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”

Roberta Fusco; Vincenza Granata; Andrea Belli; Alessandra F Perna; Giovambattista Capasso; Michele Caraglia; Ugo Pace; Paolo Delrio; Ludovico Docimo; Claudio Gambardella; Francesco Saverio Lucido; Matteo Floris; Giorgia Locci; Matteo Runfola; Denise Giannascoli; Martina Izzo; Eugenio Sorgente; Margherita Borriello; Francesco Izzo; Mariadelina Simeoni; Antonella Petrillo

doi:10.2478/raon-2026-0026

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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”

Radiology and Oncology

Volume 60 (2026): Issue 2 (June 2026)

By: Roberta Fusco, Vincenza Granata, Andrea Belli, Alessandra F Perna, Giovambattista Capasso, Michele Caraglia, Ugo Pace, Paolo Delrio, Ludovico Docimo, Claudio Gambardella, Francesco Saverio Lucido, Matteo Floris, Giorgia Locci, Matteo Runfola, Denise Giannascoli, Martina Izzo, Eugenio Sorgente, Margherita Borriello, Francesco Izzo, Mariadelina Simeoni and Antonella Petrillo

Open Access

|Apr 2026

Figures & Tables

(A)Total number of mutated cases per gene. Bar plot representing the absolute number of patients in the cancer genome atlas colon adenocarcinoma (TCGA-COAD) cohort harboring somatic mutations in the most frequently altered genes. The top mutated genes (e.g., Kirsten rat sarcoma viral oncogene homolog [KRAS], adenomatous polyposis coli [APC], tumor protein p53 [TP53]) are displayed in descending order, highlighting their prevalence in colorectal cancer. (B) Cumulative percentage of patients with mutations per gene. Bar plot showing the cumulative percentage of patients affected by somatic mutations in each gene.

Heatmap of the top 20 most variable genes.

Bar plot top 20 correlated genes with disease-free survival (DFS).

Boxplot of the top 20 correlated genes revealed group-wise differences between patients with and without disease-free survival (DFS) events.

(A) Clustering with k = 3 (optimal value identified from gap statistic); (B) revealed three distinct patient clusters, visualized via principal component analysis (PCA) projection (Figure 5B)).

Top 10 Most variable genes across 3 clusters.

Receiver operating characteristic (ROC) curves of tested classifier to predict disease-free survival (DFS).

The description of the selected top 20 genes

Probe-set ID	Gene symbol	Gene title
1554332_a_at	ALDH1A1	Aldehyde dehydrogenase 1 family, member A1
1405_i_at	CDK2	Cyclin-dependent kinase 2
1552834_at	EGFR	Epidermal growth factor receptor
1552348_at	VEGFA	Vascular endothelial growth factor A
1554436_a_at	MMP9	Matrix metallopeptidase 9
1553613_s_at	IL6	Interleukin 6
1552797_s_at	CXCL8	C-X-C motif chemokine ligand 8
1553296_at	STAT3	Signal transducer and activator of transcription 3
1552349_a_at	TP53	Tumor protein p53
1552767_a_at	CCND1	Cyclin D1
1554679_a_at	BCL2	B-cell CLL/lymphoma 2
1553589_a_at	AKT1	AKT serine/threonine kinase 1
1552502_s_at	PTEN	Phosphatase and tensin homolog
1552870_s_at	KRAS	Kirsten rat sarcoma viral oncogene homolog
213418_at	HER2	Human epidermal growth factor receptor 2
1553970_s_at	MYC	MYC proto-oncogene, bHLH transcription factor
1553830_s_at	CCNE1	Cyclin E1
1552309_a_at	CDKN1A	Cyclin-dependent kinase inhibitor 1
1553828_at	PTGS2	Prostaglandin-endoperoxide synthase 2 (COX-2)
1554195_a_at	IL10	Interleukin 10

Publicly available dataset with chronic kidney disease (CKD) or colorectal cancer (CRC) structured data

Dataset Name	Description	Use	Access	Link [reference]
Real colorectal cancer datasets from Kaggle	62 colorectal cancer patients and their respective gene expression levels.	Integration between clinical and transcriptomic data	Open access	[7]
UCI CKD dataset	400 patients, 24 clinical features	CKD stage classification	Open access	[10]
Kaggle CKD dataset	1,659 patients, clinical/lab data	Statistical analysis, ML	Open access	[11]
USRDS	National registry (CKD, ESRD)	Epidemiological studies	Request-based	[12]
UKRR CKD/AKI dataset	UK registry for CKD/AKI	Clinical audit, registry	Request via HDR UK	[13]
NHANES CKD	US survey with CKD diagnostics	Public health, epidemiology	Open access	[14]
dbGaP CKD genetics	Exome/genotype + phenotype	CKD genomics, GWAS	Request via dbGaP	[15]
PLCO CRC dataset	155,000 subjects, screening + followup	Screening, epidemiology	Request via CDAS	[16]
GENIE BPC CRC	1,485 patients, NGS + clinical	Precision medicine	Open access	[17]
TCIA CMB-CRC	Histopathology + radiology DICOM	Radiogenomics, AI	Open access	[18]
TCIA CRC_FFPE CODEX	35 multiplexed tumor images	Spatial biology, tumor microenvironment	Open access	[19]
ICCR CRC pathology	Standardized pathology templates	Diagnostic standardization	Open access	[20]
Kaggle CRC lifestyle	Diet, lifestyle, BMI, habits	Risk factor analysis	Open access	[21]
LC25000 & EBHI	25,000 histopath images in 5 classes	Image classification (CNN)	Open access	[22]
Kvasir-SEG	Polyp segmentation with masks	Endoscopic segmentation	Open access	[23]
TCGA-COAD	> 450 CRC patients, multi-omics	Oncogenomics, CMS	Open + dbGaP (raw)	[24]

Performance of tested classifier to predict disease-free survival (DFS)

Model	Accuracy	Precision	Recall	F1-score	AUC Test	AUC CV
Random forest	0.842	0.9	0.818	0.857	0.955	0.92
Logistic regression	0.842	0.833	0.909	0.87	0.909	0.939
Gradient boosting	0.789	0.818	0.818	0.818	0.818	0.703
XGBoost	0.684	0.778	0.636	0.7	0.818	0.878
Decision tree	0.632	0.7	0.636	0.667	0.631	0.616

References

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DOI: https://doi.org/10.2478/raon-2026-0026 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099

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Language: English

Page range: 227 - 243

Submitted on: Feb 5, 2026

Accepted on: Mar 15, 2026

Published on: Apr 14, 2026

Published by: Association of Radiology and Oncology

In partnership with: Paradigm Publishing Services

Keywords:

colorectal cancer,

chronic kidney disease,

Related subjects:

© 2026 Roberta Fusco, Vincenza Granata, Andrea Belli, Alessandra F Perna, Giovambattista Capasso, Michele Caraglia, Ugo Pace, Paolo Delrio, Ludovico Docimo, Claudio Gambardella, Francesco Saverio Lucido, Matteo Floris, Giorgia Locci, Matteo Runfola, Denise Giannascoli, Martina Izzo, Eugenio Sorgente, Margherita Borriello, Francesco Izzo, Mariadelina Simeoni, Antonella Petrillo, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 60 (2026): Issue 2 (June 2026)

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”

Figures & Tables

FIGURE 1.

FIGURE 2.

FIGURE 3.

FIGURE 4.

FIGURE 5.

FIGURE 6.

FIGURE 7.

FIGURE 8.

The description of the selected top 20 genes

Publicly available dataset with chronic kidney disease (CKD) or colorectal cancer (CRC) structured data

Performance of tested classifier to predict disease-free survival (DFS)

Paradigm

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