Dataset Information

Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma.

ABSTRACT: Uterine leiomyosarcoma (ULMS) is the major subtype of uterine sarcoma (US) and contributes to uterine cancer deaths. Although preoperative diagnosis of US remains challenging, frequent application of laparoscopic surgery for benign uterine leiomyomas (ULM) requires precise exclusion of US. MicroRNAs are stably present in the bloodstream, and the application of circulating miRNAs as disease biomarkers has been recognized. In the present study, we aimed to identify diagnostic biomarkers for distinguishing US from ULM by focusing on circulating miRNAs. All serum samples were collected preoperatively between 2009 and 2017, and all cases were histopathologically diagnosed. Whole miRNA profiles were obtained using a miRNA microarray. By analyzing expression levels of the miRNAs, candidate miRNAs were selected based on diagnostic performance in discriminating US from ULM, and a diagnostic model was then constructed. A total of 90 serum samples were analyzed, and clustering analyses revealed that the profiles of ULMS were distinct from those of controls. Based on leave-one-out cross-validation, seven miRNAs were selected as biomarker candidates. Based on model construction, the optimal model consisted of two miRNAs (miR-1246 and miR-191-5p), with an area under the receiver operating characteristic curve (AUC) for identifying ULMS of 0.97 (95% confidence interval [CI], 0.91-1.00). In contrast, serum lactate dehydrogenase had an AUC of only 0.64 (95% CI, 0.34-0.94). Seven serum miRNAs with high diagnostic performance for preoperative US screening were detected, and a promising diagnostic model for ULMS was generated.

SUBMITTER: Yokoi A

PROVIDER: S-EPMC6890430 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma.

Yokoi Akira A Matsuzaki Juntaro J Yamamoto Yusuke Y Tate Keisei K Yoneoka Yutaka Y Shimizu Hanako H Uehara Takashi T Ishikawa Mitsuya M Takizawa Satoko S Aoki Yoshiaki Y Kato Ken K Kato Tomoyasu T Ochiya Takahiro T

Cancer science 20191116 12

Uterine leiomyosarcoma (ULMS) is the major subtype of uterine sarcoma (US) and contributes to uterine cancer deaths. Although preoperative diagnosis of US remains challenging, frequent application of laparoscopic surgery for benign uterine leiomyomas (ULM) requires precise exclusion of US. MicroRNAs are stably present in the bloodstream, and the application of circulating miRNAs as disease biomarkers has been recognized. In the present study, we aimed to identify diagnostic biomarkers for distin ...[more]

PMID: 31599471

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Project description:The morphologic assessment of uterine leiomyosarcoma (LMS) may be challenging, and diagnostic immunohistochemical (IHC) analysis is currently lacking. We evaluated the genomic landscape of 167 uterine LMS by targeted next-generation sequencing (NGS) to identify common genomic alterations. IHC analyses corresponding to these genomic landmarks were applied to a test cohort of 16 uterine LMS, 6 smooth muscle tumors of uncertain malignant potential (STUMP), and 6 leiomyomas with NGS data and a validation cohort of 8 uterine LMS, 12 STUMP, 21 leiomyomas and leiomyoma variants, 7 low-grade endometrial stromal sarcomas, and 2 diagnostically challenging uterine smooth muscle tumors. IHC results were individually interpreted by 3 pathologists blinded to NGS data. Overall, 94% of LMS showed ≥1 genomic alteration involving TP53, RB1, ATRX, PTEN, CDKN2A, or MDM2, with 80% showing alterations in ≥2 of these genes. In the test cohort, an initial panel of p53, Rb, PTEN, and ATRX was applied, followed by a panel of DAXX, MTAP, and MDM2 in cases without abnormalities. Abnormal p53, Rb, PTEN, and ATRX IHC expression was seen in 75%, 88%, 44%, and 38% of LMS, respectively, in the test cohort. Two or more abnormal IHC results among these markers were seen in 81% of LMS. STUMPs demonstrated only 1 IHC abnormality involving these markers. No IHC abnormalities were seen in leiomyomas. In the validation cohort, abnormal p53, Rb, and PTEN IHC results were seen in LMS, whereas rare STUMP or leiomyomas with bizarre nuclei showed IHC abnormalities involving only 1 of the markers. Abnormalities in ≥2 markers were present in both diagnostically challenging smooth muscle tumors, confirming LMS. Concordance was excellent among pathologists in the interpretation of IHC (κ = 0.97) and between IHC and NGS results (κ = 0.941). Uterine LMS exhibit genomic landmark alterations for which IHC surrogates exist, and a diagnostic algorithm involving molecular-based IHC may aid in the evaluation of unusual uterine smooth muscle tumors.

Project description:Objective: The objective of this study was to estimate the accuracy of transcriptome-based classifier in differential diagnosis of uterine leiomyoma and leiomyosarcoma. Methods: We manually selected 114 normal uterine tissue and 31 leiomyosarcoma samples from publicly available transcriptome data in UCSC Xena as training/validation sets. We developed pre-processing procedure and gene selection method to sensitively find genes of larger variance in leiomyosarcoma than normal uterine tissues. Through our method, twenty genes were selected to build transcriptome-based classifier. The prediction accuracies of deep feedforward neural network (DNN), support vector machine (SVM), Random Forest (RF), and Gradient Boosting (GB) models were examined. We interpret the biological functionality of selected genes via network-based analysis using Gene-Mania. To validate the performance of trained model, we additionally collected 35 clinical samples of leiomyosarcoma and leiomyoma as a test set (18 + 17 as 1st and 2nd test sets). Results: We discovered genes expressed in a highly variable way in leiomyosarcoma while these genes are expressed in a conserved way in normal uterine samples. These genes were mainly associated with DNA replication, cell cycle, and DNA damage checkpoint. Among evaluated machine learning classifiers, the DNN had the highest accuracy and average AUC value in training data set. As gene selection and model training were made in leiomyosarcoma and uterine normal tissue, proving discriminant of ability between leiomyosarcoma and leiomyoma is necessary. Thus, further validation of trained model was conducted in newly collected clinical samples of leiomyosarcoma and leiomyoma. The DNN classifier performed AUC of 0.917 and 0.914 supporting that the selected genes in conjunction with DNN classifier are well discriminating the difference between leiomyosarcoma and leiomyoma in clinical sample. Conclusion: The transcriptome-based classifier accurately distinguished uterine leiomyoma from leiomyosarcoma.

Dataset Information

Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma.

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Serum microRNA profile enables preoperative diagnosis of uterine leiomyosarcoma.

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