Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types
Open accessAbstract
Prognostic genes are key molecules informative for cancer prognosis and treatment. Previous studies have focused on the properties of individual prognostic genes, but have lacked a global view of their system-level properties. Here we examined their properties in gene co-expression networks for four cancer types using data from 'The Cancer Genome Atlas'. We found that prognostic mRNA genes tend not to be hub genes (genes with an extremely high connectivity), and this pattern is unique to the corresponding cancer-type-specific network. In contrast, the prognostic genes are enriched in modules (a group of highly interconnected genes), especially in module genes conserved across different cancer co-expression networks. The target genes of prognostic miRNA genes show similar patterns. We identified the modules enriched in various prognostic genes, some of which show cross-tumour conservation. Given the cancer types surveyed, our study presents a view of emergent properties of prognostic genes.
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
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?
Even when no sample classification is available, subpopulation-specific modules can be resolved, an approach that has been particularly successful in classifying different cancer subtypes to provide prognostic markers.
“Even when no sample classification is available, subpopulation-specific modules can be resolved, an approach that has been particularly successful in classifying different cancer subtypes to provide prognostic markers [18–20].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [19]
The Methods (Integrative module analysis) describe using module-gene expression to classify tumor samples into subtypes, and the Results (Some prognostic modules are conserved across tumor types) report distinct survival curves for those subtypes in four cancers. The paper does not establish that the modules themselves are subpopulation-specific or explicitly demonstrate their resolution when no sample classification is available; it also describes potential clinical relevance rather than established prognostic markers. (LLM, conf=0.84)
Experimental — accuracy not yet measuredWGCNA was the first co-expression tool to be applied to RNA-seq data; it has effectively identified biologically relevant associations between phenotypes and modules, performing similarly to microarray-based analyses.
“WGCNA was the first co-expression tool to be applied to RNA-seq data; it has effectively identified biologically relevant associations between phenotypes and modules [19, 66, 67], performing similarly to microarray-based analyses.”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [19]
The Results section ('Some prognostic modules are conserved across tumor types'; page not provided) reports WGCNA-derived modules associated with patient survival, supporting biologically relevant module–phenotype associations. But Methods ('Co-expression network construction'; page not provided) says the networks were built from microarray data. The paper does not establish that WGCNA was the first co-expression tool applied to RNA-seq data or that its RNA-seq performance was similar to microarray-based analyses. (LLM, conf=0.88)
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.
Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252 · see citing works
MicroRNAs: Target Recognition and Regulatory Functions
Bartel, D. P. (2009) · Cell · doi:10.1016/j.cell.2009.01.002 · Cited by 20,068 · see citing works
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129 · see citing works
Lewis, B. P., Burge, C. B., & Bartel, D. P. (2005) · Cell · doi:10.1016/j.cell.2004.12.035 · Cited by 11,855 · see citing works
MicroRNA expression profiles classify human cancers
Lü, J., Getz, G. A., Miska, E. A., Alvarez-Saavedra, E., Lamb, J., Peck, D., Sweet-Cordero, A., Ebert, B. L., Mak, R. H., Ferrando, A. A., et al. (2005) · Nature · doi:10.1038/nature03702 · Cited by 9,497 · see citing works
Integrated genomic analyses of ovarian carcinoma
Perou, C. M. (2019) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/hvp3-wg08 · Cited by 7,927 · see citing works
Network biology: understanding the cell's functional organization
Barabási, A.-L., & Oltvai, Z. N. (2004) · Nature Reviews Genetics · doi:10.1038/nrg1272 · Cited by 7,903 · see citing works
Comprehensive genomic characterization defines human glioblastoma genes and core pathways
The Cancer Genome Atlas Research Network (2008) · Nature · doi:10.1038/nature07385 · Cited by 7,735 · see citing works
A Gene-Expression Signature as a Predictor of Survival in Breast Cancer
van de Vijver, M. J., He, Y. D., Veer, L. V. ‘., Dai, H. A., Hart, A. A. M., Voskuil, D. W., Schreiber, G. J., Peterse, J. L., Roberts, C., Marton, M. J., et al. (2002) · New England Journal of Medicine · doi:10.1056/nejmoa021967 · Cited by 6,411 · see citing works
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684 · see citing works
Lethality and centrality in protein networks
Jeong, H., Mason, S., Barabási, A., & Oltvai, Z. (2001) · Nature · doi:10.1038/35075138 · Cited by 5,327 · see citing works
MicroRNA Targeting Specificity in Mammals: Determinants beyond Seed Pairing
Grimson, A., Farh, K. K.-H., Johnston, W. K., Garrett-Engele, P., Lim, L. P., & Bartel, D. P. (2007) · Molecular Cell · doi:10.1016/j.molcel.2007.06.017 · Cited by 3,766 · see citing works
Comprehensive molecular characterization of clear cell renal cell carcinoma
The Cancer Genome Atlas Research Network (2013) · Nature · doi:10.1038/nature12222 · Cited by 3,485 · see citing works
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242 · see citing works
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,235 · see citing works
Improved scoring of functional groups from gene expression data by decorrelating GO graph structure
Alexa, A., Rahnenführer, J., & Lengauer, T. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl140 · Cited by 2,194 · see citing works
Metagenes and molecular pattern discovery using matrix factorization
Brunet, J.-P., Tamayo, P., Golub, T. R., & Mesirov, J. P. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0308531101 · Cited by 2,183 · see citing works
Sotiriou, C. P., Wirapati, P., Loi, S., Harris, A. L., Fox, S., Smeds, J., Nordgren, H., Farmer, P. J., Praz, V., Haibe‐Kains, B., et al. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj052 · Cited by 2,060 · see citing works
Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R
Langfelder, P., Zhang, B., & Horvath, S. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btm563 · Cited by 1,892 · see citing works
Evidence for dynamically organized modularity in the yeast protein–protein interaction network
Han, J. J., Bertin, N., Hao, T., Goldberg, D. S., Berriz, G. F., Zhang, L. V., Dupuy, D., Walhout, A. J. M., Cusick, M. E., Roth, F. P., et al. (2004) · Nature · doi:10.1038/nature02555 · Cited by 1,789 · see citing works
The evolution of gene regulation by transcription factors and microRNAs
Chen, K., & Rajewsky, N. (2007) · Nature Reviews Genetics · doi:10.1038/nrg1990 · Cited by 1,547 · see citing works
Buyse, M. E., Loi, S., Veer, L. V., Viale, G., Delorenzi, M., Glas, A. M., Saghatchian, M., Bergh, J. C. S., Lidereau, R., Ellis, P., et al. (2006) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djj329 · Cited by 1,235 · see citing works
MicroRNA-Cancer Connection: The Beginning of a New Tale
Calin, G. A., & Croce, C. M. (2006) · Cancer Research · doi:10.1158/0008-5472.can-06-0800 · Cited by 1,054 · see citing works
Yu, H., Kim, P. M., Sprecher, E., Trifonov, V., & Gerstein, M. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0030059 · Cited by 1,008 · see citing works
Use of Gene-Expression Profiling to Identify Prognostic Subclasses in Adult Acute Myeloid Leukemia
Bullinger, L., Döhner, K., Bair, E., Fröhling, S., Schlenk, R. F., Tibshirani, R. J., Döhner, H., & Pollack, J. R. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa031046 · Cited by 990 · see citing works
Oscier, D. G., Gardiner, A. C., Mould, S. J., et al. (2002) · Blood · doi:10.1182/blood.v100.4.1177.h81602001177_1177_1184 · Cited by 523 · see citing works
Weighted Gene Coexpression Network Analysis: State of the Art
Zhao, W., Langfelder, P., Fuller, T. F., Dong, J., Li, A., & Hovarth, S. (2010) · Journal of Biopharmaceutical Statistics · doi:10.1080/10543400903572753 · Cited by 393 · see citing works
The prognostic significance of phosphatidylinositol 3-kinase pathway activation in human gliomas
Chakravarti, A., Zhai, G., Suzuki, Y., Sarkesh, S., Black, P. M., Muzikansky, A., & Loeffler, J. S. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.07.193 · Cited by 377 · see citing works
Genomic analysis of the hierarchical structure of regulatory networks
Yu, H., & Gerstein, M. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0508637103 · Cited by 349 · see citing works
Genomic analysis of essentiality within protein networks
Yu, H., Greenbaum, D. S., Lu, H. X., Zhu, X., & Gerstein, M. (2004) · Trends in Genetics · doi:10.1016/j.tig.2004.04.008 · Cited by 326 · see citing works
Gene Expression Signature With Independent Prognostic Significance in Epithelial Ovarian Cancer
Spentzos, D., Levine, D. A., Ramoni, M. F., et al. (2004) · Journal of Clinical Oncology · doi:10.1200/jco.2004.04.070 · Cited by 257 · see citing works
Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma
Zhao, H., Ljungberg, B., Grankvist, K., Rasmuson, T., Tibshirani, R. J., & Brooks, J. D. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0030013 · Cited by 227 · see citing works
Human diseases through the lens of network biology
Furlong, L. I. (2012) · Trends in Genetics · doi:10.1016/j.tig.2012.11.004 · Cited by 194 · see citing works
Histological evaluation of AMPK signalling in primary breast cancer
Hadad, S. M., Baker, L., Quinlan, P., Robertson, K. E., Bray, S. E., Thomson, G., Kellock, D., Jordan, L. B., Purdie, C. A., Hardie, D. G., et al. (2009) · BMC Cancer · doi:10.1186/1471-2407-9-307 · Cited by 172 · see citing works
Langer, C. H., Marcucci, G., Holland, K. B., et al. (2009) · Journal of Clinical Oncology · doi:10.1200/jco.2008.20.6110 · Cited by 152 · see citing works
Hwang, J., KWAK, D. W., Sun, L., Kim, H. M., KIM, Y. M., & Park, O. J. (2007) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1397.047 · Cited by 151 · see citing works
Integrated genomic analyses of ovarian carcinoma
The Cancer Genome Atlas Research Network (2011) · Nature · doi:10.1038/nature10166 · Cited by 129 · see citing works
Gene essentiality, gene duplicability and protein connectivity in human and mouse
Liang, H., & Li, W. (2007) · Trends in Genetics · doi:10.1016/j.tig.2007.04.005 · Cited by 111 · see citing works
Self-citationA comparative study of cancer proteins in the human protein-protein interaction network
Sun, J., & Zhao, Z. (2010) · BMC Genomics · doi:10.1186/1471-2164-11-s3-s5 · Cited by 100 · see citing works
Cai, J. J., Borenstein, E., & Petrov, D. A. (2010) · Genome Biology and Evolution · doi:10.1093/gbe/evq064 · Cited by 82 · see citing works
Abba, M. C., Lacunza, E., Butti, M., et al. (2010) · Biomarker Insights · doi:10.4137/bmi.s5740 · Cited by 51 · see citing works
From Description to Causality: Mechanisms of Gene Expression Signatures in Cancer
Adler, A. S., & Chang, H. Y. (2006) · Cell Cycle · doi:10.4161/cc.5.11.2798 · Cited by 20 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 1,086 · see citing works
Identification of Distinct Psychosis Biotypes Using Brain-Based Biomarkers
Clementz, B. A., Sweeney, J. A., Hamm, J. P., Ivleva, E. I., Ethridge, L. E., Pearlson, G. D., Keshavan, M. S., & Tamminga, C. A. (2016) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2015.14091200 · Cited by 768 · see citing works
Deep learning in bioinformatics: Introduction, application, and perspective in the big data era
Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.008 · Cited by 401 · see citing works
GeneFriends: a human RNA-seq-based gene and transcript co-expression database
van Dam, S., Craig, T., & de Magalhães, J. P. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1042 · Cited by 115 · see citing works
Sudhakar, P., Verstockt, B., Cremer, J., Verstockt, S., Sabino, J., Ferrante, M., & Vermeire, S. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa281 · Cited by 49 · see citing works
Biomedical data, computational methods and tools for evaluating disease–disease associations
Xiang, J., Zhang, J., Zhao, Y., Wu, F.-X., & Li, M. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac006 · Cited by 49 · see citing works
- Authors
- Yang, Y., Han, L., Yuan, Y., Li, J., Hei, N., & Liang, H.
- Affiliations
- The University of Texas MD Anderson Cancer Center (US)
- The University of Texas Health Science Center at Houston (US)
- Corresponding author
- Liang, Han
- Venue
- Nature Communications
- Published
- 2014-02-03
- Volume
- 5
- Issue
- 1
- Pages
- 3231-3231
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC3951205
- OpenAlex
- W2171115585
- Cited by
- 423
- References
- 43
- Citations by year
- 2026: 10 · 2025: 23 · 2024: 25 · 2023: 24 · 2022: 36 · 2021: 46 · 2020: 34 · 2019: 76 · 2018: 42 · 2017: 31 · 2016: 48 · 2015: 19 · 2014: 9
- FWCI
- 14.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Institutes of Health (CA- 016672)
- National Cancer Institute (CA 016672, CA143883, CA016672 to, U24CA143883, P30CA016672)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
- Keywords
- prognostic genes, gene co-expression network, hub genes, gene modules, miRNA target genes, cancer prognosis, network topology, mRNA expression, TCGA
- MeSH terms
- Humans, Neoplasms, Prognosis, Biomarkers, Tumor, MicroRNAs
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
