The DOI matches its Crossref record; 150 of the 153 references it cites were found in a registry or an index.
Proteogenomic and metabolomic characterization of human glioblastoma
Open accessAbstract
Glioblastoma (GBM) is the most aggressive nervous system cancer. Understanding its molecular pathogenesis is crucial to improving diagnosis and treatment. Integrated analysis of genomic, proteomic, post-translational modification and metabolomic data on 99 treatment-naive GBMs provides insights to GBM biology. We identify key phosphorylation events (e.g., phosphorylated PTPN11 and PLCG1) as potential switches mediating oncogenic pathway activation, as well as potential targets for EGFR-, TP53-, and RB1-altered tumors. Immune subtypes with distinct immune cell types are discovered using bulk omics methodologies, validated by snRNA-seq, and correlated with specific expression and histone acetylation patterns. Histone H2B acetylation in classical-like and immune-low GBM is driven largely by BRDs, CREBBP, and EP300. Integrated metabolomic and proteomic data identify specific lipid distributions across subtypes and distinct global metabolic changes in IDH-mutated tumors. This work highlights biological relationships that could contribute to stratification of GBM patients for more effective treatment.
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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“High-throughput genomic, epigenomic, and transcriptomic profiling of glioblastoma cells revealed three main subtypes harboring distinct molecular and phenotypic landscapes that affect response to the therapy and clinical outcome: mesenchymal, proneural, and classical subtypes.96,97,98,99,100”
From MiR-128-3p – a gray eminence of the human central nervous system (2024) · cites this paper as [100]
Experimental — accuracy not yet measured
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
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
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
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
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Self-citationRSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Wickham, H., Averick, M., Bryan, J., Chang, W., McGowan, L., François, R., Grolemund, G., Hayes, A., Henry, L., Hester, J., et al. (2019) · Journal of Open Source Software · doi:10.21105/joss.01686 · Cited by 22,138
Radiotherapy Plus Concomitant and Adjuvant Temozolomide for Glioblastoma
Stupp, R., Mason, W. P., van den Bent, M. J., Weller, M., Fisher, B., Taphoorn, M. J. B., Belanger, K., Brandes, A. A., Marosi, C., Bogdahn, U., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa043330 · Cited by 21,807
Research works printed but not linked: 1
- Pilarczyk, 2019, Connecting Omics Signatures of Diseases, Drugs, and Mechanisms of Actions with iLINCS, BioRxiv, 826271
Cited by
Sorted by citations
Gu, Z. (2022) · iMeta · doi:10.1002/imt2.43 · Cited by 1,943
Small molecule metabolites: discovery of biomarkers and therapeutic targets
Qiu, S., Cai, Y., Yao, H., Lin, C., Xie, Y., Tang, S., & Zhang, A. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01399-3 · Cited by 993
Microglia: Immune and non-immune functions
Borst, K., Dumas, A. A., & Prinz, M. (2021) · Immunity · doi:10.1016/j.immuni.2021.09.014 · Cited by 804
Proteogenomic characterization of pancreatic ductal adenocarcinoma
Cao, L., Huang, C., Cui Zhou, D., Hu, Y., Lih, T. M., Savage, S. R., Krug, K., Clark, D. J., Schnaubelt, M., Chen, L., et al. (2021) · Cell · doi:10.1016/j.cell.2021.08.023 · Cited by 578
Self-citationZhong, Q., Xiao, X., Qiu, Y., Xu, Z., Chen, C., Chong, B., Zhao, X., Hai, S., Li, S., An, Z., et al. (2023) · MedComm · doi:10.1002/mco2.261 · Cited by 442
A proteogenomic portrait of lung squamous cell carcinoma
Satpathy, S., Krug, K., Beltran, P. M. J., Savage, S. R., Petralia, F., Kumar‐Sinha, C., Dou, Y., Reva, B. A., Kane, M. H., Avanessian, S. C., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.016 · Cited by 390
Self-citationMachine learning for multi-omics data integration in cancer
Cai, Z., Poulos, R. C., Liu, J., & Zhong, Q. (2022) · iScience · doi:10.1016/j.isci.2022.103798 · Cited by 366
Integrated Proteogenomic Characterization across Major Histological Types of Pediatric Brain Cancer
Petralia, F., Tignor, N., Reva, B., Koptyra, M., Chowdhury, S., Rykunov, D., Krek, A., Ma, W., Zhu, Y., Ji, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.10.044 · Cited by 317
Self-citationProteogenomic data and resources for pan-cancer analysis
Li, Y., Dou, Y., Leprevost, F. D. V., Geffen, Y., Calinawan, A. P., Aguet, F., Akiyama, Y., Anand, S. K., Birger, C., Cao, S., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.06.009 · Cited by 266
Self-citationVerdugo, E., Puerto, I., & Medina, M. Á. (2022) · 癌症：英文版 · doi:10.1002/cac2.12361 · Cited by 250
A review of deep learning for brain tumor analysis in MRI
Dorfner, F. J., Patel, J., Kalpathy–Cramer, J., Gerstner, E. R., & Bridge, C. P. (2025) · npj Precision Oncology · doi:10.1038/s41698-024-00789-2 · Cited by 265
Transcriptome analysis reveals tumor microenvironment changes in glioblastoma
Hoogstrate, Y., Draaisma, K., Ghisai, S. A., van Hijfte, L., Barin, N., de Heer, I., Coppieters, W., van den Bosch, T. P. P., Bolleboom, A., Gao, Z., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.02.019 · Cited by 240
Cancer proteogenomics: current impact and future prospects
Mani, D. R., Krug, K., Zhang, B., Satpathy, S., Clauser, K. R., Ding, L., Ellis, M., Gillette, M. A., & Carr, S. A. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00446-5 · Cited by 229
Self-citationZhang, Y., Chen, F., Chandrashekar, D. S., Varambally, S., & Creighton, C. J. (2022) · Nature Communications · doi:10.1038/s41467-022-30342-3 · Cited by 231
Self-citationGeffen, Y., Anand, S. K., Akiyama, Y., Yaron, T. M., Song, Y., Johnson, J. L., Govindan, A., Babur, Ö., Li, Y., Huntsman, E. M., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.013 · Cited by 217
Self-citationEpigenetic regulation during cancer transitions across 11 tumour types
Terekhanova, N. V., Karpova, A. Y., Liang, W.-W., Strzalkowski, A. J., Chen, S., Li, Y., Southard-Smith, A. N., Iglesia, M. D., Wendl, M. C., Jayasinghe, R. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06682-5 · Cited by 216
Self-citationThe E3 ligase adapter cereblon targets the C-terminal cyclic imide degron
Ichikawa, S., Flaxman, H. A., Xu, W., Vallavoju, N., Lloyd, H. C., Wang, B., Shen, D., Pratt, M. R., & Woo, C. M. (2022) · Nature · doi:10.1038/s41586-022-05333-5 · Cited by 195
Leblanc, V., Trinh, D. L. N., Aslanpour, S., Hughes, M. L., Livingstone, D., Jin, D., Ahn, B. Y., Blough, M. D., Cairncross, J. G., Chan, J. A.-W., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2022.02.016 · Cited by 159
Hong, R., Liu, W., DeLair, D., Razavian, N., & Fenyö, D. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100400 · Cited by 154
Self-citationNeutralizing IL-8 potentiates immune checkpoint blockade efficacy for glioma
Liu, H., Zhao, Q., Tan, L., Wu, X., Huang, R., Zuo, Y., Chen, L., Yang, J.-G., Zhang, Z., Ruan, W., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.03.004 · Cited by 156
Self-citationTryptophan depletion results in tryptophan-to-phenylalanine substitutants
Pataskar, A., Champagne, J., Nagel, R., Kenski, J. C. N., Laos, M., Michaux, J., Pak, H. S., Bleijerveld, O. B., Mordente, K., Navarro, J. M., et al. (2022) · Nature · doi:10.1038/s41586-022-04499-2 · Cited by 149
Spatial cellular architecture predicts prognosis in glioblastoma
Zheng, Y., Carrillo‐Pérez, F., Pizurica, M., Heiland, D. H., & Gevaert, O. (2023) · Nature Communications · doi:10.1038/s41467-023-39933-0 · Cited by 152
Andersen, R. S., Anand, A., Harwood, D. S. L., & Kristensen, B. W. (2021) · Cancers · doi:10.3390/cancers13174255 · Cited by 145
Ou, Z., Lin, S., Qiu, J., Ding, W., Ren, P., Chen, D., Wang, J., Tong, Y., Wu, D., Chen, A., et al. (2022) · Advanced Science · doi:10.1002/advs.202203040 · Cited by 152
Self-citationBrain cancer stem cells: resilience through adaptive plasticity and hierarchical heterogeneity
Gimple, R. C., Yang, K., Halbert, M. E., Agnihotri, S., & Rich, J. N. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-022-00486-x · Cited by 148
A proteogenomic analysis of clear cell renal cell carcinoma in a Chinese population
Qu, Y., Feng, J., Wu, X., Bai, L., Xu, W., Zhu, L., Liu, Y., Xu, F., Zhang, X., Yang, G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29577-x · Cited by 144
Self-citationHarnessing big data to characterize immune-related adverse events
Jing, Y., Yang, J., Johnson, D. B., Moslehi, J. J., & Han, L. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00597-8 · Cited by 125
An Epigenetic Role of Mitochondria in Cancer
Liu, Y., Chen, C., Wang, X., Sun, Y., Zhang, J., Chen, J., & Shi, Y. (2022) · Cells · doi:10.3390/cells11162518 · Cited by 131
Self-citationProteomic and phosphoproteomic landscapes of acute myeloid leukemia
Kramer, M. H., Zhang, Q., Sprung, R. W., Day, R. B., Erdmann-Gilmore, P., Li, Y., Xu, Z., Helton, N., George, D. R., Mi, Y., et al. (2022) · Blood · doi:10.1182/blood.2022016033 · Cited by 124
Self-citationMolecular classification of hormone receptor-positive HER2-negative breast cancer
Jin, X., Zhou, Y.-F., Ma, D., Zhao, S., Lin, C., Xiao, Y., Fu, T., Liu, C., Chen, Y., Xiao, W.-X., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01507-7 · Cited by 118
Pan-cancer proteogenomics connects oncogenic drivers to functional states
Li, Y., Porta‐Pardo, E., Tokheim, C. J., Bailey, M. H., Yaron, T. M., Stathias, V., Geffen, Y., Imbach, K. J., Cao, S., Anand, S. K., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.014 · Cited by 124
Self-citationIntegrated proteogenomic characterization of glioblastoma evolution
Kim, K., Migliozzi, S., Koo, H., Hong, J.-H., Park, S. M., Kim, S., Kwon, H. J., Ha, S., Garofano, L., Oh, Y. T., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2023.12.015 · Cited by 123
Self-citationLiang, W.-W., Lu, R. J.-H., Jayasinghe, R. G., Foltz, S. M., Porta-Pardo, E., Geffen, Y., Wendl, M. C., Lazcano, R., Kolodziejczak, I., Song, Y., et al. (2023) · Cancer Cell · doi:10.1016/j.ccell.2023.07.013 · Cited by 105
Self-citationExploring tumor-associated macrophages in glioblastoma: from diversity to therapy
Zhao, W., Zhang, Z., Xie, M., Ding, F., Zheng, X., Sun, S., & Du, J. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00920-x · Cited by 112
Self-citationAdvances in Diagnostic Tools and Therapeutic Approaches for Gliomas: A Comprehensive Review
Thenuwara, G., Curtin, J. F., & Tian, F. (2023) · Sensors · doi:10.3390/s23249842 · Cited by 110
Dewdney, B., Jenkins, M. R., Best, S. A., Freytag, S., Prasad, K., Holst, J., Endersby, R., & Johns, T. G. (2023) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-023-01637-8 · Cited by 107
Targeting fatty acid metabolism in glioblastoma
Miska, J. M., & Chandel, N. S. (2023) · Journal of Clinical Investigation · doi:10.1172/jci163448 · Cited by 99
Migliozzi, S., Oh, Y. T., Hasanain, M., Garofano, L., D’Angelo, F., Najac, R. D., Pïcca, A., Bielle, F., Stefano, A. L. D., Lerond, J., et al. (2023) · Nature Cancer · doi:10.1038/s43018-022-00510-x · Cited by 102
Self-citationPhosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells
Gerritsen, J. S., & White, F. M. (2021) · Expert Review of Proteomics · doi:10.1080/14789450.2021.1976152 · Cited by 100
Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome
Gardner, L., Kostarelos, K., Mallick, P., Dive, C., & Hadjidemetriou, M. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00645-x · Cited by 96
Pan-cancer proteogenomics characterization of tumor immunity
Petralia, F., Ma, W., Yaron, T. M., Caruso, F. P., Tignor, N. L., Wang, J. M., Charytonowicz, D., Johnson, J. L., Huntsman, E. M., Marino, G. B., et al. (2024) · Cell · doi:10.1016/j.cell.2024.01.027 · Cited by 104
Self-citationChen, S., Zhang, E., Guo, T., Shao, J., Wang, T., Zhang, N., Wang, X., & Zheng, J. (2022) · BMC Cancer · doi:10.1186/s12885-021-09033-7 · Cited by 96
Wen, P. Y., Weller, M., Lee, E. Q., Touat, M., Khasraw, M., Rahman, R. M., Platten, M., Lim, M. K., Winkler, F., Horbinski, C., et al. (2025) · Neuro-Oncology · doi:10.1093/neuonc/noaf177 · Cited by 113
Self-citationLam, K. H. B., Leon, A. J., Hui, W., Lee, S. C.-E. S., Batruch, I., Faust, K., Klekner, Á., Hutóczki, G., Koritzinsky, M., Richer, M., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27667-w · Cited by 97
Torrisi, F., Alberghina, C., D’Aprile, S., Pavone, A. M., Longhitano, L., Giallongo, S., Tibullo, D., Rosa, M. D., Zappalà, A., Cammarata, F. P., et al. (2022) · Biomedicines · doi:10.3390/biomedicines10040806 · Cited by 94
Emerging Role of Glioma Stem Cells in Mechanisms of Therapy Resistance
Eckerdt, F., & Platanias, L. C. (2023) · Cancers · doi:10.3390/cancers15133458 · Cited by 96
Instrumentation at the Leading Edge of Proteomics
Peters-Clarke, T. M., Coon, J. J., & Riley, N. M. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c04497 · Cited by 89
Circulating Liquid Biopsy Biomarkers in Glioblastoma: Advances and Challenges
Seyhan, A. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25147974 · Cited by 87
Upadhya, S. R., & Ryan, C. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100288 · Cited by 81
Digital profiling of gene expression from histology images with linearized attention
Pizurica, M., Zheng, Y., Carrillo‐Pérez, F., Noor, H., Yao, W., Wohlfart, C., Vladimirova, A., Marchal, K., & Gevaert, O. (2024) · Nature Communications · doi:10.1038/s41467-024-54182-5 · Cited by 84
Distinct metabolic hallmarks of WHO classified adult glioma subtypes
Björkblom, B., Wibom, C., Eriksson, M., Bergenheim, A. T., Sjöberg, R. L., Jonsson, P., Brännström, T., Antti, H., Sandström, M., & Melin, B. S. (2022) · Neuro-Oncology · doi:10.1093/neuonc/noac042 · Cited by 78
A multimodal atlas of tumour metabolism reveals the architecture of gene–metabolite covariation
Benedetti, E., Liu, E. M., Tang, C., Kuo, F., Büyüközkan, M., Park, T., Park, J., Correa, F., Hakimi, A. A., Intlekofer, A. M., et al. (2023) · Nature Metabolism · doi:10.1038/s42255-023-00817-8 · Cited by 77
Glioblastoma-instructed astrocytes suppress tumour-specific T cell immunity
Akl, C. F., Andersen, B. M., Li, Z., Giovannoni, F., Diebold, M., Sanmarco, L. M., Kilian, M., Fehrenbacher, L., Pernin, F., Rone, J. M., et al. (2025) · The Journal of Immunology · doi:10.1038/s41586-025-08997-x · Cited by 78
State of the science on brain insulin resistance and cognitive decline due to Alzheimer's disease
Rhea, E. M., Leclerc, M., Yassine, H. N., Capuano, A. W., Tong, H., Petyuk, V. A., Macauley, S. L., Fioramonti, X., Carmichael, O., Calon, F., et al. (2023) · Aging and disease · doi:10.14336/ad.2023.0814 · Cited by 70
Self-citationDrexler, R., Schüller, U., Eckhardt, A., Filipski, K., Hartung, T. I., Harter, P. N., Divé, I., Forster, M.-T., Czabanka, M., Jelgersma, C., et al. (2022) · Neuro-Oncology · doi:10.1093/neuonc/noac177 · Cited by 70
Functional Contribution and Clinical Implication of Cancer-Associated Fibroblasts in Glioblastoma
Galbo, P. M., Madsen, A. T., Liu, Y., Peng, M., Yao, W., Ciesielski, M. J., Fenstermaker, R. A., Graff, S., Montagna, C., Segall, J. E., et al. (2023) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-23-0493 · Cited by 74
Multi-scale signaling and tumor evolution in high-grade gliomas
Liu, J., Cao, S., Imbach, K. J., Gritsenko, M. A., Lih, T. M., Kyle, J. E., Yaron-Barir, T. M., Binder, Z. A., Li, Y., Strunilin, I., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.06.004 · Cited by 71
Self-citationThe pyruvate dehydrogenase complex: Life’s essential, vulnerable and druggable energy homeostat
Stacpoole, P. W., & McCall, C. E. (2023) · Mitochondrion · doi:10.1016/j.mito.2023.02.007 · Cited by 70
Integrative proteogenomic characterization of early esophageal cancer
Li, L., Jiang, D., Zhang, Q., Liu, H., Xu, F., Guo, C., Qin, Z., Wang, H., Feng, J., Liu, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37440-w · Cited by 67
Self-citationDuhamel, M., Drelich, L., Wisztorski, M., Aboulouard, S., Gimeno, J., Ogrinc, N., Devos, P., Cardon, T., Weller, M., Escande, F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34208-6 · Cited by 73
Teraiya, M., Perreault, H., & Chen, V. C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1166207 · Cited by 66
Purshouse, K. R., Friman, E. T., Boyle, S., Dewari, P. S., Grant, V., Hamdan, A., Morrison, G. M., Brennan, P. M., Beentjes, S. V., Pollard, S. M., et al. (2022) · eLife · doi:10.7554/elife.80207 · Cited by 62
Abelin, J. G., Bergstrom, E. J., Rivera, K. D., Taylor, H. B., Klaeger, S., Xu, C., Verzani, E. K., White, C. J., Woldemichael, H. B., Virshup, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37547-0 · Cited by 65
Self-citationGlioblastoma Metabolism: Insights and Therapeutic Strategies
Bernhard, C., Reita, D., Martin, S., Entz‐Werlé, N., & Dontenwill, M. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24119137 · Cited by 66
Upregulated YB-1 protein promotes glioblastoma growth through a YB-1/CCT4/mLST8/mTOR pathway
Wang, J.-Z., Zhu, H., You, P., Liu, H., Wang, W.-K., Fan, X., Yang, Y., Xu, K., Zhu, Y., Li, Q., et al. (2022) · Journal of Clinical Investigation · doi:10.1172/jci146536 · Cited by 60
Self-citationThe 2022 Report on the Human Proteome from the HUPO Human Proteome Project
Omenn, G. S., Lane, L., Overall, C. M., Pineau, C., Packer, N. H., Cristea, I. M., Lindskog, C., Weintraub, S. T., Orchard, S. E., Roehrl, M. H. A., et al. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00498 · Cited by 61
Self-citationProteomic landscape of epithelial ovarian cancer
Qian, L., Zhu, J., Xue, Z., Zhou, Y., Xiang, N., Xu, H., Sun, R., Gong, W., Cai, X., Sun, L., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50786-z · Cited by 61
Self-citationA proteogenomics data-driven knowledge base of human cancer
Liao, Y., Savage, S. R., Dou, Y., Shi, Z., Yi, X., Jiang, W., Lei, J. T., & Zhang, B. (2023) · Cell Systems · doi:10.1016/j.cels.2023.07.007 · Cited by 60
Self-citationIntercellular Communication in the Brain through Tunneling Nanotubes
Khattar, K. E., Safi, J., Rodriguez, A., & Vignais, M. (2022) · Cancers · doi:10.3390/cancers14051207 · Cited by 54
Human endogenous retrovirus K contributes to a stem cell niche in glioblastoma
Shah, A. H., Rivas, S. R., Doucet-O’Hare, T. T., Govindarajan, V., DeMarino, C., Wang, T., Ampie, L., Zhang, Y., Banasavadi‐Siddegowda, Y., Walbridge, S., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci167929 · Cited by 51
Tumor-associated monocytes promote mesenchymal transformation through EGFR signaling in glioma
Chen, Y., Huo, R., Kang, W., Liu, Y., Zhao, Z., Fu, W., Ma, R., Zhang, X., Tang, J., Zhu, Z., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101177 · Cited by 52
Self-citationShi, W., Wang, Y., Xu, C., Li, Y., Ge, S., Bai, B., Zhang, K., Wang, Y., Zheng, N., Wang, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-35797-6 · Cited by 50
Self-citationMandal, S., Bandyopadhyay, S., Tyagi, K., & Roy, A. (2021) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2021.188619 · Cited by 51
In-Depth Matrisome and Glycoproteomic Analysis of Human Brain Glioblastoma Versus Control Tissue
Sethi, M. K., Downs, M. J., Chun, S., Hackett, W. E., Phillips, J. J., & Zaia, J. (2022) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2022.100216 · Cited by 52
Mass spectrometry‐based targeted proteomics for analysis of protein mutations
Lin, T., Zhang, T., Kitata, R. B., Liu, T., Smith, R. D., Qian, W., & Shi, T. (2021) · Mass Spectrometry Reviews · doi:10.1002/mas.21741 · Cited by 51
Self-citationWNT signaling at the intersection between neurogenesis and brain tumorigenesis
Alkailani, M. I., Aittaleb, M., & Tissir, F. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.1017568 · Cited by 53
A Systems Approach to Brain Tumor Treatment
Park, J. H., de Lomana, A. L. G., Marzese, D. M., Juarez, T., Feroze, A. H., Hothi, P., Cobbs, C., Patel, A. P., Kesari, S., Huang, S., et al. (2021) · Cancers · doi:10.3390/cancers13133152 · Cited by 50
Li, B., Zhao, R., Qiu, W., Pan, Z., Zhao, S., Qi, Y., Qiu, J., Zhang, S., Guo, Q., Yang, F., et al. (2022) · Theranostics · doi:10.7150/thno.74600 · Cited by 47
Self-citationRedlich, J.-P., Feuerhake, F., Weis, J., Schaadt, N. S., Teuber‐Hanselmann, S., Buck, C., Luttmann, S., Eberle, A., Nikolin, S., Appenzeller, A., et al. (2024) · npj Imaging · doi:10.1038/s44303-024-00020-8 · Cited by 49
Hai, L., Hoffmann, D. C., Wagener, R. J., Azorín, D. D., Hausmann, D., Xie, R., Huppertz, M.-C., Hiblot, J., Sievers, P., Heuer, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45067-8 · Cited by 51
Current tools to interrogate microglial biology
Dumas, A. A., Borst, K., & Prinz, M. (2021) · Neuron · doi:10.1016/j.neuron.2021.07.004 · Cited by 47
Xu, Z., Peng, B., Kitata, R. B., Nicora, C., Weitz, K., Pu, Y., Shi, T., Cort, J., Ragauskas, A. J., & Yang, B. (2022) · Biotechnology for Biofuels and Bioproducts · doi:10.1186/s13068-022-02214-x · Cited by 46
Self-citationIntegrative proteomic characterization of adenocarcinoma of esophagogastric junction
Li, S., Li, Y., Xu, Z., Xu, J., Chen, G., Guan, X., Pan, G.-Z., Hu, C., Dong, J., Du, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36462-8 · Cited by 46
Self-citationHendriks, T., Krestensen, K. K., Mohren, R., Vandenbosch, M., Vleeschouwer, S. D., Heeren, R. M. A., & Cuypers, E. (2024) · Analytical Chemistry · doi:10.1021/acs.analchem.3c05850 · Cited by 46
Infiltrating plasma cells maintain glioblastoma stem cells through IgG-Tumor binding
Gao, J., Gu, D., Yang, K., Zhang, J., Lin, Q., Yuan, W., Xu, Z., Dixit, D., Gimple, R. C., You, H., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2024.12.006 · Cited by 47
Self-citationThe impact of hypoxia on tumor-mediated bypassing anti-PD-(L)1 therapy
Mortezaee, K., Majidpoor, J., & Kharazinejad, E. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114646 · Cited by 45
Zhu, H., Lin, Y., Lu, D., Wang, S., Liu, Y., Dong, L., Meng, Q., Gao, J., Wang, Y., Song, N., et al. (2023) · National Science Review · doi:10.1093/nsr/nwad167 · Cited by 46
Self-citationLiu, Z., Wu, Y., Xu, H., Wang, M., Weng, S., Pei, D., Chen, S., Wang, W., Yan, J., Cui, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58675-9 · Cited by 50
Self-citationThreonine fuels glioblastoma through YRDC-mediated codon-biased translational reprogramming
Wu, X., Yuan, H., Wu, Q., Gao, Y., Duan, T., Yang, K., Huang, T., Wang, S., Yuan, F., Lee, D., et al. (2024) · Nature Cancer · doi:10.1038/s43018-024-00748-7 · Cited by 45
Illuminating the dark protein-protein interactome
Tabar, M. S., Parsania, C., Chen, H., Su, X., Bailey, C. G., & Rasko, J. E. J. (2022) · Cell Reports Methods · doi:10.1016/j.crmeth.2022.100275 · Cited by 45
False discovery rate: the Achilles’ heel of proteogenomics
Aggarwal, S., Raj, A., Kumar, D., Dash, D., & Yadav, A. K. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac163 · Cited by 45
Global impact of somatic structural variation on the cancer proteome
Chen, F., Zhang, Y., Chandrashekar, D. S., Varambally, S., & Creighton, C. J. (2023) · Nature Communications · doi:10.1038/s41467-023-41374-8 · Cited by 46
Self-citationMo, Z., Xin, J., Chai, R., Woo, P. Y. M., Chan, D. T. M., & Wang, J. (2022) · Cancer Biology and Medicine · doi:10.20892/j.issn.2095-3941.2022.0418 · Cited by 41
Self-citationApplications of organoid technology to brain tumors
Wen, J., Liu, F., Cheng, Q., Weygant, N., Liang, X., Fan, F., Li, C., Zhang, L., & Liu, Z. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14272 · Cited by 44
Self-citationSaha, G., Sarkar, S., Mohanta, P. S., Kumar, K., Chakrabarti, S., Basu, M., & Ghosh, M. K. (2022) · Oncogene · doi:10.1038/s41388-022-02486-5 · Cited by 41
Wang, C., Sun, M. J., Shao, C., Schlicker, L., Zhuo, Y., Harim, Y., Peng, T., Tian, W., Stöffler, N., Schneider, M., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00500-5 · Cited by 40
Self-citationLiu, D., Li, M., Zhao, Z., Zhou, L., Zhi, F., Guo, Z., & Cui, J. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-3971 · Cited by 44
Trip, D. S. L., van Oostrum, M., Memon, D., Frommelt, F., Baptista, D., Panneerselvam, K., Bradley, G., Licata, L., Hermjakob, H., Orchard, S. E., et al. (2025) · Nature Biotechnology · doi:10.1038/s41587-025-02659-z · Cited by 46
Omenn, G. S., Lane, L., Overall, C. M., Paik, Y., Cristea, I. M., Corrales, F. J., Lindskog, C., Weintraub, S. T., Roehrl, M. H. A., Liu, S., et al. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00590 · Cited by 41
Self-citationWang, Y., Luo, R., Zhang, X., Xiang, H., Yang, B., Feng, J., Deng, M., Ran, P., Sujie, A., Zhang, F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36005-1 · Cited by 39
Self-citationRetracted
Metadata
- Authors
- Wang, L.-B., Karpova, A., Gritsenko, M. A., Kyle, J. E., Cao, S., Li, Y., Rykunov, D., Colaprico, A., Rothstein, J. H., Hong, R., Stathias, V., Cornwell, M., Petralia, F., Wu, Y., Reva, B., Krug, K., Pugliese, P., Kawaler, E., Olsen, L. K., Liang, W.-W., Song, X., Dou, Y., Wendl, M. C., Caravan, W., Liu, W., Cui Zhou, D., Ji, J., Tsai, C.-F., Petyuk, V. A., Moon, J., Ma, W., Chu, R. K., Weitz, K. K., Moore, R. J., Monroe, M. E., Zhao, R., Yang, X., Yoo, S., Krek, A., Demopoulos, A., Zhu, H., Wyczalkowski, M. A., McMichael, J. F., Henderson, B. L., Lindgren, C. M., Boekweg, H., Lu, S., Baral, J., Yao, L., Stratton, K. G., Bramer, L. M., Zink, E., Couvillion, S. P., Bloodsworth, K. J., Satpathy, S., Sieh, W., Boca, S. M., Schürer, S., Chen, F., Wiznerowicz, M., Ketchum, K. A., Boja, E. S., Kinsinger, C. R., Robles, A. I., Hiltke, T., Thiagarajan, M., Nesvizhskii, A. I., Zhang, B., Mani, D. R., Ceccarelli, M., Chen, X. S., Cottingham, S. L., Li, Q. K., Kim, A. H., Fenyö, D., Ruggles, K. V., Rodriguez, H., Mesri, M., Payne, S. H., Resnick, A. C., Wang, P., Smith, R. D., Iavarone, A., Chheda, M. G., Barnholtz-Sloan, J. S., Rodland, K. D., Liu, T., Ding, L., Agarwal, A., Amin, M., An, E., Anderson, M. L., Andrews, D. W., Bauer, T., Birger, C., Birrer, M. J., Blumenberg, L., Bocik, W. E., Borate, U., Borucki, M., Burke, M. C., Cai, S., Calinawan, A. P., Carr, S. A., Cerda, S., Chan, D. W., Charamut, A., Chen, L. S., Chesla, D., Chinnaiyan, A. M., Chowdhury, S., Cieślik, M. P., Clark, D. J., Culpepper, H., Czernicki, T., D'Angelo, F., Day, J., De Young, S., Demir, E., Dhanasekaran, S. M., Dhir, R., Domagalski, M. J., Druker, B., Duffy, E., Dyer, M., Edwards, N. J., Edwards, R., Elburn, K., Ellis, M. J., Eschbacher, J., Francis, A., Gabriel, S., Gabrovski, N., Garofano, L., Getz, G., Gillette, M. A., Godwin, A. K., Golbin, D., Hanhan, Z., Hannick, L. I., Hariharan, P., Hindenach, B., Hoadley, K. A., Hostetter, G., Huang, C., Jaehnig, E., Jewell, S. D., Ji, N., Jones, C. D., Karz, A., Kaspera, W., Kim, L., Kothadia, R. B., Kumar-Sinha, C., Lei, J., Leprevost, F. D., Li, K., Liao, Y., Lilly, J., Liu, H., Lubínski, J., Madan, R., Maggio, W., Malc, E., Malovannaya, A., Mareedu, S., Markey, S. P., Marrero-Oliveras, A., Martinez, N., Maunganidze, N., McDermott, J. E., McGarvey, P. B., McGee, J., Mieczkowski, P., Migliozzi, S., Modugno, F., Montgomery, R., Newton, C. J., Omenn, G. S., Ozbek, U., Paklina, O. V., Paulovich, A. G., Perou, A. M., Pico, A. R., Piehowski, P. D., Placantonakis, D. G., Polonskaya, L., Potapova, O., Pruetz, B., Qi, L., Ramkissoon, S., Resnick, A., Richey, S., Riggins, G., Robinson, K., Roche, N., Rohrer, D. C., Rood, B. R., Rossell, L., Savage, S. R., Schadt, E. E., Shi, Y., Shi, Z., Shutack, Y., Singh, S., Skelly, T., Sokoll, L. J., Stawicki, J., Stein, S. E., Suh, J., Szopa, W., Tabor, D., Tan, D., Tansil, D., Thangudu, R. R., Tognon, C., Traer, E., Tsang, S., Tyner, J., Um, K. S., Valley, D. R., Vasaikar, S., Vatanian, N., Velvulou, U., Vernon, M., Wan, W., Wang, J., Webster, A., Wen, B., Whiteaker, J. R., Wilson, G. D., Zakhartsev, Y., Zelt, R., Zhang, H., Zhang, L., Zhang, Z., Zhao, G., & Zhu, J.
- Affiliations
- Washington University in St. Louis (US)
- Pacific Northwest National Laboratory (US)
- Icahn School of Medicine at Mount Sinai (US)
- University of Miami (US)
- NYU Langone Health (US)
- New York University (US)
- Broad Institute (US)
- University of Sannio (IT)
- Brigham Young University (US)
- Tisch Cancer Institute (US)
- Baylor College of Medicine (US)
- Poznan University of Medical Sciences (PL)
- Northwell Health (US)
- Georgetown University Medical Center (US)
- Enterprise Science and Computing (United States) (US)
- National Cancer Institute (US)
- Frederick National Laboratory for Cancer Research (US)
- University of Michigan (US)
- Biogem (IT)
- Spectrum Health (US)
- Johns Hopkins University (US)
- Columbia University Irving Medical Center (US)
- University Hospitals of Cleveland (US)
- Corresponding author
- Rodland, Karin D.
- Liu, Tao
- Ding, Li
- ORCID
- Wang, Liang-Bo
- Karpova, Alla
- Kyle, Jennifer E.
- Cao, Song
- Li, Yize
- Rykunov, Dmitry
- Colaprico, Antonio
- Rothstein, Joseph H.
- Hong, Runyu
- Stathias, Vasileios
- Cornwell, MacIntosh
- Petralia, Francesca
- Wu, Yige
- Reva, Boris
- Krug, Karsten
- Pugliese, Pietro
- Kawaler, Emily
- Liang, Wen-Wei
- Song, Xiaoyu
- Dou, Yongchao
- Wendl, Michael C.
- Caravan, Wagma
- Liu, Wenke
- Ji, Jiayi
- Tsai, Chia-Feng
- Petyuk, Vladislav A.
- Moon, Jamie
- Ma, Weiping
- Chu, Rosalie K.
- Weitz, Karl K.
- Moore, Ronald J.
- Monroe, Matthew E.
- Zhao, Rui
- Yang, Xiaolu
- Yoo, Seungyeul
- Krek, Azra
- Wyczalkowski, Matthew A.
- McMichael, Joshua F.
- Henderson, Brittany L.
- Lindgren, Caleb M.
- Lu, Shuangjia
- Stratton, Kelly G.
- Bramer, Lisa M.
- Zink, Erika
- Couvillion, Sneha P.
- Bloodsworth, Kent J.
- Satpathy, Shankha
- Sieh, Weiva
- Boca, Simina M.
- Schürer, Stephan
- Chen, Feng
- Wiznerowicz, Maciej
- Ketchum, Karen A.
- Boja, Emily S.
- Kinsinger, Christopher R.
- Robles, Ana I.
- Hiltke, Tara
- Thiagarajan, Mathangi
- Nesvizhskii, Alexey I.
- Zhang, Bing
- Mani, D.R.
- Ceccarelli, Michele
- Li, Qing Kay
- Kim, Albert H.
- Fenyö, David
- Ruggles, Kelly V.
- Rodriguez, Henry
- Mesri, Mehdi
- Wang, Pei
- Smith, Richard D.
- Iavarone, Antonio
- Chheda, Milan G.
- Rodland, Karin D.
- Liu, Tao
- Ding, Li
- Agarwal, Anupriya
- Amin, Mitual
- An, Eunkyung
- Anderson, Matthew L.
- Andrews, David W.
- Birrer, Michael J.
- Blumenberg, Lili
- Bocik, William E.
- Borate, Uma
- Burke, Meghan C.
- Cai, Shuang
- Calinawan, Anna P.
- Carr, Steven A.
- Chan, Daniel W.
- Chen, Lin S.
- Chinnaiyan, Arul M.
- Chowdhury, Shrabanti
- Clark, David J.
- Czernicki, Tomasz
- Demir, Emek
- Dhanasekaran, Saravana Mohan
- Dhir, Rajiv
- Domagalski, Marcin J.
- Druker, Brian
- Duffy, Elizabeth
- Dyer, Maureen
- Edwards, Nathan J.
- Edwards, Robert
- Ellis, Matthew J.
- Eschbacher, Jennifer
- Francis, Alicia
- Gabriel, Stacey
- Garofano, Luciano
- Getz, Gad
- Gillette, Michael A.
- Godwin, Andrew K.
- Golbin, Denis
- Hannick, Linda I.
- Hoadley, Katherine A.
- Hostetter, Galen
- Huang, Chen
- Jaehnig, Eric
- Jewell, Scott D.
- Ji, Nan
- Jones, Corbin D.
- Karz, Alcida
- Kaspera, Wojciech
- Kothadia, Ramani B.
- Lei, Jonathan
- Leprevost, Felipe D.
- Li, Kai
- Liao, Yuxing
- Lilly, Jena
- Liu, Hongwei
- Madan, Rashna
- Malc, Ewa
- Malovannaya, Anna
- Martinez, Nina
- McGarvey, Peter B.
- Mieczkowski, Piotr
- Migliozzi, Simona
- Modugno, Francesmary
- Omenn, Gilbert S.
- Paklina, Oxana V.
- Paulovich, Amanda G.
- Pico, Alexander R.
- Piehowski, Paul D.
- Placantonakis, Dimitris G.
- Potapova, Olga
- Riggins, Gregory
- Rohrer, Daniel C.
- Rood, Brian R.
- Savage, Sara R.
- Schadt, Eric E.
- Shi, Yan
- Shi, Zhiao
- Sokoll, Lori J.
- Stawicki, Jakub
- Stein, Stephen E.
- Suh, James
- Szopa, Wojciech
- Thangudu, Ratna R.
- Tognon, Cristina
- Traer, Elie
- Tyner, Jeffrey
- Vasaikar, Suhas
- Wan, Weiqing
- Wen, Bo
- Whiteaker, Jeffrey R.
- Wilson, George D.
- Zhang, Hui
- Zhang, Zhen
- Zhao, Grace
- Zhu, Jun
- Venue
- Cancer Cell
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 39
- Issue
- 4
- Pages
- 509-528.e20
- ISSN
- 1535-6108 (ISSN-L)
- PMC
- PMC8044053
- OpenAlex
- W3126609356
- Cited by
- 661
- References
- 153
- Citations by year
- 2026: 103 · 2025: 124 · 2024: 127 · 2023: 140 · 2022: 123 · 2021: 43
- FWCI
- 62.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cancer Cell (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Institutes of Health
- U.S. Department of Energy (DE-AC05-76RL01830)
- National Human Genome Research Institute (R01HG009711, R01NS107833)
- National Cancer Institute (U01CA214125, U24CA210954, U24CA210955, U24CA210967, U24CA210972, U24CA210979, U24CA210985, U24CA210986, U24CA210993)
- Orbus Therapeutics
- Topics
- Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer, Hypoxia, and Metabolism
- Keywords
- glioblastoma, proteogenomics, metabolomics, post-translational modification, protein phosphorylation, PTPN11, phospholipase C-gamma1, EGFR, TP53, RB1, tumor immune subtypes, single-cell RNA sequencing, histone acetylation, CREB-binding protein, EP300, lipid metabolism, IDH mutation, molecular subtypes, patient stratification
- MeSH terms
- Proteogenomics, Brain Neoplasms, Glioblastoma, Humans, Mutation, Phosphorylation, Computational Biology, Proteomics, Phospholipase C gamma, Protein Tyrosine Phosphatase, Non-Receptor Type 11, Metabolomics
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
