The balance of VEGF-C and VEGFR-3 mRNA is a predictor of lymph node metastasis in non-small cell lung cancer

A positive association between vascular endothelial growth factor-C (VEGF-C) expression and lymph node metastasis has been reported in several cancers. However, the relationship of VEGF-C and lymph node metastasis in some cancers, including non-small cell lung cancer (NSCLC), is controversial. We evaluated the VEGF-C and vascular endothelial growth factor receptor-3 (VEGFR-3) expression in NSCLC samples from patients who had undergone surgery between 1998 and 2002 using real-time quantitative RT–PCR and immunohistochemical staining. We failed to find a positive association between VEGF-C and VEGFR-3 mRNA expression and lymph node metastasis in NSCLC. An immunohistological study demonstrated that VEGF-C was expressed not only in cancer cells, but also in macrophages in NSCLC, and that VEGFR-3 was expressed in cancer cells, macrophages, type II pneumocytes and lymph vessels. The VEGF-C/VEGFR-3 ratio of the node-positive group was significantly higher than that of the node-negative group. Immunohistochemical staining showed that VEGFR-3 was mainly expressed in cancer cells. The immunoreactivity of VEGF-C and VEGFR-3 was roughly correlated to the mRNA levels of VEGF-C and VEGFR-3 in real-time PCR. VEGF-C mRNA alone has no positive association with lymph node metastasis in NSCLC. The VEGF-C/VEGFR-3 ratio was positively associated with lymph node metastasis in NSCLC. This suggests that VEGF-C promotes lymph node metastasis while being influenced by the strength of the VEGF-C autocrine loop, and the VEGF-C/VEGFR-3 ratio can be a useful predictor of lymph node metastasis in NSCLC

Inferring Structural Ensembles of Flexible and Dynamic Macromolecules Using Bayesian, Maximum Entropy, and Minimal-Ensemble Refinement Methods

The flexible and dynamic nature of biomolecules and biomolecular complexes is essential for many cellularfunctions in living organisms but poses a challenge for experimental methods to determine high-resolutionstructural models. To meet this challenge, experiments are combined with molecular simulations. The latterpropose models for structural ensembles, and the experimental data can be used to steer these simulationsand to select ensembles that most likely underlie the experimental data. Here, we explain in detail how the“Bayesian Inference Of ENsembles” (BioEn) method can be used to refine such ensembles using a widerange of experimental data. The “Ensemble Refinement of SAXS” (EROS) method is a special case ofBioEn, inspired by the Gull-Daniell formulation of maximum entropy image processing and focusedoriginally on X-ray solution scattering experiments (SAXS) and then extended to integrative structuralmodeling. We also briefly sketch the “minimum ensemble method,” a maximum-parsimony refinementmethod that seeks to represent an ensemble with a minimal number of representative structures