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Unveiling prognostics biomarkers of tyrosine metabolism reprogramming in liver cancer by cross-platform gene expression analyses.

ABSTRACT: Tyrosine is mainly degraded in the liver by a series of enzymatic reactions. Abnormal expression of the tyrosine catabolic enzyme tyrosine aminotransferase (TAT) has been reported in patients with hepatocellular carcinoma (HCC). Despite this, aberration in tyrosine metabolism has not been investigated in cancer development. In this work, we conduct comprehensive cross-platform study to obtain foundation for discoveries of potential therapeutics and preventative biomarkers of HCC. We explore data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), Oncomine and Kaplan Meier plotter (KM plotter) and performed integrated analyses to evaluate the clinical significance and prognostic values of the tyrosine catabolic genes in HCC. We find that five tyrosine catabolic enzymes are downregulated in HCC compared to normal liver at mRNA and protein level. Moreover, low expression of these enzymes correlates with poorer survival in patients with HCC. Notably, we identify pathways and upstream regulators that might involve in tyrosine catabolic reprogramming and further drive HCC development. In total, our results underscore tyrosine metabolism alteration in HCC and lay foundation for incorporating these pathway components in therapeutics and preventative strategies.

SUBMITTER: Nguyen TN

PROVIDER: S-EPMC7295234 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Unveiling prognostics biomarkers of tyrosine metabolism reprogramming in liver cancer by cross-platform gene expression analyses.

Nguyen Tran N TN Nguyen Ha Q HQ Le Duc-Hau DH

PloS one 20200615 6

Tyrosine is mainly degraded in the liver by a series of enzymatic reactions. Abnormal expression of the tyrosine catabolic enzyme tyrosine aminotransferase (TAT) has been reported in patients with hepatocellular carcinoma (HCC). Despite this, aberration in tyrosine metabolism has not been investigated in cancer development. In this work, we conduct comprehensive cross-platform study to obtain foundation for discoveries of potential therapeutics and preventative biomarkers of HCC. We explore data ...[more]

PMID: 32542016

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Project description:Background Tyrosine metabolism pathway-related genes were related to prostate cancer progression, which may be used as potential prognostic markers. Aims To dissect the dysregulation of tyrosine metabolism in prostate cancer and build a prognostic signature based on tyrosine metabolism-related genes for prostate cancer. Materials and Method. Cross-platform gene expression data of prostate cancer cohorts were collected from both The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Based on the expression of tyrosine metabolism-related enzymes (TMREs), an unsupervised consensus clustering method was used to classify prostate cancer patients into different molecular subtypes. We employed the least absolute shrinkage and selection operator (LASSO) Cox regression analysis to evaluate prognostic characteristics based on TMREs to obtain a prognostic effect. The nomogram model was established and used to synthesize molecular subtypes, prognostic characteristics, and clinicopathological features. Kaplan–Meier plots and logrank analysis were used to clarify survival differences between subtypes. Results Based on the hierarchical clustering method and the expression profiles of TMREs, prostate cancer samples were assigned into two subgroups (S1, subgroup 1; S2, subgroup 2), and the Kaplan–Meier plot and logrank analysis showed distinct survival outcomes between S1 and S2 subgroups. We further established a four-gene-based prognostic signature, and both in-group testing dataset and out-group testing dataset indicated the robustness of this model. By combining the four gene-based signatures and clinicopathological features, the nomogram model achieved better survival outcomes than any single classifier. Interestingly, we found that immune-related pathways were significantly concentrated on S1-upregulated genes, and the abundance of memory B cells, CD4+ resting memory T cells, M0 macrophages, resting dendritic cells, and resting mast cells were significantly different between S1 and S2 subgroups. Conclusions Our results indicate the prognostic value of genes related to tyrosine metabolism in prostate cancer and provide inspiration for treatment and prevention strategies.

Project description:Being able to identify patients in whom immunological tolerance has been established or is developing would allow an individually tailored approach to post-transplant management of kidney allograft recipients. Ex vivo immunological monitoring was performed on samples from five groups of European renal transplant recipients (“IOT samples”): ten drug-free tolerant recipients who were functionally stable despite remaining immunosuppression-free for more than one year (Tol-DF); also functionally stable patients on minimal immunosuppression (<10 mg/day prednisone, s-LP); stable patients maintained with calcineurin inhibitors (s-CNI); stable patients maintained on CNI-free immunosuppression regimen (s-nCNI); patients showing signs of chronic rejection (CR) and healthy controls (HC). Among the investigation of other biomarkers and bioassays, gene expression profiles were generated on custom Agilent 8x15K 60mer oligonucleotide microarrays (“RISET 2.0”) on the IOT cohort (training set) and on an independent cohort of patients from the ITN (USA) that contained similar groups of patients and included 23 tolerant recipients (“ITN samples”, test set). Set of genes were identified, whose expression on whole blood allowed the identification of 100% of the tolerant recipients in the training set and 84% in the test set. Keywords: classification of clinical samples, tolerance prediction IOT samples: 13 samples from 10 Tol-DF patients, 16 samples from 11 s-LP patients, 8 samples from 8 s-nCNI patients, 40 samples from 28 s-CNI patients, 10 samples from 9 CR patients, and 8 samples from 8 HC donors were analysed on a custom Agilent 8x15K 60mer oligonucleotide microarray encomprising 5069 probes in triplicates. The microarray is dedicated to transplantation research and was designed based on current literature and published and unpublished data provided by the RISET consortium. For the probe selection procedure we specially focused on the detection of multiple transcript variants of a gene, on optimized hybridization properties of the probes, and on the avoidance of crosshybridization. The sampling dates of replicate samples from the same patient range from one day to 1.5 years. ITN samples: 31 samples from 23 Tol-DF patients, 14 samples from 11 s-LP patients, 52 samples from 34 s-CNI patients, 25 samples from 18 CR patients, and 20 samples from 20 HC donors were analysed on the same microarray platform and served as test set. The samples of the two cohorts differ in the protocol of RNA preparation.

Dataset Information

Unveiling prognostics biomarkers of tyrosine metabolism reprogramming in liver cancer by cross-platform gene expression analyses.

Publications

Unveiling prognostics biomarkers of tyrosine metabolism reprogramming in liver cancer by cross-platform gene expression analyses.

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