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

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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Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v1 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS. Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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Construction and Validation of a Combined Ferroptosis and Hypoxia Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.809672 ◽

2021 ◽

Vol 8 ◽

Author(s):

Kai Wen ◽

Yongcong Yan ◽

Juanyi Shi ◽

Lei Hu ◽

Weidong Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Tumor Staging ◽

Tumor Therapy ◽

Normal Liver ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Advanced Tumor

Background: Ferroptosis, as a unique programmed cell death modality, has been found to be closely related to the occurrence and development of hepatocellular carcinoma (HCC). Hypoxia signaling pathway has been found to be extensively involved in the transformation and growth of HCC and to inhibit anti-tumor therapy through various approaches. However, there is no high-throughput study to explore the potential link between ferroptosis and hypoxia, as well as their combined effect on the prognosis of HCC.Methods: We included 370 patients in The Cancer Genome Atlas (TCGA) database and 231 patients in the International Cancer Genome Consortium (ICGC) database. Univariate COX regression and Least Absolute Shrinkage and Selection Operator approach were used to construct ferroptosis-related genes (FRGs) and hypoxia-related genes (HRGs) prognostic signature (FHPS). Kaplan–Meier method and Receiver Operating Characteristic curves were analyzed to evaluate the predictive capability of FHPS. CIBERSOR and single-sample Gene Set Enrichment Analysis were used to explore the connection between FHPS and tumor immune microenvironment. Immunohistochemical staining was used to compare the protein expression of prognostic FRGs and HRGs between normal liver tissue and HCC tissue. In addition, the nomogram was established to facilitate the clinical application of FHPS.Results: Ten FRGs and HRGs were used to establish the FHPS. We found consistent results in the TCGA training cohort, as well as in the independent ICGC validation cohort, that patients in the high-FHPS subgroup had advanced tumor staging, shorter survival time, and higher mortality. Moreover, patients in the high-FHPS subgroup showed ferroptosis suppressive, high hypoxia, and immunosuppression status. Finally, the nomogram showed a strong prognostic capability to predict overall survival (OS) for HCC patients.Conclusion: We developed a novel prognostic signature combining ferroptosis and hypoxia to predict OS, ferroptosis, hypoxia, and immune status, which provides a new idea for individualized treatment of HCC patients.

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Identification of Key Iron Metabolism-Related Long Non-Coding RNAs in Hepatocellular Carcinoma Based on Bioinformatics Analysis

10.21203/rs.3.rs-754313/v1 ◽

2021 ◽

Author(s):

yu chen

Keyword(s):

Hepatocellular Carcinoma ◽

Iron Metabolism ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Roc Curve Analysis ◽

Cerna Network ◽

Kaplan Meier ◽

Non Coding Rnas

Abstract Background： The present study explored the regulatory mechanisms and functional roles of iron metabolism-related long non-coding RNAs (lncRNAs) in hepatocellular carcinoma (HCC) and their potential impact on prognosis of HCC patients. Methods：RNA-seq data and clinical information of HCC samples and normal samples were downloaded from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) portal. Iron metabolism-related genes were downloaded from Reactome database and AmiGo2 database. Differential expression and correlation analysis were performed to identify iron metabolism-related differentially expressed lncRNAs (DElncRNAs). Moreover, Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) analysis were used to screen the possible prognostic and diagnostic biomarkers of HCC. Results: A total of 20 differentially expressed and iron metabolism-related genes (DEIMRG) were identified by overlapping 3746 differentially expressed genes (DEGs) and 86 IMRGs. Next, ARHGAP11B, LINC00205, LINC00261 and SNHG12 were screened through Univariate Cox regression. Kaplan-Meier survival curves indicated that ARHGAP11B, LINC00205, LINC00261 and SNHG12 were related to overall survival (OS) in HCC patient in TCGA database. ARHGAP11B, LINC00205 and LINC00261 were finally identified as prognostic DEIMRGs related with OS of HCC patients after validate the survival results in ICGC portal. ARHGAP11B, LINC00205 and LINC00261 all achieved an AUC value of >0.80 in ROC curve analysis. Furthermore, LINC00205 was identified as independently prognostic factor by multivariate Cox analysis combined with clinicopathological factors. Moreover, a ceRNA network including 25 DEmRNAs, 15 DEmiRNA and 3 DElncRNAs was successfully constructed, based on prognostic DElncRNAs and key target miRNAs and mRNAs of them predicted by starBase database and miRwalk. The PPI network illustrated that CDC25A, CHEK1, CCNE2 and ANLN proteins interact more with other proteins. Conclusions: In the present study, we identified iron metabolism related LINC00205 as a prognostic and diagnostic biomarker and constructed a metabolism-related ceRNA network, which may contribute to the treatment of HCC.

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Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v2 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS.Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients. Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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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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Autophagy-related signature as Indicators for the Prognosis of Hepatocellular carcinoma

10.21203/rs.3.rs-32431/v1 ◽

2020 ◽

Author(s):

Wen Ye ◽

Zhehao Shi ◽

Zhongjing Zhang ◽

Yi Zhou ◽

Bicheng Chen ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cancer ◽

Regression Model ◽

Cox Regression ◽

Expression Profiles ◽

Risk Groups ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) is the most common and deadly type of liver cancer. Autophagy is the process of transporting damaged or aging cellular components into lysosomes for digestion and degradation. There is an accumulative evidence implies that autophagy is a key factor of the progression of cancer. The aim of this study was to determine a panel of a novel autophagy-related prognostic marker for liver cancer. Methods We conducted a comprehensive analysis of ARGs expression profiles and corresponding clinical information based on The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) database. The univariate Cox proportional regression model was used to screen candidate autophagy-related prognostic genes. In addition, the multivariate Cox proportional regression model were helped to prove five key prognostic autophagy-related genes (ATIC, BAX, BIRC5, CAPNS1 and FKBP1A), which were used to construct prognostic signature. Results Based on the prognostic signature, liver cancer patients were significantly divided into high-risk and low-risk groups in terms of overall survival (OS). Further multivariate Cox regression analysis indicated that the prognostic signature remained as an independent prognostic factor for OS. The prognostic signature in possession of a better Area Under Curves (AUC) has a better performance in predicting the survival of patients with HCC, compared with other clinical parameters. Conclusion This study provides a prospective biomarker for monitoring the outcomes in the patients with HCC.

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Identification and validation of an immune-related prognostic signature for hepatocellular carcinoma

10.21203/rs.3.rs-25879/v3 ◽

2020 ◽

Author(s):

Xinxin Xia ◽

Hui Liu ◽

Yuejun Li

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Regression Analyses ◽

Prognostic Signature ◽

Biological Mechanisms ◽

Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. The immune system plays vital roles in HCC initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients. Methods: Gene expression data were retrieved from The Cancer Genome Atlas database. Univariate Cox regression analysis was carried out to identify differentially expressed genes that associated with overall survival. The IRPS was established via Lasso and multivariate Cox regression analysis. Both Cox regression analyses were conducted to determine the independent prognostic factors for HCC. Next, the association between the IRPS and clinical-related factors were evaluated. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset. Gene set enrichment analyses (GSEA) were conducted to understand the biological mechanisms of the IRPS. Results: A total of 62 genes were identified to be candidate immune-related prognostic genes. Transcription factors-immunogenes network was generated to explore the interactions among these candidate genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be an independent risk factor influencing the prognosis of HCC patients. The relationships between the IRPS and infiltration immune cells demonstrated that the IRPS was associated with immune cell infiltration. GSEA identified significantly enriched pathways, which might assist in elucidating the biological mechanisms of the IRPS. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients. Conclusions: The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

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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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Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.611058 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhuomao Mo ◽

Daiyuan Liu ◽

Dade Rong ◽

Shijun Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Checkpoints ◽

Immune Cell Infiltration ◽

Immunosuppressive Microenvironment

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

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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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