In Science Advances, Nagoya University scientists publish their work discovering three membrane proteins associated with ovarian cancer. The research team are hopeful that their discovery could improve approaches for detecting and diagnosing the disease.
Reliable biomarkers lacking for ovarian cancer
Biomarker detection and identification is at the heart of personalized medicine, which considers how a disease affects the human body at the individual level. Through personalized medicine, clinical decisions and treatment plans can be tailored to the patient, rather than the wider patient population.
Biomarkers are biological molecules found in body fluids, blood or tissue. They might be DNA, proteins or metabolites that are involved in the molecular processes that occur in healthy cell functioning or disease. Detecting elevated levels of a biomarker that is known to be associated with a specific disease can aid physicians in the diagnostic process. The ability to detect diseases early using biomarkers can significantly help a patient’s prognosis, particularly in diseases that become increasingly difficult to treat as they progress. One example is ovarian cancer, which can be challenging to diagnose at an early stage due to the vague symptoms that occur and that are also associated with other illnesses.
Recently, scientists have established that extracellular vesicles (EVs) are implicated in the communicative processes that occur between healthy and cancerous cells.
EVs are stable membrane proteins that are found outside of the cancer cell, therefore making them a good biomarker candidate as they can be isolated from bodily fluids easily. Identifying EVs that distinguish ovarian cancer from other types of cancer, however, has proven challenging.
At Nagoya University, Dr. Akira Yokoi and Dr. Mayu Ukai from the Institute for Advanced Research extracted EVs – small and medium to large in size – from patients with high-grade serous carcinoma (HGSC), the most common type of ovarian cancer that accounts for ~75% of cases. They applied liquid chromatography-mass spectrometry, a common method utilized in biomarker discovery and validation, to search for proteins associated with HGSC specifically.
Their analyses produced three membrane proteins, FRα, Claudin-3 and TACSTD2, found in smaller EVs, that are associated with HGSC. Yokoi explains that validating these proteins was initially tough, because the EVs are “loaded” with many different molecules. For this reason, “small EVs are more suitable biomarkers than the medium and large type,” he explains. Smaller EVs are also called exosomes.
Using nanowires to “fish out” exosomes
To explore whether the smaller EVs could be captured in a manner that would aid cancer identification, the researchers sought help from Dr. Takao Yasui, a nanowire specialist at the Graduate School of Engineering at Nagoya University. Yasui created polyketone chain-coated nanowires (pNWs), which the team demonstrated could isolate exosomes from blood samples.
Extracellular vesicles (EVs) are vesicles with lipid-bilayer structures (shown in center), and three proteins expressed on small EVs called exosomes (shown in red, green, blue) are more suitable biomarkers to detect ovarian cancer than the larger extracellular vesicle. Credit: Akira Yokoi.
“pNW creation was tough,” Yokoi says. “We must have tried three to four different coatings on the nanowires. Although polyketones are a completely new material to use to coat this type of nanowire, in the end, they were such a good fit.”
“The results of this research suggest that these diagnostic biomrkers can be used as predictive markers for specific therapies. Our results allow doctors to optimize their therapeutic strategy for ovarian cancer, therefore, they may be useful for realizing personalized medicine,” Yokoi concludes.
Reference:
- Yokoi A, Ukai M, Yasui T, et al. Identifying high-grade serous ovarian carcinoma-specific extracellular vesicles by polyketone-coated nanowires. Science Advances. 2023. doi: 10.1126/sciadv.ade6958
This article was published by: Technology Networks