BackgroundCervical cancer, one of the leading causes of female deaths, remains a top cause of mortality in gynecologic oncology and tends to affect younger individuals. However, the pathogenesis of cervical cancer is still far from clear. Given the high incidence and mortality of cervical cancer, uncovering the causes and pathogenesis as well as identifying novel biomarkers are of great significance and are desperately needed.Materials and methodsFirst, raw data were downloaded from the Gene Expression Omnibus database. The Robuse Multi-Array Average algorithm and combat function of the sva package were subsequently applied to preprocess and remove batch effects. Differentially expressed genes (DEGs) analyzed with the limma package were followed by gene ontology and pathway analysis, and a protein–protein interaction (PPI) network based on the STRING website and the Cytoscape software was constructed. Weighted Correlation Network Analysis (WGCNA) was utilized to build the coexpression network. Subsequently, UALCAN websites were employed to conduct survival analysis. Finally, the oncomine database was used to validate the expression of ANLN in other datasets.ResultsGSE29570 and GSE89657, including 49 cervical cancer tissues and 20 normal cervical tissues, were screened as the datasets. Three-hundred-twenty-four DEGs were identified and, among them, 123 were upregulated, while 201 were downregulated. The DEGs PPI network complex, contained 305 nodes and 4,962 edges, and 8 clusters were calculated according to k-core =2. Among them, cluster 1, which had 65 nodes and 1,780 edges, had the highest score in these clusters. In coexpression analysis, there were 86 hubgenes from the Brown modules that were chosen for further analysis. Sixty-one key genes were identified as the intersecting genes of the Brown module of WGCNA and DEGs. In survival analysis, only ANLN was a prognostic factor, and the survival was significantly better in the low-expression ANLN group.ConclusionOur study suggested that ANLN may be a potential tumor oncogene and could serve as a biomarker for predicting the prognosis of cervical cancer patients.
In endometrial carcinoma, the clinical outcome directly correlates with the TNM stage, but the lack of sufficient information prevents accurate prediction. The molecular mechanism underlying the competing endogenous RNA (ceRNA) hypothesis has not been investigated in endometrial cancer. Multi-bioinformatic analyses, including differentially expressed gene analysis, ceRNA network construction, Cox regression analysis, function enrichment analysis, and protein-protein network analysis, were performed on the sequence data acquired from The Cancer Genome Atlas (TCGA) data bank. A ceRNA network comprising 366 mRNAs, 27 microRNAs (miRNAs), and 66 long non-coding RNAs (lncRNAs) was established. Survival analysis performed with the univariate Cox regression analysis revealed nine lncRNAs with prognostic power in endometrial carcinoma. In multivariate Cox regression analysis, a signature comprising LINC00491, LINC00483, ADARB2-AS1, and C8orf49 showed remarkable prognostic power. Risk score and neoplasm status, but not TNM stage, were independent prognostic factors of endometrial carcinoma. A ceRNA network comprising differentially expressed mRNAs, miRNAs, and lncRNAs may reveal the molecular events involved in the progression of endometrial carcinoma. In addition, the signature with prognostic value may discriminate patients with increased risk for poor outcome, which may allow physicians to take accurate decisions.
The genetic contributions to active avoidance learning in rodents have been well established, yet the molecular basis for genetically selected line differences remains poorly understood. To identify candidate genes influencing this active avoidance paradigm, we utilized the bidirectionally selected Syracuse high-and low-avoidance (SHA and SLA) rat lines that markedly differ in their two-way active avoidance behavior. Rats were phenotyped, rested to allow recovery from testing stress and then hippocampi were dissected for gene expression profiling (Affymetrix U34A chips; approximately 7000 known genes), comparing SLA to SHA. Next, a subset of differentially expressed genes was confirmed by real-time PCR (RT-PCR) in hippocampi. Additional studies at the protein level were performed for some genes. Using triplicate arrays on pooled hippocampal samples, differentially expressed genes were identified by MICROARRAY SUITE 5.0 and robust multi-array average analyses. By RT-PCR analysis in hippocampi, eight genes were nominated as potential candidate genes consistent with the differential expression from the microarray data. Four genes, Veli1 (mlin-7B), SLC3a1, Ptpro and Ykt6p, showed higher expression in SHA hippocampi than SLA. Four genes, SLC6A4, Aldh1a4, Id3a and Cd74, showed higher expression in SLA hippocampi than SHA. The active avoidance behavioral difference between lines probably emerges from 'many small things'. These potential candidate genes generate hypotheses for future testing in human association and rodent studies. Differences in levels of a pleiotropic gene like Ptpro and SLC6A4 suggest that small differences over a lifespan may contribute to large behavioral differences. In humans, the genetic contribution to temperament and personality traits is well established with heritabilities ranging from 0.3 to 0.6 (Bouchard & McGue 2003). Attempts to genetically dissect complex traits, psychiatric disorders and personality traits via linkage strategies have been challenging due to genetic heterogeneity, incomplete penetrance, phenotypic misclassification and the limits of sample size. Despite some notable successes, the power of the linkage approach has been debated for complex traits where any individual gene contributes only a small percentage of the overall phenotypic variance (Botstein & Risch 2003). As a result, candidate gene association strategies have been advocated. This approach is statistically more powerful and utilizes functional polymorphisms in genes with high face validity such as monoaminergic neurotransmission-related genes. Specifically, the dopamine D4 receptor and the serotonin transporter (5HTT) have been intensively studied by various groups for association with novelty-seeking and anxiety-related traits with conflicting results, yet a recent meta-analysis demonstrated that the 5HTT gene-linked polymorphism region was associated with anxiety-like traits and avoidance with a small effect size (Cohen d ¼ 0.1) (reviewed in Munafo et al. 2003). However, neurotransmission-related genes repre...
Given the high morbidity and the trend of younger individuals being affected observed in cervical cancer, it is important to identify sensitive and effective biomarkers for predicting the survival outcome of patients. Based on data from 307 cervical cancer cases acquired from The Cancer Genome Atlas portal, 1920 differentially expressed mRNAs, 70 microRNAs(miRNAs), and 493 long non-coding(lncRNAs) were screened by comparing cervical cancer tissues with paracancerous tissues. A competing endogenous (ceRNA) network containing 50 lncRNAs, 16 miRNAs, and 81 mRNAs was constructed. Eighteen RNAs, comprising 13 mRNAs, 2 miRNAs, and 3 lncRNAs, were identified as significant prognostic factors by univariate Cox proportional hazards regression. ETS-related gene and fatty acid synthase signatures were discovered using a multivariate Cox regression model built to identify independent prognostic factors in cervical cancer patients. Receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value for distinguishing the risk level of cervical cancer patients. High-risk patients exhibited a poorer prognosis than low-risk patients did. This study focused on ceRNA networks to provide a novel perspective and insight into cervical cancer and suggested that the identified signature can serve as an independent prognostic biomarker in cervical cancer.
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