Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data

Metastasis is the primary cause of lung adenocarcinoma (LUAD)-related death. This study evaluated the metastasis-associated genes (MAGs) in single-cell RNA sequencing (scRNA-seq) data from LUAD tissues and developed a MAG signature to predict overall survival (OS) of LUAD patients. The LUAD scRNA-se...

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Autores principales:	Jinqi He, Wenjing Zhang, Faxiang Li, Yan Yu
Formato:	article
Lenguaje:	EN
Publicado:	AIMS Press 2021
Materias:	lung cancer metastasis single-cell rna sequencing prognosis gene signature Biotechnology TP248.13-248.65 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/49cbf7accf9b4e40b5c48f18c6322f4b
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spelling	oai:doaj.org-article:49cbf7accf9b4e40b5c48f18c6322f4b2021-11-09T05:56:01ZDevelopment of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data10.3934/mbe.20212981551-0018https://doaj.org/article/49cbf7accf9b4e40b5c48f18c6322f4b2021-07-01T00:00:00Zhttps://www.aimspress.com/article/doi/10.3934/mbe.2021298?viewType=HTMLhttps://doaj.org/toc/1551-0018Metastasis is the primary cause of lung adenocarcinoma (LUAD)-related death. This study evaluated the metastasis-associated genes (MAGs) in single-cell RNA sequencing (scRNA-seq) data from LUAD tissues and developed a MAG signature to predict overall survival (OS) of LUAD patients. The LUAD scRNA-seq data was downloaded from the Gene Expression Omnibus (GEO) Database and MAGs were identified from LUAD scRNA-seq data. The LUAD transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA). Cox and LASSO regression analyses were performed to identify differentially expressed MAGs (DEMAGs) with prognostic value that were then used to construct a MAG signature and MAG-nomogram model. Finally, a functional enrichment analysis was performed via Gene Set Enrichment Analysis (GSEA). 414 MAGs and 22 prognostic DEMAGs were revealed in the study. Multivariate Cox proportional hazards regression analysis was utilized to construct a 7-MAG signature for predicting the OS of LUAD patients. Patients with high risk scores had a significantly worse OS than those with low risk scores in the training group (n = 236), and the 7-MAG signature was successfully confirmed in the testing group (n = 232) and the entire TCGA-LUAD cohort (n = 468). Furthermore, univariate and multivariate Cox regression suggested that the 7-MAG signature was an independent prognostic indicator. Additionally, based on the 7-MAG signature, a nomogram was established that could more intuitively help to predict the OS of LUAD patients. The GSEA revealed the underlying molecular mechanisms of the 7-MAG signature in LUAD metastasis. In conclusion, a 7-MAG signature was developed based on LUAD scRNA-seq data that could effectively predict LUAD patient prognosis and provide novel insights for therapeutic targets and the potential molecular mechanism of metastatic LUAD.Jinqi HeWenjing ZhangFaxiang LiYan YuAIMS Pressarticlelung cancermetastasissingle-cell rna sequencingprognosisgene signatureBiotechnologyTP248.13-248.65MathematicsQA1-939ENMathematical Biosciences and Engineering, Vol 18, Iss 5, Pp 5959-5977 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	lung cancer metastasis single-cell rna sequencing prognosis gene signature Biotechnology TP248.13-248.65 Mathematics QA1-939
spellingShingle	lung cancer metastasis single-cell rna sequencing prognosis gene signature Biotechnology TP248.13-248.65 Mathematics QA1-939 Jinqi He Wenjing Zhang Faxiang Li Yan Yu Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data
description	Metastasis is the primary cause of lung adenocarcinoma (LUAD)-related death. This study evaluated the metastasis-associated genes (MAGs) in single-cell RNA sequencing (scRNA-seq) data from LUAD tissues and developed a MAG signature to predict overall survival (OS) of LUAD patients. The LUAD scRNA-seq data was downloaded from the Gene Expression Omnibus (GEO) Database and MAGs were identified from LUAD scRNA-seq data. The LUAD transcriptomic and clinical data were obtained from The Cancer Genome Atlas (TCGA). Cox and LASSO regression analyses were performed to identify differentially expressed MAGs (DEMAGs) with prognostic value that were then used to construct a MAG signature and MAG-nomogram model. Finally, a functional enrichment analysis was performed via Gene Set Enrichment Analysis (GSEA). 414 MAGs and 22 prognostic DEMAGs were revealed in the study. Multivariate Cox proportional hazards regression analysis was utilized to construct a 7-MAG signature for predicting the OS of LUAD patients. Patients with high risk scores had a significantly worse OS than those with low risk scores in the training group (n = 236), and the 7-MAG signature was successfully confirmed in the testing group (n = 232) and the entire TCGA-LUAD cohort (n = 468). Furthermore, univariate and multivariate Cox regression suggested that the 7-MAG signature was an independent prognostic indicator. Additionally, based on the 7-MAG signature, a nomogram was established that could more intuitively help to predict the OS of LUAD patients. The GSEA revealed the underlying molecular mechanisms of the 7-MAG signature in LUAD metastasis. In conclusion, a 7-MAG signature was developed based on LUAD scRNA-seq data that could effectively predict LUAD patient prognosis and provide novel insights for therapeutic targets and the potential molecular mechanism of metastatic LUAD.
format	article
author	Jinqi He Wenjing Zhang Faxiang Li Yan Yu
author_facet	Jinqi He Wenjing Zhang Faxiang Li Yan Yu
author_sort	Jinqi He
title	Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data
title_short	Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data
title_full	Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data
title_fullStr	Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data
title_full_unstemmed	Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data
title_sort	development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell rna sequencing data
publisher	AIMS Press
publishDate	2021
url	https://doaj.org/article/49cbf7accf9b4e40b5c48f18c6322f4b
work_keys_str_mv	AT jinqihe developmentofmetastasisassociatedsevengenesignatureforpredictinglungadenocarcinomaprognosisusingsinglecellrnasequencingdata AT wenjingzhang developmentofmetastasisassociatedsevengenesignatureforpredictinglungadenocarcinomaprognosisusingsinglecellrnasequencingdata AT faxiangli developmentofmetastasisassociatedsevengenesignatureforpredictinglungadenocarcinomaprognosisusingsinglecellrnasequencingdata AT yanyu developmentofmetastasisassociatedsevengenesignatureforpredictinglungadenocarcinomaprognosisusingsinglecellrnasequencingdata
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Development of metastasis-associated seven gene signature for predicting lung adenocarcinoma prognosis using single-cell RNA sequencing data

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