BACKGROUND: To identify key genes associated with cisplatin resistance in ovarian cancer, a comprehensive analysis was conducted on three datasets from the GEO database and through experimental validation.
METHODS: Gene expression profiles were retrieved from the GEO database. DEGs were identified by comparing gene expression profiles between cisplatin-sensitive and resistant ovarian cancer cell lines. The identified genes were further subjected to GO, KEGG, and PPI network analysis. Potential inhibitors of key genes were identified through methods such as LibDock nuclear molecular docking. In vitro assays and RT-qPCR were performed to assess the expression levels of key genes in ovarian cancer cell lines. The sensitivity of cells to chemotherapy and proliferation of key gene knockout cells were evaluated through CCK8 and Clonogenic assays.
RESULTS: Results showed that 12 genes influenced the chemosensitivity of the ovarian cancer cell line SKOV3, and 9 genes were associated with the prognosis and survival outcomes of ovarian cancer patients. RT-qPCR results revealed NDRG1, CYBRD1, MT2A, CNIH3, DPYSL3, and CARMIL1 were upregulated, whereas ERBB4, ANK3, B2M, LRRTM4, EYA4, and SLIT2 were downregulated in cisplatin-resistant cell lines. NDRG1, CYBRD1, and DPYSL3 knock-down significantly inhibited the proliferation of cisplatin-resistant cell line SKOV3. Finally, photofrin, a small-molecule compound targeting CYBRD1, was identified.
CONCLUSIONS: This study reveals changes in the expression level of some genes associated with cisplatin-resistant ovarian cancer. In addition, a new small molecule compound was identified for the treatment of cisplatin-resistant ovarian cancer.

BACKGROUND: Litter size is an important index of mammalian prolificacy and is determined by the ovulation rate. The ovary is a crucial organ for mammalian reproduction and is associated with follicular development, maturation and ovulation. However, prolificacy is influenced by multiple factors, and its molecular regulation in the follicular phase remains unclear.
METHODS: Ten female goats with no significant differences in age and weight were randomly selected and divided into either the high-yielding group (n = 5, HF) or the low-yielding group (n = 5, LF). Ovarian tissues were collected from goats in the follicular phase and used to construct mRNA and miRNA sequencing libraries to analyze transcriptomic variation between high- and low-yield Yunshang black goats. Furthermore, integrated analysis of the differentially expressed (DE) miRNA-mRNA pairs was performed based on their correlation. The STRING database was used to construct a PPI network of the DEGs. RT-qPCR was used to validate the results of the predicted miRNA-mRNA pairs. Luciferase analysis and CCK-8 assay were used to detect the function of the miRNA-mRNA pairs and the proliferation of goat granulosa cells (GCs).
RESULTS: A total of 43,779 known transcripts, 23,067 novel transcripts, 424 known miRNAs and 656 novel miRNAs were identified by RNA-seq in the ovaries from both groups. Through correlation analysis of the miRNA and mRNA expression profiles, 263 negatively correlated miRNA-mRNA pairs were identified in the LF vs. HF comparison. Annotation analysis of the DE miRNA-mRNA pairs identified targets related to biological processes such as \"estrogen receptor binding (GO:0030331)\", \"oogenesis (GO:0048477)\", \"ovulation cycle process (GO:0022602)\" and \"ovarian follicle development (GO:0001541)\". Subsequently, five KEGG pathways (oocyte meiosis, progesterone-mediated oocyte maturation, GnRH signaling pathway, Notch signaling pathway and TGF-β signaling pathway) were identified in the interaction network related to follicular development, and a PPI network was also constructed. In the network, we found that CDK12, FAM91A1, PGS1, SERTM1, SPAG5, SYNE1, TMEM14A, WNT4, and CAMK2G were the key nodes, all of which were targets of the DE miRNAs. The PPI analysis showed that there was a clear interaction among the CAMK2G, SERTM1, TMEM14A, CDK12, SYNE1 and WNT4 genes. In addition, dual luciferase reporter and CCK-8 assays confirmed that miR-1271-3p suppressed the proliferation of GCs by inhibiting the expression of TXLNA.
CONCLUSIONS: These results increase the understanding of the molecular mechanisms underlying goat prolificacy. These results also provide a basis for studying interactions between genes and miRNAs, as well as the functions of the pathways in ovarian tissues involved in goat prolificacy in the follicular phase.

BACKGROUND: Ovarian cancer (OC) is a gynecological malignancy with the highest mortality rate. Cisplatin (DDP) based chemotherapy is a standard strategy for ovarian cancer. Despite good response rates for initial chemotherapy, almost 80% of the patients treated with DDP based chemotherapy will experience recurrence due to drug-resistant, which will ultimately result in fatality. The aim of the present study was to examine the pathogenesis and potential molecular markers of cisplatin-resistant OC by studying the differential expression of mRNAs and miRNAs between cisplatin resistant OC cell lines and normal cell lines.
METHODS: Two mRNA datasets (GSE58470 and GSE45553) and two miRNA sequence datasets (GSE58469 and GSE148251) were downloaded from the Gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were screened by the NetworkAnalyst. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. The protein-protein interaction network was constructed using STRING and Cytoscape software to identify the molecular mechanisms of key signaling pathways and cellular activities. FunRich and MiRNATip databases were used to identify the target genes of the DEMs.
RESULTS: A total of 380 DEGs, and 5 DEMs were identified. Protein-protein interaction (PPI) network of DEGs containing 379 nodes and 1049 edges was constructed, and 4 key modules and 24 hub genes related to cisplatin-resistant OC were screened. Two hundred ninety-nine target genes of the 5 DEMs were found out. Subsequently, one of these 299 target genes (UBB) belonging to the hub genes of GSE58470 and GSE45553 was identified by MCODE and CytoHubba,which was regulated by one miRNA (mir-454).
CONCLUSIONS: One miRNA-mRNA regulatory pairs (mir-454-UBB) was established. Taken together, our study provided evidence concerning the alteration genes involved in cisplatin-resistant OC, which will help to unravel the mechanisms underlying drug resistant.

Parkinson\'s disease (PD) is the second most common neurodegenerative disease, and it is a multifactorial disease with no definite diagnostic index. The aim of this study is to construct a molecular network to find molecules that play important roles in the progression of PD with the goal of using them diagnostically and for early intervention.
We downloaded two gene expression profiles (GSE54536 and GSE100054) from the Expression Omnibus (GEO) database to analyze possible markers. The Genes were analyzed with GEO2R. There were 1790 and 967 differentially expressed genes (DEGs) in GSE54536 and GSE100054 respectively. A total of 125 genes co-exist in the DEGs of the two data sets. KEGG pathway analysis showed that 125 DEGs were enriched in Aldosterone synthesis and secretion, Gap junctions, Platelet activation, Rap1 signaling pathway, and Estrogen signaling pathway. There were 20 hub genes among 125 DEGs analyzed by PPI that involved in Platelet activation, Inflammatory response, Innate immune response, B cell receptor signaling, Stimulatory C-type lectin receptor signaling, Lipopolysaccharide response, Leukocyte migration, and Regulation of cell proliferation. Additionally, 42 differences in miRNAs were found in GSE100054. We constructed a miRNA-mRNA regulatory network depicting interactions between the predicted genes and the 125 DEGs. 34 miRNA-mRNA pairs were obtained. We found GNAQ and TMTC2 were the most important mRNAs in the network analyzed by Cytoscape APP centiscape, and their degrees in centiscape2.2 were all 10. has-miR-142 was the most important miRNA (the highest degree is 4 in centiscape2.2), which forms miRNA-mRNA pairs with GNAQ, TMTC2, BEND2, and KYNU.
This study provides data of potential biomarkers and therapeutic targets for PD diagnosis and treatment. Among them, hsa-miR-142 is a critical miRNA in the PD network, and may be involved in PD progression by regulating GNAQ, TMTC2, BEND2, and KYNU.