Additional file 1 of Targeted proteomics-derived biomarker profile develops a multi-protein classifier in liquid biopsies for early detection of esophageal squamous cell carcinoma from a population-based case-control study

Additional file 1: Table S1. 92 proteins from the Olink multiplex Oncology II panel. Table S2. The general information of selected participants, controls and cases based on different cancer stages. Figure S1. The protein interaction of 23 preliminarily authenticated proteins. Each node represents a protein, and the gene name is marked at the top right of the node. Table S3. Gene ontology enrichment analysis of the identified 23 proteins that were differentially expressed between early ESCC and controls, covering three categories, i.e. molecular function, cellular component, and biological process. Top 5 gene ontologies in each enrichment category were selected. Data were obtained from the online ConsensusPathDB- human interaction network database http://cpdb.molgen.mpg.de/ . Table S4. Pathway enrichment analysis of the identified 23 proteins that were differentially expressed between early ESCC and controls. Top 7 enriched pathway were selected. Data were obtained from the online ConsensusPathDB- human interaction network database http://cpdb.molgen.mpg.de/ . Figure S2. An unsupervised hierarchical clustering analysis of 23 preliminarily authenticated proteins for discriminating early esophageal squamous cell carcinoma (ESCC) from healthy controls. Figure S3. The selection feature of least absolute shrinkage and selection operator (LASSO) via tenfold cross-validation based on area under the ROC curve (AUC). Selection of the tuning parameter (λ) in the LASSO model was via tenfold cross-validation based on minimum standard error. The y-axis indicates AUC. The lower x-axis indicates the log(λ). Numbers along the upper x-axis represent the average number of predictors. Red dots indicate average AUC values for each model with a given λ, and vertical bars through the red dots show the upper and lower values of AUC. The vertical black lines define the optimal values of λ, where the model provides its best fit to the data. Figure S4. A nomogram to predict individual ESCC risk based on the identified five-protein panel.