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

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.

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

BMC Cancer ◽

10.1186/s12885-021-08351-0 ◽

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.

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Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.784553 ◽

2021 ◽

Vol 8 ◽

Author(s):

Chen Jin ◽

Rui Li ◽

Tuo Deng ◽

Jialiang Li ◽

Yan Yang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Model ◽

Risk Score ◽

Cox Regression ◽

Pearson Correlation ◽

Vital Role ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Prediction Ability ◽

Cox Regression Analysis

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

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A Novel Ferroptosis-Related Gene Signature Predicts Overall Survival of Breast Cancer Patients

Biology ◽

10.3390/biology10020151 ◽

2021 ◽

Vol 10 (2) ◽

pp. 151

Author(s):

Haifeng Li ◽

Lu Li ◽

Cong Xue ◽

Riqing Huang ◽

Anqi Hu ◽

...

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Prediction Model ◽

Risk Score ◽

Cox Regression ◽

Gene Prediction ◽

Clinical Information ◽

Risk Groups ◽

Gene Signature ◽

Breast Cancer Patients

Breast cancer is the second leading cause of death in women, thus a reliable prognostic model for overall survival (OS) in breast cancer is needed to improve treatment and care. Ferroptosis is an iron-dependent cell death. It is already known that siramesine and lapatinib could induce ferroptosis in breast cancer cells, and some ferroptosis-related genes were closely related with the outcomes of treatments regarding breast cancer. The relationship between these genes and the prognosis of OS remains unclear. The data of gene expression and related clinical information was downloaded from public databases. Based on the TCGA-BRCA cohort, an 8-gene prediction model was established with the least absolute shrinkage and selection operator (LASSO) cox regression, and this model was validated in patients from the METABRIC cohort. Based on the median risk score obtained from the 8-gene model, patients were stratified into high- or low-risk groups. Cox regression analyses identified that the risk score was an independent predictor for OS. The findings from CIBERSORT and ssGSEA presented noticeable differences in enrichment scores for immune cells and pathways between the abovementioned two risk groups. To sum up, this prediction model has potential to be widely applied in future clinical settings.

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

10.21203/rs.3.rs-46933/v1 ◽

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.

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Gene signature to predict prognostic survival of hepatocellular carcinoma

Open Medicine ◽

10.1515/med-2021-0405 ◽

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.

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Development of a prognostic model based on immunogenomic landscape analysis of hepatocellular carcinoma

10.21203/rs.3.rs-238684/v1 ◽

2021 ◽

Author(s):

ligong lu ◽

Shaoqing Liu ◽

Shengni Hua ◽

Zhenlin Zhang ◽

Meixiao Zhan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Model ◽

Risk Score ◽

Immune Cells ◽

Bioinformatics Analysis ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Prognostic Indicators

Abstract Background Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer, and the systematic exploration of its prognostic indicators is urgently needed. In this study, we obtained 12 IRGs for the construction of a risk score prediction model in HCC by bioinformatics analysis. Methods Differentially expressed genes were screened using the R software edgeR package. Functional enrichment analysis was performed through gene ontology analyses as well as the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Single factor and multi-factor Cox analysis were employed for survival analysis. We used the Timer software to examine the correlation between risk score and tumor-infiltrating immune cells. Results We identified 3,215 up-regulated and 1,044 down-regulated genes in HCC tissues based on a cohort from The Cancer Genome Atlas (TCGA). Differentially expressed immune-related genes (IRGs) and survival-associated IRGs were further identified. We also integrated multivariate Cox regression analyses to obtain 12 IRGs for the construction of a risk score prediction model, whose performance was verified using the Kaplan-Meier survival and receiver operating characteristic curve analyses. Our findings suggest that the risk score was associated with clinical characteristics and the infiltration of immune cells in HCC patients. Conclusions We obtained a risk score prediction model of 12 IRGs in HCC by bioinformatics analysis and confirmed its performance.

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Identification and validation of ADME genes as prognosis and therapy markers for hepatocellular carcinoma patients

Bioscience Reports ◽

10.1042/bsr20210583 ◽

2021 ◽

Author(s):

Ju Kun Wang ◽

Ke Han ◽

Chao Zhang ◽

Xin Chen ◽

Yu Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prediction Model ◽

Cox Regression ◽

Predictive Ability ◽

Risk Groups ◽

Cancer Genome ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Bioinformatics Analyses

Purpose: ADME genes are genes involved in drug absorption, distribution, metabolism, and excretion (ADME). Previous studies report that expression levels of ADME-related genes correlate with prognosis of hepatocellular carcinoma (HCC) patients. However, the role of ADME gene expression on HCC prognosis has not been fully explored. This study sought to construct a prediction model using ADME-related genes for prognosis of HCC. Methods: Transcriptome and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), which were used as training and validation cohorts, respectively. A prediction model was constructed using univariate Cox regression and LASSO analysis. Patients were divided into high- and low-risk groups based on the median risk score. The predictive ability of the risk signature was estimated through bioinformatics analyses. Results: Six ADME-related genes (CYP2C9, ABCB6, ABCC5, ADH4, DHRS13, and SLCO2A1) were used to construct the prediction model with a good predictive ability. Univariate and multivariate Cox regression analyses showed the risk signature was an independent predictor of overall survival. A single-sample gene set enrichment analysis (ssGSEA) strategy showed a significant relationship between risk signature and immune status. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed differentially expressed genes in the high- and low-risk groups were enriched in biological process associated with metabolic and cell cycle pathways. Conclusion: A prediction model was constructed using six ADME-related genes for prediction of HCC prognosis. This signature can be used to improve HCC diagnosis, treatment, and prognosis in clinical use.

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

Bioscience Reports ◽

10.1042/bsr20202870 ◽

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.

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Co-amplification of Genes in Chromosome 8q24: a Robust Prognostic Marker in Hepatocellular Carcinoma

10.21203/rs.3.rs-272648/v2 ◽

2021 ◽

Author(s):

Yuan Cheng ◽

Yongjian Zheng ◽

Cheng Zhang ◽

Shunjun Fu ◽

Guolin He ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Marker ◽

Copy Number ◽

Molecular Mechanisms ◽

Cox Regression ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Training Dataset ◽

Tissue Samples ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the major cause of cancer related deaths worldwide, due to high 5 year postoperative recurrence rate and individual heterogeneity. Thus, prognostic model has dramatically urgently needed on HCC in recent years. Serval research have reported that copy number amplification of the 8q24 chromosomal region is associated with low survival in many cancers. The objective of this study was to construct a multi-gene model to predict the prognosis of HCC. Methods: RNAseq and copy number variant (CNV) data of tumor tissue samples of HCC from TCGA (N = 328) was used to identify differentially expressed mRNAs of genes located on chromosomal 8q24 regions by Wilcox test. Univariate Cox and Lasso Cox regression were performed to screen and construct prognostic multi-gene signature in TCGA cohort (N = 119). The multi-gene signature was validated in ICGC cohort (N = 240).A nomogram for prognosis prediction was built and Gene Set Enrichment Analysis (GSEA) was used to further study the underlying molecular mechanisms. Results: A 7-gene prognosis signature model was established for predicting HCC prognosis. Using the model, we divided individuals into high-risk and low-risk sets with significantly different overall survival in training dataset(HR = 0.17, 95% CI 0.1–0.28; P ＜ 0.001) and in testing dataset (HR = 0.42, 95% CI 0.23–0.74; P = 0.002). Multivariate Cox regression analysis indicated that this signature was an independent prognostic factor of HCC survival. Nomogram including the prognostic signature was constructed and showed better predictive performance in short year (1 and 3 year) than long year (5 year) survival. Furthermore, GSEA revealed several significantly pathways, which may help explain the underlying molecular mechanism. Conclusions: The 7-gene signature was a reliable prognostic marker in HCC, which may provide meaningful information to therapeutic customization and treatment decision making.

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

10.21203/rs.3.rs-1201594/v1 ◽

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.

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