Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patient...
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Gespeichert in:

Autor*in:	Chen Lin [verfasserIn] Tao Gaofeng [verfasserIn] Yang Mei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	papillary thyroid carcinoma immune microenvironment diagnosis machine learning biomarker autophagy rna sequencing

Übergeordnetes Werk:	In: Open Medicine - De Gruyter, 2015, 19(2024), 1, Seite 7-30
Übergeordnetes Werk:	volume:19 ; year:2024 ; number:1 ; pages:7-30

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1515/med-2024-0896

Katalog-ID:	DOAJ100843123

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520			\|a Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC.
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allfields	10.1515/med-2024-0896 doi (DE-627)DOAJ100843123 (DE-599)DOAJ5ccbfdd6c760445caec5310d5953a6cc DE-627 ger DE-627 rakwb eng Chen Lin verfasserin aut Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC. papillary thyroid carcinoma immune microenvironment diagnosis machine learning biomarker autophagy rna sequencing Medicine R Tao Gaofeng verfasserin aut Yang Mei verfasserin aut In Open Medicine De Gruyter, 2015 19(2024), 1, Seite 7-30 (DE-627)832299049 (DE-600)2829380-0 23915463 nnns volume:19 year:2024 number:1 pages:7-30 https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/article/5ccbfdd6c760445caec5310d5953a6cc kostenfrei https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/toc/2391-5463 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_603 GBV_ILN_2014 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 19 2024 1 7-30
spelling	10.1515/med-2024-0896 doi (DE-627)DOAJ100843123 (DE-599)DOAJ5ccbfdd6c760445caec5310d5953a6cc DE-627 ger DE-627 rakwb eng Chen Lin verfasserin aut Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC. papillary thyroid carcinoma immune microenvironment diagnosis machine learning biomarker autophagy rna sequencing Medicine R Tao Gaofeng verfasserin aut Yang Mei verfasserin aut In Open Medicine De Gruyter, 2015 19(2024), 1, Seite 7-30 (DE-627)832299049 (DE-600)2829380-0 23915463 nnns volume:19 year:2024 number:1 pages:7-30 https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/article/5ccbfdd6c760445caec5310d5953a6cc kostenfrei https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/toc/2391-5463 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_603 GBV_ILN_2014 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 19 2024 1 7-30
allfields_unstemmed	10.1515/med-2024-0896 doi (DE-627)DOAJ100843123 (DE-599)DOAJ5ccbfdd6c760445caec5310d5953a6cc DE-627 ger DE-627 rakwb eng Chen Lin verfasserin aut Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC. papillary thyroid carcinoma immune microenvironment diagnosis machine learning biomarker autophagy rna sequencing Medicine R Tao Gaofeng verfasserin aut Yang Mei verfasserin aut In Open Medicine De Gruyter, 2015 19(2024), 1, Seite 7-30 (DE-627)832299049 (DE-600)2829380-0 23915463 nnns volume:19 year:2024 number:1 pages:7-30 https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/article/5ccbfdd6c760445caec5310d5953a6cc kostenfrei https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/toc/2391-5463 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_603 GBV_ILN_2014 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 19 2024 1 7-30
allfieldsGer	10.1515/med-2024-0896 doi (DE-627)DOAJ100843123 (DE-599)DOAJ5ccbfdd6c760445caec5310d5953a6cc DE-627 ger DE-627 rakwb eng Chen Lin verfasserin aut Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC. papillary thyroid carcinoma immune microenvironment diagnosis machine learning biomarker autophagy rna sequencing Medicine R Tao Gaofeng verfasserin aut Yang Mei verfasserin aut In Open Medicine De Gruyter, 2015 19(2024), 1, Seite 7-30 (DE-627)832299049 (DE-600)2829380-0 23915463 nnns volume:19 year:2024 number:1 pages:7-30 https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/article/5ccbfdd6c760445caec5310d5953a6cc kostenfrei https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/toc/2391-5463 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_603 GBV_ILN_2014 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 19 2024 1 7-30
allfieldsSound	10.1515/med-2024-0896 doi (DE-627)DOAJ100843123 (DE-599)DOAJ5ccbfdd6c760445caec5310d5953a6cc DE-627 ger DE-627 rakwb eng Chen Lin verfasserin aut Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC. papillary thyroid carcinoma immune microenvironment diagnosis machine learning biomarker autophagy rna sequencing Medicine R Tao Gaofeng verfasserin aut Yang Mei verfasserin aut In Open Medicine De Gruyter, 2015 19(2024), 1, Seite 7-30 (DE-627)832299049 (DE-600)2829380-0 23915463 nnns volume:19 year:2024 number:1 pages:7-30 https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/article/5ccbfdd6c760445caec5310d5953a6cc kostenfrei https://doi.org/10.1515/med-2024-0896 kostenfrei https://doaj.org/toc/2391-5463 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_603 GBV_ILN_2014 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 19 2024 1 7-30
language	English
source	In Open Medicine 19(2024), 1, Seite 7-30 volume:19 year:2024 number:1 pages:7-30
sourceStr	In Open Medicine 19(2024), 1, Seite 7-30 volume:19 year:2024 number:1 pages:7-30
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	papillary thyroid carcinoma immune microenvironment diagnosis machine learning biomarker autophagy rna sequencing Medicine R
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container_title	Open Medicine
authorswithroles_txt_mv	Chen Lin @@aut@@ Tao Gaofeng @@aut@@ Yang Mei @@aut@@
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author	Chen Lin
spellingShingle	Chen Lin misc papillary thyroid carcinoma misc immune microenvironment misc diagnosis misc machine learning misc biomarker misc autophagy misc rna sequencing misc Medicine misc R Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
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topic_title	Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma papillary thyroid carcinoma immune microenvironment diagnosis machine learning biomarker autophagy rna sequencing
topic	misc papillary thyroid carcinoma misc immune microenvironment misc diagnosis misc machine learning misc biomarker misc autophagy misc rna sequencing misc Medicine misc R
topic_unstemmed	misc papillary thyroid carcinoma misc immune microenvironment misc diagnosis misc machine learning misc biomarker misc autophagy misc rna sequencing misc Medicine misc R
topic_browse	misc papillary thyroid carcinoma misc immune microenvironment misc diagnosis misc machine learning misc biomarker misc autophagy misc rna sequencing misc Medicine misc R
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title	Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
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title_full	Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
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title_sort	machine-learning-based prediction of a diagnostic model using autophagy-related genes based on rna sequencing for patients with papillary thyroid carcinoma
title_auth	Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
abstract	Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC.
abstractGer	Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC.
abstract_unstemmed	Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer and belongs to the category of malignant tumors of the thyroid gland. Autophagy plays an important role in PTC. The purpose of this study is to develop a novel diagnostic model using autophagy-related genes (ARGs) in patients. In this study, RNA sequencing data of PTC samples and normal samples were obtained from GSE33630 and GSE29265. Then, we analyzed GSE33630 datasets and identified 127 DE-ARGs. Functional enrichment analysis suggested that 127 DE-ARGs were mainly enriched in pathways in cancer, protein processing in endoplasmic reticulum, toll-like receptor pathway, MAPK pathway, apoptosis, neurotrophin signaling pathway, and regulation of autophagy. Subsequently, CALCOCO2, DAPK1, and RAC1 among the 127 DE-ARGs were identified as diagnostic genes by support vector machine recursive feature elimination and least absolute shrinkage and selection operator algorithms. Then, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 and its diagnostic value was confirmed in GSE29265 and our cohorts. Importantly, CALCOCO2 may be a critical regulator involved in immune microenvironment because its expression was related to many types of immune cells. Overall, we developed a novel diagnostic model using CALCOCO2, DAPK1, and RAC1 which can be used as diagnostic markers of PTC.
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title_short	Machine-learning-based prediction of a diagnostic model using autophagy-related genes based on RNA sequencing for patients with papillary thyroid carcinoma
url	https://doi.org/10.1515/med-2024-0896 https://doaj.org/article/5ccbfdd6c760445caec5310d5953a6cc https://doaj.org/toc/2391-5463
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up_date	2024-07-03T16:59:59.593Z
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