Dataset Information

Prioritization of Candidate Biomarkers for Degenerative Aortic Stenosis through a Systems Biology-Based In-Silico Approach.

ABSTRACT: Degenerative aortic stenosis is the most common valve disease in the elderly and is usually confirmed at an advanced stage when the only treatment is surgery. This work is focused on the study of previously defined biomarkers through systems biology and artificial neuronal networks to understand their potential role within aortic stenosis. The goal was generating a molecular panel of biomarkers to ensure an accurate diagnosis, risk stratification, and follow-up of aortic stenosis patients. We used in silico studies to combine and re-analyze the results of our previous studies and, with information from multiple databases, established a mathematical model. After this, we prioritized two proteins related to endoplasmic reticulum stress, thrombospondin-1 and endoplasmin, which have not been previously validated as markers for aortic stenosis, and analyzed them in a cell model and in plasma from human subjects. Large-scale bioinformatics tools allow us to extract the most significant results after using high throughput analytical techniques. Our results could help to prevent the development of aortic stenosis and open the possibility of a future strategy based on more specific therapies.

SUBMITTER: Corbacho-Alonso N

PROVIDER: S-EPMC9026876 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Prioritization of Candidate Biomarkers for Degenerative Aortic Stenosis through a Systems Biology-Based In-Silico Approach.

Corbacho-Alonso Nerea N Sastre-Oliva Tamara T Corros Cecilia C Tejerina Teresa T Solis Jorge J López-Almodovar Luis F LF Padial Luis R LR Mourino-Alvarez Laura L Barderas Maria G MG

Journal of personalized medicine 20220415 4

Degenerative aortic stenosis is the most common valve disease in the elderly and is usually confirmed at an advanced stage when the only treatment is surgery. This work is focused on the study of previously defined biomarkers through systems biology and artificial neuronal networks to understand their potential role within aortic stenosis. The goal was generating a molecular panel of biomarkers to ensure an accurate diagnosis, risk stratification, and follow-up of aortic stenosis patients. We us ...[more]

PMID: 35455758

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Project description:PurposeAlthough several reports have described the relationship between periodontal disease and cardiovascular disease, information about the association between periodontal disease and the progression of degenerative aortic stenosis (AS) is lacking. Therefore, we performed a retrospective, single-center, pilot study to provide insight into this potential association.MethodsData from 45 consecutive patients (19 men; median age, 83 years) with mild or moderate degenerative aortic stenosis were analyzed for a mean observation period of 3.3±1.9 years. The total amount of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis and titers of serum immunoglobulin G (IgG) against periodontal bacteria and high-sensitivity C-reactive protein (hs-CRP) were evaluated. Aortic valve area (AVA), maximal velocity (Vmax), mean pressure gradient (mean PG), and the Doppler velocity index (DVI) were evaluated. The change in each parameter per year ([ParameterLATEST-ParameterBASELINE]/Follow-up Years) was calculated from the retrospective follow-up echocardiographic data (baseline vs. the most recently collected data [latest]).ResultsNo correlation was found between the concentration of periodontopathic bacteria in the saliva and AS status/progression. The anti-P. gingivalis antibody titer in the serum showed a significant positive correlation with AVA and DVI. Additionally, there was a negative correlation between the anti-P. gingivalis IgG antibody titer and mean PG. The hs-CRP concentration showed positive correlations with Vmax and mean PG. Meanwhile, a negative correlation was observed between the anti-P. gingivalis IgG antibody titer and ΔAVA/year and Δmean PG/year. The hs-CRP concentration showed positive correlations with Vmax and mean PG, and it was significantly higher in patients with rapid aortic stenosis progression (ΔAVA/year <-0.1) than in their counterparts.ConclusionsOur results suggest that periodontopathic bacteria such as A. actinomycetemcomitans and P. gingivalis are not directly related to the status/progression of degenerative AS. However, inflammation and a lower immune response may be associated with disease progression.

Project description:BackgroundColorectal cancer (CRC), is the third most prevalent cancer across the globe, and is often detected at advanced stage. Late diagnosis of CRC, leave the chemotherapy and radiotherapy as the main options for the possible treatment of the disease which are associated with severe side effects. In the present study, we seek to explore CRC gene expression data using a systems biology framework to identify potential biomarkers and therapeutic targets for earlier diagnosis and treatment of the disease.MethodsThe expression data was retrieved from the gene expression omnibus (GEO). Differential gene expression analysis was conducted using R/Bioconductor package. The PPI network was reconstructed by the STRING. Cystoscope and Gephi software packages were used for visualization and centrality analysis of the PPI network. Clustering analysis of the PPI network was carried out using k-mean algorithm. Gene-set enrichment based on Gene Ontology (GO) and KEGG pathway databases was carried out to identify the biological functions and pathways associated with gene groups. Prognostic value of the selected identified hub genes was examined by survival analysis, using GEPIA.ResultsA total of 848 differentially expressed genes were identified. Centrality analysis of the PPI network resulted in identification of 99 hubs genes. Clustering analysis dissected the PPI network into seven interactive modules. While several DEGs and the central genes in each module have already reported to contribute to CRC progression, survival analysis confirmed high expression of central genes, CCNA2, CD44, and ACAN contribute to poor prognosis of CRC patients. In addition, high expression of TUBA8, AMPD3, TRPC1, ARHGAP6, JPH3, DYRK1A and ACTA1 was found to associate with decreased survival rate.ConclusionOur results identified several genes with high centrality in PPI network that contribute to progression of CRC. The fact that several of the identified genes have already been reported to be relevant to diagnosis and treatment of CRC, other highlighted genes with limited literature information may hold potential to be explored in the context of CRC biomarker and drug target discovery.

Dataset Information

Prioritization of Candidate Biomarkers for Degenerative Aortic Stenosis through a Systems Biology-Based In-Silico Approach.

Publications

Prioritization of Candidate Biomarkers for Degenerative Aortic Stenosis through a Systems Biology-Based In-Silico Approach.

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