L1CAM as an E-selectin Ligand in Colon Cancer

Deschepper, Fanny M.; Zoppi, Roberta; Pirro, Martina; Hensbergen, Paul J.; Dall’Olio, Fabio; Kotsias, Maximillianos; Gardner, Richard A.; Spencer, Daniel I. R.; Videira, Paula A.

doi:10.3390/ijms21218286

Cited by 13 publications

(9 citation statements)

References 83 publications

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“…Herein, the adhesion of HepG2 to HUVEC was significantly reduced by the treatment with C20/C22 (Figure 2D). This cancer cell-vascular endothelial cell interaction can be assisted by the interactions between selectins that are expressed on the vascular endothelium and the selectin ligand presented on cancer cells [61]. Therefore, reducing the expression of selectin ligand on cancer cells can effectively prevent the binding of cancer cells to endothelium [62].…”

Section: Discussionmentioning

confidence: 99%

Bisimidazolium Salt Glycosyltransferase Inhibitors Suppress Hepatocellular Carcinoma Progression In Vitro and In Vivo

et al. 2022

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Hepatocellular carcinoma is a leading cause of cancer death, and the disease progression has been related to glycophenotype modifications. Previously synthesized bisimidazolium salts (C20 and C22) have been shown to selectively inhibit the activity of glycosyltransferases in cultured cancer cell homogenates. The current study investigated the anticancer effects of C20/C22 and the possible pathways through which these effects are achieved. The therapeutic value of C20/C22 in terms of inhibiting cancer cell proliferation, metastasis, and angiogenesis, as well as inducing apoptosis, were examined with hepatic cancer cell line HepG2 and a xenograft mouse model. C20/C22 treatment downregulated the synthesis of SLex and Ley sugar epitopes and suppressed selectin-mediated cancer cell metastasis. C20/C22 inhibited HepG2 proliferation, induced cell-cycle arrest, increased intracellular ROS level, led to ER stress, and eventually induced apoptosis through the intrinsic pathway. Furthermore, C20/C22 upregulated the expressions of death receptors DR4 and DR5, substantially increasing the sensitivity of HepG2 to TRAIL-triggered apoptosis. In vivo, C20/C22 effectively inhibited tumor growth and angiogenesis in the xenograft mouse model without adverse effects on major organs. In summary, C20 and C22 are new promising anti-hepatic cancer agents with multiple mechanisms in controlling cancer cell growth, metastasis, and apoptosis, and they merit further development into anticancer drugs.

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Section: Discussionmentioning

confidence: 99%

Bisimidazolium Salt Glycosyltransferase Inhibitors Suppress Hepatocellular Carcinoma Progression In Vitro and In Vivo

et al. 2022

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“…Survival analysis and univariate and multivariate Cox regression analyses showed consistent results, implicating SELE as an oncogene for CRC, as supported by other related studies. 10 Numerous studies [21][22][23] confirmed that SELE was strongly linked to the generation and invasion of CRC in small samples. Other researchers 24 detected preoperative serum E-selection and CA19-9 in 152 patients with CRC and 28 healthy volunteers, and showed that patients with higher serum expression levels of E-selection and CA19-9 had a significantly lower 5-year overall survival rate.…”

Section: Discussionmentioning

confidence: 99%

SELE gene as a characteristic prognostic biomarker of colorectal cancer

Xiao

Shen

et al. 2021

J Int Med Res

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Objective To investigate the expression and clinical value of the E-selectin gene ( SELE) in colorectal cancer (CRC). Methods Using gene expression profiles and clinicopathological data for patients with CRC from The Cancer Genome Atlas, and tumor and adjacent normal tissues from 31 patients with CRC from Xianyang Central Hospital, we studied the correlation between SELE gene expression and clinical parameters using Kaplan–Meier and Cox proportional hazards regression analyses. Results Higher expression of SELE was significantly associated with a poorer prognosis and shorter survival in patients with CRC. The median expression level of SELE was significantly higher in CRC tissues compared with healthy adjacent tissue. Cox regression analysis showed that the prognosis of CRC was significantly correlated with the expression of SELE. Immunohistochemical analysis also showed that positive expression of E-selectin increased significantly in line with increasing TNM stage. Conclusion: This study confirmed that SELE gene expression is an independent prognostic factor in patients with CRC.

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“…Some genes have not been found in BRCA, but have shown that they act on the pathogenesis and development of other cancers. For example, FUT6 gene [48] has been found to be occupied an important position in the metastasis of colorectal cancer. In addition, study has shown that the expression of KIRREL2 gene [49] may cause congenital nephrotic syndrome.…”

Section: Identify the Key Biomarkers In Each Cancermentioning

confidence: 99%

SADLN: Self-Attention Based Deep Learning Network of Integrating Multi-omics Data for Cancer Subtype Recognition

Gong

Sun

Cheng

et al. 2022

Preprint

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Background: Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through deep learning model, which didn't considering the distributes differently of omics data. Moreover, these methods ignore the relationship of samples. Results :In order to tackle these problems, we proposed SADLN: a self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample’s relationship for learning a accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. Conclusions: The Self-Attention Based Deep Learning Network (SADLN) is a effective method of integrate multi-omics data for cancer subtype recognition.

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L1CAM as an E-selectin Ligand in Colon Cancer

Cited by 13 publications

References 83 publications

Bisimidazolium Salt Glycosyltransferase Inhibitors Suppress Hepatocellular Carcinoma Progression In Vitro and In Vivo

Bisimidazolium Salt Glycosyltransferase Inhibitors Suppress Hepatocellular Carcinoma Progression In Vitro and In Vivo

SELE gene as a characteristic prognostic biomarker of colorectal cancer

SADLN: Self-Attention Based Deep Learning Network of Integrating Multi-omics Data for Cancer Subtype Recognition

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