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Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma

OBJECTIVE: To screen and identify microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) using clinical samples and construct a prediction model for the prognosis of LUAD. METHODS: 160 patient samples were used to screen and identify miRNAs associated with the prognosis of LU...

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Autores principales: Dai, Hongshuang, Li, Lin, Yang, Yikun, Chen, Huang, Dong, Xin, Mao, Yousheng, Gao, Yanning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629414/
https://www.ncbi.nlm.nih.gov/pubmed/37930873
http://dx.doi.org/10.1080/07853890.2023.2241013
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author Dai, Hongshuang
Li, Lin
Yang, Yikun
Chen, Huang
Dong, Xin
Mao, Yousheng
Gao, Yanning
author_facet Dai, Hongshuang
Li, Lin
Yang, Yikun
Chen, Huang
Dong, Xin
Mao, Yousheng
Gao, Yanning
author_sort Dai, Hongshuang
collection PubMed
description OBJECTIVE: To screen and identify microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) using clinical samples and construct a prediction model for the prognosis of LUAD. METHODS: 160 patient samples were used to screen and identify miRNAs associated with the prognosis of LUAD. Differentially expressed miRNAs were analyzed using gene chip technology. The selected miRNAs were validated using samples from the validation sample group. Cox proportional hazards regression was used to construct the model and Kaplan-Meier was used to plot survival curves. Model power was assessed by testing the prognosis of the constructed model using real-time polymerase chain reaction (RT-PCR) data. RESULTS: The data showed that miR-1260b, miR-21-3p and miR-92a-3p were highly expressed in the early recurrence and metastasis group, while miR-2467-3p, miR-4659a-3p, miR-4514, miR-1471 and miR-3621 were lowly expressed. It was further confirmed that miR-21-3p was significantly highly expressed in the early recurrence and metastasis group (p = 0.02). Receiver operating characteristic (ROC) curve results showed cut-off point value of 0.0172, sensitivity of 88.2% and specificity of 100%. The predictive results of the constructed model were in good agreement with the actual prognosis of patients by using the validation sample test (Kappa = 0.426, p < 0.001), with a model sensitivity of 74.4%, a specificity of 68.3%, and an accuracy of 71.3%. CONCLUSION: miRNAs associated with the prognosis of patients with stage I LUAD were screened and validated, and a risk model for predicting the prognosis of patients was constructed. This model has good consistency with the actual prognosis of patients.
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spelling pubmed-106294142023-11-08 Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma Dai, Hongshuang Li, Lin Yang, Yikun Chen, Huang Dong, Xin Mao, Yousheng Gao, Yanning Ann Med Oncology OBJECTIVE: To screen and identify microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) using clinical samples and construct a prediction model for the prognosis of LUAD. METHODS: 160 patient samples were used to screen and identify miRNAs associated with the prognosis of LUAD. Differentially expressed miRNAs were analyzed using gene chip technology. The selected miRNAs were validated using samples from the validation sample group. Cox proportional hazards regression was used to construct the model and Kaplan-Meier was used to plot survival curves. Model power was assessed by testing the prognosis of the constructed model using real-time polymerase chain reaction (RT-PCR) data. RESULTS: The data showed that miR-1260b, miR-21-3p and miR-92a-3p were highly expressed in the early recurrence and metastasis group, while miR-2467-3p, miR-4659a-3p, miR-4514, miR-1471 and miR-3621 were lowly expressed. It was further confirmed that miR-21-3p was significantly highly expressed in the early recurrence and metastasis group (p = 0.02). Receiver operating characteristic (ROC) curve results showed cut-off point value of 0.0172, sensitivity of 88.2% and specificity of 100%. The predictive results of the constructed model were in good agreement with the actual prognosis of patients by using the validation sample test (Kappa = 0.426, p < 0.001), with a model sensitivity of 74.4%, a specificity of 68.3%, and an accuracy of 71.3%. CONCLUSION: miRNAs associated with the prognosis of patients with stage I LUAD were screened and validated, and a risk model for predicting the prognosis of patients was constructed. This model has good consistency with the actual prognosis of patients. Taylor & Francis 2023-11-06 /pmc/articles/PMC10629414/ /pubmed/37930873 http://dx.doi.org/10.1080/07853890.2023.2241013 Text en © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
spellingShingle Oncology
Dai, Hongshuang
Li, Lin
Yang, Yikun
Chen, Huang
Dong, Xin
Mao, Yousheng
Gao, Yanning
Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma
title Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma
title_full Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma
title_fullStr Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma
title_full_unstemmed Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma
title_short Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma
title_sort screening micrornas as potential prognostic biomarkers for lung adenocarcinoma
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10629414/
https://www.ncbi.nlm.nih.gov/pubmed/37930873
http://dx.doi.org/10.1080/07853890.2023.2241013
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