Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

Qin, Fanbo; Zhang, Junyong; Gong, Jianping; Zhang, Wenfeng

doi:10.1155/2021/5564040

Cited by 3 publications

(2 citation statements)

References 43 publications

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“…BIRC5 expression could reflect aggressive histological and clinical behavior of HCC, correlating with poorer OS (Kapiris et al, 2019). Present studies have shown that BIRC5 is closely related with autophagy, and several survival indexes containing BIRC5 were proved to have promising predictive value (Yang et al, 2020;Qin et al, 2021;Wang et al, 2021). Jiao et al once reported that YAP promoted sorafenib resistance in HCC by upregulating BIRC5 (Sun et al, 2021).…”

Section: Discussionmentioning

confidence: 92%

Identification and Validation of a Tumor Microenvironment-Related Gene Signature in Hepatocellular Carcinoma Prognosis

et al. 2021

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Background: Hepatocellular carcinoma (HCC) is a typical inflammatory-related malignant tumor with complex immune tolerance microenvironment and poor prognosis. In this study, we aimed to construct a novel immune-related gene signature for the prognosis of HCC patients, exploring tumor microenvironment (TME) cell infiltration characterization and potential mechanisms.Methods: A total of 364 HCC samples with follow-up information in the TCGA-LIHC dataset were analyzed for the training of the prognostic signature. The Least Absolute Shrinkage and Selector Operation (LASSO) regression based on the IRGs was conducted to identify the prognostic genes and establish an immune risk signature. The immune cell infiltration in TME was estimated via the CIBERSORT method. Gene Set Variation Analysis (GSVA) was conducted to compare the biological pathways involved in the low-risk and high-risk groups. Furthermore, paraffin sections of HCC tissue microarrays containing 77 patients from Fudan University Shanghai Cancer Center were used for IHC staining. The clinical characteristics of the 77 HCC patients were collected and summarized for survival analysis validation via the Kaplan–Meier (KM) method.Results: Three-gene signature with close immune correlation (Risk score = EPO * 0.02838 + BIRC5 * 0.02477 + SPP1 * 0.0002044) was constructed eventually and proven to be an effective prognostic factor for HCC patients. The patients were divided into a high-risk and a low-risk group according to the optimal cutoff, and the survival analysis revealed that HCC samples with high-risk immuno-score had significantly poorer outcomes than the low-risk group (p < 0.0001). The results of CIBERSORT suggested that the immune cell activation was relatively higher in the low-risk group with better prognosis. Besides, GSVA analysis showed multiple signaling differences between the high- and low-risk group, indicating that the three-gene prognostic model can affect the prognosis of patients by affecting immune-related mechanisms. Tissue microarray (TMA) results further confirmed that the expression of three genes in HCC tissues was closely related to the prognosis of patients, respectively.Conclusion: In this study, we constructed and validated a robust three-gene signature with close immune correlation in HCC, which presented a reliable performance in the prediction of HCC patients’ survival.

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Section: Discussionmentioning

confidence: 92%

Identification and Validation of a Tumor Microenvironment-Related Gene Signature in Hepatocellular Carcinoma Prognosis

et al. 2021

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“…We sought to examine the predictive value of ARGs that could be used to predict the prognosis of HCC patients. Qin et al constructed a prognostic model of ARGs for LIHC with partial data sets from TCGA (n=235) ( 36 ). However, to build the model in this study, we used the whole TCGA data set (n=346).…”

Section: Discussionmentioning

confidence: 99%

Comprehensive analysis of an autophagy-related prognostic model for predicting survival based on TCGA and ICGC database in hepatocellular carcinoma patients

An¹,

Du²,

Wang³

et al. 2022

J Gastrointest Oncol

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Background: There is accumulating evidence that autophagic activity is crucial to the development of hepatocellular carcinoma (HCC). Thus, we sought to develop a predictive model based on autophagy-related genes (ARGs) to forecast the prognosis of HCC patients.Methods: Based on expression data from The Cancer Genome Atlas (TCGA) and ARGs from Human Autophagy Database (HADb), the differentially expressed ARGs were screened. The prognosis-related ARGs were identified using a univariate Cox regression analysis. Using multivariate Cox regression analysis, a prognostic model was developed. To assess the predictive value of the model, receiver operating characteristic (ROC) curve, Kaplan-Meier curve, and multivariable Cox regression analyses were conducted.A data cohort gathered independently from the International Cancer Genome Consortium (ICGC) database further verified the model's predictive accuracy. The immune landscape was generated using the TIMER and CIBERSORT algorithms. Finally, the correlation between the prognostic signature and gene mutation status was analyzed by employing "maftools" package.Results: We identified a novel prediction model based on the ARGs of PLD1 and SLC36A1 with significant prognostic values for HCC in both univariate and multivariate Cox regression analysis, and patients were classified into high-or low-risk groups based on their risk scores. High-risk patients had significantly shorter overall survival (OS) times than low-risk patients (P=5e-4). According to the ROC curve analysis, the risk score had a higher predictive value than the other clinical characteristics. Prognostic nomograms were also performed to visualize the relationship between individual predictors and survival rates in patients with HCC. Further, an external independent cohort of ICGC patients provided additional confirmation of the predictive efficacy of the model. We subsequently analyzed the differential immune densities of the two groups and discovered that various immune cells, including naïve B cells, resting memory cluster of differentiation (CD)4 T cells, regulatory T cells, M2 macrophages, and neutrophils, had considerably larger infiltrating densities in the high-risk group than the low-risk group. Conclusions:We established a robust autophagy-related risk model having a certain prediction accuracy for predicting the prognosis of HCC patients. Our findings will contribute to the definition of prognosis and establishment of personalized treatment interventions for HCC patients.

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Autophagy-Related Gene WD Repeat Domain 45B Promotes Tumor Proliferation and Migration of Hepatocellular Carcinoma through the Akt/mTOR Signaling Pathway

Chen

Pang

et al. 2023

Diagnostics

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Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor. It has been found that autophagy plays a role both as a tumor promoter and inhibitor in HCC carcinogenesis. However, the mechanism behind is still unveiled. This study aims to explore the functions and mechanism of the key autophagy-related proteins, to shed light on novel clinical diagnoses and treatment targets of HCC. Bioinformation analyses were performed by using data from public databases including TCGA, ICGC, and UCSC Xena. The upregulated autophagy-related gene WDR45B was identified and validated in human liver cell line LO2, human HCC cell line HepG2 and Huh-7. Immunohistochemical assay (IHC) was also performed on formalin-fixed paraffin-embedded (FFPE) tissues of 56 HCC patients from our pathology archives. By using qRT-PCR and Western blots we found that high expression of WDR45B influenced the Akt/mTOR signaling pathway. Autophagy marker LC3- II/LC3-I was downregulated, and p62/SQSTM1 was upregulated after knockdown of WDR45B. The effects of WDR45B knockdown on autophagy and Akt/mTOR signaling pathways can be reversed by the autophagy inducer rapamycin. Moreover, proliferation and migration of HCC can be inhibited after the knockdown of WDR45B through the CCK8 assay, wound-healing assay and Transwell cell migration and invasion assay. Therefore, WDR45B may become a novel biomarker for HCC prognosis assessment and potential target for molecular therapy.

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Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

Cited by 3 publications

References 43 publications

Identification and Validation of a Tumor Microenvironment-Related Gene Signature in Hepatocellular Carcinoma Prognosis

Identification and Validation of a Tumor Microenvironment-Related Gene Signature in Hepatocellular Carcinoma Prognosis

Comprehensive analysis of an autophagy-related prognostic model for predicting survival based on TCGA and ICGC database in hepatocellular carcinoma patients

Autophagy-Related Gene WD Repeat Domain 45B Promotes Tumor Proliferation and Migration of Hepatocellular Carcinoma through the Akt/mTOR Signaling Pathway

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