Multi-omics Analysis Revealed that the CCN Family Regulates Cell Crosstalk, Extracellular Matrix, and Immune Escape, Leading to a Poor Prognosis of Glioma.

Publisher: Humana Press Country of Publication: United States NLM ID: 9701934 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1559-0283 (Electronic) Linking ISSN: 10859195 NLM ISO Abbreviation: Cell Biochem Biophys Subsets: MEDLINE

Original Publication: Totowa, NJ : Humana Press, c1996-

Glioma*/metabolism ; Glioma*/pathology ; Glioma*/genetics ; Extracellular Matrix*/metabolism ; CCN Intercellular Signaling Proteins*/metabolism ; CCN Intercellular Signaling Proteins*/genetics; Humans ; Prognosis ; Tumor Microenvironment ; Brain Neoplasms/metabolism ; Brain Neoplasms/pathology ; Brain Neoplasms/genetics ; Brain Neoplasms/immunology ; Single-Cell Analysis ; Cysteine-Rich Protein 61/metabolism ; Cysteine-Rich Protein 61/genetics ; Tumor Escape ; Gene Expression Regulation, Neoplastic ; Gene Expression Profiling ; Signal Transduction ; Connective Tissue Growth Factor/metabolism ; Connective Tissue Growth Factor/genetics ; Multiomics

The CCN family is a group of matricellular proteins associated with the extracellular matrix. This study aims to explore the role of the CCN family in glioma development and its implications in the tumor microenvironment. Through analysis of bulk RNA-seq cohorts, correlations between CCN family expression and glioma subtypes, patient survival, and bioactive pathway enrichment were investigated. Additionally, single-cell datasets were employed to identify novel cell subgroups, followed by analyses of cell communication and transcription factors. Spatial transcriptomic analysis was utilized to validate the CCN family's involvement in glioma. Results indicate overexpression of CYR61,CTGF, and WISP1 in glioma, associated with unfavorable subtypes and reduced survival. Enrichment analyses revealed associations with oncogenic pathways, while CTGF and WISP1 expression correlated with increased infiltration of regulatory T cells and M2 macrophages. Single-cell analysis identified MES-like cells as the highest CCN expression. Moreover, intercellular signal transduction analysis demonstrated active pathways, including SPP1-CD44, in cell subgroups with elevated CYR61 and CTGF expression. Spatial transcriptomic analysis confirmed co-localization of CYR61,CTGF and SPP1-CD44 with high oncogenic pathway activity. These findings suggest that CCN family members may serve as potential prognostic biomarkers and therapeutic targets for glioma.
(© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Wen, P. Y., & Packer, R. J. (2021). The 2021 WHO Classification of Tumors of the Central Nervous System: clinical implications. Neuro-Oncology, 23, 1215–1217. (PMID: 34185090832801710.1093/neuonc/noab120)
Weller, M., Van Den Bent, M., & Preusser, M., et al. (2021). EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nature Reviews Clinical Oncology, 18, 170–186. (PMID: 3329362910.1038/s41571-020-00447-z)
Bush, N. A., Chang, S. M., & Berger, M. S. (2017). Current and future strategies for treatment of glioma. Neurosurgical Review, 40, 1–14. (PMID: 2708585910.1007/s10143-016-0709-8)
Kalluri, A. L., Shah, P. P., & Lim, M. (2023). The Tumor Immune Microenvironment in Primary CNS Neoplasms: A Review of Current Knowledge and Therapeutic Approaches. International Journal of Molecular Sciences, 24, 2020. (PMID: 36768342991705610.3390/ijms24032020)
De Visser, K. E., & Joyce, J. A. (2023). The evolving tumor microenvironment: From cancer initiation to metastatic outgrowth. Cancer Cell, 41, 374–403. (PMID: 3691794810.1016/j.ccell.2023.02.016)
Ferrer, V. P., Moura Neto, V., & Mentlein, R. (2018). Glioma infiltration and extracellular matrix: key players and modulators. Glia, 66, 1542–1565. (PMID: 2946486110.1002/glia.23309)
Gopinath, P., Natarajan, A., & Sathyanarayanan, A., et al. (2022). The multifaceted role of Matricellular Proteins in health and cancer, as biomarkers and therapeutic targets. Gene, 815, 146137. (PMID: 3500768610.1016/j.gene.2021.146137)
Jun, J. I., & Lau, L. F. (2011). Taking aim at the extracellular matrix: CCN proteins as emerging therapeutic targets. Nature Reviews Drug Discovery, 10, 945–963. (PMID: 22129992366314510.1038/nrd3599)
Maity, G., Mehta, S., & Haque, I., et al. (2014). Pancreatic tumor cell secreted CCN1/Cyr61 promotes endothelial cell migration and aberrant neovascularization. Scientific Reports, 4, 4995. (PMID: 24833309402313110.1038/srep04995)
Wang, M., Li, X. Z., & Zhang, M. X., et al. (2021). Atractylenolide-I Sensitizes Triple-Negative Breast Cancer Cells to Paclitaxel by Blocking CTGF Expression and Fibroblast Activation. Frontiers in Oncology, 11, 738534. (PMID: 34692516852689810.3389/fonc.2021.738534)
Wang, M. Y., Chen, P. S., & Prakash, E., et al. (2009). Connective tissue growth factor confers drug resistance in breast cancer through concomitant up-regulation of Bcl-xL and cIAP1. Cancer Research, 69, 3482–3491. (PMID: 1935185910.1158/0008-5472.CAN-08-2524)
Peng, L., Wei, Y., & Shao, Y., et al. (2021). The Emerging Roles of CCN3 Protein in Immune-Related Diseases. Mediators of Inflammation, 2021, 5576059. (PMID: 34393649835602810.1155/2021/5576059)
Chen, P. C., Cheng, H. C., & Yang, S. F., et al. (2014). The CCN family proteins: modulators of bone development and novel targets in bone-associated tumors. BioMed Research International, 2014, 437096. (PMID: 245518463914550)
Sarkar, S., Ghosh, A., & Banerjee, S., et al. (2017). CCN5/WISP-2 restores ER-∝ in normal and neoplastic breast cells and sensitizes triple negative breast cancer cells to tamoxifen. Oncogenesis, 6, e340. (PMID: 28530705556933310.1038/oncsis.2017.43)
Qian, Z., Tian, X., & Miao, Y., et al. (2023). Bufalin inhibits the proliferation of lung cancer cells by suppressing Hippo-YAP pathway. Cell Signal, 109, 110746. (PMID: 3728611910.1016/j.cellsig.2023.110746)
Ishida, J., Kurozumi, K., & Ichikawa, T., et al. (2015). Evaluation of extracellular matrix protein CCN1 as a prognostic factor for glioblastoma. Brain Tumor Pathology, 32, 245–252. (PMID: 2620184210.1007/s10014-015-0227-3)
Liu, Z., Wu, J., & Ji, H., et al. (2022). Stromal protein CCN family contributes to the poor prognosis in lower-grade gioma by modulating immunity, matrix, stemness, and metabolism. Frontiers in Molecular Biosciences, 9, 1027236. (PMID: 36589241980098610.3389/fmolb.2022.1027236)
Goldman, M. J., Craft, B., & Hastie, M., et al. (2020). Visualizing and interpreting cancer genomics data via the Xena platform. Nature Biotechnology, 38, 675–678. (PMID: 32444850738607210.1038/s41587-020-0546-8)
Barrett, T., Wilhite, S. E., & Ledoux, P., et al. (2013). NCBI GEO: archive for functional genomics data sets-update. Nucleic Acids Research, 41, D991–D995. (PMID: 2319325810.1093/nar/gks1193)
Tang, Z., Li, C., & Kang, B., et al. (2017). GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Research, 45, W98–w102. (PMID: 28407145557022310.1093/nar/gkx247)
Zhao, Z., Zhang, K. N., & Wang, Q., et al. (2021). Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients. Genomics Proteomics Bioinformatics, 19, 1–12. (PMID: 33662628849892110.1016/j.gpb.2020.10.005)
Li, T., Fu, J., & Zeng, Z., et al. (2020). TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Research, 48, W509–w514. (PMID: 32442275731957510.1093/nar/gkaa407)
Vasaikar, S. V., Straub, P., & Wang, J., et al. (2018). LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Research, 46, D956–d963. (PMID: 2913620710.1093/nar/gkx1090)
Zhou, Y., Zhou, B., & Pache, L., et al. (2019). Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications, 10, 1523. (PMID: 30944313644762210.1038/s41467-019-09234-6)
Newman, A. M., Liu, C. L., & Green, M. R., et al. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12, 453–457. (PMID: 25822800473964010.1038/nmeth.3337)
Qiu, X., Hill, A., & Packer, J., et al. (2017). Single-cell mRNA quantification and differential analysis with Census. Nature Methods, 14, 309–315. (PMID: 28114287533080510.1038/nmeth.4150)
Aibar, S., González-Blas, C. B., & Moerman, T., et al. (2017). SCENIC: single-cell regulatory network inference and clustering. Nature Methods, 14, 1083–1086. (PMID: 28991892593767610.1038/nmeth.4463)
Jin, S., Guerrero-Juarez, C. F., & Zhang, L., et al. (2021). Inference and analysis of cell-cell communication using CellChat. Nature Communications, 12, 1088. (PMID: 33597522788987110.1038/s41467-021-21246-9)
Yeger, H. (2023). CCN proteins: opportunities for clinical studies-a personal perspective. Journal of Cell Communication and Signaling, 17, 333–352. (PMID: 371953811032621310.1007/s12079-023-00761-y)
Uneda, A., Kurozumi, K., & Fujimura, A., et al. (2021). Differentiated glioblastoma cells accelerate tumor progression by shaping the tumor microenvironment via CCN1-mediated macrophage infiltration. Acta Neuropathologica Communications, 9, 29. (PMID: 33618763789845510.1186/s40478-021-01124-7)
Cheng, G., Zhang, H., & Zhang, L., et al. (2015). Cyr61 promotes growth of glioblastoma in vitro and in vivo. Tumor Biology, 36, 2869–2873. (PMID: 2550170110.1007/s13277-014-2915-8)
Zuo, G. W., Kohls, C. D., & He, B. C., et al. (2010). The CCN proteins: important signaling mediators in stem cell differentiation and tumorigenesis. Histology and Histopathology, 25, 795–806. (PMID: 203767862922104)
Emon, B., Bauer, J., & Jain, Y., et al. (2018). Biophysics of Tumor Microenvironment and Cancer Metastasis - A Mini Review. Computational and Structural Biotechnology Journal, 16, 279–287. (PMID: 30128085609754410.1016/j.csbj.2018.07.003)
Holbourn, K. P., Acharya, K. R., & Perbal, B. (2008). The CCN family of proteins: structure-function relationships. Trends in Biochemical Sciences, 33, 461–473. (PMID: 18789696268393710.1016/j.tibs.2008.07.006)
Kim, H., Son, S., & Shin, I. (2018). Role of the CCN protein family in cancer. BMB Reports, 51, 486–492. (PMID: 30158025623508810.5483/BMBRep.2018.51.10.192)
Goethe, E. A., Deneen, B., & Noebels, J., et al. (2023). The Role of Hyperexcitability in Gliomagenesis. International Journal of Molecular Sciences, 24, 749. (PMID: 36614191982092210.3390/ijms24010749)
Winkler, F., Venkatesh, H. S., & Amit, M., et al. (2023). Cancer neuroscience: State of the field, emerging directions. Cell, 186, 1689–1707. (PMID: 370590691010740310.1016/j.cell.2023.02.002)
Egea, V., Von Baumgarten, L., & Schichor, C., et al. (2011). TNF-α respecifies human mesenchymal stem cells to a neural fate and promotes migration toward experimental glioma. Cell Death and Differentiation, 18, 853–863. (PMID: 2112749910.1038/cdd.2010.154)
Ramaswamy, P., Goswami, K., & Dalavaikodihalli Nanjaiah, N., et al. (2019). TNF-α mediated MEK-ERK signaling in invasion with putative network involving NF-κB and STAT-6: a new perspective in glioma.Cell. Biol Int, 43, 1257–1266.
Davidson, T. B., Lee, A., & Hsu, M., et al. (2019). Expression of PD-1 by T Cells in Malignant Glioma Patients Reflects Exhaustion and Activation. Clinical Cancer Research, 25, 1913–1922. (PMID: 3049809410.1158/1078-0432.CCR-18-1176)
Wang, G., Zhong, K., & Wang, Z., et al. (2022). Tumor-associated microglia and macrophages in glioblastoma: From basic insights to therapeutic opportunities. Frontiers in Immunology, 13, 964898. (PMID: 35967394936357310.3389/fimmu.2022.964898)
Patel, A. P., Tirosh, I., & Trombetta, J. J., et al. (2014). Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science, 344, 1396–1401. (PMID: 24925914412363710.1126/science.1254257)
Bhat, K. P. L., Balasubramaniyan, V., & Vaillant, B., et al. (2013). Mesenchymal differentiation mediated by NF-κB promotes radiation resistance in glioblastoma. Cancer Cell, 24, 331–346. (PMID: 2399386310.1016/j.ccr.2013.08.001)
Kim, Y., Varn, F. S., & Park, S. H., et al. (2021). Perspective of mesenchymal transformation in glioblastoma. Acta Neuropathologica Communications, 9, 50. (PMID: 33762019799278410.1186/s40478-021-01151-4)
Xie, S., Huang, G., & Qian, W., et al. (2023). Integrated analysis reveals the microenvironment of non-small cell lung cancer and a macrophage-related prognostic model. Translational Lung Cancer Research, 12, 277–294. (PMID: 36895934998981110.21037/tlcr-22-866)
You, G., Zheng, Z., & Huang, Y., et al. (2023). scRNA-seq and proteomics reveal the distinction of M2-like macrophages between primary and recurrent malignant glioma and its critical role in the recurrence. CNS Neuroscience and Therapeutics, 29, 3391–3405. (PMID: 371944131058034910.1111/cns.14269)
Nallasamy, P., Nimmakayala, R. K., & Karmakar, S., et al. (2021). Pancreatic Tumor Microenvironment Factor Promotes Cancer Stemness via SPP1-CD44 Axis. Gastroenterology, 161, 1998–2013.e7. (PMID: 3441844110.1053/j.gastro.2021.08.023)
Pietras, A., Katz, A. M., & Ekström, E. J., et al. (2014). Osteopontin-CD44 signaling in the glioma perivascular niche enhances cancer stem cell phenotypes and promotes aggressive tumor growth. Cell Stem Cell, 14, 357–369. (PMID: 24607407399904210.1016/j.stem.2014.01.005)

81372701 National Natural Science Foundation of China

Keywords: Bioinformatics analysis; CCN family; Glioma; Prognostic markers

0 (CCN Intercellular Signaling Proteins)
0 (Cysteine-Rich Protein 61)
139568-91-5 (Connective Tissue Growth Factor)

Date Created: 20240605 Date Completed: 20241001 Latest Revision: 20241022

20241022

10.1007/s12013-024-01323-8

38837011