Transduction motif analysis of gastric cancer based on a human signaling network

To investigate signal regulation models of gastric cancer, databases and literature were used to construct the signaling network in humans. Topological characteristics of the network were analyzed by CytoScape. After marking gastric cancer-related genes extracted from the CancerResource, GeneRIF, and COSMIC databases, the FANMOD software was used for the mining of gastric cancer-related motifs in a network with three vertices. The significant motif difference method was adopted to identify significantly different motifs in the normal and cancer states. Finally, we conducted a series of analyses of the significantly different motifs, including gene ontology, function annotation of genes, and model classification. A human signaling network was constructed, with 1643 nodes and 5089 regulating interactions. The network was configured to have the characteristics of other biological networks. There were 57,942 motifs marked with gastric cancer-related genes out of a total of 69,492 motifs, and 264 motifs were selected as significantly different motifs by calculating the significant motif difference (SMD) scores. Genes in significantly different motifs were mainly enriched in functions associated with cancer genesis, such as regulation of cell death, amino acid phosphorylation of proteins, and intracellular signaling cascades. The top five significantly different motifs were mainly cascade and positive feedback types. Almost all genes in the five motifs were cancer related, including EPOR,MAPK14, BCL2L1, KRT18,PTPN6, CASP3, TGFBR2,AR, and CASP7. The development of cancer might be curbed by inhibiting signal transductions upstream and downstream of the selected motifs.

Expression analysis of miRNA and target mRNAs in esophageal cancer

We aimed to investigate miRNAs and related mRNAs through a network-based approach in order to learn the crucial role that they play in the biological processes of esophageal cancer. Esophageal squamous-cell carcinoma (ESCC) and adenocarcinoma (EAC)-related miRNA and gene expression data were downloaded from the Gene Expression Omnibus database, and differentially expressed miRNAs and genes were selected. Target genes of differentially expressed miRNAs were predicted and their regulatory networks were constructed. Differentially expressed miRNA analysis selected four miRNAs associated with EAC and ESCC, among which hsa-miR-21 and hsa-miR-202 were shared by both diseases. hsa-miR-202 was reported for the first time to be associated with esophageal cancer in the present study. Differentially expressed miRNA target genes were mainly involved in cancer-related and signal-transduction pathways. Functional categories of these target genes were related to transcriptional regulation. The results may indicate potential target miRNAs and genes for future investigations of esophageal cancer.

Gene expression profiling analysis of lung adenocarcinoma

The present study screened potential genes related to lung adenocarcinoma, with the aim of further understanding disease pathogenesis. The GSE2514 dataset including 20 lung adenocarcinoma and 19 adjacent normal tissue samples from 10 patients with lung adenocarcinoma aged 45-73 years was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between the two groups were screened using the t-test. Potential gene functions were predicted using functional and pathway enrichment analysis, and protein-protein interaction (PPI) networks obtained from the STRING database were constructed with Cytoscape. Module analysis of PPI networks was performed through MCODE in Cytoscape. In total, 535 upregulated and 465 downregulated DEGs were identified. These included ATP5D, UQCRC2, UQCR11 and genes encoding nicotinamide adenine dinucleotide (NADH), which are mainly associated with mitochondrial ATP synthesis coupled electron transport, and which were enriched in the oxidative phosphorylation pathway. Other DEGs were associated with DNA replication (PRIM1, MCM3, and RNASEH2A), cell surface receptor-linked signal transduction and the enzyme-linked receptor protein signaling pathway (MAPK1, STAT3, RAF1, and JAK1), and regulation of the cytoskeleton and phosphatidylinositol signaling system (PIP5K1B, PIP5K1C, and PIP4K2B). Our findings suggest that DEGs encoding subunits of NADH, PRIM1, MCM3, MAPK1, STAT3, RAF1, and JAK1 might be associated with the development of lung adenocarcinoma.

Morphometric analysis of feedforward pathways from the primary somatosensory area (S1) of rats

We used biotinylated dextran amine (BDA) to anterogradely label individual axons projecting from primary somatosensory cortex (S1) to four different cortical areas in rats. A major goal was to determine whether axon terminals in these target areas shared morphometric similarities based on the shape of individual terminal arbors and the density of two bouton types: en passant (Bp) and terminaux (Bt). Evidence from tridimensional reconstructions of isolated axon terminal fragments (n=111) did support a degree of morphological heterogeneity establishing two broad groups of axon terminals. Morphological parameters associated with the complexity of terminal arbors and the proportion of beaded Bp vs stalked Bt were found to differ significantly in these two groups following a discriminant function statistical analysis across axon fragments. Interestingly, both groups occurred in all four target areas, possibly consistent with a commonality of presynaptic processing of tactile information. These findings lay the ground for additional work aiming to investigate synaptic function at the single bouton level and see how this might be associated with emerging properties in postsynaptic targets.

Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.