Login Register Cart 0
Generic placeholder image

Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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

Patterns of Gene Expression Profiles Associated with Colorectal Cancer in Colorectal Mucosa by Using Machine Learning Methods

Author(s): Jing Xin Ren, Lei Chen, Wei Guo, Kai Yan Feng, Yu-Dong Cai* and Tao Huang*

Volume 27, Issue 19, 2024

Published on: 10 November, 2023

Page: [2921 - 2934] Pages: 14

DOI: 10.2174/0113862073266300231026103844

open access plus

Abstract

Background: Colorectal cancer (CRC) has a very high incidence and lethality rate and is one of the most dangerous cancer types. Timely diagnosis can effectively reduce the incidence of colorectal cancer. Changes in para-cancerous tissues may serve as an early signal for tumorigenesis. Comparison of the differences in gene expression between para-cancerous and normal mucosa can help in the diagnosis of CRC and understanding the mechanisms of development.

Objectives: This study aimed to identify specific genes at the level of gene expression, which are expressed in normal mucosa and may be predictive of CRC risk.

Methods: A machine learning approach was used to analyze transcriptomic data in 459 samples of normal colonic mucosal tissue from 322 CRC cases and 137 non-CRC, in which each sample contained 28,706 gene expression levels. The genes were ranked using four ranking methods based on importance estimation (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (decision tree [DT], K-nearest neighbor [KNN], random forest [RF], and support vector machine [SVM]) were combined with incremental feature selection [IFS] methods to construct a prediction model with excellent performance.

Result: The top-ranked genes, namely, HOXD12, CDH1, and S100A12, were associated with tumorigenesis based on previous studies.

Conclusion: This study summarized four sets of quantitative classification rules based on the DT algorithm, providing clues for understanding the microenvironmental changes caused by CRC. According to the rules, the effect of CRC on normal mucosa can be determined.

« Previous Next »
[1]
Brustugun, O.T.; Møller, B.; Helland, Å. Years of life lost as a measure of cancer burden on a national level. Br. J. Cancer, 2014, 111(5), 1014-1020.
[http://dx.doi.org/10.1038/bjc.2014.364] [PMID: 24983370]
[2]
Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2021, 71(3), 209-249.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[3]
Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin., 2021, 71(1), 7-33.
[http://dx.doi.org/10.3322/caac.21654] [PMID: 33433946]
[4]
Fearon, E.R.; Vogelstein, B. A genetic model for colorectal tumorigenesis. Cell, 1990, 61(5), 759-767.
[http://dx.doi.org/10.1016/0092-8674(90)90186-I] [PMID: 2188735]
[5]
Amaro, A.; Chiara, S.; Pfeffer, U. Molecular evolution of colorectal cancer: From multistep carcinogenesis to the big bang. Cancer Metastasis Rev., 2016, 35(1), 63-74.
[http://dx.doi.org/10.1007/s10555-016-9606-4] [PMID: 26947218]
[6]
Paterson, C.; Clevers, H.; Bozic, I. Mathematical model of colorectal cancer initiation. Proc. Natl. Acad. Sci. USA, 2020, 117(34), 20681-20688.
[http://dx.doi.org/10.1073/pnas.2003771117] [PMID: 32788368]
[7]
Quail, D.F.; Joyce, J.A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med., 2013, 19(11), 1423-1437.
[http://dx.doi.org/10.1038/nm.3394] [PMID: 24202395]
[8]
Ombrato, L.; Nolan, E.; Kurelac, I.; Mavousian, A.; Bridgeman, V.L.; Heinze, I.; Chakravarty, P.; Horswell, S.; Gonzalez-Gualda, E.; Matacchione, G.; Weston, A.; Kirkpatrick, J.; Husain, E.; Speirs, V.; Collinson, L.; Ori, A.; Lee, J.H.; Malanchi, I. Metastatic-niche labelling reveals parenchymal cells with stem features. Nature, 2019, 572(7771), 603-608.
[http://dx.doi.org/10.1038/s41586-019-1487-6] [PMID: 31462798]
[9]
Lochhead, P.; Chan, A.T.; Nishihara, R.; Fuchs, C.S.; Beck, A.H.; Giovannucci, E.; Ogino, S. Etiologic field effect: Reappraisal of the field effect concept in cancer predisposition and progression. Mod. Pathol., 2015, 28(1), 14-29.
[http://dx.doi.org/10.1038/modpathol.2014.81] [PMID: 24925058]
[10]
Patel, A.; Tripathi, G.; Gopalakrishnan, K.; Williams, N.; Arasaradnam, R.P. Field cancerisation in colorectal cancer: A new frontier or pastures past? World J. Gastroenterol., 2015, 21(13), 3763-3772.
[http://dx.doi.org/10.3748/wjg.v21.i13.3763] [PMID: 25852261]
[11]
Hawthorn, L.; Lan, L.; Mojica, W. Evidence for field effect cancerization in colorectal cancer. Genomics, 2014, 103(2-3), 211-221.
[http://dx.doi.org/10.1016/j.ygeno.2013.11.003] [PMID: 24316131]
[12]
Chai, H.; Brown, R.E. Field effect in cancer-an update. Ann. Clin. Lab. Sci., 2009, 39(4), 331-337.
[PMID: 19880759]
[13]
Chen, L.C.; Hao, C.Y.; Chiu, Y.S.Y.; Wong, P.; Melnick, J.S.; Brotman, M.; Moretto, J.; Mendes, F.; Smith, A.P.; Bennington, J.L.; Moore, D.; Lee, N.M. Alteration of gene expression in normal-appearing colon mucosa of APC(min) mice and human cancer patients. Cancer Res., 2004, 64(10), 3694-3700.
[http://dx.doi.org/10.1158/0008-5472.CAN-03-3264] [PMID: 15150130]
[14]
Polley, A.C.J.; Mulholland, F.; Pin, C.; Williams, E.A.; Bradburn, D.M.; Mills, S.J.; Mathers, J.C.; Johnson, I.T. Proteomic analysis reveals field-wide changes in protein expression in the morphologically normal mucosa of patients with colorectal neoplasia. Cancer Res., 2006, 66(13), 6553-6562.
[http://dx.doi.org/10.1158/0008-5472.CAN-06-0534] [PMID: 16818627]
[15]
Daniel, C.R.; Bostick, R.M.; Flanders, W.D.; Long, Q.; Fedirko, V.; Sidelnikov, E.; Seabrook, M.E. TGF-alpha expression as a potential biomarker of risk within the normal-appearing colorectal mucosa of patients with and without incident sporadic adenoma. Cancer Epidemiol. Biomarkers Prev., 2009, 18(1), 65-73.
[http://dx.doi.org/10.1158/1055-9965.EPI-08-0732] [PMID: 19124482]
[16]
Maurya, N.S.; Kushwaha, S.; Chawade, A.; Mani, A. Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer. Sci. Rep., 2021, 11(1), 14304.
[http://dx.doi.org/10.1038/s41598-021-92692-0] [PMID: 34253750]
[17]
Hossain, M.J.; Chowdhury, U.N.; Islam, M.B.; Uddin, S.; Ahmed, M.B.; Quinn, J.M.W.; Moni, M.A. Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer. Comput. Biol. Med., 2021, 135, 104539.
[http://dx.doi.org/10.1016/j.compbiomed.2021.104539] [PMID: 34153790]
[18]
Koppad, S.; Basava, A.; Nash, K.; Gkoutos, G.V.; Acharjee, A. Machine learning-based identification of colon cancer candidate diagnostics genes. Biology, 2022, 11(3), 365.
[http://dx.doi.org/10.3390/biology11030365] [PMID: 35336739]
[19]
Vaughan-Shaw, P.G.; Timofeeva, M.; Ooi, L.Y.; Svinti, V.; Grimes, G.; Smillie, C.; Blackmur, J.P.; Donnelly, K.; Theodoratou, E.; Campbell, H.; Zgaga, L.; Din, F.V.N.; Farrington, S.M.; Dunlop, M.G. Differential genetic influences over colorectal cancer risk and gene expression in large bowel mucosa. Int. J. Cancer, 2021, 149(5), 1100-1108.
[http://dx.doi.org/10.1002/ijc.33616] [PMID: 33937989]
[20]
Jian, F.; Huang, F.; Zhang, Y.H.; Huang, T.; Cai, Y.D. Identifying anal and cervical tumorigenesis-associated methylation signaling with machine learning methods. Front. Oncol., 2022, 12, 998032.
[http://dx.doi.org/10.3389/fonc.2022.998032] [PMID: 36249027]
[21]
Li, H.; Wang, D.; Zhou, X.; Ding, S.; Guo, W.; Zhang, S.; Li, Z.; Huang, T.; Cai, Y.D. Characterization of spleen and lymph node cell types via CITE-seq and machine learning methods. Front. Mol. Neurosci., 2022, 15, 1033159.
[http://dx.doi.org/10.3389/fnmol.2022.1033159] [PMID: 36311013]
[22]
Liu, Z.; Meng, M.; Ding, S.; Zhou, X.; Feng, K.; Huang, T.; Cai, Y.D. Identification of methylation signatures and rules for predicting the severity of SARS-CoV-2 infection with machine learning methods. Front. Microbiol., 2022, 13, 1007295.
[http://dx.doi.org/10.3389/fmicb.2022.1007295] [PMID: 36212830]
[23]
Li, Z.; Huang, F.; Chen, L.; Huang, T.; Cai, Y.D. Identifying in vitro cultured human hepatocytes markers with machine learning methods based on single-cell RNA-Seq data. Front. Bioeng. Biotechnol., 2022, 10, 916309.
[http://dx.doi.org/10.3389/fbioe.2022.916309] [PMID: 35706505]
[24]
Huang, F.; Ma, Q.; Ren, J.; Li, J.; Wang, F.; Huang, T.; Cai, Y.D. Identification of smoking-associated transcriptome aberration in blood with machine learning methods. BioMed Res. Int., 2023, 2023, 1-13.
[http://dx.doi.org/10.1155/2023/5333361] [PMID: 36644165]
[25]
Huang, F. Analysis and prediction of protein stability based on interaction network, gene ontology, and KEGG pathway enrichment scores. Biochim. Biophys. Acta. Proteins Proteomics, 2023, 18713, 140889.
[http://dx.doi.org/10.1016/j.bbapap.2023.140889]
[26]
Zhao, X.; Chen, L.; Lu, J. A similarity-based method for prediction of drug side effects with heterogeneous information. Math. Biosci., 2018, 306, 136-144.
[http://dx.doi.org/10.1016/j.mbs.2018.09.010] [PMID: 30296417]
[27]
Tibshirani, R. Regression shrinkage and selection via the lasso: A retrospective. J. R. Stat. Soc. Series B Stat. Methodol., 2011, 73(3), 273-282.
[http://dx.doi.org/10.1111/j.1467-9868.2011.00771.x]
[28]
Pedregosa, F. Scikit-learn: Machine learning in python. J. Mach. Learn. Res., 2011, 12(85), 2825-2830.
[29]
Ke, G.; Qi, M.; Thomas, F.; Taifeng, W.; Wei, C.; Weidong, M.; Qiwei, Y.; Tie-Yan, L. LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017, pp. 3149-3157.
[30]
Dramiński, M.; Rada-Iglesias, A.; Enroth, S.; Wadelius, C.; Koronacki, J.; Komorowski, J. Monte Carlo feature selection for supervised classification. Bioinformatics, 2008, 24(1), 110-117.
[http://dx.doi.org/10.1093/bioinformatics/btm486] [PMID: 18048398]
[31]
Hanchuan, Peng Fuhui Long; Ding, C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27(8), 1226-1238.
[http://dx.doi.org/10.1109/TPAMI.2005.159] [PMID: 16119262]
[32]
Liu, H.; Setiono, R. Incremental feature selection. Appl. Intell., 1998, 9(3), 217-230.
[http://dx.doi.org/10.1023/A:1008363719778]
[33]
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International joint Conference on artificial intelligence. 1995.
[34]
Chawla, N.V.; Bowyer, K.W.; Hall, L.O.; Kegelmeyer, W.P. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res., 2002, 16, 321-357.
[http://dx.doi.org/10.1613/jair.953]
[35]
Safavian, S.R.; Landgrebe, D. A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern., 1991, 21(3), 660-674.
[http://dx.doi.org/10.1109/21.97458]
[36]
Cover, T.; Hart, P. Nearest neighbor pattern classification. IEEE Trans. Inf. Theory, 1967, 13(1), 21-27.
[http://dx.doi.org/10.1109/TIT.1967.1053964]
[37]
Breiman, L. Random forests. Mach. Learn., 2001, 45(1), 5-32.
[http://dx.doi.org/10.1023/A:1010933404324]
[38]
Cortes, C.; Vapnik, V. Support-vector networks. Mach. Learn., 1995, 20(3), 273-297.
[http://dx.doi.org/10.1007/BF00994018]
[39]
Wang, H.; Chen, L. PMPTCE-HNEA: Predicting metabolic pathway types of chemicals and enzymes with a heterogeneous network embedding algorithm. Curr. Bioinform., 2023, 18.
[http://dx.doi.org/10.2174/1574893618666230224121633]
[40]
Pan, X. Identifying protein subcellular locations with embeddings-based node2loc. IEEE/ACM Trans. Comput. Biol. Bioinform., 2022, 19(2), 666-675.
[41]
Powers, D. Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. J. Mach. Learn. Technol., 2011, 2(1), 37-63.
[42]
Tang, S.; Chen, L. iATC-NFMLP: Identifying classes of anatomical therapeutic chemicals based on drug networks, fingerprints and multilayer perceptron. Curr. Bioinform., 2022, 17(9), 814-824.
[http://dx.doi.org/10.2174/1574893617666220318093000]
[43]
Yang, Y.; Chen, L. Identification of drug–disease associations by using multiple drug and disease networks. Curr. Bioinform., 2022, 17(1), 48-59.
[http://dx.doi.org/10.2174/1574893616666210825115406]
[44]
Wu, C.; Chen, L. A model with deep analysis on a large drug network for drug classification. Math. Biosci. Eng., 2022, 20(1), 383-401.
[http://dx.doi.org/10.3934/mbe.2023018] [PMID: 36650771]
[45]
Ren, J.; Zhang, Y.; Guo, W.; Feng, K.; Yuan, Y.; Huang, T.; Cai, Y.D. Identification of genes associated with the impairment of olfactory and gustatory functions in COVID-19 via machine-learning methods. Life, 2023, 13(3), 798.
[http://dx.doi.org/10.3390/life13030798] [PMID: 36983953]
[46]
Chen, L.; Chen, K.; Zhou, B. Inferring drug-disease associations by a deep analysis on drug and disease networks. Math. Biosci. Eng., 2023, 20(8), 14136-14157.
[http://dx.doi.org/10.3934/mbe.2023632] [PMID: 37679129]
[47]
Wu, T.; Hu, E.; Xu, S.; Chen, M.; Guo, P.; Dai, Z.; Feng, T.; Zhou, L.; Tang, W.; Zhan, L.; Fu, X.; Liu, S.; Bo, X.; Yu, G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation, 2021, 2(3), 100141.
[http://dx.doi.org/10.1016/j.xinn.2021.100141] [PMID: 34557778]
[48]
Matthews, B.W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta Protein Struct., 1975, 405(2), 442-451.
[http://dx.doi.org/10.1016/0005-2795(75)90109-9] [PMID: 1180967]
[49]
Bhatlekar, S.; Addya, S.; Salunek, M.; Orr, C.R.; Surrey, S.; McKenzie, S.; Fields, J.Z.; Boman, B.M. Identification of a developmental gene expression signature, including HOX genes, for the normal human colonic crypt stem cell niche: Overexpression of the signature parallels stem cell overpopulation during colon tumorigenesis. Stem Cells Dev., 2014, 23(2), 167-179.
[http://dx.doi.org/10.1089/scd.2013.0039] [PMID: 23980595]
[50]
Sanz-Pamplona, R.; Cordero, D.; Berenguer, A.; Lejbkowicz, F.; Rennert, H.; Salazar, R.; Biondo, S.; Sanjuan, X.; Pujana, M.A.; Rozek, L.; Giordano, T.J.; Ben-Izhak, O.; Cohen, H.I.; Trougouboff, P.; Bejhar, J.; Sova, Y.; Rennert, G.; Gruber, S.B.; Moreno, V. Gene expression differences between colon and rectum tumors. Clin. Cancer Res., 2011, 17(23), 7303-7312.
[http://dx.doi.org/10.1158/1078-0432.CCR-11-1570] [PMID: 21976543]
[51]
Xu, W.; Lu, J.; Zhao, Q.; Wu, J.; Sun, J.; Han, B.; Zhao, X.; Kang, Y. Genome-wide plasma cell-free DNA methylation profiling identifies potential biomarkers for lung cancer. Dis. Markers, 2019, 2019, 1-7.
[http://dx.doi.org/10.1155/2019/4108474] [PMID: 30867848]
[52]
Harada, H.; Miyamaoto, K.; Kimura, M.; Ishigami, T.; Taniyama, K.; Okada, M. Lung cancer risk stratification using methylation profile in the oral epithelium. Asian Cardiovasc. Thorac. Ann., 2019, 27(2), 87-92.
[http://dx.doi.org/10.1177/0218492318813443] [PMID: 30417685]
[53]
Rodini, C.O.; Xavier, F.C.A.; Paiva, K.B.S.; De Souza Setúbal Destro, M.F.; Moyses, R.A.; Michaluarte, P.; Carvalho, M.B.; Fukuyama, E.E.; Tajara, E.H.; Okamoto, O.K.; Nunes, F.D. Homeobox gene expression profile indicates HOXA5 as a candidate prognostic marker in oral squamous cell carcinoma. Int. J. Oncol., 2012, 40(4), 1180-1188.
[http://dx.doi.org/10.3892/ijo.2011.1321] [PMID: 22227861]
[54]
Wang, J.; Liu, Z.; Zhang, C.; Wang, H.; Li, A.; Liu, B.; Lian, X.; Ren, Z.; Zhang, W.; Wang, Y.; Zhang, B.; Pang, B.; Gao, Y. Abnormal expression of HOXD11 promotes the malignant behavior of glioma cells and leads to poor prognosis of glioma patients. PeerJ, 2021, 9, e10820.
[http://dx.doi.org/10.7717/peerj.10820] [PMID: 33614284]
[55]
Miyamoto, K.; Fukutomi, T.; Akashi-Tanaka, S.; Hasegawa, T.; Asahara, T.; Sugimura, T.; Ushijima, T. Identification of 20 genes aberrantly methylated in human breast cancers. Int. J. Cancer, 2005, 116(3), 407-414.
[http://dx.doi.org/10.1002/ijc.21054] [PMID: 15818620]
[56]
Cai, L.; Abe, M.; Izumi, S.; Imura, M.; Yasugi, T.; Ushijima, T. Identification of PRTFDC1 silencing and aberrant promoter methylation of GPR150, ITGA8 and HOXD11 in ovarian cancers. Life Sci., 2007, 80(16), 1458-1465.
[http://dx.doi.org/10.1016/j.lfs.2007.01.015] [PMID: 17303177]
[57]
Yang, H.; Zhou, J.; Mi, J.; Ma, K.; Fan, Y.; Ning, J.; Wang, C.; Wei, X.; Zhao, H.; Li, E. HOXD10 acts as a tumor-suppressive factor via inhibition of the RHOC/AKT/MAPK pathway in human cholangiocellular carcinoma. Oncol. Rep., 2015, 34(4), 1681-1691.
[http://dx.doi.org/10.3892/or.2015.4194] [PMID: 26260613]
[58]
Chen, W.; Cai, F.; Zhang, B.; Barekati, Z.; Zhong, X.Y. The level of circulating miRNA-10b and miRNA-373 in detecting lymph node metastasis of breast cancer: potential biomarkers. Tumour Biol., 2013, 34(1), 455-462.
[http://dx.doi.org/10.1007/s13277-012-0570-5] [PMID: 23238818]
[59]
Wang, Y.; Li, Z.; Zhao, X.; Zuo, X.; Peng, Z. miR-10b promotes invasion by targeting HOXD10 in colorectal cancer. Oncol. Lett., 2016, 12(1), 488-494.
[http://dx.doi.org/10.3892/ol.2016.4628] [PMID: 27347170]
[60]
Guo, Y.; Peng, Y.; Gao, D.; Zhang, M.; Yang, W.; Linghu, E.; Herman, J.G.; Fuks, F.; Dong, G.; Guo, M. Silencing HOXD10 by promoter region hypermethylation activates ERK signaling in hepatocellular carcinoma. Clin. Epigenetics, 2017, 9(1), 116.
[http://dx.doi.org/10.1186/s13148-017-0412-9] [PMID: 29075359]
[61]
Pan, W.; Wang, K.; Li, J.; Li, H.; Cai, Y.; Zhang, M.; Wang, A.; Wu, Y.; Gao, W.; Weng, W. Restoring HOXD10 exhibits therapeutic potential for ameliorating malignant progression and 5-fluorouracil resistance in colorectal cancer. Front. Oncol., 2021, 11, 771528.
[http://dx.doi.org/10.3389/fonc.2021.771528] [PMID: 34790580]
[62]
Berx, G.; Staes, K.; van Hengel, J.; Molemans, F.; Bussemakers, M.J.G.; van Bokhoven, A.; van Roy, F. Cloning and characterization of the human invasion suppressor gene E-cadherin (CDH1). Genomics, 1995, 26(2), 281-289.
[http://dx.doi.org/10.1016/0888-7543(95)80212-5] [PMID: 7601454]
[63]
Wong, A.S.T.; Gumbiner, B.M. Adhesion-independent mechanism for suppression of tumor cell invasion by E-cadherin. J. Cell Biol., 2003, 161(6), 1191-1203.
[http://dx.doi.org/10.1083/jcb.200212033] [PMID: 12810698]
[64]
Jeanes, A.; Gottardi, C.J.; Yap, A.S. Cadherins and cancer: How does cadherin dysfunction promote tumor progression? Oncogene, 2008, 27(55), 6920-6929.
[http://dx.doi.org/10.1038/onc.2008.343] [PMID: 19029934]
[65]
Larue, L.; Bellacosa, A. Epithelial–mesenchymal transition in development and cancer: Role of phosphatidylinositol 3′ kinase/AKT pathways. Oncogene, 2005, 24(50), 7443-7454.
[http://dx.doi.org/10.1038/sj.onc.1209091] [PMID: 16288291]
[66]
Chen, X.; Wang, W.; Li, Y.; Huo, Y.; Zhang, H.; Feng, F.; Xi, W.; Zhang, T.; Gao, J.; Yang, F.; Chen, S.; Yang, A.; Wang, T. MYSM1 inhibits human colorectal cancer tumorigenesis by activating miR-200 family members/CDH1 and blocking PI3K/AKT signaling. J. Exp. Clin. Cancer Res., 2021, 40(1), 341.
[http://dx.doi.org/10.1186/s13046-021-02106-2] [PMID: 34706761]
[67]
Thierolf, M.; Hagmann, M.L.; Pfeffer, M.; Berntenis, N.; Wild, N.; Roeßler, M.; Palme, S.; Karl, J.; Bodenmüller, H.; Rüschoff, J.; Rossol, S.; Rohr, G.; Rösch, W.; Friess, H.; Eickhoff, A.; Jauch, K.W.; Langen, H.; Zolg, W.; Tacke, M. Towards a comprehensive proteome of normal and malignant human colon tissue by 2-D-LC-ESI-MS and 2-DE proteomics and identification of S100A12 as potential cancer biomarker. Proteomics Clin. Appl., 2008, 2(1), 11-22.
[http://dx.doi.org/10.1002/prca.200780046] [PMID: 21136775]
[68]
de Jong, N.S.H.; Leach, S.T.; Day, A.S. Fecal S100A12: A novel noninvasive marker in children with Crohnʼs disease. Inflamm. Bowel Dis., 2006, 12(7), 566-572.
[http://dx.doi.org/10.1097/01.ibd.0000227626.72271.91] [PMID: 16804393]
[69]
Turner, D.; Leach, S.T.; Mack, D.; Uusoue, K.; McLernon, R.; Hyams, J.; Leleiko, N.; Walters, T.D.; Crandall, W.; Markowitz, J.; Otley, A.R.; Griffiths, A.M.; Day, A.S. Faecal calprotectin, lactoferrin, M2-pyruvate kinase and S100A12 in severe ulcerative colitis: A prospective multicentre comparison of predicting outcomes and monitoring response. Gut, 2010, 59(9), 1207-1212.
[http://dx.doi.org/10.1136/gut.2010.211755] [PMID: 20801771]
[70]
Loktionov, A.; Soubieres, A.; Bandaletova, T.; Mathur, J.; Poullis, A. Colorectal cancer detection by biomarker quantification in noninvasively collected colorectal mucus: preliminary comparison of 24 protein biomarkers. Eur. J. Gastroenterol. Hepatol., 2019, 31(10), 1220-1227.
[http://dx.doi.org/10.1097/MEG.0000000000001535] [PMID: 31498281]
[71]
Spratt, D.E.; Walden, H.; Shaw, G.S. RBR E3 ubiquitin ligases: New structures, new insights, new questions. Biochem. J., 2014, 458(3), 421-437.
[http://dx.doi.org/10.1042/BJ20140006] [PMID: 24576094]
[72]
Ho, S.R.; Mahanic, C.S.; Lee, Y.J.; Lin, W.C. RNF144A, an E3 ubiquitin ligase for DNA-PKcs, promotes apoptosis during DNA damage. Proc. Natl. Acad. Sci., 2014, 111(26), E2646-E2655.
[http://dx.doi.org/10.1073/pnas.1323107111] [PMID: 24979766]
[73]
Yang, Y.L.; Zhang, Y.; Li, D.D.; Zhang, F.L.; Liu, H.Y.; Liao, X.H.; Xie, H.Y.; Lu, Q.; Zhang, L.; Hong, Q.; Dong, W.J.; Li, D.Q.; Shao, Z.M. RNF144A functions as a tumor suppressor in breast cancer through ubiquitin ligase activity-dependent regulation of stability and oncogenic functions of HSPA2. Cell Death Differ., 2020, 27(3), 1105-1118.
[http://dx.doi.org/10.1038/s41418-019-0400-z] [PMID: 31406303]
[74]
Li, Y.; Wang, J.; Wang, F.; Chen, W.; Gao, C.; Wang, J. RNF144A suppresses ovarian cancer stem cell properties and tumor progression through regulation of LIN28B degradation via the ubiquitin-proteasome pathway. Cell Biol. Toxicol., 2022, 38(5), 809-824.
[http://dx.doi.org/10.1007/s10565-021-09609-w] [PMID: 33978933]
[75]
Yin, J.; Guo, Y. HOXD13 promotes the malignant progression of colon cancer by upregulating PTPRN2. Cancer Med., 2021, 10(16), 5524-5533.
[http://dx.doi.org/10.1002/cam4.4078] [PMID: 34272834]
[76]
Xu, T.; Zong, Y.; Peng, L.; Kong, S.; Zhou, M.; Zou, J.; Liu, J.; Miao, R.; Sun, X.; Li, L. Overexpression of eIF4E in colorectal cancer patients is associated with liver metastasis. OncoTargets Ther., 2016, 9, 815-822.
[PMID: 26929650]
[77]
Hsieh, A.C.; Ruggero, D. Targeting eukaryotic translation initiation factor 4E (eIF4E) in cancer. Clin. Cancer Res., 2010, 16(20), 4914-4920.
[http://dx.doi.org/10.1158/1078-0432.CCR-10-0433] [PMID: 20702611]
[78]
Ichikawa, M.; Sowa, Y.; Iizumi, Y.; Aono, Y.; Sakai, T. Resibufogenin induces G1-phase arrest through the proteasomal degradation of cyclin D1 in human malignant tumor cells. PLoS One, 2015, 10(6), e0129851.
[http://dx.doi.org/10.1371/journal.pone.0129851] [PMID: 26121043]
[79]
Othumpangat, S. Sodium arsenite-induced inhibition of eukaryotic translation initiation factor 4E (eIF4E) results in cytotoxicity and cell death. PLoS One, 2005, 279(2), 123-131.
[80]
Chen, F.; Wang, M.; Bai, J.; Liu, Q.; Xi, Y.; Li, W.; Zheng, J. Role of RUNX3 in suppressing metastasis and angiogenesis of human prostate cancer. PLoS One, 2014, 9(1), e86917.
[http://dx.doi.org/10.1371/journal.pone.0086917] [PMID: 24475196]
[81]
Gao, M.; Zhang, X.; Li, D.; He, P.; Tian, W.; Zeng, B. Expression analysis and clinical significance of eIF4E, VEGF-C, E-cadherin and MMP-2 in colorectal adenocarcinoma. Oncotarget, 2016, 7(51), 85502-85514.
[http://dx.doi.org/10.18632/oncotarget.13453] [PMID: 27907907]
[82]
Zhao, Q.; Zhang, K.; Li, Z.; Zhang, H.; Fu, F.; Fu, J.; Zheng, M.; Zhang, S. High migration and invasion ability of pgccs and their daughter cells associated with the nuclear localization of S100A10 modified by SUMOylation. Front. Cell Dev. Biol., 2021, 9, 696871.
[http://dx.doi.org/10.3389/fcell.2021.696871] [PMID: 34336846]
[83]
Chavakis, T.; Keiper, T.; Matz-Westphal, R.; Hersemeyer, K.; Sachs, U.J.; Nawroth, P.P.; Preissner, K.T.; Santoso, S. The junctional adhesion molecule-C promotes neutrophil transendothelial migration in vitro and in vivo. J. Biol. Chem., 2004, 279(53), 55602-55608.
[http://dx.doi.org/10.1074/jbc.M404676200] [PMID: 15485832]
[84]
Khine, A.A.; Del Sorbo, L.; Vaschetto, R.; Voglis, S.; Tullis, E.; Slutsky, A.S.; Downey, G.P.; Zhang, H. Human neutrophil peptides induce interleukin-8 production through the P2Y6 signaling pathway. Blood, 2006, 107(7), 2936-2942.
[http://dx.doi.org/10.1182/blood-2005-06-2314] [PMID: 16322472]
[85]
Piccoli, M.; D’Angelo, E.; Crotti, S.; Sensi, F.; Urbani, L.; Maghin, E.; Burns, A.; De Coppi, P.; Fassan, M.; Rugge, M.; Rizzolio, F.; Giordano, A.; Pilati, P.; Mammano, E.; Pucciarelli, S.; Agostini, M. Decellularized colorectal cancer matrix as bioactive microenvironment for in vitro 3D cancer research. J. Cell. Physiol., 2018, 233(8), 5937-5948.
[http://dx.doi.org/10.1002/jcp.26403] [PMID: 29244195]
[86]
Ladwa, R.; Pringle, H.; Kumar, R.; West, K. Expression of CTGF and Cyr61 in colorectal cancer. J. Clin. Pathol., 2011, 64(1), 58-64.
[http://dx.doi.org/10.1136/jcp.2010.082768] [PMID: 21081514]
[87]
Xie, L.; Song, X.; Lin, H.; Chen, Z.; Li, Q.; Guo, T.; Xu, T.; Su, T.; Xu, M.; Chang, X.; Wang, L.K.; Liang, B.; Huang, D. Aberrant activation of CYR61 enhancers in colorectal cancer development. J. Exp. Clin. Cancer Res., 2019, 38(1), 213.
[http://dx.doi.org/10.1186/s13046-019-1217-9] [PMID: 31118064]
[88]
Huang, X.; Xiang, L.; Li, Y.; Zhao, Y.; Zhu, H.; Xiao, Y.; Liu, M.; Wu, X.; Wang, Z.; Jiang, P.; Qing, H.; Zhang, Q.; Liu, G.; Zhang, W.; Li, A.; Chen, Y.; Liu, S.; Wang, J. Snail/FOXK1/Cyr61 signaling axis regulates the epithelial–mesenchymal transition and metastasis in colorectal cancer. Cell. Physiol. Biochem., 2018, 47(2), 590-603.
[http://dx.doi.org/10.1159/000490015] [PMID: 29794466]
[89]
Wu, G.; Zhu, Y.Z.; Zhang, J.C. Sox4 up-regulates Cyr61 expression in colon cancer cells. Cell. Physiol. Biochem., 2014, 34(2), 405-412.
[http://dx.doi.org/10.1159/000363009] [PMID: 25059387]
[90]
Jeong, D.; Heo, S.; Sung Ahn, T.; Lee, S.; Park, S.; Kim, H.; Park, D.; Byung Bae, S.; Lee, S.S.; Soo Lee, M.; Kim, C.J.; Jun Baek, M. Cyr61 Expression is associated with prognosis in patients with colorectal cancer. BMC Cancer, 2014, 14(1), 164.
[http://dx.doi.org/10.1186/1471-2407-14-164] [PMID: 24606730]
[91]
Yan, J.; Yang, B.; Lin, S.; Xing, R.; Lu, Y. Downregulation of miR-142-5p promotes tumor metastasis through directly regulating CYR61 expression in gastric cancer. Gastric Cancer, 2019, 22(2), 302-313.
[http://dx.doi.org/10.1007/s10120-018-0872-4] [PMID: 30178386]
[92]
ten Bokum, A.M.; Hofland, L.J.; van Hagen, P.M. Somatostatin and somatostatin receptors in the immune system: A review. Eur. Cytokine Netw., 2000, 11(2), 161-176.
[PMID: 10903795]
[93]
Casnici, C.; Lattuada, D.; Perego, C.; Franco, P.; Marelli, O. Inhibitory effect of somatostatin on human T lymphocytes proliferation. Int. J. Immunopharmacol., 1998, 19(11-12), 721-727.
[http://dx.doi.org/10.1016/S0192-0561(97)00033-7] [PMID: 9669213]
[94]
Rosskopf, D.; Schürks, M.; Manthey, I.; Joisten, M.; Busch, S.; Siffert, W. Signal transduction of somatostatin in human B lymphoblasts. Am. J. Physiol. Cell Physiol., 2003, 284(1), C179-C190.
[http://dx.doi.org/10.1152/ajpcell.00160.2001] [PMID: 12388115]
[95]
Ruscica, M.; Arvigo, M.; Steffani, L.; Ferone, D.; Magni, P. Somatostatin, somatostatin analogs and somatostatin receptor dynamics in the biology of cancer progression. Curr. Mol. Med., 2013, 13(4), 555-571.
[http://dx.doi.org/10.2174/1566524011313040008] [PMID: 22934849]
[96]
Leiszter, K.; Sipos, F.; Galamb, O.; Krenács, T.; Veres, G.; Wichmann, B.; Fűri, I.; Kalmár, A.; Patai, Á.V.; Tóth, K.; Valcz, G.; Tulassay, Z.; Molnár, B. Promoter hypermethylation-related reduced somatostatin production promotes uncontrolled cell proliferation in colorectal cancer. PLoS One, 2015, 10(2), e0118332.
[http://dx.doi.org/10.1371/journal.pone.0118332] [PMID: 25723531]
[97]
Gatto, F.; Barbieri, F.; Arvigo, M.; Thellung, S.; Amarù, J.; Albertelli, M.; Ferone, D.; Florio, T. Biological and biochemical basis of the differential efficacy of first and second generation somatostatin receptor ligands in neuroendocrine neoplasms. Int. J. Mol. Sci., 2019, 20(16), 3940.
[http://dx.doi.org/10.3390/ijms20163940] [PMID: 31412614]
[98]
Modarai, S.R.; Opdenaker, L.M.; Viswanathan, V.; Fields, J.Z.; Boman, B.M. Somatostatin signaling via SSTR1 contributes to the quiescence of colon cancer stem cells. BMC Cancer, 2016, 16(1), 941.
[http://dx.doi.org/10.1186/s12885-016-2969-7] [PMID: 27927191]
[99]
Ma, Z.; Williams, M.; Cheng, Y.Y.; Leung, W.K. Roles of methylated DNA biomarkers in patients with colorectal cancer. Dis. Markers, 2019, 2019, 1-8.
[http://dx.doi.org/10.1155/2019/2673543] [PMID: 30944663]
[100]
Fernandez, S.; Risolino, M.; Mandia, N.; Talotta, F.; Soini, Y.; Incoronato, M.; Condorelli, G.; Banfi, S.; Verde, P. miR-340 inhibits tumor cell proliferation and induces apoptosis by targeting multiple negative regulators of p27 in non-small cell lung cancer. Oncogene, 2015, 34(25), 3240-3250.
[http://dx.doi.org/10.1038/onc.2014.267] [PMID: 25151966]
[101]
Gong, Y.; Liu, Z.; Yuan, Y.; Yang, Z.; Zhang, J.; Lu, Q.; Wang, W.; Fang, C.; Lin, H.; Liu, S. PUMILIO proteins promote colorectal cancer growth via suppressing p21. Nat. Commun., 2022, 13(1), 1627.
[http://dx.doi.org/10.1038/s41467-022-29309-1] [PMID: 35338151]
[102]
Kanai, M.; Hamada, J.; Takada, M.; Asano, T.; Murakawa, K.; Takahashi, Y.; Murai, T.; Tada, M.; Miyamoto, M.; Kondo, S.; Moriuchi, T. Aberrant expressions of HOX genes in colorectal and hepatocellular carcinomas. Oncol. Rep., 2010, 23(3), 843-851.
[PMID: 20127028]
[103]
Schimanski, C.C.; Frerichs, K.; Rahman, F.; Berger, M.; Lang, H.; Galle, P.R.; Moehler, M.; Gockel, I. High miR-196a levels promote the oncogenic phenotype of colorectal cancer cells. World J. Gastroenterol., 2009, 15(17), 2089-2096.
[http://dx.doi.org/10.3748/wjg.15.2089] [PMID: 19418581]
[104]
Mansour, M.A.; Senga, T. HOXD8 exerts a tumor-suppressing role in colorectal cancer as an apoptotic inducer. Int. J. Biochem. Cell Biol., 2017, 88, 1-13.
[http://dx.doi.org/10.1016/j.biocel.2017.04.011] [PMID: 28457970]
[105]
Planell, N.; Lozano, J.J.; Mora-Buch, R.; Masamunt, M.C.; Jimeno, M.; Ordás, I.; Esteller, M.; Ricart, E.; Piqué, J.M.; Panés, J.; Salas, A. Transcriptional analysis of the intestinal mucosa of patients with ulcerative colitis in remission reveals lasting epithelial cell alterations. Gut, 2013, 62(7), 967-976.
[http://dx.doi.org/10.1136/gutjnl-2012-303333] [PMID: 23135761]
[106]
Huang, D.; Feng, X.; Liu, Y.; Deng, Y.; Chen, H.; Chen, D.; Fang, L.; Cai, Y.; Liu, H.; Wang, L.; Wang, J.; Yang, Z. AQP9-induced cell cycle arrest is associated with RAS activation and improves chemotherapy treatment efficacy in colorectal cancer. Cell Death Dis., 2017, 8(6), e2894.
[http://dx.doi.org/10.1038/cddis.2017.282] [PMID: 28640255]
[107]
Verkman, A.S.; Hara-Chikuma, M.; Papadopoulos, M.C. Aquaporins—new players in cancer biology. J. Mol. Med., 2008, 86(5), 523-529.
[http://dx.doi.org/10.1007/s00109-008-0303-9] [PMID: 18311471]
[108]
Chen, Q.; Zhu, L.; Zheng, B.; Wang, J.; Song, X.; Zheng, W.; Wang, L.; Yang, D.; Wang, J. Effect of AQP9 expression in androgen-independent prostate cancer cell PC3. Int. J. Mol. Sci., 2016, 17(5), 738.
[http://dx.doi.org/10.3390/ijms17050738] [PMID: 27187384]
[109]
Zhang, W.; Li, C.; Liu, M.; Chen, X.; Shuai, K.; Kong, X.; Lv, L.; Mei, Z. Aquaporin 9 is down-regulated in hepatocellular carcinoma and its over-expression suppresses hepatoma cell invasion through inhibiting epithelial-to-mesenchymal transition. Cancer Lett., 2016, 378(2), 111-119.
[http://dx.doi.org/10.1016/j.canlet.2016.05.021] [PMID: 27216981]
[110]
Liu, X.; Xu, Q.; Li, Z.; Xiong, B. Integrated analysis identifies AQP9 correlates with immune infiltration and acts as a prognosticator in multiple cancers. Sci. Rep., 2020, 10(1), 20795.
[http://dx.doi.org/10.1038/s41598-020-77657-z] [PMID: 33247170]
[111]
Zajkowska, M.; Kulczyńska-Przybik, A.; Dulewicz, M.; Safiejko, K.; Juchimiuk, M.; Konopko, M.; Kozłowski, L.; Mroczko, B. Eotaxins and their receptor as biomarkers of colorectal cancer. J. Clin. Med., 2021, 10(12), 2675.
[http://dx.doi.org/10.3390/jcm10122675] [PMID: 34204490]
[112]
Cho, Y.B.; Lee, W.Y.; Choi, S.J.; Kim, J.; Hong, H.K.; Kim, S.H.; Choi, Y.L.; Kim, H.C.; Yun, S.H.; Chun, H.K.; Lee, K.U. CC chemokine ligand 7 expression in liver metastasis of colorectal cancer. Oncol. Rep., 2012, 28(2), 689-694.
[http://dx.doi.org/10.3892/or.2012.1815] [PMID: 22614322]
[113]
Cheadle, E.J.; Riyad, K.; Subar, D.; Rothwell, D.G.; Ashton, G.; Batha, H.; Sherlock, D.J.; Hawkins, R.E.; Gilham, D.E. Eotaxin-2 and colorectal cancer: A potential target for immune therapy. Clin. Cancer Res., 2007, 13(19), 5719-5728.
[http://dx.doi.org/10.1158/1078-0432.CCR-07-1145] [PMID: 17908961]
[114]
Lan, Q.; Lai, W.; Zeng, Y.; Liu, L.; Li, S.; Jin, S.; Zhang, Y.; Luo, X.; Xu, H.; Lin, X.; Chu, Z. CCL26 participates in the PRL-3–induced promotion of colorectal cancer invasion by stimulating tumor-associated macrophage infiltration. Mol. Cancer Ther., 2018, 17(1), 276-289.
[http://dx.doi.org/10.1158/1535-7163.MCT-17-0507] [PMID: 29051319]
[115]
Moore, A.J.; Devine, D.A.; Bibby, M.C. Preliminary experimental anticancer activity of cecropins. Pept. Res., 1994, 7(5), 265-269.
[PMID: 7849420]
[116]
Robertson, C.N.; Roberson, K.M.; Pinero, A.; Jaynes, J.M.; Paulson, D.F. Peptidyl membrane-interactive molecules are cytotoxic to prostatic cancer cells in vitro. World J. Urol., 1998, 16(6), 405-409.
[http://dx.doi.org/10.1007/s003450050091] [PMID: 9870289]
[117]
Ankaiah, D.; Palanichamy, E.; Antonyraj, C.B.; Ayyanna, R.; Perumal, V.; Ahamed, S.I.B.; Arul, V. Cloning, overexpression, purification of bacteriocin enterocin-B and structural analysis, interaction determination of enterocin-A, B against pathogenic bacteria and human cancer cells. Int. J. Biol. Macromol., 2018, 116, 502-512.
[http://dx.doi.org/10.1016/j.ijbiomac.2018.05.002] [PMID: 29729340]
[118]
Norouzi, Z.; Salimi, A.; Halabian, R.; Fahimi, H. Nisin, a potent bacteriocin and anti-bacterial peptide, attenuates expression of metastatic genes in colorectal cancer cell lines. Microb. Pathog., 2018, 123, 183-189.
[http://dx.doi.org/10.1016/j.micpath.2018.07.006] [PMID: 30017942]
[119]
Khusro, A.; Aarti, C.; Mahizhaveni, B.; Dusthackeer, A.; Agastian, P.; Esmail, G.A.; Ghilan, A.K.M.; Al-Dhabi, N.A.; Arasu, M.V. Purification and characterization of anti-tubercular and anticancer protein from Staphylococcus hominis strain MANF2: In silico structural and functional insight of peptide. Saudi J. Biol. Sci., 2020, 27(4), 1107-1116.
[http://dx.doi.org/10.1016/j.sjbs.2020.01.017] [PMID: 32256172]
[120]
Slaninová, J.; Mlsová, V.; Kroupová, H.; Alán, L.; Tůmová, T.; Monincová, L.; Borovičková, L.; Fučík, V.; Čeřovský, V. Toxicity study of antimicrobial peptides from wild bee venom and their analogs toward mammalian normal and cancer cells. Peptides, 2012, 33(1), 18-26.
[http://dx.doi.org/10.1016/j.peptides.2011.11.002] [PMID: 22100226]
[121]
Saleh, M.; Trinchieri, G. Innate immune mechanisms of colitis and colitis-associated colorectal cancer. Nat. Rev. Immunol., 2011, 11(1), 9-20.
[http://dx.doi.org/10.1038/nri2891] [PMID: 21151034]
[122]
Ju, Q.; Zhao, Y.J.; Dong, Y.; Cheng, C.; Zhang, S.; Yang, Y.; Li, P.; Ge, D.; Sun, B. Identification of a miRNA mRNA network associated with lymph node metastasis in colorectal cancer. Oncol. Lett., 2019, 18(2), 1179-1188.
[http://dx.doi.org/10.3892/ol.2019.10460] [PMID: 31423178]
[123]
Gamage, D.G.; Hendrickson, T.L. GPI Transamidase and GPI anchored proteins: Oncogenes and biomarkers for cancer. Crit. Rev. Biochem. Mol. Biol., 2013, 48(5), 446-464.
[http://dx.doi.org/10.3109/10409238.2013.831024] [PMID: 23978072]
[124]
Tapial, P.; López, P.; Lietha, D. FAK structure and regulation by membrane interactions and force in focal adhesions. Biomolecules, 2020, 10(2), 179.
[http://dx.doi.org/10.3390/biom10020179] [PMID: 31991559]
[125]
Záhorec, R.; Marek, V.; Waczulíková, I.; Veselovský, T.; Palaj, J.; Kečkéš, Š.; Durdík, Š. Predictive model using hemoglobin, albumin, fibrinogen, and neutrophil-to-lymphocyte ratio to distinguish patients with colorectal cancer from those with benign adenoma. Neoplasma, 2021, 68(6), 1292-1300.
[http://dx.doi.org/10.4149/neo_2021_210331N435] [PMID: 34585586]
[126]
Wallace, K.; Li, H.; Brazeal, J.G.; Lewin, D.N.; Sun, S.; Ba, A.; Paulos, C.M.; Rachidi, S.; Li, Z.; Alekseyenko, A.V. Platelet and hemoglobin count at diagnosis are associated with survival in African American and Caucasian patients with colorectal cancer. Cancer Epidemiol., 2020, 67, 101746.
[http://dx.doi.org/10.1016/j.canep.2020.101746] [PMID: 32521488]
[127]
Zhao, Z.; Zhu, A.; Bhardwaj, M.; Schrotz-King, P.; Brenner, H. Fecal microRNAs, Fecal microRNA panels, or combinations of fecal microRNAs with fecal hemoglobin for early detection of colorectal cancer and its precursors: A systematic review. Cancers, 2021, 14(1), 65.
[http://dx.doi.org/10.3390/cancers14010065] [PMID: 35008229]
[128]
Moretó, M.; Pérez-Bosque, A. Dietary plasma proteins, the intestinal immune system, and the barrier functions of the intestinal mucosa1. J. Anim. Sci., 2009, 87(S14), E92-E100.
[http://dx.doi.org/10.2527/jas.2008-1381] [PMID: 18820151]
[129]
Inoue, I.; Mukoubayashi, C.; Yoshimura, N.; Niwa, T.; Deguchi, H.; Watanabe, M.; Enomoto, S.; Maekita, T.; Ueda, K.; Iguchi, M.; Yanaoka, K.; Tamai, H.; Arii, K.; Oka, M.; Fujishiro, M.; Takeshita, T.; Iwane, M.; Mohara, O.; Ichinose, M. Elevated risk of colorectal adenoma with Helicobacter pylori-related chronic gastritis: A population-based case-control study. Int. J. Cancer, 2011, 129(11), 2704-2711.
[http://dx.doi.org/10.1002/ijc.25931] [PMID: 21225622]
[130]
Du, G.; Fang, X.; Dai, W.; Zhang, R.; Liu, R.; Dang, X. Comparative gene expression profiling of normal and human colorectal adenomatous tissues. Oncol. Lett., 2014, 8(5), 2081-2085.
[http://dx.doi.org/10.3892/ol.2014.2485] [PMID: 25295094]
[131]
Saxena, M.; Yeretssian, G. NOD-like receptors: Master regulators of inflammation and cancer. Front. Immunol., 2014, 5, 327.
[http://dx.doi.org/10.3389/fimmu.2014.00327] [PMID: 25071785]
[132]
Li, B.; Qi, Z.P.; He, D.L.; Chen, Z.H.; Liu, J.Y.; Wong, M.W.; Zhang, J.W.; Xu, E.P.; Shi, Q.; Cai, S.L.; Sun, D.; Yao, L.Q.; Zhou, P.H.; Zhong, Y.S. NLRP7 deubiquitination by USP10 promotes tumor progression and tumor-associated macrophage polarization in colorectal cancer. J. Exp. Clin. Cancer Res., 2021, 40(1), 126.
[http://dx.doi.org/10.1186/s13046-021-01920-y] [PMID: 33838681]
[133]
Huhn, S.; da Silva Filho, M.I.; Sanmuganantham, T.; Pichulik, T.; Catalano, C.; Pardini, B.; Naccarati, A.; Polakova-Vymetálkova, V.; Jiraskova, K.; Vodickova, L.; Vodicka, P.; Löffler, M.W.; Courth, L.; Wehkamp, J.; Din, F.V.N.; Timofeeva, M.; Farrington, S.M.; Jansen, L.; Hemminki, K.; Chang-Claude, J.; Brenner, H.; Hoffmeister, M.; Dunlop, M.G.; Weber, A.N.R.; Försti, A. Coding variants in NOD-like receptors: An association study on risk and survival of colorectal cancer. PLoS One, 2018, 13(6), e0199350.
[http://dx.doi.org/10.1371/journal.pone.0199350] [PMID: 29928061]
[134]
Gulifeire, T.; Yang, C.; Li, X.; Wang, Y.; Yu, X. Activation of NOD-like receptor protein 3 inflammasome mediates inflammatory response and apoptosis in septic intestinal injury model. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue, 2021, 33(7), 855-860.
[PMID: 34412757]
[135]
Zaki, M.H.; Vogel, P.; Malireddi, R.K.S.; Body-Malapel, M.; Anand, P.K.; Bertin, J.; Green, D.R.; Lamkanfi, M.; Kanneganti, T.D. The NOD-like receptor NLRP12 attenuates colon inflammation and tumorigenesis. Cancer Cell, 2011, 20(5), 649-660.
[http://dx.doi.org/10.1016/j.ccr.2011.10.022] [PMID: 22094258]
[136]
Ohashi, K.; Wang, Z.; Yang, Y.M.; Billet, S.; Tu, W.; Pimienta, M.; Cassel, S.L.; Pandol, S.J.; Lu, S.C.; Sutterwala, F.S.; Bhowmick, N.; Seki, E. NOD‐like receptor C4 inflammasome regulates the growth of colon cancer liver metastasis in NAFLD. Hepatology, 2019, 70(5), 1582-1599.
[http://dx.doi.org/10.1002/hep.30693] [PMID: 31044438]

Rights & Permissions Print Cite
© 2025 Bentham Science Publishers | Privacy Policy