LPAR1 and LPAR6 mRNA expression was normalized to that of GAPDH mRNA expression

ype EGFR genes. After each model was generated, a random cross-validation process was carried out with the software, and the percent to PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19712481 leave out and number of iterations were set at 20 and 10, respectively. To determine the accuracy of the class prediction model, the software quantifies crossvalidation and recognition capability. Cross-validation is a measure of the reliability of a model and can be used to predict how a model will behave in the future. This method is used for order Crenolanib evaluating the performance of an algorithm for a given data set and under a given parameterization. Recognition capability describes the performance of an algorithm, i.e., the proper classification of a given data set. Blind test of the classification model that most efficiently separated samples from patients with EGFR gene TKI-sensitive mutations from samples from patients with wild-type EGFR genes in the validation group. This validation was performed in a blinded manner in that MALDI-TOF-MS analysis was performed and samples were classified before the clinical outcome data were made available to the investigators. For each patient from the validation groups, a corresponding spectrum was presented to the selected classification model, which then returned a label, either “mutant” or “wild”, or PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/19711015 output a message that the spectrum was unclassifiable. The results from the selected classification model were compared with findings from ARMS in tumors to estimate the separation efficiency of the model. Statistical analysis The clinical and disease characteristics between different arms, the objective response rate and disease control rate between patients whose matched samples were labeled as “mutant” and “wild” were compared using a 2 or Fisher’s exact test. The concordance between ARMS in tumors and the serum proteomic classifier in evaluating EGFR gene mutation status was assessed using a Kappa test. Survival curves were estimated by the Kaplan–Meier method, and differences between curves were evaluated by the log-rank test. Statistical analyses 5 / 17 Classification of EGFR in NSCLC were performed with SPSS software, v19.0. A p-value less than 0.05 was considered statistically significant. Results Patient Characteristics A total of 223 patients met the enrollment criteria and were enrolled in this study. Based on the criterion of ARMS in tumors, there were 102 patients with EGFR gene TKI-sensitive mutations and 121 patients with wild-type EGFR genes. Fifty patients were randomly selected from those with EGFR gene TKI-sensitive mutations and from those with wild-type EGFR genes to form the training group, and the remaining 123 patients formed the validation group. The clinical and disease characteristics of all the patients are listed in ADC = adenocarcinoma; SCC = squamous cell carcinoma; TKI = tyrosine kinase inhibitor; EGFR = epidermal growth factor receptor; ARMS = amplification refractory mutation system; E19del = exon 19 deletion; L858R = exon 21 mutation; G719X = exon 18 mutation. doi:10.1371/journal.pone.0128970.t001 6 / 17 Classification of EGFR in NSCLC TKI-sensitive mutations and wild-type EGFR genes with respect to age, histologic type, or disease stage, but differences in sex and smoking history were observed between these two arms, with more females and more non-smokers in patients with EGFR gene TKI-sensitive mutations. Differences of peaks in serum between patients with EGFR gene TKIsensitive mutations and patients with wild-type EGFR genes in the