New Method Uses Pulsed Infrared Light to Find Cancer's 'Fingerprints' In Blood Plasma

Cancer diagnoses have traditionally relied on invasive or time-consuming procedures like tissue biopsies. Now, new research published in ACS Central Science introduces a method that utilizes pulsed infrared light to identify molecular signatures in blood plasma that may indicate the presence of certain cancers. In this proof-of-concept study, blood plasma samples from over 2,000 individuals were analyzed to correlate specific molecular patterns with lung cancer, suggesting the possibility of a unique "cancer fingerprint."

Blood plasma, the liquid component of blood, is free of cells and transports various molecules, including proteins, metabolites, lipids, and salts throughout the body. Certain molecules within plasma can serve as biomarkers for potential health issues. For example, elevated levels of prostate-specific antigen are used for prostate cancer screening. A medical test that could analyze a wide range of molecules might be capable of identifying specific patterns associated with different types of cancer, enabling faster diagnoses and reducing healthcare costs. To identify potential chemical markers of cancer, researchers from Ludwig Maximilian University of Munich (Munich, Germany) employed a method called electric-field molecular fingerprinting, which uses pulsed infrared light to analyze complex molecular mixtures in blood plasma.

In their study, the researchers applied this technique by directing ultra-short infrared light pulses through plasma samples. They then analyzed data from 2,533 participants, including individuals with lung, prostate, breast, or bladder cancer, as well as healthy controls. For each sample, they captured the "infrared molecular fingerprint," which represents the light emitted by the molecular components of the plasma. By examining these diverse molecular patterns from both cancer patients and non-cancer controls, the researchers trained a machine learning model to identify specific molecular signatures associated with the four cancer types. The model was tested on a separate set of samples to assess its ability to recognize new, unseen data. The technique achieved up to 81% accuracy in detecting lung cancer-related molecular patterns and distinguishing them from non-cancer samples. However, the model's performance was less effective in identifying the other three types of cancer. Moving forward, the researchers plan to refine and expand their approach to detect additional cancers and other health conditions.

"Laser-based infrared molecular fingerprinting detects cancer, demonstrating its potential for clinical diagnostics,” said LMU Munich researcher Mihaela Žigman. “With further technological developments and independent validation in sufficiently powered clinical studies, it could establish generalizable applications and translate into clinical practice — advancing the way we diagnose and screen for cancer today.”

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