168(12): 6173-80.
[http://dx.doi.org/10.4049/jimmunol.168.12.6173]
[103]
Fong AM, Robinson LA, Steeber DA, et al. Fractalkine and CX3CR1 mediate a novel mechanism of leukocyte capture, firm adhesion, and activation under physiologic flow. J Exp Med 1998; 188(8): 1413-9.
[http://dx.doi.org/10.1084/jem.188.8.1413]
[104]
Imai T, Hieshima K, Haskell C, et al. Identification and molecular characterization of fractalkine receptor CX3CR1, which mediates both leukocyte migration and adhesion. Cell 1997; 91(4): 521-30.
[http://dx.doi.org/10.1016/s0092-8674(00)80438-9]
[105]
Marelli G, Belgiovine C, Mantovani A, Erreni M, Allavena P. Non-redundant role of the chemokine receptor CX3CR1 in the anti-inflammatory function of gut macrophages. Immunobiology 2017; 222(2): 463-72.
[http://dx.doi.org/10.1016/j.imbio.2016.07013]
[106]
Leonardi I, Li X, Semon A, et al. CX3CR1+ mononuclear phagocytes control immunity to intestinal fungi. Science 2018; 359(6372): 232-6.
[http://dx.doi.org/10.1126/science.aao1503]
[107]
Li J, Zhou H, Fu X, Zhang M, Sun F, Fan H. Dynamic role of macrophage CX3CR1 expression in inflammatory bowel disease. Immunol Lett 2021; 232: 39-44.
[http://dx.doi.org/10.1016/j.imlet.2021.02.001]
[108]
Guglielmetti S, Mantovani A, Allavena P. Heme-oxygenase-1 Production by Intestinal CX3CR1+ Macrophages Helps to Resolve Inflammation and Prevents Carcinogenesis. Cancer Res 2017; 77(16): 4472-85.
[http://dx.doi.org/10.1158/0008-5472CAN-16-2501]
[109]
Sabate JM, Ameziane N, Lamoril J, et al. The V249I polymorphism of the CX3CR1 gene is associated with fibrostenotic disease behavior in patients with Crohn’s disease. Eur J Gastroenterol Hepatol 2008; 20(8): 748-55.
[http://dx.doi.org/10.1097/MEG.0b013e3282f824c9]
[110]
Yue Y, Zhang Q, Sun Z. CX3CR1 Acts as a Protective Biomarker in the Tumor Microenvironment of Colorectal Cancer. Front Immunol 2022; 12: 758040.
[http://dx.doi.org/10.3389/fimmu.2021.758040]

Rights & Permissions Print Cite
Article Metrics
29
4
© 2024 Bentham Science Publishers | Privacy Policy