Genomics and Prognosis Analysis of Circadian clock genes in Hepatocellular Carcinoma

Abstract Background: Circadian clock genes have been reported to exhibit a regulatory effect on the carcinogenesis and progression of numerous cancers. Nevertheless, the specific relationship between hepatocellular carcinoma (HCC) and circadian rhythm associated genes still remain to be clarified. Therefore, we evaluate the prognosis function of circadian clock genes in HCC with the online datasets of The Cancer Genome Atlas (TCGA) and the international cancer genome consortium (ICGC). Methods: In our research, the RNA-seq of the selected core circadian genes in HCC patients and their relevant clinical data were acquired from the online TCGA database and the ICGC database. R software and cBioPortal website were performed. Results: As consequence, among the 22 typical circadian clock genes, 16 genes were statistically expressed between HCC and adjacent normal tissues. Accordingly, 11 clock genes with regression coefficients were used to constitute a new risk score formula, which was related to the prognosis in HCC. Moreover, the new nomogram, which consisting risk score and several clinical traits , could be applied for the purpose of accurate prediction of the OS time for the patients. Finally, we identified a novel nomogram related with OS in HCC patients with a comprehensive analysis of circadian clock genes and other clinical characteristics profiles. It was also the first time we systematically demonstrated the relationship between clock genes and the HCC prognosis, which would contribute to the treatment of HCC. Conclusions: The current study demonstrated the potential of circadian clock genes as clinically associated biomarkers for prognosis prediction in HCC, which may make a significant contribution to the further investigations of HCC progression.

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Dysregulation of Circadian Clock Genes as Significant Clinic Factor in the Tumorigenesis of Hepatocellular Carcinoma

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/8238833 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Youfang Liang ◽

Shaoxiang Wang ◽

Xin Huang ◽

Ruihuan Chai ◽

Qian Tang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Circadian Clock ◽

Predictive Accuracy ◽

Clock Genes ◽

Close Association ◽

Therapeutic Targets ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Poor Survival ◽

Circadian Clock Genes

Hepatocellular carcinoma (HCC) is the leading cause of cancer-related mortality worldwide due to its asymptomatic onset and poor survival rate. This highlights the urgent need for developing novel diagnostic markers for early HCC detection. The circadian clock is important for maintaining cellular homeostasis and is tightly associated with key tumorigenesis-associated molecular events, suggesting the so-called chronotherapy. An analysis of these core circadian genes may lead to the discovery of biological markers signaling the onset of the disease. In this study, the possible functions of 13 core circadian clock genes (CCGs) in HCC were systematically analyzed with the aim of identifying ideal biomarkers and therapeutic targets. Profiles of HCC patients with clinical and gene expression data were downloaded from The Cancer Genome Atlas and International Cancer Genome Consortium. Various bioinformatics methods were used to investigate the roles of circadian clock genes in HCC tumorigenesis. We found that patients with high TIMELESS expression or low CRY2, PER1, and RORA expressions have poor survival. Besides, a prediction model consisting of these four CCGs, the tumor-node-metastasis (TNM) stage, and sex was constructed, demonstrating higher predictive accuracy than the traditional TNM-based model. In addition, pathway analysis showed that these four CCGs are involved in the cell cycle, PI3K/AKT pathway, and fatty acid metabolism. Furthermore, the network of these four CCGs-related coexpressed genes and immune infiltration was analyzed, which revealed the close association with B cells and nTreg cells. Notably, TIMELESS exhibited contrasting effects against CRY2, PER1, and RORA in most situations. In sum, our works revealed that these circadian clock genes TIMELESS, CRY2, PER1, and RORA can serve as potential diagnostic and prognostic biomarkers, as well as therapeutic targets, for HCC patients, which may promote HCC chronotherapy by rhythmically regulating drug sensitivity and key cellular signaling pathways.

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The m6A Methylation Gene SNRPC can be used as a Biomarker for the Prognosis and Immunotherapy of Hepatocellular Carcinoma

10.21203/rs.3.rs-320369/v1 ◽

2021 ◽

Author(s):

Jihao Cai ◽

Minglei Zhou ◽

Jianxin Xu

Keyword(s):

Hepatocellular Carcinoma ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Rna Methylation ◽

Kaplan Meier ◽

Pathogenic Factors ◽

Therapeutic Value ◽

Cancer Genome Atlas ◽

Checkpoint Inhibitor Therapy ◽

Genome Consortium

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world. Due to its complex pathogenic factors, the prognosis of HCC is poor. Therefore, a credible prognostic biomarker is urgently needed for this disease. N6-methyladenosine (m6A) RNA methylation plays an important role in the tumorigenesis, progression and prognosis of many tumors. However, studies on the prognostic and therapeutic value of this modification in HCC are lacking.Case Presentation: The HCC RNA-seq profiles in The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, including 421 LIHC and 440 LIRI samples respectively, were used in this study. The expressive distinction of 21 RNA methylation regulators between HCC and normal tissue were firstly assessed and SNRPC was obtained. Then the expression of SNRPC was validated as a risk factor for prognosis by Kaplan-Meier analysis and employed to establish a nomogram with T pathologic stage. By GSVA and GSEA analyses, we found SNRPC was mainly related to protein metabolism and immune process. Further, ESTIMATE, MCP-counter and single sample GSEA (ssGSEA) algorithm showed high-SNRPC expression group had lower stromal scores, a lower abundance of endothelial cells, fibroblasts and immune infiltration. Ultimately, Tumor Immune Dysfunction and Exclusion (TIDE) analysis exhibited high-SNRPC expression group showed non-response to immune checkpoint inhibitor therapy, especially to a PD-1 inhibitor.Conclusion: SNRPC could serve as valuable prognostic and immunotherapeutic marker in HCC. We provide here an accurate nomogram for clinical diagnosis using SNRPC as a biomarker.

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Glutamyl-Prolyl tRNA Synthetase mRNA is Highly Expressed in Hepatocellular Carcinoma and May be Associated with Poor Prognosis

10.21203/rs.3.rs-817259/v1 ◽

2021 ◽

Author(s):

Jinyong Shu ◽

Yi Gao ◽

Guifeng Zhang ◽

Pan Luo

Keyword(s):

Hepatocellular Carcinoma ◽

Histological Grade ◽

Mrna Level ◽

Trna Synthetase ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Poor Overall Survival ◽

Normal Tissues ◽

Cancer Genome Atlas ◽

Genome Atlas

Abstract BackgroundAlthough glutamyl-prolyl tRNA synthetase (EPRS) mRNA is overexpressed and plays an important role in most tumors, its role in the development and progression of hepatocellular carcinoma (HCC) remains unclear. MethodsThe expression of EPRS in tumor and adjacent tissues was queried using TIMER and The Cancer Genome Atlas. The results were validated using the real-time reverse transcription polymerase chain reaction on RNA extracted from tumor and adjacent non-tumor samples from 10 HCC patients.ResultsUsing bioinformatics analysis, we found that EPRS mRNA was overexpressed in HCC tumor tissues, and the expression level of EPRS mRNA in The Cancer Genome Atlas database was significantly correlated with tumor size (p = 0.0010), histological grade (p = 0.0002), TNM stage (p = 0.0001), and vascular invasion (p = 0.0123) of HCC. The Kaplan-Meier survival analysis demonstrated that the expression of EPRS mRNA was associated with poor overall survival (p = 0.0004). Ten pairs of tumor and adjacent normal tissues were collected from patients with HCC, and the expression of EPRS mRNA was verified. The results showed that the EPRS mRNA level in HCC tissues was higher than that in paracancerous tissues (p = 0.0401). ConclusionOverexpression of EPRS mRNA may be associated with tumorigenesis and the progression of HCC.

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Analysis of the cancer genome atlas data to determine the correlation between PROX1 and TERT in hepatocellular carcinoma

International Journal of Cancer ◽

10.1002/ijc.31888 ◽

2018 ◽

Vol 144 (7) ◽

pp. 1752-1753 ◽

Cited By ~ 2

Author(s):

Jae‐Ho Lee

Keyword(s):

Hepatocellular Carcinoma ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Atlas Data ◽

Cancer Genome Atlas ◽

Genome Atlas

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Machine Learning for Building Immune Genetic Model in Hepatocellular Carcinoma Patients

Journal of Oncology ◽

10.1155/2021/6676537 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Jun Liu ◽

Zheng Chen ◽

Wenli Li

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Risk Model ◽

Cox Model ◽

Cancer Genome ◽

Algorithm Analysis ◽

Immune Genes ◽

Normal Tissues ◽

Model Based ◽

Tumor Stages

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

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Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20203263 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Xiaofei Wang ◽

Jie Qiao ◽

Rongqi Wang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Overall Survival ◽

Risk Score ◽

Expression Profiles ◽

Univariate Analysis ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Good Prediction ◽

Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

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Diagnostic and prognostic roles of IRAK1 in hepatocellular carcinoma tissues: an analysis of immunohistochemistry and RNA-sequencing data from the cancer genome atlas

OncoTargets and Therapy ◽

10.2147/ott.s132120 ◽

2017 ◽

Vol Volume 10 ◽

pp. 1711-1723 ◽

Cited By ~ 11

Author(s):

Zhi-hua Ye ◽

Li Gao ◽

Dong-yue Wen ◽

Yun He ◽

Yu-yan Pang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Rna Sequencing ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Cancer Genome Atlas ◽

Genome Atlas

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Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5970085 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Honglan Guo ◽

Qinqiao Fan

Keyword(s):

Hepatocellular Carcinoma ◽

Roc Curve ◽

Cox Regression ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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