We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

LabMedica

Download Mobile App
Recent News Expo Clinical Chem. Molecular Diagnostics Hematology Immunology Microbiology Pathology Technology Industry Focus

Gold-Enhanced Nanopore Sensors Detect Ovarian Cancer Biomarkers in Urine Samples

By LabMedica International staff writers
Posted on 21 Feb 2024
Print article
Image: The research showed the effectiveness of a new technique to detect ovarian cancer marker peptides (Photo courtesy of VCU)
Image: The research showed the effectiveness of a new technique to detect ovarian cancer marker peptides (Photo courtesy of VCU)

The key to defeating cancer lies in its early and accurate diagnosis. Clinical data underscores this, revealing a significant 50-75% increase in the five-year survival rate when cancers are identified in their initial stages. This is true for various types of cancer, including ovarian cancer, which is notoriously challenging to diagnose. Mass spectrometry has been instrumental in discovering thousands of peptides in the urine of ovarian cancer patients, indicating their potential as biomarkers for the disease. However, the application of mass spectrometry in clinical settings is limited, prompting the need for alternative methods to detect these peptides. Now, a new study has found a novel technique to be effective in identifying specific biomarkers found in the urine of ovarian cancer patients, a development that could eventually aid doctors in diagnosing the disease more accurately.

Researchers from Virginia Commonwealth University (VCU, Richmond, VA, USA) employed a combination of gold nanoparticles and nanopore sensing to detect and categorize 13 peptides previously identified in ovarian cancer patients. Among these peptides is one from LRG-1, a protein biomarker increasingly recognized and typically found in the urine of individuals with ovarian cancer. This new technique holds the potential to simultaneously detect a wide array of peptides. Researchers hope that this comprehensive approach, when used alongside other diagnostic information (like the CA-125 blood test, transvaginal ultrasound, and family medical history), could one day provide a more accurate assessment of the presence of early-stage ovarian cancer.

“We are interested in ovarian cancer because it is particularly difficult to detect and requires the development of new sensors that could be made widely available for clinical applications,” said Joseph Reiner, Ph.D. “We envision that our approach could expand beyond ovarian cancer to other types of cancer.”

Related Links:
Virginia Commonwealth University

New
Gold Member
Human Chorionic Gonadotropin Test
hCG Quantitative - R012
Verification Panels for Assay Development & QC
Seroconversion Panels
New
Unstirred Waterbath
HumAqua 5
New
Chlamydia Trachomatis Assay
Chlamydia Trachomatis IgG

Print article

Channels

Clinical Chemistry

view channel
Image: The GlycoLocate platform uses multi-omics and advanced computational biology algorithms to diagnose early-stage cancers (Photo courtesy of AOA Dx)

AI-Powered Blood Test Accurately Detects Ovarian Cancer

Ovarian cancer ranks as the fifth leading cause of cancer-related deaths in women, largely due to late-stage diagnoses. Although over 90% of women exhibit symptoms in Stage I, only 20% are diagnosed in... Read more

Immunology

view channel
Image: The cancer stem cell test can accurately choose more effective treatments (Photo courtesy of University of Cincinnati)

Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer

Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more

Technology

view channel
Image: The new algorithms can help predict which patients have undiagnosed cancer (Photo courtesy of Adobe Stock)

Advanced Predictive Algorithms Identify Patients Having Undiagnosed Cancer

Two newly developed advanced predictive algorithms leverage a person’s health conditions and basic blood test results to accurately predict the likelihood of having an undiagnosed cancer, including ch... Read more