Identification of molecular subtypes and prognostic signature for hepatocellular carcinoma based on genes associated with homologous recombination deficiency

AbstractHepatocellular carcinoma (HCC) is a rapidly developing digestive tract carcinoma. The prognosis of patients and side effects caused by clinical treatment should be better improved. Nonnegative matrix factorization (NMF) clustering was performed using 109 homologous recombination deficiency (HRD)-related of HCC genes from The Cancer Genome Atlas (TCGA) database. Limma was applied to analyze subtype differences. Immune scores and clinical characteristics of different subtypes were compared. An HRD signature were built with least absolute shrinkage operator (LASSO) and multivariate Cox analysis. Performance of the signature system was then assessed by Kaplan–Meier curves and receiver operating characteristic (ROC) curves. We identified two molecular subtypes (C1 and C2), with C2 showing a significantly better prognosis than C1. C1 contained 3623 differentially expressed genes. A 4-gene prognostic signature for HCC was established, and showed a high predicting accuracy in validation sets, entire TCGA data set, HCCDB18 and GSE14520 queues. Moreover, the risk score was validated as an independent prognostic marker for HCC. Our research identified two molecular subtypes of HCC, and proposed a novel scoring system for evaluating the prognosis of HCC in clinical practice.

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Identification of lncRNA prognostic signature and analysis of their functions for HCV-related hepatocellular carcinoma

10.21203/rs.3.rs-50007/v1 ◽

2020 ◽

Author(s):

Kun Wang ◽

Wenxin Li ◽

Yefu Liu ◽

Zhiqiang Hao ◽

Xiangdong Hua ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Cox Regression ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Clinical Variables ◽

Kaplan Meier ◽

Underlying Mechanisms

Abstract Background Hepatitis C virus (HCV) infection is a main contribution to the increase in hepatocellular carcinoma (HCC) incidence and patients’ death recently, but prognostic biomarkers for HCV-related HCC remain rarely reported. This study was to identify an lncRNA prognostic signature for HCV-HCC patients and explore their underlying function mechanisms. Methods In total, 102 HCV-HCC samples and 50 normal control samples were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analysis were conducted to screen an lncRNA signature that could predict overall survival (OS) and then, the risk score was calculated using this signature. The prognostic potential of this risk score was evaluated by drawing Kaplan-Meier, receiver operating characteristic (ROC) curves and performing multivariate Cox regression analyses with clinical variables. Furthermore, a co-expression and competing endogenous RNA (ceRNA) networks were constructed to explore the functional mechanisms of lncRNAs. Results Multivariate Cox regression showed six lncRNAs (SLC16A1-AS1, ZFPM2-AS1, JARID2-AS1, LINC01426, USP3-AS1 and LYPLAL1-AS1) were significantly associated with OS of HCV-HCC patients. These six lncRNAs were used to establish a risk score model, which displayed a higher prognosis prediction accuracy [area under the ROC curve (AUC) = 0.95 for training set; AUC = 0.885 for testing; AUC = 0.907 for entire set]. Also, this was independent of various clinical variables. The crucial co-expression (LINC01426/SLC16A1-AS1-AURKA/SFN/CCNB1, ZFPM2-AS1/LYPLAL1-AS1/JARID2-AS1-TSSK6) or ceRNA (USP3-AS1-hsa-miR-383-SFN) interaction axes were identified. Conclusion Our study identified a novel six-lncRNA prognosis signature for HCV-HCC patients and indicated their underlying mechanisms for HCC progression.

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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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CYSTM1: A Novel Biomarker for Hepatocellular Carcinoma Prognosis

10.21203/rs.3.rs-150866/v1 ◽

2021 ◽

Author(s):

Jun Du ◽

Jinguo Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Poor Prognosis ◽

Early Stage ◽

Human Tumor ◽

The Cancer Genome Atlas ◽

Data Set ◽

Kaplan Meier ◽

Cancer Genome Atlas ◽

Cox Proportional Hazard Regression ◽

The Relationship

Abstract Background: The expression and molecular mechanism of cysteine rich transmembrane module containing 1 (CYSTM1) in human tumor cells remains unclear. The aim of this study was to determine whether CYSTM1 could be used as a potential prognostic biomarker for hepatocellular carcinoma (HCC).Methods: We first demonstrated the relationship between CYSTM1 expression and HCC in various public databases. Secondly, Kaplan–Meier analysis and Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CYSTM1 and the survival of HCC patients which data was downloaded in the cancer genome atlas (TCGA) database. Finally, we used the expression data of CYSTM1 in TCGA database to predict CYSTM1-related signaling pathways through bioinformatics analysis.Results: The expression level of CYSTM1 in HCC tissues was significantly correlated with T stage (p = 0.039). In addition, Kaplan–Meier analysis showed that the expression of CYSTM1 was significantly associated with poor prognosis in patients with early-stage HCC (p = 0.003). Multivariate analysis indicated that CYSTM1 is a potential predictor of poor prognosis in HCC patients (p = 0.036). The results of biosynthesis analysis demonstrated that the data set of CYSTM1 high expression was mainly enriched in neurodegeneration and oxidative phosphorylation pathways.Conclusion: CYSTM1 is an effective biomarker for the prognosis of patients with early-stage HCC and may play a key role in the occurrence and progression of HCC.

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An angiogenesis-related long noncoding RNA signature correlates with prognosis in patients with hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20204442 ◽

2021 ◽

Vol 41 (4) ◽

Author(s):

Dengliang Lei ◽

Yue Chen ◽

Yang Zhou ◽

Gangli Hu ◽

Fang Luo

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cancer ◽

Long Noncoding Rna ◽

Noncoding Rna ◽

Enrichment Analysis ◽

Clinical Information ◽

Roc Curves ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Highly Correlated

Abstract Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide. Neovascularization is closely related to the malignancy of tumors. We constructed a signature of angiogenesis-related long noncoding RNA (lncRNA) to predict the prognosis of patients with HCC. The lncRNA expression matrix of 424 HCC patients was downloaded from The Cancer Genome Atlas (TCGA). First, gene set enrichment analysis (GSEA) was used to distinguish the differentially expressed genes of the angiogenesis genes in liver cancer and adjacent tissues. Next, a signature of angiogenesis-related lncRNAs was constructed using univariate and multivariate analyses, and receiver operating characteristic (ROC) curves were used to assess the accuracy. The signature and relevant clinical information were used to construct the nomogram. A 5-lncRNA signature was highly correlated with overall survival (OS) in HCC patients and performed well in evaluations using the C-index, areas under the curve, and calibration curves. In summary, the 5-lncRNA model can serve as an accurate signature to predict the prognosis of patients with liver cancer, but its mechanism of action must be further elucidated by experiments.

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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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Identification of a Novel Gene Model-Based Homologous Recombination Deficiency Score to Improve Survival Prediction of TNBC.

10.21203/rs.3.rs-472194/v1 ◽

2021 ◽

Author(s):

Wenxiang Zhang ◽

Bolun Ai ◽

Xiangyi Kong ◽

Xiangyu Wang ◽

Jie Zhai ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Homologous Recombination ◽

Risk Score ◽

Cox Regression ◽

Roc Curves ◽

Cox Regression Analysis ◽

Homologous Recombination Deficiency ◽

Ajcc Stage ◽

Kaplan Meier

Abstract Background Triple-negative breast cancer (TNBC) is a specific histological type of breast cancer with a poor prognosis, early recurrence, which lacks durable chemotherapy responses and effective targeted therapies. We aimed to construct an accurate prognostic risk model based on homologous recombination deficiency (HRD) - gene expression profiles for improving prognosis prediction of TNBC. Methods Triple-negative breast cancer RNA sequencing data and sample clinical information were downloaded from the breast invasive carcinoma (BRCA) cohort in the Cancer Genome Atlas (TCGA) database. Combined with the HRD database, tumor samples were divided into two sets. We screened differentially expressed genes (DEGs) and then identified HRD-related prognostic genes using weighted gene co-expression network analysis (WGCNA) and Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to identifying key prognostic genes. Risk scores were calculated and compared with HRD score, Kaplan–Meier (KM) survival analysis were used to assess its prognostic power. GSE103091 dataset from GEO (Gene Expression Omnibus) database was used to validate the signature. Univariate and multivariate Cox regression were performed to independently verify the prognosis of the risk score. A nomogram was constructed and revealed by time-dependent ROC curves to guide clinical practice. Results We found that HRD tumor samples (HRD score > = 42) in TNBC patients were associated with poor overall survival (p = 0.027). We identified a total of 147 differential genes including 203 up-regulated and 213 down-regulated genes, among which 29 were prognosis-related genes. Through the LASSO method, 6 key prognostic genes ((MUCL1, IVL, FAM46C, CHI3L1, PRR15L, and CLEC3A) were selected and a 6-gene risk score was constructed. We found risk score was negatively associated with homologous recombination deficiency (HRD) scores (r = -0.22, p = 0.019). Compared with the low-risk group, Kaplan-Meier survival analysis shows that the high-risk group has an obvious poorer prognosis (P < 0.0001). Finally, we integrated the risk score model and clinical factors of TNBC (AJCC-stage, HRD score, T stage, and N stage) to construct a compound nomogram. Time-dependent ROC curves showed the risk score performed better in 1-, 3- and 5-year survival predictions compared with AJCC-stage. Conclusions Based on HRD gene expression data, our six HRD-related gene signature and nomogram could be practical and reliable tools for predicting OS in patients with TNBC.

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A new prognostic strategy based on four DNA repair-associated lncRNAs for hepatocellular carcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207324666210302091432 ◽

2021 ◽

Vol 24 ◽

Author(s):

Hanyi Zeng ◽

Chengdong Liu ◽

Xiaohan Zhou ◽

Li Liu

Keyword(s):

Hepatocellular Carcinoma ◽

Dna Repair ◽

Clinical Utility ◽

Prognostic Model ◽

Cox Regression ◽

Reduction Process ◽

Roc Curves ◽

Clinical Impact ◽

Functional Enrichment ◽

Prognostic Signature

Background: Hepatocellular carcinoma (HCC) is a malignant tumour with poor prognosis. The effect of DNA repair on prognosis cannot be ignored; and long non-coding RNA (lncRNA) can regulate the DNA repair process. Objective: : To obtain DNA repair-associated lncRNA (DR-lncRNA) prognostic signature for improved ability to prediction of HCC prognosis. Methods: Our study used the Cancer Genome Atlas database. Gene set variation analysis was performed to differentiate high and low levels of DNA repair to identify DR-lncRNAs. By performing univariate Cox regression, LASSO regression, and multivariate Cox regression analyses, we finally obtained a DR-lncRNA prognostic signature and constructed a nomogram prognostic model. Time-dependent receiver operating characteristic (ROC) curves, calibration plots, decision curve analysis (DCA), and clinical impact curves were used to assess predictive ability and clinical utility. Differentially expressed genes (DEGs) functional enrichment analysis was performed to further explore the underlying mechanisms that influence HCC prognosis. Results: We obtained a DR-lncRNA prognostic signature—AP002478.1, AC116351.1, LINC02580, and LINC00861. The ROC curves and calibration plots showed good discrimination and calibration properties. Combining the DR-lncRNA prognostic signature and tumour stages, we established a nomogram prognostic model. DCA and clinical impact curves showed the clinical utility of the nomogram prognostic model. DEGs of high-risk and low-risk groups predicted by the DR-lncRNA prognostic were significantly associated with cell cycle and various metabolic pathways and biological processes such as the oxidation-reduction process and cell division. Conclusion: We identified a DR-lncRNA prognostic signature and constructed a nomogram prognostic model, which could be a beneficial prognostic strategy for HCC.

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Identification of the Novel Oncogenic Role of SAAL1 and Its Therapeutic Potential in Hepatocellular Carcinoma

Cancers ◽

10.3390/cancers12071843 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1843

Author(s):

Pei-Yi Chu ◽

Shiao-Lin Tung ◽

Kuo-Wang Tsai ◽

Fang-Ping Shen ◽

Shih-Hsuan Chan

Keyword(s):

Hepatocellular Carcinoma ◽

Therapeutic Potential ◽

The Cancer Genome Atlas ◽

The Novel ◽

Advanced Hcc ◽

Independent Prognostic Marker ◽

Normal Tissues ◽

Amyloid A ◽

Mtor Phosphorylation ◽

And Migration

Hepatocellular carcinoma (HCC) is the third leading cause of cancer deaths worldwide, affecting over 700,000 people per year. The treatment effect in advanced HCC is still disappointing and prognosis of advanced HCC remains poor. Hence, to find more effective therapeutic targets to improve the treatment outcome of HCC is of urgent need. In this study, we reported the novel oncogenic function of SAAL1 (serum amyloid A-like 1) in HCC, which previously is considered as an inflammation-related gene. We found that SAAL1 was significantly upregulated in HCC tumor tissues when compared to the adjacent normal tissues and high expression of SAAL1 correlated with shorter overall survival in The Cancer Genome Atlas (TCGA) HCC database. Functionally, we showed that the depletion of SAAL1 significantly reduced cell proliferation, 3D colony formation, and migration/invasion abilities of HCC cancer cells. Furthermore, suppression of SAAL1 impaired the HGF/Met-driven Akt/mTOR phosphorylation cascade and increased the chemosensitivity of HCC cells to sorafenib and foretinib treatment. Our data indicated that SAAL1 plays an important role in HCC via mediating oncogenic HGF/Met-driven Akt/mTOR signaling and could serve as an independent prognostic marker, as well as a promising therapeutic target for HCC patients.

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Based on Integrated Bioinformatics Analysis Identification of Biomarkers in Hepatocellular Carcinoma Patients from Different Regions

BioMed Research International ◽

10.1155/2019/1742341 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Linxin Teng ◽

Kaiyuan Wang ◽

Yu Liu ◽

Yanxia Ma ◽

Weiping Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Predictive Biomarkers ◽

Principal Component ◽

Roc Curves ◽

Core Gene ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

The Core ◽

Kaplan Meier

Accumulating statistics have shown that liver cancer causes the second highest mortality rate of cancer-related deaths worldwide, of which 80% is hepatocellular carcinoma (HCC). Given the underlying molecular mechanism of HCC pathology is not fully understood yet, identification of reliable predictive biomarkers is more applicable to improve patients’ outcomes. The results of principal component analysis (PCA) showed that the grouped data from 1557 samples in Gene Expression Omnibus (GEO) came from different populations, and the mean tumor purity of tumor tissues was 0.765 through the estimate package in R software. After integrating the differentially expressed genes (DEGs), we finally got 266 genes. Then, the protein-protein interaction (PPI) network was established based on these DEGs, which contained 240 nodes and 1747 edges. FOXM1 was the core gene in module 1 and highly associated with FOXM1 transcription factor network pathway, while FTCD was the core gene in module 2 and was enriched in the metabolism of amino acids and derivatives. The expression levels of hub genes were in line with The Cancer Genome Atlas (TCGA) database. Meanwhile, there were certain correlations among the top ten genes in the up- and downregulated DEGs. Finally, Kaplan–Meier curves and receiver operating characteristic (ROC) curves were plotted for the top five genes in PPI. Apart from CDKN3, the others were closely concerned with overall survival. In this study, we detected the potential biomarkers and their involved biological processes, which would provide a new train of thought for clinical diagnosis and treatment.

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Higher prevalence of homologous recombination-deficiency in lung squamous carcinoma from African Americans

10.1101/651794 ◽

2019 ◽

Author(s):

Sanju Sinha ◽

Khadijah A. Mitchell ◽

Adriana Zingone ◽

Elise Bowman ◽

Neelam Sinha ◽

...

Keyword(s):

Homologous Recombination ◽

Copy Number ◽

Squamous Carcinoma ◽

The Cancer Genome Atlas ◽

Multiple Cancer ◽

Homologous Recombination Deficiency ◽

Molecular Features ◽

Incidence And Mortality ◽

Cancer Types ◽

Lung Squamous Carcinoma

AbstractTo improve our understanding of the longstanding disparities in incidence and mortality across multiple cancer types among minority populations, we performed a systematic comparative analysis of molecular features in tumors from African American (AA) and European American (EA) ancestry. Our pan-cancer analysis on the cancer genome atlas (TCGA) and a more focused analysis of genome-wide somatic copy number profiles integrated with tumor-normal RNA sequencing in a racially balanced cohort of 222 non-small cell lung cancers (NSCLC) reveals more aggressive genomic characteristics of AA tumors. In general, we find AA tumors exhibit higher genomic instability (GI), homologous recombination-deficiency (HRD) levels, and more aggressive molecular features such as chromothripsis across many cancer types, including lung squamous carcinoma (LUSC). GI and HRD levels are strongly correlated across AA tumors, indicating that HRD plays an important role in GI in these patients. The prevalence of germline HRD is higher in AA tumors, suggesting that the somatic differences observed have genetic ancestry origins. Finally, we identify AA-specific copy number-based arm, focal and gene level recurrent features in lung cancer, including a higher frequency of PTEN deletion and KRAS amplification and a lower frequency of CDKN2A deletion. These results highlight the importance of including minority and under-represented populations in genomics research and may have therapeutic implications.

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