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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...

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Detalles Bibliográficos
Autores principales: Xiao, Wenjing, Geng, Wei, Xu, Juanjuan, Huang, Qi, Fan, Jinshuo, Tan, Qi, Yin, Zhengrong, Li, Yumei, Yang, Guanghai, Jin, Yang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
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
Descripción
Sumario: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.