MiR-3682-3p promotes esophageal cancer progression by targeting FHL1 and activating the Wnt/ß-catenin signaling pathway.

BACKGROUND: Esophageal cancer (EC) is highly ranked among all cancers in terms of its incidence and mortality rates. MicroRNAs (miRNAs) are considered to play key regulatory parts in EC. Multiple research studies have indicated the involvement of miR-3682-3p and four and a half LIM domain protein 1 (FHL1) in the achievement of tumors. The aim of this research was to clarify the significance of these genes and their possible molecular mechanism in EC. METHODS: Data from a database and the tissue microarray were made to analyze the expression and clinical significance of miR-3682-3p or FHL1 in EC. Reverse transcription quantitative PCR and Western blotting were used to detect the expression levels of miR-3682-3p and FHL1 in EC cells. CCK8, EdU, wound healing, Transwell, flow cytometry, and Western blotting assays were performed to ascertain the biological roles of miR-3682-3p and FHL1 in EC cells. To confirm the impact of miR-3682-3p in vivo, a subcutaneous tumor model was created in nude mice. The direct interaction between miR-3682-3p and FHL1 was demonstrated through a luciferase assay, and the western blotting technique was employed to assess the levels of crucial proteins within the Wnt/ß-catenin pathway. RESULTS: The noticeable increase in the expression of miR-3682-3p and the decrease in the expression of FHL1 were observed, which correlated with a negative impact on the patients' overall survival. Upregulation of miR-3682-3p expression promoted the growth and metastasis of EC, while overexpression of FHL1 partially reversed these effects. Finally, miR-3682-3p motivates the Wnt/ß-catenin signal transduction by directly targeting FHL1. CONCLUSION: MiR-3682-3p along the FHL1 axis activated the Wnt/ß-catenin signaling pathway and thus promoted EC malignancy.

Further predictive value of lymphovascular invasion explored via supervised deep learning for lymph node metastases in breast cancer.

Lymphovascular invasion, specifically lymph-blood vessel invasion (LBVI), is a risk factor for metastases in breast invasive ductal carcinoma (IDC) and is routinely screened using hematoxylin-eosin histopathological images. However, routine reports only describe whether LBVI is present and does not provide other potential prognostic information of LBVI. This study aims to evaluate the clinical significance of LBVI in 685 IDC cases and explore the added predictive value of LBVI on lymph node metastases (LNM) via supervised deep learning (DL), an expert-experience embedded knowledge transfer learning (EEKT) model in 40 LBVI-positive cases signed by the routine report. Multivariate logistic regression and propensity score matching analysis demonstrated that LBVI (OR 4.203, 95% CI 2.809-6.290, P < 0.001) was a significant risk factor for LNM. Then, the EEKT model trained on 5780 image patches automatically segmented LBVI with a patch-wise Dice similarity coefficient of 0.930 in the test set and output counts, location, and morphometric features of the LBVIs. Some morphometric features were beneficial for further stratification within the 40 LBVI-positive cases. The results showed that LBVI in cases with LNM had a higher short-to-long side ratio of the minimum rectangle (MR) (0.686 vs. 0.480, P = 0.001), LBVI-to-MR area ratio (0.774 vs. 0.702, P = 0.002), and solidity (0.983 vs. 0.934, P = 0.029) compared to LBVI in cases without LNM. The results highlight the potential of DL to assist pathologists in quantifying LBVI and, more importantly, in exploring added prognostic information from LBVI.