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Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

[ Vol. 21 , Issue. 14 ]

Author(s):

Dawei Zhou*, Juncheng Wan and Jiang Luo   Pages 1921 - 1930 ( 10 )

Abstract:


Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer.

Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes.

Methods: We identified autophagy genes related to the prognosis of liver cancer through bioinformatics methods.

Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer.

Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

Keywords:

Primary liver cancer, autophagy, prognosis, bioinformatics, KEGG analysis, GO analysis.

Affiliation:

School of Medical Imaging, Xu Zhou Medical University, Xuzhou, Jiangsu, 221004, School of Medical Imaging, Xu Zhou Medical University, Xuzhou, Jiangsu, 221004, School of Medical Imaging, Xu Zhou Medical University, Xuzhou, Jiangsu, 221004



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