MXD3

Last update 08 May 2025

Basic Info

Synonyms

bHLHc13, Class C basic helix-loop-helix protein 13, MAD3

+ [6]

Introduction

Transcriptional repressor. Binds with MAX to form a sequence-specific DNA-binding protein complex which recognizes the core sequence 5'-CAC[GA]TG-3'. Antagonizes MYC transcriptional activity by competing for MAX and suppresses MYC dependent cell transformation (By similarity).

100 Clinical Results associated with MXD3

100 Translational Medicine associated with MXD3

0 Patents (Medical) associated with MXD3

Literatures (Medical) associated with MXD3

13 Sep 2024·Medicine

Identification of the shared gene MXD3 signatures and biological mechanism in patients with hip pain and prostate cancer

Article

Author: Gong, Binbin ; Xie, Yu ; Huang, Liang ; Shan, Hong ; Feng, Yao ; Jiang, Shusuan

Prostate cancer (PRAD) is recognized as having a significant effect on systemic illnesses. This study examined possible immune cells, metabolic pathways, and genes that may explain the interaction between PRAD and hip pain. We used information retrieved from the Cancer Genome Atlas and the Gene Expression Omnibus databases. To find common genes, we utilized differential expression analysis and weighted gene co-expression network analysis. The genes that were shared were subjected to pathway enrichment studies using Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes. Additionally, hub genes were analyzed using LASSO regression, and a receiver operating characteristic curve was generated based on the screening outcomes. The genes for the nodes were chosen in a protein–protein interaction network that was built. Single-sample gene-set enrichment analysis was performed to identify the differentially expressed genes. Immunohistochemistry staining confirmed hub gene expression, and single-sample gene-set enrichment analysis assessed immune cell infiltration. We concluded by comparing MAX dimerization protein 3 (MXD3) and MAX interactor 1 (MXI1) expression in tumor tissues using Uniform Manifold Approximation and Projection and violin plots in the Tumor lmmune Single-cell Hub database. After analyzing the intersection of the differentially expressed genes and weighted gene co-expression network analysis-significant module genes, we determined that MXD3 was the best shared diagnostic biomarker for PRAD and hip pain. One potential predictor of PRAD development was the MXI1 node gene, which was found in the protein–protein interaction network. The analyses revealed that MXD3 had a relatively positive correlation with neutrophil and T-helper cell infiltration levels, whereas MXI1 had a negative correlation with mast and Tgd cell levels. Tumors had lower levels of MXI1 expression and higher levels of MXD3 expression compared to normal tissues. Endothelial cells, induced pluripotent stem cells, and smooth muscle cells were all found to express MXI1. This is the first study to investigate the close genetic link between hip pain and PRAD using bioinformatics technologies. The 2 most significant genes involved in crosstalk between PRAD and hip pain were MXD3 and MXI1. The immunological responses triggered by T cells, mast cells, and neutrophils may be crucial in the relationship between PRAD and hip pain.

01 Feb 2024·Environmental Toxicology

Transcriptome characteristics of epithelial cells from advanced non‐small cell lung cancer were revealed by single‐cell RNA sequencing

Article

Author: Xie, Juanjuan ; Qiu, Peng ; Chen, Yutao ; Dong, Cheng ; Wang, Junwu

AbstractBackgroundLung adenocarcinoma (LUAD) is the prevalent malignancy worldwide. The aim is to explore differentially expressed genes (DEGs) associated with immune infiltration and survival time of LUAD patients, and predict transcriptional factors for shedding new light on molecular mechanisms and individual therapy of LUAD.MethodScRNA‐seq data of LUAD patients was downloaded from GSE148071 and analyzed by R packages. The clustering and protein–protein interaction network were constructed for screening DEGs. Gene Set Enrichment Analysis (GSEA) and GO enrichment analysis were performed in epithelial cell subgroups with high differentiation potential. Potential regulatory transcription factors were predicted.ResultsSixteen epithelial cell types were required and top 20 genes were identified on cell subgroup Epi4 with the highest differentiation potential associated with poor prognosis of LUAD in PPI network. GSEA and GO annotation results showed that cell subgroup Epi4 was enriched in the biological processes of cell proliferation and energy metabolism, and positively regulated the function of cell proliferation. TPI1 was significantly highly expressed in LUAD samples (p < .0001). TPI1 demonstrated a negative correlation with the infiltration levels of CD8+ T cells, CD4+ T cells, B cells, and activated mast cells, whilst manifesting a positive correlation with the infiltration levels of resident mast cells, Th2 cells, and MDSC. Epi4 was regulated by transcription factors MXD3 and GATA4.ConclusionOverexpression of TPI1 was identified as a novel biomarker for LUAD, and potential regulatory transcription factors MXD3 and GATA4 regulated the proliferation of LUAD with the poor prognosis, which may serve as potential targets to suppress the proliferation of LUAD.

01 Jan 2024·Stem Cells International

Identification and Validation of Hub Genes and Construction of miRNA‐Gene and Transcription Factor‐Gene Networks in Adipogenesis of Mesenchymal Stem Cells

Article

Author: Ouyang, Yi ; Dai, Miaomiao ; Hong, Weisheng

Background. Adipogenic differentiation stands as a crucial pathway in the range of differentiation options for mesenchymal stem cells (MSCs), carrying significant importance in the fields of regenerative medicine and the treatment of conditions such as obesity and osteoporosis. However, the exact mechanisms that control the adipogenic differentiation of MSCs are not yet fully understood. Materials and Methods. We procured datasets, namely GSE36923, GSE80614, GSE107789, and GSE113253, from the Gene Expression Omnibus database. These datasets enabled us to perform a systematic analysis, including the identification of differentially expressed genes (DEGs) pre‐ and postadipogenic differentiation in MSCs. Subsequently, we conducted an exhaustive analysis of DEGs common to all four datasets. To gain further insights, we subjected these overlapped DEGs to comprehensive gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Following the construction of protein–protein interaction (PPI) networks, we meticulously identified a cohort of hub genes pivotal to the adipogenic differentiation process and validated them using real‐time quantitative polymerase chain reaction. Subsequently, we ventured into the construction of miRNA‐gene and TF–gene interaction networks. Results. Our rigorous analysis revealed a total of 18 upregulated DEGs and 12 downregulated DEGs that consistently appeared across all four datasets. Notably, the peroxisome proliferator‐activated receptor signaling pathway, regulation of lipolysis in adipocytes, and the adipocytokine signaling pathway emerged as the top‐ranking pathways significantly implicated in the regulation of these DEGs. Subsequent to the construction of the PPI network, we identified and validated 10 key node genes, namely IL6, FABP4, ADIPOQ, LPL, PLIN1, RBP4, ACACB, NT5E, KRT19, and G0S2. Our endeavor to construct miRNA–gene interaction networks led to the discovery of the top 10 pivotal miRNAs, including hsa‐mir‐27a‐3p, hsa‐let‐7b‐5p, hsa‐mir‐1‐3p, hsa‐mir‐124‐3p, hsa‐mir‐155‐5p, hsa‐mir‐16‐5p, hsa‐mir‐101‐3p, hsa‐mir‐21‐3p, hsa‐mir‐146a‐5p, and hsa‐mir‐148b‐3p. Furthermore, the construction of TF–gene interaction networks revealed the top 10 critical TFs: ZNF501, ZNF512, YY1, EZH2, ZFP37, ZNF2, SOX13, MXD3, ELF3, and TFDP1. Conclusions. In summary, our comprehensive study has successfully unraveled the pivotal hub genes that govern the adipogenesis of MSCs. Moreover, the meticulously constructed miRNA‐gene and TF–gene interaction networks are poised to significantly augment our comprehension of the intricacies underlying MSC adipogenic differentiation, thus providing a robust foundation for future advances in regenerative biology.

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MXD3

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