EFEMP2 Inhibits Breast Cancer Invasion And Metastasis In Vitro And In Vivo

Fibulin-4 as a potential extracellular vesicle marker of fibrosis in patients with cirrhosis

Chronic liver injury leads to decreased liver function and increased fibrosis. Fibrosis is not only associated with the development of portal hypertension and carcinogenesis, but with the occurrence of events and a poor prognosis, highlighting the importance of non-invasive fibrosis assessment in patients. In the present study, we searched for markers related to liver fibrosis via proteomic analysis of small extracellular vesicles (sEVs). In the discovery cohort, proteomic analysis was carried out in the sEVs extracted from the sera of 5 patients with decompensated cirrhosis, 5 patients with compensated cirrhosis, and 5 controls without liver disease. Interestingly, in this cohort, fibulin-4 was significantly associated with cirrhosis while in the validation cohort [formed by 191 patients: 7 patients without disease, 16 patients without liver disease (other diseases), 38 patients with chronic liver disease (CLD), 75 patients with cirrhosis of Child-Pugh class A (36 without hepatocellular carcinoma [HCC], 29 with HCC), and 65 patients with cirrhosis of Child-Pugh class B-C (39 without HCC, 26 with HCC)], fibulin-4/CD9 levels increased with cirrhosis progression. Furthermore, the fibulin-4/CD9 ratio was significantly higher in patients with varices. Immunostaining also revealed strong fibulin-4 expression in cholangiocytes within the fibrous areas and mesothelial cells in liver tissue blood vessels. Taken together, our results suggest that fibulin-4, essential for lysyl oxidase activation, might be a new liver fibrosis marker found in the sEVs of patients with cirrhosis.

EFEMP2 upregulates PD-L1 expression via EGFR/ERK1/2/c-Jun signaling to promote the invasion of ovarian cancer cells

BACKGROUND

Fibulin-like extracellular matrix protein 2 (EFEMP2) has been reported to be related to the progression of various cancers. We have previously reported that EFEMP2 was highly expressed in ovarian cancer and was strongly associated with poor prognosis in patients. This study intends to further explore its interacting proteins and possible downstream signaling pathways.

METHOD

The expression of EFEMP2 was detected by RT-qPCR, ICC and western blot in 4 kinds of ovarian cancer cells with different migration and invasion ability. Cell models with strong or weak EFEMP2 expression were constructed by lentivirus transfection. The effects of the down-regulation and up-regulation of EFEMP2 on the biological behavior of ovarian cancer cells were studied through in-vitro and in-vivo functional tests. The phosphorylation pathway profiling array and KEGG database analyses identified the downstream EGFR/ERK1/2/c-Jun signaling pathway and the programmed death-1 (PD-L1) pathway enrichment. Additionally, the protein interaction between EFEMP2 and EGFR was detected by immunoprecipitation.

RESULT

EFEMP2 was positively correlated with the invasion ability of ovarian cancer cells, its down-regulation inhibited the migrative, invasive and cloning capacity of cancer cells in vitro and suppressed the tumor proliferation and intraperitoneal diffusion in vivo, while its up-regulation did the opposite. Moreover, EFEMP2 could bind to EGFR to induce PD-L1 regulation in ovarian cancer, which was caused by the activation of EGFR/ERK1/2/c-Jun signaling. Similar to EFEMP2, PD-L1 was also highly expressed in aggressive cells and had the ability to promote the invasion and metastasis of ovarian cancer cells both in vitro and in vivo, and PD-L1 upregulation was partly caused by EFEMP2 activation. Afatinib combined with trametinib had an obvious effect of inhibiting the intraperitoneal diffusion of ovarian cancer cells, especially in the group with low expression of EFEMP2, while overexpression of PD-L1 could reverse this phenomenon.

CONCLUSION

EFEMP2 could bind to EGFR to activate ERK1/2/c-Jun pathway and regulate PD-L1 expression, furthermore PD-L1 was extremely essential for EFEMP2 to promote ovarian cancer cells invasion and dissemination in vitro and in vivo. Targeted therapy against the source gene EFEMP2 is our future research direction, which may better inhibit the invasion and metastasis of ovarian cancer cells.

HOXA1, a breast cancer oncogene

More than 25 years ago, the first literature records mentioned HOXA1 expression in human breast cancer. A few years later, HOXA1 was confirmed as a proper oncogene in mammary tissue. In the following two decades, molecular data about the mode of action of the HOXA1 protein, the factors contributing to activate and maintain HOXA1 gene expression and the identity of its target genes have accumulated and provide a wider view on the association of this transcription factor to breast oncogenesis. Large-scale transcriptomic data gathered from wide cohorts of patients further allowed refining the relationship between breast cancer type and HOXA1 expression. Several recent reports have reviewed the connection between cancer hallmarks and the biology of HOX genes in general. Here we take HOXA1 as a paradigm and propose an extensive overview of the molecular data centered on this oncoprotein, from what its expression modulators, to the interactors contributing to its oncogenic activities, and to the pathways and genes it controls. The data converge to an intricate picture that answers questions on the multi-modality of its oncogene activities, point towards better understanding of breast cancer aetiology and thereby provides an appraisal for treatment opportunities.

A Weighted Gene Co-Expression Network Analysis-Derived Prognostic Model for Predicting Prognosis and Immune Infiltration in Gastric Cancer

Background

Gastric cancer (GC) is a major public health problem worldwide. In recent decades, the treatment of gastric cancer has improved greatly, but basic research and clinical application of gastric cancer remain challenges due to the high heterogeneity. Here, we provide new insights for identifying prognostic models of GC.

Methods

We obtained the gene expression profiles of GSE62254 containing 300 samples for training. GSE15459 and TCGA-STAD for validation, which contain 200 and 375 samples, respectively. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules. We performed Lasso regression and Cox regression analyses to identify the most significant five genes to develop a novel prognostic model. And we selected two representative genes within the model for immunohistochemistry staining with 105 GC specimens from our hospital to verify the prediction efficiency. Moreover, we estimated the correlation coefficient between our model and immune infiltration using the CIBERSORT algorithm. The data from GSE15459 and TCGA cohort validated the robustness and predictive accuracy of this prognostic model.

Results

Of the 12 gene modules identified, 1,198 green-yellow module genes were selected for further analysis. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using the Cox proportional hazards regression model. Finally, we constructed a five gene prognostic model: Risk Score = [(-0.7547) * Expression (ARHGAP32)] + [(-0.8272) * Expression (KLF5)] + [1.09 * Expression (MAMLD1)] + [0.5174 * Expression (MATN3)] + [1.66 * Expression (NES)]. The prognosis of samples in the high-risk group was significantly poorer than that of samples in the low-risk group (p = 6.503e-11). The risk model was also regarded as an independent predictor of prognosis (HR, 1.678, p < 0.001). The observed correlation with immune cells suggested that this risk model could potentially predict immune infiltration.

Conclusion

This study identified a potential risk model for prognosis and immune infiltration prediction in GC using WGCNA and Cox regression analysis.