16 research outputs found
Additional file 3 of Normalization of the microbiota in patients after treatment for colonic lesions
Figure S2: Summary of top 10% of important OTUs for the adenoma, advanced adenoma, and carcinoma models. A) MDA of the most important variables in the adenoma model. The dark green point represents the mean and the lighter green points are the value of each of the 100 different runs. B) Summary of important variables in the advanced adenoma model. MDA of the most important variables in the SRN model. The dark yellow point represents the mean and the lighter yellow points are the value of each of the 100 different runs. C) MDA of the most important variables in the carcinoma model. The dark red point represents the mean and the lighter red points are the value of each of the 100 different runs. (PDF 87 kb
Additional file 2 of Normalization of the microbiota in patients after treatment for colonic lesions
Figure S1: ROC curves of the adenoma, advanced adenoma, and carcinoma models. A) Adenoma ROC curve: the light green shaded areas represent the range of values of a 100 different 80/20 splits of the test set data and the dark green line represents the model using 100% of the data set and what was used for subsequent classification. B) Advanced Adenoma ROC curve: the light yellow shaded areas represent the range of values of a 100 different 80/20 splits of the test set data and the dark yellow line represents the model using 100% of the data set and what was used for subsequent classification. C) Carcinoma ROC curve: the light red shaded areas represent the range of values of a 100 different 80/20 splits of the test set data and the dark red line represents the model using 100% of the data set and what was used for subsequent classification. (PDF 92 kb
Additional file 4 of Normalization of the microbiota in patients after treatment for colonic lesions
Figure S3: Pre and post-treatment relative abundance of CRC associated OTUs within the carcinoma model. (PDF 6 kb
Isobaric Protein-Level Labeling Strategy for Serum Glycoprotein Quantification Analysis by Liquid Chromatography–Tandem Mass Spectrometry
While peptide-level labeling using
isobaric tag reagents has been
widely applied for quantitative proteomics experiments, there are
comparatively few reports of protein-level labeling. Intact protein
labeling could be broadly applied to quantification experiments utilizing
protein-level separations or enrichment schemes. Here, protein-level
isobaric labeling was explored as an alternative strategy to peptide-level
labeling for serum glycoprotein quantification. Labeling and digestion
conditions were optimized by comparing different organic solvents
and enzymes. Digestions with Asp-N and trypsin were found highly complementary;
combining the results enabled quantification of 30% more proteins
than either enzyme alone. Three commercial reagents were compared
for protein-level labeling. Protein identification rates were highest
with iTRAQ 4-plex when compared to TMT 6-plex and iTRAQ 8-plex using
higher-energy collisional dissociation on an Orbitrap Elite mass spectrometer.
The compatibility of isobaric protein-level labeling with lectin-based
glycoprotein enrichment was also investigated. More than 74% of lectin-bound
labeled proteins were known glycoproteins, which was similar to results
from unlabeled and peptide-level labeled serum samples. Finally, protein-level
and peptide-level labeling strategies were compared for serum glycoprotein
quantification. Isobaric protein-level labeling gave comparable identification
levels and quantitative precision to peptide-level labeling
Quantitative Analysis of Single Amino Acid Variant Peptides Associated with Pancreatic Cancer in Serum by an Isobaric Labeling Quantitative Method
Single
amino acid variations are highly associated with many human
diseases. The direct detection of peptides containing single amino
acid variants (SAAVs) derived from nonsynonymous single nucleotide
polymorphisms (SNPs) in serum can provide unique opportunities for
SAAV associated biomarker discovery. In the present study, an isobaric
labeling quantitative strategy was applied to identify and quantify
variant peptides in serum samples of pancreatic cancer patients and
other benign controls. The largest number of SAAV peptides to date
in serum including 96 unique variant peptides were quantified in this
quantitative analysis, of which five variant peptides showed a statistically
significant difference between pancreatic cancer and other controls
(<i>p</i>-value < 0.05). Significant differences in the
variant peptide SDNCEDTPEAGYFA<i><u>V</u></i>AVVK from serotransferrin were detected between pancreatic cancer
and controls, which was further validated by selected reaction monitoring
(SRM) analysis. The novel biomarker panel obtained by combining α-1-antichymotrypsin
(AACT), Thrombospondin-1 (THBS1) and this variant peptide showed an
excellent diagnostic performance in discriminating pancreatic cancer
from healthy controls (AUC = 0.98) and chronic pancreatitis (AUC =
0.90). These results suggest that large-scale analysis of SAAV peptides
in serum may provide a new direction for biomarker discovery research
Large-Scale Identification of Core-Fucosylated Glycopeptide Sites in Pancreatic Cancer Serum Using Mass Spectrometry
Glycosylation has significant effects
on protein function and cell
metastasis, which are important in cancer progression. It is of great
interest to identify site-specific glycosylation in search of potential
cancer biomarkers. However, the abundance of glycopeptides is low
compared to that of nonglycopeptides after trypsin digestion of serum
samples, and the mass spectrometric signals of glycopeptides are often
masked by coeluting nonglycopeptides due to low ionization efficiency.
Selective enrichment of glycopeptides from complex serum samples is
essential for mass spectrometry (MS)-based analysis. Herein, a strategy
has been optimized using LCA enrichment to improve the identification
of core-fucosylation (CF) sites in serum of pancreatic cancer patients.
The optimized strategy was then applied to analyze CF glycopeptide
sites in 13 sets of serum samples from pancreatic cancer, chronic
pancreatitis, healthy controls, and a standard reference. In total,
630 core-fucosylation sites were identified from 322 CF proteins in
pancreatic cancer patient serum using an Orbitrap Elite mass spectrometer.
Further data analysis revealed that 8 CF peptides exhibited a significant
difference between pancreatic cancer and other controls, which may
be potential diagnostic biomarkers for pancreatic cancer
Prevalence of HPV types among women from the CARE Project.
<p><i>Note.</i> Table reports n (%). HPV  =  human papillomavirus, CARE  =  Community Awareness Resources Education.</p>a<p>High- and low-risk classification scheme is the same as a recent national study that analyzed data from the National Health and Nutrition Examination Survey (NHANES) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074276#pone.0074276-Hariri1" target="_blank">[4]</a>.</p
HPV prevalence among women from the CARE Project and the NHANES.
<p><i>Note.</i> Table reports percents and 95% confidence intervals in parentheses. HPV  =  human papillomavirus, CARE  =  Community Awareness Resources Education, NHANES  =  National Health and Nutrition Examination Survey.</p>a<p>Estimates from the NHANES are for non-Hispanic white women <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074276#pone.0074276-Hariri1" target="_blank">[4]</a>, since 93.9% of women from the CARE Project were non-Hispanic white.</p>b<p>Included HPV types 16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 64, 66, 67, 68, 69, 70, 73, 82, and IS39 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074276#pone.0074276-Hariri1" target="_blank">[4]</a>.</p>c<p>Included HPV types 6, 11, 40, 42, 54, 55, 61, 62, 71, 72, 81, 83, 84, and 89 <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0074276#pone.0074276-Hariri1" target="_blank">[4]</a>.</p
Enjoy your food, but eat less [tipsheet] (2017)
DG TipSheet No. 18 December 2011 Revised October 2016, Reviewed 06/17/5M
Detection of any HPV type among women from the CARE Project (n = 1116).
<p><i>Note.</i> Totals may be less than stated sample size due to missing data. The multivariable model included data on 814 women due to missing data on correlates. The multivariable model did not include variables with dashes (--). HPV  =  human papillomavirus, CARE  =  Community Awareness Resources Education, OR  =  odds ratio, CI  =  confidence interval, ref.  =  referent group, STI  =  sexually transmitted infection.</p>*<p><i>p</i><0.05, **<i>p</i><0.001.</p