Simple and Cheap Blood Test Could Detect Cancer and Other Diseases Before Symptoms Appear

MethylScan is a low-cost blood test that detects cancers and organ diseases by analyzing DNA methylation, improving early diagnosis, and identifying disease origin.

UCLA researchers have created a simple, low-cost blood test that may be able to detect multiple cancers, liver diseases, and organ abnormalities at the same time by analyzing DNA fragments circulating in the bloodstream.

Described in the journal Proceedings of the National Academy of Sciences, the test could provide a more affordable way to catch diseases early and monitor overall health.

“Early detection is crucial,” said Dr. Jasmine Zhou, the study’s senior author, a professor of pathology and laboratory medicine and investigator at the UCLA Health Jonsson Comprehensive Cancer Center. “Survival rates are far higher when cancers are caught before they spread. If you detect cancer at stage one, outcomes are dramatically better than at stage four.”

How MethylScan Uses Cell-Free DNA

The approach, known as MethylScan, analyzes cell-free DNA (cfDNA), which consists of small fragments of genetic material released into the bloodstream as cells die. Because every organ sheds DNA, these fragments carry signals that reflect activity across the entire body.

“Every day, 50 to 70 billion cells in our body die. They don’t just disappear; their DNA goes into the bloodstream,” Zhou said. “That means we already have information from all our organs circulating in the blood.”

Using blood to detect cancer, often called a liquid biopsy, is not a new concept. Some existing tests search for mutations in tumor DNA, but they typically target a limited set of changes and can be expensive because they require deep sequencing to identify faint signals.

DNA Methylation vs Mutation-Based Testing

Rather than focusing on mutations, the UCLA team examined DNA methylation, chemical markers that regulate gene activity. These patterns vary by tissue type and shift when cells become cancerous or diseased.

“DNA methylation reflects the health status of a tissue,” said Dr. Wenyuan Li, a professor of pathology and laboratory medicine at UCLA and co-corresponding author of the study. “It’s a very informative signal.”

A major obstacle is that most cell-free DNA in the bloodstream does not come from tumors or damaged organs. About 80% to 90% originates from normal blood cells, creating background noise that makes it harder and more expensive to detect rare disease-related fragments.

Reducing Noise to Improve Detection

To solve this, the researchers developed a method, built off past work, to remove much of the background DNA before sequencing. Specialized enzymes selectively cut away unmethylated DNA, which mainly comes from blood cells. A genome-wide hybridization panel then enriches the remaining sample for methylated DNA from solid organs, including those that may be diseased.

By reducing this noise, the team can lower the amount of sequencing required while maintaining sensitivity. Reaching an effective sequencing depth of 300× per sample requires only 5 Gb of data, which could cost under $20 if sequencing costs fall below $4 per gigabase.

To evaluate performance, the researchers analyzed blood samples from 1,061 individuals, including patients with liver, lung, ovarian, and stomach cancers; people with liver diseases such as hepatitis B, hepatitis C, alcohol-related liver disease, and metabolic-associated liver disease; individuals with benign lung nodules; and healthy participants. Machine learning was used to interpret the complex methylation data.

Accuracy in Multi-Cancer and Liver Detection

The test showed strong overall performance in detecting multiple cancers. At 98% specificity, meaning very few false positives, it identified about 63% of cancers across all stages and roughly 55% of early-stage cases.

It also performed well in monitoring liver cancer among high-risk groups, such as people with cirrhosis or HBV, detecting nearly 80% of cases with a specificity just above 90%, which corresponds to a false positive rate below 10%.

In addition to detecting cancer, the methylation signals helped pinpoint where in the body the abnormal DNA originated, known as the tissue of origin.

Identifying Disease Origin and Organ Health

“Being able to trace signals back to their source is important because a positive blood test needs to be followed by imaging or other diagnostic procedures directed at the right organ,” said Li.

MethylScan functions like a system-wide health monitor. By reading DNA signals in the blood, it can detect when organs such as the liver or lungs are under stress or damaged, even before a specific disease is identified.

The study also found that the test could distinguish between different liver diseases, including viral hepatitis and metabolic-associated liver disease, correctly classifying about 85% of patients. This suggests it could reduce the need for invasive liver biopsies.

Toward a Universal Blood Test

While larger prospective studies are still needed to confirm real-world effectiveness, Zhou said the findings mark a meaningful step toward a single, affordable blood test capable of detecting a wide range of diseases earlier and more comprehensively than current approaches.

“This study demonstrates that blood-based methylation profiling can deliver clinically meaningful information across multiple diseases,” said Zhou. “It’s an exciting advancement that brings us closer to realizing the dream of a single assay for universal disease detection.”

Reference: “Toward the simultaneous detection of multiple diseases with a highly cost-effective cell-free DNA methylome test” by Weihua Zeng, Chun-Chi Liu, Shuo Li, Yonggang Zhou, Mary L. Stackpole, Ying Xiao, Ran Hu, Caitlin Tang, Qiao Liu, Wanwen Zeng, Angela Yeh, Andrew Melehy, Benjamin Tran, Zorawar Noor, Megumi Yokomizo, Dominic Amara, Shreya Gumate, Preeti Ahuja, David Yuanze Li, Junting Zhao, Inga Rose, Cattlena Walker, Sadaf Malik, Yazhen Zhu, Hsian-Rong Tseng, Edward B. Garon, Samuel W. French, Clara E. Magyar, Sarah M. Dry, Clara M. Lajonchere, Daniel Geschwind, Gina Choi, Sammy Saab, Akshay Shetty, Carrie R. Wong, Kevin G. King, David S. Lu, Steven S. Raman, Xiyan Xiang, Kirti Shetty, Lopa Mishra, Sanaz Memarzadeh, Yan Liu, Frank Albe, William Hsu, Kostyantyn Krysan, Steven M. Dubinett, Denise R. Aberle, Vatche Agopian, Steven-Huy B. Han, Wing Hung Wong, Xiaohui Ni, Wenyuan Li and Xianghong Jasmine Zhou, 6 April 2026, Proceedings of the National Academy of Sciences.
DOI: 10.1073/pnas.2518347123

The research was supported in part by grants from the National Cancer Institute.

Never miss a breakthrough: Join the SciTechDaily newsletter.
Follow us on Google and Google News.