TY - JOUR
T1 - Non-coding RNAs in Various Stages of Liver Disease Leading to Hepatocellular Carcinoma
T2 - Differential Expression of miRNAs, piRNAs, lncRNAs, circRNAs, and sno
AU - Koduru, Srinivas V.
AU - Leberfinger, Ashley N.
AU - Kawasawa, Yuka I.
AU - Mahajan, Milind
AU - Gusani, Niraj J.
AU - Sanyal, Arun J.
AU - Ravnic, Dino J.
N1 - Funding Information:
We thank Gene Arrays (An entity of Vedic Research, Inc., USA) for MetaCore data analysis free of charge and Research Informatics at Penn State College of Medicine for setting up High Performance Computing facilities. Part of this work was supported by the Department of Surgery at Penn State Health Milton S Hershey Medical Center. We thank Dr. James Broach (IPM) and Ms. Molly Pells (IPM) for providing HCC and healthy liver tissue samples, Dr. Craig Praul (Director, Penn State Genomics Core Facility, University Park, PA) for NanoString Data aquisition, and Ms. Yanfen Fu (NanoString Technologies, Seattle, WA) for data analysis. In addition, Dr. Irina Elcheva for critical commentary and correction of the manuscript.
Publisher Copyright:
© 2018 The Author(s).
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Hepatocellular carcinoma (HCC) was the fifth leading cause of cancer death in men and eighth leading cause of death in women in the United States in 2017. In our study, we sought to identify sncRNAs in various stages of development of HCC. We obtained publicly available small RNA-seq data derived from patients with cirrhosis (n = 14), low-grade dysplastic nodules (LGDN, n = 9), high grade dysplastic nodules (HGDN, n = 6), early hepatocellular carcinoma (eHCC, n = 6), and advanced hepatocellular carcinoma (HCC, n = 20), along with healthy liver tissue samples (n = 9). All samples were analyzed for various types of non-coding RNAs using PartekFlow software. We remapped small RNA-seq to miRBase to obtain differential expressions of miRNAs and found 87 in cirrhosis, 106 in LGDN, 59 in HGDN, 80 in eHCC, and 133 in HCC. Pathway analysis of miRNAs obtained from diseased samples compared to normal samples showed signaling pathways in the microRNA dependent EMT, CD44, and others. Additionally, we analyzed the data sets for piRNAs, lncRNAs, circRNAs, and sno/mt-RNAs. We validated the in silico data using human HCC samples with NanoString miRNA global expression. Our results suggest that publically available data is a valuable resource for sncRNA identification in HCC progression (FDR set to <0.05 for all samples) and that a data mining approach is useful for biomarker development.
AB - Hepatocellular carcinoma (HCC) was the fifth leading cause of cancer death in men and eighth leading cause of death in women in the United States in 2017. In our study, we sought to identify sncRNAs in various stages of development of HCC. We obtained publicly available small RNA-seq data derived from patients with cirrhosis (n = 14), low-grade dysplastic nodules (LGDN, n = 9), high grade dysplastic nodules (HGDN, n = 6), early hepatocellular carcinoma (eHCC, n = 6), and advanced hepatocellular carcinoma (HCC, n = 20), along with healthy liver tissue samples (n = 9). All samples were analyzed for various types of non-coding RNAs using PartekFlow software. We remapped small RNA-seq to miRBase to obtain differential expressions of miRNAs and found 87 in cirrhosis, 106 in LGDN, 59 in HGDN, 80 in eHCC, and 133 in HCC. Pathway analysis of miRNAs obtained from diseased samples compared to normal samples showed signaling pathways in the microRNA dependent EMT, CD44, and others. Additionally, we analyzed the data sets for piRNAs, lncRNAs, circRNAs, and sno/mt-RNAs. We validated the in silico data using human HCC samples with NanoString miRNA global expression. Our results suggest that publically available data is a valuable resource for sncRNA identification in HCC progression (FDR set to <0.05 for all samples) and that a data mining approach is useful for biomarker development.
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U2 - 10.1038/s41598-018-26360-1
DO - 10.1038/s41598-018-26360-1
M3 - Article
C2 - 29789629
AN - SCOPUS:85047632716
SN - 2045-2322
VL - 8
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 7967
ER -