Lineage-specific lncRNAs critically determine cross-species differences in tumors
Lin, J.; Liu, X.; Zhu, H.
Show abstract
Diverse mouse models have been generated to study human tumors. Although mouse and human tumors share similar differentially expressed genes and cancer hallmarks, many drugs that work in mice fail in humans. What makes mouse models poorly recapitulate human tumors remains unclear. We postulate that transcriptional regulation by lineage-specific long noncoding RNAs (LS lncRNAs) critically determines cross-species and cross-tumor differences. To test this hypothesis, we identified LS lncRNAs, predicted their target genes, integrated 9,058 RNA-seq samples from 13 human tumors and their mouse counterparts, and analyzed transcriptional regulation by LS lncRNAs across cellular contexts. LS lncRNAs substantially and tumor-specifically reconfigure transcription and signaling, and strongly influence cancer immunity and anti-cancer drug efficacy. These results provide systematic information for exploring and interpreting human tumor mouse models and for identifying human- and tumor-specific diagnostic and therapeutic targets. They also present an analytical approach applicable to other human diseases and their mouse models.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Systematic lncRNA mapping to genome-wide co-essential pathways uncovers cancer dependency on uncharacterized lncRNAs 95%
- DUX4 is a common driver of immune evasion and immunotherapy failure in metastatic cancers 95%
- Integrated evaluation of telomerase activation and telomere maintenance across cancer cell lines 94%
Similar papers in this journal
- A Modular Master Regulator Landscape Determines the Impact of Genetic Alterations on the Transcriptional Identity of Cancer Cells 96%
- Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes 95%
- Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.