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

2021 ◽  
Vol 9 ◽  
Author(s):  
Shengwei Shen ◽  
Rui Wang ◽  
Hua Qiu ◽  
Chong Li ◽  
Jinghan Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Single Cell ◽  
Multivariate Regression ◽  
Prognostic Model ◽  
Prognostic Indicator ◽  
Tumor Promoter ◽  
Prognostic Indicators ◽  
Rna Seq ◽  
Moderate Power ◽  
Clinical Tools

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.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Prognostic Model ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
Omics Data ◽  
Training Set ◽  
Test Set ◽  
Model Based ◽  
Cox Analysis

Abstract Background: Change in the genome plays a crucial role in cancerogenesis and many biomarkers can be used as effective prognostic indicators in diverse tumors. Currently, although many studies have constructed some predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance of which is unsatisfactory. To fill this shortcoming, we hope to construct a novel and accurate prognostic model with multi-omics data to guide prognostic assessments of HCC. Methods: The TCGA training set was used to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. Then the performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve, in the TCGA test set and external cohorts. Besides, a comprehensive model based on multi-omics data was constructed via multiple Cox analysis, and the performance of which was evaluated in the TCGA training set and TCGA test set. Results: We identified 16 key mRNAs, 20 key lncRNAs, 5 key miRNAs, 5 key CNV genes, and 7 key SNPs which were significantly associated with the prognosis of HCC, and constructed 5 single-omic models which showed relatively good performance in prognostic prediction with c-index ranged from 0.63 to 0.75 in the TCGA training set and test set. Besides, we validated the mRNA model and the SNP model in two independent external datasets respectively, and good discriminating abilities were observed through survival analysis (P < 0.05). Moreover, the multi-omics model based on mRNA, lncRNA, miRNA, CNV, and SNP information presented a quite strong predictive ability with c-index over 0.80 and all AUC values at 1,3,5-years more than 0.84.Conclusion: In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC, and constructed five single-omic models and an integrated multi-omics model that may provide effective and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals Identification of Multi-omics Biomarkers and Construction of the Novel Prognostic Model for Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Xiaokai Yan ◽  
Chiying Xiao ◽  
Kunyan Yue ◽  
Min Chen ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Treatment Decision ◽  
Prognostic Models ◽  
Prognostic Indicators ◽  
The Novel ◽  
Decision Curve Analysis ◽  
Treatment Decision Making ◽  
Assessment And Treatment ◽  
Cox Analysis

Abstract Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumours. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. To fill this shortcoming, we hope to build a more accurate predictive model to guide prognostic assessments of HCC. We use the TCGA to identify crucial biomarkers and construct single-omic prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of single-omic models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), and receiver operating characteristic (ROC) curve. A multi-omics model was built and evaluated by decision curve analysis (DCA), C-index, and ROC analysis. Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. Five single-omic models were constructed, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a quite predictive solid ability with a c-index over 0.80. In this study, we identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment and treatment decision-making.

scholarly journals A single-cell atlas of hepatocellular carcinoma.

2019 ◽  
Vol 5 (suppl) ◽  
pp. 46-46
Author(s):  
Ankur Sharma
Keyword(s):  
Hepatocellular Carcinoma ◽  
Endothelial Cells ◽  
Tumor Microenvironment ◽  
Liver Cancer ◽  
Single Cell ◽  
Stromal Cells ◽  
Tumor Heterogeneity ◽  
Rna Seq ◽  
Therapeutic Targeting ◽  
Complex Ecosystem

46 Background: Tumors reside and evolve in a complex ecosystem of immune and non-immune stromal cells. Methods: We employed multi-sectoral single-cell RNA-seq to catalogue intra-tumor heterogeneity in human hepatocellular carcinoma (HCC). Results: We generated single-cell atlas of ~76,000 cells from fourteen HCC patients, each consisting of 2-5 tumor and matched adjacent normal sectors. In total, we profiled 57 individual tumor and normal sectors consisting of HBV+ and HBV- HCC. By analysing matched normal and malignant sectors we observed intriguing remodelling of the tumor microenvironment (TME).We identify >70 distinct cells-states in HCC including novel, previously uncharacterized subpopulations. Specifically, we demonstrate remarkable heterogeneity in hepatocytes, fibroblast and endothelial cells. Most importantly, we consistently observed a marked remodellingof stromal cells suggesting a role in dynamic tumor-TME interactions. Conclusions: We present the first comprehensive single-cell atlas of HCC. This resource provides unprecedented insights into liver cancer, which will pave the way for early detection and therapeutic targeting.

scholarly journals The Clinical Significance of RMI2 in Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110454
Author(s):  
Bin Zheng, MS ◽  
Heng Wang, MS ◽  
Jin-xue Wang, MS ◽  
Zheng-hong Liu, MS ◽  
Pu Zhang, MD ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Genome Instability ◽  
Primary Liver Cancer ◽  
Histological Grade ◽  
Prognostic Indicator ◽  
Multivariate Regression Analysis ◽  
Prognostic Indicators ◽  
Normal Tissues ◽  
Diagnosis And Prognosis ◽  
Dismal Prognosis

Background: Hepatocellular carcinoma (HCC), which is the most common type of primary liver cancer, often presents at advanced stage with a dismal prognosis. Novel tumor biomarkers are needed to aid in HCC early detection and prognostication. Methods: Immunohistochemical staining for RecQ-mediated genome instability protein 2 (RMI2) was performed in 330 surgically resected HCC specimens and 190 adjacent normal tissues. Univariate and multivariate regression analysis were applied to identify prognostic indicators of HCC outcomes. Patient's survival was assessed with the Kaplan–Meier method. Results: RMI2 in HCC tissue was significantly higher than that in adjacent normal tissues, and was positively correlated with HCC histological grade and stage ( P < .05) but negatively correlated with the survival period. RIM2 was identified to be an independent prognostic indicator for HCC. Conclusion: The abnormal expression of RMI2 may be related to the occurrence and development of HCC. RIM2 could potentially serve as a novel tumor-specific biomarker for HCC diagnosis and prognosis prediction.

scholarly journals The oncogene Mct-1 promotes progression of hepatocellular carcinoma via enhancement of Yap-mediated cell proliferation

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wenjie Yang ◽  
Yong Ni ◽  
Shikun Yang ◽  
Yang Ji ◽  
Xinchen Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Proliferation ◽  
Molecular Mechanisms ◽  
Tumor Promoter ◽  
Luciferase Assay ◽  
Sequencing Analysis ◽  
Rna Seq ◽  
Promote Cell Proliferation

AbstractMalignant T-cell-amplified sequence 1 (Mct-1) has been reported as an oncogene in multiple malignant diseases. However, the function of Mct-1 in hepatocellular carcinoma (HCC) and the molecular mechanisms underlying tumor progression have not been explored. In this study, Mct-1 expression levels in HCC tissues and cells were detected by quantitative real-time PCR and western blotting. Mct-1 shRNAs and overexpression vector were transfected into HCC cells to downregulate or upregulate Mct-1 expression. In vitro and in vivo assays were performed to investigate the function of Mct-1 in cell proliferation and apoptosis. RNA sequencing analysis (RNA-seq) was performed to explore differences in gene expression when silenced Mct-1 expression. Mct-1 was upregulated in HCC specimens and cell lines, and higher expression of Mct-1 was predictive of poor survival. Overexpression of Mct-1 was shown to promote cell proliferation and repress cell apoptosis both in vitro and in vivo. The results of RNA-seq indicated that knockdown of Mct-1 suppressed Yap expression, while the results of the luciferase assay also revealed that Mct-1 increases the activity of the Yap promoter. Restoration of Yap expression in Mct-1 knockdown cells partially recovered the promotion of cell proliferation and inhibition of apoptosis. Collectively, these results indicate that Mct-1 acts as a tumor promoter gene in HCC progression by up-regulating Yap expression and, thus, could serve a novel potential diagnostic and prognostic biomarker for HCC.

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