An individual's inherited genetic variation may contribute to the 'angiogenic switch', which is essential for blood supply and tumor growth of microscopic and macroscopic tumors. Polymorphisms in angiogenesis-related genes potentially predispose to colorectal cancer (CRC) or affect the survival of CRC patients. We investigated the association of 392 single nucleotide polymorphisms (SNPs) in 33 angiogenesis-related genes with CRC risk and survival of CRC patients in 1754 CRC cases and 1781 healthy controls within DACHS (Darmkrebs: Chancen der Verhütung durch Screening), a German population-based case-control study. Odds ratios and 95% confidence intervals (CI) were estimated from unconditional logistic regression to test for genetic associations with CRC risk. The Cox proportional hazard model was used to estimate hazard ratios (HR) and 95% CIs for survival. Multiple testing was adjusted for by a false discovery rate. No variant was associated with CRC risk. Variants in EFNB2, MMP2 and JAG1 were significantly associated with overall survival. The association of the EFNB2 tagging SNP rs9520090 (p < 0.0001) was confirmed in two validation datasets (p-values: 0.01 and 0.05). The associations of the tagging SNPs rs6040062 in JAG1 (p-value 0.0003) and rs2241145 in MMP2 (p-value 0.0005) showed the same direction of association with overall survival in the first and second validation sets, respectively, although they did not reach significance (p-values: 0.09 and 0.25, respectively). EFNB2, MMP2 and JAG1 are known for their functional role in angiogenesis and the present study points to novel evidence for the impact of angiogenesis-related genetic variants on the CRC outcome.

One of the main disadvantages of using DNA microarrays for miRNA expression profiling is the inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e., false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA, and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data. Additionally, we obtained possible reasons why miRNA can appear as a false positive in microarray study using paired miRNA sequencing and array data. The use of DNA microarrays for estimating miRNA expression profile is limited by several factors. One of them consists of problems with comparing expression values of different miRNAs. In this work, we show that situation can be significantly improved if some additional information is taken into consideration in a comparison.