transcriptome analysis of acute phase liver graft injury in liver transplantation

transcriptome analysis of acute phase liver graft injury in liver transplantation

;Nikki P. Lee;Haiyang Wu;Kevin T.P. Ng;Ruibang Luo;Tak-Wah Lam;Chung-Mau Lo;Kwan Man
journal of healthcare engineering 2018 Vol. 6 pp. 41-
181
lee2018biomedicinestranscriptome

Abstract

Background: Liver transplantation remains the treatment of choice for a selected group of hepatocellular carcinoma (HCC) patients. However, the long-term benefit is greatly hampered by post-transplant HCC recurrence. Our previous studies have identified liver graft injury as an acute phase event leading to post-transplant tumor recurrence. Methods: To re-examine this acute phase event at the molecular level and in an unbiased way, RNA sequencing (RNA-Seq) was performed on liver graft biopsies obtained from the transplant recipients two hours after portal vein reperfusion with an aim to capture frequently altered pathways that account for post-transplant tumor recurrence. Liver grafts from recurrent recipients (n = 6) were sequenced and compared with those from recipients without recurrence (n = 5). Results: RNA expression profiles comparison pointed to several frequently altered pathways, among which pathways related to cell adhesion molecules were the most involved. Subsequent validation using quantitative polymerase chain reaction confirmed the differential involvement of two cell adhesion molecules HFE (hemochromatosis) and CD274 and their related molecules in the acute phase event. Conclusion: This whole transcriptome strategy unravels the molecular landscape of liver graft gene expression alterations, which can identify key pathways and genes that are involved in acute phase liver graft injury that may lead to post-transplant tumor recurrence.

Citation

ID: 131324
Ref Key: lee2018biomedicinestranscriptome
Use this key to autocite in SciMatic or Thesis Manager

References

Blockchain Verification

Account:
NFT Contract Address:
0x95644003c57E6F55A65596E3D9Eac6813e3566dA
Article ID:
131324
Unique Identifier:
10.3390/biomedicines6020041
Network:
Scimatic Chain (ID: 481)
Loading...
Blockchain Readiness Checklist
Authors
Abstract
Journal Name
Year
Title
5/5
Creates 1,000,000 NFT tokens for this article
Token Features:
  • ERC-1155 Standard NFT
  • 1 Million Supply per Article
  • Transferable via MetaMask
  • Permanent Blockchain Record
Blockchain QR Code
Scan with Saymatik Web3.0 Wallet

Saymatik Web3.0 Wallet