Preliminary investigation of the role of BTB domain-containing 3 gene in the proliferation and metastasis of hepatocellular carcinoma

E3 ubiquitin ligase BTBD3 inhibits tumorigenesis of colorectal cancer by regulating the TYRO3/Wnt/β-catenin signaling axis

Clinical trials and studies have implicated that E3 ubiquitin ligase BTBD3 (BTB Domain Containing 3) is a cancer-associated gene. However, the role and underlying mechanism of BTBD3 in colorectal cancer (CRC) is not fully understood yet. Herein, our study demonstrated that the mRNA and protein levels of BTBD3 were decreased in CRC tissues and associated with TYPO3 and Wnt/β-catenin pathway. Our results showed that circRAE1 knockdown and TYRO3 overexpression activated Wnt/β-catenin signaling pathway and the EMT process-associated markers, indicating that circRAE1/miR-388-3p/TYRO3 axis exacerbated tumorigenesis of CRC by activating Wnt/β-catenin signaling pathway. In addition, overexpression of BTBD3 reduced CRC cell migration and invasion in vitro and inhibited tumor growth in vivo. Our data demonstrated that BTBD3 suppressed CRC progression through negative regulation of the circRAE1/miR-388-3p/TYRO3 axis and the Wnt/β-catenin pathway. Our data further confirmed that BTBD3 bound and ubiquitinated β-catenin and led to β-catenin degradation, therefore blocked the Wnt/β-catenin pathway and suppressed the CRC tumorigenesis. This study explored the mechanism of BTBD3 involved in CRC tumorigenesis and provided a new theoretical basis for the prevention and treatment of CRC.

Prognostic value of S100A4 and Glypican-3 in hepatocellular carcinoma in cirrhotic HCV patients

AIMS

Both S100A4 and Glypican-3 have been known to be engaged in HCC development and progression. This study aimed to evaluate both S100A4 and GPC3 expression in HCC tissues as a prognostic markers.

METHODS

Tissues from 70 patients of HCC in cirrhotic HCV patients were evaluated by immunohistochemistry using antibodies against SA100A4 and GPC3 and compared with tumor-adjacent tissue (controls). All cases were followed for 40 months.

RESULTS

GPC3 was more expressed in HCC (79%) than S100A4 (21%). S100A4 was more significantly expressed in cases showing metastasis, microscopic vascular emboli, necrosis, and grade III tumors. There was no relationship between overall survival and both S100A4 and GPC3. The only significant independent predictor for recurrence was decompensation (OR 3.037), while metastasis was significantly predicted by S100A4 expression (OR 9.63) and necrosis (OR 8.33).

CONCLUSION

S100A4 might be used as a prognostic marker for HCC, while GPC3 is a reliable marker of HCC diagnosis.

Transcriptomic identification of miR-205 target genes potentially involved in metastasis and survival of cutaneous malignant melanoma

Cutaneous melanoma is an aggressive neoplasm and is responsible for the majority of skin cancer deaths. Several miRNAs are involved in melanoma tumor progression. One of them is miR-205, the loss of which contributes to the development of melanoma metastasis. We evaluated whole-genome mRNA expression profiling associated with different miR-205 expression levels in melanoma cells. Differential expression analysis identified 243 differentially expressed transcripts including inositol polyphosphate 5′-phosphatase-like protein-1 (INPPL1) and BTB/POZ Domain-Containing Protein 3 (BTBD3). INPPL1 and BTBD3 were downregulated when melanoma cells expressed miR-205, indicating that these genes are potential miR-205 targets. Additionally, the target prediction algorithm TargetScan revealed that INPPL1 and BTBD3 genes had predicted target sites of miR-205 in their 3′UTRs and functional analysis demonstrated that these genes were directly linked to miR-205. Interestingly, our clinical data showed that INPPL1 was significantly associated with lymph node metastasis-free survival (LNMFS), distant metastasis-free survival (DMFS) and melanoma specific survival (MSS). This study supports INPPL1 as a miR-205 target gene and, therefore, that the involvement of miR-205 in the metastatic dissemination of malignant melanoma is, at least in part, via INPPL1.

Identification of cancer omics commonality and difference via community fusion

The analysis of cancer omics data is a "classic" problem; however, it still remains challenging. Advancing from early studies that are mostly focused on a single type of cancer, some recent studies have analyzed data on multiple "related" cancer types/subtypes, examined their commonality and difference, and led to insightful findings. In this article, we consider the analysis of multiple omics datasets, with each dataset on one type/subtype of "related" cancers. A Community Fusion (CoFu) approach is developed, which conducts marker selection and model building using a novel penalization technique, informatively accommodates the network community structure of omics measurements, and automatically identifies the commonality and difference of cancer omics markers. Simulation demonstrates its superiority over direct competitors. The analysis of TCGA lung cancer and melanoma data leads to interesting findings.