Myocardial infarction biomarker discovery with integrated gene expression, pathways and biological networks analysis

Abstract Background Myocardial infarction (MI) is the most prevalent coronary atherosclerotic heart disease caused by the complex molecular interactions between multiple genes and environment. Molecular exploration of gene expression changes in MI patients is very crucial not just to understand the molecular basis of disease development but also to identify potential therapeutic targets. Therefore, we aim to identify potential biomarkers for the disease development mechanisms and for prognosis of MI using extensive integrated biological network analysis. Methodology Gene expression datasets (GSE66360) generated from 51 healthy controls and 49 endothelial cell samples from patients experiencing acute MI were used to analyze the differentially expressed genes (DEG), protein-protein interactions (PPI), gene network-clusters to annotate the candidate pathways relevant to MI pathogenesis. Results Bioinformatic analysis revealed 810 DEGs, between control and MI samples, with 574 up- and 236 down- regulated genes. Their functional annotations with Gene Ontology (GO) has captured several MI targeting biological processes like immune response, inflammation and platelets degranulation. Most significantly DEGs enriched KEGG pathways are related to the following functions: Cytokine-cytokine receptor interaction, TNF and NFkB signaling. By constructing the PPI network using STRING and CytoHubba, seventeen hub and bottleneck genes were found, whose involvement in MI was further confirmed by DisGeNET data. Search in the Open Target Platform reveal unique bottleneck genes as potential target for MI. Conclusion Our integrative bioinformatics analysis of large-scale gene expression data has identified several potential genetic biomarkers associated with early stage MI providing a new insight into molecular mechanism underlying the disease.

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Weighted miRNA co-expression networks analysis identifies circulating miRNA predicting overall survival in hepatocellular carcinoma patients

Scientific Reports ◽

10.1038/s41598-020-75945-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Devis Pascut ◽

Muhammad Yogi Pratama ◽

Francesca Gilardi ◽

Mauro Giuffrè ◽

Lory Saveria Crocè ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Biological Networks ◽

Biomarker Discovery ◽

Patient Survival ◽

Gene Array ◽

Significant Negative Correlation ◽

Circulating Mirnas ◽

Serum Samples ◽

Promising Candidate ◽

Networks Analysis

Abstract The weighted gene co-expression network analysis (WGCNA) has been used to explore gene expression datasets by constructing biological networks based on the likelihood expression profile among genes. In recent years, WGCNA found application in biomarker discovery studies, including miRNA. Serum samples from 20 patients with hepatocellular carcinoma (HCC) were profiled through miRNA 3.0 gene array and miRNAs biomarker candidates were identified through WGCNA. Results were validated by qRT-PCR in 102 HCC serum samples collected at diagnosis. WGCNA identified 16 miRNA modules, nine of them were significantly associated with the clinical characteristics of the patient. The Red module had a significant negative correlation with patients Survival (− 0.59, p = 0.007) and albumin (− 0.52, p = 0.02), and a positive correlation with PCR (0.61, p = 0.004) and alpha-fetoprotein (0.51, p = 0.02). In the red module, 16 circulating miRNAs were significantly associated with patient survival. MiR-3185 and miR-4507 were identified as predictors of patient survival after the validation phase. At diagnosis, high expression of circulating miR-3185 and miR-4507 identifies patients with longer survival (HR 2.02, 95% CI 1.10–3.73, p = 0.0086, and HR of 1.75, 95% CI 1.02–3.02, p = 0.037, respectively). Thought a WGCNA we identified miR-3185 and miR-4507 as promising candidate biomarkers predicting a longer survival in HCC patients.

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Faculty Opinions recommendation of Research resource: interactome of human embryo implantation: identification of gene expression pathways, regulation, and integrated regulatory networks.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.13686956.15102056 ◽

2012 ◽

Author(s):

Lois Salamonsen

Keyword(s):

Gene Expression ◽

Human Embryo ◽

Regulatory Networks ◽

Embryo Implantation ◽

Expression Pathways

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The combined detection of Amphiregulin, Cyclin A1 and DDX20/Gemin3 expression predicts aggressive forms of oral squamous cell carcinoma

British Journal of Cancer ◽

10.1038/s41416-021-01491-x ◽

2021 ◽

Author(s):

Ekaterina Bourova-Flin ◽

Samira Derakhshan ◽

Afsaneh Goudarzi ◽

Tao Wang ◽

Anne-Laure Vitte ◽

...

Keyword(s):

Gene Expression ◽

Squamous Cell ◽

Large Scale ◽

Biomarker Discovery ◽

Gene Expression Signature ◽

Predictive Biomarkers ◽

Specific Gene ◽

Specific Expression ◽

Intrinsic Nature ◽

Independent Cohort

Abstract Background Large-scale genetic and epigenetic deregulations enable cancer cells to ectopically activate tissue-specific expression programmes. A specifically designed strategy was applied to oral squamous cell carcinomas (OSCC) in order to detect ectopic gene activations and develop a prognostic stratification test. Methods A dedicated original prognosis biomarker discovery approach was implemented using genome-wide transcriptomic data of OSCC, including training and validation cohorts. Abnormal expressions of silent genes were systematically detected, correlated with survival probabilities and evaluated as predictive biomarkers. The resulting stratification test was confirmed in an independent cohort using immunohistochemistry. Results A specific gene expression signature, including a combination of three genes, AREG, CCNA1 and DDX20, was found associated with high-risk OSCC in univariate and multivariate analyses. It was translated into an immunohistochemistry-based test, which successfully stratified patients of our own independent cohort. Discussion The exploration of the whole gene expression profile characterising aggressive OSCC tumours highlights their enhanced proliferative and poorly differentiated intrinsic nature. Experimental targeting of CCNA1 in OSCC cells is associated with a shift of transcriptomic signature towards the less aggressive form of OSCC, suggesting that CCNA1 could be a good target for therapeutic approaches.

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274 Tumoral and peripheral immunophenotype of refractory vs relapse to PD-(L)1 blockade in patients with advanced non-small cell lung cancer

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0274 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A299-A299

Author(s):

Maria Ascierto ◽

Matthew Hellmann ◽

Nathan Standifer ◽

Song Wu ◽

Han Si ◽

...

Keyword(s):

Gene Expression ◽

Informed Consent ◽

Checkpoint Inhibitors ◽

Patient Response ◽

Calcium Dependent ◽

Immune Exhaustion ◽

Previously Treated ◽

Expression Pathways ◽

Relapsed Disease ◽

Written Informed Consent

BackgroundDespite the encouraging successes of immune checkpoint inhibitors, many patients do not benefit and are either refractory or relapse. The mechanisms of refractory or relapsed disease following PD-(L)1 blockade are largely unknown. To identify characteristics associated with refractory or relapsed disease we explored the immune and genomic landscape of samples derived from NSCLC patients who previously received PD-(L)1 blockade and had blood and fresh tumor biopsies collected at the time of progression.MethodsPatient response categories were defined prospectively; ‘refractory’ defined as progression within 16 weeks of initiating PD-(L)1 and ‘relapse’ defined as initial clinical benefit (CR, PR, SD) followed by progression. RNAseq (n=52) and PD-L1 IHC (n=22) were performed on tumor tissue. Immune profiling of whole blood was assessed using flow cytometry or Biomark HD (Fluidigm) gene expression panel (n=54 and n=62, respectively). Differential gene expression was defined as unadjusted p<0.05 and fold-difference >1.5. Pathways analysis was conducted by David tool. Patient samples were collected during screening for clinical trial of second line immunotherapy. Written informed consent was obtained from the patients for publication of this abstract.ResultsIn patients with NSCLC previously treated with PD-(L)1 blockade, tumors of relapsed patients were characterized by increased expression of genes associated with interferon signaling (e.g. CXCL9, SPIC, IFNg), immune suppression (e.g. ARG1, TGFB), immune exhaustion (e.g. ADORA2A), and increased PD-L1 expression (by gene expression and IHC). Refractory disease was associated with increased cadherin signaling and calcium-dependent-cell-adhesion gene expression pathways. In the periphery, reduced quantities of B cells and activated (HLA-DR+ or CD38+) or proliferating (Ki67+) CD8+ T cells were observed in refractory patients.ConclusionsThe tumor and peripheral compartments of patients with NSCLC previously treated with PD-(L)1 blockade differ based on prior response. Relapsed patients tend to have signals of sturdy immune activation and chronic inflammation thus ultimately leading to immune exhaustion. These results may help inform rational therapeutic strategies to overcome resistance to PD-(L)1 blockade in NSCLC.Trial RegistrationNCT02000947Ethics ApprovalResearch on human samples here analyzed have been performed in accordance with the Declaration of Helsinki.ConsentWritten informed consent was obtained from the patient for publication of this abstract.

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Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods

Scientific Reports ◽

10.1038/s41598-021-92725-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Joe W. Chen ◽

Joseph Dhahbi

Keyword(s):

Gene Expression ◽

Feature Selection ◽

Expression Analysis ◽

Gene Expression Analysis ◽

Biomarker Discovery ◽

Lung Squamous Cell Carcinoma ◽

Cancer Classification ◽

Selection Methods ◽

Biomarker Identification ◽

Overlapping Method

AbstractLung cancer is one of the deadliest cancers in the world. Two of the most common subtypes, lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), have drastically different biological signatures, yet they are often treated similarly and classified together as non-small cell lung cancer (NSCLC). LUAD and LUSC biomarkers are scarce, and their distinct biological mechanisms have yet to be elucidated. To detect biologically relevant markers, many studies have attempted to improve traditional machine learning algorithms or develop novel algorithms for biomarker discovery. However, few have used overlapping machine learning or feature selection methods for cancer classification, biomarker identification, or gene expression analysis. This study proposes to use overlapping traditional feature selection or feature reduction techniques for cancer classification and biomarker discovery. The genes selected by the overlapping method were then verified using random forest. The classification statistics of the overlapping method were compared to those of the traditional feature selection methods. The identified biomarkers were validated in an external dataset using AUC and ROC analysis. Gene expression analysis was then performed to further investigate biological differences between LUAD and LUSC. Overall, our method achieved classification results comparable to, if not better than, the traditional algorithms. It also identified multiple known biomarkers, and five potentially novel biomarkers with high discriminating values between LUAD and LUSC. Many of the biomarkers also exhibit significant prognostic potential, particularly in LUAD. Our study also unraveled distinct biological pathways between LUAD and LUSC.

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