A molecular signature for oncogenic BRAF in human colon cancer cells is revealed by microarray analysis.

Sporadic colorectal cancer develops through a number of functional mutations. Key events are mutually exclusive mutations in BRAF or RAS oncogenes. Signatures for BRAF oncogene have been revealed in melanoma. In a previous study we have reported a molecular signature for HRAS and KRAS mutations in colorectal cell lines that also showed an EMT phenotype for HRAS. In this study we report a molecular profile for a BRAF oncogenic mutation BRAFV600E in colon using the Illumina 45,000 gene microarray. Key differentially expressed genes have been identified from the array analysis further verified by qPCR analysis. Ingenuity pathway analysis such as microsatellite instability, kinase signalling, apoptosis, WNT and Integrin signalling is presented. MutBRAF transforms cells through cross talk with developmental pathways Hedgehog and Wnt, as well as by deregulation of colorectal cancer related kinase pathways, like PI3K. Differential gene expression of BRAFV600E in colon as compared to those associated with RAS oncogenes is presented, as well as similarities and differences between oncogenic BRAF signatures in colon as compared to thyroid and melanoma are highlighted. Novel selected genes/pathways are validated in cell lines and clinical samples bearing BRAFV600E and may serve as markers/targets for personalised diagnosis/therapy/resistance of colorectal cancer.

Enhanced Sign Language Recognition Using Weighted Intrinsic-Mode Entropy and Signer's Level of Deafness.

Sign language (SL) forms an important communication canal for the deaf. In this paper, enhanced SL recognition, by relating the individual way of signing with the signer's level of deafness (LoD) through a novel hybrid adaptive weighting (HAW) process applied to surface electromyogram and 3-D accelerometer data, is proposed. Using a LoD-driven genetic algorithm, HAW optimally weights the intrinsic modes of the acquired signals, preparing them for sample entropy (SampEn) estimation that follows. The resulting feature set, namely, weighted intrinsic-mode entropy (IMEn) (wIMEn), aims at increasing the SL-sign-classification accuracy alone or boosted by signer identification and/or signer's LoD-based group identification. The wIMEn was compared with three other feature sets, i.e., time frequency, SampEn, and IMEn, regarding their discrimination ability (both among signers and SL signs). Data from the dominant hand of nine subjects with various LoD were analyzed for the classification of 61 Greek SL (GSL) signs. Experimental results have shown that the introduced wIMEn feature set exhibited higher performance compared to others, both in signer identification and signer's LoD-based group identification and in GSL sign classification. The findings suggest that LoD could be considered in the construction of a signer-independent SL-classification system toward the enhancement of its performance.