PXD028199

PXD028199 is an original dataset announced via ProteomeXchange.

Title	Quantitative label-free proteomic analysis of follicle fluid to identify novel candidate protein biomarker for endometriosis-associated infertility
Description	Background: Gold standard for endometriosis (EM) diagnosis tends to be invasive and histopathological exams following surgery are required. EM lack precise non-invasive biomarkers and current biomarkers do not provide detailed knowledge and are therefore deficient in precision that prevents early diagnosis and treatment of EM. Methods: In the present retrospective study, label-free quantitative proteomics(LFQP) technology was used to analyze the follicular fluid (FF) of infertile patients with EM in order to recognize FF biomarkers for EM. The participants were distributed into the EM group according to the severity of EM: (i) EM-group 1 (EM-G1, stage I to Stage II, n=10); (ii) EM-group 2 (EM-G2, Stage III to Stage IV, n=10) and the control-group (CON-G, n=10, infertility due to male factors) from August 2019 to June 2020. A potential biomarker panel of FF differential protein to EM-associated infertility was also evaluated by receiver operating characteristic (ROC) curve and binary Logistic regression models. Results: A total of 237 and 236 proteins were identified in the EM-associated infertility and control groups, respectively; After untargeted proteomic analysis, 6 significant differential proteins were found in both EM-G1 VS. CON-G and EM-G2 VS. CON-G. Through bioinformatics analysis, we identified a several groups of proteins that may be closely related to EM, among which immunoglobulin lambda variable 7-46 (IGLV7-46), immunoglobulin heavy constant gamma 2 (IGHG2), glia-derived nexin (GDN) and Inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) were significantly up-regulated, while corticosteroid-binding globulin (CBG) and angiotensinogen (AGT) were significantly down regulated. According to ROC curve analysis, the area under the curve (AUC) for IGLV7-46, IGHG2, GDN, ITIH3, AGT and CBG was 0.87, 0.82, 0.78, 0.82, 0.77 and 0.81 with optimum sensitivity of 78%, 80%, 100%, 80%, 70%, 100% and specificity of 93%, 80%, 60%, 80%, 80% and 60% respectively. According to binary logistic regression and evaluated ROC analysis, the AUC for the combination of IGLV7-46, IGHG2 and ITIH3 was 0.926. Conclusions: The research use LFQP to study the FF proteome of EM-associated infertility and totally scan out 6 differentially expressed proteins. These proteins participate primarily in the inflammation and immune response phase. Additionally, ROC analysis suggests that the IGLV7-46, IGHG2 and ITIH3 are likely strong diagnostic biomarkers of EM-associated infertility, which may be a way of thinking for identifying possible minimally invasive diagnostic indicators and therapeutic goals of EM-associated infertility.
HostingRepository	iProX
AnnounceDate	2021-09-02
AnnouncementXML	Submission_2023-12-21_20:28:30.402.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	XianLing Cao
SpeciesList	scientific name: Homo sapiens; NCBI TaxID: 9606;
ModificationList	No PTMs are included in the dataset
Instrument	LTQ

Revision	Datetime	Status	ChangeLog Entry
0	2021-09-03 02:34:39	ID requested
1	2021-09-03 02:34:56	announced
⏵ 2	2023-12-21 20:28:31	announced	2023-12-22: Update publication information.

Cao XL, Song JY, Sun ZG, Quantitative label-free proteomic analysis of human follicle fluid to identify novel candidate protein biomarker for endometriosis-associated infertility. J Proteomics, 266():104680(2022) [pubmed]

submitter keyword: Endometriosis, Label-free quantitative proteomics, Follicular fluid biomarkers,Infertility, In vitro fertilization

ZhenGao Sun
contact affiliation	The First Affiliated Hospital of Shandong University of traditional Chinese Medicine
contact email	sunzhengao77@126.com
lab head
XianLing Cao
contact affiliation	Shandong Traditional Chinese Medicine University
contact email	caoxianlingling@163.com
dataset submitter

iProX dataset URI