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this study attempts to identify the independent risk factors that can predict lymph node metastasis for the patients with non-small cell lung cancer (NSCLC), and guide doctor adoption of individualized treatment for such patients.This study was approved by the Hospital's Ethics Committee and all patients had signed informed consent forms. We retrospectively reviewed NSCLC patients who had undergone surgical resection from December 2008 to December 2013.The statistical significance of evaluation variables and lymph node metastasis was determined with Pearson's Chi-square test. The risk factors of lymph node metastasis were determined through univariate and multivariate logistic regression analysis. And for the age and tumor diameter factors, optimal cutoff points were determined with a receiver operating characteristic analysis.In the present study, a total of 2623 patients were included in the study, and 779 patients with lymph node metastasis. Three independent risk factors were identified: age, tumor diameter and Ki-67 index. We found that65 years of age (Adjusted-OR:1.921), ≥2.85 cm of tumor diameter (Adjusted-OR:3.141), and 5%~25% in Ki-67 group (Adjusted-OR:2.137),≥25% (Adjusted-OR:3.341) were significant. Also we found that 307 patients with lymph node metastasis and the lymph node metastasis rate was 51.0%, when the age65 years, Ki-67 index≥25%, and the tumor diameter≥2.85 cm. On the contrary, there were only 2 patients with lymph node metastasis, and the rate of lymph node metastasis was 5.1%.Identifying three independent risk factors that predict lymph node metastasis in non-small cell patients, Among NSCLC patients in whom all three predictors were identified, and over a half of the patients showed lymph node metastasis.

Lung cancer is the leading cause of cancer deaths worldwide, compounded by late diagnosis. MicroRNAs (miRNA) are recently discovered short, noncoding genes that play essential roles in tissue differentiation during normal development and tumorigenesis. miRNA profiles across all histologic grades can provide a reliable and standardized method for the identification of lung cancer.A microRNA lung cancer dataset was analyzed. Differentially expressed microRNAs were obtained post-normalization of data using t-test (p 0.01). The data for differentially expressed microRNAs were processed using K-nearest neighbors classification method to obtain unique miRNAs expression patterns. The predicted mRNA targets were identified using TargetScan and the molecular functions associated with the predicted targets were retrieved from the Gene Ontology Consortium and represented using GO IDs in a directed acyclic graph.The results indicate that lung cancer samples can be classified using a small panel of 19 unique microRNAs (8 down-regulated and 11 up-regulated) with over 85% classification accuracy. Furthermore, using classical enrichment analysis, this study identified 66 molecular function groups which are potentially the functional signaling pathways altered by these differentially expressed microRNAs.We identified a microRNA gene signature representative of functioning as a diagnostic biomarker for lung cancer. These findings can potentially form the basis for the development of a standardized diagnostic assay that can be used for early diagnosis of lung cancer equally well from resection specimens and cytology samples.