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Construction and validation of a nomogram based on N6‐Methylandenosine‐related lncRNAs for predicting the prognosis of non‐small cell lung cancer patients
BACKGROUND: The N6‐methyladenosine (m(6)A) can modify long non‐coding RNAs (lncRNAs), thereby influencing a wide array of biological functions. However, the prognosis of m(6)A‐related lncRNAs (m(6)ARLncRNAs) in non‐small cell lung cancer (NSCLC) remains largely unknown. METHODS: Pearson correlation...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883402/ https://www.ncbi.nlm.nih.gov/pubmed/35726651 http://dx.doi.org/10.1002/cam4.4961 |
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author | Xiao, Wenjing Geng, Wei Xu, Juanjuan Huang, Qi Fan, Jinshuo Tan, Qi Yin, Zhengrong Li, Yumei Yang, Guanghai Jin, Yang |
author_facet | Xiao, Wenjing Geng, Wei Xu, Juanjuan Huang, Qi Fan, Jinshuo Tan, Qi Yin, Zhengrong Li, Yumei Yang, Guanghai Jin, Yang |
author_sort | Xiao, Wenjing |
collection | PubMed |
description | BACKGROUND: The N6‐methyladenosine (m(6)A) can modify long non‐coding RNAs (lncRNAs), thereby influencing a wide array of biological functions. However, the prognosis of m(6)A‐related lncRNAs (m(6)ARLncRNAs) in non‐small cell lung cancer (NSCLC) remains largely unknown. METHODS: Pearson correlation analysis was used to identify m(6)ARLncRNAs in 1835 NSCLC patients and with the condition (|Pearson R| > 0.4 and p < 0.001). Univariant Cox regression analysis was conducted to explore the prognostic m(6)ARLncRNAs. We filtered prognostic m(6)ARLncRNAs by LASSO regression and multivariate Cox proportional hazard regression to construct and validate an m(6)ARLncRNAs signature (m(6)ARLncSig). We analyzed the correlation between the m(6)ARLncSig score and clinical features, immune microenvironment, tumor mutation burden, and therapeutic sensitivity and conducted independence and clinical stratification analysis. Finally, we established and validated a nomogram for prognosis prediction in NSCLC patients. RESULTS: Forty‐one m(6)ARLncRNAs were identified as prognostic lncRNAs, and 12 m(6)ARLncRNAs were selected to construct m(6)ARLncSig in the TCGA training dataset. The m(6)ARLncSig was further validated in the testing dataset, GSE31210, GSE37745, GSE30219, and our NSCLC samples. In terms of m(6)ARLncSig, NSCLC patients were divided into high‐ and low‐risk groups, with significantly different overall survival (OS), clinical features (age, sex, and tumor stage), tumor‐infiltrating immune cells, chemotherapeutic sensitivity, radiotherapeutic response, and biological pathways. Moreover, m(6)ARLncSig independently predicted the OS of NSCLC patients. Finally, the robustness and clinical practicability for predicting NSCLC patient prognosis was improved by constructing a nomogram containing the m(6)ARLncSig, age, gender, and tumor stage. CONCLUSIONS: Our study demonstrated that m(6)ARLncSig could act as a potential biomarker for evaluating the prognosis and therapeutic efficacy in NSCLC patients. |
format | Online Article Text |
id | pubmed-9883402 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-98834022023-01-30 Construction and validation of a nomogram based on N6‐Methylandenosine‐related lncRNAs for predicting the prognosis of non‐small cell lung cancer patients Xiao, Wenjing Geng, Wei Xu, Juanjuan Huang, Qi Fan, Jinshuo Tan, Qi Yin, Zhengrong Li, Yumei Yang, Guanghai Jin, Yang Cancer Med Research Articles BACKGROUND: The N6‐methyladenosine (m(6)A) can modify long non‐coding RNAs (lncRNAs), thereby influencing a wide array of biological functions. However, the prognosis of m(6)A‐related lncRNAs (m(6)ARLncRNAs) in non‐small cell lung cancer (NSCLC) remains largely unknown. METHODS: Pearson correlation analysis was used to identify m(6)ARLncRNAs in 1835 NSCLC patients and with the condition (|Pearson R| > 0.4 and p < 0.001). Univariant Cox regression analysis was conducted to explore the prognostic m(6)ARLncRNAs. We filtered prognostic m(6)ARLncRNAs by LASSO regression and multivariate Cox proportional hazard regression to construct and validate an m(6)ARLncRNAs signature (m(6)ARLncSig). We analyzed the correlation between the m(6)ARLncSig score and clinical features, immune microenvironment, tumor mutation burden, and therapeutic sensitivity and conducted independence and clinical stratification analysis. Finally, we established and validated a nomogram for prognosis prediction in NSCLC patients. RESULTS: Forty‐one m(6)ARLncRNAs were identified as prognostic lncRNAs, and 12 m(6)ARLncRNAs were selected to construct m(6)ARLncSig in the TCGA training dataset. The m(6)ARLncSig was further validated in the testing dataset, GSE31210, GSE37745, GSE30219, and our NSCLC samples. In terms of m(6)ARLncSig, NSCLC patients were divided into high‐ and low‐risk groups, with significantly different overall survival (OS), clinical features (age, sex, and tumor stage), tumor‐infiltrating immune cells, chemotherapeutic sensitivity, radiotherapeutic response, and biological pathways. Moreover, m(6)ARLncSig independently predicted the OS of NSCLC patients. Finally, the robustness and clinical practicability for predicting NSCLC patient prognosis was improved by constructing a nomogram containing the m(6)ARLncSig, age, gender, and tumor stage. CONCLUSIONS: Our study demonstrated that m(6)ARLncSig could act as a potential biomarker for evaluating the prognosis and therapeutic efficacy in NSCLC patients. John Wiley and Sons Inc. 2022-06-21 /pmc/articles/PMC9883402/ /pubmed/35726651 http://dx.doi.org/10.1002/cam4.4961 Text en © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Xiao, Wenjing Geng, Wei Xu, Juanjuan Huang, Qi Fan, Jinshuo Tan, Qi Yin, Zhengrong Li, Yumei Yang, Guanghai Jin, Yang Construction and validation of a nomogram based on N6‐Methylandenosine‐related lncRNAs for predicting the prognosis of non‐small cell lung cancer patients |
title | Construction and validation of a nomogram based on N6‐Methylandenosine‐related lncRNAs for predicting the prognosis of non‐small cell lung cancer patients |
title_full | Construction and validation of a nomogram based on N6‐Methylandenosine‐related lncRNAs for predicting the prognosis of non‐small cell lung cancer patients |
title_fullStr | Construction and validation of a nomogram based on N6‐Methylandenosine‐related lncRNAs for predicting the prognosis of non‐small cell lung cancer patients |
title_full_unstemmed | Construction and validation of a nomogram based on N6‐Methylandenosine‐related lncRNAs for predicting the prognosis of non‐small cell lung cancer patients |
title_short | Construction and validation of a nomogram based on N6‐Methylandenosine‐related lncRNAs for predicting the prognosis of non‐small cell lung cancer patients |
title_sort | construction and validation of a nomogram based on n6‐methylandenosine‐related lncrnas for predicting the prognosis of non‐small cell lung cancer patients |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9883402/ https://www.ncbi.nlm.nih.gov/pubmed/35726651 http://dx.doi.org/10.1002/cam4.4961 |
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