Dataset Information

A multiomic ferroptosis-associated prognostic signature incorporating epigenetic and transcriptional biomarkers for hepatocellular carcinoma.

ABSTRACT:

Background

Ferroptosis was reported to have tremendous promise in the treatment and prognosis of hepatocellular carcinoma (HCC). Here, we identified a novel ferroptosis-related prognostic signature incorporating epigenetic and transcriptional biomarkers could help predicting survival of patients with HCC.

Methods

We employed multi-omics and clinical data from The Cancer Genome Atlas (TCGA) database to identify the ferroptosis-associated methylation CpG sites associated with HCC survival using sure independence screening (SIS). Then we utilized Kaplan-Meier curves to evaluate the prognostic significance of gene expression and DNA methylation. Receiver operating characteristic (ROC) curve was used predicting the 3- and 5-year survival. Mediation analysis of ferroptosis-related methylation and transcriptional score was performed.

Results

We firstly identified 114 significant CpG sites under the criteria of false discovery rate (FDR) <0.05 in training set. Then we screened out 5 candidate CpG sites in validation set for multivariate screening and stepwise regression. We found that the high-risk group had significantly shorter survival time than the low-risk group in the prognostic signature combined with epigenetic and transcriptional scores (HR =2.72 95% CI: 2.01-3.68, P=8.75E-11). And the predictive model involving clinical information, gene expression, and methylation data performed best for 3-year survival prediction (AUC =0.672) and 5-year survival prediction (AUC =0.742).

Conclusions

Our results suggested a signature combining clinical information, ferroptosis-related gene expression, and methylation presented a superior ability for prognostic prediction in HCC, which may bring us novel tool and targets in the treatment of HCC.

SUBMITTER: Chen J

PROVIDER: S-EPMC9372242 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A multiomic ferroptosis-associated prognostic signature incorporating epigenetic and transcriptional biomarkers for hepatocellular carcinoma.

Chen Jida J Zhu Xinli X Chen Danzhi D Jin Lidan L Xu Wenbo W Yu Wei W Zhang Liwen L

Translational cancer research 20220701 7

<h4>Background</h4>Ferroptosis was reported to have tremendous promise in the treatment and prognosis of hepatocellular carcinoma (HCC). Here, we identified a novel ferroptosis-related prognostic signature incorporating epigenetic and transcriptional biomarkers could help predicting survival of patients with HCC.<h4>Methods</h4>We employed multi-omics and clinical data from The Cancer Genome Atlas (TCGA) database to identify the ferroptosis-associated methylation CpG sites associated with HCC su ...[more]

PMID: 35966333

Similar Datasets

Project description:BackgroundHepatocellular carcinoma (HCC) is the most common type of primary liver cancer with high heterogeneity. The prognosis of HCC is quite poor and the prognostic prediction also has challenges. Ferroptosis is recently recognized as a kind of iron-dependent cell death, which is involved in tumor progression. However, further study is needed to validate the influence of drivers of ferroptosis (DOFs) on the prognosis of HCC.MethodsThe FerrDb database and the Cancer Genome Atlas (TCGA) database were applied to retrieve DOFs and information of HCC patients respectively. HCC patients were randomly divided into training and testing cohorts with a 7:3 ratio. Univariate Cox regression, LASSO and multivariate Cox regression analyses were carried out to identify the optimal prognosis model and calculate the risk score. Then, univariate and multivariate Cox regression analyses were performed to assess the independence of the signature. At last, gene functional, tumor mutation and immune-related analyses were conducted to explore the underlying mechanism. Internal and external databases were used to confirm the results. Finally, the tumor tissue and normal tissue from HCC patients were applied to validate the gene expression in the model.ResultsFive genes were identified to develop as a prognostic signature in the training cohort relying on the comprehensive analysis. Univariate and multivariate Cox regression analyses confirmed that the risk score was able to be an independent factor for the prognosis of HCC patients. Low-risk patients showed better overall survival than high-risk patients. Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Furthermore, internal and external cohorts were consistent with our results. There was a higher proportion of nTreg cell, Th1 cell, macrophage, exhausted cell and CD8+T cell in the high-risk group. The Tumor Immune Dysfunction and Exclusion (TIDE) score suggested that high-risk patients could respond better to immunotherapy. Besides, the experimental results showed that some genes were differentially expressed between tumor and normal tissues.ConclusionIn summary, the five ferroptosis gene signature showed potential in prognosis of patients with HCC and could also be regarded as a value biomarker for immunotherapy response in these patients.

Project description: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.

Project description:PurposeFerroptosis, as a novel regulated cell death form, has a close interaction with metabolism, which is largely unknown in cancer. In the present study, we conducted a comprehensive analysis of ferroptosis-related metabolic genes to delineate the metabolic signatures induced by ferroptosis and evaluate its prognostic significance in hepatocellular carcinoma (HCC).MethodsThe ferroptosis-related metabolic genes (Fer-MRGs) were identified by correlation analyses with transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus. Then, univariate and the least absolute shrinkage and selection operator Cox regression analysis was used to establish a novel risk score model. Univariate and multivariate COX analyses were used to identify independent prognostic factors for overall survival (OS) of HCC, and a nomogram was developed. The Fer-MRGs' expression was further evaluated by quantitative real-time polymerase chain reaction in HCC.ResultsA total of 77 metabolic genes were identified as Fer-MRGs, and 26 were found with prognostic values for OS of HCC. Then, a novel nine-gene (AKR1C3, ATIC, G6PD, GMPS, GNPDA1, IMPDH1, PRIM1, RRM2, and TXNRD1) risk score model was constructed. Survival analyses showed worse OS in high-risk patients both in the training and validation groups. The model was also identified as an independent prognostic factor for HCC, and a prognostic nomogram for OS was further established with superior discriminative capacity and prediction accuracy. Notably, close correlations were also identified between the risk score and the expression of immune checkpoint genes, immune subtypes of tumor, and susceptibility of HCC to chemotherapeutic agents. Finally, elevated expression of eight Fer-MRGs (except for IMPDH1) was further verified in 16 pairs of HCC tumor and adjacent tissues.ConclusionThese results indicated the intense interaction between ferroptosis and metabolism, the significant role of ferroptosis-related MRGs, and the great potential of the novel risk score model for prognosis prediction in HCC.

Project description:Background: Immune checkpoint blockers (ICBs) are increasingly being used to treat patients with advanced hepatocellular carcinoma (HCC), but only a third of these patients are sensitive to ICBs. Emerging evidence suggests that ferroptosis could be a novel target for antitumor treatment, and combined treatment with ferroptosis inducers might enhance sensitivity to immunotherapy. However, there is a lack of information on the crosstalk between ferroptosis-related lncRNAs and anti-tumor immunity. Therefore, we aim to explore prognostic value of ferroptosis-related lncRNAs and clarify potential role in ICBs of HCC. Methods: We obtained mRNA and lncRNA expression data from two independent cohorts (TCGA and GEO database). Univariate Cox, the least absolute shrinkage and selection operator (Lasso) algorithm and multivariate Cox analysis were used to construct a lncRNA signature, which was evaluated using the area under the receiver operating characteristic curve (AUC) and Kaplan-Meier curves. Tumor-infiltrating cell (TIC) profiling and the tumor immune dysfunction and exclusion (TIDE) algorithm were used to validate the signature model and immunotherapy. Finally, we adopted RT-PCR assay to evaluate the differential expression of lncRNAs in HCC tissues in our hospital. Results: The ferroptosis-related lncRNA signature included five lncRNAs, most of which were positively correlated with clinical stage and grade. The signature could stratify patients into two risk groups, with the high-risk group associated with a shorter overall survival (OS, p < 0.05) in TCGA-LIHC and GSE76427. Besides, the AUCs of the 1-, 3-, and 5-years OS were 0.772, 0.707, and 0.666, respectively. Gene set enrichment analysis (GESA) of lncRNAs revealed enrichment of oncogenic and immune-related pathways. The TIC profiling indicated a close correlation between the signature and immune cells. Furthermore, the high-risk group had a better response to immunotherapy than low-risk group. RT-PCR demonstrated these five lncRNAs were upregulated in cancerous tissue than normal tissues. Conclusions: The ferroptosis-related lncRNA signature could accurately predict the OS of HCC patients and may serve as an independent clinical factor for patients' outcomes. Ferroptosis-related lncRNAs may remodel the tumor microenvironment (TME) and affect the anti-cancer ability of ICBs, and therefore, could potentially act as an indicator for the response to immunotherapy in HCC.

Dataset Information

A multiomic ferroptosis-associated prognostic signature incorporating epigenetic and transcriptional biomarkers for hepatocellular carcinoma.

Background

Methods

Results

Conclusions

Publications

A multiomic ferroptosis-associated prognostic signature incorporating epigenetic and transcriptional biomarkers for hepatocellular carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets