Dataset Information

Label-free proteomics uncovers SMC1A expression is Down-regulated in AUB-E.

ABSTRACT:

Background

While heavy menstrual bleeding (HMB) is a prevalent symptom among women with abnormal uterine bleeding caused by endometrial disorder (AUB-E) seeking gynecologic care, the primary endometrial disorder remains poorly understood.

Methods

Five human endometrial samples from women with AUB-E and the age-matched healthy women were selected, respectively. Proteins from the samples were analyzed by a linear ion trap (LTQ)-Orbitrap Elite mass spectrometer based label-free proteomic approach. The purpose protein was validated by western blot and immunohistochemistry staining.

Results

A total of 2353 protein groups were quantified under highly stringent criteria with a false discovery rate of < 1% for protein groups, and 291 differentially expressed proteins were significantly changed between the two groups. The results showed that the down-regulation of structural maintenance of chromosomes protein 1A (SMC1A) in AUB-E patients. Next, this change in the glandular epithelial cells was validated by immunohistochemistry.

Conclusion

The results indicated a novel mechanism for the cause of AUB-E, as down-expression SMC1A potentially regulated the cell cycle progression in endometrial glandular epithelium further led to bleeding.

SUBMITTER: Jia Y

PROVIDER: S-EPMC7923474 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Publications

Label-free proteomics uncovers SMC1A expression is Down-regulated in AUB-E.

Jia Yingxian Y Luo Jie J Lan Yibing Y Li Chunming C Ma Linjuan L Zhu Xiaoming X Ruan Fei F Zhou Jianhong J

Reproductive biology and endocrinology : RB&E 20210302 1

<h4>Background</h4>While heavy menstrual bleeding (HMB) is a prevalent symptom among women with abnormal uterine bleeding caused by endometrial disorder (AUB-E) seeking gynecologic care, the primary endometrial disorder remains poorly understood.<h4>Methods</h4>Five human endometrial samples from women with AUB-E and the age-matched healthy women were selected, respectively. Proteins from the samples were analyzed by a linear ion trap (LTQ)-Orbitrap Elite mass spectrometer based label-free prote ...[more]

PMID: 33653363

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Project description:BackgroundLabel-free quantitative proteomics holds a great deal of promise for the future study of both medicine and biology. However, the data generated is extremely intricate in its correlation structure, and its proper analysis is complex. There are issues with missing identifications. There are high levels of correlation between many, but not all, of the peptides derived from the same protein. Additionally, there may be systematic shifts in the sensitivity of the machine between experiments or even through time within the duration of a single experiment.ResultsWe describe a hierarchical model for analyzing unbiased, label-free proteomics data which utilizes the covariance of peptide expression across samples as well as MS/MS-based identifications to group peptides-a strategy we call metaprotein expression modeling. Our metaprotein model acknowledges the possibility of misidentifications, post-translational modifications and systematic differences between samples due to changes in instrument sensitivity or differences in total protein concentration. In addition, our approach allows us to validate findings from unbiased, label-free proteomics experiments with further unbiased, label-free proteomics experiments. Finally, we demonstrate the clinical/translational utility of the model for building predictors capable of differentiating biological phenotypes as well as for validating those findings in the context of three novel cohorts of patients with Hepatitis C.ConclusionsMass-spectrometry proteomics is quickly becoming a powerful tool for studying biological and translational questions. Making use of all of the information contained in a particular set of data will be critical to the success of those endeavors. Our proposed model represents an advance in the ability of statistical models of proteomic data to identify and utilize correlation between features. This allows validation of predictors without translation to targeted assays in addition to informing the choice of targets when it is appropriate to generate those assays.

Dataset Information

Label-free proteomics uncovers SMC1A expression is Down-regulated in AUB-E.

Background

Methods

Results

Conclusion

Publications

Label-free proteomics uncovers SMC1A expression is Down-regulated in AUB-E.

Similar Datasets

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