A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer

<p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid deve...
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Autor*in:	Wang Yuhang [verfasserIn] Chen Lu [verfasserIn] Pei Lixia [verfasserIn] Chen Yufeng [verfasserIn] Hu Yue [verfasserIn] Hou Wenzhen [verfasserIn] Song Yafang [verfasserIn] Sun Mengzhu [verfasserIn] Sun Jianhua [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Übergeordnetes Werk:	In: BioMedica - Discover STM Publishing Ltd, 2023, 37(2021), 1, Seite 29-38
Übergeordnetes Werk:	volume:37 ; year:2021 ; number:1 ; pages:29-38

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.51441/BioMedica/5-167

Katalog-ID:	DOAJ092083919

Internformat


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520			\|a <p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p<
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allfields	10.51441/BioMedica/5-167 doi (DE-627)DOAJ092083919 (DE-599)DOAJ071743e8bc164b02a06efd7e8a67b154 DE-627 ger DE-627 rakwb eng QH301-705.5 Wang Yuhang verfasserin aut A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p< Medicine R Biology (General) Chen Lu verfasserin aut Pei Lixia verfasserin aut Chen Yufeng verfasserin aut Hu Yue verfasserin aut Hou Wenzhen verfasserin aut Song Yafang verfasserin aut Sun Mengzhu verfasserin aut Sun Jianhua verfasserin aut In BioMedica Discover STM Publishing Ltd, 2023 37(2021), 1, Seite 29-38 (DE-627)1873392885 (DE-600)3174434-5 27103471 nnns volume:37 year:2021 number:1 pages:29-38 https://doi.org/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/article/071743e8bc164b02a06efd7e8a67b154 kostenfrei https://biomedicapk.com/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/toc/2710-3471 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_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 37 2021 1 29-38
spelling	10.51441/BioMedica/5-167 doi (DE-627)DOAJ092083919 (DE-599)DOAJ071743e8bc164b02a06efd7e8a67b154 DE-627 ger DE-627 rakwb eng QH301-705.5 Wang Yuhang verfasserin aut A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p< Medicine R Biology (General) Chen Lu verfasserin aut Pei Lixia verfasserin aut Chen Yufeng verfasserin aut Hu Yue verfasserin aut Hou Wenzhen verfasserin aut Song Yafang verfasserin aut Sun Mengzhu verfasserin aut Sun Jianhua verfasserin aut In BioMedica Discover STM Publishing Ltd, 2023 37(2021), 1, Seite 29-38 (DE-627)1873392885 (DE-600)3174434-5 27103471 nnns volume:37 year:2021 number:1 pages:29-38 https://doi.org/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/article/071743e8bc164b02a06efd7e8a67b154 kostenfrei https://biomedicapk.com/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/toc/2710-3471 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_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 37 2021 1 29-38
allfields_unstemmed	10.51441/BioMedica/5-167 doi (DE-627)DOAJ092083919 (DE-599)DOAJ071743e8bc164b02a06efd7e8a67b154 DE-627 ger DE-627 rakwb eng QH301-705.5 Wang Yuhang verfasserin aut A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p< Medicine R Biology (General) Chen Lu verfasserin aut Pei Lixia verfasserin aut Chen Yufeng verfasserin aut Hu Yue verfasserin aut Hou Wenzhen verfasserin aut Song Yafang verfasserin aut Sun Mengzhu verfasserin aut Sun Jianhua verfasserin aut In BioMedica Discover STM Publishing Ltd, 2023 37(2021), 1, Seite 29-38 (DE-627)1873392885 (DE-600)3174434-5 27103471 nnns volume:37 year:2021 number:1 pages:29-38 https://doi.org/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/article/071743e8bc164b02a06efd7e8a67b154 kostenfrei https://biomedicapk.com/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/toc/2710-3471 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_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 37 2021 1 29-38
allfieldsGer	10.51441/BioMedica/5-167 doi (DE-627)DOAJ092083919 (DE-599)DOAJ071743e8bc164b02a06efd7e8a67b154 DE-627 ger DE-627 rakwb eng QH301-705.5 Wang Yuhang verfasserin aut A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p< Medicine R Biology (General) Chen Lu verfasserin aut Pei Lixia verfasserin aut Chen Yufeng verfasserin aut Hu Yue verfasserin aut Hou Wenzhen verfasserin aut Song Yafang verfasserin aut Sun Mengzhu verfasserin aut Sun Jianhua verfasserin aut In BioMedica Discover STM Publishing Ltd, 2023 37(2021), 1, Seite 29-38 (DE-627)1873392885 (DE-600)3174434-5 27103471 nnns volume:37 year:2021 number:1 pages:29-38 https://doi.org/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/article/071743e8bc164b02a06efd7e8a67b154 kostenfrei https://biomedicapk.com/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/toc/2710-3471 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_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 37 2021 1 29-38
allfieldsSound	10.51441/BioMedica/5-167 doi (DE-627)DOAJ092083919 (DE-599)DOAJ071743e8bc164b02a06efd7e8a67b154 DE-627 ger DE-627 rakwb eng QH301-705.5 Wang Yuhang verfasserin aut A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p< Medicine R Biology (General) Chen Lu verfasserin aut Pei Lixia verfasserin aut Chen Yufeng verfasserin aut Hu Yue verfasserin aut Hou Wenzhen verfasserin aut Song Yafang verfasserin aut Sun Mengzhu verfasserin aut Sun Jianhua verfasserin aut In BioMedica Discover STM Publishing Ltd, 2023 37(2021), 1, Seite 29-38 (DE-627)1873392885 (DE-600)3174434-5 27103471 nnns volume:37 year:2021 number:1 pages:29-38 https://doi.org/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/article/071743e8bc164b02a06efd7e8a67b154 kostenfrei https://biomedicapk.com/10.51441/BioMedica/5-167 kostenfrei https://doaj.org/toc/2710-3471 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_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 37 2021 1 29-38
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callnumber-first	Q - Science
author	Wang Yuhang
spellingShingle	Wang Yuhang misc QH301-705.5 misc Medicine misc R misc Biology (General) A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer
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topic_title	QH301-705.5 A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer
topic	misc QH301-705.5 misc Medicine misc R misc Biology (General)
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title	A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer
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title_full	A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer
author_sort	Wang Yuhang
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author_browse	Wang Yuhang Chen Lu Pei Lixia Chen Yufeng Hu Yue Hou Wenzhen Song Yafang Sun Mengzhu Sun Jianhua
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doi_str_mv	10.51441/BioMedica/5-167
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title_sort	6-lncrna signature to improve prognostic prediction of colon cancer
callnumber	QH301-705.5
title_auth	A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer
abstract	<p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p<
abstractGer	<p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p<
abstract_unstemmed	<p<<strong<Background and Objective:</strong<</p< <p<<strong<Background and Objective: </strong<Colorectal cancer is one of the most common malignant tumors in the world. The prognosis of colorectal cancer is still considered as worse despite the rapid development of treatment methods in the recent years. Therefore, it is important to understand the pathogenesis and development for more accurate prognostic methods. The present study aimed to identify a long non‑coding (lnc) RNAs‑based signature for prognostic determination of colon cancer patients.</p< <p<<strong<Methods: </strong<Datasets from the GEO and TCGA databases were used, differential expression of lncRNA was analyzed, and a 6‑lncRNA signature was identified. KEGG and GO was used to enrich the signal pathway to determine the biological effects of these 6-lncRNA.</p< <p<<strong<Resul</strong<t<strong<s:</strong< This study has designed a prognosis model containing “LINC01494”, “TRPM2-AS”, “ATP1A1-AS1”, “FRY-AS1”, “LINC01360”, and “RBFADN” based on data set of colorectal cancer patients available in the TCGA and GEO databases. The prognostic model of lncRNAs may predict the prognosis of patients with colorectal cancer.</p< <p<<strong<Conclusion:</strong< The present study identified a 6‑lncRNA signature that could predict the survival rate for colon cancer patients. Further studies may be carried out to strengthen the findings of the present study.</p<
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title_short	A 6-lncRNA Signature to Improve Prognostic Prediction of Colon Cancer
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