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Background Transmembrane 4 L six family member 1 (TM4SF1) is upregulated in several epithelial cancers and is closely associated with poor prognosis. However, the role of TM4SF1 and its potential mechanism in colorectal cancer (CRC) remain elusive. Methods We investigated the expression of TM4SF1 in the Oncomine, the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and confirmed the results by immunohistochemistry (IHC), qPCR and Western blotting (WB) of CRC tissues. The effect of TM4SF1 on the epithelial-to-mesenchymal transition (EMT) and cancer stemness of CRC cells was investigated by Transwell, wound healing and sphere formation assays. A series of in vitro and in vivo experiments were conducted to reveal the mechanisms by which TM4SF1 modulates EMT and cancer stemness in CRC. Results TM4SF1 expression was markedly higher in CRC tissues than in non-tumour tissues and was positively correlated with poor prognosis. Downregulation of TM4SF1 inhibited the migration, invasion and tumour sphere formation of SW480 and LoVo cells. Conversely, TM4SF1 overexpression significantly enhanced the migration, invasion and tumoursphere formation potential of CRC cells, Additionally, TM4SF1 silencing inhibited the EMT mediated by transforming growth factor-β1 (TGF-β1). Mechanistically, gene set enrichment analysis (GSEA) predicted that the Wnt signalling pathway was one of the most impaired pathways in TM4SF1-deficient CRC cells compared to controls. The results were further validated by WB, which revealed that TM4SF1 modulated SOX2 expression in a Wnt/β-catenin activation-dependent manner. Furthermore, we found that knockdown of TM4SF1 suppressed the expression of c-Myc, leading to decreased c-Myc binding to the SOX2 gene promoter. Finally, depletion of TM4SF1 inhibited metastasis and tumour growth in a xenograft mouse model. Conclusion Our study substantiates a novel mechanism by which TM4SF1 maintains cancer cell stemness and EMT via the Wnt/β-catenin/c-Myc/SOX2 axis during the recurrence and metastasis of CRC.

Information Content(IC) is an important dimensionof assessing the semantic similarity between two terms or word senses in word knowledge. The conventional method of obtaining IC of word senses is to combine knowledge of their hierarchical structure from an ontology like WordNet with actual usage in text as derived from a large corpus. In this paper, a new model of IC is presented, which relies on hierarchical structure alone. The model considers not only the hyponyms of each word sense but also its depth in the structure. The IC value is easier to calculate based on our model, and when used as the basis of a similarity approach it yields judgments that correlate more closely with human assessments than others, which using IC value obtained only considering the hyponyms and IC value got by employing corpus analysis.

Platelet-derived growth factor-D (PDGF-D) plays a crucial role in the progression of several cancers. However, its role in colorectal cancer (CRC) remains unclear. Our study showed that PDGF-D was highly expressed in CRC tissues and was positively associated with the clinicopathological features. Down-regulation of PDGF-D inhibited the tumor growth, migration and angiogenesis of SW480 cells in vitro and in vivo. Whereas up-regulation of PDGF-D promoted the malignant behaviors of HCT116 cells. Moreover, PDGF-D up-regulated the expression of Notch1 and Twist1 in CRC cells. In addition, PDGF-D expression promoted Epithelial to mesenchymal transition (EMT), which was accompanied with decreased E-cadherin and increased Vimentin expression. Consistently, PDGF-D, Notch1, and Twist1 are obviously up-regulated in transforming growth factor-beta 1 (TGF-β1) treated HCT116 cells. Since Notch1 and Twist1 play an important role in EMT and tumor progression, we examined whether there is a correlation between Notch1 and Twist1 in EMT status. Our results showed that up-regulation of Notch1 was able to rescue the effects of PDGF-D down-regulation on Twist1 expression in SW480 cells, whereas down-regulation of Notch1 reduced Twist1 expression in HCT116 cells. Furthermore, we found that Twist1 promoted EMT and aggressiveness of CRC cells. These results suggest that PDGF-D promotes tumor growth and aggressiveness of CRC, moreover, down-regulation of PDGF-D inactivates Notch1/Twist1 axis, which could reverse EMT and prevent CRC progression.

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BACKGROUND: We previously demonstrated that the pleomorphic adenoma gene like-2 (PLAGL2) is involved in the pathogenesis of Hirschsprung disease. Enhanced PLAGL2 expression was observed in several malignant tumours. However, the exact function of PLAGL2 and its underlying mechanism in colorectal cancer (CRC) remain largely unknown. METHODS: Immunohistochemical analysis of PLAGL2 was performed. A series of in vitro and in vivo experiments were conducted to reveal the role of PLAGL2 in the progression of CRC. RESULTS: Enhanced PLAGL2 expression was significantly associated with EMT-related proteins in CRC. The data revealed that PLAGL2 promotes CRC cell proliferation, migration, invasion and EMT both in vitro and in vivo. Mechanistically, PLAGL2 promoted the expression of ZEB1. PLAGL2 enhanced the expression and nuclear translocation of β-catenin by decreasing its phosphorylation. The depletion of β-catenin neutralised the regulation of ZEB1 that was caused by enhanced PLAGL2 expression. The small-molecule inhibitor PNU-74654, also impaired the enhancement of ZEB1 that resulted from the modified PLAGL2 expression. The depletion of ZEB1 could block the biological function of PLAGL2 in CRC cells. CONCLUSIONS: Collectively, our findings suggest that PLAGL2 mediates EMT to promote colorectal cancer metastasis via βcatenin-dependent regulation of ZEB1.

Multi-view Multi-instance Multi-label Learning(M3L) deals with complex objects encompassing diverse instances, represented with different feature views, and annotated with multiple labels. Existing M3L solutions only partially explore the inter or intra relations between objects (or bags), instances, and labels, which can convey important contextual information for M3L. As such, they may have a compromised performance. In this paper, we propose a collaborative matrix factorization based solution called M3Lcmf. M3Lcmf first uses a heterogeneous network composed of nodes of bags, instances, and labels, to encode different types of relations via multiple relational data matrices. To preserve the intrinsic structure of the data matrices, M3Lcmf collaboratively factorizes them into low-rank matrices, explores the latent relationships between bags, instances, and labels, and selectively merges the data matrices. An aggregation scheme is further introduced to aggregate the instance-level labels into bag-level and to guide the factorization. An empirical study on benchmark datasets show that M3Lcmf outperforms other related competitive solutions both in the instance-level and bag-level prediction.