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pmid:34359700

Comprehensive Library Generation for Identification and Quantification of Endometrial Cancer Protein Biomarkers in Cervico-Vaginal Fluid.

Kelechi Njoku · Davide Chiasserini · Bethany Geary · Andrew Pierce · Eleanor R Jones · Anthony D Whetton · Emma J Crosbie

Cancers · 2021 doi:10.3390/cancers13153804

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:34359700
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2026-11-01T15:41:47Z
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title
Comprehensive Library Generation for Identification and Quantification of Endometrial Cancer Protein Biomarkers in Cervico-Vaginal Fluid.
doi
10.3390/cancers13153804
pmid
34359700
authors array · 7 items
[0]
Kelechi Njoku
[1]
Davide Chiasserini
[2]
Bethany Geary
[3]
Andrew Pierce
[4]
Eleanor R Jones
[5]
Anthony D Whetton
[6]
Emma J Crosbie
journal
Cancers
year
2021
abstract
Endometrial cancer is the most common gynaecological malignancy in high-income countries and its incidence is rising. Early detection, aided by highly sensitive and specific biomarkers, has the potential to improve outcomes as treatment can be provided when it is most likely to effect a cure. Sequential window acquisition of all theoretical mass spectra (SWATH-MS), an accurate and reproducible platform for analysing biological samples, offers a technological advance for biomarker discovery due to its reproducibility, sensitivity and potential for data re-interrogation. SWATH-MS requires a spectral library in order to identify and quantify peptides from multiplexed mass spectrometry data. Here we present a bespoke spectral library of 154,206 transitions identifying 19,394 peptides and 2425 proteins in the cervico-vaginal fluid of postmenopausal women with, or at risk of, endometrial cancer. We have combined these data with a library of over 6000 proteins generated based on mass spectrometric analysis of two endometrial cancer cell lines. This unique resource enables the study of protein biomarkers for endometrial cancer detection in cervico-vaginal fluid. Data are available via ProteomeXchange with unique identifier PXD025925.
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Principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) plots were then used to assess degree of separation between groups and shown in Figure 10.
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sections[19] "3.3.2. Real World Application of Spectral Library for Biomarker Discovery" paragraph 4
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Principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) plots were then used to assess degree of separation between groups and shown in Figure 10.
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All datasets have been deposited in ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/ (accessed on 26 July 2021)) via the PRIDE partner repository with the dataset identifier PXD025925.
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sections[27] "Data Availability Statement" paragraph 2
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All datasets have been deposited in ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org/ (accessed on 26 July 2021)) via the PRIDE partner repository with the dataset identifier PXD025925.
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Comprehensive spectral library generation for identification and quantification of endometrial cancer protein biomarkers in cervico-vaginal fluid
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2.1. Research Ethics and Approval
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Eligibility criteria for case and control selection.
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Demographic and clinical characteristics of cases and controls whose samples were used for library generation.
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3.1. Descriptive Characteristics of the Study Population
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Demographic and clinical characteristics of cases and controls whose samples were used for library generation.
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Atypical Hyperplasia
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[3]
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[3]
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Assay statistics for the consensus cervico-vaginal fluid and endometrial cancer cell-based spectral libraries at a protein FDR of 1%.
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3.2. The Spectral Libraries
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Table 3
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Assay statistics for the consensus cervico-vaginal fluid and endometrial cancer cell-based spectral libraries at a protein FDR of 1%.
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Proteotypic
[2]
Proteotypic and Shared
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Consensus cervico-vaginal fluid spectral library
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[2]
154,206
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Endometrial cancer cell-line based spectral library
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[2]
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30,876
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Comparative overview of the various studies on the cervico-vaginal (CVF) proteome conducted in non-pregnant women.
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3.2. The Spectral Libraries
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Comparative overview of the various studies on the cervico-vaginal (CVF) proteome conducted in non-pregnant women.
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[1]
Our CVF Spectral Library
[2]
CVF Proteome by Zegel et al. [23]
[3]
CVF Proteome by Tang et al. [24]
[4]
CVF Proteome by Shaw et al. [25]
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[3]
29
[4]
5
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[2]
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[3]
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[4]
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[2]
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[3]
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[4]
Healthy female volunteers
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[0]
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[3]
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[4]
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3.3. Spectral Library Validation
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Demographic and clinical characteristics of the technical and clinical validation cohorts.
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[1]
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[1]
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[2]
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Endometrioid endometrial cancer
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[1]
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[3]
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[5]
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[1]
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[2]
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[3]
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[1]
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[3]
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3.3.1. Technical Validation and Applicability for SWATH-MS Data Analysis
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Missing values between technical replicates of the same cervico-vaginal fluid samples.
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[3]
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[4]
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[1]
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[2]
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[3]
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Reproducibility of selected biomarker candidates using the consensus cervico-vaginal fluid spectral library.
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Principal component analysis (PCA) and t-distributed stochastic neighbour embedding (t-SNE) plots were then used to assess degree of separation between groups and shown in Figure 10.
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Early detection, aided by highly sensitive and specific biomarkers, has the potential to improve outcomes as treatment can be provided when it is most likely to effect a cure. Sequential window acquisition of all theoretical mass spectra (SWATH-MS), an accurate and reproducible platform for analysing biological samples, offers a technological advance for biomarker discovery due to its reproducibility, sensitivity and potential for data re-interrogation. SWATH-MS requires a spectral library in order to identify and quantify peptides from multiplexed mass spectrometry data. Here we present a bespoke spectral library of 154,206 transitions identifying 19,394 peptides and 2425 proteins in the cervico-vaginal fluid of postmenopausal women with, or at risk of, endometrial cancer. We have combined these data with a library of over 6000 proteins generated based on mass spectrometric analysis of two endometrial cancer cell lines. 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