The extensive use of copper oxide nanoparticles (CuO‐NPs) in various industries and their wide range of applications have led to their accumulation in different ecological niches of the environment. This excess exposure raises the concern about its potential toxic effects on various organisms including humans. However, the hazardous potential of CuO‐NPs in the literature is elusive, and it is essential to study its toxicity in different biological models. Hence, we have conducted single acute dose (2000 mg/kg) and multiple dose subacute (30, 300 and 1000 mg/kg daily for 28 days) oral toxicity studies of CuO‐NPs in female albino Wistar rats following OECD guidelines 420 and 407 respectively. Blood analysis, tissue aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase and acetylcholinesterase, superoxide dismutase, catalase, lipid malondialdehyde and reduced glutathione assays, and histopathology of the tissues were carried out. The higher dose treatments of the acute and subacute study caused significant alterations in biochemical and antioxidant parameters of the liver, kidney and brain tissues of the rat. In addition, histopathological evaluation of these three organs of treated rats showed significantly high abnormalities in their histoarchitecture when compared to control rats. We infer from the results that the toxicity observed in the liver, kidney and brain of treated rats could be due to the increased generation of reactive oxygen species by CuO‐NPs.
Background: Gynecological cancers (GCs), mainly diagnosed in the late stages of the disease, remain the leading cause of global mortality in women. microRNAs (miRNAs) have been explored as diagnostic and prognostic biomarkers of cancer. Evaluating miRNA signatures to develop prognostic models could be useful in predicting high-risk patients with GC. Specifically, the identification of miRNAs associated with different stages of cancer can be beneficial in patients diagnosed with cancer Objective: This study aimed to identify potential miRNA signatures for constructing optimal prognostic models in three major GCs using The Cancer Genome Atlas (TCGA) database. Methods: Stage-specific differentially expressed microRNAs (DEmiRs) were identified and validated in public and in-house expression datasets. Moreover, various bioinformatics investigations were used to identify potential DEmiRs associated with the disease. All DEmiRs were analyzed using three penalized Cox regression models: lasso, adaptive lasso, and elastic net algorithms. The combined outcomes were evaluated using best subset regression (BSR). Prognostic DEmiR models were evaluated using Kaplan–Meier plots to predict risk scores in patients. The biological pathways of the potential DEmiRs were identified using functional enrichment analysis. Results: A total of 65 DEmiRs were identified in the three cancer types; among them, 17 demonstrated dysregulated expression in public datasets of cervical cancer, and the expression profiles of 9 DEmiRs were changed in CCLE-OV cells, whereas those of 10 were changed in CCLE-UCEC cells. Additionally, ten miRNA expression profiles were observed to be the same as DEmiRs in three OV cancer cell lines. Approximately 30 DEmiRs were experimentally validated in particular cancers. Furthermore, 23 DEmiRs were correlated with the overall survival of the patients. The combined analysis of the three penalized Cox models and BSR analysis predicted eight potential DEmiRs. A total of five models based on five DEmiRs (hsa-mir-526b, hsa-mir-508, and hsa-mir-204 in CESC and hsa-mir-137 and hsa-mir-1251 in UESC samples) successfully differentiated high-risk and low-risk patients. Functional enrichment analysis revealed that these DEmiRs play crucial roles in GCs. Conclusion: We report potential DEmiR-based prognostic models to predict the high-risk patients with GC and demonstrate the roles of miRNA signatures in the early- and late-stage of GCs.
Background: At present, all or the majority of published databases report metastasis genes based on the concept of using cancer types or hallmarks of cancer/metastasis. Since tumor metastasis is a dynamic process and involves many cellular and molecular processes, those databases cannot provide information on the sequential relations, as well as cellular and molecular mechanisms among different metastasis stages. Objective: We incorporate the concept of tumor metastasis mechanism to construct a tumor metastasis mechanism-associated gene (TMMG) database based on using the metastasis mechanism concept. Methods: We utilized the text mining tool, BioBERT to mine the titles and abstracts of the papers and identify TMMGs. Results: This tumor metastasis mechanism-associated gene database (TMMGdb) contains a wealth of annotations.To check the reliability of TMMGdb, we compared the proportions of housekeeping genes (HKGs) in TMMGdb, HCMDB, and CMgene, the results showed that around 20% of the TMMGs are HKGs, and the proportions are highly consistent among the three databases. Compared with the HCMDB and CMgene databases, TMMGdb is able to find a more recent (on or after 2017) collection of publications and TMMGs. We provided six case studies to illustrate the uniqueness of the TMMGdb database. Conclusion: TMMGdb is a comprehensive resource for the biomedical community to gain an in-depth understanding of the dynamic process, molecular features, and cellular processes involved in tumor metastasis. TMMGdb provides three interfaces; ‘Browse’, ‘Search’, and ‘Download’, for users to investigate the causal effects among different metastasis stages, the database is freely accessible at http://hmg.asia.edu.tw/TMMGdb.
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