Abstract

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.

Original languageEnglish (US)
Article number7967
JournalScientific reports
Volume8
Issue number1
DOIs
StatePublished - Dec 1 2018

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Long Noncoding RNA
Untranslated RNA
MicroRNAs
Liver Diseases
Hepatocellular Carcinoma
Small Untranslated RNA
RNA
Cause of Death
Fibrosis
Data Mining
Computer Simulation
Software
Biomarkers

All Science Journal Classification (ASJC) codes

  • General

Cite this

@article{bb94f40ce1ef46c793c69155bb1ae48f,
title = "Non-coding RNAs in Various Stages of Liver Disease Leading to Hepatocellular Carcinoma: Differential Expression of miRNAs, piRNAs, lncRNAs, circRNAs, and sno",
abstract = "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.",
author = "Koduru, {Srinivas V.} and Leberfinger, {Ashley N.} and Kawasawa, {Yuka I.} and Milind Mahajan and Gusani, {Niraj J.} and Sanyal, {Arun J.} and Ravnic, {Dino J.}",
year = "2018",
month = "12",
day = "1",
doi = "10.1038/s41598-018-26360-1",
language = "English (US)",
volume = "8",
journal = "Scientific Reports",
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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.

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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