Background & aims: Fecal tests currently used for colorectal cancer (CRC) screening show limited accuracy in detecting early tumors or precancerous lesions. In this respect, we comprehensively evaluated stool microRNA (miRNA) profiles as biomarkers for non-invasive CRC diagnosis. Methods: A total of 1,273 small RNA sequencing experiments were performed in multiple biospecimens. In a cross-sectional study, miRNA profiles were investigated in fecal samples from an Italian and a Czech cohort (155 CRC, 87 adenomas, 96 other intestinal diseases, 141 colonoscopy-negative controls). A predictive miRNA signature for cancer detection was defined by a machine learning strategy and tested in additional fecal samples from 141 CRC and 80 healthy volunteers. miRNA profiles were compared with those of 132 tumor/adenoma paired with adjacent mucosa, 210 plasma extracellular vesicles samples, and 185 fecal immunochemical tests (FIT) leftover samples. Results: Twenty-five miRNAs showed altered levels in stool of CRC patients in both cohorts (adj. P<.05). A five-miRNA signature, including miR-149-3p, miR-607-5p, miR-1246, miR-4488, and miR-6777-5p, distinguished patients from controls (AUC=0.86, 95% CI=0.79-0.94) and was validated in an independent cohort (AUC=0.96, 95% CI=0.92-1.00). The signature classified controls from low-/high-stage tumors, and advanced adenomas (AUC=0.82, 95% CI=0.71-0.97). Tissue miRNA profiles mirrored those of stool samples, while fecal profiles of different gastrointestinal diseases highlighted miRNAs specifically dysregulated in CRC. miRNA profiles in FIT leftover samples showed good correlation with those of stool collected in preservative buffer and their alterations can be detected in adenoma or CRC patients. Conclusions: Our comprehensive fecal miRNome analysis identified a signature accurately discriminating cancer aimed at improving a non-invasive diagnosis and screening strategies.

A fecal miRNA signature by small RNA sequencing accurately distinguishes colorectal cancers: results from a multicentric study

Pardini, Barbara
Co-first
;
Ferrero, Giulio
Co-first
;
Tarallo, Sonia
Co-first
;
Francavilla, Antonio;Licheri, Nicola;Clerico, Giuseppe;Cordero, Francesca
Co-last
;
2023-01-01

Abstract

Background & aims: Fecal tests currently used for colorectal cancer (CRC) screening show limited accuracy in detecting early tumors or precancerous lesions. In this respect, we comprehensively evaluated stool microRNA (miRNA) profiles as biomarkers for non-invasive CRC diagnosis. Methods: A total of 1,273 small RNA sequencing experiments were performed in multiple biospecimens. In a cross-sectional study, miRNA profiles were investigated in fecal samples from an Italian and a Czech cohort (155 CRC, 87 adenomas, 96 other intestinal diseases, 141 colonoscopy-negative controls). A predictive miRNA signature for cancer detection was defined by a machine learning strategy and tested in additional fecal samples from 141 CRC and 80 healthy volunteers. miRNA profiles were compared with those of 132 tumor/adenoma paired with adjacent mucosa, 210 plasma extracellular vesicles samples, and 185 fecal immunochemical tests (FIT) leftover samples. Results: Twenty-five miRNAs showed altered levels in stool of CRC patients in both cohorts (adj. P<.05). A five-miRNA signature, including miR-149-3p, miR-607-5p, miR-1246, miR-4488, and miR-6777-5p, distinguished patients from controls (AUC=0.86, 95% CI=0.79-0.94) and was validated in an independent cohort (AUC=0.96, 95% CI=0.92-1.00). The signature classified controls from low-/high-stage tumors, and advanced adenomas (AUC=0.82, 95% CI=0.71-0.97). Tissue miRNA profiles mirrored those of stool samples, while fecal profiles of different gastrointestinal diseases highlighted miRNAs specifically dysregulated in CRC. miRNA profiles in FIT leftover samples showed good correlation with those of stool collected in preservative buffer and their alterations can be detected in adenoma or CRC patients. Conclusions: Our comprehensive fecal miRNome analysis identified a signature accurately discriminating cancer aimed at improving a non-invasive diagnosis and screening strategies.
2023
1
22
colorectal cancer; machine learning; non-invasive diagnosis; precancerous lesions; small RNA sequencing; stool microRNAs
Pardini, Barbara; Ferrero, Giulio; Tarallo, Sonia; Gallo, Gaetano; Francavilla, Antonio; Licheri, Nicola; Trompetto, Mario; Clerico, Giuseppe; Senore, Carlo; Peyre, Sergio; Vymetalkova, Veronika; Vodickova, Ludmila; Liska, Vaclav; Vycital, Ondrej; Levy, Miroslav; Macinga, Peter; Hucl, Tomas; Budinska, Eva; Vodicka, Pavel; Cordero, Francesca; Naccarati, Alessio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1913511
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