Skip to content
2000
Volume 25, Issue 5
  • ISSN: 1566-5232
  • E-ISSN: 1875-5631

Abstract

Background

Cancer-Associated Fibroblasts (CAFs) constitute a heterogeneous group of cells critical for the remodeling of the tumor microenvironment (TME). Given their significant impact on tumor progression, particularly in skin cancers, a deeper understanding of their characteristics and functions is essential.

Methods

This study employed a single-cell transcriptomic analysis to explore the diversity of CAFs within three major types of skin cancer: basal cell carcinoma, melanoma, and head and neck squamous cell carcinoma. We applied analytical techniques, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), pseudotime tracking, metabolic profiling, and stemness assessment to delineate and define the functional attributes of identified CAF subgroups.

Results

Our analysis successfully delineated nine distinct CAF subgroups across the studied tumor types. Of particular interest, we identified a novel CAF subtype, designated as C0, exclusive to basal cell carcinoma. This subtype exhibits phenotypic traits associated with invasive and destructive capabilities, significantly correlating with the progression of basal cell carcinoma. The identification of this subgroup provides new insights into the role of CAFs in cancer biology and opens avenues for targeted therapeutic strategies.

Conclusion

A pan-cancer analysis was performed on three cancers, BCC, MA, and HNSCC, focusing on tumor fibroblasts in TME. Unsupervised clustering categorized CAF into nine subpopulations, among which the C0 subpopulation had a strong correspondence with BCC-CAF and an invasive-destructive-related phenotype.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cgt/10.2174/0115665232331353240911080642
2024-09-25
2025-10-04

  • From This Site

    /content/journals/cgt/10.2174/0115665232331353240911080642
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. HinshawD.C. ShevdeL.A. The tumor microenvironment innately modulates cancer progression.Cancer Res.201979184557456610.1158/0008‑5472.CAN‑18‑3962 31350295
     [Google Scholar]
  2. SahaiE. AstsaturovI. CukiermanE. A framework for advancing our understanding of cancer-associated fibroblasts.Nat. Rev. Cancer202020317418610.1038/s41568‑019‑0238‑1 31980749
     [Google Scholar]
  3. ZhangS. JiangC. JiangL. Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks.Front. Mol. Biosci.202310127589710.3389/fmolb.2023.1275897
     [Google Scholar]
  4. ChiH. HuangJ. YanY. Unraveling the role of disulfidptosis-related LncRNAs in colon cancer: A prognostic indicator for immunotherapy response, chemotherapy sensitivity, and insights into cell death mechanisms.Front. Mol. Biosci.202310125423210.3389/fmolb.2023.1254232
     [Google Scholar]
  5. HuangJ. LiuM. ChenH. Elucidating the influence of MPT-driven necrosis-linked LncRNAs on immunotherapy outcomes, sensitivity to chemotherapy, and mechanisms of cell death in clear cell renal carcinoma.Front. Oncol.202313127671510.3389/fonc.2023.1276715
     [Google Scholar]
  6. KalluriR. The biology and function of fibroblasts in cancer.Nat. Rev. Cancer201616958259810.1038/nrc.2016.73 27550820
     [Google Scholar]
  7. ChenX. Cancer-associated fibroblasts in immune modulation and immunotherapy.Nat. Rev. Clin. Oncol.2021187485502
     [Google Scholar]
  8. ÖhlundD. Fibroblast heterogeneity in the cancer wound.J. Exp. Med.20212181e20191720
     [Google Scholar]
  9. HwangW.L. Single-cell transcriptomic heterogeneity of bladder cancer reveals new insights into tumor microenvironment and immune landscape.Nat. Commun.2021121207
     [Google Scholar]
  10. PuramS.V. TiroshI. ParikhA.S. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer.Cell2017171716111624.e2410.1016/j.cell.2017.10.044 29198524
     [Google Scholar]
  11. LinC.Y. Single-cell analysis reveals regulatory gene expression dynamics leading to differentiation of papillary thyroid carcinoma.Oncogene2020393447462
     [Google Scholar]
  12. LambrechtsD. WautersE. BoeckxB. Phenotype molding of stromal cells in the lung tumor microenvironment.Nat. Med.20182481277128910.1038/s41591‑018‑0096‑5 29988129
     [Google Scholar]
  13. DominguezC.X. Single-cell RNA sequencing reveals conserved fibroblast heterogeneity in pancreatic ductal adenocarcinoma.Cancer Discov.2020106652669
     [Google Scholar]
  14. KiefferY. HocineH.R. GentricG. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer.Cancer Discov.20201091330135110.1158/2159‑8290.CD‑19‑1384 32434947
     [Google Scholar]
  15. ElyadaE. BolisettyM. LaiseP. Cross-species single-cell analysis of pancreatic ductal adenocarcinoma reveals antigen-presenting cancer-associated fibroblasts.Cancer Discov.2019981102112310.1158/2159‑8290.CD‑19‑0094 31197017
     [Google Scholar]
  16. LiauJ.Y. Malignant transformation of oral lichen planus: A retrospective study of 112 patients in a Taiwanese tertiary referral center.J. Formos. Med. Assoc.20211201227234
     [Google Scholar]
  17. RebeccaV.W. Melanoma subtypes: Genetic, histological, and biological features.Melanoma Res.2021313149162
     [Google Scholar]
  18. VerkouterenJ.A.C. Trends in the incidence and age at diagnosis of basal cell carcinoma in the Netherlands: A registry-based study from 1973 to 2020.Br. J. Dermatol.20211844801809
     [Google Scholar]
  19. HuangX. ChiH. GouS. An aggrephagy-related LncRNA signature for the prognosis of pancreatic adenocarcinoma.Genes202314112410.3390/genes14010124
     [Google Scholar]
  20. McGinnisC.S. MurrowL.M. GartnerZ.J. DoubletFinder: Doublet detection in single-cell RNA sequencing data using artificial nearest neighbors.Cell Syst.201984329337.e410.1016/j.cels.2019.03.003 30954475
     [Google Scholar]
  21. NieW. ZhaoZ. LiuY. Integrative single-cell analysis of cardiomyopathy identifies differences in cell stemness and transcriptional regulatory networks among fibroblast subpopulations.Cardiol. Res. Pract.20242024313163310.1155/2024/3131633
     [Google Scholar]
  22. HuangW KimBS ZhangY LinL ChaiG ZhaoZ Regulatory T cells subgroups in the tumor microenvironment cannot be overlooked: Their involvement in prognosis and treatment strategy in melanoma.Environ Toxicol2024tox.2424710.1002/tox.2424738530049
     [Google Scholar]
  23. GeQ. ZhaoZ. LiX. Deciphering the suppressive immune microenvironment of prostate cancer based on CD4+ regulatory T cells: Implications for prognosis and therapy prediction.Clin. Transl. Med.2024141e155210.1002/ctm2.1552 38239097
     [Google Scholar]
  24. ButlerA. HoffmanP. SmibertP. PapalexiE. SatijaR. Integrating single-cell transcriptomic data across different conditions, technologies, and species.Nat. Biotechnol.201836541142010.1038/nbt.4096 29608179
     [Google Scholar]
  25. KorsunskyI. MillardN. FanJ. Fast, sensitive and accurate integration of single-cell data with Harmony.Nat. Methods201916121289129610.1038/s41592‑019‑0619‑0 31740819
     [Google Scholar]
  26. DingY. ZhaoZ. CaiH. Single-cell sequencing analysis related to sphingolipid metabolism guides immunotherapy and prognosis of skin cutaneous melanoma.Front. Immunol.202314130446610.3389/fimmu.2023.1304466
     [Google Scholar]
  27. ZhaoZ. DingY. Innovative breakthroughs facilitated by single-cell multi-omics: Manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells.Front. Immunol.202314119689210.3389/fimmu.2023.1196892
     [Google Scholar]
  28. LiX.Y. ZhaoZ.J. WangJ.B. m7G methylation-related genes as biomarkers for predicting overall survival outcomes for hepatocellular carcinoma.Front. Bioeng. Biotechnol.20221084975610.3389/fbioe.2022.849756
     [Google Scholar]
  29. AshburnerM. BallC.A. BlakeJ.A. Gene Ontology: Tool for the unification of biology.Nat. Genet.2000251252910.1038/75556 10802651
     [Google Scholar]
  30. CarbonS. DouglassE. GoodB.M. The Gene Ontology resource: Enriching a GOld mine.Nucleic Acids Res.202149D1D325D33410.1093/nar/gkaa1113 33290552
     [Google Scholar]
  31. MiH. EbertD. MuruganujanA. PANTHER version 16: A revised family classification, tree-based classification tool, enhancer regions and extensive API.Nucleic Acids Res.202149D1D394D40310.1093/nar/gkaa1106 33290554
     [Google Scholar]
  32. JiangH. ZhangZ. WangL. LiY. ChenQ. Metabolomic profiling reveals potential biomarkers of demyelination disorders in the mouse corpus callosum.J. Neurochem.2022162453054010.1111/jnc.15624
     [Google Scholar]
  33. SmithT. JohnsonM. LeeP. Advances in the management of chronic respiratory diseases: A comprehensive review.Respir. Med. Res.202118211212510.1016/j.rmr.2021.01.002
     [Google Scholar]
  34. SubramanianA. TamayoP. MoothaV.K. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci. USA200510243155451555010.1073/pnas.0506580102 16199517
     [Google Scholar]
  35. YuG. WangL.G. HanY. HeQ.Y. clusterProfiler: An R package for comparing biological themes among gene clusters.OMICS201216528428710.1089/omi.2011.0118 22455463
     [Google Scholar]
  36. StreetK. RissoD. FletcherR.B. Slingshot: Cell lineage and pseudotime inference for single-cell transcriptomics.BMC Genomics201819147710.1186/s12864‑018‑4772‑0
     [Google Scholar]
  37. BibbyJ. Profiling metabolic heterogeneity in breast cancer cells and tumours using single-cell RNA-sequencing.Nat. Commun.2019101111 30602773
     [Google Scholar]
  38. XiaoZ. DaiZ. LocasaleJ.W. Metabolic landscape of the tumor microenvironment at single-cell resolution.Nat. Commun.2019101376310.1038/s41467‑019‑11738‑0
     [Google Scholar]
  39. AibarS. González-BlasC.B. MoermanT. SCENIC: Single-cell regulatory network inference and clustering.Nat. Methods201714111083108610.1038/nmeth.4463 28991892
     [Google Scholar]
  40. StuartT. ButlerA. HoffmanP. Comprehensive integration of single-cell data.Cell2019177718881902.e2110.1016/j.cell.2019.05.031 31178118
     [Google Scholar]
  41. TiroshI. IzarB. PrakadanS.M. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq.Science2016352628218919610.1126/science.aad0501 27124452
     [Google Scholar]
  42. MaynardA. Interrogation of human tumors and cell lines from single-cell RNA-seq data reveals heterogeneity in immune and stromal cell populations.Nat. Biotechnol.2020387800808
     [Google Scholar]
  43. FinakG. MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.Genome Biol.201516127810.1186/s13059‑015‑0844‑5
     [Google Scholar]
  44. QianJ. A pan-cancer blueprint of the heterogeneous tumor microenvironment revealed by single-cell profiling.Cell2021184410141032
     [Google Scholar]
  45. WolockS.L. LopezR. KleinA.M. Scrublet: Computational identification of cell doublets in single-cell transcriptomic data.Cell Syst.201984281291.e910.1016/j.cels.2018.11.005 30954476
     [Google Scholar]
  46. HanX. WangR. ZhouY. Mapping the mouse cell atlas by microwell-seq.Cell2018172510911107.e1710.1016/j.cell.2018.02.001 29474909
     [Google Scholar]
  47. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris.Nature2018562772736737210.1038/s41586‑018‑0590‑4 30283141
     [Google Scholar]
  48. van DijkD. SharmaR. NainysJ. Recovering gene interactions from single-cell data using data diffusion.Cell20181743716729.e2710.1016/j.cell.2018.05.061 29961576
     [Google Scholar]
  49. De SimoneM. ArrigoniA. RossettiG. Transcriptional landscape of human tissue lymphocytes unveils uniqueness of tumor-infiltrating T regulatory cells.Immunity20164551135114710.1016/j.immuni.2016.10.021 27851914
     [Google Scholar]
  50. ZhangM. YangH. WanL. Single-cell transcriptomic architecture and intercellular crosstalk of human intrahepatic cholangiocarcinoma.J. Hepatol.20207351118113010.1016/j.jhep.2020.05.039 32505533
     [Google Scholar]
  51. ChenB. ChanW.N. XieF. The molecular classification of cancer‐associated fibroblasts on a pan‐cancer single‐cell transcriptional atlas.Clin. Transl. Med.20231312e151610.1002/ctm2.1516 38148640
     [Google Scholar]
  52. LavieD. Ben-ShmuelA. ErezN. Scherz-ShouvalR. Cancer-associated fibroblasts in the single-cell era.Nat. Can.20223779380710.1038/s43018‑022‑00411‑z 35883004
     [Google Scholar]
  53. HuangH. WangZ. ZhangY. Mesothelial cell-derived antigen-presenting cancer-associated fibroblasts induce expansion of regulatory T cells in pancreatic cancer.Cancer Cell2022406656673.e710.1016/j.ccell.2022.04.011 35523176
     [Google Scholar]
  54. MaC. YangC. PengA. Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment.Mol. Cancer202322117010.1186/s12943‑023‑01876‑x 37833788
     [Google Scholar]
  55. CordsL. TietscherS. AnzenederT. Cancer-associated fibroblast classification in single-cell and spatial proteomics data.Nat. Commun.2023141429410.1038/s41467‑023‑39762‑1 37463917
     [Google Scholar]
  56. KijimaT. PrinceT. NeckersL. KogaF. FujiiY. Heat shock factor 1 (HSF1)-targeted anticancer therapeutics: Overview of current preclinical progress.Expert Opin. Ther. Targets201923536937710.1080/14728222.2019.1602119 30931649
     [Google Scholar]
  57. GrunbergN. Pevsner-FischerM. Goshen-LagoT. Cancer-associated fibroblasts promote aggressive gastric cancer phenotypes via heat shock factor 1–mediated secretion of extracellular vesicles.Cancer Res.20218171639165310.1158/0008‑5472.CAN‑20‑2756 33547159
     [Google Scholar]
  58. SharonY. RazY. CohenN. Tumor-derived osteopontin reprograms normal mammary fibroblasts to promote inflammation and tumor growth in breast cancer.Cancer Res.201575696397310.1158/0008‑5472.CAN‑14‑1990 25600648
     [Google Scholar]
  59. QuanteM. TuS.P. TomitaH. Bone marrow-derived myofibroblasts contribute to the mesenchymal stem cell niche and promote tumor growth.Cancer Cell201119225727210.1016/j.ccr.2011.01.020 21316604
     [Google Scholar]
  60. RazY. CohenN. ShaniO. Bone marrow–derived fibroblasts are a functionally distinct stromal cell population in breast cancer.J. Exp. Med.2018215123075309310.1084/jem.20180818 30470719
     [Google Scholar]
  61. KiddS. SpaethE. WatsonK. Origins of the tumor microenvironment: Quantitative assessment of adipose-derived and bone marrow-derived stroma.PLoS One201272e3056310.1371/journal.pone.0030563 22363446
     [Google Scholar]
  62. KrenningG. ZeisbergE.M. KalluriR. The origin of fibroblasts and mechanism of cardiac fibrosis.J. Cell. Physiol.2010225363163710.1002/jcp.22322 20635395
     [Google Scholar]
  63. Rynne-VidalA. Jiménez-HeffernanJ. Fernández-ChacónC. López-CabreraM. SandovalP. The mesothelial origin of carcinoma associated-fibroblasts in peritoneal metastasis.Cancers2015741994201110.3390/cancers7040872 26426054
     [Google Scholar]
  64. ChenY. McAndrewsK.M. KalluriR. Clinical and therapeutic relevance of cancer-associated fibroblasts.Nat. Rev. Clin. Oncol.2021181279280410.1038/s41571‑021‑00546‑5 34489603
     [Google Scholar]
  65. SongB. WuP. LiangZ. A novel necroptosis-related gene signature in skin cutaneous melanoma prognosis and tumor microenvironment.Front. Genet.20221391700710.3389/fgene.2022.917007
     [Google Scholar]
/content/journals/cgt/10.2174/0115665232331353240911080642
Loading
Pan-Cancer Single-Cell Analysis Revealing the Heterogeneity of Cancer-Associated Fibroblasts in Skin Tumors
Curr. Gene Ther. 25, 793 (2025); https://doi.org/10.2174/0115665232331353240911080642
/content/journals/cgt/10.2174/0115665232331353240911080642
/content/journals/cgt/10.2174/0115665232331353240911080642
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): basal cell carcinoma; cancer-associated fibroblasts (CAFs); extracellular matrix (ECM); head and neck squamous carcinoma; melanoma; Pan-cancer; single-cell analysis

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