Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma
Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842...
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MDPI AG
2020-11-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/21/22/8479 |
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| author | Zhaodong Li Fangyuan Qi Fan Li |
| author_facet | Zhaodong Li Fangyuan Qi Fan Li |
| author_sort | Zhaodong Li |
| collection | DOAJ |
| description | Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (<i>TTK</i>, <i>HMMR</i>, <i>ASPM</i>, <i>CDCA8</i>, <i>KIF2C</i>, <i>CCNA2</i>, <i>CCNB2</i>, and <i>MKI67</i>) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD. |
| first_indexed | 2024-03-10T14:56:02Z |
| format | Article |
| id | doaj.art-9a5927343e1d41b4afbc0f6978f6f632 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-10T14:56:02Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-9a5927343e1d41b4afbc0f6978f6f6322023-11-20T20:34:02ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672020-11-012122847910.3390/ijms21228479Establishment of a Gene Signature to Predict Prognosis for Patients with Lung AdenocarcinomaZhaodong Li0Fangyuan Qi1Fan Li2The Key Laboratory of Zoonosis, Department of Pathogenobiology, College of Basic Medicine, Jilin University, Changchun 130021, ChinaThe Key Laboratory of Zoonosis, Department of Pathogenobiology, College of Basic Medicine, Jilin University, Changchun 130021, ChinaThe Key Laboratory of Zoonosis, Department of Pathogenobiology, College of Basic Medicine, Jilin University, Changchun 130021, ChinaAccumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (<i>TTK</i>, <i>HMMR</i>, <i>ASPM</i>, <i>CDCA8</i>, <i>KIF2C</i>, <i>CCNA2</i>, <i>CCNB2</i>, and <i>MKI67</i>) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD.https://www.mdpi.com/1422-0067/21/22/8479lung adenocarcinomagene signatureoverall survivalprognosisbioinformatic analysis |
| spellingShingle | Zhaodong Li Fangyuan Qi Fan Li Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma International Journal of Molecular Sciences lung adenocarcinoma gene signature overall survival prognosis bioinformatic analysis |
| title | Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma |
| title_full | Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma |
| title_fullStr | Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma |
| title_full_unstemmed | Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma |
| title_short | Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma |
| title_sort | establishment of a gene signature to predict prognosis for patients with lung adenocarcinoma |
| topic | lung adenocarcinoma gene signature overall survival prognosis bioinformatic analysis |
| url | https://www.mdpi.com/1422-0067/21/22/8479 |
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