Integrating Single-cell and Bulk RNA Sequencing Reveals Stemness Phenotype Associated with Clinical Outcomes and Potential Immune Evasion Mechanisms in Hepatocellular Carcinoma


Cite item

Full Text

Abstract

Aims:Bulk and single-cell RNA sequencing data were analyzed to explore the association of stemness phenotype with dysfunctional anti-tumor immunity and its impact on clinical outcomes of primary and relapse HCC.

Background:The stemness phenotype is gradually acquired during cancer progression; however, it remains unclear the effect of stemness phenotype on recurrence and clinical outcomes in hepatocellular carcinoma (HCC).

Methods:The stemness index (mRNAsi) calculated by a one-class logistic regression algorithm in multiple HCC cohorts was defined as the stemness phenotype of the patient. Using single-cell profiling in primary or early-relapse HCC, cell stemness phenotypes were evaluated by developmental potential. Differential analysis of stemness phenotype, gene expression and interactions between primary and recurrent samples revealed the underlying immune evasion mechanisms.

Results:A strong correlation was discovered between mRNAsi and clinical outcomes in patient with HCC. The high and low mRNAsi groups had distinct tumor immune microenvironments. Cellular stemness phenotype varied by cell type. Moreover, compared with primary tumors, early-relapse tumors had increased stemness of dendritic cells and tumor cells and reduced stemness of T cells and B cells. Moreover, in relapse tumors, CD8+ T cells displayed a low stemness state, with a high exhausted state, unlike the high stemness state observed in primary HCC.

Conclusions:The comprehensive characterization of the HCC stemness phenotype provides insights into the clinical outcomes and immune escape mechanisms associated with recurrence.

About the authors

Xiaojing Zhu

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Xing Wang

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Hao Wang

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Yanqi Xiao

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Minghui Jiang

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Minwei Wang

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Nan Zhang

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Aimin Xie

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Hongyan Yuan

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Zixin Zhang

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Jiaxing Zhang

College of Bioinformatics Science and Technology, Harbin Medical University

Email: info@benthamscience.net

Yan Xu

College of Bioinformatics Science and Technology, Harbin Medical University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Llovet JM, Kelley RK, Villanueva A, et al. Hepatocellular carcinoma. Nat Rev Dis Primers 2021; 7(1): 6. doi: 10.1038/s41572-020-00240-3 PMID: 33479224
  2. Sangro B, Sarobe P, Hervás-Stubbs S, Melero I. Advances in immunotherapy for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2021; 18(8): 525-43. doi: 10.1038/s41575-021-00438-0 PMID: 33850328
  3. Liu YC, Yeh CT, Lin KH. Cancer stem cell functions in hepatocellular carcinoma and comprehensive therapeutic strategies. Cells 2020; 9(6): 1331. doi: 10.3390/cells9061331 PMID: 32466488
  4. Zhou J, Sun H, Wang Z, et al. Guidelines for the diagnosis and treatment of hepatocellular carcinoma(2019 Edition). Liver Cancer 2020; 9(6): 682-720. doi: 10.1159/000509424 PMID: 33442540
  5. Zheng J, Kuk D, Gönen M, et al. Actual 10-year survivors after resection of hepatocellular carcinoma. Ann Surg Oncol 2017; 24(5): 1358-66. doi: 10.1245/s10434-016-5713-2 PMID: 27921192
  6. Liang N, Yang T, Huang Q, et al. Mechanism of cancer stemness maintenance in human liver cancer. Cell Death Dis 2022; 13(4): 394. doi: 10.1038/s41419-022-04848-z PMID: 35449193
  7. Cooper J, Giancotti FG. Integrin signaling in cancer: Mechanotransduction, stemness, epithelial plasticity, and therapeutic resistance. Cancer Cell 2019; 35(3): 347-67. doi: 10.1016/j.ccell.2019.01.007 PMID: 30889378
  8. Gehart H, Clevers H. Tales from the crypt: New insights into intestinal stem cells. Nat Rev Gastroenterol Hepatol 2019; 16(1): 19-34. doi: 10.1038/s41575-018-0081-y PMID: 30429586
  9. Naik S, Larsen SB, Cowley CJ, Fuchs E. Two to tango: Dialog between immunity and stem cells in health and disease. Cell 2018; 175(4): 908-20. doi: 10.1016/j.cell.2018.08.071 PMID: 30388451
  10. Tsui YM, Chan LK, Ng IOL. Cancer stemness in hepatocellular carcinoma: Mechanisms and translational potential. Br J Cancer 2020; 122(10): 1428-40. doi: 10.1038/s41416-020-0823-9 PMID: 32231294
  11. Li J, Zhang C, Yuan X, Ren Z, Yu Z. Correlations between stemness indices for hepatocellular carcinoma, clinical characteristics, and prognosis. Am J Transl Res 2020; 12(9): 5496-510. PMID: 33042433
  12. Xu Q, Xu H, Chen S, Huang W. Immunological value of prognostic signature based on cancer stem cell characteristics in hepatocellular carcinoma. Front Cell Dev Biol 2021; 9: 710207. doi: 10.3389/fcell.2021.710207 PMID: 34409040
  13. Malta TM, Sokolov A, Gentles AJ, et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 2018; 173(2): 338-354.e15. doi: 10.1016/j.cell.2018.03.034 PMID: 29625051
  14. Chen D, Liu J, Zang L, et al. Integrated machine learning and bioinformatic analyses constructed a novel stemness-related classifier to predict prognosis and immunotherapy responses for hepatocellular carcinoma patients. Int J Biol Sci 2022; 18(1): 360-73. doi: 10.7150/ijbs.66913 PMID: 34975338
  15. Zhang Y, Zhang R, Zeng L, et al. Identification and validation of a potential stemness-associated biomarker in hepatocellular carcinoma. Stem Cells Int 2022; 2022: 1-18. doi: 10.1155/2022/1534593 PMID: 35859724
  16. Yung WK, Shapiro JR, Shapiro WR. Heterogeneous chemosensitivities of subpopulations of human glioma cells in culture. Cancer Res 1982; 42(3): 992-8. PMID: 7199383
  17. Visvader JE, Clevers H. Tissue-specific designs of stem cell hierarchies. Nat Cell Biol 2016; 18(4): 349-55. doi: 10.1038/ncb3332 PMID: 26999737
  18. Reitman ZJ, Paolella BR, Bergthold G, et al. Mitogenic and progenitor gene programmes in single pilocytic astrocytoma cells. Nat Commun 2019; 10(1): 3731. doi: 10.1038/s41467-019-11493-2 PMID: 31427603
  19. Tirosh I, Venteicher AS, Hebert C, et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 2016; 539(7628): 309-13. doi: 10.1038/nature20123 PMID: 27806376
  20. Patel AP, Tirosh I, Trombetta JJ, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 2014; 344(6190): 1396-401. doi: 10.1126/science.1254257 PMID: 24925914
  21. Puram SV, Tirosh I, Parikh AS, et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 2017; 171(7): 1611-1624.e24. doi: 10.1016/j.cell.2017.10.044 PMID: 29198524
  22. Moncada R, Barkley D, Wagner F, et al. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat Biotechnol 2020; 38(3): 333-42. doi: 10.1038/s41587-019-0392-8 PMID: 31932730
  23. Baron M, Tagore M, Hunter MV, et al. The stress-like cancer cell state is a consistent component of tumorigenesis. Cell Syst 2020; 11(5): 536-546.e7. doi: 10.1016/j.cels.2020.08.018 PMID: 32910905
  24. Karmaus PWF, Chen X, Lim SA, et al. Metabolic heterogeneity underlies reciprocal fates of TH17 cell stemness and plasticity. Nature 2019; 565(7737): 101-5. doi: 10.1038/s41586-018-0806-7 PMID: 30568299
  25. Ho DWH, Tsui YM, Sze KMF, et al. Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and stemness-related subpopulations in liver cancer. Cancer Lett 2019; 459: 176-85. doi: 10.1016/j.canlet.2019.06.002 PMID: 31195060
  26. Prasetyanti PR, Medema JP. Intra-tumor heterogeneity from a cancer stem cell perspective. Mol Cancer 2017; 16(1): 41. doi: 10.1186/s12943-017-0600-4 PMID: 28209166
  27. Rosenberg SA, Restifo NP. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 2015; 348(6230): 62-8. doi: 10.1126/science.aaa4967 PMID: 25838374
  28. Li W, Lu L, Lu J, et al. cGAS-STING–mediated DNA sensing maintains CD8 + T cell stemness and promotes antitumor T cell therapy. Sci Transl Med 2020; 12(549): eaay9013. doi: 10.1126/scitranslmed.aay9013 PMID: 32581136
  29. Crespo J, Sun H, Welling TH, Tian Z, Zou W. T cell anergy, exhaustion, senescence, and stemness in the tumor microenvironment. Curr Opin Immunol 2013; 25(2): 214-21. doi: 10.1016/j.coi.2012.12.003 PMID: 23298609
  30. Sun Y, Wu L, Zhong Y, et al. Single-cell landscape of the ecosystem in early-relapse hepatocellular carcinoma. Cell 2021; 184(2): 404-421.e16. doi: 10.1016/j.cell.2020.11.041 PMID: 33357445
  31. Goldman MJ, Craft B, Hastie M, et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 2020; 38(6): 675-8. doi: 10.1038/s41587-020-0546-8 PMID: 32444850
  32. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 2018; 36(5): 411-20. doi: 10.1038/nbt.4096 PMID: 29608179
  33. Zhang M, Hu S, Min M, et al. Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing. Gut 2021; 70(3): 464-75. doi: 10.1136/gutjnl-2019-320368 PMID: 32532891
  34. Gulati GS, Sikandar SS, Wesche DJ, et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 2020; 367(6476): 405-11. doi: 10.1126/science.aax0249 PMID: 31974247
  35. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15(12): 550. doi: 10.1186/s13059-014-0550-8 PMID: 25516281
  36. Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 2013; 14(1): 7. doi: 10.1186/1471-2105-14-7 PMID: 23323831
  37. Yu G, Wang LG, Han Y, He QY. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7. doi: 10.1089/omi.2011.0118 PMID: 22455463
  38. Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013; 4(1): 2612. doi: 10.1038/ncomms3612 PMID: 24113773
  39. Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015; 12(5): 453-7. doi: 10.1038/nmeth.3337 PMID: 25822800
  40. Charoentong P, Finotello F, Angelova M, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep 2017; 18(1): 248-62. doi: 10.1016/j.celrep.2016.12.019 PMID: 28052254
  41. Qiu X, Mao Q, Tang Y, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods 2017; 14(10): 979-82. doi: 10.1038/nmeth.4402 PMID: 28825705
  42. Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 2021; 12(1): 1088. doi: 10.1038/s41467-021-21246-9 PMID: 33597522
  43. Friedmann-Morvinski D, Verma IM. Dedifferentiation and reprogramming: Origins of cancer stem cells. EMBO Rep 2014; 15(3): 244-53. doi: 10.1002/embr.201338254 PMID: 24531722
  44. Tian N, Shangguan W, Zhou Z. yao Y, Fan C, Cai L. Lin28b is involved in curcumin-reversed paclitaxel chemoresistance and associated with poor prognosis in hepatocellular carcinoma. J Cancer 2019; 10(24): 6074-87. doi: 10.7150/jca.33421 PMID: 31762817
  45. Zhang J, Hu K, Yang Y, et al. LIN28B-AS1-IGF2BP1 binding promotes hepatocellular carcinoma cell progression. Cell Death Dis 2020; 11(9): 741. doi: 10.1038/s41419-020-02967-z PMID: 32917856
  46. Thiery JP, Acloque H, Huang RYJ, Nieto MA. Epithelial-mesenchymal transitions in development and disease. Cell 2009; 139(5): 871-90. doi: 10.1016/j.cell.2009.11.007 PMID: 19945376
  47. Kumar MP, Du J, Lagoudas G, et al. Analysis of single-cell RNA-Seq identifies cell-cell communication associated with tumor characteristics. Cell Rep 2018; 25(6): 1458-1468.e4. doi: 10.1016/j.celrep.2018.10.047 PMID: 30404002
  48. Gattinoni L, Klebanoff CA, Restifo NP. Paths to stemness: Building the ultimate antitumour T cell. Nat Rev Cancer 2012; 12(10): 671-84. doi: 10.1038/nrc3322 PMID: 22996603
  49. Cha JH, Chan LC, Li CW, Hsu JL, Hung MC. Mechanisms controlling PD-L1 expression in cancer. Mol Cell 2019; 76(3): 359-70. doi: 10.1016/j.molcel.2019.09.030 PMID: 31668929
  50. Boucher K, Parquet N, Widen R, et al. Stemness of B-cell progenitors in multiple myeloma bone marrow. Clin Cancer Res 2012; 18(22): 6155-68. doi: 10.1158/1078-0432.CCR-12-0531 PMID: 22988056
  51. Xu MM, Pu Y, Han D, et al. Dendritic cells but not macrophages sense tumor mitochondrial DNA for cross-priming through signal regulatory protein α signaling. Immunity 2017; 47(2): 363-373.e5. doi: 10.1016/j.immuni.2017.07.016 PMID: 28801234
  52. Annalise MM. Leukocyte Ig-like receptor complex (LRC) in mice and men. Trends Immunol 2002; 23(2): 81.
  53. Rahbari NN, Mehrabi A, Mollberg NM, et al. Hepatocellular carcinoma. Ann Surg 2011; 253(3): 453-69. doi: 10.1097/SLA.0b013e31820d944f PMID: 21263310
  54. Bosch FX, Ribes J, Díaz M, Cléries R. Primary liver cancer: Worldwide incidence and trends. Gastroenterology 2004; 127(5): S5-S16. doi: 10.1053/j.gastro.2004.09.011 PMID: 15508102
  55. Lee TKW, Guan XY, Ma S. Cancer stem cells in hepatocellular carcinoma - from origin to clinical implications. Nat Rev Gastroenterol Hepatol 2022; 19(1): 26-44. doi: 10.1038/s41575-021-00508-3 PMID: 34504325
  56. Vanegas NDP, Ruiz-Aparicio PF, Uribe GI, Linares-Ballesteros A, Vernot JP. Leukemia-induced cellular senescence and stemness alterations in mesenchymal stem cells are reversible upon withdrawal of B-cell acute lymphoblastic leukemia cells. Int J Mol Sci 2021; 22(15): 8166. doi: 10.3390/ijms22158166 PMID: 34360930
  57. Shen S, Wang R, Qiu H, et al. Development of an autophagy-based and stemness-correlated prognostic model for hepatocellular carcinoma using bulk and single-cell RNA-sequencing. Front Cell Dev Biol 2021; 9: 743910. doi: 10.3389/fcell.2021.743910 PMID: 34820373
  58. Chaudhary K, Poirion OB, Lu L, Garmire LX. Deep learning–based multi-omics integration robustly predicts survival in liver cancer. Clin Cancer Res 2018; 24(6): 1248-59. doi: 10.1158/1078-0432.CCR-17-0853 PMID: 28982688
  59. Magalhães-Novais S, Bermejo-Millo JC, Loureiro R, et al. Cell quality control mechanisms maintain stemness and differentiation potential of P19 embryonic carcinoma cells. Autophagy 2020; 16(2): 313-33. doi: 10.1080/15548627.2019.1607694 PMID: 30990357
  60. Tiberio L, Del Prete A, Schioppa T, Sozio F, Bosisio D, Sozzani S. Chemokine and chemotactic signals in dendritic cell migration. Cell Mol Immunol 2018; 15(4): 346-52. doi: 10.1038/s41423-018-0005-3 PMID: 29563613
  61. Dudek AM, Martin S, Garg AD, Agostinis P. Immature, semi-mature, and fully mature dendritic cells: Toward a DC-cancer cells interface that augments anticancer immunity. Front Immunol 2013; 4: 438. doi: 10.3389/fimmu.2013.00438 PMID: 24376443
  62. Gonzalez NM, Zou D, Gu A, Chen W. Schrödinger’s T cells: Molecular insights into stemness and exhaustion. Front Immunol 2021; 12: 725618. doi: 10.3389/fimmu.2021.725618 PMID: 34512656

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Copyright (c) 2024 Bentham Science Publishers