ArticleRadiology and oncology2026
Toward multimodal integration of colorectal cancer and chronic kidney disease: transcriptomic modeling as a framework for the SIRIO study "Spatial radiomics and transcriptomics to the discovery of the cross-link between colon cancer and chronic kidney disease".
Article in Radiology and oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundColorectal cancer (CRC) and chronic kidney disease (CKD) are major contributors to global morbidity and mortality. Increasing epidemiological and genetic evidence suggests a biologically plausible interplay between renal dysfunction and colorectal tumorigenesis. However, publicly available datasets rarely integrate structured renal function parameters with multi-omic cancer data, limiting mechanistic and prognostic investigations of the CRC-CKD axis.
methodsWe systematically evaluated public resources, including the cancer genome atlas (TCGA) - colon adenocarcinoma (COAD) and an open-access transcriptomic survival dataset, to assess the feasibility of integrating clinical and genomic information for biomarker discovery. Transcriptomic data from 62 CRC patients were analyzed using unsupervised clustering, correlation-based feature selection, and multiple supervised machine learning classifiers to identify gene signatures associated with disease-free survival (DFS).
resultsTCGA-COAD confirmed the canonical mutational landscape of CRC but lacked structured renal function data. In contrast, the survival dataset enabled integrative DFS modelling. Unsupervised analysis identified three transcriptionally distinct subgroups. Random forest and logistic regression achieved the highest predictive performance. Feature importance analysis highlighted
conclusionsOur findings expose a critical gap in current public repositories regarding integrated CRC-CKD data and demonstrate the feasibility of transcriptomic-driven prognostic modelling. This analysis provides a hypothesisgenerating computational framework to support future multimodal investigations integrating renal, molecular, and imaging parameters in CRC that will be the main objectives of the SIRIO study.
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