Skip to content
2000
image of Innovative Insights into Liver Cancer: Multi-Omics Reveals Critical 
Subtypes and Hub Genes

Abstract

Introduction/Objective

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignant tumor, characterized by elevated mortality rates and poor diagnostic outcomes. Accurate identification of cancer subtypes is crucial for guiding personalized treatment and improving patient prognosis.

Methods

A method for precisely identifying HCC subtypes by integrating multi-omics data was presented. This approach combines the GRACES dimensionality reduction technique with the hMKL subtype identification model to analyze data from 266 HCC patients.

Results

We identified two subtypes more accurately, both significantly associated with overall survival. Their respective three-year mortality rates were 55.9% and 27.9%. Additionally, we observed significant differences in the activity of five pathways between these two subtypes, along with notable variations in the abundance and status of seven types of immune cells. Through further determination of the PPI network and centrality indicators, 13 up-regulated hub genes and 14 down-regulated hub genes were identified.

Discussion

Based on the above results, we compared and discussed the hub genes with the textual data, examined differences in gene upregulation and downregulation, and evaluated findings from other bioinformatics analyses to identify potential biomarkers.

Conclusion

Limited research on ENPP3 and C3 in HCC suggests their potential as biomarkers. Additionally, low expression levels of PIK3R1, KDR, and CYP3A5, along with high expression levels of EGLN3 and EPO, may indicate a higher risk of liver cancer in patients. Single-gene survival analysis highlighted the significant impact of highly expressed genes on HCC prognosis, with PKM, RRM2, and EPO playing crucial roles in the risk scores.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936365348250331112230
2025-04-24
2025-08-16

  • From This Site

    /content/journals/cbio/10.2174/0115748936365348250331112230
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Vogel A. Meyer T. Sapisochin G. Salem R. Saborowski A. Hepatocellular carcinoma. Lancet 2022 400 10360 1345 1362 10.1016/S0140‑6736(22)01200‑4 36084663
    [Google Scholar]
  2. Zheng R.S. Chen R. Han B.F. Wang S.M. Li L. Sun K.X. Zeng H.M. Wei W.W. He J. Cancer incidence and mortality in China, 2022. Zhonghua Zhong Liu Za Zhi 2024 46 3 221 231 10.3760/cma.j.cn112152‑20240119‑00035 38468501
    [Google Scholar]
  3. Rumgay H. Arnold M. Ferlay J. Lesi O. Cabasag C.J. Vignat J. Laversanne M. McGlynn K.A. Soerjomataram I. Global burden of primary liver cancer in 2020 and predictions to 2040. J. Hepatol. 2022 77 6 1598 1606 10.1016/j.jhep.2022.08.021 36208844
    [Google Scholar]
  4. Papadakos S.P. Stergiou I.E. Gkolemi N. Arvanitakis K. Theocharis S. Unraveling the significance of EPH/Ephrin signaling in liver cancer: Insights into tumor progression and therapeutic implications. Cancers 2023 15 13 3434 10.3390/cancers15133434 37444544
    [Google Scholar]
  5. Chen F. Wang J. Wu Y. Gao Q. Zhang S. Potential biomarkers for liver cancer diagnosis based on multi-omics strategy. Front. Oncol. 2022 12 822449 10.3389/fonc.2022.822449 35186756
    [Google Scholar]
  6. Bruix J. Endpoints in clinical trials for liver cancer and their value in evidence-based clinical decision making: An unresolved Gordian knot. J. Hepatol. 2021 74 6 1483 1488 10.1016/j.jhep.2021.01.033 33556420
    [Google Scholar]
  7. Wang S. Huang H. Hu X. Xiao M. Yang K. Bu H. Jiang Y. Huang Z. A novel amino acid‐related gene signature predicts overall survival in patients with hepatocellular carcinoma. Cancer Rep. 2024 7 7 e2131 10.1002/cnr2.2131 39041652
    [Google Scholar]
  8. Fu J. Wang H. Precision diagnosis and treatment of liver cancer in China. Cancer Lett. 2018 412 283 288 10.1016/j.canlet.2017.10.008 29050983
    [Google Scholar]
  9. Li L. Wang H. Heterogeneity of liver cancer and personalized therapy. Cancer Lett. 2016 379 2 191 197 10.1016/j.canlet.2015.07.018 26213370
    [Google Scholar]
  10. Ning Z. Tan G. Cancer metabolism: A novel perspective on precision diagnosis and treatment for liver cancer. Zhonghua Wai Ke Za Zhi 2020 58 1 31 36 10.3760/cma.j.issn.0529‑5815.2020.01.008 31902167
    [Google Scholar]
  11. Chen F. Zhang W. Gao X. Yuan H. Liu K. The role of small interfering RNAs in hepatocellular carcinoma. J. Gastrointest. Cancer 2024 55 1 26 40 10.1007/s12029‑023‑00911‑w 37432548
    [Google Scholar]
  12. Hytiroglou P. Bioulac-Sage P. Theise N.D. Sempoux C. Etiology, pathogenesis, diagnosis, and practical implications of hepatocellular neoplasms. Cancers 2022 14 15 3670 10.3390/cancers14153670 35954333
    [Google Scholar]
  13. Dai W.C. Fan R. Sun A.H. He F.C. Hou J.L. [Multi-omics research contributes to early screening, diagnosis and treatment of liver cancer]. Zhonghua Gan Zang Bing Za Zhi 2022 30 8 793 796 10.3760/cma.j.cn501113‑20220628‑00357 36207934
    [Google Scholar]
  14. Gu S. Lv L. Lin X. Li X. Dai J. Zhang J. Kong R. Xie W. Li J. Using structural analysis to explore the role of hepatitis B virus mutations in immune escape from liver cancer in Chinese, European and American populations. J. Biomol. Struct. Dyn. 2022 40 4 1586 1596 10.1080/07391102.2020.1830852 33030111
    [Google Scholar]
  15. Huang L. Sun H. Sun L. Shi K. Chen Y. Ren X. Ge Y. Jiang D. Liu X. Knoll W. Zhang Q. Wang Y. Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning. Nat. Commun. 2023 14 1 48 10.1038/s41467‑022‑35696‑2 36599851
    [Google Scholar]
  16. Imamura T. Okamura Y. Ohshima K. Uesaka K. Sugiura T. Yamamoto Y. Ashida R. Ohgi K. Nagashima T. Yamaguchi K. Molecular characterization‐based multi‐omics analyses in primary liver cancer using the Japanese version of the genome atlas. J. Hepatobiliary Pancreat. Sci. 2023 30 3 269 282 10.1002/jhbp.1223 35918906
    [Google Scholar]
  17. Pan M. Chen P. Zhang Q. Yang Y. Hu W. Hu G. Wang R. Chen X. Comprehensive profiling of cell subsets of gastric cancer and liver metastasis based on single cell RNA-sequencing analysis. Transl. Cancer Res. 2024 13 1 330 347 10.21037/tcr‑23‑1532 38410212
    [Google Scholar]
  18. Wang J. Miao Y. Li L. Wu Y. Ren Y. Cui Y. Cao H. Multi-omics data integration for hepatocellular carcinoma subtyping with multi-kernel learning. Front. Genet. 2022 13 962870 10.3389/fgene.2022.962870 36147508
    [Google Scholar]
  19. Ayton S.G. Pavlicova M. Robles-Espinoza C.D. Tamez Peña J.G. Treviño V. Multiomics subtyping for clinically prognostic cancer subtypes and personalized therapy: A systematic review and meta-analysis. Genet. Med. 2022 24 1 15 25 10.1016/j.gim.2021.09.006 34906494
    [Google Scholar]
  20. Shen Y. Xiong W. Gu Q. Zhang Q. Yue J. Liu C. Wang D. Multi-omics integrative analysis uncovers molecular subtypes and mrnas as therapeutic targets for liver cancer. Front. Med. 2021 8 654635 10.3389/fmed.2021.654635 34109194
    [Google Scholar]
  21. Shaw T.I. Zhao B. Li Y. Wang H. Wang L. Manley B. Stewart P.A. Karolak A. Multi-omics approach to identifying isoform variants as therapeutic targets in cancer patients. Front. Oncol. 2022 12 1051487 10.3389/fonc.2022.1051487 36505834
    [Google Scholar]
  22. Chaudhary K. Poirion O.B. Lu L. Garmire L.X. Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 2018 24 6 1248 1259 10.1158/1078‑0432.CCR‑17‑0853 28982688
    [Google Scholar]
  23. Guo L.Y. Wu A.H. Wang Y. Zhang L. Chai H. Liang X.F. Deep learning-based ovarian cancer subtypes identification using multi-omics data. BioData Min. 2020 13 1 10 10.1186/s13040‑020‑00222‑x 32863885
    [Google Scholar]
  24. Neagu A.N. Whitham D. Bruno P. Morrissiey H. Darie C.A. Darie C.C. Omics-based investigations of breast cancer. Molecules 2023 28 12 4768 10.3390/molecules28124768 37375323
    [Google Scholar]
  25. Rappoport N. Shamir R. Multi-omic and multi-view clustering algorithms: Review and cancer benchmark. Nucleic Acids Res. 2018 46 20 10546 10562 10.1093/nar/gky889 30295871
    [Google Scholar]
  26. Salimy S. Lanjanian H. Abbasi K. Salimi M. Najafi A. Tapak L. Masoudi-Nejad A. A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data. Heliyon 2023 9 7 e17653 10.1016/j.heliyon.2023.e17653 37455955
    [Google Scholar]
  27. Wei T. Fa B. Luo C. Johnston L. Zhang Y. Yu Z. An efficient and easy-to-use network-based integrative method of multi-omics data for cancer genes discovery. Front. Genet. 2021 11 613033 10.3389/fgene.2020.613033 33488678
    [Google Scholar]
  28. Zhao L. Yan H. MCNF: A novel method for cancer subtyping by integrating multi-omics and clinical data. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2020 17 5 1682 1690 10.1109/TCBB.2019.2910515 30990192
    [Google Scholar]
  29. Sun H. Chang Z. Li H. Tang Y. Liu Y. Qiao L. Feng G. Huang R. Han D. Yin D. Multi-omics analysis-based macrophage differentiation-associated papillary thyroid cancer patient classifier. Transl. Oncol. 2024 43 101889 10.1016/j.tranon.2024.101889 38382228
    [Google Scholar]
  30. Vahabi N. Michailidis G. Unsupervised multi-omics data integration methods: A comprehensive review. Front. Genet. 2022 13 854752 10.3389/fgene.2022.854752 35391796
    [Google Scholar]
  31. Wei Y. Li L. Zhao X. Yang H. Sa J. Cao H. Cui Y. Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning. Brief. Bioinform. 2023 24 1 bbac488 10.1093/bib/bbac488 36433785
    [Google Scholar]
  32. Chen C. Weiss S.T. Liu Y.Y. Graph convolutional network-based feature selection for high-dimensional and low-sample size data. Bioinformatics 2023 39 4 btad135 10.1093/bioinformatics/btad135 37084264
    [Google Scholar]
  33. Guan L. Luo Q. Liang N. Liu H. A prognostic prediction system for hepatocellular carcinoma based on gene co‑expression network. Exp. Ther. Med. 2019 17 6 4506 4516 10.3892/etm.2019.7494 31086582
    [Google Scholar]
  34. Ramazzotti D. Lal A. Wang B. Batzoglou S. Sidow A. Multi-omic tumor data reveal diversity of molecular mechanisms that correlate with survival. Nat. Commun. 2018 9 1 4453 10.1038/s41467‑018‑06921‑8 30367051
    [Google Scholar]
  35. Gusev A. Lee S.H. Trynka G. Finucane H. Vilhjálmsson B.J. Xu H. Zang C. Ripke S. Bulik-Sullivan B. Stahl E. Kähler A.K. Hultman C.M. Purcell S.M. McCarroll S.A. Daly M. Pasaniuc B. Sullivan P.F. Neale B.M. Wray N.R. Raychaudhuri S. Price A.L. Schizophrenia Working Group of the Psychiatric Genomics ConsortiumSWE-SCZ ConsortiumSchizophrenia Working Group of the Psychiatric Genomics Consortium SWE-SCZ Consortium Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 2014 95 5 535 552 10.1016/j.ajhg.2014.10.004 25439723
    [Google Scholar]
  36. Hamilton W.L. Ying R. Leskovec J. Inductive representation learning on large graphs. arXiv 2017 1706.02216
    [Google Scholar]
  37. Liu B. Wei Y. Zhang Y. Yang Q. Deep neural networks for high dimension, low sample size data. IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence Melbourne, Australia, 19 August 2017, pp. 2287–2293. 10.24963/ijcai.2017/318
    [Google Scholar]
  38. Barakat A. Mittal A. Ricketts D. Rogers B.A. Understanding survival analysis: Actuarial life tables and the Kaplan–Meier plot. Br. J. Hosp. Med. 2019 80 11 642 646 10.12968/hmed.2019.80.11.642 31707885
    [Google Scholar]
  39. Love M.I. Huber W. Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014 15 12 550 10.1186/s13059‑014‑0550‑8 25516281
    [Google Scholar]
  40. Ritchie M.E. Phipson B. Wu D. Hu Y. Law C.W. Shi W. Smyth G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015 43 7 e47 10.1093/nar/gkv007 25605792
    [Google Scholar]
  41. Schubert M. Klinger B. Klünemann M. Sieber A. Uhlitz F. Sauer S. Garnett M.J. Blüthgen N. Saez-Rodriguez J. Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat. Commun. 2018 9 1 20 10.1038/s41467‑017‑02391‑6 29295995
    [Google Scholar]
  42. Zhengdong A. Xiaoying X. Shuhui F. Rui L. Zehui T. Guanbin S. Li Y. Xi T. Wanqian L. Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma. Cancer Cell Int. 2024 24 1 130 10.1186/s12935‑024‑03306‑4 38584256
    [Google Scholar]
  43. Newman A.M. Liu C.L. Green M.R. Gentles A.J. Feng W. Xu Y. Hoang C.D. Diehn M. Alizadeh A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 2015 12 5 453 457 10.1038/nmeth.3337 25822800
    [Google Scholar]
  44. Chen T. Yao L. Liu W. Luan J. Wang Y. Yang C. Zhou X. Ji C. Guo X. Wang Z. Song N. Epididymal segment-specific miRNA and mRNA regulatory network at the single cell level. Cell Cycle 2023 22 19 2194 2209 10.1080/15384101.2023.2280170 37982230
    [Google Scholar]
  45. Bader G.D. Hogue C.W.V. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 2003 4 1 2 10.1186/1471‑2105‑4‑2 12525261
    [Google Scholar]
  46. Wu T. Hu E. Xu S. Chen M. Guo P. Dai Z. Feng T. Zhou L. Tang W. Zhan L. Fu X. Liu S. Bo X. Yu G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2021 2 3 100141 10.1016/j.xinn.2021.100141 34557778
    [Google Scholar]
  47. Ding Z. Deng Z. Li H. Single-cell transcriptome analysis reveals the key genes associated with macrophage polarization in liver cancer. Hepatol. Commun. 2023 7 11 e0304 10.1097/HC9.0000000000000304 37889536
    [Google Scholar]
  48. Shao M. Tao Q. Xu Y. Xu Q. Shu Y. Chen Y. Shen J. Zhou Y. Wu Z. Chen M. Yang J. Shi Y. Wen T. Bu H. Glutamine synthetase-negative hepatocellular carcinoma has better prognosis and response to sorafenib treatment after hepatectomy. Chin. Med. J. 2023 136 17 2066 2076 10.1097/CM9.0000000000002380 37249521
    [Google Scholar]
  49. Wang M. Guo H. Zhang B. Shang Y. Zhang S. Liu X. Cao P. Fan Y. Tan K. Transcription factors-related molecular subtypes and risk prognostic model: exploring the immunogenicity landscape and potential drug targets in hepatocellular carcinoma. Cancer Cell Int. 2024 24 1 9 10.1186/s12935‑023‑03185‑1 38178084
    [Google Scholar]
  50. Geisbrecht B.V. Lambris J.D. Gros P. Complement component C3: A structural perspective and potential therapeutic implications. Semin. Immunol. 2022 59 101627 10.1016/j.smim.2022.101627 35760703
    [Google Scholar]
  51. Chen L. Zhang C. Xue R. Liu M. Bai J. Bao J. Deep whole-genome analysis of 494 hepatocellular carcinomas. Nature 2024 627 8004 586 593 10.1038/s41586‑024‑07054‑3
    [Google Scholar]
  52. Li X. Liu C. Zhang Z. Li X. Yao Z. Dong Y. Wang X. Chen Z. Hepatocyte-specific Wtap deficiency promotes hepatocellular carcinoma by activating GRB2–ERK depending on downregulation of proteasome-related genes. J. Biol. Chem. 2023 299 11 105301 10.1016/j.jbc.2023.105301 37777158
    [Google Scholar]
  53. Li J. Gao F. Su J. Pan T. Bioinformatics identification and validation of aging‑related molecular subtype and prognostic signature in breast cancer. Medicine 2023 102 19 e33605 10.1097/MD.0000000000033605 37171324
    [Google Scholar]
  54. Cobleigh M.A. Layng K.V. Mauer E. Mahon B. Hockenberry A.J. Abukhdeir A.M. Comparative genomic analysis of PIK3R1-mutated and wild-type breast cancers. Breast Cancer Res. Treat. 2024 204 2 407 414 10.1007/s10549‑023‑07196‑4 38153569
    [Google Scholar]
  55. Huang M. Yang S. Tai W.C.S. Zhang L. Zhou Y. Cho W.C.S. Chan L.W.C. Wong S.C.C. Bioinformatics identification of regulatory genes and mechanism related to hypoxia-induced PD-L1 inhibitor resistance in hepatocellular carcinoma. Int. J. Mol. Sci. 2023 24 10 8720 10.3390/ijms24108720 37240068
    [Google Scholar]
  56. Du J. Shen E. Anthocyanin-mediated autophagy in hepatocellular carcinoma: Gene associations and prognostic implications. Epub 2024 20240408 10.2174/0118715303280877240130065512 38616759
    [Google Scholar]
  57. Wan Y. Jiang H. Liu Z. Bai C. Lian Y. Zhang C. Zhang Q. Huang J. Exploring the molecular mechanisms of huaier on modulating metabolic reprogramming of hepatocellular carcinoma: A study based on network pharmacology, molecular docking and bioinformatics. Curr. Pharm. Des. 2024 30 24 1894 1911 10.2174/0113816128287535240429043610 38747231
    [Google Scholar]
  58. Yoo J.E. Nahm J.H. Kim Y.J. Jeon Y. Park Y.N. The dual role of transforming growth factor-beta signatures in human B viral multistep hepatocarcinogenesis: Early and late responsive genes. J. Liver Cancer 2022 22 2 115 124 10.17998/jlc.2022.04.20 37383409
    [Google Scholar]
  59. Xu C. Cheng S. Chen K. Song Q. Liu C. Fan C. Zhang R. Zhu Q. Wu Z. Wang Y. Fan J. Zheng H. Lu L. Chen T. Zhao H. Jiao Y. Qu C. Sex differences in genomic features of hepatitis b–associated hepatocellular carcinoma with distinct antitumor immunity. Cell. Mol. Gastroenterol. Hepatol. 2023 15 2 327 354 10.1016/j.jcmgh.2022.10.009 36272708
    [Google Scholar]
  60. Zhou X. Tan F. Zhang S. Wang A. Zhang T. A strategy based on bioinformatics and machine learning algorithms reveals potential mechanisms of shelian capsule against hepatocellular carcinoma. Curr. Pharm. Des. 2024 30 5 377 405 10.2174/0113816128284465240108071554 38310567
    [Google Scholar]
  61. Zhu L. Lou Y. Xiao Q. Wang L. Chen G. Yang W. Wang T. Establishment and evaluation of exosomes-related gene risk model in hepatocellular carcinoma. Biochem. Genet. 2024 62 2 698 717 10.1007/s10528‑023‑10441‑6 37405532
    [Google Scholar]
  62. Yang C. Guo L. Du J. Zhang Q. Zhang L. SPINK1 overexpression correlates with hepatocellular carcinoma treatment resistance revealed by single cell RNA-sequencing and spatial transcriptomics. Biomolecules 2024 14 3 265 10.3390/biom14030265 38540686
    [Google Scholar]
  63. Chen Y. Pan B. Qiu J. Chen Z. Wang X. Tang N. Increased effects of 2,5-dimethylcelecoxib on sensitivity of hepatocellular carcinoma cells to sorafenib via CYP3A5 expression and activation of AMPK. Toxicol. In Vitro 2021 76 105226 10.1016/j.tiv.2021.105226 34293431
    [Google Scholar]
  64. Humpton T.J. Hall H. Kiourtis C. Nixon C. Clark W. Hedley A. Shaw R. Bird T.G. Blyth K. Vousden K.H. p53-mediated redox control promotes liver regeneration and maintains liver function in response to CCl4. Cell Death Differ. 2022 29 3 514 526 10.1038/s41418‑021‑00871‑3 34628485
    [Google Scholar]
  65. Ma W.K. Voss D.M. Scharner J. Costa A.S.H. Lin K.T. Jeon H.Y. Wilkinson J.E. Jackson M. Rigo F. Bennett C.F. Krainer A.R. ASO-based PKM splice-switching therapy inhibits hepatocellular carcinoma growth. Cancer Res. 2022 82 5 900 915 10.1158/0008‑5472.CAN‑20‑0948 34921016
    [Google Scholar]
  66. Wu Y.J. Liu S. Tian Y.Q. Fan Z.J. Zhang L. Liu S.Y. [Screening and validation of pivotal genes in hepatitis B virus-associated hepatocellular carcinoma]. Zhonghua Gan Zang Bing Za Zhi 2023 31 8 869 876 10.3760/cma.j.cn501113‑20220420‑00213 37723070
    [Google Scholar]
  67. Xu Y. Xu X. Ni X. Pan J. Chen M. Lin Y. Zhao Z. Zhang L. Ge N. Song G. Zhang J. Gene-based cancer-testis antigens as prognostic indicators in hepatocellular carcinoma. Heliyon 2023 9 3 e13269 10.1016/j.heliyon.2023.e13269 36950598
    [Google Scholar]
  68. Lee H. Choi J.Y. Joung J.G. Joh J.W. Kim J.M. Hyun S.H. Metabolism-associated gene signatures for FDG avidity on PET/CT and prognostic validation in hepatocellular carcinoma. Front. Oncol. 2022 12 845900 10.3389/fonc.2022.845900 35174098
    [Google Scholar]
  69. Qiu X. Dong L. Wang K. Zhong X. Xu H. Xu S. Guo H. Wei X. Chen W. Xu X. Development and validation of a novel nomogram integrated with hypoxic and lactate metabolic characteristics for prognosis prediction in hepatocellular carcinoma. J. Hepatocell. Carcinoma 2024 11 241 255 10.2147/JHC.S446313 38333220
    [Google Scholar]
  70. Mardjuki R. Wang S. Carozza J.A. Abhiraman G.C. Lyu X. Li L. Identification of extracellular membrane protein Enpp3 as a major cgamp hydrolase, cementing cgamp’s role as an immunotransmitter. bioRxiv 2024 20240113 10.1101/2024.01.12.575449
    [Google Scholar]
  71. Hamdy H. Yang Y. Cheng C. Liu Q. Identification of potential hub genes related to aflatoxin b1, liver fibrosis and hepatocellular carcinoma via integrated bioinformatics analysis. Biology 2023 12 2 205 10.3390/biology12020205 36829489
    [Google Scholar]
  72. Wen H. Ji T. Lin L. Cheng N. Zhu K. Cao L. High expression of ten eleven translocation 1 is associated with poor prognosis in hepatocellular carcinoma. Mediators Inflamm. 2023 2023 1 22 10.1155/2023/2664370 37181808
    [Google Scholar]
  73. Zhang Y. Zhang R. Zeng L. Wang H. Peng R. Zhang M. Zhang H. Yang Z. Gao L. Wang M. Liu J. Identification and validation of a potential stemness-associated biomarker in hepatocellular carcinoma. Stem Cells Int. 2022 2022 1 18 10.1155/2022/1534593 35859724
    [Google Scholar]
  74. Zhang Z. Mou L. Pu Z. Zhuang X. Construction of a hepatocytes-related and protein kinase–related gene signature in HCC based on ScRNA-Seq analysis and machine learning algorithm. J. Physiol. Biochem. 2023 79 4 771 785 10.1007/s13105‑023‑00973‑1 37458958
    [Google Scholar]
  75. Hasan M.A.M. Maniruzzaman M. Shin J. Gene expression and metadata based identification of key genes for hepatocellular carcinoma using machine learning and statistical models. IEEE/ACM Trans. Comput. Biol. Bioinformatics 2023 20 6 3786 3799 10.1109/TCBB.2023.3322753 37812547
    [Google Scholar]
  76. Liu F. Wu Y. Zhang B. Yang S. Shang K. Li J. Zhang P. Deng W. Chen L. Zheng L. Gai X. Zhang H. Oncogenic β-catenin-driven liver cancer is susceptible to methotrexate-mediated disruption of nucleotide synthesis. Chin. Med. J. 2024 137 2 181 189 10.1097/CM9.0000000000002816 37612257
    [Google Scholar]
  77. Song Y. Park I.S. Kim J. Seo H.R. Actinomycin D. Actinomycin D inhibits the expression of the cystine/glutamate transporter xCT via attenuation of CD133 synthesis in CD133+ HCC. Chem. Biol. Interact. 2019 309 108713 10.1016/j.cbi.2019.06.026 31226288
    [Google Scholar]
  78. Hu Y.J. Zhang X. Lv H.M. Liu Y. Li S.Z. Protein O ‐GlcNAcylation: The sweet hub in liver metabolic flexibility from a (patho)physiological perspective. Liver Int. 2024 44 2 293 315 10.1111/liv.15812 38110988
    [Google Scholar]
  79. Zhang Y. Yang X. Zhou L. Gao X. Wu X. Chen X. Hou J. Wang L. Immune-related lincRNA pairs predict prognosis and therapeutic response in hepatocellular carcinoma. Sci. Rep. 2022 12 1 4259 10.1038/s41598‑022‑08225‑w 35277569
    [Google Scholar]
  80. Guo Z. Chen W. Dai G. Huang Y. Cordycepin suppresses the migration and invasion of human liver cancer cells by downregulating the expression of CXCR4. Int. J. Mol. Med. 2020 45 1 141 150 31746344
    [Google Scholar]
  81. Espinoza AF Patel RH Patel KR Badachhape AA Whitlock R Srivastava RK Novel treatment strategy utilizing panobinostat for high-risk and treatment-refractory hepatoblastoma. J Hepatol 2024 80 4 610 621 10.1016/j.jhep.2024.01.003
    [Google Scholar]
  82. Nazzal M. Sur S. Steele R. Khatun M. Patra T. Phillips N. Long J. Ray R. Ray R.B. Establishment of a patient‐derived xenograft tumor from hepatitis C–associated liver cancer and evaluation of imatinib treatment efficacy. Hepatology 2020 72 2 379 388 10.1002/hep.31298 32356575
    [Google Scholar]
  83. Wang Z. Wang M. Zhang M. Xu K. Zhang X. Xie Y. Zhang Y. Chang C. Li X. Sun A. He F. High-affinity SOAT1 ligands remodeled cholesterol metabolism program to inhibit tumor growth. BMC Med. 2022 20 1 292 10.1186/s12916‑022‑02436‑8 35941608
    [Google Scholar]
  84. Liu C. Mu X. Wang X. Zhang C. Zhang L. Yu B. Sun G. Ponatinib inhibits proliferation and induces apoptosis of liver cancer cells, but its efficacy is compromised by its activation on PDK1/Akt/mTOR signaling. Molecules 2019 24 7 1363 10.3390/molecules24071363 30959969
    [Google Scholar]
  85. Zhang B. Zhang X. Zhou T. Liu J. Clinical observation of liver cancer patients treated with axitinib and cabozantinib after failed sorafenib treatment: A case report and literature review. Cancer Biol. Ther. 2015 16 2 215 218 10.4161/15384047.2014.962318 25668362
    [Google Scholar]
  86. Sun M. Zhang J. Liu S. Liu Y. Zheng D. Sp1 is involved in 8-chloro-adenosine-upregulated death receptor 5 expression in human hepatoma cells. Oncol. Rep. 2008 19 1 177 185 10.3892/or.19.1.177 18097593
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936365348250331112230
Loading
Innovative Insights into Liver Cancer: Multi-Omics Reveals Critical 
Subtypes and Hub Genes
Bentham Science Publishers ; https://doi.org/10.2174/0115748936365348250331112230
/content/journals/cbio/10.2174/0115748936365348250331112230
/content/journals/cbio/10.2174/0115748936365348250331112230
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keywords: immunocell infiltration analysis ; multi-omics data ; Feature selection ; multi-core learning ; subtype recognition ; hepatocellular carcinoma

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test