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10.1038/s41598-018-26360-1 http://scihub22266oqcxt.onion/10.1038/s41598-018-26360-1 C5964116!5964116 !29789629     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 276.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 276.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 276.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\29789629 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Sci+Rep 2018 ; 8 (1 ): 7967 Nephropedia Template TP {{tp|p=29789629 |t=2018. Non-coding RNAs in Various Stages of Liver Disease Leading to Hepatocellular Carcinoma: Differential Expression of miRNAs, piRNAs, lncRNAs, circRNAs, and sno/mt-RNAs |pdf=|usr=}}{{29789629 }} gab.com Text Non-coding RNAs in Various Stages of Liver Disease Leading to Hepatocellular Carcinoma: Differential Expression of miRNAs, piRNAs, lncRNAs, circRNAs, and sno/mt-RNAs JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29789629 #FOAvip 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. Twit Text FOAVip Non-coding RNAs in Various Stages of Liver Disease Leading to Hepatocellular Carcinoma: Differential Expression of miRNAs, piRNAs, lncRNAs, circRNAs, and sno/mt-RNAs ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29789629 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29789629 #FOAvip English Wikipedia <ref>{{Cite pmid|29789629 }}</ref> Non-coding RNAs in Various Stages of Liver Disease Leading to Hepatocellular Carcinoma: Differential Expression of miRNAs, piRNAs, lncRNAs, circRNAs, and sno/mt-RNAs #MMPMID29789629 Koduru SV ; Leberfinger AN ; Kawasawa YI ; Mahajan M ; Gusani NJ ; Sanyal AJ ; Ravnic DJ Sci Rep 2018[May]; 8 (1 ): 7967 PMID29789629 show ga 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. |Biomarkers, Tumor/analysis/genetics [MESH] |Carcinoma, Hepatocellular/diagnosis/*genetics/*pathology [MESH] |Case-Control Studies [MESH] |Disease Progression [MESH] |Female [MESH] |Gene Expression Profiling [MESH] |Gene Expression Regulation, Neoplastic [MESH] |Humans [MESH] |Liver Cirrhosis/diagnosis/genetics/pathology [MESH] |Liver Neoplasms/diagnosis/*genetics/*pathology [MESH] |Male [MESH] |MicroRNAs/analysis/genetics [MESH] |Microarray Analysis [MESH] |Neoplasm Grading [MESH] |Neoplasm Staging [MESH] |Precancerous Conditions/diagnosis/genetics/pathology [MESH] |RNA, Circular [MESH] |RNA, Long Noncoding/*analysis/genetics [MESH] |RNA, Small Interfering/analysis/genetics [MESH] |RNA, Small Nucleolar/genetics [MESH]Non-coding RNAs in Various Stages of Liver Disease Leading to Hepatoce(epmc).pdf DeepDyve Pubget Overpricing