CircRUNX1 drives the malignant phenotypes of lung adenocarcinoma through mediating the miR-5195-3p/HMGB3 network.

BACKGROUND: Circular RNAs (circRNAs) are key factors in the regulation of cancer progression. However, the role of circRUNX1 in lung adenocarcinoma (LUAD) progression is unclear. METHODS: The expression levels of circRUNX1, microRNA (miR)-5195-3p, and high-mobility group protein B3 (HMGB3) were detected by quantitative real-time PCR. Cell proliferation, migration, invasion and apoptosis were analyzed by EdU staining, colony formation assay, transwell assay and flow cytometry. Protein levels were measured using western blot analysis. The interaction between miR-5195-3p and circRUNX1 or HMGB3 was verified by dual-luciferase reporter assay and RIP assay. Animal experiments were performed to investigate the role of circRUNX1 in LUAD tumorigenesis. RESULTS: We found that circRUNX1 was upregulated in LUAD tumor tissues and cells. CircRUNX1 knockdown suppressed LUAD cell proliferation and metastasis, while promoted apoptosis. In terms of mechanism, we found that circRUNX1 could sponge miR-5195-3p, and miR-5195-3p inhibitor abolished the regulation of circRUNX1 knockdown on LUAD cell proliferation, metastasis and apoptosis. In addition, miR-5195-3p could target HMGB3, and HMGB3 overexpression reversed the inhibition effect of miR-5195-3p on LUAD progression. Moreover, circRUNX1 knockdown reduced LUAD tumorigenesis. CONCLUSION: CircRUNX1 facilitated LUAD proliferation and metastasis by regulating the miR-5195-3p/HMGB3 axis, suggesting that it might be a possible therapeutic target for LUAD.

Tumor DNA Methylation Profiles Enable Diagnosis, Prognosis Prediction, and Screening for Cervical Cancer.

Background: DNA-methylation-based machine learning algorithms have demonstrated powerful diagnostic capabilities, and these tools are currently emerging in many fields of tumor diagnosis and patient prognosis prediction. This work aimed to identify novel DNA methylation diagnostic biomarkers for differentiating cervical cancer (CC) from normal tissues, as well as a prognostic prediction model to predict survival of CC patients. Methods: The methylation profiles with the available clinical characteristics were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) program. We first screened out the differential methylation sites in CC and normal tissues and performed multiple statistical analyses to discover DNA methylation diagnostic markers that are used to distinguish CC and normal control. Then, we developed a methylation-based survival model to improve risk stratification. Results: A diagnostic prediction panel consists of five CpG markers that could predict cervical cancer versus normal tissue with highly correct rate of 100%, and cg16428251, cg22341310, and cg23316360 which in diagnostic prediction panel all could yield high sensitivity and specificity for detection of CC and normal in six cohorts (area under curve [AUC] > 0.8), in addition to excellent performance in discriminating between CC and normal sample. The diagnostic marker panel also effectively predicted the CIN3 versus normal tissue with high accuracy in two datasets (AUC = 0.80, 0.789, respectively). Furthermore, a prognostic prediction model aggregated two CpG markers that effectively stratified the prognosis of high-risk and low-risk groups (training cohort: hazard ratio [HR] 4, 95% CI: 1.7-9.6, P = 0.0021; testing cohort: hazard ratio [HR] 1.9, 95% CI: 1.2-3.1, P = 0.0072). Conclusion: The findings of our study showed that DNA methylation markers are of great value in the diagnosis and prognosis of CC.