Researchers develop ramanome-based metastatic assessment index to enhance cancer metastasis prediction

Researchers from Qingdao University and the Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) of the Chinese Academy of Sciences have developed a novel method for rapidly and accurately assessing the metastatic potential of cancer cells. The new tool— combining Raman spectroscopy and machine learning—introduces the Ramanome-based Metastasis Index (RMI), offering an innovative means of diagnosing and managing cancer.

This study, published in Small Methods, addresses a gap in current cancer research. Cancer metastasis, a leading cause of cancer-related deaths, involves a complex, multi-stage process. Traditional methods for assessing metastatic potential, such as transwell assays and histopathological analysis, are often time-consuming and labor-intensive, with limited accessibility to samples.

The RMI leverages single-cell Raman spectra to provide a comprehensive molecular profile of cancer cells. This label-free, non-invasive technique enables the rapid extraction of key information about metastatic behavior.

The researchers validated the RMI across multiple tumor cell lines and a mouse model of pancreatic ductal adenocarcinoma. The results showed that RMI had a strong correlation with traditional methods, which confirmed its reliability.

A key finding of this study was the pivotal role of lipid-related Raman peaks in determining the RMI. Lipidomic analysis revealed strong associations between metastatic potential and specific lipids, such as phosphatidylcholine, phosphatidylethanolamine, and cholesteryl ester. These findings highlight the critical role of lipid metabolism in cancer progression, presenting it as a potential therapeutic target. 

"This study not only provides a tool for predicting tumor metastatic potential but also offers new insights into the mechanisms underlying metastasis," said Prof. REN He from Qingdao University. "This method addresses the challenges associated with time-consuming and labor-intensive biological experiments, as well as the limitations related to sample availability and the technical proficiency of operators. By replacing traditional biological methods with more objective, stable, and user-friendly physical techniques, RMI provides accurate predictions of tumor metastasis. Meanwhile, understanding the link between metabolic changes and metastatic potential could guide the development of therapies targeting these metabolic pathways."

Prof. XU Jian, the head of the Single-Cell Center at QIBEBT, remarked, "Our next step is to continue advancing Ramanome-based tumor classification methods and rapid metabolic profiling techniques."

This study demonstrates how Raman spectroscopy, coupled with machine learning and lipidomics, can change cancer diagnosis and treatment. By identifying lipids linked to metastatic potential, it paves the way for innovative strategies in cancer therapy.