Estrogen receptor α promotes lung cancer cell invasion via increase of and cross-talk with infiltrated macrophages through the CCL2/CCR2/MMP9 and CXCL12/CXCR4 signaling pathways.
Miao HeWeiwei YuChawnshang ChangHiroshi MiyamotoXiaohong LiuKe JiangShuyuan YehPublished in: Molecular oncology (2020)
Data analysis of clinical samples suggests that higher estrogen receptor α (ERα) expression could be associated with worse overall survival in some patients with non-small-cell lung cancer (NSCLC). Immunofluorescence results further showed that higher ERα expression was linked to larger numbers of infiltrated macrophages in NSCLC tissues. However, the detailed mechanisms underlying this phenomenon remain unclear. Results from in vitro studies with multiple cell lines revealed that, in NSCLC cells, ERα can activate the CCL2/CCR2 axis to promote macrophage infiltration, M2 polarization, and MMP9 production, which can then increase NSCLC cell invasion. Mechanistic studies using chromatin immunoprecipitation and promoter luciferase assays demonstrated that ERα could bind to estrogen response elements (EREs) on the CCL2 promoter to increase CCL2 expression. Furthermore, ERα-increased macrophage infiltration can induce a positive feedback mechanism to increase lung cancer cell ERα expression via the up-regulation of the CXCL12/CXCR4 pathway. Targeting these newly identified pathways, NSCLC ERα-increased macrophage infiltration or the macrophage-to-NSCLC CXCL12/CXCR4/ERα signal, with anti-estrogens or CCR2/CXCR4 antagonists, may help in the development of new alternative therapies to better treat NSCLC.
Keyphrases
- estrogen receptor
- small cell lung cancer
- advanced non small cell lung cancer
- poor prognosis
- adipose tissue
- gene expression
- brain metastases
- endoplasmic reticulum
- cell migration
- liver injury
- dna methylation
- liver fibrosis
- binding protein
- dendritic cells
- signaling pathway
- induced apoptosis
- regulatory t cells
- high throughput
- dna damage
- epidermal growth factor receptor
- drug induced
- machine learning
- drug delivery
- artificial intelligence
- pi k akt
- cancer therapy
- big data
- cell cycle arrest