Skip to main content

Advertisement

SpringerLink
Log in

Detecting disease genes of non-small lung cancer based on consistently differential interactions

  • Published:
Cancer and Metastasis Reviews Aims and scope Submit manuscript

Abstract

Systematic identification of causal disease genes can shed light on the mechanisms underlying complex diseases and provide crucial information to develop efficient biomarkers or design suitable therapies. The present paper describes a novel approach to detect potential disease genes for lung cancer, based on consistently differential interaction (CDI) scheme from heterogeneous disease datasets. In particular, reliable disordered regulations in disease states were discovered by identifying the CDIs, from which the disease genes were further detected based on their topological structures on the network. As an application of the CDI-based method, the RNA-seq data of two subtypes of non-small lung cancer were used to identify CDIs from normal to cancer onset. The results of analysis well agree with the prior knowledge as well as the experiments, thereby implying the predictive power of the CDI-based method. The comparison with other approaches also indicated the superiority of the CDI-based method in terms of accuracy and effectiveness on detecting disease-specific genes for lung cancer and metastasis. In contrast to conventional molecular biomarkers, the identified CDIs as novel network biomarkers or edge biomarkers can be applied to predict patient survival for both subtypes of lung cancers, and the interactions among CDIs can be further used as new edgetic targets for network drug design. In addition, a potential molecular mechanism was developed to explain the key roles of the identified CDIs in lung cancer and metastasis from a network perspective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Bandyopadhyay, S., Mehta, M., Kuo, D., Sung, M. K., Chuang, R., Jaehnig, E. J., et al. (2010). Rewiring of genetic networks in response to DNA damage. Science, 330(6009), 1385–1389. doi:10.1126/science.1195618.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  2. Liu, X. P., Liu, Z. P., Zhao, X. M., & Chen, L. N. (2012). Identifying disease genes and module biomarkers by differential interactions. Journal of the American Medical Informatics Association, 19(2), 241–248. doi:10.1136/amiajnl-2011-000658.

    Article  PubMed Central  PubMed  Google Scholar 

  3. Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D., & Ideker, T. (2007). Network-based classification of breast cancer metastasis. Molecular Systems Biology, 3, doi: Artn 140. doi:10.1038/Msb4100180.

  4. Metzker, M. L. (2010). Sequencing technologies—the next generation. Nature Reviews Genetics, 11(1), 31–46. doi:10.1038/Nrg2626.

  5. Schena, M., Shalon, D., Davis, R. W., & Brown, P. O. (1995). Quantitative monitoring of gene-expression patterns with a complementary-DNA microarray. Science, 270(5235), 467–470. doi:10.1126/science.270.5235.467.

    Article  CAS  PubMed  Google Scholar 

  6. Chen, L.N., Liu, R., Liu, Z.P., Li, M.Y., & Aihara, K. (2012). Detecting early-warning signals for sudden deterioration of complex diseases by dynamical network biomarkers. Scientific Reports, 2, Artn 342. doi:10.1038/Srep00342.

  7. Yu, X. T., Li, G. J., & Chen, L. N. (2014). Prediction and early diagnosis of complex diseases by edge-network. Bioinformatics, 30(6), 852–859. doi:10.1093/bioinformatics/btt620.

    Article  CAS  PubMed  Google Scholar 

  8. Zhang, W., Zeng, T., & Chen, L. (2014). EdgeMarker: identifying differentially correlated molecule pairs as edge-biomarkers. Journal of Theoretical Biology, 362, 35–43. doi:10.1016/j.jtbi.2014.05.041.

    Article  CAS  PubMed  Google Scholar 

  9. Edgar, R., Domrachev, M., & Lash, A. E. (2002). Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research, 30(1), 207–210. doi:10.1093/Nar/30.1.207.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  10. Guo, Y., Graber, A., McBurney, R.N., & Balasubramanian, R. (2010). Sample size and statistical power considerations in high-dimensionality data settings: a comparative study of classification algorithms. Bmc Bioinformatics, 11, Artn 447. doi:10.1186/1471-2105-11-447.

  11. Ma, C., Blackwell, T., Boehnke, M., Scott, L. J., & Investigators, G. D. (2013). Recommended joint and meta-analysis strategies for case–control association testing of single low-count variants. Genetic Epidemiology, 37(6), 539–550. doi:10.1002/Gepi.21742.

    Article  PubMed Central  PubMed  Google Scholar 

  12. Molina, J. R., Yang, P. G., Cassivi, S. D., Schild, S. E., & Adjei, A. A. (2008). Non-small cell lung cancer: epidemiology, risk factors, treatment, and survivorship. Mayo Clinic Proceedings, 83(5), 584–594.

    Article  PubMed Central  PubMed  Google Scholar 

  13. D’Addario, G., Fruh, M., Reck, M., Baumann, P., Klepetko, W., Felip, E., et al. (2010). Metastatic non-small-cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of Oncology, 21, v116–v119. doi:10.1093/annonc/mdq189.

    Article  PubMed  Google Scholar 

  14. Niklinski, J., Niklinska, W., Laudanski, J., Chyczewska, E., & Chyczewski, L. (2001). Prognostic molecular markers in non-small cell lung cancer. Lung Cancer, 34, S53–S58. doi:10.1016/S0169-5002(01)00345-2.

    Article  PubMed  Google Scholar 

  15. Coate, L. E., John, T., Tsao, M. S., & Shepherd, F. A. (2009). Molecular predictive and prognostic markers in non-small-cell lung cancer. Lancet Oncology, 10(10), 1001–1010.

    Article  CAS  PubMed  Google Scholar 

  16. Li, X.L., Shi, Y.R., Yin, Z.H., Xue, X.X., & Zhou, B.S. (2014). An eight-miRNA signature as a potential biomarker for predicting survival in lung adenocarcinoma. Journal of Translational Medicine, 12, Artn 159. doi:10.1186/1479-5876-12-159.

  17. Cordell, H. J. (2009). Detecting gene-gene interactions that underlie human diseases. Nature Reviews Genetics, 10(6), 392–404. doi:10.1038/Nrg2579.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  18. Dutkowski, J., & Ideker, T. (2011). Protein networks as logic functions in development and cancer. Plos Computational Biology, 7(9), ARTN e1002180. doi:10.1371/journal.pcbi.1002180.

  19. Colotta, F., Allavena, P., Sica, A., Garlanda, C., & Mantovani, A. (2009). Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis, 30(7), 1073–1081. doi:10.1093/carcin/bgp127.

    Article  CAS  PubMed  Google Scholar 

  20. Cavallo, F., De Giovanni, C., Nanni, P., Forni, G., & Lollini, P. L. (2011). 2011: the immune hallmarks of cancer. Cancer Immunology, Immunotherapy, 60(3), 319–326. doi:10.1007/s00262-010-0968-0.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: the next generation. Cell, 144(5), 646–674. doi:10.1016/j.cell.2011.02.013.

    Article  CAS  PubMed  Google Scholar 

  22. Yarden, R. I., & Brody, L. C. (1999). BRCA1 interacts with components of the histone deacetylase complex. Proceedings of the National Academy of Sciences of the United States of America, 96(9), 4983–4988. doi:10.1073/pnas.96.9.4983.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  23. Gatei, M., Scott, S. P., Filippovitch, I., Soronika, N., Lavin, M. F., Weber, B., et al. (2000). Role for ATM in DNA damage-induced phosphorylation of BRCA1. Cancer Research, 60(12), 3299.

    CAS  PubMed  Google Scholar 

  24. Ouchi, T., Lee, S. W., Ouchi, M., Aaronson, S. A., & Horvath, C. M. (2000). Collaboration of signal transducer and activator of transcription 1 (STAT1) and BRCA1 in differential regulation of IFN-gamma target genes. Proceedings of the National Academy of Sciences of the United States of America, 97(10), 5208–5213. doi:10.1073/pnas.080469697.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  25. Cousineau, I., Abaji, C., & Belmaaza, A. (2005). BRCA1 regulates RAD51 function in response to DNA damage and suppresses spontaneous sister chromatid replication slippage: implications for sister chromatid cohesion, genome stability, and carcinogenesis. Cancer Research, 65(24), 11384–11391. doi:10.1158/0008-5472.Can-05-2156.

    Article  CAS  PubMed  Google Scholar 

  26. Chang, S.H., Wang, R.H., Akagi, K., Kim, K.A., Martin, B.K., Cavallone, L., et al. (2011). Tumor suppressor BRCA1 epigenetically controls oncogenic microRNA-155. Nature Medicine, 17(10), 1275-U1308. doi:10.1038/Nm.2459.

  27. Rosell, R., Skrzypski, M., Jassem, E., Taron, M., Bartolucci, R., Sanchez, J.J., et al. (2007). BRCA1: a novel prognostic factor in resected non-small-cell lung cancer. Plos One, 2(11), Artn E1129.doi:10.1371/Journal.Pone.0001129.

  28. Schaller-Schonitz, M., Barzan, D., Williamson, A.J.K., Griffiths, J.R., Dallmann, I., Battmer, K., et al. (2014). BCR-ABL affects STAT5A and STAT5B differentially. Plos One, 9(5), ARTN e97243. doi:10.1371/journal.pone.0097243.

  29. Clauson, C., Scharer, O.D., & Niedernhofer, L. (2013). Advances in understanding the complex mechanisms of DNA interstrand cross-link repair. Cold Spring Harbor Perspectives in Biology, 5(10), ARTN a012732. doi:10.1101/cshperspect.a012732.

  30. Rebhan, M., Chalifa-Caspi, V., Prilusky, J., & Lancet, D. (1998). GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinformatics, 14(8), 656–664. doi:10.1093/bioinformatics/14.8.656.

    Article  CAS  PubMed  Google Scholar 

  31. Rekhtman, N., Ang, D. C., Sima, C. S., Travis, W. D., & Moreira, A. L. (2011). Immunohistochemical algorithm for differentiation of lung adenocarcinoma and squamous cell carcinoma based on large series of whole-tissue sections with validation in small specimens. Modern Pathology, 24(10), 1348–1359. doi:10.1038/modpathol.2011.92.

    Article  PubMed  Google Scholar 

  32. Chen, Z., Fillmore, C. M., Hammerman, P. S., Kim, C. F., & Wong, K. K. (2014). Non-small-cell lung cancers: a heterogeneous set of diseases. Nature Reviews Cancer, 14(8), 535–546. doi:10.1038/Nrc3775.

    Article  CAS  PubMed  Google Scholar 

  33. Wu, Y. Q., Borde, M., Heissmeyer, V., Feuerer, M., Lapan, A. D., Stroud, J. C., et al. (2006). FOXP3 controls regulatory T cell function through cooperation with NFAT. Cell, 126(2), 375–387. doi:10.1016/j.cell.2006.05.042.

    Article  CAS  PubMed  Google Scholar 

  34. Nam, H. J., & van Deursen, J. M. (2014). Cyclin B2 and p53 control proper timing of centrosome separation. Nature Cell Biology, 16(10), 1027–1027 (vol 16, pg 535, 2014).

    Article  CAS  Google Scholar 

  35. Ladd, J. J., Busald, T., Johnson, M. M., Zhang, Q., Pitteri, S. J., Wang, H., et al. (2012). Increased plasma levels of the APC-interacting protein MAPRE1, LRG1, and IGFBP2 preceding a diagnosis of colorectal cancer in women. Cancer Prevention Research, 5(4), 655–664. doi:10.1158/1940-6207.Capr-11-0412.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  36. Horiuchi, M., Itoh, A., Pleasure, D., Ozato, K., & Itoh, T. (2011). Cooperative contributions of Interferon regulatory factor 1 (IRF1) and IRF8 to interferon-gamma-mediated cytotoxic effects on oligodendroglial progenitor cells. Journal of Neuroinflammation, 8, Artn 8. doi:10.1186/1742-2094-8-8.

  37. Hutchinson, E. (2008). Developmental block. Nature Reviews Cancer, 8(3), 160–160. doi:10.1038/Nrc2335.

    Article  CAS  Google Scholar 

  38. Prevot, D., Voeltzel, T., Birot, A. M., Morel, A. P., Rostan, M. C., Magaud, J. P., et al. (2000). The leukemia-associated protein Btg1 and the p53-regulated protein Btg2 interact with the homeoprotein Hoxb9 and enhance its transcriptional activation. Journal of Biological Chemistry, 275(1), 147–153. doi:10.1074/jbc.275.1.147.

    Article  CAS  PubMed  Google Scholar 

  39. López-Malpartida, A. V., Ludeña, M. D., Varela, G., & García Pichel, J. (2009). Differential ErbB receptor expression and intracellular signaling activity in lung adenocarcinomas and squamous cell carcinomas. Lung Cancer, 65(1), 25–33. doi:10.1016/j.lungcan.2008.10.009.

    Article  PubMed  Google Scholar 

  40. Hartman, Z., Zhao, H., & Agazie, Y. M. (2013). HER2 stabilizes EGFR and itself by altering autophosphorylation patterns in a manner that overcomes regulatory mechanisms and promotes proliferative and transformation signaling. Oncogene, 32(35), 4169–4180. doi:10.1038/Onc.2012.418.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  41. Spoerke, J. M., O’Brien, C., Huw, L., Koeppen, H., Fridlyand, J., Brachmann, R. K., et al. (2012). Phosphoinositide 3-kinase (PI3K) pathway alterations are associated with histologic subtypes and are predictive of sensitivity to PI3K inhibitors in lung cancer preclinical models. Clinical Cancer Research, 18(24), 6771–6783. doi:10.1158/1078-0432.Ccr-12-2347.

    Article  CAS  PubMed  Google Scholar 

  42. Wang, J.G., Huang, Q., Liu, Z.P., Wang, Y., Wu, L.Y., Chen, L.N., et al. (2011). NOA: a novel network ontology analysis method. Nucleic Acids Research, 39(13), ARTN e87. doi:10.1093/nar/gkr251.

  43. Perlikos, F., Harrington, K. J., & Syrigos, K. N. (2013). Key molecular mechanisms in lung cancer invasion and metastasis: a comprehensive review. Critical Reviews in Oncology Hematology, 87(1), 1–11. doi:10.1016/j.critrevonc.2012.12.007.

    Article  Google Scholar 

  44. Moreno-Bueno, G., Portillo, F., & Cano, A. (2008). Transcriptional regulation of cell polarity in EMT and cancer. Oncogene, 27(55), 6958–6969. doi:10.1038/Onc.2008.346.

    Article  CAS  PubMed  Google Scholar 

  45. Schimmack, S, Taylor, A Lawrence, B, Alaimo, D, Schmitz-Winnenthal, H, Buchler, M.W., et al. (2014). A mechanistic role for the chromatin modulator, NAP1L1, in pancreatic neuroendocrine neoplasm proliferation and metastases. Epigenetics & Chromatin, 7, Artn 15. doi:10.1186/1756-8935-7-15.

  46. Sun, A.Q., Yu, G.Z., Dou, X.Y., Yan, X.W., Yang, W.N., & Lin, Q. (2014). Nedd4-1 is an exceptional prognostic biomarker for gastric cardia adenocarcinoma and functionally associated with metastasis. Molecular Cancer, 13, Unsp 248. doi:10.1186/1476-4598-13-248.

  47. Yue, D.S., Li,H., Che, J.J., Zhang, Y., Tseng, H.H., Jin, J.Q., et al. (2014). Hedgehog/Gli promotes epithelial-mesenchymal transition in lung squamous cell carcinomas. Journal of Experimental & Clinical Cancer Research, 33, Artn 34. doi:10.1186/1756-9966-33-34.

  48. Cai, P. F., Guo, W. J., Yuan, H. Q., Li, Q., Wang, W. C., Sun, Y., et al. (2014). Expression and clinical significance of tyrosine phosphatase SHP-2 in colon cancer. Biomedicine & Pharmacotherapy, 68(3), 285–290. doi:10.1016/j.biopha.2013.10.012.

    Article  CAS  Google Scholar 

  49. Jechlinger, M., Sommer, A., Moriggl, R., Seither, P., Kraut, N., Capodiecci, P., et al. (2006). Autocrine PDGFR signaling promotes mammary cancer metastasis. Journal of Clinical Investigation, 116(6), 1561–1570. doi:10.1172/Jc124652.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  50. Pegoraro, S., Ros, G., Piazza, S., Sommaggio, R., Ciani, Y., Rosato, A., et al. (2013). HMGA1 promotes metastatic processes in basal-like breast cancer regulating EMT and stemness. Oncotarget, 4(8), 1293–1308.

    PubMed Central  PubMed  Google Scholar 

  51. Tan, M. Y., Gong, H., Zeng, Y. G., Tao, L., Wang, J., Jiang, J. T., et al. (2014). Downregulation of homeodomain-interacting protein kinase-2 contributes to bladder cancer metastasis by regulating Wnt signaling. Journal of Cellular Biochemistry, 115(10), 1762–1767. doi:10.1002/Jcb.24842.

    Article  CAS  PubMed  Google Scholar 

  52. Zhou, F.F., Drabsch, Y., Dekker, T.J., de Vinuesa, A.G., Li, Y.H., Hawinkels, L.J.A.C., et al. (2014). Nuclear receptor NR4A1 promotes breast cancer invasion and metastasis by activating TGF-beta signalling. Nature Communications, 5, Artn 3388. doi:10.1038/Ncomms4388.

  53. Muller, A., Homey, B., Soto, H., Ge, N. F., Catron, D., Buchanan, M. E., et al. (2001). Involvement of chemokine receptors in breast cancer metastasis. Nature, 410(6824), 50–56. doi:10.1038/35065016.

    Article  CAS  PubMed  Google Scholar 

  54. Li, H.Y., Yang, L., Fu, H., Yan, J.S., Wang, Y., Guo, H., et al. (2013). Association between G alpha i2 and ELMO1/Dock180 connects chemokine signalling with Rac activation and metastasis. Nature Communications, 4, Artn 1706. doi:10.1038/Ncomms2680.

  55. Eberl, M., Klingler, S., Mangelberger, D., Loipetzberger, A., Damhofer, H., Zoidl, K., et al. (2012). Hedgehog-EGFR cooperation response genes determine the oncogenic phenotype of basal cell carcinoma and tumour-initiating pancreatic cancer cells. EMBO Molecular Medicine, 4(3), 218–233. doi:10.1002/emmm.201100201.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  56. Kanehisa, M. (2002). The KEGG database. In Silico Simulation of Biological Processes, 247, 91–103.

    Article  CAS  Google Scholar 

  57. Kochetkova, M., Kumar, S., & McColl, S. R. (2009). Chemokine receptors CXCR4 and CCR7 promote metastasis by preventing anoikis in cancer cells. Cell Death and Differentiation, 16(5), 664–673. doi:10.1038/Cdd.2008.190.

    Article  CAS  PubMed  Google Scholar 

  58. Stark, C., Breitkreutz, B. J., Chatr-aryamontri, A., Boucher, L., Oughtred, R., Livstone, M. S., et al. (2011). The BioGRID interaction database: 2011 update. Nucleic Acids Research, 39, D698–D704. doi:10.1093/Nar/Gkq1116.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  59. Liu, B. L., & Bo, H. (2007). HPRD: a high performance RDF database. Network and Parallel Computing, Proceedings, 4672, 364–374.

    Article  Google Scholar 

  60. Hermjakob, H., Montecchi-Palazzi, L., Lewington, C., Mudali, S., Kerrien, S., Orchard, S., et al. (2004). IntAct: an open source molecular interaction database. Nucleic Acids Research, 32, D452–D455. doi:10.1093/Nar/Gkh052.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  61. Chatr-Aryamontri, A., Ceol, A., Palazzi, L. M., Nardelli, G., Schneider, M. V., Castagnoli, L., et al. (2007). MINT: the molecular interaction database. Nucleic Acids Research, 35, D572–D574. doi:10.1093/Nar/Gkl950.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  62. Croft, D., O’Kelly, G., Wu, G. M., Haw, R., Gillespie, M., Matthews, L., et al. (2011). Reactome: a database of reactions, pathways and biological processes. Nucleic Acids Research, 39, D691–D697. doi:10.1093/Nar/Gkq1018.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  63. Xenarios, I., Salwinski, L., Duan, X. Q. J., Higney, P., Kim, S. M., & Eisenberg, D. (2002). DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Research, 30(1), 303–305. doi:10.1093/Nar/30.1.303.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  64. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., et al. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13(11), 2498–2504. doi:10.1101/Gr.1239303.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  65. Zhang, W., Zeng, T., Liu, X., Chen, L. (2015). Diagnosing phenotypes of single-sample individuals by edge biomarkers. Journal of Molecular Cell Biology. doi:10.1093/jmcb/mjv025.

  66. Zhang, X., Zhao, J., Hao, J., Zhao, X., & Chen, L. (2014). Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks. Nucleic Acids Research. doi:10.1093/nar/gku1315.

    Google Scholar 

  67. Zeng, T., Zhang, C., Zhang, W., Liu, R., Liu, J., & Chen, L. (2014). Deciphering early development of complex diseases by progressive module network. Methods. doi:10.1016/j.ymeth.2014.01.021.

    Google Scholar 

  68. Liu, R., Yu, X., Liu, X., Xu, D., Aihara, K., & Chen, L. (2014). Identifying critical transitions of complex diseases based on a single sample. Bioinformatics. doi:10.1093/bioinformatics/btu084.

    Article  Google Scholar 

  69. Zeng, T., Sun, S., Wang, Y., Zhu, H., & Chen, L. (2013). Network biomarkers reveal dysfunctional gene regulations during disease progression. FEBS Journal. doi:10.1111/febs.12536.

    PubMed Central  Google Scholar 

Download references

Acknowledgments

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (No. XDB13040700), the National Program on Key Basic Research Project (No. 2014CB910504), and the National Natural Science Foundation of China (NSFC) (Nos. 91439103, 61134013, 61403363, 31200987). This work was supported by the Knowledge Innovation Program of SIBS of CAS (No. 2013KIP218) and China Postdoctoral Science Foundation (Nos. 2014T70441, 2013M541565). The work was supported by Zhongshan Distinguished Professor Grant (XDW), NSFC (91230204, 81270099, 81320108001, 81270131, 81300010), The Shanghai Committee of Science and Technology (12JC1402200, 12431900207, 11410708600, 14431905100), Operation funding of Shanghai Institute of Clinical Bioinformatics, and Ministry of Education, Academic Special Science and Research Foundation for PhD Education (20130071110043).

Author contributions

LC and XL planed this study. QS, XL, and TZ designed the experiments. QS wrote the manuscript. All authors read and approved the final manuscript.

Competing financial interests

The authors declare no competing financial interests.

Author information

Authors and Affiliations

  1. Key Laboratory of Systems Biology, Innovation Center for Cell Signaling Network, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Qianqian Shi, Xiaoping Liu, Tao Zeng & Luonan Chen

  2. Department of Biomedical Science, University College of London, London, UK

    William Wang

  3. School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China

    Luonan Chen

Authors
  1. Qianqian Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoping Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. William Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Luonan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luonan Chen.

Additional information

Qianqian Shi and Xiaoping Liu contributed equally to this work.

Electronic supplementary material

Below is the link to the electronic supplementary material.

ESM 1

(DOCX 20 kb)

Supplementary Fig. 1

Network modules capture specific-cancer hallmarks for LUSC. (A) The 42 network modules with 292 interactions among 333 genes for LUSC were obtained from the integrated PINs identified by CDI, and the most abundant feature sets with more than 3 genes were kept. Blue edges indicate the down correlated gene-pairs in tumor compared to normal, and red edges indicate up correlated gene-pairs in tumor. (B) General cancer and LUSC-associated genes were identified from the predicted gene sets by CDI or the same number of top gene sets created by DI or DG. (C) Clinical covariate association test for differential expressed nodes and edges in LUSC predicted modules using Fisher’s exact test. The postfix for methods, like “-nonbatch” or “-batch” is added for distinguishing method’s characteristic in the comparative analysis, e.g., “batch” means meta process considering K datasets. The solid black line in (B) and (C) represents the enrichment significance of –log(0.05). (PDF 2653 kb)

Supplementary Fig. 2

The relations among the predicted results and prior information are different. Those interaction sets identified by CDI, DI and extracted from GeneCards share different edges for LUAD (A) and LUSC (C). Interactions for GeneCards are defined as those edges with as least one gene from GeneCards. Those gene sets identified by CDI, DG and extracted from GeneCards share different nodes for LUAD (B) and LUSC (D). (PDF 265 kb)

Supplementary Fig. 3

The prognostic role of clinical outcomes in non-small cell lung cancer. Kaplan–Meier analysis of overall survival based on N stage (A), T stage (B) and disease stage (C) for LUAD and LUSC. (PDF 243 kb)

Supplementary Fig. 4

Edges associated with prognosis in TCGA lung squamous cell cancer cohort and 12 signature predictor analysis. (A) The matrix visualizes the significant HRs for the 18 edges in the TCGA LUSC cohort. Numbers in the rectangles indicate the HRs for expression with significant univariate Cox regression (P- value < 0.1). Red rectangles indicate HRs >1 and blue rectangles indicate HRs < 1. (B) 12 edges are finally identified as the predictor signature. The Kaplan–Meier curves for LUSC (C) and for LUAD (D) risk groups were obtained from the TCGA cohort divided by the median cutoff point as shown in the right up plot of the predictor-score distribution. (PDF 290 kb)

Supplementary Table 1

Information of LUAD and LUSC samples from TCGA. Eight datasets expression profiles are for lung squamous cell carcinoma and 18 for lung adenocarcinoma. For disease samples column, each row represents a dataset we used. (PDF 111 kb)

Supplementary Table 2

Available clinical information of the cohort for LUAD and LUSC. Age, gender, disease stage, N stage, M stage, T stage and smoking status outcomes are collected (e.g., Nonsmoker: lifetime nonsmoker or current reformed smoker for >15 years) (PDF 121 kb)

Supplementary Table 3

All the consensus network modules for LUAD by CDI, with sign consensus value as weight, which from −1 to +1 corresponds to down and up correlated level. (CSV 20 kb)

Supplementary Table 4

All the consensus network modules for LUSC by CDI, with sign consensus value as weight, which from −1 to +1 corresponds to down and up correlated level. (CSV 24 kb)

Supplementary Table 5

Intersection between the disease gene sets for LUAD and LUSC by CDI, with sign-consistent value as weight, which from −1 to +1 corresponds to down and up differential expression level by DG. (TXT 1 kb)

Table 3

Correlation P values between edge prognostic signatures and known NSCLC metastasis gene markers for LUSC. Table cells with P value > 0.05 are removed (PDF 166 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, Q., Liu, X., Zeng, T. et al. Detecting disease genes of non-small lung cancer based on consistently differential interactions. Cancer Metastasis Rev 34, 195–208 (2015). https://doi.org/10.1007/s10555-015-9561-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10555-015-9561-5

Keywords

Search

Navigation