Identification of a Five-Immune Gene Model as an Independent Prognostic Factor in Hepatocellular Carcinoma

Mapping Intimacies ◽

10.21203/rs.3.rs-41766/v1 ◽

2020 ◽

Author(s):

Haitao Chen ◽

Jianchun Guo

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factor ◽

Risk Model ◽

Independent Prognostic Factor ◽

The Cancer Genome Atlas ◽

Entire Group ◽

Immune Gene ◽

Gene Model ◽

Immune Genes ◽

Cox Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is a common cancer with a poor prognosis. We purposed to identify a prognostic risk model of HCC according to the differentially expressed (DE) immune genes.Methods: The DE immune genes were identified based on 374 HCC and 50 adjacent normal samples from the Cancer Genome Atlas database. Univariate Cox analysis, Lasso regression, and multivariate Cox analysis were used to determine the immune genes used to construct the model based on the training group. The testing group and the entire group were applied for the validation of the model.Results: A five-immune gene model comprising HSPA4, ISG20L2, NDRG1, EGF, and IL17D was identified. Based on the model, overall survival was significantly different between the high-risk and low-risk groups (P = 7.953e-06). The AUC for the model at 1- and 3-year was 0.849 and 0.74, respectively. The validating groups confirmed the reliability of the model. The risk score was identified as an independent prognostic factor and was closely related to the content of immune cells from HCC samples.Conclusion: We identified a five-immune gene model, which could be treated as an independent prognostic factor of HCC.

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Identification of a five-immune gene model as an independent prognostic factor in hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08012-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Haitao Chen ◽

Yueying Li ◽

Shu-Yuan Xiao ◽

Jianchun Guo

Keyword(s):

Hepatocellular Carcinoma ◽

Model Performance ◽

Training Group ◽

The Cancer Genome Atlas ◽

Entire Group ◽

Immune Gene ◽

Lasso Regression ◽

Gene Model ◽

Immune Genes ◽

Cox Analysis

Abstract Background Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis. We aimed to identify a new prognostic model of HCC based on differentially expressed (DE) immune genes. Methods The DE immune genes were identified based on an analysis of 374 cases of HCC and 50 adjacent non-tumor specimens from the Cancer Genome Atlas (TCGA) database. Univariate Cox analysis, Lasso regression, and multivariate Cox analysis were used to construct the model based on the training group. Survival analysis and the receiver operating characteristic (ROC) curves were used to evaluate model performance. The testing group and the entire group were subsequently used for validation of the model. Results A five-immune gene model consisted of HSPA4, ISG20L2, NDRG1, EGF, and IL17D was identified. Based on the model, the overall survival was significantly different between the high-risk and low-risk groups (P = 7.953e-06). The AUCs for the model at 1- and 3-year were 0.849 and 0.74, respectively. The reliability of the model was confirmed using the validation groups. The risk score was identified as an independent prognostic parameter and closely related to the content of immune cells from human HCC specimens. Conclusion We identified a five-immune gene model that can be used as an independent prognostic marker for HCC.

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Identification of hepatocellular carcinoma prognostic markers based on 10-immune gene signature

Bioscience Reports ◽

10.1042/bsr20200894 ◽

2020 ◽

Vol 40 (8) ◽

Author(s):

Kaifei Zhao ◽

Lin Xu ◽

Feng Li ◽

Jin Ao ◽

Guojun Jiang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Prediction ◽

Clinical Features ◽

Risk Model ◽

Risk Group ◽

External Validation ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Test Set

Abstract Background: Due to the heterogeneity of hepatocellular carcinoma (HCC), hepatocelluarin-associated differentially expressed genes were analyzed by bioinformatics methods to screen the molecular markers for HCC prognosis and potential molecular targets for immunotherapy. Methods: RNA-seq data and clinical follow-up data of HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Multivariate Cox analysis and Lasso regression were used to identify robust immunity-related genes. Finally, a risk prognosis model of immune gene pairs was established and verified by clinical features, test set and Gene Expression Omnibus (GEO) external validation set. Results: A total of 536 immune-related gene (IRGs) were significantly associated with the prognosis of patients with HCC. Ten robust IRGs were finally obtained and a prognostic risk prediction model was constructed by feature selection of Lasso. The risk score of each sample is calculated based on the risk model and is divided into high risk group (Risk-H) and low risk group (Risk-L). Risk models enable risk stratification of samples in training sets, test sets, external validation sets, staging and subtypes. The area under the curve (AUC) in the training set and the test set were all >0.67, and there were significant overall suvival (OS) differences between the Risk-H and Risk-L samples. Compared with the published four models, the traditional clinical features of Grade, Stage and Gender, the model performed better on the risk prediction of HCC prognosis. Conclusion: The present study constructed 10-gene signature as a novel prognostic marker for predicting survival in patients with HCC.

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A Four-Immune Gene Prognostic Risk Model for Colorectal Adenocarcinoma Based on the TCGA and Immport Data Sets

10.21203/rs.3.rs-136144/v1 ◽

2020 ◽

Author(s):

Boyang Xu ◽

Ziqi Peng ◽

Yue An ◽

Xue Yao ◽

Mingjun Sun

Keyword(s):

T Cells ◽

Colorectal Adenocarcinoma ◽

Immune Cell ◽

Risk Model ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Data Sets ◽

Immune Gene ◽

Immune Genes ◽

Follicular Helper

Abstract BackgroundAs one of the hot spots in oncology field, immune research provides new ideas for the diagnosis and treatment of tumors. Different histological types of colorectal cancer are different. Adenocarcinoma, as the type with the highest proportion, has a high research value. This study aims to build an immune gene prognostic risk model for colorectal adenocarcinoma to improve the diagnosis and prognosis prediction of colorectal adenocarcinoma.MethodsThe differentially expressed immune genes could be obtained from the gene expression data downloaded from The Cancer Genome Atlas (TCGA) and the immune gene data downloaded from the ImmPort Database. Univariate COX and multivariate COX analyses were used to construct the immune gene prognostic risk model of and the clinical application potential of this model. The correlation between the model and the immune cells infiltration and the influence of each immune cell on the survival were analyzed.Results5975 differentially expressed genes were obtained, and 497 differentially expressed immune genes were selected by combining the information of immune genes. Among them, 36 immune genes were associated with prognosis, and 4 immune genes (THRB, IL1RL2, LGR6, LTB4R2) were included in the prognostic risk model of immune genes. Patients with higher Risk Score had shorter survival. Compared with gender, age and pathological stage, the model has better prediction potential. In addition, the model was correlated with Macrophages M0, Macrophages M1, T cells follicular helper and NK cells activated. Among them, T cells follicular helper and Macrophages M0 were related to the survival of patients.ConclusionWe developed a prognostic risk model containing four immune genes, THRB, IL1RL2, LGR6 and LTB4R2, which accurately described the prognosis of the patient, and affected the survival of patients by influencing the infiltration of Macrophages M0 and T cells follicular helper.

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A Four-Immune Gene Prognostic Risk Model for Colorectal Adenocarcinoma Based on the TCGA and ImmPort Data Sets

10.21203/rs.3.rs-120261/v1 ◽

2020 ◽

Author(s):

Boyang Xu ◽

Ziqi Peng ◽

Yue An ◽

Xue Yao ◽

Mingjun Sun

Keyword(s):

T Cells ◽

Colorectal Adenocarcinoma ◽

Immune Cell ◽

Risk Model ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Data Sets ◽

Immune Gene ◽

Immune Genes ◽

Follicular Helper

Abstract Background: As one of the hot spots in oncology field, immune research provides new ideas for the diagnosis and treatment of tumors. Different histological types of colorectal cancer are different. Adenocarcinoma, as the type with the highest proportion, has a high research value. This study aims to build an immune gene prognostic risk model for colorectal adenocarcinoma to improve the diagnosis and prognosis prediction of colorectal adenocarcinoma.Methods: The differentially expressed immune genes could be obtained from the gene expression data downloaded from The Cancer Genome Atlas (TCGA) and the immune gene data downloaded from the ImmPort Database. Univariate COX and multivariate COX analyses were used to construct the immune gene prognostic risk model of and the clinical application potential of this model. The correlation between the model and the immune cells infiltration and the influence of each immune cell on the survival were analyzed.Results: 5975 differentially expressed genes were obtained, and 497 differentially expressed immune genes were selected by combining the information of immune genes. Among them, 36 immune genes were associated with prognosis, and 4 immune genes (THRB, IL1RL2, LGR6, LTB4R2) were included in the prognostic risk model of immune genes. Patients with higher Risk Score had shorter survival. Compared with gender, age and pathological stage, the model has better prediction potential. In addition, the model was correlated with Macrophages M0, Macrophages M1, T cells follicular helper and NK cells activated. Among them, T cells follicular helper and Macrophages M0 were related to the survival of patients.Conclusion: We developed a prognostic risk model containing four immune genes, THRB, IL1RL2, LGR6 and LTB4R2, which accurately described the prognosis of the patient, and affected the survival of patients by influencing the infiltration of Macrophages M0 and T cells follicular helper.

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Machine Learning for Building Immune Genetic Model in Hepatocellular Carcinoma Patients

Journal of Oncology ◽

10.1155/2021/6676537 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Jun Liu ◽

Zheng Chen ◽

Wenli Li

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Risk Model ◽

Cox Model ◽

Cancer Genome ◽

Algorithm Analysis ◽

Immune Genes ◽

Normal Tissues ◽

Model Based ◽

Tumor Stages

Background. Hepatocellular carcinoma (HCC) is the leading liver cancer with special immune microenvironment, which played vital roles in tumor relapse and poor drug responses. In this study, we aimed to explore the prognostic immune signatures in HCC and tried to construct an immune-risk model for patient evaluation. Methods. RNA sequencing profiles of HCC patients were collected from the cancer genome Atlas (TCGA), international cancer genome consortium (ICGC), and gene expression omnibus (GEO) databases (GSE14520). Differentially expressed immune genes, derived from ImmPort database and MSigDB signaling pathway lists, between tumor and normal tissues were analyzed with Limma package in R environment. Univariate Cox regression was performed to find survival-related immune genes in TCGA dataset, and in further random forest algorithm analysis, significantly changed immune genes were used to generate a multivariate Cox model to calculate the corresponding immune-risk score. The model was examined in the other two datasets with recipient operation curve (ROC) and survival analysis. Risk effects of immune-risk score and clinical characteristics of patients were individually evaluated, and significant factors were then used to generate a nomogram. Results. There were 52 downregulated and 259 upregulated immune genes between tumor and relatively normal tissues, and the final immune-risk model (based on SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV and MAP4K2) can better differentiate patients into high and low immune-risk subpopulations, in which high score patients showed worse outcomes after resection ( p < 0.05 ). The differentially enriched pathways between the two groups were mainly about cell proliferation and cytokine production, and calculated immune-risk score was also highly correlated with immune infiltration levels. The nomogram, constructed with immune-risk score and tumor stages, showed high accuracy and clinical benefits in prediction of 1-, 3- and 5-year overall survival, which is useful in clinical practice. Conclusion. The immune-risk model, based on expression of SPP1, BRD8, NDRG1, KITLG, HSPA4, TRAF3, ITGAV, and MAP4K2, can better differentiate patients into high and low immune-risk groups. Combined nomogram, using immune-risk score and tumor stages, could make accurate prediction of 1-, 3- and 5-year survival in HCC patients.

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Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.657051 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shenglan Cai ◽

Xingwang Hu ◽

Ruochan Chen ◽

Yiya Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Immune Genes ◽

Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

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Onodera’s prognostic nutritional index is a strong prognostic indicator for patients with hepatocellular carcinoma after initial hepatectomy, especially patients with preserved liver function

BMC Surgery ◽

10.1186/s12893-020-00917-2 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Akihiro Tanemura ◽

Shugo Mizuno ◽

Aoi Hayasaki ◽

Kazuyuki Gyoten ◽

Takehiro Fujii ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

Prognostic Factor ◽

Liver Function ◽

Prognostic Nutritional Index ◽

Independent Prognostic Factor ◽

Related Factors ◽

Impaired Liver Function ◽

Nutritional Index ◽

Initial Hepatectomy

Abstract Background Several inflammation-based scores are used to assess the surgical outcomes of hepatocellular carcinoma (HCC). The aim of the present study was to elucidate the prognostic value of the prognostic nutritional index (PNI) in HCC patients who underwent hepatectomy with special attention to preoperative liver functional reserve. Methods Preoperative demographic and tumor-related factors were analyzed in 189 patients with HCC undergoing initial hepatectomy from August 2005 to May 2016 to identify significant prognostic factors. Results Multivariate analysis for overall survival (OS) revealed that female sex (p = 0.005), tumor size (p < 0.001) and PNI (p = 0.001) were independent prognostic factors. Compared to the High PNI group (PNI ≥ 37, n = 172), the Low PNI group (PNI < 37, n = 17) had impaired liver function and significantly poorer OS (13% vs. 67% in 5-year OS, p = 0.001) and recurrence-free survival (RFS) (8 vs. 25 months in median PFS time, p = 0.002). In the subgroup of patients with a preserved liver function of LHL15 ≥ 0.9, PNI was also independent prognostic factor, and OS (21% vs. 70% in 5-year OS, p = 0.008) and RFS (8 vs. 28 months in median PFS time, p = 0.018) were significantly poorer in the Low PNI group than the High PNI group. Conclusions PNI was an independent prognostic factor for HCC patients who underwent hepatectomy. Patients with PNI lower than 37 were at high risk for early recurrence and poor patient survival, especially in the patients with preserved liver function of LHL ≥ 0.9.

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