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

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.

scholarly journals Novel Hypoxia-Related Gene Signature for Risk Stratification and Prognosis in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Quanxiao Li ◽  
Limin Jin ◽  
Meng Jin
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Stratification ◽  
Risk Group ◽  
External Validation ◽  
Gene Signature ◽  
Cancer Genome ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Individual Risk ◽  
Tumor Node Metastasis Stage

Hepatocellular carcinoma (HCC) is the most common form of liver cancer with limited therapeutic options and low survival rate. The hypoxic microenvironment plays a vital role in progression, metabolism, and prognosis of malignancies. Therefore, this study aims to develop and validate a hypoxia gene signature for risk stratification and prognosis prediction of HCC patients. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used as a training cohort, and one Gene Expression Omnibus database (GSE14520) was served as an external validation cohort. Our results showed that eight hypoxia-related genes (HRGs) were identified by the least absolute shrinkage and selection operator analysis to develop the hypoxia gene signature and demarcated HCC patients into the high- and low-risk groups. In TCGA, ICGC, and GSE14520 datasets, patients in the high-risk group had worse overall survival outcomes than those in the low-risk group (all log-rank P < 0.001). Besides, the risk score derived from the hypoxia gene signature could serve as an independent prognostic factor for HCC patients in the three independent datasets. Finally, a nomogram including the gene signature and tumor-node-metastasis stage was constructed to serve clinical practice. In the present study, a novel hypoxia signature risk model could reflect individual risk classification and provide therapeutic targets for patients with HCC. The prognostic nomogram may help predict individualized survival.

scholarly journals Gene signature to predict prognostic survival of hepatocellular carcinoma

Open Medicine ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. 135-150
Author(s):  
Li Li ◽  
Yundi Cao ◽  
YingRui Fan ◽  
Rong Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Noncoding Rna ◽  
Cox Regression ◽  
External Validation ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Hypergeometric Test ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Hepatocellular carcinoma (HCC) has a high incidence and poor prognosis and is the second most fatal cancer, and certain HCC patients also show high heterogeneity. This study developed a prognostic model for predicting clinical outcomes of HCC. RNA and microRNA (miRNA) sequencing data of HCC were obtained from the cancer genome atlas. RNA dysregulation between HCC tumors and adjacent normal liver tissues was examined by DESeq algorithms. Survival analysis was conducted to determine the basic prognostic indicators. We identified competing endogenous RNA (ceRNA) containing 15,364 pairs of mRNA–long noncoding RNA (lncRNA). An imbalanced ceRNA network comprising 8 miRNAs, 434 mRNAs, and 81 lncRNAs was developed using hypergeometric test. Functional analysis showed that these RNAs were closely associated with biosynthesis. Notably, 53 mRNAs showed a significant prognostic correlation. The least absolute shrinkage and selection operator’s feature selection detected four characteristic genes (SAPCD2, DKC1, CHRNA5, and UROD), based on which a four-gene independent prognostic signature for HCC was constructed using Cox regression analysis. The four-gene signature could stratify samples in the training, test, and external validation sets (p <0.01). Five-year survival area under ROC curve (AUC) in the training and validation sets was greater than 0.74. The current prognostic gene model exhibited a high stability and accuracy in predicting the overall survival (OS) of HCC patients.

A 13-immune gene set signature for prediction of colon cancer prognosis

2020 ◽  
Vol 23 ◽  
Author(s):  
Zhuo Lu ◽  
Jin Chen ◽  
JiongYi Yan ◽  
QiaoMing Liu ◽  
Fang Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
External Validation ◽  
Therapeutic Targets ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Immune Gene ◽  
Training Set ◽  
Test Set ◽  
Gene Set ◽  
Validation Set

Background: Colon cancer is one of the most common cancer worldwide and has a poor prognosis. Through the analysis of transcriptome and clinical data of colon cancer, immune gene-set signature was identified by single sample enrichment analysis (ssGSEA) scoring to predict patient survival and discover new therapeutic targets. Objective: To study the role of immune gene-set signature in colon cancer. Methods: First, RNASeq and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA). Immune gene-related gene sets were collected from ImmPort database. Genes and immunological pathways related to prognosis were screened in the training set and integrated for feature selection using random forest. Immune gene-related prognosis model was verified in the entire TCGA test set and GEO validation set and compared with immune cells scores and matrix score. Results: 1650 prognostic genes and 13 immunological pathways were identified. These genes and pathways are closely related to the development of tumors. 13-immune gene-set signature was established, which is an independent prognostic factor for patients with colon cancer. Risk stratification of samples could be carried out in the training set, test set and external validation set. The AUC of five-year survival in the training set and validation set is greater than 0.6. Immunosuppression occurs in high-risk samples. Compared with published models, Riskscore has better prediction effect. Conclusion: This study constructed 13-immune gene-set signature as a new prognostic marker to predict the survival of patients with colon cancer, and provided new diagnostic/prognostic biomarkers and therapeutic targets for colon cancer.

scholarly journals Molecular and clinicopathological characterization of a prognostic immune gene signature associated with MGMT methylation in glioblastoma

2020 ◽  
Author(s):  
Liang Zhao ◽  
Jiayue Zhang ◽  
Shurui Xuan ◽  
Zhiyuan Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Risk Group ◽  
Methylation Status ◽  
Gene Signature ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Immune Gene ◽  
Mgmt Methylation ◽  
Genome Atlas

AbstractBackgroundO6-methylguanine-DNA methyltransferase (MGMT) methylation status affects tumor chemo-resistance and the prognosis of glioblastoma (GBM) patients. We aimed to investigate the role of MGMT methylation in the regulation of GBM immunophenotype and discover an effective biomarker to improve prognosis prediction of GBM patients.MethodsA total of 769 GBM patients with clinical information from five independent cohorts were enrolled in the present study. Samples from the Cancer Genome Atlas (TCGA) dataset were used as the training set, whereas transcriptome data from the Chinese Glioma Genome Atlas (CGGA) RNA-seq, CGGA microarray, GSE16011, and the Repository for Molecular Brain Neoplasia (REMBRANDT) cohort were used for validation. A series of bioinformatics approaches were carried out to construct a prognostic signature based on immune-related genes, which were tightly related with the MGMT methylation status. The influence of the signature on immunosuppression and remodeling of the tumor microenvironment were comprehensively investigated. Then, the utility of this immune gene signature was analyzed by the development and evaluation of a nomogram.ResultsWe found that MGMT unmethylation was closely associated with immune-related biological processes in GBM. Sixty-five immune genes were more highly expressed in the MGMT unmethylated than the MGMT methylated group. An immune gene-based risk model was further established to divide patients into high and low-risk groups, and the prognostic value of this signature was validated in several GBM cohorts. Functional analyses manifested a universal up-regulation of immune-related pathways in the high-risk group as compared to the low-risk group. Furthermore, the risk score was highly correlated to the immune cell infiltration, immunosuppression, inflammatory activities, as well as the expression levels of immune checkpoints. Finally, a nomogram was developed for clinical application.ConclusionsMGMT methylation is strongly related to the immune responses in GBM. The immune gene-based signature we identified may have potential implications in predicting the prognosis of GBM patients and mechanisms underlying the role of MGMT methylation.

scholarly journals Immune Implication of ASF1B Gene in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Tao Meng ◽  
Zhong Tong ◽  
Ming-Ya Yang ◽  
Jing-Jing Wang ◽  
Li-Xin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
External Validation ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Test Set

Abstract Background: Anti-silencing function 1B (ASF1B) has been demonstrated to contribute to tumorigenesis. However, its carcinogenic and immune effects in hepatocellular carcinoma (HCC) have not been reported. This study aimed to identify immune role of ASF1B in HCC.Methods: HCC datasets obtained from The Cancer Genome Atlas (TCGA) database were used to investigate the role of ASF1B gene in HCC, followed by validation using Gene Expression Omnibus (GEO) datasets and Gene Expression Profiling Interactive Analysis (GEPIA) website. CIBERSORT analysis was performed to evaluate immune cell infiltration levels. The TISIDB and cBioPortal network tool were used to seek ASF1B-associated immunomodulators and its co-expressed genes. TCGA cohort was divided into train set and test set according to the ratio of 7:3. Cox regression was used to identify ASF1B-associated prognostic immunomodulators in train set, followed by internal validation using the test set. Based on the median risk-score, HCC patients were divided into high- and low-risk group for the further survival curves and receiver operating characteristic (ROC) analysis, as well as nomogram and calibration curves analysis. Finally, the dataset collected from the GEO was adopted for external validation.Results: ASF1B was over-expressed in TCGA HCC cohort and contributed poor prognosis, which was verified in two GEO datasets (GSE14520 and GSE6764) and GEPIA, as well as Kaplan Meier Plotter network tool. The immune cell infiltration levels were found to be associated with the ASF1B copy numbers and mRNA expression. A total of 78 ASF1B-associated genes were screened out, including 7 immunoinhibitors, 21 immunostimulators and 50 tightly co-expressed genes. Finally, 5 ASF1B-associated genes (TNFSF4, TNFRSF4, KDR, MICB and CST7) were identified to be strongly related to HCC survival. Survival analysis demonstrated that the prognosis of patients in high-risk group was poor. The prognosis predict model, which was established by nomogram based on risk-score, and was validated in both TCGA test set and GEO validated datasets, exerted excellent predictive power in this study.Conclusion: Our findings showed that the ASF1B was associated with HCC immunity. The selected ASF1B-asociated immune markers could be promising biomarkers for the prognosis of HCC.

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

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.

scholarly journals Identification of an Autophagy-related Gene Signature Predicting Overall Survival for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Wenfang Xu ◽  
Wenke Guo ◽  
Ping Lu ◽  
Duan Ma ◽  
Lei Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Cox Regression Analysis ◽  
Prognostic Features ◽  
Tcga Dataset

The poor prognosis of hepatocellular carcinoma (HCC) calls for the development of accurate prognostic models. The growing number of studies indicating a correlation between autophagy activity and HCC indicates there is a commitment to finding solutions for the prognosis of HCC from the perspective of autophagy. We used a cohort in The Cancer Genome Atlas (TCGA) to evaluate the expression of autophagy-related genes in 371 HCC samples using univariate Cox and lasso Cox regression analysis, and the prognostic features were identified. A prognostic model was established by combining the expression of selected genes with the multivariate Cox regression coefficient of each gene. Eight autophagy-related genes were selected as prognostic features of HCC. We established the HCC prognostic risk model in TCGA dataset using these identified prognostic genes. The model’s stability was confirmed in two independent verification sets (GSE14520 and GSE36376). The model had a good predictive power for the overall survival (OS) of HCC (Hazard Ratio=2.32, 95% Confidence Interval=1.76–3.05, p&lt;0.001). Moreover, the risk score computed by the model did not depend on other clinical parameters. Finally, the applicability of the model was demonstrated through a nomogram (C-index=0.701). In this study, we established an autophagy-related risk model having a high prediction accuracy for OS in HCC. Our findings will contribute to the definition of prognosis and establishment of personalized therapy for HCC patients.

scholarly journals Identification and development of a novel invasion-related gene signature for prognosis prediction in colon adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiahua Liu ◽  
Chunhui Jiang ◽  
Chunjie Xu ◽  
Dongyang Wang ◽  
Yuguang Shen ◽  
...  
Keyword(s):  
T Cells ◽  
External Validation ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Immune Infiltration ◽  
Prognosis Prediction ◽  
Protein Levels ◽  
Limma Package ◽  
Cox Analysis

AbstractThe overall survival of metastatic colon adenocarcinoma (COAD) remains poor, so it is important to explore the mechanisms of metastasis and invasion. This study aimed to identify invasion-related genetic markers for prognosis prediction in patients with COAD. Three molecular subtypes (C1, C2, and C3) were obtained based on 97 metastasis-related genes in 365 COAD samples from The Cancer Genome Atlas (TCGA). A total of 983 differentially expressed genes (DEGs) were identified among the different subtypes by using the limma package. A 6-gene signature (ITLN1, HOXD9, TSPAN11, GPRC5B, TIMP1, and CXCL13) was constructed via Lasso-Cox analysis. The signature showed strong robustness and could be used in the training, testing, and external validation (GSE17537) cohorts with stable predictive efficiency. Compared with other published signatures, our model showed better performance in predicting outcomes. Pan-cancer expression analysis results showed that ITLN1, TSPAN11, CXCL13, and GPRC5B were downregulated and TIMP1 was upregulated in most tumor samples, including COAD, which was consistent with the results of the TCGA and GEO cohorts. Western blot analysis and immunohistochemistry were performed to validate protein expression. Tumor immune infiltration analysis results showed that TSPAN11, GPRC5B, TIMP1, and CXCL13 protein levels were significantly positively correlated with CD4+ T cells, macrophages, neutrophils, and dendritic cells. Further, the TIMP1 and CXCL13 proteins were significantly related to the tumor immune infiltration of CD8+ T cells. We recommend using our signature as a molecular prognostic classifier to assess the prognostic risk of patients with COAD.

scholarly journals A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang Hong ◽  
Yu Zhou ◽  
Xiangbang Xie ◽  
Wanrui Wu ◽  
Changsheng Shi ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Prognostic Significance ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cumulative Distribution ◽  
Gene Module ◽  
Expression Levels

Abstract Background Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. Methods Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature. Results We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature. Conclusions Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC.

scholarly journals Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

2020 ◽  
Author(s):  
Dai Zhang ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
Jia Yao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gynecological Cancer ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.

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