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.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ZeptoMetrix an Antylia scientific company

Download Mobile App




New Protein Biomarkers to Improve Diagnostic Tools for Colorectal Cancer

By LabMedica International staff writers
Posted on 22 Jan 2025
Print article
Image: Three newly identified protein biomarkers have the potential to improve diagnostic tools for colorectal cancer (Photo courtesy of Adobe Stock)
Image: Three newly identified protein biomarkers have the potential to improve diagnostic tools for colorectal cancer (Photo courtesy of Adobe Stock)

Colorectal cancer is a leading cause of cancer-related deaths globally, and its incidence is expected to rise in the coming decades. This cancer begins when abnormal cells grow uncontrollably in the large bowel, comprising the colon and rectum. Early detection is crucial for effective treatment, underscoring the need for reliable diagnostic tools. Currently, diagnosis involves the removal of tissue from the bowel, which is then tested in the lab to identify cancer and determine suitable treatments. Advances that simplify the detection process and enable earlier identification of colorectal cancer would be highly beneficial. Researchers have now identified three new protein biomarkers that could improve diagnostic tools for the disease.

Researchers at the University of Birmingham (Birmingham, UK) employed machine learning and artificial intelligence (AI) techniques to analyze large health datasets and identify proteins with strong predictive potential for colorectal cancer. In their study published in Frontiers in Oncology, the team analyzed one of the largest UK Biobank datasets, comparing protein profiles from healthy individuals and colorectal cancer patients. They identified three key proteins—TFF3, LCN2, and CEACAM5—associated with cell adhesion and inflammation, processes that are closely linked to cancer development. The next steps include further validating these biomarkers, which may eventually lead to the creation of new diagnostic tools. The team used three machine learning models and AI to recognize patterns in the data.

“In our study, we used advanced machine learning and artificial intelligence (AI) models combined with protein network analysis to identify key protein biomarkers that could aid in diagnosing colorectal cancer,” said Dr. Animesh Acharjee who led the study. “The biomarkers show promise but further large-scale validation study is needed to look into the relationships and mechanistic properties of these potential new biomarkers.”

Gold Member
Chagas Disease Test
CHAGAS Cassette
Verification Panels for Assay Development & QC
Seroconversion Panels
New
Ultrasonic Cleaner
UC 300 Series
New
Herpes Simplex Virus ELISA
HSV 2 IgG – ELISA

Print article

Channels

Clinical Chemistry

view channel
Image: Professor Nicole Strittmatter (left) and first author Wei Chen stand in front of the mass spectrometer with a tissue sample (Photo courtesy of Robert Reich/TUM)

Mass Spectrometry Detects Bacteria Without Time-Consuming Isolation and Multiplication

Speed and accuracy are essential when diagnosing diseases. Traditionally, diagnosing bacterial infections involves the labor-intensive process of isolating pathogens and cultivating bacterial cultures,... 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

Industry

view channel
Image: The collaboration aims to leverage Oxford Nanopore\'s sequencing platform and Cepheid\'s GeneXpert system to advance the field of sequencing for infectious diseases (Photo courtesy of Cepheid)

Cepheid and Oxford Nanopore Technologies Partner on Advancing Automated Sequencing-Based Solutions

Cepheid (Sunnyvale, CA, USA), a leading molecular diagnostics company, and Oxford Nanopore Technologies (Oxford, UK), the company behind a new generation of sequencing-based molecular analysis technologies,... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.