Cargando…

Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma

BACKGROUND: The objective of the present study was to predict candidate genes with prognostic information for lung adenocarcinoma (LUAD). METHODS: Weighted correlation network analysis (WGCNA) was utilized to build the co-expression network of deferentially expressed genes (DEGs) in GSE32863. Key ge...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhu, Huijun, Yue, Haiying, Xie, Yiting, Chen, Binlin, Zhou, Yanhua, Liu, Wenqi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947492/
https://www.ncbi.nlm.nih.gov/pubmed/33717590
http://dx.doi.org/10.21037/jtd-21-49
_version_ 1783663240220246016
author Zhu, Huijun
Yue, Haiying
Xie, Yiting
Chen, Binlin
Zhou, Yanhua
Liu, Wenqi
author_facet Zhu, Huijun
Yue, Haiying
Xie, Yiting
Chen, Binlin
Zhou, Yanhua
Liu, Wenqi
author_sort Zhu, Huijun
collection PubMed
description BACKGROUND: The objective of the present study was to predict candidate genes with prognostic information for lung adenocarcinoma (LUAD). METHODS: Weighted correlation network analysis (WGCNA) was utilized to build the co-expression network of deferentially expressed genes (DEGs) in GSE32863. Key genes were identified as the intersecting genes of the modules of WGCNA and DEGs. Kaplan-Meier plotter was employed to conduct survival analysis. Enrichment analysis was performed. The expression of key genes in LUAD was validated. Then, we performed in vitro experiments to explore functions of key genes. We overexpressed DYNLRB2 in A549 cell. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting were test expression levels and functional analyses were performed, including cell viability, apoptosis. RESULTS: A total of 1,587 DEGs in GSE32863 were identified, including 649 up-regulated genes and 938 down-regulated genes. In coexpression analysis, there were 1,271 hubgenes from the modules that were chosen for further analysis. 15 key genes were identified as the intersecting genes of the modules of WGCNA and DEGs. The expressions of dynein light chain roadblock-type 2 (DYNLRB2) and mouse homolog of ß1 spectrin (SPTBN1) were lower in LUAD, and were associated with survival time of LUAD patients. GSEA results showed that high expressed DYNLRB2 and SPTBN1 were enriched in Drug metabolism cytochrome P450, Cardiac muscle contraction, Retinol metabolism. Down-regulated DYNLRB2 and SPTBN1 were associated with Homologous recombination, Progesterone mediated oocyte maturation, Base excision repair. The in vitro experiment confirmed the overexpression of DYNLRB2 in A549 transferred cells. The overexpress DYNLRB2 inhibited cell viability and induced apoptosis. CONCLUSIONS: Our study suggested that DYNLRB2 and SPTBN1 might be potential tumor suppressor genes and could serve as biomarkers for predicting the prognosis of LUAD patients.
format Online
Article
Text
id pubmed-7947492
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher AME Publishing Company
record_format MEDLINE/PubMed
spelling pubmed-79474922021-03-12 Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma Zhu, Huijun Yue, Haiying Xie, Yiting Chen, Binlin Zhou, Yanhua Liu, Wenqi J Thorac Dis Original Article BACKGROUND: The objective of the present study was to predict candidate genes with prognostic information for lung adenocarcinoma (LUAD). METHODS: Weighted correlation network analysis (WGCNA) was utilized to build the co-expression network of deferentially expressed genes (DEGs) in GSE32863. Key genes were identified as the intersecting genes of the modules of WGCNA and DEGs. Kaplan-Meier plotter was employed to conduct survival analysis. Enrichment analysis was performed. The expression of key genes in LUAD was validated. Then, we performed in vitro experiments to explore functions of key genes. We overexpressed DYNLRB2 in A549 cell. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting were test expression levels and functional analyses were performed, including cell viability, apoptosis. RESULTS: A total of 1,587 DEGs in GSE32863 were identified, including 649 up-regulated genes and 938 down-regulated genes. In coexpression analysis, there were 1,271 hubgenes from the modules that were chosen for further analysis. 15 key genes were identified as the intersecting genes of the modules of WGCNA and DEGs. The expressions of dynein light chain roadblock-type 2 (DYNLRB2) and mouse homolog of ß1 spectrin (SPTBN1) were lower in LUAD, and were associated with survival time of LUAD patients. GSEA results showed that high expressed DYNLRB2 and SPTBN1 were enriched in Drug metabolism cytochrome P450, Cardiac muscle contraction, Retinol metabolism. Down-regulated DYNLRB2 and SPTBN1 were associated with Homologous recombination, Progesterone mediated oocyte maturation, Base excision repair. The in vitro experiment confirmed the overexpression of DYNLRB2 in A549 transferred cells. The overexpress DYNLRB2 inhibited cell viability and induced apoptosis. CONCLUSIONS: Our study suggested that DYNLRB2 and SPTBN1 might be potential tumor suppressor genes and could serve as biomarkers for predicting the prognosis of LUAD patients. AME Publishing Company 2021-02 /pmc/articles/PMC7947492/ /pubmed/33717590 http://dx.doi.org/10.21037/jtd-21-49 Text en 2021 Journal of Thoracic Disease. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Zhu, Huijun
Yue, Haiying
Xie, Yiting
Chen, Binlin
Zhou, Yanhua
Liu, Wenqi
Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma
title Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma
title_full Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma
title_fullStr Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma
title_full_unstemmed Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma
title_short Bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma
title_sort bioinformatics and integrated analyses of prognosis-associated key genes in lung adenocarcinoma
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947492/
https://www.ncbi.nlm.nih.gov/pubmed/33717590
http://dx.doi.org/10.21037/jtd-21-49
work_keys_str_mv AT zhuhuijun bioinformaticsandintegratedanalysesofprognosisassociatedkeygenesinlungadenocarcinoma
AT yuehaiying bioinformaticsandintegratedanalysesofprognosisassociatedkeygenesinlungadenocarcinoma
AT xieyiting bioinformaticsandintegratedanalysesofprognosisassociatedkeygenesinlungadenocarcinoma
AT chenbinlin bioinformaticsandintegratedanalysesofprognosisassociatedkeygenesinlungadenocarcinoma
AT zhouyanhua bioinformaticsandintegratedanalysesofprognosisassociatedkeygenesinlungadenocarcinoma
AT liuwenqi bioinformaticsandintegratedanalysesofprognosisassociatedkeygenesinlungadenocarcinoma