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Background: Colon cancer patients with the same stage show diverse clinical behavior due to tumor heterogeneity. We aimed to discover distinct classes of tumors based on microarray expression patterns, to analyze whether the molecular classification correlated with the histopathological stages or other clinical parameters and to study differences in the survival. Methods: Hierarchical clustering was performed for class discovery in 88 colon tumors (stages I to IV). Pathways analysis and correlations between clinical parameters and our classification were analyzed. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the main subtype was generated using the 3-Nearest-Neighbor method. Coincidences with other prognostic predictors were assesed. Results: Hierarchical clustering identified four robust tumor subtypes with biologically and clinically distinct behavior. Stromal components (p<0.001), nuclear ß-catenin (p = 0.021), mucinous histology (p = 0.001), microsatellite-instability (p = 0.039) and BRAF mutations (p<0.001) were associated to this classification but it was independent of Dukes stages (p = 0.646). Molecular subtypes were established from stage I. High-stroma-subtype showed increased levels of genes and altered pathways distinctive of tumour-associated-stroma and components of the extracellular matrix in contrast to Low-stroma-subtype. Mucinous-subtype was reflected by the increased expression of trefoil factors and mucins as well as by a higher proportion of MSI and BRAF mutations. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the Low-stromasubtype distinguished low risk patients from high risk patients in the external cohort (Dukes B and C:HR = 8.56(2.53- 29.01); Dukes B,C and D:HR = 1.87(1.07-3.25)). Eight different reported survival gene signatures segregated our tumors into two groups the Low-stroma-subtype and the other tumor subtypes. Conclusions: We have identified novel molecular subtypes in colon cancer with distinct biological and clinical behavior that are established from the initiation of the tumor. Tumor microenvironment is important for the classification and for the malignant power of the tumor. Differential gene sets and biological pathways characterize each tumor subtype reflecting underlying mechanisms of carcinogenesis that may be used for the selection of targeted therapeutic procedures. This classification may contribute to an improvement in the management of the patients with CRC and to a more comprehensive prognosis. [ABSTRACT FROM AUTHOR]

Background: Colon cancer patients with the same stage show diverse clinical behavior due to tumor heterogeneity. We aimed to discover distinct classes of tumors based on microarray expression patterns, to analyze whether the molecular classification correlated with the histopathological stages or other clinical parameters and to study differences in the survival.Methods: Hierarchical clustering was performed for class discovery in 88 colon tumors (stages I to IV). Pathways analysis and correlations between clinical parameters and our classification were analyzed. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the main subtype was generated using the 3-Nearest-Neighbor method. Coincidences with other prognostic predictors were assesed.Results: Hierarchical clustering identified four robust tumor subtypes with biologically and clinically distinct behavior. Stromal components (p < 0.001), nuclear β-catenin (p = 0.021), mucinous histology (p = 0.001), microsatellite-instability (p = 0.039) and BRAF mutations (p < 0.001) were associated to this classification but it was independent of Dukes stages (p = 0.646). Molecular subtypes were established from stage I. High-stroma-subtype showed increased levels of genes and altered pathways distinctive of tumour-associated-stroma and components of the extracellular matrix in contrast to Low-stroma-subtype. Mucinous-subtype was reflected by the increased expression of trefoil factors and mucins as well as by a higher proportion of MSI and BRAF mutations. Tumor subtypes were validated using an external set of 78 patients. A 167 gene signature associated to the Low-stroma-subtype distinguished low risk patients from high risk patients in the external cohort (Dukes B and C:HR = 8.56(2.53-29.01); Dukes B,C and D:HR = 1.87(1.07-3.25)). Eight different reported survival gene signatures segregated our tumors into two groups the Low-stroma-subtype and the other tumor subtypes.Conclusions: We have identified novel molecular subtypes in colon cancer with distinct biological and clinical behavior that are established from the initiation of the tumor. Tumor microenvironment is important for the classification and for the malignant power of the tumor. Differential gene sets and biological pathways characterize each tumor subtype reflecting underlying mechanisms of carcinogenesis that may be used for the selection of targeted therapeutic procedures. This classification may contribute to an improvement in the management of the patients with CRC and to a more comprehensive prognosis. [ABSTRACT FROM AUTHOR]

Simple Summary: Colorectal cancer is one of the most significant causes of cancer mortality worldwide. Patients stratification is central to improve clinical practice and the Consensus Molecular Subtypes (CMS) have been validated as a useful tool to predict both prognosis and treatment response. This is the first study describing that microRNA profiling can define colorectal cancer CMS subtypes as well as mRNA profiling. MicroRNAs small size facilitates its analysis in serum facilitating a real-time analysis of the disease course. Three microRNA subtypes are identified: miR-LS is associated with the low-stroma/CMS2-subtype; miR-MI with the mucinous-MSI/CMS1-subtype and miR-HS with the high-stroma/CMS4-subtype. MicroRNA novel subtypes and association to the CMS classification were externally validated using TGCA data. Analyzing both mRNAs and miRs in the same population enabled identification of miR target genes and altered biological pathways. A miR-mRNA interaction screening and regulatory network selected major miR targets and was functionally validated for the miR30b/SCL6A4 pair. Colorectal cancer consensus molecular subtypes (CMSs) are widely accepted and constitutes the basis for patient stratification to improve clinical practice. We aimed to find whether miRNAs could reproduce molecular subtypes, and to identify miRNA targets associated to the High-stroma/CMS4 subtype. The expression of 939 miRNAs was analyzed in tumors classified in CMS. TALASSO was used to find gene-miRNA interactions. A miR-mRNA regulatory network was constructed using Cytoscape. Candidate gene-miR interactions were validated in 293T cells. Hierarchical-Clustering identified three miRNA tumor subtypes (miR-LS; miR-MI; and miR-HS) which were significantly associated (p < 0.001) to the reported mRNA subtypes. miR-LS correlated with the low-stroma/CMS2; miR-MI with the mucinous-MSI/CMS1 and miR-HS with high-stroma/CMS4. MicroRNA tumor subtypes and association to CMSs were validated with TCGA datasets. TALASSO identified 1462 interactions (p < 0.05) out of 21,615 found between 176 miRs and 788 genes. Based on the regulatory network, 88 miR-mRNA interactions were selected as candidates. This network was functionally validated for the pair miR-30b/SLC6A6. We found that miR-30b overexpression silenced 3′-UTR-SLC6A6-driven luciferase expression in 293T-cells; mutation of the target sequence in the 3′-UTR-SLC6A6 prevented the miR-30b inhibitory effect. In conclusion CRC subtype classification using a miR-signature might facilitate a real-time analysis of the disease course and treatment response. [ABSTRACT FROM AUTHOR]

Notch signaling regulates cell fate decisions during development through local cell interactions. Signaling is triggered by the interaction of the Notch receptor with its transmembrane ligands expressed on adjacent cells. Recent studies suggest that Delta is cleaved to release an extracellular fragment, DIEC, by a mechanism that involves the activity of the metalloprotease Kuzbanian; however, the functional significance of that cleavage remains controversial. Using independent functional assays in vitro and in vivo, we examined the biological activity of purified soluble Delta forms and conclude that Delta cleavage is an important down-regulating event in Notch signaling. The data support a model whereby Delta inactivation is essential for providing the critical ligand/receptor expression differential between neighboring ceils in order to distinguish the signaling versus the receiving partner. [ABSTRACT FROM AUTHOR]

BACKGROUND: MicroRNAs (miRs) are frequently altered in colorectal cancer (CRC) and can be used as prognostic factors. OBJECTIVE: To confirm in stage III CRC patients a reported miR signature that was associated to the presence of metastatic disease. To correlate miR expression with microsatellite instability (MSI) and mutations in RAS and BRAF. METHODS: miR-21, miR-135a, miR-206, miR-335 and miR-Let-7a expression was analyzed by RT-qPCR in 150 patients out of the 329 patients used to analyze MSI and RAS and BRAF mutations. Association with disease free survival (DFS) and overall survival (OS) was analyzed. Data was confirmed by a multivariate analysis. RESULTS: MiR-21 high expression (p = 0.034) and miR-335 low expression (p = 0.0061) were significantly associated with MSI-H. A positive trend (p = 0.0624) between miR-135a high expression and RAS mutations was found. Lower miR-21 expression levels are associated with DFS (HR = 2.654, 95% CI: 1.066–6.605, p = 0.036) and a trend with OS (HR = 2.419, 95% CI: 0.749–7.815, p = 0.140). MiR-21 high expression significantly improves DFS of the poor prognosis group (T4 or N2) (p = 0.03). CONCLUSIONS: Association of increased expression of miR-21 and better prognosis in the poor prognostic group may be of interest and could be explored in future prospective clinical trials. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented which discusses a study investigating the impact of high RNA concentrations in the real time reverse transcription quantitative polymerase chain reaction (RT-qPCR).