Interactive bioinformatics analysis for the screening of hub genes and molecular docking of phytochemicals present in kitchen spices to inhibit CDK1 in cervical cancer.

Cervical cancer (CC) is the world's fourth most prevalent cancer among women. The mortality rate of cervical cancer increases each year due to a lack of early diagnosis. Our study aims to find potential genes linked to cervical cancer and validate the findings using docking analysis. The microarray datasets (GSE6791, GSE7803, GSE9750, GSE39001, GSE52903, GSE63514, and GSE75132) were downloaded from the GEO (Gene Expression Omnibus) database. A total of 1160 Differentially Expressed Genes (DEGs) were discovered using the R statistical language, including 825 up-regulated and 335 down-regulated genes. STRING, which predicts the potential interaction between genes at the protein level, was used to build the PPI network of these DEGs. Moreover, hub gene expression analysis was carried out by CytoHubba plugin Cytoscape. CDK1 was considered for subsequent molecular docking because of its frequent appearance throughout the analysis. CDK1 was docked with the 399 phytochemicals of Indian kitchen spices. The top three compounds namely, Vicenin 2, 2-O,3-O,4-O,6-O-Tetragalloyl-d-glucopyranose and Pentagalloylglucose, were chosen based on their docking scores and their interactions with the key amino acids present in the ATP binding pocket, like the positive control Dinaciclib. In conclusion, the findings of this study may lead to new insights on CC diagnosis, aetiology, and treatment options. In the future, it may be possible to develop particular diagnostics and therapies for CC by identifying hub genes and studying overexpressed proteins as therapeutic targets.

Identification of crucial hub genes and potential molecular mechanisms in breast cancer by integrated bioinformatics analysis and experimental validation.

Breast cancer (BC) is a malignancy that affects a large number of women around the world. The purpose of the current study was to use bioinformatics analysis to uncover gene signatures during BC and their potential mechanisms. The gene expression profiles (GSE29431, GSE10810, and GSE42568) were retrieved from the Gene Expression Omnibus database, and the differential expressed genes (DEGs) were identified in normal tissues and tumour tissue samples from BC patients. In total, 296 DEGs were identified in BC, including 46 upregulated genes and 250 downregulated genes. GO and KEGG pathway analysis were performed. A PPI network of the DEGs was also constructed. GO analysis results showed that upregulated DEGs were significantly enriched in biological processes (BP), including cell division, mitotic cell cycle, chromosome separation, and cell division. MF analysis showed that upregulated DEGs controlled the microtubule cytoskeleton, the microtubule organising center, the cytoskeleton, and the chromosome-centric region. KEGG analysis revealed the upregulated DEGs mainly regulated p53 signaling, while the downregulated DEGs were enriched in the AMPK signalling pathway and PPAR signalling pathway. Moreover, five hub genes with a high degree of stability were identified, including NUSAP1, MELK, CENPF, TOP2A, and PPARG. Experimental validation showed that all five hub genes had the same expression trend as predicted. The overall survival and expression levels of hub genes were detected by Kaplan-Meier-plotter and the UALCAN database and were further validated using the Human Protein Atlas database. Taken together, the identified key genes enhance our understanding of the molecular pathways that underpin BC pathogenesis. As a result, our novel findings could be used as molecular targets and diagnostic biomarkers in the treatment of BC. This study is based on empirical evidence, making it an appealing read for the global scientific community.