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image of Fibroblast Heterogeneity in Hepatocellular Carcinoma and Identification of Prognostic Markers Based on Single-cell Transcriptome Analysis

Abstract

Background

HCC is a malignant tumor with high morbidity and mortality. Fibroblasts play a key role in the tumor microenvironment (TME). However, the transcriptional regulatory mechanisms of fibroblasts remained unclear in HCC.

Aim

The aim of this study was to explore the complex role of fibroblasts in hepatocellular carcinoma (HCC) and to reveal their transcriptional regulatory mechanisms.

Objective

The goal of this study was to discover potential prognostic markers for HCC by analyzing the genetic variations and differentiation process of fibroblasts.

Methods

Single-cell transcriptome data from the non-tumor liver site and primary tumor site of HCC were acquired from GSE149614, processed, and clustered using the Seurat pipeline. The inferCNV algorithm was applied to infer copy number variations (CNVs) in fibroblasts. Subsequently, the mechanism underlying the interaction between fibroblasts and other cells in the TME of HCC was analyzed using CellChat software. The trajectory of cellular differentiation of fibroblasts from normal state to malignant state was examined using Monocle 2. SCENIC analysis was performed to identify key transcription factors (TFs) in fibroblasts and assess their correlation with HCC prognosis. Finally, qRT-PCR and Transwell assays were carried out to analyze the mRNA expression and cell metastasis.

Results

We identified a total of nine different cell types (B cells, cycling cells, endothelial cells, epithelial cells, fibroblasts, hepatocytes, macrophages, plasma cells, and T cells) based on the single-cell transcriptomic data of HCC. Among them, fibroblasts were highly enriched at the primary tumor site, and their number increased with advanced stages. In addition, significant deletions were detected on chromosome 6p of fibroblasts, and genes in this region were remarkably enriched in pathways associated with antigen processing and presentation. Intercellular communication showed that epithelial cells regulated fibroblasts the most. The differentiation of fibroblasts was mainly accompanied by a transition from normal to malignant state. Importantly, CEBPD and FOSB, the TFs most associated with the putative timing of fibroblasts, were under-expressed in human hepatocytes and showed a significant correlation with HCC prognosis. Overexpressed CEBPD inhibited HCC cell migration and invasion.

Conclusion

In conclusion, our study revealed that fibroblast recruitment and differentiation, as well as copy number loss at chromosome 6p, were associated with a higher degree of malignancy and immune dysfunction in HCC. The current discoveries provided new insights into the clinical treatment and diagnosis of HCC.

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2024-11-08
2025-01-18
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References

  1. Siegel R.L. Giaquinto A.N. Jemal A. Cancer statistics, 2024. CA Cancer J. Clin. 2024 74 1 12 49 10.3322/caac.21820
    [Google Scholar]
  2. Papadakos S.P. Arvanitakis K. Stergiou I.E. Koutsompina M.L. Germanidis G. Theocharis S. γδ T cells: A game changer in the future of hepatocellular carcinoma immunotherapy. Int. J. Mol. Sci. 2024 25 3 1381 10.3390/ijms25031381
    [Google Scholar]
  3. Kinsey E. Lee H.M. Management of hepatocellular carcinoma in 2024: The multidisciplinary paradigm in an evolving treatment landscape. Cancers 2024 16 3 666 10.3390/cancers16030666
    [Google Scholar]
  4. Cannella R. Zins M. Brancatelli G. ESR essentials: Diagnosis of hepatocellular carcinoma—practice recommendations by ESGAR. Eur. Radiol. 2024 34 4 2127 2139 10.1007/s00330‑024‑10606‑w
    [Google Scholar]
  5. Su F. Su W. Wang H. Wang Y. Ye L. Zhu P. Gu J. NMR-based metabolomic techniques identify the anticancer effects of three polyphyllins in HepG2 cells. Curr. Pharm. Anal. 2022 18 4 415 426 10.2174/1573412917666210823090145
    [Google Scholar]
  6. Yan D. Li C. Zhou Y. Yan X. Zhi W. Qian H. Han Y. Exploration of combinational therapeutic strategies for HCC based on TCGA HCC database. Oncologie 2022 24 1 101 111 10.32604/oncologie.2022.020357
    [Google Scholar]
  7. Wang Y. Wang P. Zhang Z. Zhou J. Fan J. Sun Y. Dissecting the tumor ecosystem of liver cancers in the single-cell era. Hepatol. Commun. 2023 7 9 e0248 10.1097/HC9.0000000000000248
    [Google Scholar]
  8. Chew V. Chuang C.H. Hsu C. Translational research on drug development and biomarker discovery for hepatocellular carcinoma. J. Biomed. Sci. 2024 31 1 22 10.1186/s12929‑024‑01011‑y
    [Google Scholar]
  9. Chhabra Y. Weeraratna A.T. Fibroblasts in cancer: Unity in heterogeneity. Cell 2023 186 8 1580 1609 10.1016/j.cell.2023.03.016
    [Google Scholar]
  10. Yamamoto Y. Kasashima H. Fukui Y. Tsujio G. Yashiro M. Maeda K. The heterogeneity of cancer-associated fibroblast subpopulations: Their origins, biomarkers, and roles in the tumor microenvironment. Cancer Sci. 2023 114 1 16 24 10.1111/cas.15609
    [Google Scholar]
  11. Kalluri R. The biology and function of fibroblasts in cancer. Nat. Rev. Cancer 2016 16 9 582 598 10.1038/nrc.2016.73
    [Google Scholar]
  12. Sahai E. Astsaturov I. Cukierman E. DeNardo D.G. Egeblad M. Evans R.M. Fearon D. Greten F.R. Hingorani S.R. Hunter T. Hynes R.O. Jain R.K. Janowitz T. Jorgensen C. Kimmelman A.C. Kolonin M.G. Maki R.G. Powers R.S. Puré E. Ramirez D.C. Shouval S.R. Sherman M.H. Stewart S. Tlsty T.D. Tuveson D.A. Watt F.M. Weaver V. Weeraratna A.T. Werb Z. A framework for advancing our understanding of cancer-associated fibroblasts. Nat. Rev. Cancer 2020 20 3 174 186 10.1038/s41568‑019‑0238‑1
    [Google Scholar]
  13. Akkız H. Emerging role of cancer-associated fibroblasts in progression and treatment of hepatocellular carcinoma. Int. J. Mol. Sci. 2023 24 4 3941 10.3390/ijms24043941
    [Google Scholar]
  14. Zulibiya A. Wen J. Yu H. Chen X. Xu L. Ma X. Zhang B. Single-cell RNA sequencing reveals potential for endothelial-to-mesenchymal transition in tetralogy of fallot. Congenit. Heart Dis. 2023 18 6 611 625 10.32604/chd.2023.047689
    [Google Scholar]
  15. 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 5 411 420 10.1038/nbt.4096
    [Google Scholar]
  16. Korsunsky I. Millard N. Fan J. Slowikowski K. Zhang F. Wei K. Baglaenko Y. Brenner M. Loh P. Raychaudhuri S. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 2019 16 12 1289 1296 10.1038/s41592‑019‑0619‑0
    [Google Scholar]
  17. Xiong Z. Yang Q. Li X. Effect of intra- and inter-tumoral heterogeneity on molecular characteristics of primary IDH-wild type glioblastoma revealed by single-cell analysis. CNS Neurosci. Ther. 2020 26 9 981 989 10.1111/cns.13396
    [Google Scholar]
  18. Gao R. Bai S. Henderson Y.C. Lin Y. Schalck A. Yan Y. Kumar T. Hu M. Sei E. Davis A. Wang F. Shaitelman S.F. Wang J.R. Chen K. Moulder S. Lai S.Y. Navin N.E. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes. Nat. Biotechnol. 2021 39 5 599 608 10.1038/s41587‑020‑00795‑2
    [Google Scholar]
  19. Jin S. Juarez G.C.F. Zhang L. Chang I. Ramos R. Kuan C.H. Myung P. Plikus M.V. Nie Q. Inference and analysis of cell-cell communication using cellchat. Nat. Commun. 2021 12 1 1088 10.1038/s41467‑021‑21246‑9
    [Google Scholar]
  20. Qiu X. Mao Q. Tang Y. Wang L. Chawla R. Pliner H.A. Trapnell C. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 2017 14 10 979 982 10.1038/nmeth.4402
    [Google Scholar]
  21. Aibar S. Blas G.C.B. Moerman T. Thu H.V.A. Imrichova H. Hulselmans G. Rambow F. Marine J-C. Geurts P. Aerts J. Oord V.D.J. Atak Z.K. Wouters J. Aerts S. SCENIC: Single-cell regulatory network inference and clustering. Nat. Methods 2017 14 11 1083 1086 10.1038/nmeth.4463
    [Google Scholar]
  22. Kassambara A. Drawing survival curves using 'ggplot2' [R package survminer version 0.2.0]. Available from: https://rpkgs.datanovia.com/survminer/ 2017
  23. Therneau TM Grambsch PM Modeling survival data: Extending the cox model Springer Science & Business Media 2013 350
    [Google Scholar]
  24. Spandidos A. Wang X. Wang H. Seed B. PrimerBank: A resource of human and mouse PCR primer pairs for gene expression detection and quantification. Nucleic Acids Res. 2010 38 Database issue Suppl. 1 D792 D799 10.1093/nar/gkp1005
    [Google Scholar]
  25. Affo S. Yu L.X. Schwabe R.F. The role of cancer-associated fibroblasts and fibrosis in liver cancer. Annu. Rev. Pathol. 2017 12 1 153 186 10.1146/annurev‑pathol‑052016‑100322
    [Google Scholar]
  26. Affo S. Filliol A. Gores G.J. Schwabe R.F. Fibroblasts in liver cancer: Functions and therapeutic translation. Lancet Gastroenterol. Hepatol. 2023 8 8 748 759 10.1016/S2468‑1253(23)00111‑5
    [Google Scholar]
  27. Chochi Y. Kawauchi S. Nakao M. Furuya T. Hashimoto K. Oga A. Oka M. Sasaki K. A copy number gain of the 6p arm is linked with advanced hepatocellular carcinoma: An array-based comparative genomic hybridization study. J. Pathol. 2009 217 5 677 684 10.1002/path.2491
    [Google Scholar]
  28. Borchers C.H. Kast J. Foster L.J. Siu K.W.M. Overall C.M. Binkowski T.A. Hildebrand W.H. Scherer A. Mansoor M. Keown P.A. The human proteome organization chromosome 6 consortium: Integrating chromosome-centric and biology/disease driven strategies. J. Proteomics 2014 100 60 67 10.1016/j.jprot.2013.08.001
    [Google Scholar]
  29. Peng H. Zhu E. Zhang Y. Advances of cancer-associated fibroblasts in liver cancer. Biomark. Res. 2022 10 1 59 10.1186/s40364‑022‑00406‑z
    [Google Scholar]
  30. Zhang R. Qi F. Zhao F. Li G. Shao S. Zhang X. Yuan L. Feng Y. Cancer-associated fibroblasts enhance tumor-associated macrophages enrichment and suppress NK cells function in colorectal cancer. Cell Death Dis. 2019 10 4 273 10.1038/s41419‑019‑1435‑2
    [Google Scholar]
  31. Nagarsheth N. Wicha M.S. Zou W. Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy. Nat. Rev. Immunol. 2017 17 9 559 572 10.1038/nri.2017.49
    [Google Scholar]
  32. Karnoub A.E. Dash A.B. Vo A.P. Sullivan A. Brooks M.W. Bell G.W. Richardson A.L. Polyak K. Tubo R. Weinberg R.A. Mesenchymal stem cells within tumour stroma promote breast cancer metastasis. Nature 2007 449 7162 557 563 10.1038/nature06188
    [Google Scholar]
  33. Tan W. Zhang W. Strasner A. Grivennikov S. Cheng J.Q. Hoffman R.M. Karin M. Tumour-infiltrating regulatory T cells stimulate mammary cancer metastasis through RANKL–RANK signalling. Nature 2011 470 7335 548 553 10.1038/nature09707
    [Google Scholar]
  34. Zhao X. Ding L. Lu Z. Huang X. Jing Y. Yang Y. Chen S. Hu Q. Ni Y. Diminished CD68+ cancer-associated fibroblast subset induces regulatory T-Cell (Treg) infiltration and predicts poor prognosis of oral squamous cell carcinoma patients. Am. J. Pathol. 2020 190 4 886 899 10.1016/j.ajpath.2019.12.007
    [Google Scholar]
  35. Yoo S.A. Leng L. Kim B.J. Du X. Tilstam P.V. Kim K.H. Kong J-S. Yoon H-J. Liu A. Wang T. Song Y. Sauler M. Bernhagen J. Ritchlin C.T. Lee P. Cho C-S. Kim W-U. Bucala R. MIF allele-dependent regulation of the MIF coreceptor CD44 and role in rheumatoid arthritis. Proc. Natl. Acad. Sci. 2016 113 49 E7917 E26 10.1073/pnas.1612717113
    [Google Scholar]
  36. Klasen C. Ohl K. Sternkopf M. Shachar I. Schmitz C. Heussen N. Hobeika E. Zerdoun L.E. Tenbrock K. Reth M. Bernhagen J. Bounkari E.O. MIF promotes B cell chemotaxis through the receptors CXCR4 and CD74 and ZAP-70 signaling. J. Immunol. 2014 192 11 5273 5284 10.4049/jimmunol.1302209
    [Google Scholar]
  37. Liu Y. Zhang L. Ju X. Wang S. Qie J. Single-cell transcriptomic analysis reveals macrophage–tumor crosstalk in hepatocellular carcinoma. Front. Immunol. 2022 13 955390 10.3389/fimmu.2022.955390
    [Google Scholar]
  38. Zhou M. Lu F. Jiang L. Chen C. Chen S. Geng L. Sun R. Li Q. Duan S. Zhang B. Mao H. Xing C. Yuan Y. Decoding the intercellular cross-talking between immune cells and renal innate cells in diabetic kidney disease by bioinformatics. J. Inflamm. Res. 2023 16 3049 3062 10.2147/JIR.S409017
    [Google Scholar]
  39. Zhang Y. Zuo C. Liu L. Hu Y. Yang B. Qiu S. Li Y. Cao D. Ju Z. Ge J. Wang Q. Wang T. Bai L. Yang Y. Li G. Shao Z. Gao Y. Li Y. Bian R. Miao H. Li L. Li X. Jiang C. Yan S. Wang Z. Wang Z. Cui X. Huang W. Xiang D. Wang C. Li Q. Wu X. Gong W. Liu Y. Shao R. Liu F. Li M. Chen L. Liu Y. Single-cell RNA-sequencing atlas reveals an MDK-dependent immunosuppressive environment in ErbB pathway-mutated gallbladder cancer. J. Hepatol. 2021 75 5 1128 1141 10.1016/j.jhep.2021.06.023
    [Google Scholar]
  40. Gao Y. Xu Q. Li X. Guo Y. Zhang B. Jin Y. zhu C. Shen Y. Yang P. Shi Y. Jin R. Liu D. Ouyang Y. Liu X. Wang W. Chen D. Yang T. Heterogeneity induced GZMA-F2R communication inefficient impairs antitumor immunotherapy of PD-1 mAb through JAK2/STAT1 signal suppression in hepatocellular carcinoma. Cell Death Dis. 2022 13 3 213 10.1038/s41419‑022‑04654‑7
    [Google Scholar]
  41. Yu X. Xie L. Ge J. Li H. Zhong S. Liu X. Integrating single-cell RNA-seq and spatial transcriptomics reveals MDK-NCL dependent immunosuppressive environment in endometrial carcinoma. Front. Immunol. 2023 14 1145300 10.3389/fimmu.2023.1145300
    [Google Scholar]
  42. Huang J. Tsang W.Y. Li Z.H. Guan X.Y. The origin, differentiation, and functions of cancer-associated fibroblasts in gastrointestinal cancer. Cell. Mol. Gastroenterol. Hepatol. 2023 16 4 503 511 10.1016/j.jcmgh.2023.07.001
    [Google Scholar]
  43. Liu P. Cao W. Ma B. Li M. Chen K. Sideras K. Duitman J-W. Sprengers D. Tran K.T.C. Ijzermans J.N.M. Biermann K. Verheij J. Spek C.A. Kwekkeboom J. Pan Q. Peppelenbosch M.P. Action and clinical significance of CCAAT/enhancer-binding protein delta in hepatocellular carcinoma. Carcinogenesis 2019 40 1 155 163 10.1093/carcin/bgy130
    [Google Scholar]
  44. Gao S. Gang J. Yu M. Xin G. Tan H. Computational analysis for identification of early diagnostic biomarkers and prognostic biomarkers of liver cancer based on GEO and TCGA databases and studies on pathways and biological functions affecting the survival time of liver cancer. BMC Cancer 2021 21 1 791 10.1186/s12885‑021‑08520‑1
    [Google Scholar]
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