Expression And Biological Interaction Network Of RHOC For Hepatic Carcinoma With Metastasis In PBMC Samples.

Abstract

OBJECTIVES:

Hepatic carcinoma with metastasis remains incurable, and clinical diagnostic methods lacked adequate sensitivity and specificity. Therefore, seeking effectively diagnostic biomarkers is still essential for it. RHOC was reported to be linked to metastasis of hepatic carcinoma. However, almost all of the studies used tissues as detection samples, which was not ideal for clinical course minoring. Therefore, here, it was aimed to use PBMC samples that were not only easily accessible but also minimally invasive to determine the expression and biological interaction network of RHOC for hepatic carcinoma with metastasis.

METHODS:

PBMC samples were isolated. Then, RNA-seq was performed to identify the DEGs between hepatic carcinoma with metastasis and hepatic carcinoma with solitary tumor. Subsequently, q-RT-PCR was used to verify the expression level of RHOC. Finally, bioinformatic analysis was used to present the biological interaction network of RHOC for hepatic carcinoma with metastasis in PBMC samples.

RESULTS:

The results of both RNA-seq and q-RT-PCR showed that the expression level of RHOC was significantly higher in the PBMC samples of hepatic carcinoma with metastasis than in those of hepatic carcinoma with solitary tumor. By using variety of bioinformatic analysis platforms, in PBMCs, 18 co-expression genes with RHOC were identified and their interaction network showed that MYL9 and RHOC had the highest edge evidence, and were involved in some cell migration-related pathways.

CONCLUSION:

Our results indicated that RHOC in PBMCs could be potentially minimally invasive indicators for the diagnosis and clinical course supervision of hepatic carcinoma with metastasis, and its biological interaction network determined based on bioinformatic methods would lay a foundation for further study of the role of RHOC in tumor invasion and metastasis.

• Authors: Yanting Shen, Lu Bu, Rui Li, Zhenzhu Chen, Fei Tian, Qinyu Ge
• Journal: OncoTargets and therapy (Onco Targets Ther) Vol. 12 Pg. 9117-9128 ( 2019) ISSN: 1178-6930 [Print] New Zealand
• PMID: 31806997 (Publication Type: Journal Article)
• Copyright: © 2019 Shen et al.