A5040609768- Advanced Proteomics Techniques and Applications
- Mass Spectrometry Techniques and Applications
- Metabolomics and Mass Spectrometry Studies
- Identification and Quantification in Food
- Radiomics and Machine Learning in Medical Imaging
- Health and Well-being Studies
- Prostate Cancer Treatment and Research
- Enhanced Recovery After Surgery
- Prostate Cancer Diagnosis and Treatment
- Head and Neck Cancer Studies
- Emotional Intelligence and Performance
- Cancer, Lipids, and Metabolism
- Esophageal Cancer Research and Treatment
- Resilience and Mental Health
- Histone Deacetylase Inhibitors Research
- Protein Degradation and Inhibitors
- Cancer Diagnosis and Treatment
- Ferroptosis and cancer prognosis
- Viral Infectious Diseases and Gene Expression in Insects
- Veterinary Equine Medical Research
- Nutrition and Health in Aging
- Plant Stress Responses and Tolerance
- Assisted Reproductive Technology and Twin Pregnancy
- Wnt/β-catenin signaling in development and cancer
- Gaze Tracking and Assistive Technology
Children's Medical Research Institute
2021-2025
Guy's and St Thomas' NHS Foundation Trust
2021-2024
The University of Sydney
2021-2024
Westmead Institute
2020-2023
Bahauddin Zakariya University
2023
University of Haripur
2022
COMSATS University Islamabad
2022
Macquarie University
2018-2021
University of Engineering and Technology Taxila
2020
Quaid-i-Azam University
2015-2017
Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding application this technology across all elements medicine biology. Proteomics, as branch biochemistry, has in recent years played pivotal role extending developing science accurately identifying biology interactions groups proteins or proteomes. Proteomics primarily used mass spectrometry (MS)-based techniques for proteins, although other including affinity-based...
Abstract Although HPV-positive OPSCC is associated with better prognosis than HPV-negative disease, ~30% of cases relapse despite curative-intent radiotherapy (+/- chemotherapy). We aimed to develop a proteomic signature risk recurrence within HPV+OPSCC. analysed tumor specimens from 124 patients T1-4N0-3M0 HPV+OPSCC: 50 residual or recurrent disease 5 years treatment and 74 age performance-status matched no recurrence. Proteomic analysis was performed on archival FFPE primary core biopsy...
<p>Tables of all identified peptides for protein groups represented by the 26-peptide signature. The differentially abundant peptide (DAPep) that is part signature highlighted in yellow.</p>
<p>Top 15 cellular functions and pathways for DAPeps in tumor vs to NAT samples</p>
<div>Abstract<p>Although human papillomavirus (HPV)–positive oropharyngeal squamous cell carcinoma (OPSCC) is associated with better prognosis than HPV-negative disease, ∼30% of cases relapse despite curative-intent radiotherapy (±chemotherapy). We aimed to develop a proteomic signature risk recurrence within HPV+OPSCC. analyzed tumor specimens from 124 patients T1–4N0–3M0 HPV+OPSCC: 50 residual or recurrent disease 5 years treatment and 74 age performance status–matched no...
<p>Differential abundance of 233 unique protein groups identified from 405 DAPeps discriminates R NR. <b>A,</b> Volcano plot shows the distribution proteins significantly up- or downregulated in relative to NR samples patients with HPV+OPSCC. A total 87 peptides (from 52 groups) were R, and 318 181 upregulated at an adjusted <i>P</i> value 0.01 (1% FDR). Axes show FC > 1.5 (adj. P Val) < 0.01. Significantly are indicated red (increased R) blue color...
<p>(Table S1) Differentially abundant peptides tumour vs NAT. (Table S2) recurrence non-recurrence. S3) 26-peptide signature.</p>
<p>The 26-peptide signature. List of the signature (amino acid sequence and predicted gene name) with HRs, 95% CIs, <i>P</i> values in multivariate model, ranked according to their significance from top bottom. The was identified by Lasso-regularized Cox proportional hazard model as input a forward feature selection algorithm has C-index 0.947. squares intervals plot represent HRs respectively. Each peptide is different protein group. UniMod:4 an iodoacetamide derivative...
<p>Distribution of low- and high-risk peptide signature groups between categories clinical variables</p>
<p>26-peptide signature stratifies HPV+ OPSCC patients into three risk groups for recurrence free survival (RFS)</p>
<p>Differential abundance 1,614 unique protein groups from 4,834 DAPeps discriminates tumor and NAT patient samples</p>
<p>Multivariate Cox regression model and ROC curves incorporating the 26-peptide signature. <b>A,</b> Forest plot showing important clinicopathologic variables along with risk signature their association disease-specific survival using a multivariate model. Mean HRs are shown as squares, whiskers represent 95% CI. <b>B,</b> curve at 5 years after date of treatment for The predicted AUC is comparing proteomic six variables.</p>
<p>Multivariate Cox regression models incorporating the 26-peptide signature</p>
<p>Top 15 cellular functions and pathways for DAPep tumor samples from recurrence vs non-recurrence</p>
<p>Methods used for (i) Tissue lysis and digestion (ii) Data-independent acquisition (iii) DIA-NN search parameters</p>
<p>Summary of the clinicopathologic characteristics OPSCC cohort</p>
<p>Overview of the DIA-MS dataset. Number peptides and proteins identified using samples from all 124 patients (“All” includes both tumor NATs). The number is shown in each filtering step with 405 being associated RFS (<i>q</i> value < 0.05) used to obtain prognostic signature. In peptide intensity step, ≤15 raw were discarded. C-index DAPep signature multivariate Cox modeling was 0.947.</p>
<p>A 26-peptide signature stratifies patients with HPV+OPSCC into three risk groups for RFS or OS. KM curves 95% CIs based on the stratification of are shown. The signatures were separated low-, intermediate- and high-risk (<b>A</b>) (<b>B</b>) OS median cut-off, respective numbers samples in each group log rank test was used to assess <i>P</i> value differences between curves. built using coefficients from training dataset. shaded area around curve...
<p>Study samples and workflow. <b>A,</b> Summary of included in the study. <b>B,</b> proteomic In brief, tumor core NAT underwent high pressure temperature treatment for lysis digestion proteins to peptides by trypsin/LysC. Samples were analyzed duplicate via DIA-MS two separate instruments. Proteomic data was processed using DIA-NN software, quantitative on obtained. Protein abundance inferred, single-peptide present ≥20% downstream analyses.</p>
<p>Distribution of the quantified peptides among number patient tumor samples.</p>
Gleason grading is an important prognostic indicator for prostate adenocarcinoma and crucial patient treatment decisions. However, intermediate-risk patients diagnosed in the grade group (GG) 2 GG3 can harbour either aggressive or non-aggressive disease, resulting under- overtreatment of a significant number patients. Here, we performed proteomic, differential expression, machine learning, survival analyses 1,348 matched tumour benign sample runs from 278 Three proteins (F5, TMEM126B, EARS2)...
Histone deacetylases (HDAC) are metal-dependent enzymes and considered as important targets for cell functioning. Particularly, higher expression of class I HDACs is common in the onset multiple malignancies which results deregulation many target genes involved growth, differentiation survival. Although substantial attempts have been made to control irregular functioning by employing various inhibitors with high sensitivity towards transformed cells, limited success has achieved epigenetic...
Abstract Background Prehabilitation is safe, feasible and may improve a range of outcomes in patients with oesophago-gastric cancer (OGC). Recent studies have suggested the potential prehabilitation to body composition, sarcopenia physical fitness, reduce surgical complications quality life. Despite this, services are not offered throughout all OGC centres UK. Where offered, delivery definitions vary significantly, as do funding sources access. Methods A professional association endorsed...
Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed mixture selected recombinant DDA libraries to subsequently identify (not quantify) cancer-associated SWATH/DIA. The exemplar protein spectral library (rPSL) was derived tryptic digestion 36 that had been previously implicated as possible cancer biomarkers both our...