Identification of Molecular Biomarkers and Key Pathways for Esophageal Carcinoma (EsC): A Bioinformatics Approach

Advancing gastrointestinal cancer diagnostics: a systematic review and meta-analysis of circulating microRNA-1246 as a non-invasive biomarker

BACKGROUND

Despite numerous reports on the alterations of microRNA-1246 (miR-1246) expression level in digestive system cancers, its role in gastrointestinal cancers (GICs) remains unclear. This meta-analysis aimed to assess the diagnostic potential of circulating miR-1246 in GICs.

METHODS

Meta-disc version 1.4 and Comprehensive Meta-Analysis (CMA) version 3.7 software were used to calculate pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), area under the curve (AUC), Q*index and summary receiver-operating characteristic (SROC). Subgroup analyses were conducted for cancer type, sample type and geographical region. Publication bias was assessed using Begg's and Egger's tests.

RESULTS

A total of 14 articles involving 18 studies and 1526 participants (972 cases and 554 controls) were included. The diagnostic accuracy of miRNA-1246 in GICs was as follows: pooled sensitivity: 0.81 (95% CI: 0.79 - 0.83), specificity: 0.74 (95% CI: 0.71 - 0.77), PLR: 3.315 (95% CI: 2.33 - 4.72), NLR: 0.221 (95% CI: 0.153 - 0.319), DOR: 16.87 (95% CI: 9.45 - 30.09), AUC: 0.891, and Q*-index: 0.807. No publication bias was found based on Begg's (p = 0.172) and Egger's (p = 0.113) tests.

CONCLUSION

Circulating miR-1246 shows promise as a non-invasive biomarker for early detection of GICs.

Exploring gene regulatory interaction networks and predicting therapeutic molecules for hypopharyngeal cancer and EGFR-mutated lung adenocarcinoma

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases.

Retracted: Identification of Molecular Biomarkers and Key Pathways for Esophageal Carcinoma (EsC): A Bioinformatics Approach

[This retracts the article DOI: 10.1155/2022/5908402.].

Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities

Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics

Objective

To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment.

Methods

Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool.

Results

CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate.

Conclusion

CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.