Cargando…

A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs

INTRODUCTION: Colorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor–node–metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC....

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Guoqiang, Yang, Mei, Wang, Qiaoli, Zhao, Liufang, Zhu, Sijin, Zhu, Lixiu, Xu, Tianrui, Cao, Ruixue, Li, Cheng, Liu, Qiuyan, Xiong, Wei, Su, Yan, Dong, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531750/
https://www.ncbi.nlm.nih.gov/pubmed/34692467
http://dx.doi.org/10.3389/fonc.2021.613949
_version_ 1784586929719738368
author Xu, Guoqiang
Yang, Mei
Wang, Qiaoli
Zhao, Liufang
Zhu, Sijin
Zhu, Lixiu
Xu, Tianrui
Cao, Ruixue
Li, Cheng
Liu, Qiuyan
Xiong, Wei
Su, Yan
Dong, Jian
author_facet Xu, Guoqiang
Yang, Mei
Wang, Qiaoli
Zhao, Liufang
Zhu, Sijin
Zhu, Lixiu
Xu, Tianrui
Cao, Ruixue
Li, Cheng
Liu, Qiuyan
Xiong, Wei
Su, Yan
Dong, Jian
author_sort Xu, Guoqiang
collection PubMed
description INTRODUCTION: Colorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor–node–metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment. METHODS: CRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence in situ hybridization. Additionally, the protein expression was detected by Western blot. RESULTS: A prognostic prediction model of CRC was built based on nine ARlncRNAs (NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, LINC00957, and CD27-AS1). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with NCM460. After autophagy activation, LINCC00174 expression was significantly downregulated both in NCM460 and HT29, while NKILA expression was significantly increased. CONCLUSION: The new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment.
format Online
Article
Text
id pubmed-8531750
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-85317502021-10-23 A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs Xu, Guoqiang Yang, Mei Wang, Qiaoli Zhao, Liufang Zhu, Sijin Zhu, Lixiu Xu, Tianrui Cao, Ruixue Li, Cheng Liu, Qiuyan Xiong, Wei Su, Yan Dong, Jian Front Oncol Oncology INTRODUCTION: Colorectal cancer (CRC) is the most common gastrointestinal cancer and has a low overall survival rate. Tumor–node–metastasis staging alone is insufficient to predict patient prognosis. Autophagy and long noncoding RNAs play important roles in regulating the biological behavior of CRC. Therefore, establishing an autophagy-related lncRNA (ARlncRNA)-based bioinformatics model is important for predicting survival and facilitating clinical treatment. METHODS: CRC data were retrieved from The Cancer Genome Atlas. The database was randomly divided into train set and validation set; then, univariate and multivariate Cox regression analyses were performed to screen prognosis-related ARlncRNAs for prediction model construction. Interactive network and Sankey diagrams of ARlncRNAs and messenger RNAs were plotted. We analyzed the survival rate of high- and low-risk patients and plotted survival curves and determined whether the risk score was an independent predictor of CRC. Receiver operating characteristic curves were used to evaluate model sensitivity and specificity. Then, the expression level of lncRNA was detected by quantitative real-time polymerase chain reaction, and the location of lncRNA was observed by fluorescence in situ hybridization. Additionally, the protein expression was detected by Western blot. RESULTS: A prognostic prediction model of CRC was built based on nine ARlncRNAs (NKILA, LINC00174, AC008760.1, LINC02041, PCAT6, AC156455.1, LINC01503, LINC00957, and CD27-AS1). The 5-year overall survival rate was significantly lower in the high-risk group than in the low-risk group among train set, validation set, and all patients (all p < 0.001). The model had high sensitivity and accuracy in predicting the 1-year overall survival rate (area under the curve = 0.717). The prediction model risk score was an independent predictor of CRC. LINC00174 and NKILA were expressed in the nucleus and cytoplasm of normal colonic epithelial cell line NCM460 and colorectal cancer cell lines HT29. Additionally, LINC00174 and NKILA were overexpressed in HT29 compared with NCM460. After autophagy activation, LINCC00174 expression was significantly downregulated both in NCM460 and HT29, while NKILA expression was significantly increased. CONCLUSION: The new ARlncRNA-based model predicts CRC patient prognosis and provides new research ideas regarding potential mechanisms regulating the biological behavior of CRC. ARlncRNAs may play important roles in personalized cancer treatment. Frontiers Media S.A. 2021-10-08 /pmc/articles/PMC8531750/ /pubmed/34692467 http://dx.doi.org/10.3389/fonc.2021.613949 Text en Copyright © 2021 Xu, Yang, Wang, Zhao, Zhu, Zhu, Xu, Cao, Li, Liu, Xiong, Su and Dong https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Xu, Guoqiang
Yang, Mei
Wang, Qiaoli
Zhao, Liufang
Zhu, Sijin
Zhu, Lixiu
Xu, Tianrui
Cao, Ruixue
Li, Cheng
Liu, Qiuyan
Xiong, Wei
Su, Yan
Dong, Jian
A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs
title A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs
title_full A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs
title_fullStr A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs
title_full_unstemmed A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs
title_short A Novel Prognostic Prediction Model for Colorectal Cancer Based on Nine Autophagy-Related Long Noncoding RNAs
title_sort novel prognostic prediction model for colorectal cancer based on nine autophagy-related long noncoding rnas
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8531750/
https://www.ncbi.nlm.nih.gov/pubmed/34692467
http://dx.doi.org/10.3389/fonc.2021.613949
work_keys_str_mv AT xuguoqiang anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT yangmei anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT wangqiaoli anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT zhaoliufang anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT zhusijin anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT zhulixiu anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT xutianrui anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT caoruixue anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT licheng anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT liuqiuyan anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT xiongwei anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT suyan anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT dongjian anovelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT xuguoqiang novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT yangmei novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT wangqiaoli novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT zhaoliufang novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT zhusijin novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT zhulixiu novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT xutianrui novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT caoruixue novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT licheng novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT liuqiuyan novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT xiongwei novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT suyan novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas
AT dongjian novelprognosticpredictionmodelforcolorectalcancerbasedonnineautophagyrelatedlongnoncodingrnas