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Microarray-based data mining reveals key genes and potential therapeutic drugs for Cadmium-induced prostate cell malignant transformation
داده کاوی مبتنی بر ریزآرایه ژنهای کلیدی و داروهای درمانی بالقوه را برای تحول بدخیم سلولهای پروستات ناشی از کادمیوم-2019 Increasing evidence showed that Cadmium (Cd) can accumulate in the body and damage cells, resulting in
cancerigenesis of the prostate with complex mechanisms. In the present study, we aimed to explore the possible
key genes, pathways and therapeutic drugs using bioinformatics methods. Microarray-based data were retrieved
and analyzed to screen differentially expressed genes (DEGs) between Cd-treated prostate cells and controls.
Then, functions of the DEGs were annotated and hub genes were screened. Next, key genes were selected from
the hub genes via validation in a prostate cancer cohort from The Cancer Genome Atlas (TCGA). Afterward,
potential drugs were further predicted. Consequently, a gene expression profile, GSE9951, was retrieved. Then,
361 up-regulated and 30 down-regulated DEGs were screened out, which were enriched in various pathways.
Among the DEGs, seven hub genes (HSPA5, HSP90AB1, RHOA, HSPD1, MAD2L1, SKP2, and CCT2) were dysregulated
in prostate cancer compared to normal controls, and two of them (HSPD1 and CCT2) might influence
the prostate cancer prognosis. Lastly, ionomycin was predicted to be a potential agent reversing Cd-induced
prostate cell malignant transformation. In summary, the present study provided novel evidence regarding the
mechanisms of Cd-induced prostate cell malignant transformation, and identified ionomycin as a potential small
molecule against Cd toxicity. Keywords: Cadmium | Differentially expressed genes | Prostate carcinoma | TCGA | Bioinformatics |
مقاله انگلیسی |
2 |
Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
پیش بینی مراحل گلیوما بر اساس الگوریتم یادگیری ماشین همراه با شبکه های تعامل پروتئین-پروتئین-2019 Background: Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study
the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was
designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined
with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using
the protein-protein interaction (PPI) networks.
Results: As a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes
between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas
stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to
build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the
prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected
genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs
involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence
of gliomas or, at the very least, that they are useful for tumor researchers.
Conclusion: Machine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve
our understanding of the biological functions involved in glioma growth. Keywords: DEGs | Machine learning | PPI networks | GO | KEGG | SVM | ANN | Random forest | Couple naïve Bayes |
مقاله انگلیسی |