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Cancer-Associated Fibroblast-Related Genes Are Associated with Prognosis of Patients with Ovarian Cancer

  • HUMAN GENETICS
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Abstract

This study aimed to investigate the effects of cancer-associated fibroblasts (CAFs)-related genes on prognosis and immunity in ovarian cancer. The gene expression profile data and clinical data of ovarian cancer were obtained from TCGA and GEO databases. The differentially expressed genes (DEGs) between tumor and normal tissues were selected, and the infiltration of immune cells, stromal cells and CAFs in each sample was evaluated. WGCNA was used for CAFs signature genes identification. CAFs score was calculated, followed by comparison of prognosis, immune cells, pathway and drug susceptibility between CAFs score-high and -low groups. There were significant differences in immune and stromal score as well as CAFs ratio between two groups. A total of 6474 DEGs were identified. By WGCNA, six CAFs signature genes, including MMP1, DPYSL4, PDGFRA, OGN, FBXL7 and NUDT10, were identified. CAFs score was an independent prognostic factor. There was a significant correlation between the actual prognosis and the different CAFs score groups. The proportion of five immune cells was significantly different between two CAFs score groups. Nine signaling pathways, such as epithelial-mesenchymal transition and angiogenesis, were different between two groups. Five chemotherapeutic drugs, such as paclitaxel and gefitinib, were found to be significantly different in IC50 levels between two groups. The six CAFs signature genes, including MMP1, DPYSL4, PDGFRA, OGN, FBXL7 and NUDT10, may serve as prognostic biomarkers and therapeutic targets of ovarian cancer.

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DATA AVAILABILITY STATEMENTS

The data used to support the findings of this study are available from the corresponding author upon request.

REFERENCES

  1. Fukagawa, D., Sugai, T., Osakabe, M., Suga, Y., Nagasawa, T., Itamochi, H. and Sugiyama, T., Protein expression patterns in cancer-associated fibroblasts and cells undergoing the epithelial- mesenchymal transition in ovarian cancers, Oncotarget, 2018, vol. 9, no. 44, pp. 27514—27524.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., and Jemal, A., Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, C. A. Cancer J. Clin., 2018, vol. 68, no. 6, pp. 394—424.

    Article  Google Scholar 

  3. Merritt, M.A., Rice, M.S., Barnard, M.E., Hankinson, S.E., Matulonis, U.A., Poole, E.M., and Tworoger, S.S., Pre-diagnosis and post-diagnosis use of common analgesics and ovarian cancer prognosis (NHS/NHSII): a cohort study, Lancet Oncol., 2018, vol. 19, no. 8, pp. 1107—1116.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Emmings, E., Mullany, S., Chang, Z., Landen, C.N., Jr., and Linder, S., Targeting mitochondria for treatment of chemoresistant ovarian cancer, Int. J. Mol. Sci., 2019, vol. 20, no. 1, p. 229.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Guo, T., Dong, X., Xie, S., Zhang, L., Zeng, P., and Zhang, L., Cellular mechanism of gene mutations and potential therapeutic targets in ovarian cancer, Cancer Manage. Res., 2021, vol. 13, p. 3081.

    Article  CAS  Google Scholar 

  6. Wang, F.T., Sun, W., Zhang, J.T., and Fan, Y.Z., Cancer‑associated fibroblast regulation of tumor neo‑angiogenesis as a therapeutic target in cancer, Oncol. Lett., 2019, vol. 17, no. 3, pp. 3055—3065.

    PubMed  PubMed Central  CAS  Google Scholar 

  7. Liotta, L.A. and Kohn, E.C., The microenvironment of the tumour—host interface, Nature, 2001, vol. 411, no. 6835, pp. 375—379.

    Article  PubMed  CAS  Google Scholar 

  8. Ghoneum, A., Afify, H., Salih, Z., Kelly, M., and Said, N., Role of tumor microenvironment in the pathobiology of ovarian cancer: insights and therapeutic opportunities, Cancer Med., 2018, vol. 7, no. 10, pp. 5047—5056.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Taddei, M.L., Giannoni, E., Comito, G., and Chiarugi, P., Microenvironment and tumor cell plasticity: an easy way out, Cancer Lett., 2013, vol. 341, no. 1, pp. 80—96.

    Article  PubMed  CAS  Google Scholar 

  10. Paulsson, J. and Micke, P., Prognostic relevance of cancer-associated fibroblasts in human cancer, Semin. Cancer Biol., 2014, vol. 25, pp. 61—68.

    Article  PubMed  CAS  Google Scholar 

  11. Junttila, M.R. and De Sauvage, F.J., Influence of tumour micro-environment heterogeneity on therapeutic response, Nature, 2013, vol. 501, no. 7467, pp. 346—354.

    Article  PubMed  CAS  Google Scholar 

  12. Lau, E.Y.T., Lo, J., Cheng, B.Y.L., Ma, M.K.F., Lee, J.M.F., Ng, J.K.Y., Chai, S., Lin, C.H., Tsang, S.Y., and Ma, S., Cancer-associated fibroblasts regulate tumor-initiating cell plasticity in hepatocellular carcinoma through c-Met/FRA1/HEY1 signaling, Cell Rep., 2016, vol. 15, no. 6, pp. 1175—1189.

    Article  PubMed  CAS  Google Scholar 

  13. Mhaidly, R. and Mechta-Grigoriou, F., Role of cancer-associated fibroblast subpopulations in immune infiltration, as a new means of treatment in cancer, Immunol. Rev., 2021, vol. 302, no. 1, pp. 259—272.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Costa, A., Kieffer, Y., Scholer-Dahirel, A., Pelon, F., Bourachot, B., Cardon, M., Sirven, P., Magagna, I., Fuhrmann, L., and Bernard, C., Fibroblast heterogeneity and immunosuppressive environment in human breast cancer, Cancer Cell, 2018, vol. 33, no. 3, pp. 463—479.

    Article  PubMed  CAS  Google Scholar 

  15. Öhlund, D., Handly-Santana, A., Biffi, G., Elyada, E., Almeida, A.S., Ponz-Sarvise, M., Corbo, V., Oni, T.E., Hearn, S.A., and Lee, E.J., Distinct populations of inflammatory fibroblasts and myofibroblasts in pancreatic cancer, J. Exp. Med., 2017, vol. 214, no. 3, pp. 579—596.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Moran-Jones, K., Gloss, B.S., Murali, R., Chang, D.K., Colvin, E.K., Jones, M.D., Yuen, S., Howell, V.M., Brown, L.M., and Wong, C.W., Connective tissue growth factor as a novel therapeutic target in high grade serous ovarian cancer, Oncotarget, 2015, vol. 6, no. 42, pp. 44551—44562.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mieulet, V., Garnier, C., Kieffer, Y., Guilbert, T., Nemati, F., Marangoni, E., Renault, G., Chamming’s, F., Vincent-Salomon, A., and Mechta-Grigoriou, F., Stiffness increases with myofibroblast content and collagen density in mesenchymal high grade serous ovarian cancer, Sci. Rep., 2021, vol. 11, no. 1, pp. 1—20.

    Article  Google Scholar 

  18. Yoshihara, K., Tajima, A., Yahata, T., Kodama, S., Fujiwara, H., Suzuki, M., Onishi, Y., Hatae, M., Sueyoshi, K., and Fujiwara, H., Gene expression profile for predicting survival in advanced-stage serous ovarian cancer across two independent datasets, PLoS One, 2010, vol. 5, no. 3, p. e9615.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Xu, Q., Xu, H., Deng, R., Wang, Z., Li, N., Qi, Z., Zhao, J., and Huang, W., Multi-omics analysis reveals prognostic value of tumor mutation burden in hepatocellular carcinoma, Cancer Cell Int., 2021, vol. 21, no. 1, pp. 1—15.

    Article  Google Scholar 

  20. Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P.W., and Levine, D.A., Inferring tumour purity and stromal and immune cell admixture from expression data, Nat. Commun., 2013, vol. 4, no. 1, pp. 1—11.

    Article  CAS  Google Scholar 

  21. Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W. and Smyth, G.K., limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 2015, vol. 43, no. 7, р. е47.

  22. Langfelder, P. and Horvath, S., WGCNA: an R package for weighted correlation network analysis, BMC Bioinf., 2008, vol. 9, p. 559.

    Article  Google Scholar 

  23. Liu, B., Chen, X., Zhan, Y., Wu, B. and Pan, S., Identification of a gene signature for renal cell carcinoma–associated fibroblasts mediating cancer progression and affecting prognosis, Front. Cell Dev. Biol., 2021, p. 1914.

  24. Rizvi, A.A., Karaesmen, E., Morgan, M., Preus, L., Wang, J., Sovic, M., Hahn, T. and Sucheston-Campbell, L.E., gwasurvivr: an R package for genome-wide survival analysis, Bioinformatics, 2019, vol. 35, no. 11, pp. 1968—1970.

    Article  PubMed  CAS  Google Scholar 

  25. Chen, B., Khodadoust, M.S., Liu, C.L., Newman, A.M., and Alizadeh, A.A., Profiling tumor infiltrating immune cells with CIBERSORT, in Cancer Systems Biology, Clifton, NJ, Springer, 2018, vol. 1711, pp. 243–259. https://doi.org/10.1007/978-1-4939-7493-1_12.

  26. Reimand, J., Isserlin, R., Voisin, V., Kucera, M., Tannus-Lopes, C., Rostamianfar, A., Wadi L., Meyer, M., Wong, J., and Xu, C., Pathway enrichment analysis and visualization of omics data using g: Profiler, GSEA, Cytoscape and Enrichment Map, Nat. Protoc., 2019, vol. 14, no. 2, pp. 482—517.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Geeleher, P., Cox, N., and Huang, R.S., pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels, PLoS One, 2014, vol. 9, no. 9, p. e107468.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Suzuki, T., Curow, C., Wang, H., Martinez, A., Hojo, N., and Unternaehrer, J., Snail-mediated regulation of MMP1 expression in the context of ovarian cancer invasiveness, Proceedings of the American Association for Cancer Research Annual Meeting 2021, Philadelphia (PA): AACR, Cancer Res., 2021, vol. 81, suppl. 13, р. 2881.

  29. Nagano, H., Hashimoto, N., Nakayama, A., Suzuki, S., Miyabayashi, Y., Yamato, A., Higuchi, S., Fujimoto, M., Sakuma, I., and Beppu, M., p53-inducible DPYSL4 associates with mitochondrial supercomplexes and regulates energy metabolism in adipocytes and cancer cells, Proc. Natl. Acad. Sci., 2018, vol. 115, no. 33, pp. 8370—8375.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Rakoczy, J., Fernandez-Valverde, S.L., Glazov, E.A., Wainwright, E.N., Sato, T., Takada, S., Combes, A.N., Korbie, D.J., Miller, D., and Grimmond, S.M., MicroRNAs-140-5p/140-3p modulate Lеуdig cell numbers in the developing mouse testis, Biol. Reprod., 2013, vol. 88, no. 6, p. 143. https://doi.org/10.1095/biolreprod.113.107607

    Article  PubMed  Google Scholar 

  31. Matei, D., Emerson, R., Lai, Y., Baldridge, L., Rao, J., Yiannoutsos, C., and Donner, D., Autocrine activation of PDGFRα promotes the progression of ovarian cancer, Oncogene, 2006, vol. 25, no. 14, pp. 2060—2069.

    Article  PubMed  CAS  Google Scholar 

  32. Tanaka, K.-I., Matsumoto, E., Higashimaki, Y., Katagiri, T., Sugimoto, T., Seino, S. and Kaji, H., Role of osteoglycin in the linkage between muscle and bone, J. Biol. Chem., 2012, vol. 287, no. 15, pp. 11616—11628.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Chen, H., Yang, L., and Sun, W., Elevated OGN expression correlates with the EMT signature and poor prognosis in ovarian carcinoma, Int. J. Clin. Exp. Pathol., 2019, vol. 12, no. 2, p. 584.

    PubMed  PubMed Central  CAS  Google Scholar 

  34. Chiu, H.-W., Chang, J.-S., Lin, H.-Y., Lee, H.-H., Kuei, C.-H., Lin, C.-H., Huang, H.-M., and Lin, Y.-F., FBXL7 upregulation predicts a poor prognosis and associates with a possible mechanism for paclitaxel resistance in ovarian cancer, J. Clin. Med., 2018, vol. 7, no. 10, p. 330.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Ziani, L., Chouaib, S., and Thiery, J., Alteration of the antitumor immune response by cancer-associated fibroblasts, Front. Immunol., 2018, p. 414.

  36. Sun, Q., Zhang, B., Hu, Q., Qin, Y., Xu, W., Liu, W., Yu, X., and Xu, J., The impact of cancer-associated fibroblasts on major hallmarks of pancreatic cancer, Theranostics, 2018, vol. 8, no. 18, p. 5072.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Baker, A.T., Abuwarwar, M.H., Poly, L., Wilkins, S., and Fletcher, A.L., Cancer-associated fibroblasts and T cells: from mechanisms to outcomes, J. Immunol., 2021, vol. 206, no. 2, pp. 310—320.

    Article  PubMed  CAS  Google Scholar 

  38. dos Santos Pereira, J., de Oliveira Nóbrega, F.J., Vasconcelos, R.G., Câmara, A.C.d.S.M., de Souza, L.B., and Queiroz, L.M.G., Myofibroblasts and mast cells: influences on biological behavior of odontogenic lesions, Ann. Diagn. Pathol., 2018, vol. 34, pp. 66—71.

    Article  Google Scholar 

  39. Steinman, R.M., The dendritic cell system and its role in immunogenicity, Annu. Rev. Immunol., 1991, vol. 9, no. 1, pp. 271—296.

    Article  PubMed  CAS  Google Scholar 

  40. Cheng, J., Deng, Y., Yi, H., Wang, G., Fu, B., Chen, W., Liu, W., Tai, Y., Peng, Y., and Zhang, Q., Hepatic carcinoma-associated fibroblasts induce IDO-producing regulatory dendritic cells through IL-6-mediated STAT3 activation, Oncogenesis, 2016, vol. 5, no. 2, р. е198.

  41. Cai, J., Gong, L., Li G., Guo, J., Yi, X., and Wang, Z., Exosomes in ovarian cancer as cites promote epithelial–mesenchymal transition of ovarian cancer cells by delivery of miR-6780b-5p, Cell Death Dis., 2021, vol. 12, no. 2, pp. 1—17.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Vergara, D., Merlot, B., Lucot, J.-P., Collinet, P., Vinatier, D., Fournier, I., and Salzet, M., Epithelial–mesenchymal transition in ovarian cancer, Cancer Lett., 2010, vol. 291, no. 1, pp. 59—66.

    Article  PubMed  CAS  Google Scholar 

  43. He, L., Zhu, W., Chen, Q., Yuan, Y., Wang, Y., Wang, J., and Wu, X., Ovarian cancer cell-secreted exosomal miR-205 promotes metastasis by inducing angiogenesis, Theranostics, 2019, vol. 9, no. 26, p. 8206.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Pokhriyal, R., Hariprasad, R., Kumar, L. and Hariprasad, G., Chemotherapy resistance in advanced ovarian cancer patients, Biomarkers Cancer, 2019, vol. 11, p. 1179299X19860815.

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This work was supported by ongoing institutional funding. No additional grants to carry out or direct this particular research were obtained.

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Jing Bai, Aijun Chen and Youzhen Luo carried out the conception and design of the research, Zhijun Jiang participated in the acquisition of data. Xiuping Zhao carried out the analysis and interpretation of data. Nana Wang participated in the design of the study and performed the statistical analysis. Jing Bai, Aijun Chen and Youzhen Luo conceived of the study, and participated in its design and coordination and drafted the manuscript and revision of manuscript for important intellectual content. All authors read and approved the final manuscript.

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Correspondence to A. J. Chen or Y. Z. Luo.

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Bai, J., Jiang, Z.J., Zhao, X.P. et al. Cancer-Associated Fibroblast-Related Genes Are Associated with Prognosis of Patients with Ovarian Cancer. Russ J Genet 59 (Suppl 2), S208–S218 (2023). https://doi.org/10.1134/S1022795423140028

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