Cargando…
Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis
BACKGROUND: Non-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approac...
Autores principales: | , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831708/ https://www.ncbi.nlm.nih.gov/pubmed/36688087 http://dx.doi.org/10.1155/2023/6782732 |
_version_ | 1784867902273355776 |
---|---|
author | Yu, Liyan Liang, Xuemei Wang, Jianwei Ding, Guangxiang Tang, Jinhai Xue, Juan He, Xin Ge, Jingxuan Jin, Xianzhang Yang, Zhiyi Li, Xianwei Yao, Hehuan Yin, Hongtao Liu, Wu Yin, Shengchen Sun, Bing Sheng, Junxiu |
author_facet | Yu, Liyan Liang, Xuemei Wang, Jianwei Ding, Guangxiang Tang, Jinhai Xue, Juan He, Xin Ge, Jingxuan Jin, Xianzhang Yang, Zhiyi Li, Xianwei Yao, Hehuan Yin, Hongtao Liu, Wu Yin, Shengchen Sun, Bing Sheng, Junxiu |
author_sort | Yu, Liyan |
collection | PubMed |
description | BACKGROUND: Non-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC by bioinformatics analysis. METHODS: From the Gene Expression Omnibus database, GSE118370 and GSE10072 microarray datasets were obtained. Identifying the differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples was done. By using bioinformatics tools, a protein-protein interaction (PPI) network was constructed, modules were analyzed, and enrichment analyses were performed. The expression and prognostic values of 14 hub genes were validated by the GEPIA database, and the correlation between hub genes and survival in lung adenocarcinoma was assessed by UALCAN, cBioPortal, String and Cytoscape, and Timer tools. RESULTS: We found three genes (PIK3R1, SPP1, and PECAM1) that have a clear correlation with OS in the lung adenocarcinoma patient. It has been found that lung adenocarcinoma exhibits high expression of SPP1 and that this has been associated with poor prognosis, while low expression of PECAM1 and PIK3R1 is associated with poor prognosis (P < 0.05). We also found that the expression of SPP1 was associated with miR-146a-5p, while the high expression of miR-146a-5p was related to good prognosis (P < 0.05). On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients (P < 0.05). Finally, we found that the immune checkpoint genes CD274(PD-L1) and PDCD1LG2(PD-1) were also related to SPP1 in lung adenocarcinoma. CONCLUSIONS: The results indicated that SPP1 is a cancer promoter (oncogene), while PECAM1 and PIK3R1 are cancer suppressor genes. These genes take part in the regulation of biological activities in lung adenocarcinoma, which provides a basis for improving detection and immunotherapeutic targets for lung adenocarcinoma. |
format | Online Article Text |
id | pubmed-9831708 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-98317082023-01-19 Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis Yu, Liyan Liang, Xuemei Wang, Jianwei Ding, Guangxiang Tang, Jinhai Xue, Juan He, Xin Ge, Jingxuan Jin, Xianzhang Yang, Zhiyi Li, Xianwei Yao, Hehuan Yin, Hongtao Liu, Wu Yin, Shengchen Sun, Bing Sheng, Junxiu Genet Res (Camb) Research Article BACKGROUND: Non-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC by bioinformatics analysis. METHODS: From the Gene Expression Omnibus database, GSE118370 and GSE10072 microarray datasets were obtained. Identifying the differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples was done. By using bioinformatics tools, a protein-protein interaction (PPI) network was constructed, modules were analyzed, and enrichment analyses were performed. The expression and prognostic values of 14 hub genes were validated by the GEPIA database, and the correlation between hub genes and survival in lung adenocarcinoma was assessed by UALCAN, cBioPortal, String and Cytoscape, and Timer tools. RESULTS: We found three genes (PIK3R1, SPP1, and PECAM1) that have a clear correlation with OS in the lung adenocarcinoma patient. It has been found that lung adenocarcinoma exhibits high expression of SPP1 and that this has been associated with poor prognosis, while low expression of PECAM1 and PIK3R1 is associated with poor prognosis (P < 0.05). We also found that the expression of SPP1 was associated with miR-146a-5p, while the high expression of miR-146a-5p was related to good prognosis (P < 0.05). On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients (P < 0.05). Finally, we found that the immune checkpoint genes CD274(PD-L1) and PDCD1LG2(PD-1) were also related to SPP1 in lung adenocarcinoma. CONCLUSIONS: The results indicated that SPP1 is a cancer promoter (oncogene), while PECAM1 and PIK3R1 are cancer suppressor genes. These genes take part in the regulation of biological activities in lung adenocarcinoma, which provides a basis for improving detection and immunotherapeutic targets for lung adenocarcinoma. Hindawi 2023-01-03 /pmc/articles/PMC9831708/ /pubmed/36688087 http://dx.doi.org/10.1155/2023/6782732 Text en Copyright © 2023 Liyan Yu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Yu, Liyan Liang, Xuemei Wang, Jianwei Ding, Guangxiang Tang, Jinhai Xue, Juan He, Xin Ge, Jingxuan Jin, Xianzhang Yang, Zhiyi Li, Xianwei Yao, Hehuan Yin, Hongtao Liu, Wu Yin, Shengchen Sun, Bing Sheng, Junxiu Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis |
title | Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis |
title_full | Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis |
title_fullStr | Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis |
title_full_unstemmed | Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis |
title_short | Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis |
title_sort | identification of key biomarkers and candidate molecules in non-small-cell lung cancer by integrated bioinformatics analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9831708/ https://www.ncbi.nlm.nih.gov/pubmed/36688087 http://dx.doi.org/10.1155/2023/6782732 |
work_keys_str_mv | AT yuliyan identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT liangxuemei identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT wangjianwei identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT dingguangxiang identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT tangjinhai identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT xuejuan identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT hexin identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT gejingxuan identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT jinxianzhang identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT yangzhiyi identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT lixianwei identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT yaohehuan identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT yinhongtao identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT liuwu identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT yinshengchen identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT sunbing identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis AT shengjunxiu identificationofkeybiomarkersandcandidatemoleculesinnonsmallcelllungcancerbyintegratedbioinformaticsanalysis |