Profiling Cell-state Fingerprints Based on Deep Learning Model with Meta-programs of Pan-cancer Article Swipe
Zebin Wen
,
Siwen Xu
,
Jiahong Wang
,
Shuyi Cao
,
Xingguang Luο
,
Yan Chen
,
Patrick Wai-Hang Kwong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/gpbjnl/qzaf123
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/gpbjnl/qzaf123
Cell states within cancer have garnered significant attention, yet the mechanisms through which malignant cells assert dominance in pan-cancer commonalities remain elusive. In this study, we employed label-free multiplexed single-cell RNA sequencing (scRNA-seq) to analyze cell states in 159,372 cells across 245 cell lines spanning 14 tissue types, integrating both public and proprietary datasets. We identified 21 meta-programs (MPs) representing shared characteristics across pan-cancer landscapes, encompassing 16 biological processes. Subsequently, we developed a deep learning model StateNet to generate cell-state fingerprints for delineating the individuality of each cell line based on these MPs. Leveraging StateNet, we pinpointed ACAT2 as a potential mediator bridging hypoxia and the lipid metabolism pathway, and we also showcased that epithelial–mesenchymal transition programs are vital for classifying cell lines through perturbation experiments. StateNet not only elucidates the overarching manifold structure of scRNA-seq data but also furnishes cell-state fingerprints of cell clusters, unveiling prognosis-related programs and distinguishing between patients with varying survival outcomes. Utilizing these prognosis-related programs on 3210 cancer samples, we constructed Cox models and identified risk-associated programs and genes responsible for different cancer types. StateNet thus emerges as a novel and efficient tool for cancer profiling, unraveling the shared commonalities and distinct individualities of pan-cancer cells across expansive datasets.
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Profiling Cell-state Fingerprints Based on Deep Learning Model with Meta-programs of Pan-cancerWork title
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2025Year of publication
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2025-12-02Full publication date if available
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Zebin Wen, Siwen Xu, Jiahong Wang, Shuyi Cao, Xingguang Luο, Yan Chen, Patrick Wai-Hang KwongList of authors in order
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https://doi.org/10.1093/gpbjnl/qzaf123Publisher landing page
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| abstract_inverted_index.hypoxia | 104 |
| abstract_inverted_index.through | 12, 124 |
| abstract_inverted_index.varying | 154 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.StateNet | 77, 127, 180 |
| abstract_inverted_index.bridging | 103 |
| abstract_inverted_index.distinct | 197 |
| abstract_inverted_index.elusive. | 22 |
| abstract_inverted_index.employed | 27 |
| abstract_inverted_index.garnered | 6 |
| abstract_inverted_index.generate | 79 |
| abstract_inverted_index.learning | 75 |
| abstract_inverted_index.manifold | 133 |
| abstract_inverted_index.mediator | 102 |
| abstract_inverted_index.pathway, | 109 |
| abstract_inverted_index.patients | 152 |
| abstract_inverted_index.programs | 117, 148, 160, 172 |
| abstract_inverted_index.samples, | 164 |
| abstract_inverted_index.spanning | 45 |
| abstract_inverted_index.survival | 155 |
| abstract_inverted_index.StateNet, | 95 |
| abstract_inverted_index.Utilizing | 157 |
| abstract_inverted_index.clusters, | 145 |
| abstract_inverted_index.datasets. | 54, 204 |
| abstract_inverted_index.developed | 72 |
| abstract_inverted_index.different | 177 |
| abstract_inverted_index.dominance | 17 |
| abstract_inverted_index.efficient | 187 |
| abstract_inverted_index.expansive | 203 |
| abstract_inverted_index.furnishes | 140 |
| abstract_inverted_index.malignant | 14 |
| abstract_inverted_index.outcomes. | 156 |
| abstract_inverted_index.potential | 101 |
| abstract_inverted_index.scRNA-seq | 136 |
| abstract_inverted_index.showcased | 113 |
| abstract_inverted_index.structure | 134 |
| abstract_inverted_index.unveiling | 146 |
| abstract_inverted_index.Leveraging | 94 |
| abstract_inverted_index.attention, | 8 |
| abstract_inverted_index.biological | 68 |
| abstract_inverted_index.cell-state | 80, 141 |
| abstract_inverted_index.elucidates | 130 |
| abstract_inverted_index.identified | 56, 170 |
| abstract_inverted_index.label-free | 28 |
| abstract_inverted_index.mechanisms | 11 |
| abstract_inverted_index.metabolism | 108 |
| abstract_inverted_index.pan-cancer | 19, 64, 200 |
| abstract_inverted_index.pinpointed | 97 |
| abstract_inverted_index.processes. | 69 |
| abstract_inverted_index.profiling, | 191 |
| abstract_inverted_index.sequencing | 32 |
| abstract_inverted_index.transition | 116 |
| abstract_inverted_index.unraveling | 192 |
| abstract_inverted_index.(scRNA-seq) | 33 |
| abstract_inverted_index.classifying | 121 |
| abstract_inverted_index.constructed | 166 |
| abstract_inverted_index.delineating | 83 |
| abstract_inverted_index.integrating | 49 |
| abstract_inverted_index.landscapes, | 65 |
| abstract_inverted_index.multiplexed | 29 |
| abstract_inverted_index.overarching | 132 |
| abstract_inverted_index.proprietary | 53 |
| abstract_inverted_index.responsible | 175 |
| abstract_inverted_index.significant | 7 |
| abstract_inverted_index.single-cell | 30 |
| abstract_inverted_index.encompassing | 66 |
| abstract_inverted_index.experiments. | 126 |
| abstract_inverted_index.fingerprints | 81, 142 |
| abstract_inverted_index.perturbation | 125 |
| abstract_inverted_index.representing | 60 |
| abstract_inverted_index.Subsequently, | 70 |
| abstract_inverted_index.commonalities | 20, 195 |
| abstract_inverted_index.individuality | 85 |
| abstract_inverted_index.meta-programs | 58 |
| abstract_inverted_index.distinguishing | 150 |
| abstract_inverted_index.characteristics | 62 |
| abstract_inverted_index.individualities | 198 |
| abstract_inverted_index.risk-associated | 171 |
| abstract_inverted_index.prognosis-related | 147, 159 |
| abstract_inverted_index.epithelial–mesenchymal | 115 |
| cited_by_percentile_year | |
| countries_distinct_count | 4 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |