scholarly journals Comprehensive Analysis of Immune Prognostic‐Related Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Weike Gao ◽  
Luan Li ◽  
Chengzhen Li ◽  
Guanying Yu ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Prognostic Index ◽  
Risk Groups ◽  
Predictive Biomarker ◽  
Vital Role ◽  
Great Promise ◽  
Immune Genes ◽  
The Difference ◽  
Difference Expression

Abstract Background: The percentage of death resulted from hepatocellular carcinoma (HCC) remains high worldwide, despite surgical and chemotherapy treatment. Immunotherapy offers great promise in the treatment of a rapidly expanding spectrum of HCC. Therefore, further exploration of the immune-related signatures in the tumor microenvironment, which plays a vital role in tumor initiation and progression for immunotherapy is currently needed. Methods: In this present research, 866 immune-related difference expression genes (DEGs) were identified by integrating the DEGs between TCGA HCC and normal tissue and the immune genes from databases (Innate DB; Imm Port), and 144 candidate prognostic genes were defined through weighted gene co-expression network analysis (WGCNA). Results: Seven prognostic immune-related DEGs were determined with LASSO Cox PH model, which was followed by constructing the ImmuneRiskScore model based on the prognostic immune-related DEGs. The prognostic index of the ImmuneRiskScore was validated then in the dependent dataset. Patients were divided into high- and low-risk groups according to ImmuneRiskScore. The difference in ImmuneRiskScore and infiltration of immune cells between groups was detected and the correlation analysis for immunotherapy biomarkers was further explored. Conclusion:The ImmuneRiskScore of HCC could provide a prognostic signature and reflect immune characteristics within tumor microenvironment. Furthermore, it also may provide novel immunotherapy predictive biomarker for HCC patients in the near future.

scholarly journals Comprehensive analysis of immune prognostic‐related genes in the tumor microenvironment of hepatocellular carcinoma

2020 ◽  
Author(s):  
Weike Gao ◽  
Luan Li ◽  
Chengzhen Li ◽  
Guanying Yu ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Prognostic Index ◽  
Risk Groups ◽  
Predictive Biomarker ◽  
Vital Role ◽  
Great Promise ◽  
Immune Genes ◽  
The Difference ◽  
Difference Expression

Abstract Background : The percentage of death resulted from hepatocellular carcinoma (HCC) remains high worldwide, despite surgical and chemotherapy treatment. Immunotherapy offers great promise in the treatment of a rapidly expanding spectrum of HCC. Therefore, further exploration of the immune-related signatures in the tumor microenvironment, which plays a vital role in tumor initiation and progression for immunotherapy is currently needed. Methods: In this present research, 866 immune-related difference expression genes (DEGs) were identified by integrating the DEGs between TCGA HCC and normal tissue and the immune genes from databases (Innate DB; Imm Port), and 144 candidate prognostic genes were defined through weighted gene co-expression network analysis (WGCNA). Results: Seven prognostic immune-related DEGs were determined by LASSO Cox PH model, which were followed by constructing the ImmuneRiskScore model based on the prognostic immune-related DEGs. The prognostic index of the ImmuneRiskScore was validated then in the dependent dataset. Patients were divided into high- and low-risk groups according to ImmuneRiskScore. The difference in ImmuneRiskScore and infiltration of immune cells between groups was detected and the correlation analysis for immunotherapy biomarkers was further explored. Conclusion: The ImmuneRiskScore of HCC could provide a prognostic signature and reflect immune characteristics within tumor microenvironment. Furthermore, it also may provide novel immunotherapy predictive biomarker for HCC patients in the near future.

scholarly journals Comparison of immune profiles between hepatocellular carcinoma subtypes

2020 ◽  
Vol 6 (1) ◽  
pp. 19-32
Author(s):  
Xuemin Pan ◽  
Ping Lin ◽  
Fangyoumin Feng ◽  
Jia Li ◽  
Yuan-Yuan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Checkpoint Inhibitors ◽  
Wnt Signaling Pathway ◽  
Tumor Infiltrating Lymphocytes ◽  
Cytolytic Activity ◽  
Immune Genes ◽  
Cancer Hallmarks ◽  
Activation Level ◽  
The Difference ◽  
Immune Related Genes

AbstractImmunotherapy, especially immune checkpoint inhibitors, is becoming a promising treatment for hepatocellular carcinoma (HCC). However, the response rate remains limited due to the heterogeneity of HCC samples. Molecular subtypes of HCC vary in genomic background, clinical features, and prognosis. This study aims to compare the immune profiles between HCC subtypes and find subtype-specific immune characteristics that might contribute to the prognosis and potential of immunotherapy. The immune profiles consist of immune-related genes, cytolytic activity, immune pathways, and tumor-infiltrating lymphocytes. HCC-c1 samples showed an overall higher activation level of immune genes and pathways, and this pattern was consistent in validation sets. We associated the difference in immune profiles with the activation level of cancer hallmarks and genomic mutations. There was a negative correlation between most of the metabolism pathway and immune-related pathways in HCC samples. CTNNB1/WNT signaling pathway mutation, one of the common mutations in HCC, appears to be associated with the expression of immune genes as well. These results reveal the difference of immune profiles between HCC subtypes and possible reasons and influence, which may also deepen our understanding of the carcinogenesis process.

scholarly journals An lncRNA Model for Predicting the Prognosis of Hepatocellular Carcinoma Patients and ceRNA Mechanism

2021 ◽  
Vol 8 ◽  
Author(s):  
Hao Zhang ◽  
Renzheng Liu ◽  
Lin Sun ◽  
Xiao Hu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Survival Rate ◽  
Liver Cancer ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
The Difference ◽  
Experimental Group

Liver cancer is a highly malignant tumor. Notably, recent studies have found that long non-coding RNAs (lncRNAs) play a prominent role in the prognosis of patients with liver cancer. Herein, we attempted to construct an lncRNA model to accurately predict the survival rate in liver cancer. Based on The Cancer Genome Atlas (TCGA) database, we first identified 1066 lncRNAs with differential expression. The patient data obtained from TCGA were divided into the experimental group and the verification group. According to the difference in lncRNAs, we used single-factor and multi-factor Cox regression to select the genes needed to build the model in the experimental group, which were verified in the verification group. The results showed that the model could accurately predict the survival rate of patients in the high and low risk groups. The reliability of the model was also confirmed by the area under the receiver operating characteristic curve. Our model is significantly correlated with different clinicopathological features. Finally, we built a ceRNA network based on lncRNAs, which was used to display miRNAs and mRNAs related to lncRNAs. In summary, we constructed an lncRNA model to predict the survival rate of patients with hepatocellular carcinoma.

Prognostic and predictive significance of the tumor microenvironment in hepatocellular carcinoma

2021 ◽  
pp. 1-12
Author(s):  
Jibing Liu ◽  
Shuwen Kuang ◽  
Yiling Zheng ◽  
Mei Liu ◽  
Liming Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Roc Curve ◽  
Cox Regression ◽  
Predictive Biomarker ◽  
Foxp3 Expression ◽  
Tissue Samples ◽  
Logistic Analysis ◽  
Kaplan Meier ◽  
Tumor Number

BACKGROUND: Identification of molecular markers that reflect the characteristics of the tumor microenvironment (TME) may be beneficial to predict the prognosis of post-operative hepatocellular carcinoma (HCC) patients. OBJECTIVE AND METHODS: A total of 100 tissue samples from HCC patients were separately stained by immunohistochemistry to examine the expression levels of CD56, CD8α, CD68, FoxP3, CD31 and pan-Keratin. The prognostic values were analyzed by Cox regression and the Kaplan-Meier method. RESULTS: Univariate and multivariate logistic analysis showed that FoxP3 was the independent factor associated with microvascular invasion (MVI), tumor size and envelop invasion; CD68 was associated with envelope invasion and AFP. Kaplan-Meier survival curves revealed that CD68 and FoxP3 expression were significantly associated with relapse free survival (RFS) of HCC patients (P< 0.05). The ROC curve indicated that the combination of tumor number, MVI present and CD68 expression yielded a ROC curve area of 82.3% (86.36% specificity, 68.75% sensitivity) to evaluate the prognosis of HCC patients, which was higher than the classifier established by the combination of tumor number and MVI (78.8% probability, 63.64% specificity and 85.42% sensitivity). CONCLUSIONS: Our study indicated that CD68 and FoxP3 are associated with prognosis of HCC patients, and CD68 can be considered as a potential prognostic and predictive biomarker.

scholarly journals Development and Validation of a Prognostic Signature Associated With Tumor Microenvironment Based on Autophagy-Related lncRNA Analysis in Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Yan Deng ◽  
Feng Zhang ◽  
Zhen-Gang Sun ◽  
Shuai Wang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Functional Analysis ◽  
High Risk ◽  
Tumor Microenvironment ◽  
Predictive Power ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Training Cohort

Objective: The present study aimed to establish a prognostic signature based on the autophagy-related long non-coding RNAs (lncRNAs) analysis in patients with hepatocellular carcinoma (HCC).Methods: Patients with HCC from The Cancer Genome Atlas (TCGA) were taken as the training cohort, and patients from the International Cancer Genome Consortium (ICGC) were treated as the validation cohort. Autophagy-related lncRNAs were obtained via a co-expression network analysis. According to univariate and multivariate analyses, a multigene prognostic signature was constructed in the training cohort. The predictive power of the signature was confirmed in both cohorts. The detailed functions were investigated using functional analysis. The single-sample gene set enrichment analysis (ssGSEA) score was used to evaluate the tumor microenvironment. The expression levels of immunotherapy and targeted therapy targets between the two risk groups were compared. Finally, a nomogram was constructed by integrating clinicopathological parameters with independently predictive value and the risk score.Results: Four autophagy-related lncRNAs were identified to establish a prognostic signature, which separated patients into high- and low-risk groups. Survival analysis showed that patients in the high-risk group had a shorter survival time in both cohorts. A time-independent receiver-operating characteristic (ROC) curve and principal component analysis (PCA) confirmed that the prognostic signature had a robust predictive power and reliability in both cohorts. Functional analysis indicated that the expressed genes in the high-risk group are mainly enriched in autophagy- and cancer-related pathways. ssGSEA revealed that the different risk groups were associated with the tumor microenvironment. Moreover, the different risk groups had positive correlations with the expressions of specific mutant genes. Multivariate analysis showed that the risk score also exhibited excellent predictive power irrespective of clinicopathological characteristics in both cohorts. A nomogram was established. The nomogram showed good discrimination, with Harrell's concordance index (C-index) of 0.739 and good calibration.Conclusion: The four autophagy-related lncRNAs could be used as biological biomarkers and therapeutic targets. The prognostic signature and nomogram might aid clinicians in individual treatment optimization and clinical decision-making for patients with HCC.

Status and Development of The Tumor Microenvironment in Hepatocellular Carcinoma*

2012 ◽  
Vol 39 (5) ◽  
pp. 416-422
Author(s):  
Zhi-Lei LIU ◽  
Wei SUN ◽  
Fu-Chu HE ◽  
Xian-Ling CONG ◽  
Ying JIANG
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment

scholarly journals PPM1G promotes the progression of hepatocellular carcinoma via phosphorylation regulation of alternative splicing protein SRSF3

2021 ◽  
Vol 12 (8) ◽  
Author(s):  
Dawei Chen ◽  
Zhenguo Zhao ◽  
Lu Chen ◽  
Qinghua Li ◽  
Jixue Zou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Alternative Splicing ◽  
Protein Function ◽  
Vital Role ◽  
Normal Tissues ◽  
Mechanistic Analysis ◽  
Highly Correlated ◽  
Splicing Patterns

AbstractEmerging evidence has demonstrated that alternative splicing has a vital role in regulating protein function, but how alternative splicing factors can be regulated remains unclear. We showed that the PPM1G, a protein phosphatase, regulated the phosphorylation of SRSF3 in hepatocellular carcinoma (HCC) and contributed to the proliferation, invasion, and metastasis of HCC. PPM1G was highly expressed in HCC tissues compared to adjacent normal tissues, and higher levels of PPM1G were observed in adverse staged HCCs. The higher levels of PPM1G were highly correlated with poor prognosis, which was further validated in the TCGA cohort. The knockdown of PPM1G inhibited the cell growth and invasion of HCC cell lines. Further studies showed that the knockdown of PPM1G inhibited tumor growth in vivo. The mechanistic analysis showed that the PPM1G interacted with proteins related to alternative splicing, including SRSF3. Overexpression of PPM1G promoted the dephosphorylation of SRSF3 and changed the alternative splicing patterns of genes related to the cell cycle, the transcriptional regulation in HCC cells. In addition, we also demonstrated that the promoter of PPM1G was activated by multiple transcription factors and co-activators, including MYC/MAX and EP300, MED1, and ELF1. Our study highlighted the essential role of PPM1G in HCC and shed new light on unveiling the regulation of alternative splicing in malignant transformation.

scholarly journals A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongdong Zhou ◽  
Xiaoli Liu ◽  
Xinhui Wang ◽  
Fengna Yan ◽  
Peng Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Surgical Therapy ◽  
Calibration Curve ◽  
Cox Regression ◽  
Validation Cohort ◽  
Risk Group ◽  
Risk Groups ◽  
High Risk Group ◽  
Alpha Fetoprotein ◽  
Clinicopathologic Characteristics

Abstract Background Alpha-fetoprotein-negative hepatocellular carcinoma (AFP-NHCC) (< 8.78 ng/mL) have special clinicopathologic characteristics and prognosis. The aim of this study was to apply a new method to establish and validate a new model for predicting the prognosis of patients with AFP-NHCC. Methods A total of 410 AFP-negative patients with clinical diagnosed with HCC following non-surgical therapy as a primary cohort; 148 patients with AFP-NHCC following non-surgical therapy as an independent validation cohort. In primary cohort, independent factors for overall survival (OS) by LASSO Cox regression were all contained into the nomogram1; by Forward Stepwise Cox regression were all contained into the nomogram2. Nomograms performance and discriminative power were assessed with concordance index (C-index) values, area under curve (AUC), Calibration curve and decision curve analyses (DCA). The results were validated in the validation cohort. Results The C-index of nomogram1was 0.708 (95%CI: 0.673–0.743), which was superior to nomogram2 (0.706) and traditional modes (0.606–0.629). The AUC of nomogram1 was 0.736 (95%CI: 0.690–0.778). In the validation cohort, the nomogram1 still gave good discrimination (C-index: 0.752, 95%CI: 0.691–0.813; AUC: 0.784, 95%CI: 0.709–0.847). The calibration curve for probability of OS showed good homogeneity between prediction by nomogram1 and actual observation. DCA demonstrated that nomogram1 was clinically useful. Moreover, patients were divided into three distinct risk groups for OS by the nomogram1: low-risk group, middle-risk group and high-risk group, respectively. Conclusions Novel nomogram based on LASSO Cox regression presents more accurate and useful prognostic prediction for patients with AFP-NHCC following non-surgical therapy. This model could help patients with AFP-NHCC following non-surgical therapy facilitate a personalized prognostic evaluation.

Baseline apparent diffusion coefficients: Validation study of new predictor of survival in patients with unresectable hepatocellular carcinoma following chemoembolization

2021 ◽  
pp. 1-10
Author(s):  
Lichao Xu ◽  
Shiqin Wang ◽  
Shengping Wang ◽  
Ying Wang ◽  
Wentao Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Cox Regression ◽  
Response To Treatment ◽  
Roc Curve Analysis ◽  
Adc Value ◽  
Apparent Diffusion Coefficients ◽  
Apparent Diffusion ◽  
Response Biomarker ◽  
The Difference

OBJECTIVES: To investigate whether the baseline apparent diffusion coefficient (ADC) can predict survival in the hepatocellular carcinoma (HCC) patients receiving chemoembolization. MATERIALS AND METHODS: Diffusion-weighted MR imaging of HCC patients is performed within 2 weeks before chemoembolization. The ADC of the largest index lesion is recorded. Responses are assessed by mRECIST after the start of the second course of chemoembolization. Receiver operating characteristic (ROC) curve analysis is performed to evaluate the diagnostic performance and determine optimal cut-off values. Cox regression and Kaplan–Meier survival analyses are used to explore the differences in overall survival (OS) between the responders and non-responders. RESULTS: The difference is statistically significant in the baseline ADC between the responders and non-responders (P <  0.001). ROC analyses indicate that the baseline ADC value is a good predictor of response to treatment with an area under the ROC curve (AUC) of 0.744 and the optimal cut-off value of 1.22×10–3 mm2/s. The Cox regression model shows that the baseline ADC is an independent predictor of OS, with a 57.2% reduction in risk. CONCLUSION: An optimal baseline ADC value is a functional imaging response biomarker that has higher discriminatory power to predict tumor response and prolonged survival following chemoembolization in HCC patients.

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