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Research topics

Integrative cancer genomics

Reading several layers of genomic information together to reach mechanism and therapeutic targets — two decades across liver cancer and infectious disease.

One idea

This strand records a research programme that pushed a single idea for nearly two decades — that integrating multiple layers of genomic information yields mechanism and clinically actionable insight that no single layer provides. Sixty-one primary publications, read in sequence, trace that idea from early microarray-based recurrence signatures of hepatocellular carcinoma into multiomics tumour classification, structure-guided antiviral therapeutics, computational infrastructure, and molecular oncology.

Hepatocellular carcinoma as the proving ground

The anchor arc is liver cancer. It runs from expression signatures and recurrence prediction through driver gene discovery, exome and epigenome integration, and stepwise models of hepatocarcinogenesis, toward fusion-directed peptide therapeutics and antibody-based immunotherapy. Along the way sit the mitochondrial defect axis, stem cell traits, and the spectrum of tumour subtypes.

The same logic elsewhere

A parallel arc applied the same structural and computational logic to SARS-CoV-2 — receptor binding analyses, immune escape maps, inhibitory peptides, engineered antibodies, pan-variant vaccine concepts. The target changed, but the methodological signature did not: integrate across layers, validate functionally, model structurally.

What holds it up

Both arcs rest on reusable pipelines, curated databases, single cell atlases, and explainable machine learning models for plasma proteomic and imaging biomarkers. Leaving tools behind mattered as much as leaving results.

What contextBio does

That lineage becomes the current services — reproducible omics processing is AURORA, and structure-first molecular design is PepDesigner. The reading side continues in spatial biology in cancer, the design side in generative binder design.

References

61 items

This page draws on the review manuscript Integrative Multiomics Genomics and Structural Therapeutics: A Narrative Review of a Translational Cancer and Infectious Disease Research Program (2007 to 2026); below is the literature it cites.

  1. Kwon SM, Cho H, Choi JH, Jee BA, Jo Y, Woo HG. Perspectives of integrative cancer genomics in next generation sequencing era. Genomics Inform. 2012. doi:10.5808/GI.2012.10.2.69
  2. Woo HG, Park ES, Thorgeirsson SS, Kim YJ. Exploring genomic profiles of hepatocellular carcinoma. Mol Carcinog. 2011. doi:10.1002/mc.20691
  3. Woo HG, Kim YJ. Multiplatform genomic roadmap of hepatocellular carcinoma: a matter of molecular heterogeneity. Hepatology. 2018. doi:10.1002/hep.29925
  4. Woo HG, Park ES, Cheon JH, Kim JH, Lee JS, Park BJ, et al. Gene expression-based recurrence prediction of hepatitis B virus-related human hepatocellular carcinoma. Clin Cancer Res. 2008. doi:10.1158/1078-0432.CCR-07-1473
  5. Woo HG, Wang XW, Budhu A, Kim YH, Kwon SM, Tang ZY, et al. Association of TP53 mutations with stem cell-like gene expression and survival of patients with hepatocellular carcinoma. Gastroenterology. 2011. doi:10.1053/j.gastro.2010.11.034
  6. Woo HG, Park ES, Lee JS, Lee YH, Ishikawa T, Kim YJ, et al. Identification of potential driver genes in human liver carcinoma by genomewide screening. Cancer Res. 2009. doi:10.1158/0008-5472.CAN-09-0164
  7. Woo HG, Lee JH, Yoon JH, Kim CY, Lee HS, Jang JJ, et al. Identification of a cholangiocarcinoma-like gene expression trait in hepatocellular carcinoma. Cancer Res. 2010. doi:10.1158/0008-5472.CAN-09-2823
  8. Seok JY, Na DC, Woo HG, Roncalli M, Kwon SM, Yoo JE, et al. A fibrous stromal component in hepatocellular carcinoma reveals a cholangiocarcinoma-like gene expression trait and epithelial-mesenchymal transition. Hepatology. 2012. doi:10.1002/hep.25570
  9. Kwon SM, Kim DS, Won NH, Park SJ, Chwae YJ, Kang HC, et al. Genomic copy number alterations with transcriptional deregulation at 6p identify an aggressive HCC phenotype. Carcinogenesis. 2013. doi:10.1093/carcin/bgt095
  10. Woo HG, Kim SS, Cho H, Kwon SM, Cho HJ, Ahn SJ, et al. Profiling of exome mutations associated with progression of HBV-related hepatocellular carcinoma. PLoS One. 2014. doi:10.1371/journal.pone.0115152
  11. Choi JH, Kim MJ, Park YK, Im JY, Kwon SM, Kim HC, et al. Mutations acquired by hepatocellular carcinoma recurrence give rise to an aggressive phenotype. Oncotarget. 2017. doi:10.18632/oncotarget.14248
  12. Kim TH, Lee EJ, Choi JH, Yim SY, Lee S, Kang J, et al. Identification of novel susceptibility loci associated with hepatitis B surface antigen seroclearance in chronic hepatitis B. PLoS One. 2018. doi:10.1371/journal.pone.0199094
  13. Choi JH, Kim YB, Ahn JM, Kim MJ, Bae WJ, Han SU, et al. Identification of genomic aberrations associated with lymph node metastasis in diffuse-type gastric cancer. Exp Mol Med. 2018. doi:10.1038/s12276-017-0009-6
  14. Woo HG, Choi JH, Yoon S, Jee BA, Cho EJ, Lee JH, et al. Integrative analysis of genomic and epigenomic regulation of the transcriptome in liver cancer. Nat Commun. 2017. doi:10.1038/s41467-017-00991-w
  15. Jee BA, Choi JH, Rhee H, Yoon S, Kwon SM, Nahm JH, et al. Dynamics of genomic, epigenomic, and transcriptomic aberrations during stepwise hepatocarcinogenesis. Cancer Res. 2019. doi:10.1158/0008-5472.CAN-19-0991
  16. Kim D, Shah M, Kim JH, Kim J, Baek YH, Jeong JS, et al. Integrative transcriptomic and genomic analyses unveil the IFI16 variants and expression as MASLD progression markers. Hepatology. 2025. doi:10.1097/HEP.0000000000000805
  17. Lee YK, Jee BA, Kwon SM, Yoon YS, Xu WG, Wang HJ, et al. Identification of a mitochondrial defect gene signature reveals NUPR1 as a key regulator of liver cancer progression. Hepatology. 2015. doi:10.1002/hep.27976
  18. Lee YK, Woo HG, Yoon G. Mitochondrial defect-responsive gene signature in liver-cancer progression. BMB Rep. 2015. doi:10.5483/bmbrep.2015.48.11.180
  19. Rhee H, Ko JE, Chung T, Jee BA, Kwon SM, Nahm JH, et al. Transcriptomic and histopathological analysis of cholangiolocellular differentiation trait in intrahepatic cholangiocarcinoma. Liver Int. 2018. doi:10.1111/liv.13492
  20. Jang B, Kwon SM, Kim JH, Kim JM, Chung T, Yoo JE, et al. Transcriptomic profiling of intermediate cell carcinoma of the liver. Hepatol Commun. 2024. doi:10.1097/HC9.0000000000000505
  21. Jeon Y, Kwon SM, Rhee H, Yoo JE, Chung T, Woo HG, et al. Molecular and radiopathologic spectrum between HCC and intrahepatic cholangiocarcinoma. Hepatology. 2023. doi:10.1002/hep.32397
  22. Yoon S, Choi JH, Shah M, Kwon SM, Yang J, Park YN, et al. USO1 isoforms differentially promote liver cancer progression by dysregulating the ER-Golgi network. Carcinogenesis. 2021. doi:10.1093/carcin/bgab067
  23. Moon SU, Shah M, Thao TT, Woo HG. Long non-coding RNA TPRG1-AS1 interacts with CLTC in liver cancer cells. Anticancer Res. 2024. doi:10.21873/anticanres.17307
  24. Shah M, Hussain M, Woo HG. Structural insights into antibody-based immunotherapy for hepatocellular carcinoma. Genomics Inform. 2025. doi:10.1186/s44342-024-00033-0
  25. Shah M, Moon SU, Choi JH, Kim MJ, Woo HG. Peptide-based therapeutics targeting the SLC39A14-PIWIL2 fusion in hepatocellular carcinoma. Genomics Inform. 2025. doi:10.1186/s44342-025-00060-5
  26. Shah M, Moon SU, Choi JH, Kim MJ, Woo HG. Correction: peptide-based therapeutics targeting the SLC39A14-PIWIL2 fusion in hepatocellular carcinoma. Genomics Inform. 2026. doi:10.1186/s44342-025-00066-z
  27. Shah M, Ahmad B, Choi S, Woo HG. Mutations in the SARS-CoV-2 spike RBD are responsible for stronger ACE2 binding and poor anti-SARS-CoV mAbs cross-neutralization. Comput Struct Biotechnol J. 2020. doi:10.1016/j.csbj.2020.11.002
  28. Shah M, Woo HG. Omicron: a heavily mutated SARS-CoV-2 variant exhibits stronger binding to ACE2 and potently escapes approved COVID-19 therapeutic antibodies. Front Immunol. 2021. doi:10.3389/fimmu.2021.830527
  29. Shah M, Woo HG. Molecular perspectives of SARS-CoV-2: pathology, immune evasion, and therapeutic interventions. Mol Cells. 2021. doi:10.14348/molcells.2021.0026
  30. Shah M, Woo HG. The paradigm of immune escape by SARS-CoV-2 variants and strategies for repositioning subverted mAbs against escaped VOCs. Mol Ther. 2022. doi:10.1016/j.ymthe.2022.08.020
  31. Shah M, Moon SU, Kim JH, Thao TT, Woo HG. SARS-CoV-2 pan-variant inhibitory peptides deter S1-ACE2 interaction and neutralize delta and omicron pseudoviruses. Comput Struct Biotechnol J. 2022. doi:10.1016/j.csbj.2022.04.030
  32. Shah M, Shin JY, Woo HG. Rational strategies for enhancing mAb binding to SARS-CoV-2 variants through CDR diversification and antibody-escape prediction. Front Immunol. 2023. doi:10.3389/fimmu.2023.1113175
  33. Shah M, Woo HG. Assessment of neutralization susceptibility of Omicron subvariants XBB.1.5 and BQ.1.1 against broad-spectrum neutralizing antibodies through epitopes mapping. Front Mol Biosci. 2023. doi:10.3389/fmolb.2023.1236617
  34. Shah M, Moon SU, Shin JY, Choi JH, Kim D, Woo HG. Pan-variant SARS-CoV-2 vaccines induce protective immunity by targeting conserved epitopes. Adv Sci (Weinh). 2025. doi:10.1002/advs.202409919
  35. Pham NT, Ko J, Shah M, Rakkiyappan R, Woo HG, Manavalan B. Leveraging deep transfer learning and explainable AI for accurate COVID-19 diagnosis: insights from a multi-national chest CT scan study. Comput Biol Med. 2025. doi:10.1016/j.compbiomed.2024.109461
  36. Ko J, Park S, Woo HG. Optimization of vision transformer-based detection of lung diseases from chest X-ray images. BMC Med Inform Decis Mak. 2024. doi:10.1186/s12911-024-02591-3
  37. Yang JO, Charny P, Lee B, Kim S, Bhak J, Woo HG. GS2PATH: a web-based integrated analysis tool for finding functional relationships using gene ontology and biochemical pathway data. Bioinformation. 2007. doi:10.6026/97320630002194
  38. Joo T, Choi JH, Lee JH, Park SE, Jeon Y, Jung SH, et al. SEQprocess: a modularized and customizable pipeline framework for NGS processing in R package. BMC Bioinformatics. 2019. doi:10.1186/s12859-019-2676-x
  39. Choi JH, Hong SE, Woo HG. Pan-cancer analysis of systematic batch effects on somatic sequence variations. BMC Bioinformatics. 2017. doi:10.1186/s12859-017-1627-7
  40. Choi JH, Kim HI, Woo HG. scTyper: a comprehensive pipeline for the cell typing analysis of single-cell RNA-seq data. BMC Bioinformatics. 2020. doi:10.1186/s12859-020-03700-5
  41. Choi JH, Choi HS, Cho SH, Lee JH, Woo HG. CGV: Cancer Genome Viewer, a web service for integrative cancer genome and pharmacogenomic data analysis. Bioinformatics. 2022. doi:10.1093/bioinformatics/btac642
  42. Noh JY, Lee HI, Choi JH, Cho SH, Yi YH, Lim JH, et al. CCIDB: a manually curated cell-cell interaction database with cell context information. Database (Oxford). 2023. doi:10.1093/database/baad057
  43. Choi JH, Lee BS, Jang JY, Lee YS, Kim HJ, Roh J, et al. Single-cell transcriptome profiling of the stepwise progression of head and neck cancer. Nat Commun. 2023. doi:10.1038/s41467-023-36691-x
  44. Park S, Lee DG, Kim J, Shah M, Shin H, Woo HG. BIGPN: biologically informed graph propagational network for plasma proteomic profiling of neurodegenerative biomarkers. Artif Intell Med. 2025. doi:10.1016/j.artmed.2025.103241
  45. Park S, Lee DG, Kim J, Kim D, Nam Y, Park B, et al. Explainable multiplex graph propagational network with multimodal neuroimage integration for dementia subtype diagnosis. Neural Netw. 2025. doi:10.1016/j.neunet.2025.107971
  46. Park S, Kim D, Choi JH, Hong CH, Son SJ, Roh HW, et al. NeuroFANN: identification of neuropathological subtypes in dementia with plasma proteins by using functionally annotated neural network. Brief Bioinform. 2025. doi:10.1093/bib/bbaf366
  47. Park S, Lee DG, Kim J, Kim SH, Hwang HJ, Shin H, et al. PPIxGPN: plasma proteomic profiling of neurodegenerative biomarkers with protein-protein interaction-based explainable graph propagational network. Brief Bioinform. 2025. doi:10.1093/bib/bbaf213
  48. Park S, Kim D, Lee H, Hong CH, Son SJ, Roh HW, et al. Plasma protein-based identification of neuroimage-driven subtypes in mild cognitive impairment via protein-protein interaction aware explainable graph propagational network. Comput Biol Med. 2024. doi:10.1016/j.compbiomed.2024.109303
  49. Park S, Hong CH, Son SJ, Roh HW, Kim D, Shin H, et al. Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network. Brief Bioinform. 2024. doi:10.1093/bib/bbae428
  50. Kim D, Sohn JY, Cho JH, Choi JH, Oh GY, Woo HG. KF-NIPT: K-mer and fetal fraction-based estimation of chromosomal anomaly from NIPT data. BMC Bioinformatics. 2025. doi:10.1186/s12859-025-06127-y
  51. Ka HI, Woo HG. Mitochondrial transfer in cancer: mechanisms, immune evasion, and therapeutic opportunities. Genomics Inform. 2026. doi:10.1186/s44342-025-00064-1
  52. Moon SU, Lee JH, Shah M, Yoon S, Woo HG. RRM2 is a CTNNB1 transport regulator promoting colon cancer progression. Anticancer Res. 2024. doi:10.21873/anticanres.17054
  53. Moon SU, Kim JH, Woo HG. Tumor suppressor RBM24 inhibits nuclear translocation of CTNNB1 and TP63 expression in liver cancer cells. Oncol Lett. 2021. doi:10.3892/ol.2021.12935
  54. Yoon S, Shin B, Woo HG. Endoplasmic reticulum stress induces CAP2 expression promoting epithelial-mesenchymal transition in liver cancer cells. Mol Cells. 2021. doi:10.14348/molcells.2021.0031
  55. Yoon S, Choi JH, Kim SJ, Lee EJ, Shah M, Choi S, et al. EPHB6 mutation induces cell adhesion-mediated paclitaxel resistance via EPHA2 and CDH11 expression. Exp Mol Med. 2019. doi:10.1038/s12276-019-0261-z
  56. Yoon S, Lee EJ, Choi JH, Chung T, Kim DY, Im JY, et al. Recapitulation of pharmacogenomic data reveals that invalidation of SULF2 enhance sorafenib susceptibility in liver cancer. Oncogene. 2018. doi:10.1038/s41388-018-0291-3
  57. Jung HJ, Byun HO, Jee BA, Min S, Jeoun UW, Lee YK, et al. The ubiquitin-like with PHD and ring finger domains 1 (UHRF1)/DNA methyltransferase 1 (DNMT1) axis is a primary regulator of cell senescence. J Biol Chem. 2017. doi:10.1074/jbc.M116.750539
  58. Park SH, Jang KY, Kim MJ, Yoon S, Jo Y, Kwon SM, et al. Tumor suppressive effect of PARP1 and FOXO3A in gastric cancers and its clinical implications. Oncotarget. 2015. doi:10.18632/oncotarget.6264
  59. Kwon SM, Kang SH, Park CK, Jung S, Park ES, Lee JS, et al. Recurrent glioblastomas reveal molecular subtypes associated with mechanistic implications of drug-resistance. PLoS One. 2015. doi:10.1371/journal.pone.0140528
  60. Jee BA, Lim H, Kwon SM, Jo Y, Park MC, Lee IJ, et al. Molecular classification of basal cell carcinoma of skin by gene expression profiling. Mol Carcinog. 2015. doi:10.1002/mc.22233
  61. Kim YM, Byun HO, Jee BA, Cho H, Seo YH, Kim YS, et al. Implications of time-series gene expression profiles of replicative senescence. Aging Cell. 2013. doi:10.1111/acel.12087

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