已发表论文

检测参与肝细胞癌发展的癌症相关成纤维细胞的新型 12-标志物组

 

Authors Zou B, Liu X, Gong Y, Cai C, Li P, Xing S, Pokhrel B, Zhang B, Li J

Received 2 June 2018

Accepted for publication 20 August 2018

Published 5 November 2018 Volume 2018:10 Pages 5303—5311

DOI https://doi.org/10.2147/CMAR.S176152

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Dr Colin Mak

Peer reviewer comments 2

Editor who approved publication: Dr Beicheng Sun

Background/Aim: Cancer-associated fibroblasts (CAFs) are important factors in the progression of hepatocellular carcinoma (HCC). But the characterization of these cells remains incomplete. This study aims to identify a panel of markers for CAFs that are associated with HCC progression.
Materials and methods: The sequencing data and clinicopathological characteristics of 366 patients were obtained from the Cancer Genome Atlas (TCGA) database (366 HCC tissues and there were 50/366 cases with corresponding normal liver tissues). In vitro validation of the markers was performed by quantitative real-time PCR using the hepatic stellate cell line LX2 induced by the HCC cell line Huh7. The activation of LX2 was confirmed by α-smooth muscle actin and fibroblast activation protein, using quantitative real-time PCR and immunofluorescence staining. In vivo detections of the 12 markers were done in 40 tissue samples (30 HCC and 10 normal).
Results: We successfully identified 12 CAF markers from TCGA data: FGF5, CXCL5, IGFL2, MMP1, ADAM32, ADAM18, IGFL1, FGF8, FGF17, FGF19, FGF4, and FGF23. The 12-marker panel was associated with the pathological and clinical progressions of HCC. All 12 markers were upregulated in vitro. In vivo expressions of these markers were paralleled with those in TCGA data.
Conclusion: A 12-marker panel of CAFs in HCC is identified, which is associated with both pathological and clinical progressions of cancer.
Keywords: hepatocellular carcinoma, cancer-associated fibroblasts, CAFs marker panel, TCGA database analysis, transcriptome profiling




Figure 1 Differential gene expression using the TCGA mRNA sequencing data of HCC.