The Different Effect of VEGF Polymorphisms on the Prognosis of Non-Small Cell Lung Cancer according to Tumor Histology

Vascular endothelial growth factor (VEGF) contributes to tumor angiogenesis. The role of VEGF single nucleotide polymorphisms (SNPs) in lung cancer susceptibility and its prognosis remains inconclusive and controversial. This study was performed to investigate whether VEGF polymorphisms affect survival outcomes of patients with early stage non-small cell lung cancer (NSCLC) after surgery. Three potentially functional VEGF SNPs (rs833061T>C, rs2010963G>C, and rs3025039C>T) were genotyped. A total of 782 NSCLC patients who were treated with surgical resection were enrolled. The association of the SNPs with overall survival (OS) and disease free survival (DFS) was analyzed. In overall population, none of the three polymorphisms were significantly associated with OS or DFS. However, when the patients were stratified by tumor histology, squamous cell carcinoma (SCC) and adenocarcinoma (AC) had significantly different OS (Adjusted hazard ratio [aHR] = 0.76, 95% CI = 0.56-1.03 in SCC; aHR = 1.33, 95% CI = 0.98-1.82 in AC; P for heterogeneity = 0.01) and DFS (aHR = 0.75, 95% CI = 0.58-0.97 in SCC; aHR = 1.26, 95% CI = 1.00-1.60 in AC; P for heterogeneity = 0.004) according to the rs833061T>C genotypes. Our results suggest that the prognostic role of VEGF rs833061T>C may differ depending on tumor histology.

MONGKIE: an integrated tool for network analysis and visualization for multi-omics data

BACKGROUND

Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed.

RESULTS

Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers.

CONCLUSION

We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. .