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Last update 08 May 2025

TRHDE

Last update 08 May 2025

Basic Info

Synonyms

PAP-II, PGPEP2, pyroglutamyl aminopeptidase II

+ [9]

Introduction

Specific inactivation of TRH after its release.

Drugs associated with TRHDE

CN118176179

Patent Mining

Target

TRH x TRHDE

Mechanism

TRH inhibitors [+1]

Active Org.

Mitsubishi Tanabe Pharma Corp.

Originator Org.

Mitsubishi Tanabe Pharma Corp.

Active Indication

Nervous System Diseases [+3]

Inactive Indication-

Drug Highest PhaseDiscovery

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with TRHDE

100 Translational Medicine associated with TRHDE

0 Patents (Medical) associated with TRHDE

104

Literatures (Medical) associated with TRHDE

04 Apr 2025·Journal of Proteome Research

Metabolomics- and Proteomics-Based Disease Diagnostic Classifier Model for the Prediction and Diagnosis of Colorectal Carcinoma

Article

Author: Lei, Ningjing ; Zhang, Haozhe ; Sun, Mengyao ; Liu, Na ; Wang, Zhaorui ; Li, Tianyuan ; Feng, Zhikun

BACKGROUNDColorectal carcinoma (CRC) is a leading cause of cancer-related deaths globally. Diagnostic biomarkers are essential for risk stratification and early detection, potentially enhancing patient survival. Our study aimed to explore the potential biomarkers of CRC at the protein and metabolic levels.METHODSBlood serum from CRC patients and healthy controls was analyzed using metabolomic and proteomic techniques. A conjoint analysis was conducted, and samples were split into training and validation sets (7:3 ratio) to develop and evaluate a disease diagnosis classifier model. Immunohistochemistry (IHC) analyses were conducted to validate the results.RESULTSWe identified 631 differential metabolites and 61 differentially expressed proteins (DEPs) in CRC, involved in pathways such as arginine and proline metabolism, central carbon metabolism in cancer, and signaling pathways including TGF-β, mTOR, PI3K-Akt, and others. Key proteins (CILP2, SLC3A2, EXTL2, hydroxypyruvate isomerase (HYI), ENPEP, LRG1, CTSS, thyrotropin-releasing hormone-degrading ectoenzyme (TRHDE), SELE, and HSPA1A) showed significant expression differences between CRC patients and controls. IHC results showed that compared with the paracancerous tissues, the expression of CILP2, EXTL2, and HYI was significantly downregulated in the CRC tissues (P < 0.05). The classifier model, comprising l-arginine, Harden-Young ester, l-aspartic acid, oxoglutaric acid, l-proline, octopine, l-valine, and progesterone, achieved AUC values of 0.998 and 0.914 in training and validation data sets, respectively.CONCLUSIONSThe identified metabolites and DEPs are promising CRC biomarkers. The developed classifier model based on eight metabolites demonstrates high accuracy for CRC assessment and diagnosis.

01 Feb 2025·Molecular Genetics & Genomic Medicine

Identification and Validation of Biomarkers in Metabolic Dysfunction‐Associated Steatohepatitis Using Machine Learning and Bioinformatics

Article

Author: Zhang, Yu‐Ying ; Yu, Peng ; Liu, Shuang ; Liu, Jian‐Ping ; Luo, Yun‐Fei ; Li, Jin‐E ; Zeng, Hai‐Xia

ABSTRACTBackgroundThe incidence of metabolic dysfunction‐associated steatohepatitis (MASH) is increasing annually. MASH can progress to cirrhosis and hepatocellular carcinoma. However, the early diagnosis of MASH is challenging.AimTo screen prospective biomarkers for MASH and verify their effectiveness through in vitro and in vivo experiments.MethodsMicroarray datasets (GSE89632, GSE48452, and GSE63067) from the Gene Expression Omnibus database were used to identify differentially expressed genes (DEGs) between patients with MASH and healthy controls. Machine learning methods such as support vector machine recursive feature elimination and least absolute shrinkage and selection operator were utilized to identify optimum feature genes (OFGs). OFGs were validated using the GSE66676 dataset. CIBERSORT was utilized to illustrate the variations in immune cell abundance between patients with MASH and healthy controls. The correlation between OFGs and immune cell populations was evaluated. The OFGs were validated at both transcriptional and protein levels.ResultsInitially, 37 DEGs were identified in patients with MASH compared with healthy controls. In the enrichment analysis, the DEGs were mainly related to inflammatory responses and immune signal‐related pathways. Subsequently, using machine learning algorithms, five genes (FMO1, PEG10, TP53I3, ME1, and TRHDE) were identified as OFGs. The candidate biomarkers were validated in the testing dataset and through experiments with animal and cell models. The malic enzyme (ME1) gene (HGNC:6983) expression was significantly upregulated in MASH samples compared to controls (0.4353 ± 0.2262 vs. −0.06968 ± 0.3222, p = 0.00076). Immune infiltration analysis revealed a negative correlation between ME1 expression and plasma cells (R = −0.77, p = 0.0033).ConclusionThis study found that ME1 plays a regulatory role in early MASH, which may affect disease progression by mediating plasma cells and T cells gamma delta to regulate immune microenvironment. This finding provides a new idea for the early diagnosis, monitoring and potential therapeutic intervention of MASH.

04 Jan 2025·Journal of Animal Science

A meta-analysis of genome-wide association studies to identify candidate genes associated with feed efficiency traits in pigs

Review

Author: Marques, Daniele B D ; Veroneze, Renata ; Silva, Delvan A da ; Silva, Maria Rita Gonçalves da ; Lopes, Paulo S ; Brito, Luiz F ; Machado, Inaê I

AbstractPig production is an agricultural sector of great economic and social relevance to Brazil and global markets. Feed efficiency traits directly influence the sustainability of pig production due to the economic impact of feed costs on the production system and the environmental footprint of the industry. Therefore, breeding for improved feed efficiency has been a target of worldwide pig breeding programs. Genome-wide association studies (GWAS) enable the assessment of the genetic background of complex traits, which contributes to a better understanding of the biological mechanisms regulating their phenotypic expression. In this context, the primary objective of this study was to identify and validate genomic regions and candidate genes associated with feed conversion ratio (FCR) and residual feed intake (RFI) in pigs based on a comprehensive systematic review and meta-analysis of GWAS. The METAL software was used to implement the meta-analysis and the Bonferroni multiple testing correction considering a significance threshold 0.05. The significant single nucleotide polymorphisms (SNPs) in the meta-analysis were used to identify candidate genes, followed by a functional genomic enrichment analysis. The systematic review identified 13 studies, of which 7 evaluated FCR, 3 evaluated RFI, and 3 studies investigated both traits, with 160 and 96 SNPs identified for FCR and RFI, respectively. After the meta-analysis, 145 markers were significantly associated with FCR and 90 with RFI. The gene annotation process resulted in 105 and 114 genes for FCR and RFI, respectively. The enrichment analysis for FCR resulted in 16 significant gene ontology (GO) terms, while 6 terms were identified for RFI. The main GO terms were actin cytoskeleton (GO_BP:0030036), membrane (GO_CC:0016020), integral components of the peroxisomal membrane (GO_CC:0005779), and carbohydrate-binding (GO_MF:0030246). The main candidate genes identified were MED18, PHACTR4, ABCC2, TRHDE, FRS2, FAR2 and FIS1 for FCR, and ADGRL2, ASGR1, ASGR2, and MAN2B1 for RFI. These findings contribute to a better understanding of the genetic mechanisms associated with feed efficiency traits in pigs, providing a foundation for future improvements in pig breeding programs.

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TRHDE

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