PCsRNAdb: a comprehensive resource of small noncoding RNAs across cancers

Sep 14, 2025·
Ren-De HUANG
Ren-De HUANG
1st Author
,
Jiang Li
Co-1st Author
,
Qi Cao
Co-1st Author
,
Lixia Wang
Co-1st Author
,
Zhixiong Shao
,
Haochun Yang
,
Xinlei Zhang
,
Chuanlai Yang
,
Xiangya Kong
,
Qiuyue Gu
,
Jianmin Wu
,
Tsan-Yu Chiu
,
Penghu Lian
Co-Corresponding Author
,
Kui Wu
Co-Corresponding Author
Feng GAO
Feng GAO
Co-Corresponding Author
,
Zhongxu Zhu
Corresponding Author
· 0 min read
Abstract
Small noncoding RNAs (sncRNAs) constitute a diverse class of endogenous transcripts, including miRNAs, piRNAs, tRNA-derived fragments (tDRs), rRNA-derived fragments (rRFs), and other small RNAs. They have been identified as pivotal regulatory elements in a wide array of biological processes and diseases. Among these, miRNA is the most extensively studied and confirmed as a key regulatory and diagnostic biomarker in tumorigenesis and development. However, the current understanding of sncRNAs other than miRNA remains limited, particularly in the field of oncology. To this end, we collected 190 datasets from GEO/SRA database, encompassing 11 114 samples across 19 cancer types, and built a user-friendly database, the Pan-Cancer Small Non-Coding RNA Database (PCsRNAdb, http://pcsrnadb.cloudna.cn/#/Home), which is developed to provide abundant resources of sncRNAs specifically designed to investigate the association between sncRNAs and cancers. PCsRNAdb comprehensively provides basic information such as the sequence, length, abundance, and target genes of sncRNAs, and further offers four advanced functionalities, including differential expression analysis, survival analysis, expression profiling analysis, and network regulatory analysis. We anticipate that such platform offers invaluable resources and effective analytical tools for researchers in related fields and is expected to facilitate cancer-related research in this domain.
Type
Publication
Nucleic Acids Research
publication
Ren-De HUANG
Authors
Research Student
I am a research student working on AI methods for colorectal cancer analysis and clinical decision support.
Feng GAO
Authors
Professor
My research leverages AI and big data to improve diagnostics, prognostics, and ultimately, outcomes in cancer and other biomedical fields.