Yutao He , Bin Du , Weiran Liao , Wei Wang, Jifeng Su, Chen Guo, Kai Zhang, Zhitian Shi
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引用次数: 0
Abstract
Background
Hepatocellular carcinoma (HCC) is a globally prevalent disease. Our article evaluates risk models based on autophagy- and HCC-related genes and their prognostic value by bioinformatics analytical methods to provide a scientific basis for clinical treatment.
Methods
Prognostic genes were identified by univariate and multivariate Cox analyses, and risk scores were calculated. The value of risk models was analysed by receiver operating characteristic curve (ROC), immune microenvironment and drug sensitivity. Prognostic gene-related regulatory mechanisms based on network database.
Results
We screened four prognosis-related genes (SQSTM1, GABARAPL1, CDKN2A, HSPB8) for model construction. The AUC for 1-, 2- and 3-year survival was higher than 0.6 in both the training and validation sets. The nomogram constructed based on risk scores, pathologic_T predicted the outcome better. There were differences in the tumour microenvironment between the high and low risk groups, as evidenced by differences in the distribution of immune cells and differences in the expression of immune checkpoints.
Conclusion
Our results illustrate that models, nomograms and risk scores were valuable for tumour progression.
期刊介绍:
Open access, online only, peer-reviewed international journal in the Life Sciences, established in 2014 Biochemistry and Biophysics Reports (BB Reports) publishes original research in all aspects of Biochemistry, Biophysics and related areas like Molecular and Cell Biology. BB Reports welcomes solid though more preliminary, descriptive and small scale results if they have the potential to stimulate and/or contribute to future research, leading to new insights or hypothesis. Primary criteria for acceptance is that the work is original, scientifically and technically sound and provides valuable knowledge to life sciences research. We strongly believe all results deserve to be published and documented for the advancement of science. BB Reports specifically appreciates receiving reports on: Negative results, Replication studies, Reanalysis of previous datasets.