Metastasis is a common phenomenon and the major lethal cause of lung adenocarcinoma (AdC). To discover novel potential biomarkers associated with lymph node metastasis and prognosis in lung AdC, we assessed differences in protein expression between primary lung AdC with (LNM AdC) and without lymph node metastasis (non‐LNM AdC) using a quantitative proteomic approach. Laser capture microdissection was performed to purify the cancer cells from primary lung AdC tissues. The differential proteins between the pooled microdissected non‐LNM AdC and LNM AdC tissues were identified by two‐dimensional difference gel electrophoresis (2D‐DIGE) coupled with mass spectrometry (MS). In this study, twenty proteins were found to be differentially expressed in two types of lung AdC. ANXA3, significantly up‐regulated in LNM AdC compared with non‐LNM AdC, was validated by western blotting. Immunohistochemistry showed that ANXA3 over‐expression was frequently observed in LNM AdCs and matched lymph node metastases compared with non‐LNM AdCs. ANXA3 over‐expression was significantly associated with advanced clinical stage (p < 0.001) and lymph node metastasis (p < 0.001) and increased relapse rate (p < 0.001) and decreased overall survival (p < 0.001) in lung AdCs. Cox regression analysis indicated ANXA3 over‐expression was an independent prognostic factor. Our results indicate that ANXA3 might serve as a novel biomarker for lymph node metastasis and prognosis in lung AdC, and play an important role in lung AdC progression. Copyright © 2008 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Proteins and their interactions are fundamental to biological system. With the scientific paradigm shifting to systems biology, functional study of proteins from a network viewpoint to get a deep understanding of their roles in human life and diseases being increasingly essential. Although several methods already existed for protein-protein interaction (PPI) network building, the precise reconstruction of disease associated PPI network remains a challenge. In this paper we introduce a novel concept of comprehensive influence of proteins in network, in which direct and indirect connections are adopted for the calculation of influential effects of a protein with different weights. With the optimized weights, we calculate and select the important proteins and their interactions to reconstruct the PPI network for further validation and confirmation. To evaluate the performance of the method, we compared our model with the six existed ones by using five standard data sets. The results indicated that our method outperforms the existed ones. We then applied our model to prostate cancer and Parkinson's disease to predict novel disease associated proteins for the future experimental validation. Author SummaryThe diverse protein-protein interaction networks have dramatic effects on biological system. The disease associated PPI networks are generally reconstructed from experimental data with computational models but with limited accuracy. We developed a novel concept of comprehensive influence of proteins in network for reconstructing the PPI network. Our model outperforms the state-of-the-art ones and we then applied our model to identify novel interactions for further validation. / 21experimental methods and the complexity of biomedical systems, the experimental results often present high false-positive and false-negative rates (Cheng, et al., 2016). Moreover, experimental verification of the PPI network is time consuming and expensive. Therefore, rapid and accurate computational methods represent useful alternative for predicting protein interactions, with such results providing guidance for experiments, including determination of unknown relationships between disease-causing genes and protein interactions (Zhu, et al., 2015). Such knowledge can be useful to the understanding of protein structure and evolution, as well as the overall function of the network and its associated dynamic processes (Cowen, et al., 2017;Hoeng, et al., 2014).The unknown interaction between proteins can be predicted by the theory of complex network. PPI network consists of nodes and edges, with the nodes representing proteins and the edges representing associations or interactions between the two proteins (Gani, et al., 2015). Libennowell et al. considered the influence of common neighbor nodes in the network and introduced similarity indices to include common neighbors (Libennowell and Kleinberg, 2007). Pujari et al. suggested that each attribute of a pair of connected proteins represents different information (Pujari and Kanawati, 2012), and L...