Background: Several studies reported the dysregulation of miR-541 in the progression of some human malignancies. Osteosarcoma (OS) is one of the most common primary malignant bone tumors. This study aimed to assess the expression and clinical significance of miR-541 in OS patients and explore the biological function of miR-541 in tumor progression. Methods: Expression of miR-541 was detected by quantitative real-time PCR, and its prognostic value was evaluated using Kaplan-Meier survival analysis. The biological function of miR-541 was examined by analyzing its effects on OS cell proliferation, migration and invasion. Additionally, the underlying potential target of miR-541 was predicated and analyzed. Results: The expression of miR-541 was significantly decreased in OS tissues and cell lines. The deregulated expression of miR-541 in tumor tissues was associated with the overall survival of OS patients and was a potential independent prognostic indicator. In OS cells, the overexpression of miR-541 could inhibit cell proliferation, migration and invasion. The luciferase activity results indicated that TGIF2 was a potential target of miR-541. Conclusion: The results of this study revealed that the decreased miR-541 expression in OS patients may serve as a prognostic biomarker, and that the overexpression of miR-541 in OS cells results in inhibited cell proliferation, migration and invasion, indicating the potential of miR-541 as a therapeutic target in OS treatment.

Osteosarcoma (OS) is a type of primary bone tumor, which is one of the leading causes of cancer-related death. MicroRNA (miR)-605 has been demonstrated to act as a prognostic biomarker and therapeutic target in various cancers, such as breast cancer and non-small cell lung cancer, but its function in OS remains unclear. The aim of the present study was to investigate the prognostic value of miR-605 in patients with OS by evaluating its expression levels and to explore the biological function of miR-605 in OS progression. For this purpose, tumor tissues and adjacent normal tissues were collected from OS patients, and the expression of miR-605 in the collected tissues and OS MG63, U2OS, HOS, and SAOS-2 cell lines was detected by quantitative real-time PCR. The prognostic value of miR-605 was evaluated by Kaplan-Meier survival curves and Cox regression analysis. The effects of miR-605 on OS cell proliferation, migration and invasion were analyzed by the CCK-8 and transwell assays, respectively. The results of the present study revealed that miR-605 was significantly downregulated in OS tissues compared with adjacent normal tissues, which was associated with the clinical stage and distant metastasis of patients. Additionally, the downregulation of miR-605 predicted the poor prognosis of patients with OS and served as an independent prognostic indicator. The downregulation of miR-605 enhanced cell proliferation, migration, and invasion of OS cells, which suggested that miR-605 may be involved in the progression of OS. The findings of the present study provide a new therapeutic target for the treatment of patients with OS.

In order to help badminton players make reasonable training plans and realize a comprehensive grasp of the training process, this paper mainly recognizes and perceives the posture of badminton athletes based on the method of moving edge calculation. Firstly, from the perspective of moving edge motion analysis, considering the vector field formed by moving edge vector as movable spatial distribution information, the spatial distribution model of moving edge field is realized. Secondly, while athletes interact with the computer through limb movement, the overall posture of athletes is divided into several parts, and each part is perceived separately. Finally, in the human posture evaluation module, an algorithm for human posture evaluation in the image pixel plane is proposed. Through comparative experiments, the motion recognition algorithm can effectively recognize the three typical swing movements of badminton players in the video and improve the overall performance of the existing recognition algorithms.