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Pathological discrimination between luteinized thecoma associated with sclerosing peritonitis and thecoma. | LitMetric

Pathological discrimination between luteinized thecoma associated with sclerosing peritonitis and thecoma.

Medicine (Baltimore)

Department of Obstetrics & Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Hubei Province, China.

Published: June 2023

Background: Similarities between luteinized thecoma associated with sclerosing peritonitis (LTSP) and thecoma, cause difficulty in clinical differential diagnoses. To improve the situation, we selected 10 specified molecular pathological markers that are frequently used in clinical pathology of ovarian sex cord-stromal tumors to determine whether they exert a discriminatory effect.

Methods: Applying immunohistochemistry, we analyzed the expression of alpha-1,6-mannosylglycoprotein 6-beta-n-acetylglucosaminyltransferase B (MGAT5B), nuclear receptor coactivator 3 (NCOA3), proliferation marker protein Ki-67 (MKI67), estrogen receptor, progesterone receptor, Vimentin, receptor tyrosine-protein kinase erbB-2, Catenin beta-1 (β-Catenin), CD99 antigen (CD99) and Wilms tumor protein (WT1) in 102 cases of diseases containing 11 LTSP and 91 thecoma. Whole-exome sequencing and fluorescence in situ hybridization were used to examine the MGAT5B-NCOA3 fusion gene in LTSP. Statistical analysis was performed using t test, one-way analysis of variance test, and post hoc test.

Results: Six significant markers were verified for the discrimination between LTSP and thecoma, containing 4 upregulating indicators MGAT5B, NCOA3, MKI67, β-Catenin, and 2 downregulating markers CD99 and WT1 in luteinized cells. In addition, the MGAT5B-NCOA3 fusion gene was identified in LTSP for the first time with significantly rich expression compared to thecoma.

Conclusions: We verified 6 significant molecular pathological markers containing MGAT5B, NCOA3, MKI67, β-Catenin, CD99, and WT1 and identified MGAT5B-NCOA3 fusion gene in LTSP; this work will help clinicians to discriminate between medical conditions and treat patients accurately.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256399PMC
http://dx.doi.org/10.1097/MD.0000000000033911DOI Listing

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