Cancer is a fatal malignancy and its increasing worldwide prevalence demands the discovery of more sensitive and reliable molecular biomarkers. To investigate the GINS1 expression level and its prognostic value in distinct human cancers using a series of multi-layered in silico approach may help to establish it as a potential shared diagnostic and prognostic biomarker of different cancer subtypes. The GINS1 mRNA, protein expression, and promoter methylation were analyzed using UALCAN and Human Protein Atlas (HPA), while mRNA expression was further validated via GENT2. The potential prognostic values of GINS1 were evaluated through KM plotter. Then, cBioPortal was utilized to examine the GINS1-related genetic mutations and copy number variations (CNVs), while pathway enrichment analysis was performed using DAVID. Moreover, a correlational analysis between GINS1 expression and CD8+ T immune cells and a the construction of gene-drug interaction network was performed using TIMER, CDT, and Cytoscape. The GINS1 was found down-regulated in a single subtypes of human cancer while commonly up-regulated in 23 different other subtypes. The up-regulation of GINS1 was significantly correlated with the poor overall survival (OS) of Liver Hepatocellular Carcinoma (LIHC), Lung Adenocarcinoma (LUAD), and Kidney renal clear cell carcinoma (KIRC). The GINS1 was also found up-regulated in LIHC, LUAD, and KIRC patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of GINS1 in two diverse pathways, while few interesting correlations were also documented between GINS1 expression and its promoter methylation level, CD8+ T immune cells level, and CNVs. Moreover, we also predicted few drugs that could be used in the treatment of LIHC, LUAD, and KIRC by regulating the GINS1 expression. The expression profiling of GINS1 in the current study has suggested it a novel shared diagnostic and prognostic biomarker of LIHC, LUAD, and KIRC.
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Introduction The heterogeneity-specific nature of the available colorectal cancer (CRC) biomarkers is significantly contributing to the cancer-associated high mortality rate worldwide. Hence, this study was initiated to investigate a system of novel CRC biomarkers that could commonly be employed to the CRC patients and helpful to overcome the heterogenetic-specific barrier. Methods Initially, CRC-related hub genes were extracted through PubMed based literature mining. A protein-protein interaction (PPI) network of the extracted hub genes was constructed and analyzed to identify few more closely CRC-related hub genes (real hub genes). Later, a comprehensive bioinformatics approach was applied to uncover the diagnostic and prognostic role of the identified real hub genes in CRC patients of various clinicopathological features. Results Out of 210 collected hub genes, in total 6 genes (CXCL12, CXCL8, AGT, GNB1, GNG4, and CXCL1) were identified as the real hub genes. We further revealed that all the six real hub genes were significantly dysregulated in colon adenocarcinoma (COAD) patients of various clinicopathological features including different races, cancer stages, genders, age groups, and body weights. Additionally, the dysregulation of real hub genes has shown different abnormal correlations with many other parameters including promoter methylation, overall survival (OS), genetic alterations and copy number variations (CNVs), and CD8+T immune cells level. Finally, we identified a potential miRNA and various chemotherapeutic drugs via miRNA, and real hub genes drug interaction network that could be used in the treatment of CRC by regulating the expression of real hub genes. Conclusion In conclusion, we have identified six real hub genes as potential biomarkers of CRC patients that could help to overcome the heterogenetic-specific barrier across different clinicopathological features.
During their injecting career, injecting drug users (IDUs) are exposed to some infections, like hepatitis C virus (HCV) infection and human immunodeficiency virus (HIV) infection, due to their injecting behavioral risk factors, such as sharing syringes or other paraphernalia containing infected blood, or sexual behavior risk factors. If we consider that these IDUs might belong to a social network of people where these behavioral risk factors are spread, then HCV and HIV infections might be associated at both the individual and the population level. In this paper, we study the association between HCV and HIV infection at the population level using aggregate data. Our aim is to define a hierarchy of structured models with which the association between HCV and HIV infection at population level and the time trend of prevalence can be investigated. The data analyzed in the paper are “diagnostic testing data,” which consist of repeated cross-sectional prevalence measurements from 1998 to 2006 for HCV and HIV infection, obtained from a sample of 515 drug treatment centers spread among the 20 regions in Italy, where subjects went for a serum diagnostic test. Since we do not have any individual data, it is not possible to relate these prevalence data to socio-demographic or behavioral risk data. Each region defines a cluster with repeated prevalence data for HCV and HIV infection over time. Several modeling approaches, such as generalized linear mixed models (GLMMs) and hierarchical Bayesian models are applied to the data. First, we test different covariance structures for the region-specific random effects in the GLMM context; second, a hierarchical Bayesian model is used to refit the best GLMM in order to obtain the posterior distribution for the parameters of primary interest. We found that the correlation at population level between HCV and HIV is approximately 0.68 and the prevalence of the two infections generally decreased over the years, compared to the situation in 1998.
Background: The association between human papillomavirus (HPV) and human breast cancer (BC) has already been thoroughly studied worldwide with contradictory findings. Although the researchers have tried to minimize the conflict using statistical meta-analysis, because of its shortcomings, there is still a need to evaluate the correlation between HPV and BC using any additional method. Objectives: This study was launched to investigate the correlation between HPV and BC through the application of Bradford Hill criteria postulates. Methods: Population-wide studies associating HPV with BC were searched using the PubMed database. Then, the information of HPV burden in BC, normal/benign samples was analyzed, and ultimately Bradford Hill criteria postulates were applied on the collected evidence to explore the relationship between HPV and BC. In addition, to make the outcomes more authentic, we also reviewed the methodologies of previous studies to address the propensity of false results. Results: After a careful evaluation of the obtained data against major Bradford Hill criteria postulates, it was noted that all these postulates including strength, consistency, biological gradient, temporality, plausibility, experiment, specificity, and analogy were not fulfilled. Conclusion: The results of the present study have failed to establish a casual association between HPV and BC rather suggested HPV as a cause-effective agent or at least a co-participant in the pathogenesis of BC. The weakness of association especially the low level of consistency across studies, and the lack of specificity of effect, there is a need for more experiments concerning Bradford Hill criteria postulates.
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