Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma
Objective: Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. The present study aimed to screen the key genes involved in LUAD development and prognosis. Methods: The transcriptome data for 515 LUAD and 347 normal samples were downloaded from The Cancer Genome Atlas and Ge...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2022-04-01
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| Series: | Inquiry: The Journal of Health Care Organization, Provision, and Financing |
| Online Access: | https://doi.org/10.1177/00469580221096259 |
| _version_ | 1811232462860189696 |
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| author | Xuan Luo PhD Jian Guo Xu PhD ZhiYuan Wang PhD XiaoFang Wang PhD QianYing Zhu master's degree Juan Zhao master's degree Li Bian PhD |
| author_facet | Xuan Luo PhD Jian Guo Xu PhD ZhiYuan Wang PhD XiaoFang Wang PhD QianYing Zhu master's degree Juan Zhao master's degree Li Bian PhD |
| author_sort | Xuan Luo PhD |
| collection | DOAJ |
| description | Objective: Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. The present study aimed to screen the key genes involved in LUAD development and prognosis. Methods: The transcriptome data for 515 LUAD and 347 normal samples were downloaded from The Cancer Genome Atlas and Genotype Tissue Expression databases. The weighted gene co-expression network and differentially expressed genes were used to identify the central regulatory genes for the development of LUAD. Univariate Cox, LASSO, and multivariate Cox regression analyses were utilized to identify prognosis-related genes. Results: The top 10 central regulatory genes of LUAD included IL6, PECAM1, CDH5, VWF, THBS1, CAV1, TEK , HGF, SPP1, and ENG . Genes that have an impact on survival included PECAM1, HGF, SPP1, and ENG . The favorable prognosis genes included KDF1, ZNF691, DNASE2B, and ELAPOR1, while unfavorable prognosis genes included RPL22, ENO1, PCSK9, SNX7, and LCE5A. The areas under the receiver operating characteristic curves of the risk score model in the training and testing datasets were .78 and .758, respectively. Conclusion: Bioinformatics methods were used to identify genes involved in the development and prognosis of LUAD, which will provide a basis for further research on the treatment and prognosis of LUAD. |
| first_indexed | 2024-04-12T11:03:12Z |
| format | Article |
| id | doaj.art-2b26f9a2f3be45d0834bea7b0c95ecb4 |
| institution | Directory Open Access Journal |
| issn | 0046-9580 1945-7243 |
| language | English |
| last_indexed | 2024-04-12T11:03:12Z |
| publishDate | 2022-04-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Inquiry: The Journal of Health Care Organization, Provision, and Financing |
| spelling | doaj.art-2b26f9a2f3be45d0834bea7b0c95ecb42022-12-22T03:35:52ZengSAGE PublishingInquiry: The Journal of Health Care Organization, Provision, and Financing0046-95801945-72432022-04-015910.1177/00469580221096259Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung AdenocarcinomaXuan Luo PhDJian Guo Xu PhDZhiYuan Wang PhDXiaoFang Wang PhDQianYing Zhu master's degreeJuan Zhao master's degreeLi Bian PhDObjective: Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis. The present study aimed to screen the key genes involved in LUAD development and prognosis. Methods: The transcriptome data for 515 LUAD and 347 normal samples were downloaded from The Cancer Genome Atlas and Genotype Tissue Expression databases. The weighted gene co-expression network and differentially expressed genes were used to identify the central regulatory genes for the development of LUAD. Univariate Cox, LASSO, and multivariate Cox regression analyses were utilized to identify prognosis-related genes. Results: The top 10 central regulatory genes of LUAD included IL6, PECAM1, CDH5, VWF, THBS1, CAV1, TEK , HGF, SPP1, and ENG . Genes that have an impact on survival included PECAM1, HGF, SPP1, and ENG . The favorable prognosis genes included KDF1, ZNF691, DNASE2B, and ELAPOR1, while unfavorable prognosis genes included RPL22, ENO1, PCSK9, SNX7, and LCE5A. The areas under the receiver operating characteristic curves of the risk score model in the training and testing datasets were .78 and .758, respectively. Conclusion: Bioinformatics methods were used to identify genes involved in the development and prognosis of LUAD, which will provide a basis for further research on the treatment and prognosis of LUAD.https://doi.org/10.1177/00469580221096259 |
| spellingShingle | Xuan Luo PhD Jian Guo Xu PhD ZhiYuan Wang PhD XiaoFang Wang PhD QianYing Zhu master's degree Juan Zhao master's degree Li Bian PhD Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma Inquiry: The Journal of Health Care Organization, Provision, and Financing |
| title | Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma |
| title_full | Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma |
| title_fullStr | Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma |
| title_full_unstemmed | Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma |
| title_short | Bioinformatics Identification of Key Genes for the Development and Prognosis of Lung Adenocarcinoma |
| title_sort | bioinformatics identification of key genes for the development and prognosis of lung adenocarcinoma |
| url | https://doi.org/10.1177/00469580221096259 |
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