| Autor: |
Wanting Song, Yi Bai, Jialin Zhu, Fanxin Zeng, Chunmeng Yang, Beibei Hu, Mingjun Sun, Chenyan Li, Shiqiao Peng, Moye Chen, Xuren Sun |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
World Journal of Surgical Oncology, Vol 19, Iss 1, Pp 1-13 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
1477-7819 |
| DOI: |
10.1186/s12957-021-02329-9 |
| Popis: |
Abstract Background Gastric cancer (GC) represents a major malignancy and is the third deathliest cancer globally. Several lines of evidence indicate that the epithelial-mesenchymal transition (EMT) has a critical function in the development of gastric cancer. Although plentiful molecular biomarkers have been identified, a precise risk model is still necessary to help doctors determine patient prognosis in GC. Methods Gene expression data and clinical information for GC were acquired from The Cancer Genome Atlas (TCGA) database and 200 EMT-related genes (ERGs) from the Molecular Signatures Database (MSigDB). Then, ERGs correlated with patient prognosis in GC were assessed by univariable and multivariable Cox regression analyses. Next, a risk score formula was established for evaluating patient outcome in GC and validated by survival and ROC curves. In addition, Kaplan-Meier curves were generated to assess the associations of the clinicopathological data with prognosis. And a cohort from the Gene Expression Omnibus (GEO) database was used for validation. Results Six EMT-related genes, including CDH6, COL5A2, ITGAV, MATN3, PLOD2, and POSTN, were identified. Based on the risk model, GC patients were assigned to the high- and low-risk groups. The results revealed that the model had good performance in predicting patient prognosis in GC. Conclusions We constructed a prognosis risk model for GC. Then, we verified the performance of the model, which may help doctors predict patient prognosis. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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