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An autophagy-related gene expression signature for survival prediction in multiple cohorts of hepatocellular carcinoma patients.

ABSTRACT: Prognostic signatures have been proposed as clinical tools to estimate prognosis in hepatocellular carcinoma (HCC), which is the second most common contributor to cancer-related death at present globally. Autophagy-related genes play a dynamic and fundamental role in HCC, but knowledge of their utility as prognostic markers is limited. Here, we facilitated univariate and multivariate Cox proportional hazards regression analyses to reveal that 3 autophagy-related genes (BIRC5, FOXO1 and SQSTM1) were closely related to the survival of HCC. Then, we generated a prognosis index (PI) for predicting overall survival (OS) based on the three genes, which was an independent prognostic indicator for the OS of HCC (HR = 1.930, 95% CI: 1.200-3.104, P = 0.007). The PI showed moderate performance for predicting the survival of HCC patients and its efficacy was validated by data from three microarrays (GSE10143, GSE10186 and GSE17856). Furthermore, we deeply mined the integrated large-scale datasets from public microarrays and immunohistochemistry to validate the overexpression of BIRC5 and SQSTM1 while down-regulated FOXO1 expression in HCC. Bioinformatic analysis offered the hypothesis that proliferative signals in high-risk HCC patients were disturbing and thereby facilitated inferior clinical outcomes. Collectively, the prognostic signature we proposed is a promising biomarker for monitoring outcome of HCC. Nevertheless, prospective experimental studies are needed to validate the clinical utility.

SUBMITTER: Lin P

PROVIDER: S-EPMC5915122 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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An autophagy-related gene expression signature for survival prediction in multiple cohorts of hepatocellular carcinoma patients.

Lin Peng P He Rong-Quan RQ Dang Yi-Wu YW Wen Dong-Yue DY Ma Jie J He Yun Y Chen Gang G Yang Hong H

Oncotarget 20180109 25

Prognostic signatures have been proposed as clinical tools to estimate prognosis in hepatocellular carcinoma (HCC), which is the second most common contributor to cancer-related death at present globally. Autophagy-related genes play a dynamic and fundamental role in HCC, but knowledge of their utility as prognostic markers is limited. Here, we facilitated univariate and multivariate Cox proportional hazards regression analyses to reveal that 3 autophagy-related genes (BIRC5, FOXO1 and SQSTM1) w ...[more]

PMID: 29707114

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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.

Dataset Information

An autophagy-related gene expression signature for survival prediction in multiple cohorts of hepatocellular carcinoma patients.

Publications

An autophagy-related gene expression signature for survival prediction in multiple cohorts of hepatocellular carcinoma patients.

Similar Datasets

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