Predictive computational obesity risk framework through integration of gene expression profiles and genetic risk score.
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE109597 | GEO | 2018/01/25
REPOSITORIES: GEO
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Project description:Predictive computational obesity risk framework through integration of gene expression profiles and genetic risk score.
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Project description:Reliable non-invasive tools to diagnose at risk metabolic dysfunction-associated steatohepatitis (MASH) are urgently needed to improve management. We developed a risk stratification score incorporating proteomics-derived serum markers with clinical variables to identify high risk MASH patients (NAFLD Activity Score (NAS) >4 and fibrosis score >2). In this three-phase proteomic study of biopsy-proven metabolic dysfunction-associated steatotic fatty liver disease (MASLD), we first developed a multi-protein predictor for discriminating NAS>4 based on SOMAscan proteomics quantifying 1,305 serum proteins from 57 US patients. Four key predictor proteins were verified by ELISA in the expanded US cohort (N=168), and enhanced by adding clinical variables to create the 9-feature MASH Dx Score which predicted MASH and also high risk MASH (F2+). The MASH Dx Score was validated in two independent, external cohorts from Germany (N=139) and Brazil (N=177). The discovery phase identified a 6-protein classifier that achieved an AUC of 0.93 for identifying MASH. Significant elevation of four proteins (THBS2, GDF15, SELE, IGFBP7) was verified by ELISA in the expanded discovery and independently in the two external cohorts. MASH Dx Score incorporated these proteins with established MASH risk factors (age, BMI, ALT, diabetes, hypertension) to achieve good discrimination between MASH and MASLD without MASH (AUC:0.87- discovery; 0.83- pooled external validation cohorts), with similar performance when evaluating high risk MASH F2-4 (vs. MASH F0-1 and MASLD without MASH). The MASH Dx Score offers the first reliable non-invasive approach combining novel, biologically plausible ELISA-based fibrosis markers and clinical parameters to detect high risk MASH in patient cohorts from the US, Brasil and Europe.
2023-12-22 | GSE251855 | GEO
Project description:Mortality risk score in postsurgical patients based on gene expression
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Project description:Platelets are key mediators of atherothrombosis; yet, limited tools exist to identify individuals with a hyperreactive platelet phenotype. Here we derive and validate a Platelet Reactivity ExpreSsion Score (PRESS) integrating platelet aggregation responses and RNA sequencing in multiple cohorts. PRESS performs well in identifying a hyperreactive phenotype in patients with cardiovascular disease (AUC [cross-validation] 0.81, 95% CI 0.68 to 0.94), with similar discrimination in an independent cohort of healthy participants (AUC [validation] 0.77, 95% CI 0.75 to 0.79); accuracy 80%, sensitivity 71%, and specificity 86%. Next, we validate PRESS in multiple cohorts of patients with platelet RNASeq available. Following multivariable adjustment, PRESS was significantly higher in MI (β=1.8, p=0.02), and individuals with a score above the median more frequently develop a future cardiovascular event (adjusted HR 1.90, CI 1.07 to 3.36), p=0.027). Altogether, we provide strong evidence that PRESS is a robust prognostic marker, which could have an essential role in facilitating a personalized approach to cardiovascular risk management.
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Project description:Background: Asthma is common chronic inflammatory disease of the airways with a heterogenous clinical presentation. Individual differences in asthma susceptibility remain poorly understood, although genetics is thought to play a major role. Aim: To build a polygenic risk score (PRS) for asthma and determine whether predictive genetic variants can be epigenomically linked to specific pathophysiological mechanisms. Methods: PRSs were constructed using data from genome-wide association studies and performance was validated using data generated in the Rotterdam Study, a Dutch prospective cohort of 14,926 individuals. Outcomes used were asthma, childhood-onset asthma, adulthood-onset asthma, eosinophilic asthma and exacerbations. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) data from 14 primary cell types, including lung epithelial cells and T lymphocytes was used for epigenomic PRS partitioning. Results: All PRSs successfully predicted risk to develop asthma and related outcomes, with the strongest predictive power (2.42 odds ratios per PRS standard deviation, area under the curve of 0.736) achieved for childhood-onset asthma. PRSs allowed for stratification of the Rotterdam Study cohort into groups at low or high risk to develop asthma. PRS partitioning using genome-wide epigenomic profiles identified 5 clusters of variants within gene regulatory regions linked to specific asthma-relevant cells, genes and biological pathways. Conclusions: PRSs can predict whether individuals in a Dutch cohort developed asthma and asthma-related phenotypes, which is most effective for childhood-onset asthma. Importantly, we show that PRS partitioning based on epigenomics data dissects a genetic risk score into blocks of regulatory variants with differential predictive power, which likely represent distinct genetically driven disease pathways. These findings have potential implications for personalized risk mitigation and treatment strategies.
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Project description:Despite continual efforts to establish pre-operative prognostic model of gastric cancer by using clinical and pathological parameters, a staging system that reliably separates patients with early and advanced gastric cancer into homogeneous groups with respect to prognosis does not exist. With use of microarray and quantitative RT-PCR technologies, we exploited series of experiments in combination with complementary data analyses on tumor specimens from 161 gastric cancer patients. Various statistical analyses were applied to gene expression data to uncover subgroups of gastric cancer, to identify potential biomarkers associated with prognosis, and to construct molecular predictor of risk from identified prognostic biomarkers.Two subgroups of gastric cancer with strong association with prognosis were uncovered. The robustness of prognostic gene expression signature was validated in independent patient cohort with use of support vector machines prediction model. For easy translation of our finding to clinics, we develop scoring system based on expression of six genes that can predict the likelihood of recurrence after curative resection of tumors. In multivariate analysis, our novel risk score was an independent predictor of recurrence (P=0.004) in cohort of 96 patients, and its robustness was validated in two other independent cohorts. We identified novel prognostic subgroups of gastric cancer that are distinctive in gene expression patterns. Six-gene signature and risk score derived from them has been validated for predicting the likelihood of survival at diagnosis. 65 primary gastric adenocarcinoma, 6 GIST and 19 surrounding normal fresh frozen tissues were used for microarray. All the tissues were obtained after curative resection after pathologic confirm at Yonsei cancer center(Seoul, Korea). Microarray experiment and data analysis were done at Dept. of systems biology, MDACC DNA microarray (Illumina human V3)
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Project description:Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign samples from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.
2023-11-30 | PXD041005 | Pride