Lung adenocarcinoma (LUAD) is the main subtype of lung cancer. In this study, we found that RBP Mex3a was significantly upregulated in LUAD tissues and elevated Mex3a expression was associated with poor LUAD prognosis and metastasis. Furthermore, we demonstrated that Mex3a knockdown significantly inhibited LUAD cell migration and invasion in vitro and metastasis in nude mice. Transcriptome sequencing indicated that Mex3a affected gene expression linked to ECM-receptor interactions, including laminin subunit alpha 2(LAMA2). RNA immunoprecipitation (RIP) assay revealed Mex3a directly bound to LAMA2 mRNA and Mex3a increased the instability of LAMA2 mRNA in LUAD cells. Furthermore, we discovered that LAMA2 was surprisingly downregulated in LUAD and inhibited LUAD metastasis. LAMA2 knockdown partially reverse the decrease of cell migration and invasion caused by Mex3a knockdown. In addition, we found that both Mex3a and LAMA2 could influence PI3K-AKT pathway, which are downstream effectors of the ECM-receptor pathway. Moreover, the reduced activation of PI3K-AKT pathway in caused by Mex3a depletion was rescued by LAMA2 knockdown. In conclusion, we demonstrated that Mex3a downregulates LAMA2 expression to exert a prometastatic role in LUAD. Our study revealed the prognostic and prometastatic effects of Mex3a in LUAD, suggesting that Mex3a can serve as a prognostic biomarker and a target for metastatic therapy.
Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. The role of molecular alterations and the immune microenvironment in ESCC development has not been fully elucidated. The present study aimed to elucidate key candidate genes and immune cell infiltration characteristics in ESCC by integrated bioinformatics analysis. Nine gene expression datasets from the Gene Expression Omnibus (GEO) database were analysed to identify robust differentially expressed genes (DEGs) using the robust rank aggregation (RRA) algorithm. Functional enrichment analyses showed that the 152 robust DEGs are involved in multiple processes in the tumor microenvironment (TME). Immune cell infiltration analysis based on the 9 normalized GEO microarray datasets was conducted with the CIBERSORT algorithm. The changes in macrophages between ESCC and normal tissues were particularly obvious. In ESCC tissues, M0 and M1 macrophages were increased dramatically, while M2 macrophages were decreased. A robust DEG-based protein–protein interaction (PPI) network was used for hub gene selection with the CytoHubba plugin in Cytoscape. Nine hub genes (CDA, CXCL1, IGFBP3, MMP3, MMP11, PLAU, SERPINE1, SPP1 and VCAN) had high diagnostic efficiency for ESCC according to receiver operating characteristic (ROC) curve analysis. The expression of all hub genes except MMP3 and PLAU was significantly related to macrophage infiltration. Univariate and multivariate regression analyses showed that a 7-gene signature constructed from the robust DEGs was useful for predicting ESCC prognosis. Our results might facilitate the exploration of potential targeted TME therapies and prognostic evaluation in ESCC.
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