A multiomics approach reveals RNA dynamics promote cellular sensitivity to DNA hypomethylation
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Abstract
Epigenetic dysregulation drives many of the hallmarks of cancer by enabling aberrant gene expression programs which underlie cancer cellular plasticity and tumor heterogeneity phenotypes that promote cancer initiation, progression, metastasis and drug resistance 1 . Indeed, one of the key findings of the genomics era in cancer biology has been that most cancer genomes are epigenetically abnormal and mutations in genes that regulate DNA methylation, such as DNMT3A/B , TET1-3 and IDH1/2 , are prevalent 2 , 3 . Together, these observations suggest that epigenetic dysregulation promotes cancer but may also represent a targetable vulnerability. As such, there has been substantial interest in the development of anti-cancer strategies which modulate cancer associated epigenetic programs and dependencies 4 , 5 , 6 . One such promising strategy which has shown success in the context of certain subtypes of acute myeloid leukemia (AML) is to inhibit the activity of key enzymes required for maintenance and regulation of DNA methylation by small molecule drugs, such as decitabine, resulting in global DNA hypomethylation. There is clear evidence of clinical benefit of decitabine treatment for AML patients who have cytogenetic abnormalities associated with unfavorable risk, TP53 mutations or both (defined hereafter as high-risk AML patients) 7 , 8 . Unfortunately, despite this benefit, most AML patients eventually progress following decitabine treatment with a median overall survival of less than 1 year. Problematically, relatively little progress has been made on improving the clinical activity of DNA hypomethylating agents (HMA) such as decitabine in AML or other cancers in part because the molecular determinants of response to HMAs are unclear.
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CRISPRi experiments in other cancer cell types (AML, CML, pancreatic cancer and lung cancer cell lines) demonstrated that, unlike PSMA1, PTGES3 is not an essential gene upon knockdown.
“Furthermore, CRISPRi experiments in other cancer cell types (AML, CML, pancreatic cancer and lung cancer cell lines54–56) demonstrated that, unlike PSMA1, which is a positive control commonly essential gene, PTGES3 is not an essential gene upon knockdown (Extended Data Fig. 10d–g).”
From Genome-scale CRISPR screens identify PTGES3 as a direct modulator of androgen receptor function in advanced prostate cancer (2025) · cites this paper as [54]
The Results section describes CRISPRi screens in AML cell lines, but does not report whether PTGES3 knockdown is nonessential relative to PSMA1. Searches for PTGES3, PSMA1, and the other named cancer cell types found no passage in our copy addressing that comparison. (LLM, conf=0.76)
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Cites
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106 · see citing works
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041 · see citing works
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
limma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
Cutadapt removes adapter sequences from high-throughput sequencing reads
Martin, M. (2011) · EMBnet.journal · doi:10.14806/ej.17.1.200 · Cited by 33,889 · see citing works
BEDTools: a flexible suite of utilities for comparing genomic features
Quinlan, A. R., & Hall, I. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq033 · Cited by 27,437 · see citing works
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807 · cited 2× · see citing works
“…analyses: (1) To identify smaller, focused pathways associated with drug sensitivity or resistance, we performed GSEA analysis on genes ranked by ρ phenotype and defined minimum and maximum thresholds for gene set size when running the `gsea` function (`min_size = 15` and `max_size = 150`) 31,73 .”
InfluentialSalmon provides fast and bias-aware quantification of transcript expression
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · see citing works
Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications
Krueger, F., & Andrews, S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr167 · Cited by 5,161 · see citing works
N6-methyladenosine-dependent regulation of messenger RNA stability
Wang, X., Lu, Z., Gomez, A., Hon, G. C., Yue, Y., Han, D., Fu, Y., Parisien, M., Dai, Q., Jia, G., et al. (2014) · Nature · doi:10.1038/nature12730 · Cited by 4,558 · see citing works
Comprehensive Analysis of mRNA Methylation Reveals Enrichment in 3′ UTRs and near Stop Codons
Meyer, K. D., Saletore, Y., Zumbo, P., Elemento, O., Mason, C. E., & Jaffrey, S. R. (2012) · Cell · doi:10.1016/j.cell.2012.05.003 · Cited by 4,223 · see citing works
Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences
Soneson, C., Love, M. I., & Robinson, M. D. (2015) · F1000Research · doi:10.12688/f1000research.7563.1 · Cited by 3,929 · see citing works
N6-methyladenosine Modulates Messenger RNA Translation Efficiency
Wang, X., Zhao, B. S., Roundtree, I. A., Lu, Z., Han, D., Ma, H., Weng, X., Chen, K., Shi, H., & He, C. (2015) · Cell · doi:10.1016/j.cell.2015.05.014 · Cited by 3,483 · see citing works
Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation
Gilbert, L. A., Horlbeck, M. A., Adamson, B., Villalta, J. E., Chen, Y., Whitehead, E. H., Guimaraes, C., Panning, B., Ploegh, H. L., Bassik, M. C., et al. (2014) · Cell · doi:10.1016/j.cell.2014.09.029 · Cited by 2,728 · see citing works
Self-citationReading, writing and erasing mRNA methylation
Zaccara, S., Ries, R. J., & Jaffrey, S. R. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0168-5 · Cited by 2,597 · see citing works
UpSet: Visualization of Intersecting Sets
Lex, A., Gehlenborg, N., Strobelt, H., Vuillemot, R., & Pfister, H. (2014) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2014.2346248 · Cited by 2,340 · see citing works
methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles
Akalin, A., Kormáksson, M., Li, S., Garrett-Bakelman, F. E., Figueroa, M. E., Melnick, A. M., & Mason, C. E. (2012) · Genome biology · doi:10.1186/gb-2012-13-10-r87 · Cited by 2,206 · see citing works
Smith, T. S., Heger, A., & Sudbery, I. M. (2017) · Genome Research · doi:10.1101/gr.209601.116 · Cited by 2,072 · see citing works
Chiappinelli, K. B., Strissel, P. L., Desrichard, A., Li, H., Henke, C., Akman, B., Hein, A., Rote, N. S., Cope, L. M., Snyder, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.011 · Cited by 2,022 · see citing works
Gene expression regulation mediated through reversible m6A RNA methylation
Fu, Y., Dominissini, D., Rechavi, G., & He, C. (2014) · Nature Reviews Genetics · doi:10.1038/nrg3724 · Cited by 1,926 · see citing works
Cunningham, F., Allen, J. E., Allen, J., Alvarez-Jarreta, J., Amode, M. R., Armean, I. M., Austine-Orimoloye, O., Azov, A. G., Barnes, I., Bennett, R., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab1049 · Cited by 1,923 · see citing works
Cellular differentiation, cytidine analogs and DNA methylation
Jones, P. A., & Taylor, S. M. (1980) · Cell · doi:10.1016/0092-8674(80)90237-8 · Cited by 1,831 · see citing works
DiNardo, C. D., Pratz, K., Pullarkat, V., Jonas, B. A., Arellano, M., Becker, P. S., Frankfurt, O., Konopleva, M., Wei, A. H., Kantarjian, H. M., et al. (2019) · Blood · doi:10.1182/blood-2018-08-868752 · Cited by 1,825 · see citing works
Roulois, D., Loo Yau, H., Singhania, R., Wang, Y., Danesh, A., Shen, S. Y., Han, H., Liang, G., Jones, P. A., Pugh, T. J., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.056 · Cited by 1,527 · see citing works
N6-methyladenosine marks primary microRNAs for processing
Alarcón, C. R., Lee, H., Goodarzi, H., Halberg, N., & Tavazoie, S. F. (2015) · Nature · doi:10.1038/nature14281 · Cited by 1,394 · see citing works
Self-citationEpigenetic plasticity and the hallmarks of cancer
Flavahan, W. A., Gaskell, E., & Bernstein, B. E. (2017) · Science · doi:10.1126/science.aal2380 · Cited by 1,340 · see citing works
Frankish, A., Diekhans, M., Jungreis, I., Lagarde, J., Loveland, J. E., Mudge, J. M., Sisu, C., Wright, J. C., Armstrong, J., Barnes, I., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1087 · Cited by 1,334 · see citing works
Functional genomic landscape of acute myeloid leukaemia
Tyner, J. W., Tognon, C. E., Bottomly, D., Wilmot, B., Kurtz, S. E., Savage, S. L., Long, N., Schultz, A. R., Traer, E. A., Abel, M. L., et al. (2018) · Nature · doi:10.1038/s41586-018-0623-z · Cited by 1,330 · see citing works
Vu, L., Pickering, B. F., Cheng, Y., Zaccara, S., Nguyen, D. T. T., Minuesa, G., Chou, T., Chow, A., Saletore, Y., MacKay, M. J., et al. (2017) · Nature Medicine · doi:10.1038/nm.4416 · Cited by 1,250 · see citing works
Dynamic m6A mRNA methylation directs translational control of heat shock response
Zhou, J. L., Wan, J., Gao, X., Zhang, X., Jaffrey, S. R., & Qian, S. (2015) · Nature · doi:10.1038/nature15377 · Cited by 1,228 · see citing works
Small molecule inhibition of METTL3 as a strategy against myeloid leukaemia
Yankova, E., Blackaby, W., Albertella, M., Rak, J., Braekeleer, E. D., Tsagkogeorga, G., Pilka, E., Aspris, D., Leggate, D., Hendrick, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03536-w · Cited by 1,207 · see citing works
Modes of action of the DNA methyltransferase inhibitors azacytidine and decitabine
Stresemann, C., & Lyko, F. (2008) · International Journal of Cancer · doi:10.1002/ijc.23607 · Cited by 954 · see citing works
Horlbeck, M. A., Gilbert, L. A., Villalta, J. E., Adamson, B., Pak, R. A., Chen, Y., Fields, A. P., Park, C. Y., Corn, J. E., Kampmann, M., et al. (2016) · eLife · doi:10.7554/elife.19760 · Cited by 894 · see citing works
Self-citationVisualizing Genomic Data Using Gviz and Bioconductor
Hahne, F., & Ivánek, R. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3578-9_16 · Cited by 893 · see citing works
<i>TP53</i> and Decitabine in Acute Myeloid Leukemia and Myelodysplastic Syndromes
Welch, J. S., Petti, A. A., Miller, C. A., Fronick, C. C., O’Laughlin, M., Fulton, R. S., Wilson, R. K., Baty, J. D., Duncavage, E. J., Tandon, B., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1605949 · Cited by 796 · see citing works
Tsai, H.-C., Li, H., Neste, L. V., Cai, Y., Robert, C., Rassool, F. V., Shin, J., Harbom, K., Beaty, R. M., Pappou, E. P., et al. (2012) · Cancer Cell · doi:10.1016/j.ccr.2011.12.029 · Cited by 651 · see citing works
Transcriptome-wide measurement of translation by ribosome profiling
McGlincy, N. J., & Ingolia, N. T. (2017) · Methods · doi:10.1016/j.ymeth.2017.05.028 · Cited by 547 · see citing works
Sykes, D. B., Kfoury, Y. S., Mercier, F. E., Wawer, M. J., Law, J. M., Haynes, M. K., Lewis, T. A., Schajnovitz, A., Jain, E., Lee, D., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.057 · Cited by 500 · see citing works
Epigenetic Therapies for Cancer
Bates, S. E. (2020) · New England Journal of Medicine · doi:10.1056/nejmra1805035 · Cited by 493 · see citing works
Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing
Replogle, J. M., Norman, T. M., Xu, A., Hussmann, J. A., Chen, J., Cogan, J. Z., Meer, E. J., Terry, J. M., Riordan, D. P., Srinivas, N., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0470-y · Cited by 465 · see citing works
Li, H., Chiappinelli, K. B., Guzzetta, A. A., Easwaran, H., Yen, R.-W. C., Vatapalli, R. J., Topper, M. J., Luo, J., Connolly, R. M., Azad, N. S., et al. (2014) · Oncotarget · doi:10.18632/oncotarget.1782 · Cited by 428 · see citing works
Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics
Ding, L., Bailey, M. H., Porta-Pardo, E., Thorsson, V., Colaprico, A., Bertrand, D., Gibbs, D. L., Weerasinghe, A., Huang, K.-L., Tokheim, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.033 · Cited by 399 · see citing works
m6A in mRNA coding regions promotes translation via the RNA helicase-containing YTHDC2
Mao, Y., Dong, L., Liu, X., Guo, J., Ma, H., Shen, B., & Qian, S. (2019) · Nature Communications · doi:10.1038/s41467-019-13317-9 · Cited by 397 · see citing works
Meng, J., Lu, Z., Liu, H., Zhang, L., Zhang, S., Chen, Y., Rao, M. K., & Huang, Y. (2014) · Methods · doi:10.1016/j.ymeth.2014.06.008 · Cited by 375 · see citing works
Epigenetic Therapeutics: A New Weapon in the War Against Cancer
Ahuja, N., Sharma, A. R., & Baylin, S. B. (2016) · Annual Review of Medicine · doi:10.1146/annurev-med-111314-035900 · Cited by 364 · see citing works
DNMT and HDAC inhibitors induce cryptic transcription start sites encoded in long terminal repeats
Brocks, D., Schmidt, C. R., Daskalakis, M., Jang, H. S., Shah, N. M., Li, D., Li, J., Zhang, B., Hou, Y., Laudato, S., et al. (2017) · Nature Genetics · doi:10.1038/ng.3889 · Cited by 339 · see citing works
Crosstalk between epitranscriptomic and epigenetic mechanisms in gene regulation
Kan, R. L., Chen, J., & Sallam, T. (2022) · Trends in Genetics · doi:10.1016/j.tig.2021.06.014 · Cited by 308 · see citing works
A Universal Framework for Regulatory Element Discovery across All Genomes and Data Types
Elemento, O., Slonim, N., & Tavazoie, S. (2007) · Molecular Cell · doi:10.1016/j.molcel.2007.09.027 · Cited by 306 · see citing works
m6A RNA methylation regulates the fate of endogenous retroviruses
Chełmicki, T., Roger, E., Teissandier, A., Dura, M., Bonneville, L., Rucli, S., Dossin, F., Fouassier, C., Lameiras, S., & Bourc’his, D. (2021) · Nature · doi:10.1038/s41586-020-03135-1 · Cited by 299 · see citing works
Epigenetic therapy induces transcription of inverted SINEs and ADAR1 dependency
Mehdipour, P., Marhon, S. A., Ettayebi, I., Chakravarthy, A., Hosseini, A., Wang, Y., de Castro, F. A., Yau, H. L., Ishak, C. A., Abelson, S., et al. (2020) · Nature · doi:10.1038/s41586-020-2844-1 · Cited by 298 · see citing works
Enhancer RNA m6A methylation facilitates transcriptional condensate formation and gene activation
Lee, J.-H., Wang, R., Xiong, F., Krakowiak, J., Liao, Z., Nguyen, P. T.-M., Moroz, E., Shao, J., Zhu, X., Bolt, M. J., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2021.07.024 · Cited by 275 · see citing works
Mechanisms of resistance to 5-aza-2′-deoxycytidine in human cancer cell lines
Qin, T., Jelı́nek, J., Si, J., Shu, J., & Issa, J. J. (2008) · Blood · doi:10.1182/blood-2008-02-140038 · Cited by 238 · see citing works
DNA methylation disruption reshapes the hematopoietic differentiation landscape
Izzo, F., Lee, S. C., Poran, A., Chaligne, R., Gaiti, F., Gross, B., Murali, R. R., Deochand, S. D., Ang, C., Jones, P. W., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0595-4 · Cited by 236 · see citing works
Self-citationRevealing Global Regulatory Perturbations across Human Cancers
Goodarzi, H., Elemento, O., & Tavazoie, S. (2009) · Molecular Cell · doi:10.1016/j.molcel.2009.11.016 · Cited by 201 · see citing works
Self-citationRefined RIP-seq protocol for epitranscriptome analysis with low input materials
Zeng, Y., Wang, S., Gao, S., Soares, F., Ahmed, M., Guo, H., Wang, M., Hua, J. T., Guan, J., Moran, M. F., et al. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2006092 · Cited by 188 · see citing works
RNA m6A regulates transcription via DNA demethylation and chromatin accessibility
Deng, S., Zhang, J., Su, J., Zuo, Z., Zeng, L., Liu, K., Zheng, Y., Huang, X., Bai, R., Zhuang, L., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01173-1 · Cited by 187 · see citing works
A gene ontology inferred from molecular networks
Dutkowski, J., Kramer, M., Surma, M. A., Balakrishnan, R., Cherry, J. M., Krogan, N. J., & Ideker, T. (2013) · Nature Biotechnology · doi:10.1038/nbt.2463 · Cited by 184 · see citing works
Tsao, T., Shi, Y., Kornblau, S. M., Lu, H., Konoplev, S. N., Antony, A., Ruvolo, V. R., Qiu, Y. H., Zhang, N., Coombes, K. R., et al. (2012) · Annals of Hematology · doi:10.1007/s00277-012-1537-8 · Cited by 171 · see citing works
DNA Hypomethylating Drugs in Cancer TherapyPreprint
Sato, T., Issa, J. J., & Kropf, P. L. (2017) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a026948 · Cited by 166 · see citing works
THE CALCULATION OF MICROBIAL ASSAYS
Bliss, C. I. (1956) · Bacteriological Reviews · doi:10.1128/br.20.4.243-258.1956 · Cited by 163 · see citing works
Cui, X., Wei, Z., Zhang, L., Liu, H., Sun, L., Zhang, S., Huang, Y., & Meng, J. (2016) · BioMed Research International · doi:10.1155/2016/8367534 · Cited by 160 · see citing works
Bogenberger, J. M., Delman, D., Hansen, N., Valdez, R., Fauble, V. D. S., Mesa, R. A., & Tibes, R. (2014) · Leukemia & lymphoma/Leukemia and lymphoma · doi:10.3109/10428194.2014.910657 · Cited by 152 · see citing works
Li, Y., & Kiledjian, M. (2010) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.15 · Cited by 145 · see citing works
New insights into decapping enzymes and selective mRNA decay
Grudzien‐Nogalska, E., & Kiledjian, M. (2016) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1379 · Cited by 137 · see citing works
Yamauchi, T., Masuda, T., Canver, M. C., Seiler, M., Semba, Y., Shboul, M., Al‐Raqad, M., Maeda, M., Schoonenberg, V. A. C., Cole, M. A., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.01.012 · Cited by 134 · see citing works
Dynamic Gene Regulatory Networks of Human Myeloid Differentiation
Ramírez, R. N., El-Ali, N. C., Mager, M. A., Wyman, D., Conesa, A., & Mortazavi, A. (2017) · Cell Systems · doi:10.1016/j.cels.2017.03.005 · Cited by 114 · see citing works
Alkallas, R., Fish, L., Goodarzi, H., & Najafabadi, H. (2017) · Nature Communications · doi:10.1038/s41467-017-00867-z · Cited by 102 · see citing works
Self-citationCashen, A. F., Shah, A. K., Todt, L. M., Fisher, N. M., & DiPersio, J. F. (2007) · Cancer Chemotherapy and Pharmacology · doi:10.1007/s00280-007-0531-7 · Cited by 98 · see citing works
RADAR: differential analysis of MeRIP-seq data with a random effect model
Zhang, Z. S., Zhang, Q., Eckert, M. A., Zhu, A. C., Chryplewicz, A., Jesus, D. F. D., Ren, D., Kulkarni, R., Lengyel, E. R., He, C., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1915-9 · Cited by 98 · see citing works
Kampmann, M., Bassik, M. C., & Weissman, J. S. (2014) · Nature Protocols · doi:10.1038/nprot.2014.103 · Cited by 87 · see citing works
Nuclear TARBP2 Drives Oncogenic Dysregulation of RNA Splicing and Decay
Fish, L., Navickas, A., Culbertson, B., Xu, Y., Nguyen, H. C. B., Zhang, S., Hochman, M., Okimoto, R. A., Dill, B. D., Molina, H., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.06.001 · Cited by 87 · see citing works
Self-citationHERVd: the Human Endogenous RetroViruses Database: update
Pačes, J. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh075 · Cited by 84 · see citing works
CGAT: computational genomics analysis toolkit
Sims, D., Ilott, N. E., Sansom, S. N., Sudbery, I. M., Johnson, J. S., Fawcett, K. A., Berlanga‐Taylor, A. J., Luna-Valero, S., Ponting, C. P., & Heger, A. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt756 · Cited by 74 · see citing works
Chen, Y., Shen, J., Chen, D., Wu, C., Guo, R., Zhang, P., Lv, J., Li, W., Wang, Z., & Chen, Y. (2020) · Journal of Hematology & Oncology · doi:10.1186/s13045-020-00854-w · Cited by 71 · see citing works
Orta, M. L., Calderón‐Montaño, J. M., Domı́nguez, I., Pastor, N., Burgos‐Morón, E., López‐Lázaro, M., Cortés, F., Mateos, S., & Helleday, T. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt270 · Cited by 70 · see citing works
Mahoney, C. E., Pirman, D. A., Chubukov, V., Sleger, T., Hayes, S., Fan, Z. P., Allen, E. L., Chen, Y., Huang, L., Liu, M., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-07959-4 · Cited by 69 · see citing works
A guide to creating design matrices for gene expression experiments
Law, C. W., Zeglinski, K., Dong, X., Alhamdoosh, M., Smyth, G. K., & Ritchie, M. E. (2020) · F1000Research · doi:10.12688/f1000research.27893.1 · Cited by 69 · see citing works
Publisher Correction: Reading, writing and erasing mRNA methylation
Zaccara, S., Ries, R. J., & Jaffrey, S. R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00654-3 · Cited by 54 · see citing works
Ku, Y., Park, J., Cho, R., Lee, Y.-K., Park, H.-M., Kim, M. A., Hur, K., Byun, S. Y., Liu, J., Lee, Y.-S., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016289118 · Cited by 49 · see citing works
Lachmann, A., Xie, Z., & Ma’ayan, A. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac076 · Cited by 45 · see citing works
Shareef, S. J., Bevill, S. M., Raman, A. T., Aryee, M. J., van Galen, P., Hovestadt, V., & Bernstein, B. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00910-x · Cited by 43 · see citing works
An mRNA processing pathway suppresses metastasis by governing translational control from the nucleus
Navickas, A., Asgharian, H., Winkler, J., Fish, L., Garcia, K., Markett, D., Dodel, M., Culbertson, B., Miglani, S., Joshi, T. V., et al. (2023) · Nature Cell Biology · doi:10.1038/s41556-023-01141-9 · Cited by 43 · see citing works
Self-citationSorrentino, V. G., Thota, S., Gonzalez, E. A., Rameshwar, P., Chang, V. T.-S., & Etchegaray, J. (2021) · Pharmaceuticals · doi:10.3390/ph14070641 · Cited by 37 · see citing works
A field guide to endogenous retrovirus regulatory networks
Dopkins, N., O’Mara, M. M., Lawrence, E., Fei, T., Sandoval‐Motta, S., Nixon, D. F., & Bendall, M. L. (2022) · Molecular Cell · doi:10.1016/j.molcel.2022.09.011 · Cited by 37 · see citing works
Kayamori, K., Nagai, Y., Zhong, C. F., Kaito, S., Shinoda, D., Koide, S., Kuribayashi, W., Oshima, M., Nakajima‐Takagi, Y., Yamashita, M., et al. (2021) · Blood Advances · doi:10.1182/bloodadvances.2020001461 · Cited by 35 · see citing works
The hENT1 and DCK genes underlie the decitabine response in patients with myelodysplastic syndrome
Wu, P., Geng, S., Weng, J., Deng, C., Lu, Z., Luo, C., & Du, X. (2014) · Leukemia Research · doi:10.1016/j.leukres.2014.08.016 · Cited by 34 · see citing works
Targeting the key cholesterol biosynthesis enzyme squalene monooxygenasefor cancer therapy
Zou, Y., Zhang, H., Bi, F., Tang, Q., & Xu, H. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.938502 · Cited by 33 · see citing works
DcpS is a transcript-specific modulator of RNA in mammalian cells
Zhou, M., Bail, S., Plasterer, H. L., Rusche, J. R., & Kiledjian, M. (2015) · RNA · doi:10.1261/rna.051573.115 · Cited by 26 · see citing works
Recurrent transcriptional responses in AML and MDS patients treated with decitabine
Upadhyay, P., Beales, J. T., Shah, N. M., Gruszczyńska, A., Miller, C. A., Petti, A. A., Ramakrishnan, S. M., Link, D. C., Ley, T. J., & Welch, J. S. (2022) · Experimental Hematology · doi:10.1016/j.exphem.2022.04.002 · Cited by 20 · see citing works
Middeke, J. M., Teipel, R., Röllig, C., Stasik, S., Zebisch, A., Sill, H., Krämer, M., Scholl, S., Hochhaus, A., Jost, E., et al. (2021) · Leukemia & lymphoma/Leukemia and lymphoma · doi:10.1080/10428194.2020.1864354 · Cited by 13 · see citing works
Branstrom, A. A., Cao, L., Furia, B., Trotta, C. R., Santaguida, M. A., Graci, J. D., Colacino, J. M., Ray, B., Li, W., Sheedy, J., et al. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.832816 · Cited by 13 · see citing works
When RNA methylation meets DNA methylation
Xu, W., & Shen, H. (2022) · Nature Genetics · doi:10.1038/s41588-022-01166-0 · Cited by 13 · see citing works
DCK confers sensitivity of DCTD-positive cancer cells to oxidized methylcytidines
Zhao, Y., Jiang, W., Gao, H. F., Pang, G.-Z., Wu, Y., Wang, Y.-X., Sheng, M., Xie, J., Wu, W., Ji, Z.-J., et al. (2022) · Protein & Cell · doi:10.1093/procel/pwac028 · Cited by 6 · see citing works
Yogodzinski, C., Arab, A., Pritchard, J. R., Goodarzi, H., & Gilbert, L. A. (2021) · Genome Medicine · doi:10.1101/2021.01.08.425918 · Cited by 3 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Li, H., Melnyk, J. E., Fu, B. X. H., Shrestha, R., Zhang, M., Sjöström, M., Feng, S., Anderson, J. A., Han, W., Chesner, L. N., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02388-8 · Cited by 10 · see citing works
Self-citationDecoding disease complexity: Multi-Omics integration and AI in precision medicine
Amini, H., & Wang, J. (2026) · The Journal of Precision Medicine Health and Disease · doi:10.1016/j.premed.2026.100047 · Cited by 3 · see citing works
Elucidating the risk factors and oncogene drivers of acute myeloid leukemia
Sanya, D. R. A., & Onésime, D. (2025) · Human Cell · doi:10.1007/s13577-025-01294-9 · Cited by 1 · see citing works
Jadhav, V. J., Kale, N., Javale, D. P., & Gore, R. W. (2026) · Automation and Remote Control · doi:10.1134/s0005117925601423
- Authors
- Ge, A. Y., Arab, A., Dai, R., Navickas, A., Fish, L., Garcia, K., Asgharian, H., Goudreau, J., Lee, S., Keenan, K., Pappalardi, M. B., McCabe, M. T., Przybyla, L., Goodarzi, H., & Gilbert, L. A.
- Affiliations
- University of California, San Francisco (US)
- GlaxoSmithKline (United States) (US)
- Corresponding author
- Goodarzi, Hani
- Gilbert, Luke A.
- ORCID
- Venue
- Scientific Reports
- Published
- 2024-10-29
- Volume
- 14
- Issue
- 1
- Pages
- 25940-25940
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC11522420
- OpenAlex
- W4403877082
- Cited by
- 4
- References
- 95
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 0.54
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Scientific Reports (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (1s10od028511-01, W81XWH)
- University of California, San Francisco (1S10OD028511-01)
- National Cancer Institute (R01CA240984, DP2CA239597, R01CA244634)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Health Sciences › Medicine › Hematology › Acute Myeloid Leukemia Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA modifications and cancer
- Keywords
- DNA hypomethylation, decitabine, acute myeloid leukemia, DNA methylation, RNA dynamics, epigenetic dysregulation, hypomethylating agents, drug response, multi-omics, tumor heterogeneity, TP53 mutation, chromosomal abnormalities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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