A metabolic bone disorder called osteoporosis is characterized by decreased bone mass and compromised microarchitecture. This condition can deteriorate bones and raise the risk of fractures. The two main causes of osteoporosis are an increase in osteoclast activity or quantity and a decrease in osteoblast viability. Numerous mechanisms, including estrogen shortage, aging, chemical agents, and decreased mechanical loads, have been linked to osteoporosis. Inflammation and oxidative stress have recently been linked to osteoporosis, according to an increasing number of studies. The two primary medications used to treat osteoporosis at the moment are bisphosphonates and selective estrogen receptor modulators (SERMs). These medications work well for osteoporosis brought on by aging and estrogen deprivation, however, they do not target inflammation and oxidative stress-induced osteoporosis. In addition, these drugs have some limitations that are attributed to various side effects that have not been overcome. Traditional Chinese medicine (TCM) has been applied in osteoporosis for many years and has a high safety profile. Therefore, in this review, literature related to botanical drugs that have an effect on inflammation and oxidative stress-induced osteoporosis was searched for. Moreover, the pharmacologically active ingredients of these herbs and the pathways were discussed and may contribute to the discovery of more safe and effective drugs for the treatment of osteoporosis.
Background: Lung cancer is a high occurrence rate and mortality rate cancer. Non-small cell lung cancer (NSCLC) is confirmed in 80–85% of lung cancer cases. Lung squamous cell carcinoma (LUSC) is frequently diagnosed at the advanced stage with poor prognoses. The size of tumor was an important indicator of the prognosis. Methods: TCGA database and GEO database were performed to download transcriptome data and clinical information of LUSC. Firstly, we identified differentially expressed genes (DEGs) between TNM stage as T3-T4 and T1-T2 of LUSC patients in TCGA datasets. Furthermore, PPI was applied to identify proteins that interact actively during the process of tumorigenesis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the enriched biological processes and pathways of the DEGs. After that, LASSO Cox regression algorithms were employed to confirm tumor size-related signature. In addition, survival analysis (including nomogram, Kaplan–Meier method, ROC curve, GSVA, and correlation matrix) was performed to achieve a accurate prognostic model. Finally, the GEO database was applied to check the tumor size-related prognostic features. Results: 1267 genes were identified as DEGs. And we can conclude that DEGs primarily concentrated in membranes, defence response to bacterium , transmembrane signaling receptor activity and olfactory transduction by the results from GO functions and KEGG pathways analysis. Five genes about tumour size-related risk signature including PCGF2, ULK3, MCRIP1,UCKL1, and CCDC18-AS1 were selected to forecast overall survival of LUSC patients. The credibility of prediction model was verified in GSE68825 and GSE68793. The LUSC patients were divided into low-risk score and high-risk score groups according to average value of risk score. Scatter plots show that patients in high-risk score group had shorter survival time. Conclusion: Our study identified five biomarkers that were related to tumor size in the LUSC. The prognostic model can efficiently predict the survival status of patients. In addition, the several biomarkers are conductive to further investigate therapies and forecast prognosis of LUSC.
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