Pan‐cancer analysis of iron metabolic landscape across the Cancer Genome Atlas

Zhang, Su; Chang, Wei; Wu, Hao; Wang, Yu Han; Gong, Yu; Zhao, You Li; Liu, Shan Hui; Wang, Han Zhang; Svatek, Robert S.; Rodríguez, Ronald; Wang, Zhi Ping

doi:10.1002/jcp.29017

Cited by 59 publications

(62 citation statements)

References 28 publications

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“…Equally, no studies of pancreatic cancer have focused explicitly on segregating the patients based on the proteomic difference, and explored the subtype-specific alterations in the cellular processes and signalling pathways. Furthermore, recent studies have conducted a proteomic analysis of pancreatic cancer [20][21][22][23][24][25][26]; however, they have not integrated the proteomic subtypes with alterations in various signalling pathways, to the specific cancer gene mutations and/or the disease outcomes of the afflicted patients.…”

Section: Introductionmentioning

confidence: 99%

Proteogenomic Analysis of Pancreatic Cancer Subtypes

Kafita

Nkhoma

Zulu

et al. 2020

Preprint

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Pancreatic cancer remains a significant public health problem with an ever-rising incidence of disease. Cancers of the pancreas are characterised by various molecular aberrations, including changes in the proteomics and genomics landscape of the tumour cells. There is a need, therefore, to identify the proteomic landscape of pancreatic cancer and the specific genomic and molecular alterations associated with disease subtypes. Here, we carry out an integrative bioinformatics analysis of The Cancer Genome Atlas dataset that includes proteomics and whole-exome sequencing data collected from pancreatic cancer patients. We apply unsupervised clustering on the proteomics dataset to reveal the two distinct subtypes of pancreatic cancer. Using functional and pathway analysis, we demonstrate the different molecular processes and signalling aberrations of the pancreatic cancer subtypes.We explore the clinical characteristic of these subtypes to show differences in disease outcome. Using datasets of mutations and copy number alterations, we show that various signalling pathways are altered among pancreatic tumours, including the Wnt pathway, Notch pathway and PI3K-mTOR pathways. Altogether, we reveal the proteogenomic landscape of pancreatic cancer subtypes and the altered molecular processes which can be leveraged to devise more effective treatments.

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Section: Introductionmentioning

confidence: 99%

Proteogenomic Analysis of Pancreatic Cancer Subtypes

Kafita

Nkhoma

Zulu

et al. 2020

Preprint

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“…Seventy iron metabolism-related genes were collected from the published literature (18). RNA-seq transcriptome and clinical data sets were downloaded from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC)databases.…”

Section: Methods 1 Data Sourcesmentioning

confidence: 99%

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

Chen¹,

Hu²,

Pan³

et al. 2020

Preprint

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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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“…Acquisition of ferroptosis-and iron metabolism-related genes associated with HCC Ferroptosis-related genes were obtained in the ferroptosis pathway (map04216) from the KEGG PATHWAY Database (https://www.genome.jp/kegg/pathway.html). Genes related to iron metabolism were obtained in the pathway of iron uptake and transport (R-HSA-917937) from the Reactome Pathway Database (https://reactome.org/) and cellular iron ion homeostasis (GO:0006879) from the AmiGo2 database (http://amigo.geneontology.org/amigo) [28]. We searched and comprehensively analyzed the source literature of these genes, eliminated unrelated genes and added newly reported related genes, and then integrated them for subsequent research.…”

Section: Methodsmentioning

confidence: 99%

The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

Tang

Zhu

et al. 2020

Preprint

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Background : In this study, we comprehensively analyzed genes related to ferroptosis and iron metabolism to construct diagnostic and prognostic models and explore the relationship with the immune microenvironment in HCC. Methods : Integrated analysis, cox regression and the least absolute shrinkage and selection operator (LASSO) method of 104 ferroptosis- and iron metabolism-related genes and HCC-related RNA sequencing were performed to identify HCC-related ferroptosis and iron metabolism genes. Results : four genes (ABCB6, FLVCR1, SLC48A1 and SLC7A11) were identified to construct prognostic and diagnostic models. Poorer overall survival (OS) was exhibited in the high-risk group than that in the low-risk group in both the training cohort (P < 0.001, HR = 0.27) and test cohort (P < 0.001, HR = 0.27). The diagnostic models successfully distinguished HCC from normal samples and proliferative nodule samples. Compared with low-risk groups, high-risk groups had higher TMB; higher fractions of macrophages, follicular helper T cells, memory B cells, and neutrophils; and exhibited higher expression of CD83, B7H3, OX40 and CD134L. As an inducer of ferroptosis, erastin inhibited HCC cell proliferation and progression, and it was showed to affect Th17 cell differentiation and IL-17 signaling pathway through bioinformatics analysis, indicating it a potential agent of cancer immunotherapy. Conclusions: The prognostic and diagnostic models based on the four genes indicated superior diagnostic and predictive performance, indicating new possibilities for individualized treatment of HCC patients.

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Pan‐cancer analysis of iron metabolic landscape across the Cancer Genome Atlas

Cited by 59 publications

References 28 publications

Proteogenomic Analysis of Pancreatic Cancer Subtypes

Proteogenomic Analysis of Pancreatic Cancer Subtypes

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

The ferroptosis and iron-metabolism signature robustly predicts clinical diagnosis, prognosis and immune microenvironment for hepatocellular carcinoma

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