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Cited by 168Endometriosis Research and TreatmentEndometrial and Cervical Cancer TreatmentsEctopic Pregnancy Diagnosis and Management

Molecular classification of endometriosis and disease stage using high-dimensional genomic data

Open access
Tamaresis, John S., Irwin, Juan C., Goldfien, Gabriel A., Rabban, Joseph T., Burney, Richard O., Nezhat, Camran, DePaolo, Louis V., Giudice, Linda C.·2014·Endocrinology
DOI 10.1210/en.2014-1490PMC PMC4239429
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Abstract

Endometriosis (E), an estrogen-dependent, progesterone-resistant, inflammatory disorder, affects 10% of reproductive-age women. It is diagnosed and staged at surgery, resulting in an 11-year latency from symptom onset to diagnosis, underscoring the need for less invasive, less expensive approaches. Because the uterine lining (endometrium) in women with E has altered molecular profiles, we tested whether molecular classification of this tissue can distinguish and stage disease. We developed classifiers using genomic data from n = 148 archived endometrial samples from women with E or without E (normal controls or with other common uterine/pelvic pathologies) across the menstrual cycle and evaluated their performance on independent sample sets. Classifiers were trained separately on samples in specific hormonal milieu, using margin tree classification, and accuracies were scored on independent validation samples. Classification of samples from women with E or no E involved 2 binary decisions, each based on expression of specific genes. These first distinguished presence or absence of uterine/pelvic pathology and then no E from E, with the latter further classified according to severity (minimal/mild or moderate/severe). Best performing classifiers identified E with 90%-100% accuracy, were cycle phase-specific or independent, and used relatively few genes to determine disease and severity. Differential gene expression and pathway analyses revealed immune activation, altered steroid and thyroid hormone signaling/metabolism, and growth factor signaling in endometrium of women with E. Similar findings were observed with other disorders vs controls. Thus, classifier analysis of genomic data from endometrium can detect and stage pelvic E with high accuracy, dependent or independent of hormonal milieu. We propose that limited classifier candidate genes are of high value in developing diagnostics and identifying therapeutic targets. Discovery of endometrial molecular differences in the presence of E and other uterine/pelvic pathologies raises the broader biological question of their impact on the steroid hormone response and normal functions of this tissue.

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  • Similar gene expression patterns between women with endometriosis and other pelvic/uterine pathology (adenomyosis, uterine prolapse, uterine fibroids) suggest that the gene expression pattern of endometrium might be similarly perturbed across women with different pelvic pathologies.

    “Indeed, similar gene expression patterns between women with endometriosis and other pelvic/ uterine pathology (adenomyosis, uterine prolapse, and uterine fibroids) were also observed by Tamaresis et al., which suggested that the gene expression pattern of endometrium might be similarly perturbed across women with different pelvic pathologies (93).”

    From Biomarkers in endometriosis: Challenges and opportunities (2017) · cites this paper as [93]

  • Relatively few genes were required to delineate endometriosis from other benign pelvic conditions and to classify disease severity.

    “Interestingly, relatively few genes were required to delineate endometriosis from other benign pelvic conditions and to classify disease severity.”

    From Update on biomarkers for the detection of endometriosis (2015) · cites this paper as [118]

  • The two best performing proliferative and early-secretory phase-specific disease classifiers achieved 100% accuracy using less than 100 genes for each disease classification decision.

    “For example, the two best performing proliferative and early-secretory phase-specific disease classifiers achieved 100% accuracy using less than 100 genes for each disease classification decision.”

    From Update on biomarkers for the detection of endometriosis (2015) · cites this paper as [118]

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Authors
Tamaresis, J. S., Irwin, J. C., Goldfien, G. A., Rabban, J. T., Burney, R. O., Nezhat, C., DePaolo, L. V., & Giudice, L. C.
Affiliations
  • University of California, San Francisco (US)
  • Madigan Army Medical Center (US)
  • Stanford Medicine (US)
  • National Institutes of Health (US)
Corresponding author
Giudice, Linda C.
ORCID
Venue
Endocrinology
Publisher
The Endocrine Society
Published
2014-12-01
Volume
155
Issue
12
Pages
4986-4999
ISSN
  • 0013-7227 (ISSN-L)
  • 1945-7170
DOI
10.1210/en.2014-1490
PMC
PMC4239429
OpenAlex
W2007347214
Cited by
168
References
58
Citations by year
2026: 8 · 2025: 16 · 2024: 17 · 2023: 13 · 2022: 14 · 2021: 9 · 2020: 28 · 2019: 13 · 2018: 15 · 2017: 13 · 2016: 13 · 2015: 9
FWCI
14.17
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Endocrinology (publishedVersion, cc-by-nc)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • University of Virginia
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD055764, P50HD055764)
Topics
  • Health Sciences › Medicine › Reproductive Medicine › Endometriosis Research and Treatment
  • Health Sciences › Medicine › Obstetrics and Gynecology › Endometrial and Cervical Cancer Treatments
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ectopic Pregnancy Diagnosis and Management
Keywords
endometriosis, endometriosis staging, endometrium, molecular classification, gene expression classifier, menstrual cycle, immune activation, steroid hormone signaling, thyroid hormone signaling, growth factor signaling, uterine pathology, non-invasive diagnosis, differential gene expression
MeSH terms
Adult, Artificial Intelligence, Decision Trees, Endometriosis, Endometrium, Female, Humans, Menstrual Cycle, Middle Aged, Severity of Illness Index, Case-Control Studies, Gene Expression Profiling, Genomics, Molecular Diagnostic Techniques, Transcriptome

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