PAX5-miR-142 feedback loop promotes breast cancer proliferation by regulating DNMT1 and ZEB1.

BACKGROUND: Breast cancer is one of the most common malignancies occurred in female around the globe. Recent studies have revealed the crucial characters of miRNA and genes, as well as the essential roles of epigenetic regulation in breast cancer initiation and progression. In our previous study, miR-142-3p was identified as a tumor suppressor and led to G2/M arrest through targeting CDC25C. However, the specific mechanism is still uncertain. METHODS: We identified PAX5 as the upstream regulator of miR-142-5p/3p through ALGGEN website and verified by series of assays in vitro and in vivo. The expression of PAX5 in breast cancer was detected by qRT-PCR and western blot. Besides, bioinformatics analysis and BSP sequencing were performed to analyze the methylation of PAX5 promoter region. Finally, the binding sites of miR-142 on DNMT1 and ZEB1 were predicted by JASPAR, and proved by luciferase reporter assay, ChIP analysis and co-IP. RESULTS: PAX5 functioned as a tumor suppressor by positive regulation of miR-142-5p/3p both in vitro and in vivo. The expression of PAX5 was regulated by the methylation of its promoter region induced by DNMT1 and ZEB1. In addition, miR-142-5p/3p could regulate the expression of DNMT1 and ZEB1 through binding with their 3'UTR region, respectively. CONCLUSION: In summary, PAX5-miR-142-DNMT1/ZEB1 constructed a negative feedback loop to regulate the progression of breast cancer, which provided emerging strategies for breast cancer therapy.

CMTM7 inhibits breast cancer progression by regulating Wnt/ß-catenin signaling.

BACKGROUND: Breast cancer is the major cause of death in females globally. Chemokine-like factor like MARVEL transmembrane domain containing 7 (CMTM7) is reported as a tumor suppressor and is involved in epidermal growth factor receptor degradation and PI3K/AKT signaling in previous studies. However, other molecular mechanisms of CMTM7 remain unclear. METHODS: The expression level of CMTM7 in breast cancer cells and tissues was detected by qRT-PCR and western blot, and the methylation of CMTM7 promoter was detected by BSP sequencing. The effect of CMTM7 was verified both in vitro and in vivo, including MTT, colony formation, EdU assay, transwell assay and wound healing assay. The interaction between CMTM7 and CTNNA1 was investigated by co-IP assay. The regulation of miR-182-5p on CMTM7 and TCF3 on miR-182-5p was detected by luciferase reporter assay and ChIP analysis. RESULTS: This study detected the hypermethylation levels of the CMTM7 promoter region in breast cancer tissues and cell lines. CMTM7 was performed as a tumor suppressor both in vitro and in vivo. Furthermore, CMTM7 was a direct miR-182-5p target. Besides, we found that CMTM7 could interact with Catenin Alpha 1 (CTNNA1) and regulate Wnt/ß-catenin signaling. Finally, transcription factor 3 (TCF3) can regulate miR-182-5p. We identified a feedback loop with the composition of miR-182-5p, CMTM7, CTNNA1, CTNNB1 (ß-catenin), and TCF3, which play essential roles in breast cancer progression. CONCLUSION: These findings reveal the emerging character of CMTM7 in Wnt/ß-catenin signaling and bring new sights of gene interaction. CMTM7 and other elements in the feedback loop may serve as emerging targets for breast cancer therapy.

HS3ST3A1 and CAPN8 Serve as Immune-Related Biomarkers for Predicting the Prognosis in Thyroid Cancer.

Background: Thyroid cancer (TC) tends to be a common malignancy worldwide and results in various outcomes due to its different subtypes. The tumor microenvironment (TME) was demonstrated to play crucial roles in various malignancies, including thyroid cancer. This study combined the ESTIMATE and CIBERSORT algorithms, identified four TME-related genes, and evaluated their correlation with clinical characteristics. These findings revealed the malignant performance of TME in TC, and the TME-related DEGs might serve as prognostic biomarkers, which can be utilized for the prediction of immunotherapy effects in patients with TC. Methods: The clinical and gene expression profiles of TC patients were collected from the TCGA dataset. The ESTIMATE algorithm was utilized to estimate stromal and immune scores and predict the level of stromal and immune cell infiltration. The differential expressed genes related to TME were filtered by the "limma" package in R, and the PPI network was constructed by a string website. KEGG pathway and GO analyses were performed to investigate the biological progression and molecular functions of TME-related DEGs. Then, univariate Cox regression analysis was employed to screen four genes correlated with clinical characteristics. GSEA was conducted to assess their roles in the TME of TC. To further investigate the association between TME-related genes and tumor-infiltrating immune cells (TIICs), the CIBERSORT algorithm was performed. Finally, the malignancy behaviors of the two genes were verified by RT-qPCR, IHC, MTT, colony formation, and transwell assays. Results: Four TME-related DEGs, LRRN4CL, HS3ST3A1, PCOLCE2, and CAPN8, were identified and were significantly predictive of poor overall survival. KEGG and GO pathway analysis established that the TME-related DEGs were involved in immune responses and pathways in cancer. Furthermore, the malignancy behaviors of HS3ST3A1 and CAPN8 were verified by cellular functional experiments. These results revealed that the TME-related genes HS3ST3A1 and CAPN8 were able to serve as predictors of prognosis in patients with TC. Conclusion: HS3ST3A1 and CAPN8 may serve as valuable prognostic biomarkers and TME indicators, which can be utilized for the prediction of immunotherapy effects and provide novel treatment strategies for patients with TC.