The miSignal test currently detects risks for seven types of cancer, including pancreatic and breast cancer, and is generating revenue through various distribution channels in Japan.

The newly acquired funding will facilitate Craif's entry into the U.S., with clinical trials for miSignal expected to complete by late 2026 and FDA approval anticipated in 2027.

Urine samples used by Craif are cleaner than blood samples, resulting in clearer test results and reduced costs for both patients and healthcare providers.

The company plans to expand miSignal's capabilities to detect more than ten cancer types and is also exploring applications for early detection of non-cancerous diseases like dementia.

To date, Craif has raised a total of $57 million, including a recent $22 million Series C funding round led by X&KSK, aimed at supporting its expansion into the U.S. market.

With a valuation nearing $100 million, Craif's recent funding round included participation from notable investors like Unreasonable Group and aims to bolster its research and development efforts.

Craif has established an R&D lab in Irvine, California, and plans to open a business development office in San Diego, collaborating with 30 medical institutions across 15 U.S. states.

The startup was co-founded by Ryuichi Onose and Takao Yasui, with Onose's personal experiences with cancer in his family motivating the development of better detection methods.

Founded in 2018 as a spin-off from Nagoya University, Craif is a Japanese biotech startup that utilizes microRNA (miRNA) and artificial intelligence for non-invasive cancer detection.

Craif has developed a urine-based test called miSignal, which enables early cancer detection, even at Stage 1, addressing the limitations of traditional diagnostic methods that can be invasive.

Cancer remains a leading global cause of death, with the National Cancer Institute estimating millions of new cases annually, highlighting the urgent need for innovative detection methods.

Craif's test utilizes machine learning algorithms to analyze urinary microRNA samples, achieving high sensitivity and specificity in distinguishing between cancerous and non-cancerous samples.