Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer
This work was aimed at analyzing the correlation between the methylation level of the M6A gene in esophageal cancer (EC) and the prognosis of patients based on bioinformatics technology and evaluating the prognostic predictive values of different data mining models. 80 EC patients and 80 healthy peo...
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| description | This work was aimed at analyzing the correlation between the methylation level of the M6A gene in esophageal cancer (EC) and the prognosis of patients based on bioinformatics technology and evaluating the prognostic predictive values of different data mining models. 80 EC patients and 80 healthy people were selected, and the serum of the patients was collected to detect the level of DNA methyltransferase. During the radical resection of EC, tumor tissues and adjacent normal tissues were collected from patients to detect the methylation level of the M6A gene. COX regression analysis was employed to analyze the independent risk factors (IRFs) of M6A gene methylation and other treatments affecting the prognosis of EC patients. The particle swarm optimization (PSO) algorithm was introduced to improve the fuzzy C-means clustering (FCM) algorithm. The differences in the prognostic prediction efficiency of logistic regression analysis (LRA), decision tree (DT) C5.0, artificial neural network (ANN), support vector machine (SVM), and improved FCM (IFCM) models were compared. The levels of DNA methyltransferase and human histone deacetylase 1 (HSD-1) in EC patients were increased greatly (P |
| doi_str_mv | 10.1155/2022/4420717 |
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| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2715335237</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2715335237</sourcerecordid><originalsourceid>FETCH-LOGICAL-c361t-a2c873dde5d2f66368aaed3719a5cc7bf6a5b58240fc1407f3ba3162240acd543</originalsourceid><addsrcrecordid>eNp9kE1LAzEQhoMoWKs3f0DAo67NxyZZj7W2VWhRRMHbMs1m25RtUpOtsv31bmnRm6cZXp53GB6ELim5pVSIHiOM9dKUEUXVEepQmalEMZkd_-7i4xSdxbgkRHLFeQd9jTbbbYMfoAY8tc66OQZX4HvrrSt9WEFtdcR9B1UTbcTW4ampF03V5t795b7EU9nHY-MMfgl-5WsT8KuZ76C2M4x-vYC5gQoPwGkTztFJCVU0F4fZRe-j4dvgMZk8j58G_UmiuaR1AkxniheFEQUrpeQyAzAFV_QOhNZqVkoQM5GxlJSapkSVfAacStYGoAuR8i662t9dB_-5MbHOl34T2q9jzhQVnAvWeuiimz2lg48xmDJfB7uC0OSU5Duz-c5sfjDb4td7fGFdAd_2f_oHNuZ4Zg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2715335237</pqid></control><display><type>article</type><title>Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><source>Wiley-Blackwell Open Access Titles</source><source>Alma/SFX Local Collection</source><creator>Wu, Shuoming ; Yang, Xiangbao ; Qiu, Xie ; Pan, Yinpeng</creator><contributor>Bhattacharya, Sweta ; Sweta Bhattacharya</contributor><creatorcontrib>Wu, Shuoming ; Yang, Xiangbao ; Qiu, Xie ; Pan, Yinpeng ; Bhattacharya, Sweta ; Sweta Bhattacharya</creatorcontrib><description>This work was aimed at analyzing the correlation between the methylation level of the M6A gene in esophageal cancer (EC) and the prognosis of patients based on bioinformatics technology and evaluating the prognostic predictive values of different data mining models. 80 EC patients and 80 healthy people were selected, and the serum of the patients was collected to detect the level of DNA methyltransferase. During the radical resection of EC, tumor tissues and adjacent normal tissues were collected from patients to detect the methylation level of the M6A gene. COX regression analysis was employed to analyze the independent risk factors (IRFs) of M6A gene methylation and other treatments affecting the prognosis of EC patients. The particle swarm optimization (PSO) algorithm was introduced to improve the fuzzy C-means clustering (FCM) algorithm. The differences in the prognostic prediction efficiency of logistic regression analysis (LRA), decision tree (DT) C5.0, artificial neural network (ANN), support vector machine (SVM), and improved FCM (IFCM) models were compared. The levels of DNA methyltransferase and human histone deacetylase 1 (HSD-1) in EC patients were increased greatly (P<0.05). The methylation rates and methylation levels of M6A methylation regulators (ALKBH5, HNRNPC, METTL3, WTAP, RBM15, YTHDC1, YTHDF1, and FTO) in EC tissues were obviously higher (P<0.05). The survival time of high-risk EC patients was much shorter than that of low-risk patients (P<0.05). Univariate and multivariate COX regression analysis showed that gender, tumor grade, TNM grade, degree of infiltration, and methylation of ALKBH5, HNRNPC, and METTL3 genes were IRFs for the prognosis of EC patients (P<0.05). The areas under the ROC curve (AUCs) of LRA, DT C5.0, ANN, SVM, and IFCM algorithms for predicting the prognosis of patients were 0.813, 0.857, 0.895, 0.926, and 0.958, respectively, and the IFCM model had the best diagnostic effect. In conclusion, the detection of bioinformatics technology showed no obvious DNA methylation in EC patients, and the elevated levels of M6A methylation regulators in patients were an IRF affecting the prognosis of patients. In addition, the fuzzy data mining model can be undertaken as the preferred method for prognosis prediction of EC patients.</description><identifier>ISSN: 1687-725X</identifier><identifier>EISSN: 1687-7268</identifier><identifier>DOI: 10.1155/2022/4420717</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Algorithms ; Antibodies ; Artificial neural networks ; Bioinformatics ; Biomarkers ; Cancer ; Clustering ; Data mining ; Decision analysis ; Decision trees ; Deoxyribonucleic acid ; DNA ; DNA methylation ; Epigenetics ; Esophageal cancer ; Gene expression ; Hospitals ; Medical prognosis ; Medical research ; Mortality ; Particle swarm optimization ; Patients ; Pneumothorax ; Prognosis ; Proteins ; Regression analysis ; Risk analysis ; Support vector machines ; Technology assessment ; Tumors</subject><ispartof>Journal of sensors, 2022-09, Vol.2022, p.1-12</ispartof><rights>Copyright © 2022 Shuoming Wu et al.</rights><rights>Copyright © 2022 Shuoming Wu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c361t-a2c873dde5d2f66368aaed3719a5cc7bf6a5b58240fc1407f3ba3162240acd543</cites><orcidid>0000-0002-9078-1247</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><contributor>Bhattacharya, Sweta</contributor><contributor>Sweta Bhattacharya</contributor><creatorcontrib>Wu, Shuoming</creatorcontrib><creatorcontrib>Yang, Xiangbao</creatorcontrib><creatorcontrib>Qiu, Xie</creatorcontrib><creatorcontrib>Pan, Yinpeng</creatorcontrib><title>Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer</title><title>Journal of sensors</title><description>This work was aimed at analyzing the correlation between the methylation level of the M6A gene in esophageal cancer (EC) and the prognosis of patients based on bioinformatics technology and evaluating the prognostic predictive values of different data mining models. 80 EC patients and 80 healthy people were selected, and the serum of the patients was collected to detect the level of DNA methyltransferase. During the radical resection of EC, tumor tissues and adjacent normal tissues were collected from patients to detect the methylation level of the M6A gene. COX regression analysis was employed to analyze the independent risk factors (IRFs) of M6A gene methylation and other treatments affecting the prognosis of EC patients. The particle swarm optimization (PSO) algorithm was introduced to improve the fuzzy C-means clustering (FCM) algorithm. The differences in the prognostic prediction efficiency of logistic regression analysis (LRA), decision tree (DT) C5.0, artificial neural network (ANN), support vector machine (SVM), and improved FCM (IFCM) models were compared. The levels of DNA methyltransferase and human histone deacetylase 1 (HSD-1) in EC patients were increased greatly (P<0.05). The methylation rates and methylation levels of M6A methylation regulators (ALKBH5, HNRNPC, METTL3, WTAP, RBM15, YTHDC1, YTHDF1, and FTO) in EC tissues were obviously higher (P<0.05). The survival time of high-risk EC patients was much shorter than that of low-risk patients (P<0.05). Univariate and multivariate COX regression analysis showed that gender, tumor grade, TNM grade, degree of infiltration, and methylation of ALKBH5, HNRNPC, and METTL3 genes were IRFs for the prognosis of EC patients (P<0.05). The areas under the ROC curve (AUCs) of LRA, DT C5.0, ANN, SVM, and IFCM algorithms for predicting the prognosis of patients were 0.813, 0.857, 0.895, 0.926, and 0.958, respectively, and the IFCM model had the best diagnostic effect. In conclusion, the detection of bioinformatics technology showed no obvious DNA methylation in EC patients, and the elevated levels of M6A methylation regulators in patients were an IRF affecting the prognosis of patients. In addition, the fuzzy data mining model can be undertaken as the preferred method for prognosis prediction of EC patients.</description><subject>Algorithms</subject><subject>Antibodies</subject><subject>Artificial neural networks</subject><subject>Bioinformatics</subject><subject>Biomarkers</subject><subject>Cancer</subject><subject>Clustering</subject><subject>Data mining</subject><subject>Decision analysis</subject><subject>Decision trees</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA methylation</subject><subject>Epigenetics</subject><subject>Esophageal cancer</subject><subject>Gene expression</subject><subject>Hospitals</subject><subject>Medical prognosis</subject><subject>Medical research</subject><subject>Mortality</subject><subject>Particle swarm optimization</subject><subject>Patients</subject><subject>Pneumothorax</subject><subject>Prognosis</subject><subject>Proteins</subject><subject>Regression analysis</subject><subject>Risk analysis</subject><subject>Support vector machines</subject><subject>Technology assessment</subject><subject>Tumors</subject><issn>1687-725X</issn><issn>1687-7268</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE1LAzEQhoMoWKs3f0DAo67NxyZZj7W2VWhRRMHbMs1m25RtUpOtsv31bmnRm6cZXp53GB6ELim5pVSIHiOM9dKUEUXVEepQmalEMZkd_-7i4xSdxbgkRHLFeQd9jTbbbYMfoAY8tc66OQZX4HvrrSt9WEFtdcR9B1UTbcTW4ampF03V5t795b7EU9nHY-MMfgl-5WsT8KuZ76C2M4x-vYC5gQoPwGkTztFJCVU0F4fZRe-j4dvgMZk8j58G_UmiuaR1AkxniheFEQUrpeQyAzAFV_QOhNZqVkoQM5GxlJSapkSVfAacStYGoAuR8i662t9dB_-5MbHOl34T2q9jzhQVnAvWeuiimz2lg48xmDJfB7uC0OSU5Duz-c5sfjDb4td7fGFdAd_2f_oHNuZ4Zg</recordid><startdate>20220907</startdate><enddate>20220907</enddate><creator>Wu, Shuoming</creator><creator>Yang, Xiangbao</creator><creator>Qiu, Xie</creator><creator>Pan, Yinpeng</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SP</scope><scope>7U5</scope><scope>7XB</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>D1I</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KB.</scope><scope>L6V</scope><scope>L7M</scope><scope>M0N</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-9078-1247</orcidid></search><sort><creationdate>20220907</creationdate><title>Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer</title><author>Wu, Shuoming ; 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During the radical resection of EC, tumor tissues and adjacent normal tissues were collected from patients to detect the methylation level of the M6A gene. COX regression analysis was employed to analyze the independent risk factors (IRFs) of M6A gene methylation and other treatments affecting the prognosis of EC patients. The particle swarm optimization (PSO) algorithm was introduced to improve the fuzzy C-means clustering (FCM) algorithm. The differences in the prognostic prediction efficiency of logistic regression analysis (LRA), decision tree (DT) C5.0, artificial neural network (ANN), support vector machine (SVM), and improved FCM (IFCM) models were compared. The levels of DNA methyltransferase and human histone deacetylase 1 (HSD-1) in EC patients were increased greatly (P<0.05). The methylation rates and methylation levels of M6A methylation regulators (ALKBH5, HNRNPC, METTL3, WTAP, RBM15, YTHDC1, YTHDF1, and FTO) in EC tissues were obviously higher (P<0.05). The survival time of high-risk EC patients was much shorter than that of low-risk patients (P<0.05). Univariate and multivariate COX regression analysis showed that gender, tumor grade, TNM grade, degree of infiltration, and methylation of ALKBH5, HNRNPC, and METTL3 genes were IRFs for the prognosis of EC patients (P<0.05). The areas under the ROC curve (AUCs) of LRA, DT C5.0, ANN, SVM, and IFCM algorithms for predicting the prognosis of patients were 0.813, 0.857, 0.895, 0.926, and 0.958, respectively, and the IFCM model had the best diagnostic effect. In conclusion, the detection of bioinformatics technology showed no obvious DNA methylation in EC patients, and the elevated levels of M6A methylation regulators in patients were an IRF affecting the prognosis of patients. In addition, the fuzzy data mining model can be undertaken as the preferred method for prognosis prediction of EC patients.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/4420717</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-9078-1247</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Antibodies Artificial neural networks Bioinformatics Biomarkers Cancer Clustering Data mining Decision analysis Decision trees Deoxyribonucleic acid DNA DNA methylation Epigenetics Esophageal cancer Gene expression Hospitals Medical prognosis Medical research Mortality Particle swarm optimization Patients Pneumothorax Prognosis Proteins Regression analysis Risk analysis Support vector machines Technology assessment Tumors |
| title | Fuzzy Data Mining and Bioinformatics Analysis in Methylation Analysis of M6A Gene Promoter Region in Esophageal Cancer |
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