Dataset Information

Development and Validation of an Autophagy-Related Gene Signature for Predicting the Prognosis of Hepatocellular Carcinoma

ABSTRACT:

Purpose

Autophagy is a lysosomal degradation pathway that is essential for maintaining the homeostasis of the intracellular environment. Mounting evidence indicates that autophagy plays an essential role in the occurrence and development of hepatocellular cancer (HCC). This research is aimed at exploring the prognostic value of autophagy-related genes (ARGs) in HCC patients.

Methods

The Wilcoxon test was used to identify differentially expressed ARGs in The Cancer Genome Atlas (TCGA) HCC cohort. Then, the TCGA cohort was randomly divided into training and testing groups. Cox and LASSO regression models were used to screen for autophagy-related genes that affect overall survival (OS) in the TCGA training group. Based on the coefficient of risk genes, we constructed an autophagy-related gene signature for predicting the prognosis of HCC patients. Finally, we validated the prognostic significance of autophagy-related gene signature using the TCGA testing group and three external datasets.

Results

ATG10, BIRC5, GAPDH, and TMEM74 are risk genes for OS. According to the optimal cutoff value of risk score in each HCC dataset, HCC patients can divide into high- and low-risk groups. ARG risk score can significantly distinguish HCC patients with different survival outcomes. Meanwhile, the ARG risk score is independently correlated with OS in multiple HCC cohorts.

Conclusions

The autophagy-related risk score can effectively screen high-risk HCC patients and provide guidance for clinical prevention and treatment of HCC.

SUBMITTER: Zhang J

PROVIDER: S-EPMC8568514 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Hepatocellular carcinoma (HCC) has been recognized as the third leading cause of cancer-related deaths worldwide. There is increasing evidence that the abnormal expression of autophagy-related genes plays an important role in the occurrence and development of HCC. Therefore, the study of autophagy-related genes can further elucidate the genetic drivers of cancer and provide valuable therapeutic targets for clinical treatment. In this study, we used 232 autophagy-related genes extracted from the Human Autophagy Database (HADb) and Molecular Signatures Database (MSigDB) to construct 1884 autophagy-related gene pairs. On this basis, we developed a prognostic model based on autophagy-related gene pairs using least absolute shrinkage and selection operator (LASSO) Cox regression to evaluate the prognosis of patients after liver cancer resection. We then used 845 liver cancer samples from three different databases to test the reliability of the risk signature through survival analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate analysis. To further explore the underlying biological mechanisms, we conducted an enrichment analysis of autophagy-related genes. Finally, we combined the signature with independent prognostic factors to construct a nomogram. Based on the autophagy-related gene pair (ARGP) signature, we can divide patients into high- or low-risk groups. Survival analysis and ROC curve analysis verified the validity of the signature (AUC: 0.786-0.828). Multivariate Cox regression showed that the risk score can be used as an independent predictor of the clinical outcomes of liver cancer patients. Notably, this model has a more accurate predictive effect than most prognostic models for hepatocellular carcinoma. Moreover, our model is a powerful supplement to the HCC staging indicator, and a nomogram comprising both indicators can provide a better prognostic effect. Based on pairs of multiple autophagy-related genes, we proposed a prognostic model for predicting the overall survival rate of HCC patients after surgery, which is a promising prognostic indicator. This study confirms the importance of autophagy in the occurrence and development of HCC, and also provides potential biomarkers for targeted treatments.

Project description:BackgroundHepatocellular carcinoma (HCC) is a malignancy with a bleak prognosis. Although emerging research increasingly supports the involvement of chromatin regulators (CRs) in cancer development, CRs in HCC patients have not received proportionate attention. This study aimed to investigate the role and prognostic significance of CRs in HCC patients, providing new insights for clinical diagnosis and treatment strategies.MethodsWe analyzed 424 samples in The Cancer Genome Atlas-Liver hepatocellular carcinoma (TCGA-LIHC) data to identify key CR genes associated with HCC prognosis by differential expression and univariate Cox regression analyses. LASSO-multivariate Cox regression method was used for construction of a prognostic signature and development of a CR-related prognosis model. The prognosis capacity of the model was evaluated via Kaplan-Meier method. Relationship between the model and tumor microenvironment (TME) was evaluated. Additionally, clinical variables and the model were incorporated to create a nomogram. The role of the prognostic gene MRG-binding protein (MRGBP) in HCC was elucidated by immunohistochemistry and semiquantitative analysis.ResultsA risk score model, comprising B-lymphoma Mo-MLV insertion region 1 (BMI1), chromobox 2 (CBX2), and MRGBP, was constructed. The area under the curve (AUC) of the CR-based signature is 0.698 (P<0.05), exhibiting robust predictive power. Functional and pathway analyses illuminated the biological relevance of these genes. Immune microenvironment analysis suggested potential implications for immunotherapy. Drug sensitivity analysis identified agents for targeted treatment. Clinical samples show that MRGBP is highly expressed in HCC tissues.ConclusionsThis CR-based signature shows promise as a valuable prognostic tool for HCC patients. It demonstrates predictive capabilities, independence from other clinical factors, and potential clinical applicability. In addition, we need more experiments to validate our findings. These findings offer insights into HCC prognosis and treatment, with implications for personalized medicine and improved patient outcomes.