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Ovarian cancer (OV) is the most lethal gynecologic malignancy. One major reason of the high mortality of the disease is due to platinum-based chemotherapy resistance. Increasing evidence reveal the important biological functions and clinical significance of zinc finger proteins (ZNFs) in OV. In the present study, the relationship between the zinc finger protein 76 (ZNF76) and clinical outcome and platinum resistance in patients with OV was explored. We further analyzed ZNF76 expression via multiple gene expression databases and identified its functional networks using cBioPortal. RT-qPCR and IHC assay shown that the ZNF76 mRNA and protein expression were significantly lower in OV tumor than that in normal ovary tissues. A strong relationship between ZNF76 expression and platinum resistance was determined in patients with OV. The low expression of ZNF76 was associated with worse survival in OV. Multivariable analysis showed that the low expression of ZNF76 was an independent factor predicting poor outcome in OV. The prognosis value of ZNF76 in pan-cancer was validated from multiple cohorts using the PrognoScan database and GEPIA 2. A gene-clinical nomogram was constructed by multivariate cox regression analysis, combined with clinical characterization and ZNF76 expression in TCGA. Functional network analysis suggested that ZNF76 was involved in several biology progressions which associated with OV. Ten hub genes (CDC5L, DHX16, SNRPC, LSM2, CUL7, PFDN6, VARS, HSD17B8, PPIL1, and RGL2) were identified as positively associated with the expression of ZNF76 in OV. In conclusion, ZNF76 may serve as a promising prognostic-related biomarker and predict the response to platinum in OV patients. [ABSTRACT FROM AUTHOR]

Ovarian cancer (OV) is the main cause of deaths worldwide in female reproductive system malignancies. Enhancer RNAs (eRNAs) are derived from the transcription of enhancers and has attracted increasing attention in cancers recently. However, the biological functions and clinical significance of eRNAs in OV have not been well described presently. We used an integrated data analysis to identify prognostic-related eRNAs in OV. Tissue-specific enhancer-derived RNAs and their regulating genes were considered as putative eRNA-target pairs using the computational pipeline PreSTIGE. Gene expression profiles and clinical data of OV and 32 other cancer types were obtained from the UCSC Xena platform. Altogether, 71 eRNAs candidates showed significant correlation with overall survival (OS) of OV samples (Kaplan-Meier log-rank test, P<0.05). Among which, 23 were determined to be correlated with their potential target genes (Spearman's r > 0.3, P<0.001). It was found that among the 23 prognostic-related eRNAs, the expression of forkhead box P4 antisense RNA 1 (FOXP4-AS1) had the highest positive correlation with its predicted target gene FOXP4 (Spearman's r = 0.61). Moreover, the results were further validated by RT-qPCR analysis in an independent OV cohort. Our results suggested the eRNA FOXP4-AS1 expression index may be a favorable independent prognostic biomarker candidate in OV. [ABSTRACT FROM AUTHOR]

RNA-seq analysis was used to identify differentially expressed genes (DEGs) at the genetic level in the longissimus dorsi muscle from two pigs to investigate the genetic mechanisms underlying the difference in meat quality between Debao pigs and Landrace pigs. Then, these DEGs underwent functional annotation, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein interaction (PPI) analyses. Finally, the expression levels of specific DEGs were assessed using qRT-PCR. The reference genome showed gene dosage detection of all samples which showed that the total reference genome comprised 22342 coding genes, including 14743 known and 190 unknown genes. For detection of the Debao pig genome, we obtained 14168 genes, including 13994 known and 174 unknown genes. For detection of the Landrace pig genome, we obtained 14404 genes, including 14223 known and 181 unknown genes. GO analysis and KEGG signaling pathway analysis show that DEGs are significantly related to metabolic regulation, amino acid metabolism, muscular tissue, muscle structure development etc. We identified key genes in these processes, such as FOS, EGR2, and IL6, by PPI network analysis. qRT-PCR confirmed the differential expression of six selected DEGs in both pig breeds. In conclusion, the present study revealed key genes and related signaling pathways that influence the difference in pork quality between these breeds and could provide a theoretical basis for improving pork quality in future genetic thremmatology. [ABSTRACT FROM AUTHOR]