scholarly journals Circular RNA expression profiles during the differentiation of mouse neural stem cells

2018 ◽  
Vol 12 (S8) ◽  
Author(s):  
Qichang Yang ◽  
Jing Wu ◽  
Jian Zhao ◽  
Tianyi Xu ◽  
Zhongming Zhao ◽  
...  
2019 ◽  
Vol 13 (1) ◽  
Author(s):  
Qichang Yang ◽  
Jing Wu ◽  
Jian Zhao ◽  
Tianyi Xu ◽  
Zhongming Zhao ◽  
...  

2020 ◽  
Vol 20 (6) ◽  
pp. 1-1
Author(s):  
Mengbi Lin ◽  
Yue Zheng ◽  
Qian Li ◽  
Yufang Liu ◽  
Qingfang Xu ◽  
...  

Epigenomics ◽  
2021 ◽  
Author(s):  
Congxia Bai ◽  
Tingting Liu ◽  
Yingying Sun ◽  
Hao Li ◽  
Ning Xiao ◽  
...  

Aim: To investigate the expression profiles of circRNAs after intracerebral hemorrhage (ICH). Materials & methods: RNA sequencing and qRT-PCR were used to investigate and validate circRNA expression levels. Bioinformatics analysis was performed to explore potential functions of the circRNAs. Results: Expression levels of 15 circRNAs were consistently altered in patients with ICH compared with their expression levels in hypertension. Three circRNAs, hsa_circ_0001240, hsa_circ_0001947 and hsa_circ_0001386, individually or combined, were confirmed as promising biomarkers for predicting and diagnosing ICH. The circRNAs were involved mainly in lysine degradation and the immune system. Conclusion: This is the first study to report expression profiles of circRNAs after ICH and to propose that three circRNAs are potential biomarkers for ICH.


Oncotarget ◽  
2017 ◽  
Vol 8 (49) ◽  
pp. 86625-86633 ◽  
Author(s):  
Ya-Li Gao ◽  
Ming-Yun Zhang ◽  
Bo Xu ◽  
Li-Jie Han ◽  
Shou-Feng Lan ◽  
...  

2019 ◽  
Vol 120 (10) ◽  
pp. 18031-18040 ◽  
Author(s):  
Shuai Xiang ◽  
Zeng Li ◽  
Yanyan Bian ◽  
Xisheng Weng

Epigenomics ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 1027-1040 ◽  
Author(s):  
Chenjing Zhang ◽  
Jingya Wang ◽  
Xiaoge Geng ◽  
Jiangfeng Tu ◽  
Huiqin Gao ◽  
...  

Aims: To profile and characterize the circular RNA (circRNA) expression pattern in poorly differentiated gastric adenocarcinoma (PDGA). Methods & materials: CircRNA expression profiles in PDGA and adjacent nontumor tissues were analyzed by microarray. Five randomly selected differentiated expressed circRNAs (DECs) were validated by real-time quantitative PCR. m6A qualification of the top 20 DECs was conducted by m6A-immunoprecipitation and real-time quantitative PCR. Results: A total of 65 DECs were found in PDGA compared with the control. Hsa_circRNA_0077837 had the largest area under the curve. Most DECs had m6A modifications, the trend of m6A modification alteration was mainly consistent with the circRNA expression level. Conclusion: Our study revealed a set of DECs and their m6A modification alterations, which may provide new insight for their potential function in PDGA.


Sign in / Sign up

Export Citation Format

Share Document