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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2508.05692 |
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| _version_ | 1866908953001066496 |
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| author | Cai, Xiao-Xuan Liao, Jing-Shan Ma, Jia-Jun Pang, Yu-Xuan Chen, Yi-Gang Lin, Yang-Chi-Dung Chen, Yi-Dan Cao, Xin Zhang, Yi-Cheng Xu, Tao-Sheng Lee, Tzong-Yi Huang, Hsi-Yuan Huang, Hsien-Da |
| author_facet | Cai, Xiao-Xuan Liao, Jing-Shan Ma, Jia-Jun Pang, Yu-Xuan Chen, Yi-Gang Lin, Yang-Chi-Dung Chen, Yi-Dan Cao, Xin Zhang, Yi-Cheng Xu, Tao-Sheng Lee, Tzong-Yi Huang, Hsi-Yuan Huang, Hsien-Da |
| contents | microRNA are pivotal post-transcriptional regulators whose single-cell behavior has remained largely inaccessible owing to technical barriers in single-cell small-RNA profiling. We present SiCmiR, a two-layer neural network that predicts miRNA expression profile from only 977 LINCS L1000 landmark genes reducing sensitivity to dropout of single-cell RNA-seq data. Proof-of-concept analyses illustrate how SiCmiR can uncover candidate hub-miRNAs in bulk-seq cell lines and hepatocellular carcinoma, scRNA-seq pancreatic ductal carcinoma and ACTH-secreting pituitary adenoma and extracellular-vesicle-mediated crosstalk in glioblastoma. Trained on 6462 TCGA paired miRNA-mRNA samples, SiCmiR attains state-of-the-art accuracy on held-out cancers and generalizes to unseen cancer types, drug perturbations and scRNA-seq. We next constructed SiCmiR-Atlas, containing 632 public datasets, 9.36 million cells, 726 cell types, which is the first dedicated database of single-cell mature miRNA expression--providing interactive visualization, biomarker identification and cell-type-resolved miRNA-target networks. SiCmiR transforms bulk-derived statistical power into a single-cell view of miRNA biology and provides a community resource SiCmiR Atlas for biomarker discovery. SiCmiR Atlas is avilable at https://awi.cuhk.edu.cn/~SiCmiR/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05692 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SiCmiR Atlas: Single-Cell miRNA Landscapes Reveals Hub-miRNA and Network Signatures in Human Cancers Cai, Xiao-Xuan Liao, Jing-Shan Ma, Jia-Jun Pang, Yu-Xuan Chen, Yi-Gang Lin, Yang-Chi-Dung Chen, Yi-Dan Cao, Xin Zhang, Yi-Cheng Xu, Tao-Sheng Lee, Tzong-Yi Huang, Hsi-Yuan Huang, Hsien-Da Genomics microRNA are pivotal post-transcriptional regulators whose single-cell behavior has remained largely inaccessible owing to technical barriers in single-cell small-RNA profiling. We present SiCmiR, a two-layer neural network that predicts miRNA expression profile from only 977 LINCS L1000 landmark genes reducing sensitivity to dropout of single-cell RNA-seq data. Proof-of-concept analyses illustrate how SiCmiR can uncover candidate hub-miRNAs in bulk-seq cell lines and hepatocellular carcinoma, scRNA-seq pancreatic ductal carcinoma and ACTH-secreting pituitary adenoma and extracellular-vesicle-mediated crosstalk in glioblastoma. Trained on 6462 TCGA paired miRNA-mRNA samples, SiCmiR attains state-of-the-art accuracy on held-out cancers and generalizes to unseen cancer types, drug perturbations and scRNA-seq. We next constructed SiCmiR-Atlas, containing 632 public datasets, 9.36 million cells, 726 cell types, which is the first dedicated database of single-cell mature miRNA expression--providing interactive visualization, biomarker identification and cell-type-resolved miRNA-target networks. SiCmiR transforms bulk-derived statistical power into a single-cell view of miRNA biology and provides a community resource SiCmiR Atlas for biomarker discovery. SiCmiR Atlas is avilable at https://awi.cuhk.edu.cn/~SiCmiR/. |
| title | SiCmiR Atlas: Single-Cell miRNA Landscapes Reveals Hub-miRNA and Network Signatures in Human Cancers |
| topic | Genomics |
| url | https://arxiv.org/abs/2508.05692 |