Osteosarcoma is a highly aggressive cancer common in children and adolescents. There is still a lack of effective treatments for metastatic or recurrent osteosarcoma. The role of long non-coding RNAs (lncRNAs) in osteosarcoma has gradually attracted attention. Here, we identified lncRNAs that were abnormally expressed in metastatic osteosarcoma through analyzing the sequencing data of osteosarcoma tissues and selected upregulated lncRNA MELTF-AS1 for detailed study. The qRT-PCR analysis showed that the expression of MELTF-AS1 was increased in osteosarcoma tissues and cells, and the high expression of MELTF-AS1 indicated a poor prognosis of osteosarcoma patients. The high expression of MELTF-AS1 in osteosarcoma was partly due to the transcriptional activation of RREB1. The results of transwell assays, scratch wound healing assays, and the tail vein injection lung metastasis model demonstrated that knocking down MELTF-AS1 inhibited metastasis ability of osteosarcoma cells. Furthermore, the results of RNA pull-down assays, luciferase reporter assays, and RNA immunoprecipitation (RIP) assays revealed that MELTF-AS1 could regulate MMP14 expression through interaction with miR-485-5p. Our study suggested that MELTF-AS1 functioned as a pro-metastasis gene in osteosarcoma by upregulating MMP14 and that it could be a potential therapeutic and diagnostic target for osteosarcoma.

Background Osteoarthritis, a common degenerative disease of articular cartilage, is characterized by degeneration of articular cartilage, changes in subchondral bone structure, and formation of osteophytes, with main clinical manifestations including increasingly serious swelling, pain, stiffness, deformity, and mobility deficits of the knee joints. With the advent of the big data era, the processing of mass data has evolved into a hot topic and gained a solid foundation from the steadily developed and improved machine learning algorithms. Aiming to provide a reference for the diagnosis and treatment of osteoarthritis, this paper using machine learning identifies the key feature genes of osteoarthritis and explores its relationship with immune infiltration, thereby revealing its pathogenesis at the molecular level. Methods From the GEO database, GSE55235 and GSE55457 data were derived as training sets and GSE98918 data as a validation set. Differential gene expressions of the training sets were analyzed, and the LASSO regression model and support vector machine model were established by applying machine learning algorithms. Moreover, their intersection genes were regarded as feature genes, the receiver operator characteristic (ROC) curve was drawn, and the results were verified using the validation set. In addition, the expression spectrum of osteoarthritis was analyzed by immunocyte infiltration and the co-expression correlation between feature genes and immunocytes was construed. Conclusion EPYC and KLF9 can be viewed as feature genes for osteoarthritis. The silencing of EPYC and the overexpression of KLF9 are associated with the occurrence of osteoarthritis and immunocyte infiltration.

Introducing bone regeneration–promoting factors into scaffold materials to improve the bone induction property is crucial in the fields of bone tissue engineering and regenerative medicine. This study aimed to develop a Sr-HA/PTH1-34-loaded composite hydrogel system with high biocompatibility. Teriparatide (PTH1-34) capable of promoting bone regeneration was selected as the bioactive factor. Strontium-substituted hydroxyapatite (Sr-HA) was introduced into the system to absorb PTH1-34 to promote the bioactivity and delay the release cycle. PTH1-34-loaded Sr-HA was then mixed with the precursor solution of the hydrogel to prepare the composite hydrogel as bone-repairing material with good biocompatibility and high mechanical strength. The experiments showed that Sr-HA absorbed PTH1-34 and achieved the slow and effective release of PTH1-34. In vitro biological experiments showed that the Sr-HA/PTH1-34-loaded hydrogel system had high biocompatibility, allowing the good growth of cells on the surface. The measurement of alkaline phosphatase activity and osteogenesis gene expression demonstrated that this composite system could promote the differentiation of MC3T3-E1 cells into osteoblasts. In addition, the in vivo cranial bone defect repair experiment confirmed that this composite hydrogel could promote the regeneration of new bones. In summary, Sr-HA/PTH1-34 composite hydrogel is a highly promising bone repair material.

Background：Breast cancer is the most common female cancer in the world. Many scholars have devoted themselves to elucidating the pathogenesis of Breast cancer. In the past, dCTP pyrophosphatase 1 (DCTPP1) was thought to be overexpressed in several cancers. However, The mechanism by which DCTPP1 is regulated by non-coding RNA in Breast cancer and its relationship with immune infiltration have not been elucidated.Results：In this study, reliable databases from the Cancer Genome Atlas (TCGA) and Gene Expression Integration (GEO) showed that the expression of DCTPP1 in Breast cancer tissues was higher than in normal tissues. Bioinformatics analysis showed that DCTPP1 was negatively correlated with the expression of hsa-miR-125b-5p in BRCA,The expression of LncRNA LGALS8-AS1 is positively correlated with the expression of DCTPP1, and negatively correlated with the expression of hsa-miR-125b-5p. Therefore, we speculate that lncRNA LGALS8-AS1 promotes tumor progression through sponge hsa-miR-125b-5p and maintains the overexpression of DCTPP1 in Breast cancer. The survival analysis of 3 genes showed that the overexpression of DCTPP1 and LGALS8-AS1 is related to the poor prognosis of patients. By analyzing the relationship between DCTPP1 and immune infiltration, we found that the high copy of DCTPP1 is related to the infiltration of CD8+ T cells, and the high expression of DCTPP1 is related to the infiltration of CD4+ T cells in basal-like Breast cancer. DCTPP1 is positively correlated with the expression of immune checkpoint B7-H3.Conclusion: LNC LGALS8-AS1 can upregulate DCTPP1 by sponging with hsa-miR-125b-5p. DCTPP1 can be used as a new prognostic marker for B7-H3 antibody treatment of breast cancer.

Background: Osteoarthritis is a common degenerative disease of articular cartilage. Its typical features include articular cartilage degeneration, subchondral bone structural changes and osteophyte formation. The main clinical manifestations are increasingly severe knee joint swelling, stiffness, deformity and limited mobility. With the advent of the era of big data, the processing of massive data has become a hot topic, and the continuous development and improvement of machine learning algorithms have laid the foundation for the era of big data. This paper uses machine learning methods to identify the real key characteristic genes of osteoarthritis and explore the relationship between them and immune infiltration, so as to reveal the pathogenic mechanism of osteoarthritis at the molecular biology level, aiming to provide osteoarthritis diagnosis and treatment. Method: Download the GSE55235 and GSE55457 datasets from the GEO database, merge the two as the training set, and download the GSE98918 data as the validation set. Gene differential expression analysis was performed on the training set, lasso regression model and support vector machine model were constructed by machine learning algorithm, and the intersection genes were taken as feature genes and receiver operating characteristic curves were drawn. Finally, the expression profile of osteoarthritis was analyzed by immune cell infiltration and the correlation between the co-expression of characteristic genes and immune cells was analyzed. Conclusion: EPYC and KLF9 can be used as the characteristic genes of osteoarthritis.The silencing of EPYC and the overexpression of KLF9 are related to the occurrence of osteoarthritis and the infiltration of immune cells.