High Level of GMFG Correlated to Poor Clinical Outcome and Promoted Cell Migration and Invasion through EMT Pathway in Triple-Negative Breast Cancer.
Yonglin ZhaoXing WeiJia LiYan DiaoChangyou ShanWeimiao LiShuqun ZhangFei WuPublished in: Genes (2023)
Triple-negative breast cancer (TNBC) has a very poor prognosis due to the disease's lack of established targeted treatment options. Glia maturation factor γ (GMFG), a novel ADF/cofilin superfamily protein, has been reported to be differentially expressed in tumors, but its expression level in TNBC remains unknown. The question of whether GMFG correlates with the TNBC prognosis is also unclear. In this study, data from the Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), Human Protein Atlas (HPA), and Genotype-Tissue Expression (GTEx) databases were used to analyze the expression of GMFG in pan-cancer and the correlation between clinical factors. Gene Set Cancer Analysis (GSCA) and Gene Set Enrichment Analysis (GSEA) were also used to analyze the functional differences between the different expression levels and predict the downstream pathways. GMFG expression in breast cancer tissues, and its related biological functions, were further analyzed by immunohistochemistry (IHC), immunoblotting, RNAi, and function assay; we found that TNBC has a high expression of GMFG, and this higher expression was correlated with a poorer prognosis in TCGA and collected specimens of the TNBC. GMFG was also related to TNBC patients' clinicopathological data, especially those with histological grade and axillary lymph node metastasis. In vitro, GMFG siRNA inhibited cell migration and invasion through the EMT pathway. The above data indicate that high expression of GMFG in TNBC is related to malignancy and that GMFG could be a biomarker for the detection of TNBC metastasis.
Keyphrases
- poor prognosis
- long non coding rna
- binding protein
- papillary thyroid
- single cell
- squamous cell carcinoma
- big data
- gene expression
- endothelial cells
- mesenchymal stem cells
- lymph node
- electronic health record
- small molecule
- cancer therapy
- early stage
- copy number
- stem cells
- cell therapy
- machine learning
- deep learning
- genome wide
- bone marrow
- label free