FIMICS: A panel of long noncoding RNAs for cardiovascular conditions.
FIMICS: A panel of long noncoding RNAs for cardiovascular conditions.
复制标题
FIMICE：一组长的非编码RNA，用于心血管疾病。
DOI：
10.1016/j.heliyon.2023.e13087
复制
发表时间：
2023-01
期刊：
影响因子：
4
通讯作者：
Devaux, Yvan
中科院分区：
文献类型：
--
作者：
Firat, Huseyin;Zhang, Lu;Baksi, Shounak;Leszek, Przemyslaw;Schordan, Eric;Ounzain, Samir;Kottwitz, Jan;Patriki, Dimitri;Heidecker, Bettina;Luscher, Thomas F.;Pedrazzini, Thierry;Devaux, Yvan
关键词：
心血管疾病如心力衰竭是导致死亡的主要原因。通过鉴定新的生物标志物进行患者分层可以改善复杂病因的心血管疾病的管理。长链非编码RNA（Long-noncoding RNA，lncRNA）具有高度的组织特异性，已成为人类疾病的重要生物标志物。在这项研究中，我们的目的是确定心脏富集的lncRNA作为心血管疾病的潜在生物标志物。深度RNA测序和定量PCR鉴定了衰竭和非衰竭心脏之间差异表达的lncRNA。使用独立的数据集来评估正常心脏中lncRNA的富集。我们鉴定了一组2906个lncRNA，命名为FIMICS，它们在心脏富集或在衰竭和非衰竭心脏之间差异表达。心肌炎与急性心肌梗死患者血清lncRNAs表达的差异我们在此提出FIMICS小组，一个现成的工具，以提供深入了解心血管病理，这可能是有助于诊断，监测和预后的目的。
Cardiovascular disorders such as heart failure are leading causes of mortality. Patient stratification via identification of novel biomarkers could improve management of cardiovascular diseases of complex etiologies. Long-noncoding RNAs (lncRNAs) are highly tissue-specific in nature and have emerged as important biomarkers in human diseases. In this study, we aimed to identify cardiac-enriched lncRNAs as potential biomarkers for cardiovascular conditions. Deep RNA sequencing and quantitative PCR identified differentially expressed lncRNAs between failing and non-failing hearts. An independent dataset was used to evaluate the enrichment of lncRNAs in normal hearts. We identified a panel of 2906 lncRNAs, named FIMICS, that were either cardiac-enriched or differentially expressed between failing and non-failing hearts. Expression of lncRNAs in blood samples differentiated patients with myocarditis and acute myocardial infarction. We hereby present the FIMICS panel, a readily available tool to provide insights into cardiovascular pathologies and which could be helpful for diagnosis, monitoring and prognosis purposes.
登录
查看更多内容
影响因子：
56.9
作者：
Cheng, J;Kapranov, P;Gingeras, TR
通讯作者：
Gingeras, TR
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
7
作者：
Harrow J;Frankish A;Gonzalez JM;Tapanari E;Diekhans M;Kokocinski F;Aken BL;Barrell D;Zadissa A;Searle S;Barnes I;Bignell A;Boychenko V;Hunt T;Kay M;Mukherjee G;Rajan J;Despacio-Reyes G;Saunders G;Steward C;Harte R;Lin M;Howald C;Tanzer A;Derrien T;Chrast J;Walters N;Balasubramanian S;Pei B;Tress M;Rodriguez JM;Ezkurdia I;van Baren J;Brent M;Haussler D;Kellis M;Valencia A;Reymond A;Gerstein M;Guigó R;Hubbard TJ
通讯作者：
Hubbard TJ
影响因子：
39.3
作者：
Ounzain S;Micheletti R;Beckmann T;Schroen B;Alexanian M;Pezzuto I;Crippa S;Nemir M;Sarre A;Johnson R;Dauvillier J;Burdet F;Ibberson M;Guigó R;Xenarios I;Heymans S;Pedrazzini T
通讯作者：
Pedrazzini T
影响因子：
14.9
作者：
Kolesnikov N;Hastings E;Keays M;Melnichuk O;Tang YA;Williams E;Dylag M;Kurbatova N;Brandizi M;Burdett T;Megy K;Pilicheva E;Rustici G;Tikhonov A;Parkinson H;Petryszak R;Sarkans U;Brazma A
通讯作者：
Brazma A