New software sheds light on cancer’s hidden genetic networks

(Press-News.org) University of Navarra (Spain) researchers have developed RNACOREX, a new open-source software capable of identifying gene regulation networks with applications in cancer survival analysis. The tool, created by scientists at the Institute of Data Science and Artificial Intelligence (DATAI), members of the Cancer Center Clínica Universidad de Navarra, has been validated with data from thirteen tumor types from the international consortium The Cancer Genome Atlas (TCGA).

Published in PLOS Computational Biology, RNACOREX analyzes thousands of molecules simultaneously to detect key interactions that often remain undetected using conventional analytical approaches. The tool provides researchers with an interpretable molecular “map” that improves the understanding of tumors and opens new venues for revealing the mechanisms that drive tumor progression.

Decoding Cancer’s Hidden Genetic Structure Inside our cells, different types of molecules — such as microRNAs (miRNAs) and messenger RNA (mRNA)— communicate through highly complex regulatory networks. When these connections break down, cancers and other diseases can emerge. 

“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 addresses this problem by combining information from international databases with real gene-expression data analysis to rank the most biologically relevant miRNA–mRNA interactions. Using this information, it derives increasingly complex regulatory networks that could also serve as powerful probabilistic models.

Better interpretation and prediction To assess its performance, researchers evaluated RNACOREX on thirteen cancer types — from breast and colon to lung, stomach, melanoma, and head and neck — using data from The Cancer Genome Atlas (TCGA) consortium. “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”, adds Aitor Oviedo-Madrid, a researcher at the Digital Medicine Laboratory of DATAI and first author of the study.

RNACOREX not only identifies regulatory networks associated with clinical outcomes, but also uncovers molecular patterns shared across tumor types and highlights individual molecules of particular biomedical interest. These findings open the door to new hypotheses about the mechanisms that regulate tumor growth and suggest valuable clues for future diagnostic or therapeutic targets. “Our tool provides a reliable molecular ‘map’ that helps prioritize new biological targets, speeding up cancer research”, he concludes.

An evolving open-source tool RNACOREX is an open-source program available on GitHub and PyPI (Python Package Index), and includes automated database downloads to streamline its use in laboratories and research centers. The project has been partially funded by the Government of Navarra (ANDIA 2021 program) and the ERA PerMed JTC2022 PORTRAIT.

“As artificial intelligence in genomics accelerates, RNACOREX positions itself as an explainable, easy-to-interpret solution and an alternative to ‘black-box’ models, helping bring omics data into biomedical practice,” says Armañanzas.

The University of Navarra is already developing new functionalities — including pathway analysis and additional interaction layers — to develop models that better explain the mechanisms driving tumor growth and progression. These advances reflect the institution’s commitment to interdisciplinary research that integrates biomedicine, AI, and data science to improve understanding and management of cancer through personalized and precision medicine.

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