A new tool is revealing the invisible networks inside cancer
Spanish researchers have created a powerful new open-source tool that helps uncover the hidden genetic networks driving cancer. Called RNACOREX, the software can analyze thousands of molecular interactions at once, revealing how genes communicate inside tumors and how those signals relate to patient survival. Tested across 13 different cancer types using international data, the tool matches the predictive power of advanced AI systems—while offering something rare in modern analytics: clear, interpretable explanations that help scientists understand why tumors behave the way they do.
Researchers at the University of Navarra in Spain have created RNACOREX, an open-source software platform designed to identify gene regulation networks linked to cancer survival. The tool was developed by scientists at the Institute of Data Science and Artificial Intelligence (DATAI), in collaboration with members of the Cancer Center Clínica Universidad de Navarra. Its performance has been tested using data from thirteen different tumor types provided by the international consortium The Cancer Genome Atlas (TCGA).
RNACOREX was published in the journal PLOS Computational Biology. It can analyze thousands of biological molecules at the same time, allowing it to detect important molecular interactions that are often missed by traditional analysis methods. By producing a clear and interpretable molecular "map," the software helps researchers better understand how tumors function and offers new ways to explore the biological processes that drive cancer progression.
Decoding Cancer's Hidden Genetic Structure
Within human cells, different types of molecules such as microRNAs (miRNAs) and messenger RNA (mRNA) -- communicate through highly complex regulatory networks. When these networks fail to function properly, diseases including cancer can develop.
"Understanding the architecture of these networks is crucial for detecting, studying, and classifying different tumor types. However, reliably identifying these networks is a challenge due to the vast amount of available data, the presence of many false signals, and the lack of accessible and precise tools capable of distinguishing which molecular interactions are truly associated with each disease," says Rubén Armañanzas, head of the Digital Medicine Laboratory at DATAI and one of the study's lead authors.
RNACOREX was designed to overcome these challenges. It integrates curated information from international biological databases with real-world gene expression data to rank the most biologically meaningful miRNA-mRNA interactions. From this foundation, the software builds progressively more complex regulatory networks that can also function as probabilistic models for studying disease behavior.
Predicting Survival With Interpretable Results
To evaluate how well the tool performs, the research team applied RNACOREX to data from thirteen different cancers, including breast, colon, lung, stomach, melanoma, and head and neck tumors, using information from The Cancer Genome Atlas (TCGA).
"The software predicted patient survival with accuracy on par with sophisticated AI models, but with something many of those systems lack: clear, interpretable explanations of the molecular interactions behind the results," says Aitor Oviedo-Madrid, a researcher at the Digital Medicine Laboratory of DATAI and first author of the study.
Beyond survival prediction, RNACOREX can identify regulatory networks linked to clinical outcomes, detect molecular patterns shared across multiple tumor types, and spotlight individual molecules with strong biomedical relevance. These insights may help researchers generate new hypotheses about how tumors grow and progress, while also pointing toward promising future diagnostic markers or treatment targets. "Our tool provides a reliable molecular 'map' that helps prioritize new biological targets, speeding up cancer research," Oviedo-Madrid adds.