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 Development and validation of a novel ten-gene signature predicting prognosis in Hepatocellular carcinoma

2020 ◽  
Author(s):  
Guanbao Zhou ◽  
Genjie Lu ◽  
Liang Yang ◽  
Yangfang Lu
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Hierarchical Clustering ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Signature ◽  
Molecular Biomarkers ◽  
The Cancer Genome Atlas ◽  
Expression Levels ◽  
Gene Expression Levels

Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver cancer with relatively poor prognosis. Thus, we aimed to identify novel molecular biomarkers to effectively predict the prognosis of HCC patients and eventually guide treatment. Methods: Prognosis-associated genes were determined by Kaplan-Meier and multivariate Cox regression analyses using the expression and clinical data of 373 HCC patients from The Cancer Genome Atlas (TCGA) database and validated in an independent Gene Expression Omnibus (GEO) dataset. The classification of AML was performed by unsupervised hierarchical clustering of ten gene expression levels. A prognostic risk score was established based on a linear combination of ten gene expression levels using the regression coefficients derived from the multivariate Cox regression models. Results: A total of 183 genes were significantly associated with prognosis in HCC. SLC25A15, RAB8A, GOT2, SORBS2, IL18RAP were top five protective genes, while FHL3, AMD1, DCAF13, UBE2E1, PTDSS2 were top five risk genes in HCC. SLC25A15, GOT2, IL18RAP were significantly down-regulated and DCAF13, PTDSS2 and SORBS2 were significantly up-regulated in the HCC samples and these genes exhibited high accuracy in differentiating HCC tissues from normal liver tissues. Hierarchical clustering analysis of the ten genes discovered three clusters of HCC patients. HCC tumors of cluster1 and 2 were significantly associated with more favourable OS than those of cluster3, cluster2 tumors showed higher pathologic stage than cluster3 tumors. The risk score was predictive of increased mortality rate in HCC patients. Conclusions: The ten-gene signature and the risk score may turn out to be novel molecular biomarkers and stratification of HCC patients to considerably ameliorate the prognostic prediction.

scholarly journals A Nomogram Based on a Three-Gene Signature Derived from AATF Coexpressed Genes Predicts Overall Survival of Hepatocellular Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Zhanzhong Ma ◽  
Wenli Li
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Clinical Practice ◽  
Correlation Coefficient ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Cancer Genome ◽  
Survival Prediction

Background. Hepatocellular carcinoma (HCC) is a common cancer with an extremely high mortality rate. Therefore, there is an urgent need in screening key biomarkers of HCC to predict the prognosis and develop more individual treatments. Recently, AATF is reported to be an important factor contributing to HCC. Methods. We aimed to establish a gene signature to predict overall survival of HCC patients. Firstly, we examined the expression level of AATF in the Gene Expression Omnibus (GEO), the Cancer Genome Atlas (TCGA), and the International Union of Cancer Genome (ICGC) databases. Genes coexpressed with AATF were identified in the TCGA dataset by the Poisson correlation coefficient and used to establish a gene signature for survival prediction. The prognostic significance of this gene signature was then validated in the ICGC dataset and used to build a combined prognostic model for clinical practice. Results. Gene expression data and clinical information of 2521 HCC patients were downloaded from three public databases. AATF expression in HCC tissue was higher than that in matched normal liver tissues. 644 genes coexpressed with AATF were identified by the Poisson correlation coefficient and used to establish a three-gene signature (KIF20A, UCK2, and SLC41A3) by the univariate and multivariate least absolute shrinkage and selection operator Cox regression analyses. This three-gene signature was then used to build a combined nomogram for clinical practice. Conclusion. This integrated nomogram based on the three-gene signature can predict overall survival for HCC patients well. The three-gene signature may be a potential therapeutic target in HCC.

scholarly journals An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weifeng Zheng ◽  
Chaoying Chen ◽  
Jianghao Yu ◽  
Chengfeng Jin ◽  
Tiemei Han
Keyword(s):  
Energy Metabolism ◽  
Cox Regression ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Prognostic Gene ◽  
Kaplan Meier ◽  
Esophagus Carcinoma

Abstract Background The essence of energy metabolism has spread to the field of esophageal cancer (ESC) cells. Herein, we tried to develop a prognostic prediction model for patients with ESC based on the expression profiles of energy metabolism associated genes. Materials and methods The overall survival (OS) predictive gene signature was developed, internally and externally validated based on ESC datasets including The Cancer Genome Atlas (TCGA), GSE54993 and GSE19417 datasets. Hub genes were identified in each energy metabolism related molecular subtypes by weighted gene correlation network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, nomogram, decision curve analysis (DCA), and restricted mean survival time (EMST) were used to assess the performance of the gene signature. Results A novel energy metabolism based eight-gene signature (including UBE2Z, AMTN, AK1, CDCA4, TLE1, FXN, ZBTB6 and APLN) was established, which could dichotomize patients with significantly different OS in ESC. The eight-gene signature demonstrated independent prognostication potential in patient with ESC. The prognostic nomogram constructed based on the gene signature showed excellent predictive performance, whose robustness and clinical usability were higher than three previous reported prognostic gene signatures. Conclusions Our study established a novel energy metabolism based eight-gene signature and nomogram to predict the OS of ESC, which may help in precise clinical management.

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.

scholarly journals Identification of prognostic alternative splicing events related to the immune microenvironment of hepatocellular carcinoma

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Shanshan Yu ◽  
Luya Cai ◽  
Chuan Liu ◽  
Ruihong Gu ◽  
Lingyi Cai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Alternative Splicing ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Microenvironment ◽  
The Impact

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world, and its 5-year survival rate is less than 20%, despite various treatments being available. Increasing evidence indicates that alternative splicing (AS) plays a nonnegligible role in the formation and development of the tumor microenvironment (TME). However, the comprehensive analysis of the impact on prognostic AS events on immune-related perspectives in HCC is lacking but urgently needed. Methods The transcriptional data and clinical information of HCC patients were downloaded from TCGA (The Cancer Genome Atlas) database for calculating immune and stromal scores by ESTIMATE algorithm. We then divided patients into high/low score groups and explored their prognostic significance using Kaplan–Meier curves. Based on stromal and immune scores, differentially expressed AS events (DEASs) were screened and evaluated with functional enrichment analysis. Additionally, a risk score model was established by applying univariate and multivariate Cox regression analyses. Finally, gene set variation analysis (GSVA) was adopted to explore differences in biological behaviors between the high- and low-risk subgroups. Results A total of 370 HCC patients with complete and qualified corresponding data were included in the subsequent analysis. According to the results of ESTIMATE analysis, we observed that the high immune/stromal score group had a longer survival probability, which was significantly correlated with prognosis in HCC patients. In addition, 467 stromal/immune score-related DEASs were identified, and enrichment analysis revealed that DEASs were significantly enriched in pathways related to HCC tumorigenesis and the immune microenvironment. More importantly, the final prognostic signature containing 16 DEASs showed powerful predictive ability. Finally, GSVA demonstrated that activation of carcinogenic pathways and immune-related pathways in the high-risk group may lead to poor prognosis. Conclusions Collectively, these outcomes revealed prognostic AS events related to carcinogenesis and the immune microenvironment, which may yield new directions for HCC immunotherapy.

scholarly journals Identification of Subtypes and a Prognostic Gene Signature in Colon Cancer Using Cell Differentiation Trajectories

2021 ◽  
Vol 9 ◽  
Author(s):  
Renshen Xiang ◽  
Jincheng Fu ◽  
Yuhang Ge ◽  
Jun Ren ◽  
Wei Song ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cell Differentiation ◽  
Clinical Decision Making ◽  
Molecular Subtypes ◽  
Predictive Performance ◽  
Clinical Decision ◽  
Gene Signature ◽  
Clinicopathological Characteristics ◽  
The Cancer Genome Atlas ◽  
Sequencing Data

Research on the heterogeneity of colon cancer (CC) cells is limited. This study aimed to explore the CC cell differentiation trajectory and its clinical implication and to construct a prognostic risk scoring (RS) signature based on CC differentiation-related genes (CDRGs). Cell trajectory analysis was conducted on the GSE148345 dataset, and CDRG-based molecular subtypes were identified from the GSE39582 dataset. A CDRG-based prognostic RS signature was constructed using The Cancer Genome Atlas as the training set and GSE39582 as the validation set. Two subsets with distinct differentiation states, involving 40 hub CDRGs regulated by YY1 and EGR2, were identified by single-cell RNA sequencing data, of which subset I was related to hypoxia, metabolic disorders, and inflammation, and subset II was associated with immune responses and ferroptosis. The CDRG-based molecular subtypes could successfully predict the clinical outcomes of the patients, the tumor microenvironment status, the immune infiltration status, and the potential response to immunotherapy and chemotherapy. A nomogram integrating a five-CDRG-based RS signature and prognostic clinicopathological characteristics could successfully predict overall survival, with strong predictive performance and high accuracy. The study emphasizes the relevance of CC cell differentiation for predicting the prognosis and therapeutic response of patients to immunotherapy and chemotherapy and proposes a promising direction for CC treatment and clinical decision-making.

scholarly journals Identification of a thirteen-gene signature predicting overall survival for hepatocellular carcinoma

2021 ◽  
Author(s):  
Xiaohan Zhou ◽  
Chengdong Liu ◽  
Hanyi Zeng ◽  
Dehua Wu ◽  
Li Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
R Package ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Pathway Enrichment Analysis ◽  
Score Model ◽  
Predictive Efficiency

Background: Hepatocellular carcinoma (HCC) is a malignant tumor of the digestive system characterized by mortality rate and poor prognosis. To indicate the prognosis of HCC patients, lots of genes have been screened as prognostic indicators. However, the predictive efficiency of single gene is not enough. Therefore, it is essential to identify a risk-score model based on gene signature to elevate predictive efficiency. Methods: lasso regression analysis followed by univariate cox regression was employed to establish a risk-score model for HCC prognosis prediction based on The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) dataset GSE14520. R package “clusterProfiler” was used to conduct function and pathway enrichment analysis. The infiltration level of various immune and stromal cells in the tumor microenvironment (TME) were evaluated by ssGSEA of R package “GSVA”. Results: This prognostic model is an independent prognostic factor for predicting the prognosis of HCC patients and can be more effective combining with clinical data through the construction of nomogram model. Further analysis showed patients in high-risk group possess more complex TME and immune cell composition. Conclusions: Taken together, our research suggests the thirteen-gene signature to possess potential prognostic value for HCC patients and provide new information for immunological research and treatment in HCC.

scholarly journals A Necroptosis-Related Gene Signature for Predicting Prognosis, Immune Landscape, and Therapy Sensitivity in Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Junliang Chen ◽  
Huaitao Wang ◽  
Lei Zhou ◽  
Zhihao Liu ◽  
Hui Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Targeted Therapy ◽  
Molecular Targeted Therapy ◽  
Clustering Algorithm ◽  
Cox Regression ◽  
Gene Signature ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Comprehensive Treatment ◽  
Related Gene

Abstract Background: Hepatocellular carcinoma (HCC) remains a growing threat to global health. Necroptosis is a newly discovered regulated cell necrosis that plays a vital role in cancer development. Thus, we conducted this study to develop a predictive signature based on necroptosis-related genes.Methods: The tumor samples in The Cancer Genome Atlas (TCGA) Liver Hepatocellular Carcinoma (LIHC) cohort were subtyped using the consensus clustering algorithm. Univariate Cox regression and LASSO-Cox analysis were performed to construct a gene signature model from differentially expressed genes between tumor clusters. Then we integrated TNM stage and the prognostic model to build a nomogram. The gene signature and the nomogram were externally validated in the GSE14520 cohort from the gene expression omnibus (GEO) and LIRP-JP cohort from the International Cancer Genome Consortium (ICGC). Predictive performance evaluation was conducted using Kaplan-Meier plot, time-dependent receiver operating characteristic curve, principal components analysis, concordance index, and decision curve analysis. The tumor microenvironment was estimated using seven published methods. Finally, we also predicted the drug responses to immunotherapy, conventional chemotherapy and molecular-targeted therapy using two algorithms and two datasets. Results: We identified two necroptosis-related clusters and a ten-gene signature (MTMR2, CDCA8, S100A9, ANXA10, G6PD, SLC1A5, SLC2A1, SPP1, PLOD2, and MMP1). The gene signature and the nomogram had good predictive ability in TCGA, ICGC, and GEO cohorts. The risk score was positively associated with the degree of necroptosis and immune infiltration (especially immunosuppressive cells). The high-risk group could benefit more from immunotherapy. Chemotherapy and molecular-targeted therapy should be adapted to the molecular profiles of each patient.Conclusion: The necroptosis-related gene signature provides reliable evidence for prognosis prediction, comprehensive treatment, and new therapeutic targets for HCC patients. The nomogram can further improve predictive accuracy.

scholarly journals Development of a Bile Acid-Related Gene Signature for Predicting Survival in Patients with Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Gang Wang ◽  
Jun Guan ◽  
Qin Yang ◽  
Fengtian Wu ◽  
Junwei Shao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Bile Acid ◽  
Prediction Model ◽  
Cox Regression ◽  
De Novo ◽  
Gene Prediction ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Tissue Samples ◽  
Regulated Expression

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common diseases, threatening millions of patients annually. Increasing evidence supports that bile acid (BA) has an impact on HCC through inflammation, DNA damage or other mechanisms. Methods: With the data from The Cancer Genome Atlas portal, a total of 127 BA-associated genes were analyzed in HCC tumor and non-tumor samples, and then, using univariate and multivariate Cox regression, genes with correlations to the prognosis of HCC patients were identified. Then, a prediction model with identified genes was constructed for evaluating the risk of HCC patients for prognosis. Results: Twenty-six genes with differential expression between HCC and control tissue samples were identified, of which 19 genes had up-regulated expression and 7 genes had down-regulated expression in tumor samples. Three genes, NPC1, ABCC1 and SLC51B, were extrapolated to construct a prediction model for prognosis of HCC patients. Conclusion: The three-gene prediction model was more reliable than the pathological staging characters of the tumor for the prognosis and survival of HCC patients. Additionally, the up-regulated genes facilitating the transport of BAs are associated with poor prognosis of HCC patients and genes of de novo synthesis of BAs benefits HCC patients.

scholarly journals Identification of an RNA binding protein-related gene signature in hepatocellular carcinoma patients

2020 ◽  
Author(s):  
Li Wang ◽  
Na Zhou ◽  
Jialin Qu ◽  
Man Jiang ◽  
Xiaochun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Characteristic Curve ◽  
Gene Signature ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters

Abstract Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality among all human cancers. Studies have demonstrated that RNA binding proteins (RBPs) involved in the biological process of cancers including hepatocellular cancer. In this study, we aim to identify clinical value of RNA binding proteins for hepatocellular carcinoma.Methods: We analyses the data of HCC that downloaded from the Cancer Genome Atlas (TCGA) database and determined the differently expressed of RBPs between cancer and normal tissues. We further elucidate the function of RBPs by utilized Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gene signature of SMG5, EZH2, FBLL1, ZNF239, IGF2BP3 were generated by performed the univariate and multivariate Cox regression and LASSO regression analysis. CIBERSORT analysis was used to evaluation of tumor-infiltrating immune cells in different group. We built and verify a prognosis nomogram base on RBPs-related genes. Gene signature was validated by the International Cancer Genome Consortium (ICGC) database. The expressions of RBPs-related genes were validated by using Oncomine database, and the Human Protein Atlas.Result: Most of RBPs-related genes were significantly different in cancer and normal tissue. The survival of patients in the different group was statistically different. The Gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters (AUC=0.758). The patients in the high-risk group were more likely to have a higher Macrophages M0. Conclusion: Gene signature constructed by RNA binding proteins can be independent risk factors for hepatocellular carcinoma patients.

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