Functional & Integrative Genomics

, Volume 19, Issue 1, pp 191–204 | Cite as

Integrated analysis of transcription factors and targets co-expression profiles reveals reduced correlation between transcription factors and target genes in cancer

  • Jinsheng Liang
  • Ying Cui
  • Yuhuan Meng
  • Xingsong Li
  • Xueping Wang
  • Wanli Liu
  • Lizhen Huang
  • Hongli DuEmail author
Original Article


Transcription factors are recognized as the key regulators of gene expression. However, the changes in the correlation of transcription factors and their target genes between normal and tumor tissues are usually ignored. In this research, we used mRNA expression profile data from The Cancer Genome Atlas which included 5726 samples across 11 major human cancers to perform co-expression analysis by the Pearson correlation coefficients. Then, integrating 81,357 pairs of transcription factors and target genes from transcription factors databases to find out the changes in the co-expression correlation of these gene pairs from normal to tumor tissues. Based on the changes in the number of co-expressed TF-TG pairs and changes in the level of co-expression, we found the generally reduced correlation between transcription factors and their target genes in cancer. Additionally, we screened out universal and specific transcription factors-target genes pairs which may significant influence particular cancer. Then, we obtained 423 cancer cell line expression profiles from Broad Institute Cancer Cell Line Encyclopedia to verify our results. Some of these pairs like XRCC5-XRCC6 have been reported to involve in multiple cancers, while pairs like IRF1-PSMB9 without any previous articles related to tumor but involve in the biological processes of cancer, which are of great potential to be therapeutic targets. Our research may provide insights to better understand the tumor development mechanisms and find potential therapeutic targets.


Co-expression Integrative analysis Transcription factor Tumorigenesis Therapeutic target 



This work was partially supported by the National key research and development program (SQ2018YFC090062), Science and Technology Planning Project of Guangzhou (201704020176, 201508020040, 201510010044), and the Fundamental Research Funds for the Central Universities (2015ZZ125).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


We declare that the experiments comply with the current laws of the country in which they were performed.

Supplementary material

10142_2018_636_MOESM1_ESM.xlsx (12 kb)
ESM 1 (XLSX 12 kb)
10142_2018_636_MOESM2_ESM.xlsx (19 kb)
ESM 2 (XLSX 19 kb)
10142_2018_636_MOESM3_ESM.xlsx (10 kb)
ESM 3 (XLSX 9 kb)
10142_2018_636_MOESM4_ESM.xlsx (204 kb)
ESM 4 (XLSX 203 kb)
10142_2018_636_MOESM5_ESM.xlsx (102 kb)
ESM 5 (XLSX 102 kb)
10142_2018_636_MOESM6_ESM.xlsx (121 kb)
ESM 6 (XLSX 121 kb)
10142_2018_636_MOESM7_ESM.xlsx (105 kb)
ESM 7 (XLSX 104 kb)
10142_2018_636_MOESM8_ESM.xlsx (57 kb)
ESM 8 (XLSX 57 kb)
10142_2018_636_MOESM9_ESM.xlsx (224 kb)
ESM 9 (XLSX 224 kb)
10142_2018_636_MOESM10_ESM.xlsx (388 kb)
ESM 10 (XLSX 388 kb)
10142_2018_636_MOESM11_ESM.xlsx (187 kb)
ESM 11 (XLSX 187 kb)
10142_2018_636_MOESM12_ESM.xlsx (301 kb)
ESM 12 (XLSX 300 kb)
10142_2018_636_MOESM13_ESM.xlsx (366 kb)
ESM 13 (XLSX 365 kb)
10142_2018_636_MOESM14_ESM.xlsx (84 kb)
ESM 14 (XLSX 84 kb)
10142_2018_636_MOESM15_ESM.xlsx (70 kb)
ESM 15 (XLSX 70 kb)
10142_2018_636_MOESM16_ESM.xlsx (74 kb)
ESM 16 (XLSX 73 kb)
10142_2018_636_MOESM17_ESM.xlsx (326 kb)
ESM 17 (XLSX 325 kb)
10142_2018_636_MOESM18_ESM.xlsx (91 kb)
ESM 18 (XLSX 90 kb)
10142_2018_636_MOESM19_ESM.xlsx (19 kb)
ESM 19 (XLSX 18 kb)
10142_2018_636_MOESM20_ESM.xlsx (48 kb)
ESM 20 (XLSX 47 kb)
10142_2018_636_MOESM21_ESM.xlsx (16 kb)
ESM 21 (XLSX 16 kb)
10142_2018_636_MOESM22_ESM.xlsx (24 kb)
ESM 22 (XLSX 23 kb)
10142_2018_636_MOESM23_ESM.xlsx (15 kb)
ESM 23 (XLSX 14 kb)


  1. Bargiela-Iparraguirre J, Prado-Marchal L, Pajuelo-Lozano N, Jiménez B, Perona R, Sánchez-Pérez I (2014) Mad2 and BubR1 modulates tumourigenesis and paclitaxel response in MKN45 gastric cancer cells. Cell Cycle 13:3590–3601. CrossRefPubMedPubMedCentralGoogle Scholar
  2. Barretina J, Caponigro G, Stransky N, Venkatesan K et al (2012) The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483:603–607. CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bau DT, Tsai CW, Wu CN (2011) Role of the XRCC5/XRCC6 dimer in carcinogenesis and pharmacogenomics. Pharmacogenomics 12:515–534. CrossRefPubMedGoogle Scholar
  4. Bouhlel MA, Lambert M, David-Cordonnier MH (2015) Targeting transcription factor binding to DNA by competing with DNA binders as an approach for controlling gene expression. Curr Top Med Chem 15:1323–1358CrossRefGoogle Scholar
  5. Brennan DJ, O’Connor DP, Rexhepaj E et al (2010) Antibody-based proteomics: fast-tracking molecular diagnostics in oncology. Nat Rev Cancer 10:605–617. CrossRefPubMedGoogle Scholar
  6. Cancer Genome Atlas Research N et al (2017) Integrated genomic and molecular characterization of cervical cancer. Nature 543:378–384. CrossRefGoogle Scholar
  7. Chen H, Zou Y, Yang H, Wang J (2014) Downregulation of FoxM1 inhibits proliferation, invasion and angiogenesis of HeLa cells in vitro and in vivo. Int J Oncol 45:2355–2364. CrossRefPubMedGoogle Scholar
  8. Colaprico A et al (2016) TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 44:e71. CrossRefPubMedGoogle Scholar
  9. da Huang W, Sherman BT, Lempicki RA (2009a) Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37:1–13CrossRefGoogle Scholar
  10. da Huang W, Sherman BT, Lempicki RA (2009b) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57. CrossRefGoogle Scholar
  11. Deng M, Brägelmann J, Schultze JL, Perner S (2016) Web-TCGA: an online platform for integrated analysis of molecular cancer data sets. BMC Bioinformatics 17:72CrossRefGoogle Scholar
  12. Dillies MA et al (2013) A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis. Brief Bioinform 14:671–683. CrossRefPubMedGoogle Scholar
  13. Du Y et al (2006) Depression of MAD2 inhibits apoptosis of gastric cancer cells by upregulating Bcl-2 and interfering mitochondrion pathway. Biochem Biophys Res Commun 345:1092–1098. CrossRefPubMedGoogle Scholar
  14. Engin H, Ustundag Y, Ozel Tekin I, Gokmen A (2012) Plasma concentrations of Ang-1, Ang-2 and Tie-2 in gastric cancer. Eur Cytokine Netw 23:21–24. CrossRefPubMedGoogle Scholar
  15. Frittoli E, Palamidessi A, Marighetti P, Confalonieri S, Bianchi F (2014) A RAB5/RAB4 recycling circuitry induces a proteolytic invasive program and promotes tumor dissemination. J Cell Biol 206:307–328. CrossRefPubMedPubMedCentralGoogle Scholar
  16. Garcia-Alonso L, Iorio F, Matchan A, Fonseca N et al (2018) Transcription factor activities enhance markers of drug sensitivity in cancer. Cancer Res 78:769–780. CrossRefPubMedGoogle Scholar
  17. Han H et al (2015) TRRUST: a reference database of human transcriptional regulatory interactions. Sci Rep 5:11432. CrossRefPubMedPubMedCentralGoogle Scholar
  18. Hornsveld M, Smits LMM, Meerlo M, van Amersfoort M et al (2018) FOXO transcription factors both suppress and support breast cancer progression. Cancer Res.
  19. Huang T, Wang G, Yang L, Peng B (2017) Transcription factor YY1 modulates lung cancer progression by activating lncRNA-PVT1. DNA Cell Biol 36:947–958. CrossRefPubMedGoogle Scholar
  20. Jiang C, Xuan Z, Zhao F, Zhang MQ (2007) TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 35:D137–D140. CrossRefPubMedPubMedCentralGoogle Scholar
  21. Kersey PJ, Allen JE, Allot A, Barba M et al (2018) Ensembl Genomes 2018: an integrated omics infrastructure for non-vertebrate species. Nucleic Acids Res 46:D802–D808. CrossRefPubMedGoogle Scholar
  22. Kim HS et al (2005) Frequent mutations of human Mad2, but not Bub1, in gastric cancers cause defective mitotic spindle checkpoint. Mutat Res 578:187–201. CrossRefPubMedGoogle Scholar
  23. Kim MY, Bae JS, Kim TH, Park JM, Ahn YH (2012) Role of transcription factor modifications in the pathogenesis of insulin resistance. Exp Diabetes Res 2012:716425. CrossRefPubMedGoogle Scholar
  24. Kotarba G, Krzywinska E, Grabowska AI, Taracha A (2018) TFCP2/TFCP2L1/UBP1 transcription factors in cancer. Cancer Lett 420:72–79. CrossRefPubMedGoogle Scholar
  25. Landthaler M, Gaidatzis D, Rothballer A et al (2008) Molecular characterization of human Argonaute-containing ribonucleoprotein complexes and their bound target mRNAs. RNA 14:2580–2596. CrossRefPubMedPubMedCentralGoogle Scholar
  26. Lin PC et al (2015) Clinical relevance of plasma DNA methylation in colorectal cancer patients identified by using a genome-wide high-resolution array. Ann Surg Oncol 22(Suppl 3):S1419–S1427. CrossRefPubMedGoogle Scholar
  27. Liu SS, Chen XM, Zheng HX, Shi SL, Li Y (2011) Knockdown of Rab5a expression decreases cancer cell motility and invasion through integrin-mediated signaling pathway. J Biomed Sci 18.
  28. Liu X, Li Y, Wei J (2012) Role of Ang-2, Tie-2 and VEGFR-2 in angiogenesis in colorectal carcinoma and their prognostic value. J South Med Univ 32.
  29. Lu J, Tan M, Cai Q, (2015) The Warburg effect in tumor progression: Mitochondrial oxidative metabolism as an anti-metastasis mechanism. Cancer Lett 356 (2):156-164Google Scholar
  30. Magnenat L, Schwimmer LJ, Barbas CF (2008) Drug-inducible and simultaneous regulation of endogenous genes by single-chain nuclear receptor-based zinc-finger transcription factor gene switches. Gene Ther 15:1223–1232. CrossRefPubMedPubMedCentralGoogle Scholar
  31. Mandric I, Temate-Tiagueu Y, Shcheglova T, Al Seesi S, Zelikovsky A, Mandoiu II (2017) Fast bootstrapping-based estimation of confidence intervals of expression levels and differential expression from RNA-Seq data. Bioinformatics 33:3302–3304. CrossRefPubMedGoogle Scholar
  32. Marum L (2012) Cancer Cell Line Encyclopedia launched by Novartis and Broad Institute. Future Med Chem 4:947CrossRefGoogle Scholar
  33. Marusyk A, Polyak K (2010) Tumor heterogeneity: causes and consequences. Biochim Biophys Acta 1805:105–117PubMedGoogle Scholar
  34. Mroz RM, Korniluk M, Panek B, Ossolinska M, Chyczewska E (2013) sVEGF R1 and Tie-2 levels during chemotherapy of lung cancer patients. Adv Exp Med Biol 756:313–319. CrossRefPubMedGoogle Scholar
  35. Nolens G, Pignon JC, Koopmansch B, Elmoualij B, Zorzi W, De Pauw E, Winkler R (2009) Ku proteins interact with activator protein-2 transcription factors and contribute to ERBB2 overexpression in breast cancer cell lines. Breast Cancer Res 11:R83. CrossRefPubMedPubMedCentralGoogle Scholar
  36. Parker NR, Hudson AL, Khong P, Parkinson JF, Dwight T, Ikin RJ, Zhu Y, Cheng ZJ, Vafaee F, Chen J, Wheeler HR, Howell VM (2016) Intratumoral heterogeneity identified at the epigenetic, genetic and transcriptional level in glioblastoma. Sci Rep 6:22477CrossRefGoogle Scholar
  37. Paulo P et al (2012) FLI1 is a novel ETS transcription factor involved in gene fusions in prostate cancer. Genes Chromosom Cancer 51:240–249. CrossRefPubMedGoogle Scholar
  38. Peng C, Wang M, Shen Y et al (2013) Reconstruction and analysis of transcription factor-miRNA co-regulatory feed-forward loops in human cancers using filter-wrapper feature selection. PLoS One 8:e78197. CrossRefPubMedPubMedCentralGoogle Scholar
  39. Pucholt P, Sjodin P, Weih M, Ronnberg-Wastljung AC, Berlin S (2015) Genome-wide transcriptional and physiological responses to drought stress in leaves and roots of two willow genotypes. BMC Plant Biol 15:244. CrossRefPubMedPubMedCentralGoogle Scholar
  40. Rainey L, van der Waal D, Jervaeus A, Wengström Y (2018) Are we ready for the challenge of implementing risk-based breast cancer screening and primary prevention? Breast 9:24–32. CrossRefGoogle Scholar
  41. Rooj AK, Bronisz A, Godlewski J (2016) The role of octamer binding transcription factors in glioblastoma multiforme. Biochim Biophys Acta 1859:805–811. CrossRefPubMedPubMedCentralGoogle Scholar
  42. Sakurai T et al (2007) Functional roles of Fli-1, a member of the Ets family of transcription factors, in human breast malignancy. Cancer Sci 98:1775–1784. CrossRefPubMedGoogle Scholar
  43. Samur MK (2014) RTCGAToolbox: a new tool for exporting TCGA Firehose data. PLoS One 9:e106397. CrossRefPubMedPubMedCentralGoogle Scholar
  44. Sang MM, Du WQ, Zhang RY, Zheng JN, Pei DS (2015) Suppression of CSN5 promotes the apoptosis of gastric cancer cells through regulating p53-related apoptotic pathways. Bioorg Med Chem Lett 25:2897–2901. CrossRefPubMedGoogle Scholar
  45. Segaran T (2007) Programming collective intelligence: building smart web 2.0 applications. O’Reilly Media, SebastopolGoogle Scholar
  46. Song W, Hu L, Li W (2014) Oncogenic Fli-1 is a potential prognostic marker for the progression of epithelial ovarian cancer. BMC Cancer 14.
  47. Stine ZE, Walton ZE, Altman BJ et al (2015) MYC, metabolism, and cancer. Cancer Discov 5:1024–1039. CrossRefPubMedPubMedCentralGoogle Scholar
  48. Su Z, Yemul S, Estabrook A, Friedman R et al (1995) Transcriptional switching model for the regulation of tumorigenesis and metastasis by the ha-ras oncogene - transcriptional changes in the ha-ras tumor-suppressor gene lysyl oxidase. Int J Oncol 7:1279–1284PubMedGoogle Scholar
  49. Sui J, Li YH, Zhang Y-Q et al (2016) Integrated analysis of long non-coding RNA-associated ceRNA network reveals potential lncRNA biomarkers in human lung adenocarcinoma. Int J Oncol 49:2023–2036. CrossRefPubMedGoogle Scholar
  50. Tan H, Bao J, Zhou X (2015) Genome-wide mutational spectra analysis reveals significant cancer-specific heterogeneity. Sci Rep 5:12566. CrossRefPubMedPubMedCentralGoogle Scholar
  51. Testa AC, Forrest ARR (2016) Transcription factor NKX6.3 sheds light on gastric cancer progression. EBioMedicine 9:9–10. CrossRefPubMedPubMedCentralGoogle Scholar
  52. Tomczak K, Czerwinska P, Wiznerowicz M (2015) The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol 19:A68–A77. CrossRefGoogle Scholar
  53. Uddin S, Hussain AR, Ahmed M, Siddiqui K (2012) Overexpression of FoxM1 offers a promising therapeutic target in diffuse large B-cell lymphoma. Haematologica 97:1092–1100. CrossRefPubMedPubMedCentralGoogle Scholar
  54. Uhlen M et al (2017) A pathology atlas of the human cancer transcriptome. Science 357:660. CrossRefGoogle Scholar
  55. Vidhyasekaran P (2016) Molecular manipulation of transcription factors, the master regulators of PAMP-triggered signaling systems. 255–358.
  56. Wagner GP, Kin K, Lynch VJ (2012) Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131:281–285.
  57. Wang C, Yang S, Liu CM, Jiang TT (2018) Screening and identification of four serum miRNAs as novel potential biomarkers for cured pulmonary tuberculosis. Tuberculosis 108:26–34.
  58. Wei L, Jin Z, Yang S, Xu Y (2017) TCGA-assembler 2: software pipeline for retrieval and processing of TCGA/CPTAC data. Bioinformatics.
  59. White E, Mehnert JM, Chan CS (2015) Autophagy, metabolism, and cancer. Clin Cancer Res 21:5037–5046. CrossRefPubMedPubMedCentralGoogle Scholar
  60. Xiao YC, Yang ZB, Cheng XS, Fang XB (2015) CXCL8, overexpressed in colorectal cancer, enhances the resistance of colorectal cancer cells to anoikis. Cancer Lett 361:22–32. CrossRefPubMedGoogle Scholar
  61. Xue YJ, Xiao RH, Long DZ, Zou XF (2012) Overexpression of FoxM1 is associated with tumor progression in patients with clear cell renal cell carcinoma. J Transl Med 10.
  62. Yang M et al (2009) Functional FEN1 polymorphisms are associated with DNA damage levels and lung cancer risk. Hum Mutat 30:1320–1328. CrossRefPubMedGoogle Scholar
  63. Yang PS, Yin PH, Tseng LM, Yang CH (2011) Rab5A is associated with axillary lymph node metastasis in breast cancer patients. Cancer Sci 102:2172–2178CrossRefGoogle Scholar
  64. Yang C, Chen H, Yu L, Shan L (2013) Inhibition of FOXM1 transcription factor suppresses cell proliferation and tumor growth of breast cancer. Cancer Gene Ther 20:117–124. CrossRefPubMedGoogle Scholar
  65. Yoshida Y, Tomiyama T, Maruta T, Tomita M, Ishikawa T, Arakawa K (2016) De novo assembly and comparative transcriptome analysis of Euglena gracilis in response to anaerobic conditions. BMC Genomics 17:182. CrossRefPubMedPubMedCentralGoogle Scholar
  66. Zerbino DR et al (2018) Ensembl 2018. Nucleic Acids Res 46:D754–D761. CrossRefPubMedGoogle Scholar
  67. Zhang J-H et al (2014) Expression of Ang-2/Tie-2 and PI3K/AKT in colorectal cancer. Asian Pac J Cancer Prev 15:8651–8656. CrossRefPubMedGoogle Scholar
  68. Zhang W, Edwards A, Flemington EK, Zhang K (2017) Racial disparities in patient survival and tumor mutation burden, and the association between tumor mutation burden and cancer incidence rate. Sci Rep 7:13639. CrossRefPubMedPubMedCentralGoogle Scholar
  69. Zheng G et al (2008) ITFP: an integrated platform of mammalian transcription factors. Bioinformatics 24:2416–2417. CrossRefPubMedGoogle Scholar
  70. Zhou W, Liotta LA, Petricoin EF (2017) The Warburg effect and mass spectrometry-based proteomic analysis. Cancer Genomics Proteomics 14:211–218. CrossRefPubMedPubMedCentralGoogle Scholar
  71. Zhu F, Zykova TA, Kang BS, Wang Z (2007) Bidirectional signals transduced by TOPK-ERK interaction increase tumorigenesis of HCT116 colorectal cancer cells. Gastroenterology 133:219–231CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Biology and Biological EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Department of Laboratory Medicine, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer MedicineSun Yat-sen University Cancer CenterGuangzhouChina

Personalised recommendations