MACIR

Last update 08 May 2025

Basic Info

Synonyms

C5orf30, FLJ25291, MACIR

+ [1]

Introduction

Regulates the macrophage function, by enhancing the resolution of inflammation and wound repair functions mediated by M2 macrophages (PubMed:30659109). The regulation of macrophage function is, due at least in part, to its ability to inhibit glycolysis (PubMed:30659109). May also play a role in trafficking of proteins via its interaction with UNC119 and UNC119B cargo adapters: may help the release of UNC119 and UNC119B cargo or the recycling of UNC119 and UNC119B (PubMed:22085962). May play a role in ciliary membrane localization via its interaction with UNC119B and protein transport into photoreceptor cells (PubMed:22085962).

100 Clinical Results associated with MACIR

100 Translational Medicine associated with MACIR

0 Patents (Medical) associated with MACIR

Literatures (Medical) associated with MACIR

01 Jul 2024·The American Journal of Pathology

Serum Fusion Transcripts to Assess the Risk of Hepatocellular Carcinoma and the Impact of Cancer Treatment through Machine Learning

Article

Author: Yu, Yan-Ping ; Liu, Silvia ; Geller, David ; Luo, Jian-Hua

Hepatocellular carcinoma (HCC) is one of the most fatal malignancies. Early diagnosis of HCC is crucial in reducing the risk for mortality. This study analyzed a panel of nine fusion transcripts in serum samples from 61 patients with HCC and 75 patients with non-HCC conditions, using TaqMan real-time quantitative RT-PCR. Seven of the nine fusions frequently detected in patients with HCC included: MAN2A1-FER (100%), SLC45A2-AMACR (62.3%), ZMPSTE24-ZMYM4 (62.3%), PTEN-NOLC1 (57.4%), CCNH-C5orf30 (55.7%), STAMBPL1-FAS (26.2%), and PCMTD1-SNTG1 (16.4%). Machine-learning models were constructed based on serum fusion-gene levels to predict HCC in the training cohort, using the leave-one-out cross-validation approach. One machine-learning model, called the four fusion genes logistic regression model (MAN2A1-FER≤40, CCNH-C5orf30≤38, SLC45A2-AMACR≤41, and PTEN-NOLC1≤40), produced accuracies of 91.5% and 83.3% in the training and testing cohorts, respectively. When serum α-fetal protein level was incorporated into the machine-learning model, a two fusion gene (MAN2A1-FER≤40, CCNH-C5orf30≤38) + α-fetal protein logistic regression model was found to generate an accuracy of 94.8% in the training cohort. The same model resulted in 95% accuracy in both the testing and combined cohorts. Cancer treatment was associated with reduced levels of most of the serum fusion transcripts. Serum fusion-gene machine-learning models may serve as important tools in screening for HCC and in monitoring the impact of HCC treatment.

01 Nov 2023·Immunobiology

Human macrophage immunometabolism regulator (MACIR) in patients with periodontitis

Article

Author: Czerewaty, Michał ; Mazurek-Mochol, Małgorzata ; Serwin, Karol ; Pawlik, Andrzej ; Piotrowska, Katarzyna ; Kozak, Małgorzata

OBJECTIVEPeriodontitis is a local inflammatory reaction caused by bacterial infection in which immune cells, including macrophages, are involved. Recent studies have shown that an important regulator of macrophage function is the human macrophage immunometabolism regulator (MACIR). This gene has been shown to play a key role in modulating the immune response by affecting the activity of fibroblasts and macrophages. In this study, we investigated the expression of MACIR in the gingival tissues of patients with periodontal disease, as well as the effect of IL-1β and TNF-α on the expression of MACIR gene and protein in human gingival fibroblasts.METHODSMACIR mRNA expression in gingival tissue samples was determined using Real-time PCR. Expression of MACIR protein was determined using immunofluorescent staining and western blotting.RESULTSThe MACIR mRNA expression in gingival tissue samples in patients with periodontitis was statistically significantly lower than in gingival tissue samples from healthy controls (p = 0.009). The stimulation of human gingival fibroblasts with IL-1β and TNF-α resulted in a statistically significant decrease of MACIR gene mRNA expression. In western blotting and immunofluorescent analysis, we confirmed that the stimulation of the primary culture of human gingival fibroblasts by both IL-1β and TNF-α decreases the expression of MACIR protein.CONCLUSIONThe results of the study suggest that MACIR is an important regulator of the inflammatory process in patients with periodontitis. Decreased expression of the MACIR gene may activate macrophages to secrete mediators that increase inflammation and cause periodontal tissue destruction.

28 Feb 2022·Journal of Clinical and Translational HepatologyQ2 · MEDICINE

Detection of Prognostic Biomarkers for Hepatocellular Carcinoma through CircRNA-associated CeRNA Analysis

Q2 · MEDICINE

Article

Author: Huang, Xia ; Han, Li ; Xu, Hanlin ; Wei, Lili ; Yang, Yuling ; Wang, Maolong

BACKGROUND AND AIMSThe prognosis of hepatocellular carcinoma (HCC) is extremely poor; therefore, there is an urgent need for novel prognostic molecular biomarkers of HCC. The current investigation utilized circular (circ)RNA-associated competing endogenous (ce)RNAs analysis in order to identify significant prognostic biomarkers of HCC.METHODSCircRNAs and mRNAs that were differentially expressed between normal and HCC tissues were identified. Their respective functions were predicted with Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A nomogram was used for model verification.RESULTSA ceRNA network composed of differentially expressed circRNAs and mRNAs was constructed. Significant hub nodes in the ceRNA network were hsa_circ_0004662, hsa_circ_0005735, hsa_circ_0006990, hsa_circ_0018403 and hsa_circ_0100609. By using this information, a prognostic risk assessment tool was developed based on the expressions of seven genes (PLOD2, TARS, RNF19B, CCT2, RAN, C5orf30 and MCM10). Furthermore, multivariate Cox regression analysis revealed risk and T-stage parameters as independent prognostic factors. The nomograms that were constructed from risk and T-stage groups were used to further assess the prediction of HCC patient survival rates. The nomogram, which consisted of risk and T-stage scores assessment models, was found to be an independent factor for predicting prognosis of HCC.CONCLUSIONSFive circRNAs, including hsa_circ_0004662, hsa_circ_0005735, hsa_circ_0006990, hsa_circ_0018403 and hsa_circ_0100609, that may play key roles in the progression of HCC were identified. Seven gene signatures were identified, which were associated with the aforementioned circRNAs, including PLOD2, TARS, RNF19B, CCT2, RAN, C5orf30 and MCM10, all of which were significant genes involved in the pathophysiology of HCC. These genes may be used as a prognosticating tool in HCC patients.

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MACIR

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