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2000
Volume 32, Issue 13
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

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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2025-06-16
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References

  1. SiegelR.L. GiaquintoA.N. JemalA. Cancer statistics, 2024.CA Cancer J. Clin.2024741124910.3322/caac.21820
    [Google Scholar]
  2. PapadakosS.P. ArvanitakisK. StergiouI.E. KoutsompinaM.L. GermanidisG. TheocharisS. γδ T cells: A game changer in the future of hepatocellular carcinoma immunotherapy.Int. J. Mol. Sci.2024253138110.3390/ijms25031381
    [Google Scholar]
  3. KinseyE. LeeH.M. Management of hepatocellular carcinoma in 2024: The multidisciplinary paradigm in an evolving treatment landscape.Cancers202416366610.3390/cancers16030666
    [Google Scholar]
  4. CannellaR. ZinsM. BrancatelliG. ESR essentials: Diagnosis of hepatocellular carcinoma—practice recommendations by ESGAR.Eur. Radiol.20243442127213910.1007/s00330‑024‑10606‑w
    [Google Scholar]
  5. SuF. SuW. WangH. WangY. YeL. ZhuP. GuJ. NMR-based metabolomic techniques identify the anticancer effects of three polyphyllins in HepG2 cells.Curr. Pharm. Anal.202218441542610.2174/1573412917666210823090145
    [Google Scholar]
  6. YanD. LiC. ZhouY. YanX. ZhiW. QianH. HanY. Exploration of combinational therapeutic strategies for HCC based on TCGA HCC database.Oncologie202224110111110.32604/oncologie.2022.020357
    [Google Scholar]
  7. WangY. WangP. ZhangZ. ZhouJ. FanJ. SunY. Dissecting the tumor ecosystem of liver cancers in the single-cell era.Hepatol. Commun.202379e024810.1097/HC9.0000000000000248
    [Google Scholar]
  8. ChewV. ChuangC.H. HsuC. Translational research on drug development and biomarker discovery for hepatocellular carcinoma.J. Biomed. Sci.20243112210.1186/s12929‑024‑01011‑y
    [Google Scholar]
  9. ChhabraY. WeeraratnaA.T. Fibroblasts in cancer: Unity in heterogeneity.Cell202318681580160910.1016/j.cell.2023.03.016
    [Google Scholar]
  10. YamamotoY. KasashimaH. FukuiY. TsujioG. YashiroM. MaedaK. The heterogeneity of cancer-associated fibroblast subpopulations: Their origins, biomarkers, and roles in the tumor microenvironment.Cancer Sci.20231141162410.1111/cas.15609
    [Google Scholar]
  11. KalluriR. The biology and function of fibroblasts in cancer.Nat. Rev. Cancer201616958259810.1038/nrc.2016.73
    [Google Scholar]
  12. SahaiE. AstsaturovI. CukiermanE. DeNardoD.G. EgebladM. EvansR.M. FearonD. GretenF.R. HingoraniS.R. HunterT. HynesR.O. JainR.K. JanowitzT. JorgensenC. KimmelmanA.C. KoloninM.G. MakiR.G. PowersR.S. PuréE. RamirezD.C. ShouvalS.R. ShermanM.H. StewartS. TlstyT.D. TuvesonD.A. WattF.M. WeaverV. WeeraratnaA.T. WerbZ. A framework for advancing our understanding of cancer-associated fibroblasts.Nat. Rev. Cancer202020317418610.1038/s41568‑019‑0238‑1
    [Google Scholar]
  13. AkkızH. Emerging role of cancer-associated fibroblasts in progression and treatment of hepatocellular carcinoma.Int. J. Mol. Sci.2023244394110.3390/ijms24043941
    [Google Scholar]
  14. ZulibiyaA. WenJ. YuH. ChenX. XuL. MaX. ZhangB. Single-cell RNA sequencing reveals potential for endothelial-to-mesenchymal transition in tetralogy of fallot.Congenit. Heart Dis.202318661162510.32604/chd.2023.047689
    [Google Scholar]
  15. ButlerA. HoffmanP. SmibertP. PapalexiE. SatijaR. Integrating single-cell transcriptomic data across different conditions, technologies, and species.Nat. Biotechnol.201836541142010.1038/nbt.4096
    [Google Scholar]
  16. KorsunskyI. MillardN. FanJ. SlowikowskiK. ZhangF. WeiK. BaglaenkoY. BrennerM. LohP. RaychaudhuriS. Fast, sensitive and accurate integration of single-cell data with Harmony.Nat. Methods201916121289129610.1038/s41592‑019‑0619‑0
    [Google Scholar]
  17. XiongZ. YangQ. LiX. Effect of intra- and inter-tumoral heterogeneity on molecular characteristics of primary IDH-wild type glioblastoma revealed by single-cell analysis.CNS Neurosci. Ther.202026998198910.1111/cns.13396
    [Google Scholar]
  18. GaoR. BaiS. HendersonY.C. LinY. SchalckA. YanY. KumarT. HuM. SeiE. DavisA. WangF. ShaitelmanS.F. WangJ.R. ChenK. MoulderS. LaiS.Y. NavinN.E. Delineating copy number and clonal substructure in human tumors from single-cell transcriptomes.Nat. Biotechnol.202139559960810.1038/s41587‑020‑00795‑2
    [Google Scholar]
  19. JinS. JuarezG.C.F. ZhangL. ChangI. RamosR. KuanC.H. MyungP. PlikusM.V. NieQ. Inference and analysis of cell-cell communication using cellchat.Nat. Commun.2021121108810.1038/s41467‑021‑21246‑9
    [Google Scholar]
  20. QiuX. MaoQ. TangY. WangL. ChawlaR. PlinerH.A. TrapnellC. Reversed graph embedding resolves complex single-cell trajectories.Nat. Methods2017141097998210.1038/nmeth.4402
    [Google Scholar]
  21. AibarS. BlasG.C.B. MoermanT. ThuH.V.A. ImrichovaH. HulselmansG. RambowF. MarineJ-C. GeurtsP. AertsJ. OordV.D.J. AtakZ.K. WoutersJ. AertsS. SCENIC: Single-cell regulatory network inference and clustering.Nat. Methods201714111083108610.1038/nmeth.4463
    [Google Scholar]
  22. KassambaraA. Drawing survival curves using 'ggplot2' (R package survminer version 0.2.0). Available from: https://rpkgs.datanovia.com/survminer/ 2017
  23. TherneauTM GrambschPM Modeling survival data: Extending the cox modelSpringer Science & Business Media2013350
    [Google Scholar]
  24. SpandidosA. WangX. WangH. SeedB. PrimerBank: A resource of human and mouse PCR primer pairs for gene expression detection and quantification.Nucleic Acids Res.201038Database issueSuppl. 1D792D79910.1093/nar/gkp1005
    [Google Scholar]
  25. AffoS. YuL.X. SchwabeR.F. The role of cancer-associated fibroblasts and fibrosis in liver cancer.Annu. Rev. Pathol.201712115318610.1146/annurev‑pathol‑052016‑100322
    [Google Scholar]
  26. AffoS. FilliolA. GoresG.J. SchwabeR.F. Fibroblasts in liver cancer: Functions and therapeutic translation.Lancet Gastroenterol. Hepatol.20238874875910.1016/S2468‑1253(23)00111‑5
    [Google Scholar]
  27. ChochiY. KawauchiS. NakaoM. FuruyaT. HashimotoK. OgaA. OkaM. SasakiK. A copy number gain of the 6p arm is linked with advanced hepatocellular carcinoma: An array-based comparative genomic hybridization study.J. Pathol.2009217567768410.1002/path.2491
    [Google Scholar]
  28. BorchersC.H. KastJ. FosterL.J. SiuK.W.M. OverallC.M. BinkowskiT.A. HildebrandW.H. SchererA. MansoorM. KeownP.A. The human proteome organization chromosome 6 consortium: Integrating chromosome-centric and biology/disease driven strategies.J. Proteomics2014100606710.1016/j.jprot.2013.08.001
    [Google Scholar]
  29. PengH. ZhuE. ZhangY. Advances of cancer-associated fibroblasts in liver cancer.Biomark. Res.20221015910.1186/s40364‑022‑00406‑z
    [Google Scholar]
  30. ZhangR. QiF. ZhaoF. LiG. ShaoS. ZhangX. YuanL. FengY. Cancer-associated fibroblasts enhance tumor-associated macrophages enrichment and suppress NK cells function in colorectal cancer.Cell Death Dis.201910427310.1038/s41419‑019‑1435‑2
    [Google Scholar]
  31. NagarshethN. WichaM.S. ZouW. Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy.Nat. Rev. Immunol.201717955957210.1038/nri.2017.49
    [Google Scholar]
  32. KarnoubA.E. DashA.B. VoA.P. SullivanA. BrooksM.W. BellG.W. RichardsonA.L. PolyakK. TuboR. WeinbergR.A. Mesenchymal stem cells within tumour stroma promote breast cancer metastasis.Nature2007449716255756310.1038/nature06188
    [Google Scholar]
  33. TanW. ZhangW. StrasnerA. GrivennikovS. ChengJ.Q. HoffmanR.M. KarinM. Tumour-infiltrating regulatory T cells stimulate mammary cancer metastasis through RANKL–RANK signalling.Nature2011470733554855310.1038/nature09707
    [Google Scholar]
  34. ZhaoX. DingL. LuZ. HuangX. JingY. YangY. ChenS. HuQ. NiY. 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.2020190488689910.1016/j.ajpath.2019.12.007
    [Google Scholar]
  35. YooS.A. LengL. KimB.J. DuX. TilstamP.V. KimK.H. KongJ-S. YoonH-J. LiuA. WangT. SongY. SaulerM. BernhagenJ. RitchlinC.T. LeeP. ChoC-S. KimW-U. BucalaR. MIF allele-dependent regulation of the MIF coreceptor CD44 and role in rheumatoid arthritis.Proc. Natl. Acad. Sci.201611349E7917E2610.1073/pnas.1612717113
    [Google Scholar]
  36. KlasenC. OhlK. SternkopfM. ShacharI. SchmitzC. HeussenN. HobeikaE. ZerdounL.E. TenbrockK. RethM. BernhagenJ. BounkariE.O. MIF promotes B cell chemotaxis through the receptors CXCR4 and CD74 and ZAP-70 signaling.J. Immunol.2014192115273528410.4049/jimmunol.1302209
    [Google Scholar]
  37. LiuY. ZhangL. JuX. WangS. QieJ. Single-cell transcriptomic analysis reveals macrophage–tumor crosstalk in hepatocellular carcinoma.Front. Immunol.20221395539010.3389/fimmu.2022.955390
    [Google Scholar]
  38. ZhouM. LuF. JiangL. ChenC. ChenS. GengL. SunR. LiQ. DuanS. ZhangB. MaoH. XingC. YuanY. Decoding the intercellular cross-talking between immune cells and renal innate cells in diabetic kidney disease by bioinformatics.J. Inflamm. Res.2023163049306210.2147/JIR.S409017
    [Google Scholar]
  39. ZhangY. ZuoC. LiuL. HuY. YangB. QiuS. LiY. CaoD. JuZ. GeJ. WangQ. WangT. BaiL. YangY. LiG. ShaoZ. GaoY. LiY. BianR. MiaoH. LiL. LiX. JiangC. YanS. WangZ. WangZ. CuiX. HuangW. XiangD. WangC. LiQ. WuX. GongW. LiuY. ShaoR. LiuF. LiM. ChenL. LiuY. Single-cell RNA-sequencing atlas reveals an MDK-dependent immunosuppressive environment in ErbB pathway-mutated gallbladder cancer.J. Hepatol.20217551128114110.1016/j.jhep.2021.06.023
    [Google Scholar]
  40. GaoY. XuQ. LiX. GuoY. ZhangB. JinY. zhuC. ShenY. YangP. ShiY. JinR. LiuD. OuyangY. LiuX. WangW. ChenD. YangT. Heterogeneity induced GZMA-F2R communication inefficient impairs antitumor immunotherapy of PD-1 mAb through JAK2/STAT1 signal suppression in hepatocellular carcinoma.Cell Death Dis.202213321310.1038/s41419‑022‑04654‑7
    [Google Scholar]
  41. YuX. XieL. GeJ. LiH. ZhongS. LiuX. Integrating single-cell RNA-seq and spatial transcriptomics reveals MDK-NCL dependent immunosuppressive environment in endometrial carcinoma.Front. Immunol.202314114530010.3389/fimmu.2023.1145300
    [Google Scholar]
  42. HuangJ. TsangW.Y. LiZ.H. GuanX.Y. The origin, differentiation, and functions of cancer-associated fibroblasts in gastrointestinal cancer.Cell. Mol. Gastroenterol. Hepatol.202316450351110.1016/j.jcmgh.2023.07.001
    [Google Scholar]
  43. LiuP. CaoW. MaB. LiM. ChenK. SiderasK. DuitmanJ-W. SprengersD. TranK.T.C. IjzermansJ.N.M. BiermannK. VerheijJ. SpekC.A. KwekkeboomJ. PanQ. PeppelenboschM.P. Action and clinical significance of CCAAT/enhancer-binding protein delta in hepatocellular carcinoma.Carcinogenesis201940115516310.1093/carcin/bgy130
    [Google Scholar]
  44. GaoS. GangJ. YuM. XinG. TanH. 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 Cancer202121179110.1186/s12885‑021‑08520‑1
    [Google Scholar]
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