Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

The role of kinesin family members in hepatobiliary carcinomas: from bench to bedside

As a major component of the digestive system malignancies, tumors originating from the hepatic and biliary ducts seriously endanger public health. The kinesins (KIFs) are molecular motors that enable the microtubule-dependent intracellular trafficking necessary for mitosis and meiosis. Normally, the stability of KIFs is essential to maintain cell proliferation and genetic homeostasis. However, aberrant KIFs activity may destroy this dynamic stability, leading to uncontrolled cell division and tumor initiation. In this work, we have made an integral summarization of the specific roles of KIFs in hepatocellular and biliary duct carcinogenesis, referring to aberrant signal transduction and the potential for prognostic evaluation. Additionally, current clinical applications of KIFs-targeted inhibitors have also been discussed, including their efficacy advantages, relationship with drug sensitivity or resistance, the feasibility of combination chemotherapy or other targeted agents, as well as the corresponding clinical trials. In conclusion, the abnormally activated KIFs participate in the regulation of tumor progression via a diverse range of mechanisms and are closely associated with tumor prognosis. Meanwhile, KIFs-aimed inhibitors also carry out a promising tumor-targeted therapeutic strategy that deserves to be further investigated in hepatobiliary carcinoma (HBC).

Construction and validation of prognostic signature for hepatocellular carcinoma basing on hepatitis B virus related specific genes

BACKGROUND

Hepatocellular carcinoma (HCC) is a frequent primary liver cancer, and it is one of the leading cause of cancer-related deaths. Hepatitis B virus (HBV) infection is a crucial risk factor for HCC. Thus, this study aimed to explore the prognostic role of HBV-positive HCC related specific genes in HCC.

METHODS

The HCC related data were downloaded from three databases, including The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO). Univariate Cox regression analysis and LASSO Cox regression analysis were conducted to build the Risk score. Multivariate Cox regression analysis and survival analysis determined the independent prognostic indicators.

RESULTS

After cross analysis of differentially expressed genes (DEGs), we have identified 106 overlapped DEGs, which were probably HBV-positive HCC related specific genes. These 106 DEGs were significantly enriched in 213 GO terms and 8 KEGG pathways. Among that, 11 optimal genes were selected to build a Risk score, and Risk score was an independent prognostic factor for HCC. High risk HCC patients had worse OS. Moreover, five kinds of immune cells were differentially infiltrated between high and low risk HCC patients.

CONCLUSION

The prognostic signature, based on HMMR, MCM6, TPX2, KIF20A, CCL20, RGS2, NUSAP1, FABP5, FZD6, PBK, and STK39, is conducive to distinguish different prognosis of HCC patients.

HBx Mediated Increase of DDX17 Contributes to HBV-Related Hepatocellular Carcinoma Tumorigenesis

HBV is strongly associated with HCC development and DEAD-box RNA helicase 17 (DDX17) is a very important member of the DEAD box family that plays key roles in HCC development by promoting cancer metastasis. However, the important role of DDX17 in the pathogenesis of HBV-related HCC remains unclear. In this study, we investigated the role of DDX17 in the replication of HBV and the development of HBV-associated HCC. Based on data from the GEO database and HBV-infected cells, we found that DDX17 was upregulated by the HBV viral protein X (HBx). Mechanistically, increased DDX17 expression promoted HBV replication and transcription by upregulating ZWINT. Further study showed that DDX17 could promote HBx-mediated HCC metastasis. Finally, the promotive effect of DDX17 on HBV and HBV-related HCC was confirmed in vivo. In summary, the results revealed the novel role of DDX17 in the replication of HBV and the metastasis of HBV-associated HCC.

Identification of the miRNA signature and key genes in colorectal cancer lymph node metastasis

BACKGROUND

Because its metastasis to the lymph nodes are closely related to poor prognosis, miRNAs and mRNAs can serve as biomarkers for the diagnosis, prognosis, and therapy of colorectal cancer (CRC). This study aimed to identify novel gene signatures in the lymph node metastasis of CRC.

METHODS

GSE56350, GSE70574, and GSE95109 datasets were downloaded from the Gene Expression Omnibus (GEO) database, while data from 569 colorectal cancer cases were also downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed miRNAs (DE-miRNAs) were calculated using R programming language (Version 3.6.3), while gene ontology and enrichment analysis of target mRNAs were performed using FunRich ( http://www.funrich.org ). Furthermore, the mRNA-miRNA network was constructed using Cytoscape software (Version 3.8.0). Gene expression levels were verified using the GEO datasets. Similarly, quantitative real-time PCR (qPCR) was used to examine expression profiles from 20 paired non-metastatic and metastatic lymph node tissue samples obtained from patients with CRC.

RESULTS

In total, five DE-miRNAs were selected, and 34 mRNAs were identified after filtering the results. Moreover, two key miRNAs (hsa-miR-99a, hsa-miR-100) and one gene (heparan sulfate-glucosamine 3-sulfotransferase 2 [HS3ST2]) were identified. The GEO datasets analysis and qPCR results showed that the expression of key miRNA and genes were consistent with that obtained from the bioinformatic analysis. A novel miRNA-mRNA network capable of predicting the prognosis and confirmed experimentally, hsa-miR-99a-HS3ST2-hsa-miR-100, was found after expression analysis in metastasized lymph node tissue from CRC samples.

CONCLUSION

In summary, miRNAs and genes with potential as biomarkers were found and a novel miRNA-mRNA network was established for CRC lymph node metastasis by systematic bioinformatic analysis and experimental validation. This network may be used as a potential biomarker in the development of lymph node metastatic CRC.