Dataset Information

Quantitative proteomic analysis of MCM3 in ThinPrep samples of patients with cervical preinvasive cancer

ABSTRACT: Triage methods for cervical cancer detection show moderate accuracy and present considerable false-negative and false-positive result rates. A complementary diagnostic parameter could help improve the accuracy of identifying patients who need treatment. A pilot study was performed using a targeted proteomics approach with opportunistic ThinPrep samples obtained from women collected at the hospital’s outpatient clinic to determine the concentration levels of minichromosome maintenance-3 (MCM3) and envoplakin (EVPL) proteins. Forty samples with ’’negative for intraepithelial lesion or malignancy’ (NILM), 21 samples with ’atypical squamous cells of undetermined significance’ (ASC-US), and 33 samples with ’low-grade squamous intraepithelial lesion and worse’ (≥LSIL) were analyzed, using cytology and the patients’ histology reports.

INSTRUMENT(S): Orbitrap Fusion Lumos, Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Endocervical Epithelium, Ectocervical Epithelium

DISEASE(S): Cervical Cancer

SUBMITTER: Coskun Guzel

LAB HEAD: Theo Luider

PROVIDER: PXD042919 | Pride | 2023-10-24

REPOSITORIES: Pride

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Publications

Quantitative Proteomic Analysis of MCM3 in ThinPrep Samples of Patients with Cervical Preinvasive Cancer.

Köse Büşra B Laar Ralf van de RV Beekhuizen Heleen van HV Kemenade Folkert van FV Baykal Ahmet Tarik AT Luider Theo T Güzel Coşkun C

International journal of molecular sciences 20230621 13

Triage methods for cervical cancer detection show moderate accuracy and present considerable false-negative and false-positive result rates. A complementary diagnostic parameter could help improve the accuracy of identifying patients who need treatment. A pilot study was performed using a targeted proteomics approach with opportunistic ThinPrep samples obtained from women collected at the hospital's outpatient clinic to determine the concentration levels of minichromosome maintenance-3 (MCM3) an ...[more]

PMID: 37445651

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Project description:Background. MicroRNAs (miRNAs) are short (~22 nt) non-coding regulatory RNAs that control gene expression at the translational level. Deregulation of miRNA expression has been discovered in a wide variety of tumours and it is now clear that they contribute to cancer development and progression. This prompted the development of miRNA-chips for cancer diagnosis or prognosis, opening a new door to understand carcinogenesis. Cervical cancer is one of the most common cancers in women worldwide. Therefore, there is a strong need for a non-invasive, fast and efficient method to diagnose the disease. We investigated miRNA expression profiles in cervical cancer using a microarray platform developed in house containing probes for mature miRNAs. Results. We have evaluated miRNA expression profiles of a heterogeneous set of cervical tissues from 25 different patients. This set included 19 normal cervical tissues, 4 squamous cell carcinoma, 5 high-grade squamous intraepithelial lesion (HSIL) and 9 low-grade squamous intraepithelial lesion (LSIL) samples. We observed high variability in miRNA expression especially among normal cervical samples, which prevented us from obtaining a unique miRNA expression signature for this tumour type. However, miRNAs deregulation in malignant and pre-malignant cervical tissues was detected after tackling the high variability observed. We were also able to identify putative targets of relevant candidate miRNAs. Conclusions. Our results show that miRNA deregulation may play an important role in the malignant transformation of cervical squamous cells. In addition, deregulated miRNAs highlight new candidate targets allowing a better understanding of the molecular mechanism of this tumour type. In this study we used a common reference design experiment where the common reference used was a commercial RNA from normal cervix (Ambion) and the test samples were 4 pre-treatment squamous cell cervical carcinoma, 7 high-grade Squamous Intraepithelial Lesion (CINII, n=2 and CIN III, n=5) sample, 9 low-grade Squamous Intraepithelial Lesion (CIN I) samples, 19 normal cervix samples and 4 pools of normal cervix samples.

Project description:Background. Although gene expression arrays have been used to generate molecular predictors of relapse and drug sensitivity in breast cancer, no large study describes genes and exons differentially expressed between breast cancer and benign lesions. Methods. One hundred and sixty-five tumour samples were obtained by fine needle aspiration (FNA). cDNA were hybridized on Splice ArrayTM. A nearest centroid prediction rule was developed to classify lesions as malignant or benign on a training set, and its performance evaluated on an independent validation set. A two-way ANOVA model was used to identify probesets that present a differential expression between cancer and benign lesions while adjusting for scan dates. P-values were adjusted for False Discovery Rate. Findings. Overall 120 breast cancers and 45 benign lesions were included in the study. A 1228-probeset molecular classifier for breast cancer diagnosis was generated from the training set (n=94). This signature accurately classified all samples (100% accuracy, 95% exact CI: 96-100%). In the validation set (n=71), the molecular predictor accurately classified 68 out of 71 tumours (96%, 95% CI: 88-99%). When the 165 samples were taken into account, 37 858 exon-probesets (5.4%) and 3733 genes (20%) were found to be differentially expressed between malignant and benign conditions (adjusted p-value<0.05). Pathway analyses showed that genes involved in spliceosome assembly were significantly enriched in malignant condition (permutation p=0.002). In the same population of 165 samples, 956 exon-probesets presented both a higher intensity and higher splice index in breast cancer, although located on unchanged genes. Interpretation. The present study provides a thorough description of differentially expressed exons between breast cancer and benign lesions, and emphasizes the contribution of spliceosome and alternative transcripts to the molecular portrait of breast malignancy. This allowed the development of a molecular classifier for breast cancer diagnosis using FNA.

Dataset Information

Quantitative proteomic analysis of MCM3 in ThinPrep samples of patients with cervical preinvasive cancer

Publications

Quantitative Proteomic Analysis of MCM3 in ThinPrep Samples of Patients with Cervical Preinvasive Cancer.

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