How to Build CHD@ZJU

CHD related Articles were retrieved from Pubmed, by entering keywords "coronary heart disease" and constrict the publish date from 2000/1/1 to now (2013/1/23). As a result, totally 115898 articles were found and their abstracts were downloaded for text mining. Since some articles didn't contain abstracts, only 88396 abstracts remained.
The text-mining process to get CHD related genes could be divided in to 5 following steps:

  • 1) Extracting all keywords from abstracts and ignoring those keywords start with numbers. 101402 keywords were extracted.

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  • 2) Input these keywords into Gene library in ArrayTrack and find possible related genes. 4674 genes were then found.

  • 3) Put these 4674 genes again into pubmed abstracts to find related aticles. Only genes which offical name or there keyword description (such as prolactin for gene PRL) could be found in the abstract would be remained. As a result, 1247 genes were remained.

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  • 4) Manually examined on the 1247 genes to validate it was acutally related to CHD. Some genes would be filtered if it represents other meanings (such as gene CAD, Entrez ID:790, carbamoyl-phosphate synthetase 2, is mostly meant coronary arterial disease in articles). 681 genes were then validated with at least one reference.

  • 5) All genes was compared with 1078 CHD genes in RGD database, and 370 genes were overlapped. These 370 genes were labels as "RGD_Supported" and the other 293 genes were labels as "REFERED". All 663 genes had supported references in CHD@ZJU which were examined by step 4.

    • How To contact Us

    Collaboration Information: Prof. Xiaohui Fan (fanxh@zju.edu.cn)

    Website using assistance : Leihong Wu (11019004@zju.edu.cn)




    Cystatin C and estimated glomerular filtration rate as predictors for adverse outcome in patients with ST-elevation and non-ST-elevation acute coronary syndromes: results from the Platelet Inhibition and Patient Outcomes study.
  • Author:"Akerblom, Axel;Wallentin, Lars;Siegbahn, Agneta;Becker, Richard C;Budaj, Andrzej;Buck, Kristen;Giannitsis, Evangelos;Horrow, Jay;Husted, Steen;Katus, Hugo A;Steg, Philippe Gabriel;Storey, Robert F;Asenblad, Nils;James, Stefan K"

  • Published Year:2012

  • Journal:Clinical chemistry

  • Abstract:"BACKGROUND: We evaluated the predictive ability of cystatin C and creatinine-based estimations of glomerular filtration rate (eGFR), including the Chronic Kidney Disease-Epidemiology (CKD-EPI) equation, in acute coronary syndrome (ACS) patients with (STE-ACS) or without (NSTE-ACS) ST elevation in a large contemporary ACS population. METHODS: Concentrations of cystatin C and creatinine, as well as eGFR at randomization, were measured in 16 401 patients in the Platelet Inhibition and Patient Outcomes (PLATO) study and evaluated as predictors of the composite end point of cardiovascular death or myocardial infarction within 1 year. Two Cox proportional hazards models were used, the first adjusting for clinical characteristics and the second for clinical characteristics plus the biomarkers N-terminal pro-B-type natriuretic peptide, troponin I, and C-reactive protein. RESULTS: The median cystatin C value was 0.83 mg/L. Increasing quartiles of cystatin C were strongly associated with poor outcome (6.9%, 7.1%, 9.5%, and 16.2%). The fully adjusted hazard ratios per SD of cystatin C in the NSTE-ACS and STE-ACS populations were 1.12 (95% CI 1.04-1.20) (n=8053) and 1.06 (95% CI 0.97-1.17) (n=5278), respectively. There was no significant relationship of cystatin C with type of ACS (STE or NSTE). c Statistics ranged from 0.6923 (cystatin C) to 0.6941 (CKD-EPI). CONCLUSIONS: Cystatin C concentration contributes independently in predicting the risk of cardiovascular death or myocardial infarction in NSTE-ACS, with no interaction by type of ACS. CKD-EPI exhibited the largest predictive value of all renal markers. Nevertheless, the additive predictive value of cystatin C or creatinine-based eGFR measures in the unselected ACS patient is small."

  • 10.1373/clinchem.2011.171520

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