br Shortest signed directed path
Shortest signed directed path analysis using OmniPath
The pathway analysis was performed for all signaling relationships between overexpressed POIs and measured SB 431542 sites using OmniPath (http://omnipathdb.org/), a collection of literature curated human signaling pathways integrated from 25 data-bases (pathway databases: TRIP, SPIKE, SignaLink3, Guide2Pharma, CA1, ARN, NRF2ome, Macrophage, DeathDomain, PDZBase, Signor; interaction databases: BioGRID, CancerCellMap, MPPI, DIP, InnateDB, MatrixDB; PTM databases: PhosphoSite, DEPOD, LMPID, phosphoELM, ELM, DOMINO, dbPTM, HPRD-phos) (Turei€ et al., 2016). The shortest path was determined based on based on Breadth-First Search methods, computed through a Python module called pyPath (Turei€ et al., 2016).
For each signaling relationship between a phosphorylation site and an overexpressed protein, the median phosphorylation abun-dance in each pre-defined bin was calculated using arcsinh transformed data. K-means shape-based clustering was performed with the package ‘kml’ (Genolini et al., 2015) in R for all strong POI abundance-dependent signaling relationships after 0-1 normal-ization on phosphorylation abundance (in Figure 1D), or for signaling trajectories over 1-hour EGF stimulation time course without data normalization (in Figure S6). Euclidean distance was used as similarity measure.
Selection of strong signaling dynamic influencing POIs
For each pair of signaling relationships between an overexpressed POI and a measured phosphorylation site, the delta BP-R2 score was calculated as the signed-BP-R2 value with 10-minute EGF stimulation minus the signed-BP-R2 value in unstimulated cells. We selected the 10 POIs with the largest positive differences in signed-BP-R2, the 10 POIs with the largest negative difference in signed-BP-R2, the 20 POIs with the most signaling relationships in the 99th percentile of the difference in signed-BP-R2, and the 10 central signaling dynamic regulators in the MAPK/ERK and AKT pathways known from the literature (Steelman et al., 2011). Some POIs were in more than one set, so this resulted in 39 kinases and phosphatases.
Signaling amplitudes analysis
The signaling amplitudes analysis was adapted from our previous methods (Lun et al., 2017). The fold change of median phosphor-ylation abundance for each bin in EGF-stimulated samples over the corresponding bin of the unstimulated sample (EGF min) was calculated using the raw count. The amplitude for each bin was identified as the maximal fold change over all time points. Amplitude ratios between the second highest and the second lowest bin amplitudes were computed for all samples, and the highest amplitude ratio in all FLAG-GFP overexpression and untransfected controls was used to determine the cutoff for robust and strong abundance-dependent changes.
DATA AND SOFTWARE AVAILABILITY
All raw data and pre-analyzed data are available at Mendeley Data under the following link:
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/vibspec
Analysis of the lipid profile of saliva in ovarian and endometrial cancer by IR T fourier spectroscopy
Lyudmila V. Bel’skayaa, , Elena A. Sarfa, Denis V. Solomatinb, Victor K. Kosenokc
a Department of Biology and Biological Education, Omsk State Pedagogical University, 14, Tukhachevsky str, Omsk, 644043, Russia
b Department of Mathematics and Mathematics Teaching Methods, Omsk State Pedagogical University, 14, Tukhachevsky str, Omsk, 644043, Russia
c Department of Oncology, Omsk State Medical University, 12, Lenina str, Omsk, 644099, Russia
Background and objectives: Currently, the question of finding new, fast and non-invasive diagnostic tools for the
detection of ovarian and endometrial cancer is secretion relevant. Objective: the use of IR spectroscopy to assess changes in the lipid profile of saliva in ovarian and endometrial cancer.
Materials and methods: The case-control study included 107 patients, which were divided into 3 groups: the main group (patients with diagnosed ovarian and endometrial cancer, n = 51), the reference group (patients with non-malignant ovarian and endometrial pathologies, n = 26) and the control group (healthy individuals, n = 30). The content of lipids, the level of lipid peroxidation products (diene and triene conjugates, Schiﬀ bases, malondialdehyde) were determined in all saliva samples. A modified Folch method was used to obtain a lipid extract of saliva, which was analyzed using FT-IR spectroscopy. Non-parametric statistical methods are used for data processing.