A New Hope for the Early Diagnosis of Endometriosis

Around the world, endometriosis affects millions of women. It is a benign, inflammatory condition in which tissue that normally grows in the lining of the uterus – the endometrium –  begins to abnormally grow in areas outside the uterus, typically around the pelvic region. Approximately 11% of females in the world who are of reproductive age have endometriosis. Some women experience heavy bleeding, discomfort during intercourse or urination, and even fertility issues. Individuals with endometriosis are also at higher risk of ovarian cancer, cardiovascular disease, and autoimmune diseases. Unfortunately, due to the lack of non-invasive modes of diagnosis, this condition is difficult to identify and treat. On average, it takes around seven years from the start of symptoms for endometriosis to be diagnosed. Current diagnosis measures heavily rely on the experience of the healthcare provider, and also involve taking medical history, physical exams, imaging, laparoscopic surgery and histopathology. There are also potential risks to the invasive surgery, including development of post-surgical adhesions and damage to nerves, reproductive organs, or blood vessels.

The good news is there may be a change in the game after researchers in Australia have identified a panel of proteins (biomarkers) that could serve as indicators of endometriosis. Published in December 2024 in the Human Reproduction journal, Schoeman and colleagues at Proteomics International in Western Australia, along with collaborators, ran a research study to identify proteins that could potentially be ideal diagnosis markers for the disease. Several research stages in this project, consisting of protein biomarker discovery, analytical validation, and clinical validation, allowed the identification of candidates for prospective non-invasive diagnosis of endometriosis. This study marks a key step toward replacing invasive surgery with faster, accurate, and non-surgical methods for diagnosing endometriosis.

The discovery phase of this study involved analyzing the plasma of individuals from the general population (19 samples), from symptomatic controls who were free of lesions but had endometriosis symptoms (15 samples), and from individuals that were confirmed to have endometriosis by laparoscopy (22 samples). Forty-eight proteins were found to be uniquely under-expressed or over-expressed in the plasma of endometriosis patients compared to symptomatic controls and the general population. These proteins then underwent analytical validation to establish reliable and robust experimental procedures that could corroborate their association with the disease. 

Put simply, the analytical validation phase of this project consisted of taking the plasma from patients and putting it through what is known as a Multiple Reaction Monitoring (MRM) Mass Spectrometry machine. The machine was then configured to accurately identify differentially expressed proteins in endometriosis patients, compared to samples from the general population and symptomatic controls. From the analytical validation stage, thirty-nine of the forty-eight proteins produced consistent results, and went into clinical validation studies to be harnessed to develop statistical models, where biomarkers could be used to predict endometriosis. Twelve additional proteins that have previously been linked to the disease were also put into the clinical validation studies. 

Using the highly refined and robust MRM Mass spectrometry experiments that were established in the analytical validation stage, data from the clinical validation studies were input into different statistical models, developed to calculate the probability of each biomarker's ability to discriminate endometriosis patients from symptomatic controls and general population samples. A total of ten protein biomarkers had differential expression that showed a strong correlation with the presence of endometriosis, independent of other clinical factors. Of these ten biomarkers, three were able to accurately distinguish endometriosis samples from the general population. The remaining seven, after adjusting for age and BMI, were also effective in distinguishing endometriosis patients from symptomatic controls, as well as identifying different severity of endometriosis-positive cases. These ten biomarker candidates were then characterized, and determined to be proteins important in the blood clotting system, the immune system, and protein-fat transport systems, as seen in Table 1.

Table 1. Top 10 candidate biomarkers for identifying endometriosis samples

Protein	Differential Expression	Relative to	Role in the body
Vitamin K-dependent protein S	Lower	General population	Coagulation
Hemoglobin subunit beta	Higher	General population	Coagulation (indirectly)
Serum paraoxonase/arylesterase 1	Higher	General population	Lipid transport
Afamin	Lower	Symptomatic controls	Lipid transport
Coagulation Factor XII	Lower	Symptomatic controls	Coagulation
Complement component C9	Higher	Symptomatic controls	Immune system
Neuropilin-1	Lower	Symptomatic controls	Immune system
Inter-alpha-trypsin inhibitor light chain	Lower	Symptomatic controls	Immune system
Selenoprotein P	Higher	Symptomatic controls	Lipid transport
Proteoglycan 4	Lower	Symptomatic controls	Immune system

When compared to previous literature, the results from this study revealed that there were differences in the results of some of the proteins' association with endometriosis. These discrepancies reflect the difficulty in establishing a definitive set of biomarkers for the diagnosis of endometriosis. Irrespective, this study presents a compelling case due to its large sample size, which enabled the drawing of more reliable conclusions with minimal bias. The use of well-defined clinical populations—whose endometriosis status was confirmed via laparoscopy prior to discovery and clinical analysis—further strengthens its validity. Additionally, categorizing the identified proteins offers insight into the disease’s pathology and may contribute to future advancements in its treatment. Overall, this study highlights significant progress in the divergence from invasive surgical methods toward fast, accurate, accessible, and non-invasive diagnosis of endometriosis. Such advancements have the potential to allow more relevant and personalized care for patients.

1. Schoeman, M., et al. (2024). Identification of plasma protein biomarkers for endometriosis and the development of statistical models for disease diagnosis.

2. Mayo Clinic. (2024). Endometriosis – Symptoms and causes. Mayo Clinic. Retrieved April 23, 2025, from https://www.mayoclinic.org/diseases-conditions/endometriosis/symptoms-causes/syc-20354656

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