Additional file 1: of Impact of preoperative patient education on the prevention of postoperative complications after major visceral surgery: the cluster randomized controlled PEDUCAT trial

CONSORT checklist. (DOCX 36 kb

Barplots detailing intensity values recorded for particular miRNAs that exhibited significant expression variations.

<p>Only one typical result each is shown for blood and tissue. In the two panels on the left, the intensity values of miR-320a in blood samples are presented. The two panels to the right show the values of miRNA-103 as recorded in tissue samples. The horizontal lines in each panel represent the respective median.</p

ROC curves calculated on the basis of the miRNA measurements.

<p>Receiver operating characteristic (ROC) curves are a widely accepted indicator of diagnostic utility. Measure of accuracy is the corresponding area under the ROC curve, denoted as AUC. It ranges in value from 0.5 (chance) to 1.0 (perfect discrimination).</p

Clinical-pathological parameters of pancreatic cancer patients.

*<p> Logrank <i>P</i>-value for the differences in survival.</p>†<p> Median survival upper limit not calculable due to insufficient number of events.</p

Multivariate Cox regression analysis for the effect of mutations on survival in malignant exocrine cancer patients.

*<p> Hazard ratio and corresponding P-value for effect of mutations on survival calculated after adjusting with gender, age, TNM status, tumor differentiation grade and histology.</p

Multivariate Cox regression analysis for the effect of mutations on survival in PDAC patients.

*<p> Hazard ratio and corresponding P-value for effect of mutations on survival calculated after adjusting with gender, age, TNM status, and tumor differentiation grade.</p

Monte Carlo cross-validation workflow to evaluate the accuracy of methods for ranking genes for outcome prediction.

<p>The full dataset is a gene expression matrix with 8,000 features (the genes) as rows and 30 samples (the patients) as columns. For each patient, the outcome (poor or good) is given (1). The dataset is randomly divided into a training and a test set (2). Within the training set, genes are ranked by how different they are between patients with poor and good outcome (3). The most different genes are selected (4). They are used to train a machine learning classifier on the training set (5). After training, the classifier is asked to predict the outcome of the test set patients (6). The predicted outcome is compared with the true outcome and the number of correctly classified patients is noted (7). Steps 2–7 are repeated 1,000 times, and the resulting final accuracy is obtained by averaging over the 1,000 accuracies of step 7.</p

Signature to predict risk in patients with and without adjuvant therapy.

<p>(<b>A</b>) Signature to predict risk in patients with adjuvant therapy. The signature was developed with patients receiving adjuvant therapy separated by their median survival into two groups, a high risk group with shorter survival and a low risk group with longer survival. A classifier trained with the signature using leave-one-out cross-validation shows a significant difference between the predicted low and high risk group (, logrank test). (<b>B</b>) Signature to predict risk in patients without adjuvant therapy. The signature was developed with patients not receiving adjuvant therapy separated by their median survival into two groups, a high risk group with shorter survival and a low risk group with longer survival. A classifier trained with the signature using leave-one-out cross-validation shows a significant difference between the predicted low and high risk group (, logrank test).</p

Regulatory network around signature genes.

<p>(<b>A</b>) All direct neighbors for the seven candidates STAT3, FOS, JUN, SP1, CDX2, CEBPA, and BRCA1 (marked yellow). Transcription factors are marked with a dot. Genes reported in the literature associated with pancreatic cancer survival according to GoGene are represented with larger circles. The absolute correlation coefficient of gene expression with survival in the screening dataset is shown in red. (<b>B</b>) Selection of the network showing genes that are regulated by FOS and SP1. It contains many literature-associated and highly correlated genes. (<b>C</b>) Protein–protein interactions among all signature genes, representing physical interactions between the transcription factors SP1, STAT3, JUN, FOS and the transcription coactivator BRCA1.</p

Clinical characteristics of patients used in this study.

<p>The screening dataset (genome-wide gene expression profiling) comprises 30 samples of surgically resected pancreatic ductal adenocarcinoma from patients without adjuvant chemotherapy. The validation dataset (immunohistochemistry of seven marker candidates) comprises samples from 412 patients, of which 172 had received adjuvant therapy and 240 had not. Significant differences between the adjuvant and no adjuvant therapy subgroups were found for regional lymph nodes status (, Fisher's exact test) and for the stage groupings (, Fisher's exact test). Differences in all other variables were not significant.</p>†<p>Based on postsurgical histopathological assessment (indicated by the p prefix).</p>‡<p>Stage was assessed by the American Joint Committee on Cancer 2006 guidelines.</p