Signature Construction and Molecular Subtype Identification Based on Pyroptosis-Related Genes for Better Prediction of Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. However, there is a lack of adequate means of treatment prognostication for HCC. Pyroptosis is a newly discovered way of programmed cell death. However, the prognostic role of pyroptosis in HCC has not been thoroughly investigated. Here, we generated a novel prognostic signature to evaluate the prognostic value of pyroptosis-related genes (PRGs) using the data from The Cancer Genome Atlas (TCGA) database. The accuracy of the signature was validated using survival analysis through the International Cancer Genome Consortium cohort ( n = 231 ) and the First Affiliated Hospital of Wenzhou Medical University cohort ( n = 180 ). Compared with other clinical factors, the risk score of the signature was found to be associated with better patient outcomes. The enrichment analysis identified multiple pathways related with pyroptosis in HCC. Furthermore, drug sensitivity testing identified six potential chemotherapeutic agents to provide possible treatment avenues. Interestingly, patients with low risk were confirmed to be associated with lower tumor mutation burden (TMB). However, patients at high risk were found to have a higher count of immune cells. Consensus clustering was performed to identify two main molecular subtypes (named clusters A and B) based on the signature. It was found that compared with cluster B, better survival outcomes and lower TMB were observed in cluster A. In conclusion, signature construction and molecular subtype identification of PRGs could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC treatment.

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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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Identification of Iron Metabolism-Related Genes Signature and Novel Role of FLVCR1 in Hepatocellular Carcinoma

10.21203/rs.3.rs-45663/v1 ◽

2020 ◽

Author(s):

Jianhui Chen ◽

Chuan HU ◽

Reguang Pan ◽

Xuedan Du ◽

Haotian Fu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Iron Metabolism ◽

Clustering Analysis ◽

Cox Regression ◽

Treatment Strategies ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Lasso Regression ◽

Prognostic Signature

Abstract Background: Hepatocellular carcinoma (HCC) is the main and highly malignant histological subtype of liver cancer. We tried to construct a novel signature with iron metabolism-related genes to provide new therapeutic targets and improve the prognosis for HCC patients.Methods: The gene expression data of 70 iron metabolism-related genes and its relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. Consensus clustering analysis was performed to determine clusters of HCC patients with different OS. Cox regression and LASSO regression analyses were used to establish a prognostic signature. Receiver operating characteristic (ROC) and Kaplan–Meier analyses were carried out to examine the predicated performance of the signature.Results: Consensus clustering analysis determined two clusters of HCC patients with different OS(p<0.01), TNM stage(p<0.05) and pathological grade(p<0.05). A nine-gene prognostic signature established with iron metabolism-related genes can independently predicate the prognostic of HCC patients. The ROC curves showed a great performance of the signature. In addition, FLVCR1, a hub gene with the highest mutation frequency in our signature, showed the significantly prognostic value in HCC patients. High FLVCR1 expression was significantly associated with poor prognosis and aggressive progression in HCC patients. The promoter methylation level of FLVCR1 was lower in HCC samples with aggressive progression status. The FLVCR1 expression was positively correlated with the infiltration level of B cell, CD4+ T cell, macrophage, neutrophil and dendritic cell. Conclusion: Our study first established a signature related to iron metabolism and identified FLVCR1 as a potential therapeutic target. These findings provided more treatment strategies for HCC patients.

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A hypoxia-related signature for clinically predicting diagnosis, prognosis and immune microenvironment of hepatocellular carcinoma patients.

10.21203/rs.3.rs-17783/v1 ◽

2020 ◽

Author(s):

Baohui Zhang ◽

Bufu Tang ◽

Jianyao Gao ◽

Jiatong Li ◽

Lingming Kong ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Enrichment Analysis ◽

High Risk Group ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Diagnostic Model ◽

Potential Biomarker

Abstract Background Hypoxia plays an indispensable role in the development of hepatocellular carcinoma (HCC). However, there are few studies on the application of hypoxia molecules in the prognosis predicting of HCC. We aimed to identify the hypoxia-related genes in HCC and construct reliable models for diagnosis, prognosis and recurrence of HCC patients as well as exploring the potential mechanism.Methods Differentially expressed genes (DEGs) analysis was performed using The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and four clusters were determined by a consistent clustering analysis. Three DEGs closely related to overall survival（OS）were identified using Cox regression and LASSO analysis and the hypoxia-related signature was developed and validated in TCGA and International Cancer Genome Consortium (ICGC) database. Then the Gene Set Enrichment Analysis (GSEA) was performed to explore signaling pathways regulated by the signature and the CIBERSORT was used for estimating the fractions of immune cell types.Results A total of 397 hypoxia-related DEGs were detected and three genes (PDSS1, CDCA8 and SLC7A11) were selected to construct a prognosis, recurrence and diagnosis model. Then patients were divided into high- and low-risk groups. Our hypoxia-related signature was significantly associated with worse prognosis and higher recurrence rate. The diagnostic model also accurately distinguished HCC from normal samples and nodules. Furthermore, the hypoxia-related signature could positively regulate immune response and the high-risk group had higher fractions of macrophages, B memory cells and follicle-helper T cells, and exhibited higher expression of immunocheckpoints such as PD1and PDL1.Conclusions Altogether, our study showed that hypoxia-related signature is a potential biomarker for diagnosis, prognosis and recurrence of HCC, and it provided an immunological perspective for developing personalized therapies.

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An m6A-Related Prognostic Biomarker Associated With the Hepatocellular Carcinoma Immune Microenvironment

Frontiers in Pharmacology ◽

10.3389/fphar.2021.707930 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yingxi Du ◽

Yarui Ma ◽

Qing Zhu ◽

Tongzheng Liu ◽

Yuchen Jiao ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Checkpoint ◽

Immune Cell ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Risk Scores ◽

Survival Prediction ◽

Consensus Clustering ◽

Immune Microenvironment ◽

Cluster 2

Background: N6-methyladenosine (m6A) is related to the progression of multiple cancers. However, the underlying influences of m6A-associated genes on the tumor immune microenvironment in hepatocellular carcinoma (HCC) remain poorly understood. Therefore, we sought to construct a survival prediction model using m6A-associated genes to clarify the molecular and immune characteristics of HCC.Methods: HCC case data were downloaded from The Cancer Genome Atlas (TCGA). Then, by applying consensus clustering, we identified two distinct HCC clusters. Next, four m6A-related genes were identified to construct a prognostic model, which we validated with Gene Expression Omnibus (GEO) and International Cancer Genome Consortium (ICGC) datasets. Additionally, the molecular and immune characteristics in different subgroups were analyzed.Results: m6A RNA methylation regulators were differentially expressed between HCC and normal samples and linked with immune checkpoint expression. Using consensus clustering, we divided HCC samples into two subtypes with distinct clinical features. Cluster 2 was associated with unfavorable prognosis, higher immune checkpoint expression and immune cell infiltration levels. In addition, the immune and carcinogenic signaling pathways were enriched in cluster 2. Furthermore, we constructed a risk model using four m6A-associated genes. Patients with different risk scores had distinct survival times, expression levels of immunotherapy biomarkers, TP53 mutation rates, and sensitivities to chemotherapy and targeted therapy. Similarly, the model exhibited an identical impact on overall survival in the validation cohorts.Conclusion: The constructed m6A-based signature may be promising as a biomarker for prognostics and to distinguish immune characteristics in HCC.

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Novel Biomarker NEDD1 and Its Analysis in Hepatocellular Carcinoma

10.21203/rs.3.rs-1192410/v1 ◽

2021 ◽

Author(s):

Xiaopeng Ding ◽

Jiahao Yu ◽

Xin Shi ◽

Kangwei Li ◽

Shuoyi Ma ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

External Validation ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Related Data ◽

Significant Difference

Abstract Background: NEDD1 (NEDD1 Gamma-Tubulin Ring Complex Targeting Factor) plays a crucial impact in regulating cell cycle and the development of scirrhous gastric cancer. However, the role of NEDD1 hasn’t been reported in hepatocellular carcinoma (HCC) so far. The aim of this research is to explore the role of NEDD1 on the development and prognosis of HCC. Methods: HCC-related data were download from The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and gene set enrichment analysis (GSEA) were conducted by the LinkedOmics database. Results: The expression of NEDD1 has significant difference between tumor and adjacent normal tissues in HCC (P＜0.01). We also found that NEDD1 was an independent risk factor in HCC patients (HR 1.643, 95%CI 1.125–2.398; P = 0.01). The study also demonstrated that NEDD1 expression was significantly relevant to the expression of several immune checkpoint genes, including CTLA-4, PD-L1 and PD-1. GSEA revealed that Cell cycle, MicroRNAs in cancer and Ribosome pathways were significantly enriched in NEDD1 overexpression phenotype. By integrating NEDD1 with other relevant factors, we constructed the prognostic nomogram to help the improvement of the prognosis for patients with HCC. The data from the International Cancer Genome Consortium (ICGC) database were used as an independent external validation of our prognostic model. Conclusion: The expression level of NEDD1 was negatively correlated to the prognosis of HCC patients and it may be a promising therapeutic target of HCC, which probably be able to predict the efficacy of immunotherapy for HCC patients.

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Gene signatures and prognostic values of m1A-related regulatory genes in hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-020-72178-1 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 1

Author(s):

Qingmiao Shi ◽

Chen Xue ◽

Xin Yuan ◽

Yuting He ◽

Zujiang Yu

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Enrichment Analysis ◽

Cancer Genome ◽

Regulatory Genes ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Clinical Prognosis ◽

Cox Regression Analysis ◽

Genes Expression

Abstract Hepatocellular carcinoma (HCC) ranks fourth in cancer-related mortality worldwide. N1-methyladenosine (m1A), a methylation modification on RNA, is gaining attention for its role across diverse biological processes. However, m1A-related regulatory genes expression, its relationship with clinical prognosis, and its role in HCC remain unclear. In this study, we utilized The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) database to investigate alterations within 10 m1A-related regulatory genes and observed a high mutation frequency (23/363). Cox regression analysis and least absolute shrinkage and selection operator were used to explore the association between m1A-related regulatory genes expression and HCC patient survival and identified four regulators that were remarkably associated with HCC patient prognosis. Additionally, an independent cohort from International Cancer Genome Consortium was studied to validate our discoveries and found to be consistent with those in the TCGA dataset. In terms of mechanism, gene set enrichment analysis linked these four genes with various physiological roles in cell division, the MYC pathway, protein metabolism, and mitosis. Kyoto Encyclopedia of Genes and Genomes analysis revealed that PI3K/Akt signaling pathway had potential relevance to m1A-related regulatory genes in HCC. These findings indicate that m1A-related regulatory genes may play crucial roles in regulating HCC progression and be exploited for diagnostic and prognostic purposes.

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Identification of prognostic and immune-related lncRNAs in hepatocellular carcinoma based on the cancer genome atlas data mining

Materials Express ◽

10.1166/mex.2020.1736 ◽

2020 ◽

Vol 10 (8) ◽

pp. 1189-1196

Author(s):

Kaikai Ren ◽

Jiakang Ma ◽

Bo Zhou ◽

Xiaoyan Lin ◽

Mingyu Hou ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Expression Profiles ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

High Rate ◽

Prognostic Indicators ◽

Cancer Genome Atlas ◽

Genome Atlas

Hepatocellular carcinoma (HCC) is a malignancy originating from hepatocytes with a high rate of distant metastasis and recurrence. HCC prognosis remains poorly understood, although its diagnosis and treatment have improved globally. Therefore, it is necessary to identify reliable predictive and prognostic indicators of HCC. HCC gene expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas. Seven lncRNAs (C10orf91, AC011352.3, AC015722.2, AC006372.1, PICSAR, AC110285.3, and AP001972.4) associated with immune and clinicopathological features were identified as biomarker candidates for HCC prognosis based on single-sample gene set enrichment analysis, the ESTIMATE algorithm, and Cox PHR analyses. Altogether, the findings revealed that the seven immune-related lncRNAs may provide a reference for improving HCC prognosis.

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Integration Analysis of m6A Regulators and m6A-Related Genes in Hepatocellular Carcinoma

BIO Integration ◽

10.15212/bioi-2021-0002 ◽

2021 ◽

Author(s):

Jingdun Xie ◽

Zhenhua Qi ◽

Xiaolin Luo ◽

Fang Yan ◽

Wei Xing ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Patient Group ◽

Risk Score ◽

Mutation Frequency ◽

Cox Regression ◽

Cancer Genome ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Cox Regression Analysis

Background: N6-Methyladenosine (m6A) RNA methylation of eukaryotic mRNA is involved in the progression of various tumors. We aimed to investigate m6A-related genes and m6A regulators in hepatocellular carcinoma (HCC) and their association with prognosis in HCC.Methods: We downloaded liver cancer sample data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium database. A total of 21 m6A regulators and 1258 m6A-related genes were then analyzed by consensus clustering, Spearman’s correlation, GO, KEGG, LASSO Cox regression, and univariate Cox regression analyses. Finally, we constructed a risk prognostic model.Results: We obtained 192 candidate m6A-related genes and 3 m6A regulators, including YTHDF1, YTHDF2, and YTHDC1. The expression of these genes and regulators differed significantly in different stages of HCC. Based on Cox regression analysis, 19 of 98 m6A-related prognostic genes were obtained to construct a risk score model. The 1- and 3-year area under the curves (AUCs) among HCC patients were greater than 0.7. Finally, based on analysis of mutation differences between high- and low-risk score groups, we determined that TP53 had the highest mutation frequency in the high-risk HCC patient group, whereas titin (TTN) had the highest mutation frequency in the low-risk HCC patient group.Conclusion: This study comprehensively analyzed m6A regulators and m6A-related genes through an integrated bioinformatic analysis, including expression, clustering, protein–protein interaction, and prognosis, thus providing novel insights into the roles of m6A regulators and m6A-related genes in HCC.

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Gene Signatures and Prognostic Values of N6-Methyladenosine Related Genes in Ovarian Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.542457 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhijing Na ◽

Ling Fan ◽

Xiuxia Wang

Keyword(s):

Ovarian Cancer ◽

Bioinformatics Analysis ◽

Treatment Strategies ◽

Disease Free Survival ◽

Enrichment Analysis ◽

Vital Role ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Consensus Clustering ◽

Mrna Expression Analysis

N6-Methyladenosine (m6A) is one of the most prominent modification regulating RNA processing and metabolism. Increasing studies have illuminated the vital role of m6A methylation in carcinogenesis. However, little is known about the interaction between m6A-related genes and survival of ovarian cancer (OC) patients. The purpose of this study was to obtain more reliable m6A-related genes that could be used as prognostic markers of OC using bioinformatics analysis performed on the RNA-seq data of OC. Gene expression datasets of all m6A-related genes as well as corresponding clinical data were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases. We detected differential expressed m6A-related candidate genes as well as their relationship and interaction. m6A RNA methylation regulator ALKBH5 and 35 m6A-related genes are dysregulated in OC. A gene set that could be used as a potential independent prognostic risk feature was further screened including NEBL, PDGFRA, WDR91, and ZBTB4. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. We applied consensus clustering analysis on the expression of the four prognostic genes and obtained four OC subgroups TM1-TM4. There were significant differences in age, stage and grade among the subgroups, and the overall survival (OS) as well as Disease-free survival (DFS) of TM2 group were shorter than those of the other three groups. Further GO and KEGG enrichment analysis indicated that these differential genes were closely related to biological processes and key signaling pathways involved in OC. In summary, our study has indicated that m6A-related genes are key factors in the progression of OC and have potential effects on the prognostic stratification of OC and the development of treatment strategies.

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Integrated analysis of iron metabolic-related genes in hepatocellular carcinoma

10.21203/rs.3.rs-147085/v1 ◽

2021 ◽

Author(s):

Li Wang ◽

Jialin Qu ◽

Man Jiang ◽

Na Zhou ◽

Zhixuan Ren ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Integrated Analysis ◽

Consensus Clustering ◽

Cox Regression Analysis

Abstract Background Iron is a nutrient essential for hemoglobin synthesis, DNA synthesis, and energy metabolism in all mammals. Iron metabolic involved in numerous types of cancers including hepatocellular cancer. In this study, we aim to identify prognostic model that based on iron metabolic-related genes that could effectively predict the prognosis for HCC patients. Methods The RNA microarray and clinical data of HCC patients that obtained from The Cancer Genome Atlas (TCGA) database. We identify the clusters of HCC patients with different clinical outcome performed by consensus clustering analysis. Four iron metabolic-related genes (FLVCR1, FTL, HIF1A, HMOX1) were screen for prognostic model by performed the Cox regression analysis. The efficacy of prognostic model was validated by the International Cancer Genome Consortium (ICGC) database. Meantime, the expressions value of FLVCR1, FTL, HIF1A, HMOX1 was performed using Oncomine database, the Human Protein Atlas and Kaplan Meier-plotter. Result The patients with low-risk score have better prognosis than high risk score both in TCGA cohort and ICGC cohort. The prognostic model showed well performance for predicting the prognosis of HCC patients than other clinicopathological parameters by OS-related ROC curves. Conclusion Our survival models that based on Iron metabolic can be independent risk factors for hepatocellular carcinoma patients.

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