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Last update 08 May 2025

C10orf99

Last update 08 May 2025

Basic Info

Synonyms

Antimicrobial peptide with 57 amino acid residues, Antimicrobial peptide-57, AP-57

+ [10]

Introduction

Highly cationic protein that has multiple functions. Acts as a chemotactic factor that mediates lymphocytes recruitment to epithelia through binding and activation of the G-protein coupled receptor GPR15 (PubMed:28900043, PubMed:28936214). May be a tumor suppressor; together with SUSD2 has a growth inhibitory effect on colon cancer cells which includes G1 cell cycle arrest (PubMed:25351403). May regulate keratinocyte proliferation (PubMed:29872130). In addition, through activation of Mas-related G protein-coupled receptors (MRGPRs) contributes to pruritogenesis by activating itch-selective sensory neurons and mast cells degranulation (PubMed:35704588). Has antimicrobial activity against Gram-positive bacteria, including Staphylococcus aureus and Actinomyces spec., and Mycoplasma hominis and lentivirus (PubMed:25585381).

Drugs associated with C10orf99

CN117186202

Patent Mining

Target

C10orf99

Mechanism-

Active Org.

Peking University Third Hospital [+1]

Originator Org.

Peking University [+1]

Active Indication

Eye Diseases [+1]

Inactive Indication-

Drug Highest PhaseDiscovery

First Approval Ctry. / Loc.-

First Approval Date20 Jan 1800

100 Clinical Results associated with C10orf99

100 Translational Medicine associated with C10orf99

0 Patents (Medical) associated with C10orf99

Literatures (Medical) associated with C10orf99

01 May 2025·Environmental Toxicology

RETRACTED: Development of gene panel for predicting recurrence in early‐stage cervical cancer patients

Article

Author: Zhu, Weipei ; Ma, Yun

AbstractCervical cancer (CC) is a common malignancy affecting women worldwide. Our objective was to develop a consensus‐based gene panel using multi‐omics data that could effectively predict recurrence in early‐stage cervical cancer patients. We utilized the “Multi‐Omics Consensus Integration Analysis (MOVICS)” package for consensus clustering design to integrate multiple omics datasets and improve the molecular classification landscape of early‐stage CC. We identified the “resting and naive” tumor microenvironment (TME) as cancer subtype (CS) 2. Leveraging the feature genes from the CS classifier, we employed machine learning algorithms to identify a gene panel, including ALDH1A1, CLDN10, MUC13, and C10orf99, which could generate a consensus machine learning‐driven score (CMLS) for each patient. Stratifying patients into high and low CMLS groups resulted in Kaplan–Meier curves demonstrating a significant difference in recurrence rates between the two groups. This difference remained significant even after adjusting for clinical features in multivariate Cox regression analysis, with the risk ratio of CMLS surpassing that of clinical characteristics. Furthermore, the TME exhibited notable differences between the different CMLS groups, suggesting that patients with low CMLS may exhibit a better response to immunotherapy. This study highlights the potential of the CMLS approach in predicting recurrence in early‐stage cervical cancer patients and provides a screening model for selecting patients suitable for immunotherapy.

01 Aug 2024·Skin Research and Technology

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients

Article

Author: Chen, Yibo ; Zhang, Wenjuan ; Cheng, Wenli ; Tan, Luyi ; Zhang, Wenji ; Zhang, Xinyu ; Zhu, Chenyu ; Li, Min

AbstractBackgroundPsoriasis is an immune‐mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention.Materials and methodsExpression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell‐type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI).ResultsA total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine‐mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine‐cytokine receptor interaction and IL‐17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine‐cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores.ConclusionHigh levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

01 Aug 2024·Fundamental & Clinical Pharmacology

Evaluation of potential antiviral activities of antimicrobial peptides in fish mucus

Article

Author: Er, Ayşe ; Tufan, Öznur ; Dik, Irmak ; Dik, Burak

AbstractBackgroundFish skin mucus contains innate immune factors and acts as the first line of physical or chemical defense against pathogens.ObjectiveThe primary aim of this study was to determine the antiviral activity of sea bream (SBr), rainbow trout (RT), and sea bass (SBa) fish skin mucus against herpes simplex virus (HSV)‐1. In addition, it was aimed to associate possible antiviral activity with antimicrobial peptides (AMPs) such as cathelicidin, hepcidin, galectin 2, and C10ORF99, whose levels were determined in the mucus.MethodsThe antiviral activity and oxidative/antioxidant status of mucus against HSV‐1 virus was evaluated. In addition, AMPs, SOD, and CAT activities, and immunoglobulin M levels were also analyzed in mucus of fish.ResultsAntiviral activity mucus of SBr, RT, and SBa against HSV‐1 were determined as 2−4, 2−5, and 2−2, respectively. The higher antiviral activity of SBr and RT mucus compared to the mucus of SBa can be associated with higher AMP levels in them.ConclusionThe skin mucus of SBr and RT may be nutritional supplement, adjuvant, and a new agent that can potentiate the effects of antimicrobial/antiviral agents.

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C10orf99

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