The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
The Molecular Signatures Database Hallmark Gene Set Collection
Open accessAbstract
The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Performing gene set enrichment analysis using the MSigDB Hallmark gene sets (Liberzon et al. 2015), we observed that Myc target genes and DNA repair pathways were up-regulated, whereas TRP53 pathway genes were down-regulated (Fig. 4D).”
From Murine single-cell RNA-seq reveals cell-identity- and tissue-specific trajectories of aging (2019) · cites this paper as Liberzon et al. 2015
The cited source says what the claim attributes to it.
In the citing paper
“Gene set enrichment analyses were performed according to standard procedures [39, 40] on two published RNA-seq datasets [31, 48]”
From A gene toolbox for monitoring autophagy transcription (2021) · cites this paper as [40]
The cited source says what the claim attributes to it.
In the citing paper
“( F ) Hallmark gene set (81) enrichment plot for genes upregulated in control reactive astrocytes as compared to Ts21 reactive astrocytes (DEGs: adj. p<0.01, Wilcoxon Rank Sum test).”
From Molecular and cellular processes disrupted in the early postnatal Down syndrome prefrontal cortex (2026) · cites this paper as [81]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Moreover, carriers of MNAT1 gene, which is one of the leading genes in the G2/M checkpoint pathway58, were linked to better treatment outcome of FOLFOX54.”
From Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer (2025) · cites this paper as [58]
Experimental — accuracy not yet measuredIn the citing paper
“Significant enrichment of hallmark gene sets (33) for inflammatory response and NF-κB signaling were found in the MDP-l,d dataset (Table S8).”
From Enterococcus peptidoglycan remodeling promotes checkpoint inhibitor cancer immunotherapy (2021) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Self-citationSystematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
Ostrom, Q. T., Gittleman, H. R., Farah, P., Ondracek, A., Chen, Y., Wolinsky, Y., Stroup, N. E., Kruchko, C., & Barnholtz‐Sloan, J. S. (2013) · Neuro-Oncology · doi:10.1093/neuonc/not151 · Cited by 12,063
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
Self-citationThe basics of epithelial-mesenchymal transition
Kalluri, R., & Weinberg, R. A. (2009) · Journal of Clinical Investigation · doi:10.1172/jci39104 · Cited by 10,196
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950
Self-citationVerhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Self-citationMolecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Items 1,101–1,112 of 1,112
A comparative transcriptomic analysis of mouse demyelination models and multiple sclerosis lesions
Aboelnour, E. L., Vanoverbeke, V. R., Maupin, E. A., Hatfield, M. M., & Adams, K. L. (2026) · Nature Communications · doi:10.1038/s41467-026-72383-y · Cited by 4
Balestra, C., Maj, C., Müller, E., & Mayr, A. (2023) · PLOS ONE · doi:10.1371/journal.pone.0282699 · Cited by 4
Lin, S., & Jia, P. (2024) · GigaScience · doi:10.1093/gigascience/giae108 · Cited by 4
He, S., Tan, Y., Ye, Q., M Gubin, M., Dede, M., Rafei, H., Peng, W., Rezvani, K., Mohanty, V., & Chen, K. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-025-00060-4 · Cited by 3
Type 1 interferon perturbates clonal competition by reshaping human blood development
Lama, C., Isakov, D., Rosenberg, S., Quijada-Álamo, M., Saurty-Seerunghen, M. S., Moein, S., Nozais, M., Abera, T.-A., Sakaguchi, O., Totwani, M., et al. (2026) · Nature Genetics · doi:10.1038/s41588-026-02751-3 · Cited by 3
Zhong, J., Li, M., Dai, Z., & Wan, J. (2025) · Neurochemistry International · doi:10.1016/j.neuint.2025.106049 · Cited by 3
Inhibiting tumor cell-intrinsic UBA6 by inosine augments tumor immunogenicity
He, B., Zhang, L., Jiang, L., Yu, L., Li, Q., Tian, X., He, J.-Q., Zeng, L., Yang, Y., Wang, C., et al. (2021) · doi:10.21203/rs.3.rs-620390/v1 · Cited by 82
EXOSC3 G191 Variants Trigger System-Wide Recalibration of RNA Processing Machinery
Wijeratne, H. R. S., Runnebohm, A. M., Barron, M. P., Hampton, A. J., Li, R., Peck Justice, S. A., Lenceski, S. H., Rooney, J. D., Smith-Kinnaman, W. R., Chang, G. S., et al. (2026) · doi:10.64898/2026.01.26.701775 · Cited by 1
“Gene Ontology (GO) gene sets were retrieved from the MSigDB (Human) using the msigdbr R package 155 , and filtered to include only pathways containing between 10 and 500 quantified proteins.”
Analysis of intracellular and intercellular crosstalk from omics data
Chiodi, A., Pelucchi, P., & Mosca, E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.08.10.552776
Puluca, N., Vafadarnejad, E., Saleem, T., Rammoser, C., Wirth, F., Schwerdt, V., Dreßen, M., Gottmann, I., Reiminger, X., Yan, Y., et al. (2026) · Cardiovascular Research · doi:10.1093/cvr/cvag189 · Cited by 2
Integration of polygenic risk with single cell methylation data using met-scDRS
Li, X., Shi, Z., Hou, K., Eyring, K. W., Liu, C., Heffel, M. G., Luo, C., Geschwind, D. H., & Pasaniuc, B. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag751
Taurine lowers inflammaging by regulating the NLRP3 inflammasome in macrophages
Guan, C., Ryu, S., Dong, M., Zhu, Y., Maeda, R., Youm, Y.-H., Mohanty, S., Kim, H.-H., Orliaguet, L., Dlugos, T., et al. (2026) · Cell · doi:10.1016/j.cell.2026.09.061
Metadata
- Authors
- Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Mesirov, Jill P.
- Tamayo, Pablo
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2015-12
- Volume
- 1
- Issue
- 6
- Pages
- 417-425
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC4707969
- OpenAlex
- W2214074259
- Cited by
- 13,807
- References
- 37
- Citations by year
- 2026: 2174 · 2025: 2294 · 2024: 2415 · 2023: 1982 · 2022: 1708 · 2021: 1298 · 2020: 926 · 2019: 534 · 2018: 270 · 2017: 154 · 2016: 44
- FWCI
- 68.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health
- National Cancer Institute (R01CA154480, U54CA112962, R01CA121941)
- National Institute of General Medical Sciences (R01GM074024)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- Hallmark gene sets, gene set enrichment analysis, biological processes, cancer hallmarks
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
