Perioperative gene expression analysis for prediction of postoperative sepsis.

نویسندگان

  • Carl Hinrichs
  • Katja Kotsch
  • Sandra Buchwald
  • Marit Habicher
  • Nicole Saak
  • Herwig Gerlach
  • Hans-Dieter Volk
  • Didier Keh
چکیده

BACKGROUND Postoperative sepsis is one of the main causes of death after major abdominal surgery; however, the immunologic factors contributing to the development of sepsis are not completely understood. In this study, we evaluated gene expression in patients who developed postoperative sepsis and in patients with an uncomplicated postoperative course. METHODS We enrolled 220 patients in a retrospective matched-pair, case-control pilot study to investigate the perioperative expression of 23 inflammation-related genes regarding their properties for predicting postoperative sepsis. Twenty patients exhibiting symptoms of sepsis in the first 14 days after surgery (case group) were matched with 20 control patients with an uncomplicated postoperative course. Matching criteria were sex, age, main diagnosis, type of surgery, and concomitant diseases. Blood samples were drawn before surgery and on the first and second postoperative days. Relative gene expression was analyzed with real-time reverse-transcription PCR. RESULTS Significant differences (P < 0.005) in gene expression between the 2 groups were observed for IL1B (interleukin 1, beta), TNF [tumor necrosis factor (TNF superfamily, member 2)], CD3D [CD3d molecule, delta (CD3-TCR complex)], and PRF1 [perforin 1 (pore forming protein)]. Logistic regression analysis and a subsequent ROC curve analysis revealed that the combination of TNF, IL1B, and CD3D expression had a specificity and specificity of 90% and 85%, respectively, and predicted exclusion of postoperative sepsis with an estimated negative predictive value of 98.1%. CONCLUSIONS These data suggest that gene expression analysis may be an effective tool for differentiating patients at high and low risk for sepsis after abdominal surgery.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

O-3: Drug Repositioning by Merging Gene Expression Data Analysis and Cheminformatics Target Prediction Approaches

The transcriptional responses of drug treatments combined with a protein target prediction algorithm was utilised to associate compounds to biological genomic space. This enabled us to predict efficacy of compounds in cMap and LINCS against 181 databases of diseases extracted from GEO. 18/30 of top drugs predicted for leukemia (e.g. Leflunomide and Etoposide) and breast cancer (e.g. Tamoxifen a...

متن کامل

Multivariate Feature Extraction for Prediction of Future Gene Expression Profile

Introduction: The features of a cell can be extracted from its gene expression profile. If the gene expression profiles of future descendant cells are predicted, the features of the future cells are also predicted. The objective of this study was to design an artificial neural network to predict gene expression profiles of descendant cells that will be generated by division/differentiation of h...

متن کامل

Elevated plasma D-dimer as a predictor of postoperative complications after radical cystectomy.

Detailed preoperative evaluation is essential in prevention of perioperative complications. As thorough anamnesis, physical examination and standard laboratory investigation do not contribute much in prediction of perioperative complications and outcome, and detection of tumor markers is also insufficient in means of prognosis, some molecular marker have emerged lately as prognostic markers in ...

متن کامل

Multivariate Feature Extraction for Prediction of Future Gene Expression Profile

Introduction: The features of a cell can be extracted from its gene expression profile. If the gene expression profiles of future descendant cells are predicted, the features of the future cells are also predicted. The objective of this study was to design an artificial neural network to predict gene expression profiles of descendant cells that will be generated by division/differentiation of h...

متن کامل

Prediction of Blasting Cost in Limestone Mines Using Gene Expression Programming Model and Artificial Neural Networks

The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (G...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Clinical chemistry

دوره 56 4  شماره 

صفحات  -

تاریخ انتشار 2010