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Development of an Autophagy-Based and Stemness-Correlated Prognostic Model for Hepatocellular Carcinoma Using Bulk and Single-Cell RNA-Sequencing

ABSTRACT: Accumulating evidence has proved that autophagy serves as a tumor promoter in formed malignancies, and the autophagy-related prognostic signatures have been constructed as clinical tools to predict prognosis in many high-mortality cancers. Autophagy-related genes have participated in the development and metastasis of hepatocellular carcinoma (HCC), but the understanding of their prognostic value is limited. Thereafter, LIMMA and survival analysis were conducted in both ICGC and TCGA databases and a total of 10 hub autophagy-related genes, namely, NPC1, CDKN2A, RPTOR, SPHK1, HGS, BIRC5, SPNS1, BAK1, ATIC, and MAPK3, were collected. Then, GO, KEGG, correlation, consensus, and PCA analyses were utilized to reveal their potential targeted role in HCC treatment. Single-cell RNA-seq of cancer stem cells also indicated that there was a positive correlation between these genes and stemness. In parallel, we applied univariate, LASSO, and multivariate regression analyses to study the autophagy-related genes and finally proposed that ATIC and BIRC5 were the valuable prognostic indicators of HCC. The signature based on ATIC and BIRC5 exhibited moderate power for predicting the survival of HCC in the ICGC cohort, and its efficacy was further validated in the TCGA cohort. Taken together, we suggested that 10 aforementioned hub genes are promising therapeutic targets of HCC and the ATIC/BIRC5 prognostic signature is a practical prognostic indicator for HCC patients.

SUBMITTER: Shen S

PROVIDER: S-EPMC8606524 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDisulfidptosis is a new type of cellular death triggered in response to disulfide stress and is strongly linked to the progression of malignancies. Hepatocellular carcinoma (HCC) is a very common malignancy. Some reports have suggested a link between disulfidptosis-related genes (DRGs) and cancer; however, further research needs to be conducted.MethodsIn this study, HCC data from the Cancer Genome Atlas-Liver Hepatocellular Carcinoma and Gene Expression Omnibus data sets were collected and analyzed. A univariate Cox regression analysis, least absolute shrinkage and selection operator, and multivariate Cox regression analysis were conducted to identify the hub DRGs signature for prognosis. The HCC patients were allocated to high- and low-risk groups based on their disulfidptosis risk scores. The model was validated with a high degree of precision using both internal and external validation data sets. "ESTIMATE" and "CIBERSORT" packages were employed to assess the immunological landscapes and immune cell infiltration. The IMvigor210 cohort was chosen to validate the immunotherapy results. A drug sensitivity analysis was conducted to identify targeted medications. The expression of the hub DRGs in the HCC cells was confirmed using cytological techniques.ResultsThe bioinformatic analysis revealed that 16 genes showed differential expression. A prognostic model was developed based on four genes: RPN1, SLC2A1, SLC2A4, and SLC7A11. A notable difference in prognosis was observed between the two risk groups. Based on the results of the immune microenvironment, tumor mutation burden, immunotherapy, and drug screening analyses, the DRGs signature can be employed in HCC immunotherapy decision making. Further, the expression levels of the hub DRGs were significantly upregulated in the HCC cells.ConclusionsOur four-DRGs signature could be used to predict HCC prognosis. Further, this study showed that the hub DRGs could serve as biomarkers for immunotherapy prediction and could potentially guide targeted therapies.

Project description:Background The application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients’ prognosis and immunotherapeutic response. Methods NK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA–LIHC data set, GSE14520 data set, GSE76427 data set and ICGC–LIRI–JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set. Results With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients’ prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS–PD1 blocker, IPS–CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group. Conclusions Overall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s40001-023-01300-6.

Dataset Information

Development of an Autophagy-Based and Stemness-Correlated Prognostic Model for Hepatocellular Carcinoma Using Bulk and Single-Cell RNA-Sequencing

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