Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods

Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Li, Chunyang [verfasserIn] Zeng, Xiaoxi Yu, Haopeng Gu, Yonghong Zhang, Wei

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	Pancreatic cancer Bioinformatics analysis Differentially expressed genes Hub genes Diagnosis

Anmerkung:	© The Author(s). 2018

Übergeordnetes Werk:	Enthalten in: World journal of surgical oncology - London : Biomed Central, 2003, 16(2018), 1 vom: 14. Nov.
Übergeordnetes Werk:	volume:16 ; year:2018 ; number:1 ; day:14 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s12957-018-1519-y

Katalog-ID:	SPR028841832

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520			\|a Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical.
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allfields	10.1186/s12957-018-1519-y doi (DE-627)SPR028841832 (SPR)s12957-018-1519-y-e DE-627 ger DE-627 rakwb eng Li, Chunyang verfasserin aut Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. Pancreatic cancer (dpeaa)DE-He213 Bioinformatics analysis (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Hub genes (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Zeng, Xiaoxi aut Yu, Haopeng aut Gu, Yonghong aut Zhang, Wei aut Enthalten in World journal of surgical oncology London : Biomed Central, 2003 16(2018), 1 vom: 14. Nov. (DE-627)369082907 (DE-600)2118383-1 1477-7819 nnns volume:16 year:2018 number:1 day:14 month:11 https://dx.doi.org/10.1186/s12957-018-1519-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 14 11
spelling	10.1186/s12957-018-1519-y doi (DE-627)SPR028841832 (SPR)s12957-018-1519-y-e DE-627 ger DE-627 rakwb eng Li, Chunyang verfasserin aut Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. Pancreatic cancer (dpeaa)DE-He213 Bioinformatics analysis (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Hub genes (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Zeng, Xiaoxi aut Yu, Haopeng aut Gu, Yonghong aut Zhang, Wei aut Enthalten in World journal of surgical oncology London : Biomed Central, 2003 16(2018), 1 vom: 14. Nov. (DE-627)369082907 (DE-600)2118383-1 1477-7819 nnns volume:16 year:2018 number:1 day:14 month:11 https://dx.doi.org/10.1186/s12957-018-1519-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 14 11
allfields_unstemmed	10.1186/s12957-018-1519-y doi (DE-627)SPR028841832 (SPR)s12957-018-1519-y-e DE-627 ger DE-627 rakwb eng Li, Chunyang verfasserin aut Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. Pancreatic cancer (dpeaa)DE-He213 Bioinformatics analysis (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Hub genes (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Zeng, Xiaoxi aut Yu, Haopeng aut Gu, Yonghong aut Zhang, Wei aut Enthalten in World journal of surgical oncology London : Biomed Central, 2003 16(2018), 1 vom: 14. Nov. (DE-627)369082907 (DE-600)2118383-1 1477-7819 nnns volume:16 year:2018 number:1 day:14 month:11 https://dx.doi.org/10.1186/s12957-018-1519-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 14 11
allfieldsGer	10.1186/s12957-018-1519-y doi (DE-627)SPR028841832 (SPR)s12957-018-1519-y-e DE-627 ger DE-627 rakwb eng Li, Chunyang verfasserin aut Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. Pancreatic cancer (dpeaa)DE-He213 Bioinformatics analysis (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Hub genes (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Zeng, Xiaoxi aut Yu, Haopeng aut Gu, Yonghong aut Zhang, Wei aut Enthalten in World journal of surgical oncology London : Biomed Central, 2003 16(2018), 1 vom: 14. Nov. (DE-627)369082907 (DE-600)2118383-1 1477-7819 nnns volume:16 year:2018 number:1 day:14 month:11 https://dx.doi.org/10.1186/s12957-018-1519-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 14 11
allfieldsSound	10.1186/s12957-018-1519-y doi (DE-627)SPR028841832 (SPR)s12957-018-1519-y-e DE-627 ger DE-627 rakwb eng Li, Chunyang verfasserin aut Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2018 Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. Pancreatic cancer (dpeaa)DE-He213 Bioinformatics analysis (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Hub genes (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213 Zeng, Xiaoxi aut Yu, Haopeng aut Gu, Yonghong aut Zhang, Wei aut Enthalten in World journal of surgical oncology London : Biomed Central, 2003 16(2018), 1 vom: 14. Nov. (DE-627)369082907 (DE-600)2118383-1 1477-7819 nnns volume:16 year:2018 number:1 day:14 month:11 https://dx.doi.org/10.1186/s12957-018-1519-y kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 16 2018 1 14 11
language	English
source	Enthalten in World journal of surgical oncology 16(2018), 1 vom: 14. Nov. volume:16 year:2018 number:1 day:14 month:11
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topic_facet	Pancreatic cancer Bioinformatics analysis Differentially expressed genes Hub genes Diagnosis
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container_title	World journal of surgical oncology
authorswithroles_txt_mv	Li, Chunyang @@aut@@ Zeng, Xiaoxi @@aut@@ Yu, Haopeng @@aut@@ Gu, Yonghong @@aut@@ Zhang, Wei @@aut@@
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Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. 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author	Li, Chunyang
spellingShingle	Li, Chunyang misc Pancreatic cancer misc Bioinformatics analysis misc Differentially expressed genes misc Hub genes misc Diagnosis Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods
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topic_title	Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods Pancreatic cancer (dpeaa)DE-He213 Bioinformatics analysis (dpeaa)DE-He213 Differentially expressed genes (dpeaa)DE-He213 Hub genes (dpeaa)DE-He213 Diagnosis (dpeaa)DE-He213
topic	misc Pancreatic cancer misc Bioinformatics analysis misc Differentially expressed genes misc Hub genes misc Diagnosis
topic_unstemmed	misc Pancreatic cancer misc Bioinformatics analysis misc Differentially expressed genes misc Hub genes misc Diagnosis
topic_browse	misc Pancreatic cancer misc Bioinformatics analysis misc Differentially expressed genes misc Hub genes misc Diagnosis
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title	Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods
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title_full	Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods
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author_browse	Li, Chunyang Zeng, Xiaoxi Yu, Haopeng Gu, Yonghong Zhang, Wei
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author-letter	Li, Chunyang
doi_str_mv	10.1186/s12957-018-1519-y
title_sort	identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods
title_auth	Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods
abstract	Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. © The Author(s). 2018
abstractGer	Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. © The Author(s). 2018
abstract_unstemmed	Background Pancreatic cancer is one of the most lethal tumors with poor prognosis, and lacks of effective biomarkers in diagnosis and treatment. The aim of this investigation was to identify hub genes in pancreatic cancer, which would serve as potential biomarkers for cancer diagnosis and therapy in the future. Methods Combination of two expression profiles of GSE16515 and GSE22780 from Gene Expression Omnibus (GEO) database was served as training set. Differentially expressed genes (DEGs) with top 25% variance followed by protein-protein interaction (PPI) network were performed to find candidate genes. Then, hub genes were further screened by survival and cox analyses in The Cancer Genome Atlas (TCGA) database. Finally, hub genes were validated in GSE15471 dataset from GEO by supervised learning methods k-nearest neighbor (kNN) and random forest algorithms. Results After quality control and batch effect elimination of training set, 181 DEGs bearing top 25% variance were identified as candidate genes. Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. Our results provide a novel prospect for diagnosis and treatment of pancreatic cancer, which may have a further application in clinical. © The Author(s). 2018
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title_short	Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods
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author2	Zeng, Xiaoxi Yu, Haopeng Gu, Yonghong Zhang, Wei
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up_date	2024-07-03T22:03:17.371Z
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Then, two hub genes, MMP7 and ITGA2, correlating with diagnosis and prognosis of pancreatic cancer were screened as hub genes according to above-mentioned bioinformatics methods. Finally, hub genes were demonstrated to successfully differ tumor samples from normal tissues with predictive accuracies reached to 93.59 and 81.31% by using kNN and random forest algorithms, respectively. Conclusions All the hub genes were associated with the regulation of tumor microenvironment, which implicated in tumor proliferation, progression, migration, and metastasis. 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Identification of hub genes with diagnostic values in pancreatic cancer by bioinformatics analyses and supervised learning methods

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