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Cited by 2Cancer-related molecular mechanisms researchHepatocellular Carcinoma Treatment and PrognosisFerroptosis and cancer prognosis

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A Novel and Robust Prognostic Model for Hepatocellular Carcinoma Based on Enhancer RNAs-Regulated Genes

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Zhang, Wei, Chen, Kegong, Tian, Wei, Zhang, Qi, Sun, Lin, Wang, Yupeng, Liu, Meina, Zhang, Qiuju·2022·Frontiers in Oncology
DOI 10.3389/fonc.2022.849242PMC PMC9133429
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Abstract

Evidence has demonstrated that enhancer RNAs (eRNAs) play a vital role in the progression and prognosis of cancers, but few studies have focused on the prognostic ability of eRNA-regulated genes (eRGs) for hepatocellular carcinoma (HCC). Using gene expression profiles of HCC patients from the TCGA-LIHC and eRNA expression profiles from the enhancer RNA in cancers (eRic) data portal, we developed a novel and robust prognostic signature composed of 10 eRGs based on Lasso-penalized Cox regression analysis. According to the signature, HCC patients were stratified into high- and low-risk groups, which have been shown to have significant differences in tumor immune microenvironment, immune checkpoints, HLA-related genes, DNA damage repair-related genes, Gene-set variation analysis (GSVA), and the lower half-maximal inhibitory concentration (IC50) of Sorafenib. The prognostic nomogram combining the signature, age, and TNM stage had good predictive ability in the training set (TCGA-LIHC) with the concordance index (C-index) of 0.73 and the AUCs for 1-, 3-, and 5-year OS of 0.82, 0.77, 0.74, respectively. In external validation set (GSE14520), the nomogram also performed well with the C-index of 0.71 and the AUCs for 1-, 3-, and 5-year OS of 0.74, 0.77, 0.74, respectively. In addition, an important eRG (AKR1C3) was validated using two HCC cell lines (Huh7 and MHCC-LM3) in vitro, and the results demonstrated the overexpression of AKR1C3 is related to cell proliferation, migration, and invasion in HCC. Altogether, our eRGs signature and nomogram can predict prognosis accurately and conveniently, facilitate individualized treatment, and improve prognosis for HCC patients.

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  • Studies that evaluate robustness to various data distributions through external validation inherently address the generalizability of the model.

    “studies that evaluate the robustness to various data distributions through external validation inherently address the generalizability of the model19–22.”

    From A scoping review of robustness concepts for machine learning in healthcare (2025) · cites this paper as [22]

    Methods §2.2 says the authors used an external HCC dataset to assess the model’s predictive capability and generalization, and Discussion §4 links its external-validation performance to robustness. The paper does not report testing robustness across various data distributions; Discussion §4 also says a multicenter study is needed to further establish generalizability. Page numbers were not provided in our copy. (LLM, conf=0.82)

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Research works printed but not linked: 60

  1. Sung, 2021, Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries, CA: Cancer J Clin, 71, 10.3322/caac.21660 · DOI · Paper page
  2. Singal, 2015, Hepatocellular Carcinoma From Epidemiology to Prevention: Translating Knowledge Into Practice, Clin Gastroenterol Hepatol, 13, 10.1016/j.cgh.2015.08.014 · DOI · Paper page
  3. Rastogi, 2018, Changing Role of Histopathology in the Diagnosis and Management of Hepatocellular Carcinoma, World J Gastroenterol, 24, 10.3748/wjg.v24.i35.4000 · DOI
  4. Llovet, 1999, Prognosis of Hepatocellular Carcinoma: The BCLC Staging Classification, Semin Liv Dis, 19, 10.1055/s-2007-1007122 · DOI · Paper page
  5. Yau, 2014, Development of Hong Kong Liver Cancer Staging System With Treatment Stratification for Patients With Hepatocellular Carcinoma, Gastroenterology, 146, 10.1053/j.gastro.2014.02.032 · DOI
  6. Li, 2016, Heterogeneity of Liver Cancer and Personalized Therapy, Cancer Lett, 379, 10.1016/j.canlet.2015.07.018 · DOI
  7. Yang, 2010, Epidemiology and Management of Hepatocellular Carcinoma, Infect Dis Clin North Am, 24, 899, 10.1016/j.idc.2010.07.004 · DOI
  8. Pan, 2003, Overexpression of Osteopontin Is Associated With Intrahepatic Metastasis, Early Recurrence, and Poorer Prognosis of Surgically Resected Hepatocellular Carcinoma, Cancer, 98, 10.1002/cncr.11487 · DOI
  9. Giordano, 2014, Met as a Therapeutic Target in HCC: Facts and Hopes, J Hepatol, 60, 10.1016/j.jhep.2013.09.009 · DOI · Paper page
  10. Toyoda, 2006, Prognostic Significance of Simultaneous Measurement of Three Tumor Markers in Patients With Hepatocellular Carcinoma, Clin Gastroenterol Hepatol, 4, 10.1016/S1542-3565(05)00855-4 · DOI
  11. He, 2020, SIX4 Promotes Hepatocellular Carcinoma Metastasis Through Upregulating YAP1 and C-MET, Oncogene, 39, 10.1038/s41388-020-01500-y · DOI
  12. Huang, 2020, ITGBL1 Promotes Cell Migration and Invasion Through Stimulating the TGF-β Signalling Pathway in Hepatocellular Carcinoma, Cell Prolif, 53, 10.1111/cpr.12836 · DOI
  13. Hong, 2020, SSR2 Overexpression Associates With Tumorigenesis and Metastasis of Hepatocellular Carcinoma Through Modulating EMT, J Cancer, 11, 10.7150/jca.44788 · DOI
  14. Wang, 2016, Long Noncoding RNA CPS1-IT1 Suppresses the Metastasis of Hepatocellular Carcinoma by Regulating HIF-1α Activity and Inhibiting Epithelial-Mesenchymal Transition, Oncotarget, 7, 10.18632/oncotarget.9635 · DOI
  15. Zhang, 2020, A Novel Molecular-Clinicopathologic Nomogram to Improve Prognosis Prediction of Hepatocellular Carcinoma, Aging, 12, 10.18632/aging.103350 · DOI
  16. Liu, 2020, Identification of a Four-Gene Metabolic Signature Predicting Overall Survival for Hepatocellular Carcinoma, J Cell Physiol, 235, 10.1002/jcp.29081 · DOI
  17. Zhao, 2018, Identification of a Five-Long Non-Coding RNA Signature to Improve the Prognosis Prediction for Patients With Hepatocellular Carcinoma, World J Gastroenterol, 24, 10.3748/wjg.v24.i30.3426 · DOI
  18. Kim, 2010, Widespread Transcription at Neuronal Activity-Regulated Enhancers, Nature, 465, 10.1038/nature09033 · DOI · Paper page
  19. Li, 2016, Enhancers as Non-Coding RNA Transcription Units: Recent Insights and Future Perspectives, Nat Rev Genet, 17, 10.1038/nrg.2016.4 · DOI · Paper page
  20. Li, 2015, Condensin I and II Complexes License Full Estrogen Receptor α-Dependent Enhancer Activation, Mol Cell, 59, 188, 10.1016/j.molcel.2015.06.002 · DOI
  21. Hsieh, 2014, Enhancer RNAs Participate in Androgen Receptor-Driven Looping That Selectively Enhances Gene Activation, Proc Natl Acad Sci USA, 111, 10.1073/pnas.1324151111 · DOI · Paper page
  22. Melo, 2013, eRNAs Are Required for P53-Dependent Enhancer Activity and Gene Transcription, Mol Cell, 49, 10.1016/j.molcel.2012.11.021 · DOI · Paper page
  23. Lee, 2020, Enhancer RNAs in Cancer: Regulation, Mechanisms and Therapeutic Potential, RNA Biol, 17, 10.1080/15476286.2020.1712895 · DOI
  24. Hnisz, 2015, Convergence of Developmental and Oncogenic Signaling Pathways at Transcriptional Super-Enhancers, Mol Cell, 58, 10.1016/j.molcel.2015.02.014 · DOI · Paper page
  25. Lovén, 2013, Selective Inhibition of Tumor Oncogenes by Disruption of Super-Enhancers, Cell, 153, 10.1016/j.cell.2013.03.036 · DOI · Paper page
  26. Andersson, 2014, An Atlas of Active Enhancers Across Human Cell Types and Tissues, Nature, 507, 10.1038/nature12787 · DOI · Paper page
  27. 2012, An Integrated Encyclopedia of DNA Elements in the Human Genome, Nature, 489, 57, 10.1038/nature11247 · DOI · Paper page
  28. Bernstein, 2010, The NIH Roadmap Epigenomics Mapping Consortium, Nat Biotechnol, 28, 10.1038/nbt1010-1045 · DOI · Paper page
  29. Zhang, 2019, Transcriptional Landscape and Clinical Utility of Enhancer RNAs for eRNA-Targeted Therapy in Cancer, Nat Commun, 10, 4562, 10.1038/s41467-019-12543-5 · DOI
  30. Grimes, 2008, The Nomogram Epidemic: Resurgence of a Medical Relic, Ann Internal Med, 149, 10.7326/0003-4819-149-4-200808190-00010 · DOI
  31. Balachandran, 2015, Nomograms in Oncology: More Than Meets the Eye, Lancet Oncol, 16, 10.1016/s1470-2045(14)71116-7 · DOI
  32. Hnzelmann, 2013, GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data, BMC Bioinf, 14, 7, 10.1186/1471-2105-14-7 · DOI · Paper page
  33. Liu, 2019, Immune Checkpoint Inhibitors in Hepatocellular Carcinoma: Opportunities and Challenges, Oncology, 24, S3, 10.1634/theoncologist.2019-IO-S1-s01 · DOI
  34. Newman, 2015, Robust Enumeration of Cell Subsets From Tissue Expression Profiles, Nat Methods, 12, 10.1038/nmeth.3337 · DOI · Paper page
  35. Li, 2017, TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells, Cancer Res, 77, 10.1158/0008-5472.CAN-17-0307 · DOI
  36. Aykul, 2016, Determination of Half-Maximal Inhibitory Concentration Using Biosensor-Based Protein Interaction Analysis, Analytic Biochem, 508, 97, 10.1016/j.ab.2016.06.025 · DOI
  37. Llovet, 2008, Sorafenib in Advanced Hepatocellular Carcinoma, N Engl J Med, 359, 10.1056/NEJMoa0708857 · DOI · Paper page
  38. Geeleher, 2014, Prrophetic: An R Package for Prediction of Clinical Chemotherapeutic Response From Tumor Gene Expression Levels, PloS One, 9, 10.1371/journal.pone.0107468 · DOI
  39. Yang, 2013, Genomics of Drug Sensitivity in Cancer (GDSC): A Resource for Therapeutic Biomarker Discovery in Cancer Cells, Nucleic Acids Res, 41, 10.1093/nar/gks1111 · DOI · Paper page
  40. Zhang, 2020, Development and Validation of a 14-Gene Signature for Prognosis Prediction in Hepatocellular Carcinoma, Genomics, 112, 10.1016/j.ygeno.2020.03.013 · DOI
  41. Li, 2017, Development and Validation of a Three-Gene Prognostic Signature for Patients With Hepatocellular Carcinoma, Sci Rep, 7, 5517, 10.1038/s41598-017-04811-5 · DOI
  42. Zhang, 2020, Construction of a Risk Score Prognosis Model Based on Hepatocellular Carcinoma Microenvironment, World J Gastroenterol, 26, 10.3748/wjg.v26.i2.134 · DOI
  43. Chelius, 2001, Expression, Purification and Characterization of the Structure and Disulfide Linkages of Insulin-Like Growth Factor Binding Protein-4, J Endocrinol, 168, 10.1677/joe.0.1680283 · DOI
  44. Lee, 2018, Loss of Tumor Suppressor IGFBP4 Drives Epigenetic Reprogramming in Hepatic Carcinogenesis, Nucleic Acids Res, 46, 10.1093/nar/gky589 · DOI
  45. Jin, 2013, SUOX Is a Promising Diagnostic and Prognostic Biomarker for Hepatocellular Carcinoma, J Hepatol, 59, 10.1016/j.jhep.2013.04.028 · DOI
  46. Zhu, 2020, Clinical Significance and Function of RDH16 as a Tumor-Suppressing Gene in Hepatocellular Carcinoma, Hepatol Res Off J Jap Soc Hepatol, 50, 10.1111/hepr.13432 · DOI
  47. Ouyang, 2020, A Robust Twelve-Gene Signature for Prognosis Prediction of Hepatocellular Carcinoma, Cancer Cell Int, 20, 207, 10.1186/s12935-020-01294-9 · DOI
  48. Cho, 2020, Activation of Tumor-Promoting Pathways Implicated in Hepatocellular Adenoma/Carcinoma, A Long-Term Complication of Glycogen Storage Disease Type Ia, Biochem Biophys Res Commun, 522, 1, 10.1016/j.bbrc.2019.11.061 · DOI
  49. Xu, 2020, Large-Scale Transcriptome Profiles Reveal Robust 20-Signatures Metabolic Prediction Models and Novel Role of G6PC in Clear Cell Renal Cell Carcinoma, J Cell Mol Med, 24, 10.1111/jcmm.15536 · DOI
  50. Luo, 2021, SSRP1 Is a Prognostic Biomarker Correlated With CD8(+) T Cell Infiltration in Hepatocellular Carcinoma (HCC), BioMed Res Int, 2021, 9409836, 10.1155/2021/9409836 · DOI
  51. Ding, 2016, SSRP1 Contributes to the Malignancy of Hepatocellular Carcinoma and Is Negatively Regulated by miR-497, Mol Ther J Am Soc Gene Ther, 24, 10.1038/mt.2016.9 · DOI
  52. Pandita, 2015, Single-Strand DNA-Binding Protein SSB1 Facilitates TERT Recruitment to Telomeres and Maintains Telomere G-Overhangs, Cancer Res, 75, 10.1158/0008-5472.Can-14-2289 · DOI
  53. Xiong, 2019, High Throughput circRNA Sequencing Analysis Reveals Novel Insights Into the Mechanism of Nitidine Chloride Against Hepatocellular Carcinoma, Cell Death Dis, 10, 658, 10.1038/s41419-019-1890-9 · DOI
  54. Zhu, 2021, In Silico Identification of Contradictory Role of ADAMTS5 in Hepatocellular Carcinoma, Technol Cancer Res Treat, 20, 1533033820986826, 10.1177/1533033820986826 · DOI
  55. Hu, 2020, Development and Verification of the Hypoxia-Related and Immune-Associated Prognosis Signature for Hepatocellular Carcinoma, J Hepatocell Carcinom, 7, 10.2147/jhc.S272109 · DOI
  56. Ding, 2021, Ras Related GTP Binding D Promotes Aerobic Glycolysis of Hepatocellular Carcinoma, Ann Hepatol, 23, 10.1016/j.aohep.2021.100307 · DOI
  57. Jin, 2007, Aldo-Keto Reductases and Bioactivation/Detoxication, Annu Rev Pharmacol Toxicol, 47, 10.1146/annurev.pharmtox.47.120505.105337 · DOI
  58. Liu, 2019, AKR1C3 Promotes AR-V7 Protein Stabilization and Confers Resistance to AR-Targeted Therapies in Advanced Prostate Cancer, Mol Cancer Ther, 18, 10.1158/1535-7163.Mct-18-1322 · DOI
  59. Huebbers, 2019, Upregulation of AKR1C1 and AKR1C3 Expression in OPSCC With Integrated HPV16 and HPV-Negative Tumors Is an Indicator of Poor Prognosis, Int J Cancer, 144, 10.1002/ijc.31954 · DOI
  60. Liu, 2021, Development and Validation of a Robust Immune-Related Risk Signature for Hepatocellular Carcinoma, Medicine, 100, 10.1097/md.0000000000024683 · DOI

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Metadata

Authors
Zhang, W., Chen, K., Tian, W., Zhang, Q., Sun, L., Wang, Y., Liu, M., & Zhang, Q.
Affiliations
  • Harbin Medical University (CN)
  • University of Science and Technology of China (CN)
Corresponding author
  • Liu, Meina
  • Zhang, Qiuju
ORCID
Venue
Frontiers in Oncology
Publisher
Frontiers Media SA
Published
2022-05-12
Volume
12
Pages
849242-849242
ISSN
2234-943X (ISSN-L)
DOI
10.3389/fonc.2022.849242
PMC
PMC9133429
OpenAlex
W4280508191
Cited by
2
References
1
Citations by year
2025: 1 · 2023: 1
FWCI
0.21
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Oncology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Science and Technology Major Project (2016ZX08011005)
  • National Natural Science Foundation of China (82073666, 82003556)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer-related molecular mechanisms research
  • Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Ferroptosis and cancer prognosis
Keywords
hepatocellular carcinoma, enhancer RNA, prognostic signature, prognostic nomogram, risk stratification, tumor immune microenvironment, immune checkpoint, HLA genes, DNA repair, AKR1C3, cell proliferation, migration and invasion, TCGA-LIHC, ERIC

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