Prognostic and Immunotherapeutic Value of Regulatory T Cell Marker Gene Signature in Melanoma
,
Abstract
Regulatory T cells (Tregs) are central to establishing an immunosuppressive tumor microenvironment (TME), which promotes cancer progression and influences therapeutic outcomes. However, the prognostic significance of Treg-related genes (TRGs) in predicting immunotherapy response in melanoma remains insufficiently characterized. This study seeks to elucidate the role of TRGs in the antitumor immune response of melanoma.
The ordinary transcriptome and single-cell RNA sequencing (scRNA-seq) data were obtained from the gene expression omnibus and the cancer genome atlas databases. A multi-tiered quality control process was applied to scRNA-seq data, followed by cell annotation, cell-cell communication, and enrichment analysis to investigate Treg function in the melanoma microenvironment. Weighted gene coexpression network analysis (WGCNA) was employed to identify modules associated with Treg infiltration.
Key prognostic genes were identified using univariate Cox regression analysis and integrated into a prognostic model through least absolute shrinkage and selection operator and stepwise regression methods. The analysis revealed a Treg-related gene signature (TRGS) comprising CHD3, FOSB, SEMA4D, PSME1, FYN, PRKACB, and ARID5A. Higher TRGS-based risk scores were significantly associated with worse prognoses, immune cell infiltration, and stromal scores.
TRGS was identified as an independent prognostic indicator for melanoma, offering novel insights into the role of Tregs in modulating the TME. This study highlights the potential clinical utility of TRGs in melanoma diagnostics and personalized immunotherapy, providing a robust foundation for future therapeutic strategies.
1. Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402(10400):485-502.
2. Liu J, Pei S, Zhang P, Jiang K, Luo B, Hou Z, et al. Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma. Bmc Cancer. 2023;23(1):388.
3. Wang JY, Wang EB, Swetter SM. What Is Melanoma? Jama-J Am Med Assoc. 2023;329(11):948.
4. Michielin O, Atkins MB, Koon HB, Dummer R, Ascierto PA. Evolving impact of long-term survival results on metastatic melanoma treatment. J Immunother Cancer. 2020;8(2):e000948.
5. Robert C, Grob JJ, Stroyakovskiy D, Karaszewska B, Hauschild A, Levchenko E, et al. Five-Year Outcomes with Dabrafenib plus Trametinib in Metastatic Melanoma. New Engl J Med. 2019;381(7):626-36.
6. Wolchok JD, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Lao CD, et al. Long-Term Outcomes With Nivolumab Plus Ipilimumab or Nivolumab Alone Versus Ipilimumab in Patients With Advanced Melanoma. J Clin Oncol. 2022;40(2):127-37.
7. Dimitriou F, Long GV, Menzies AM. Novel adjuvant options for cutaneous melanoma. Ann Oncol. 2021;32(7):854-65.
8. Tay C, Tanaka A, Sakaguchi S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy. Cancer Cell. 2023;41(3):450-65.
9. Li L, Yu R, Cai T, Chen Z, Lan M, Zou T, et al. Effects of immune cells and cytokines on inflammation and immunosuppression in the tumor microenvironment. Int Immunopharmacol. 2020;88:106939.
10. Moreno AM, Li Z, DuPage M. Treg programming and therapeutic reprogramming in cancer. Immunology. 2019;157(3):198-209.
11. Togashi Y, Shitara K, Nishikawa H. Regulatory T cells in cancer immunosuppression - implications for anticancer therapy. Nat Rev Clin Oncol. 2019;16(6):356-71.
12. Tanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27(1):109-18.
13. Shan F, Somasundaram A, Bruno TC, Workman CJ, Vignali D. Therapeutic targeting of regulatory T cells in cancer. Trends Cancer. 2022;8(11):944-61.
14. Harris RJ, Willsmore Z, Laddach R, Crescioli S, Chauhan J, Cheung A, et al. Enriched circulating and tumor-resident TGF-beta(+) regulatory B cells in patients with melanoma promote FOXP3(+) Tregs. Oncoimmunology. 2022;11(1):2104426.
15. Correll A, Tuettenberg A, Becker C, Jonuleit H. Increased regulatory T-cell frequencies in patients with advanced melanoma correlate with a generally impaired T-cell responsiveness and are restored after dendritic cell-based vaccination. Exp Dermatol. 2010;19(8):e213-21.
16. Noyes D, Bag A, Oseni S, Semidey-Hurtado J, Cen L, Sarnaik AA, et al. Tumor-associated Tregs obstruct antitumor immunity by promoting T cell dysfunction and restricting clonal diversity in tumor-infiltrating CD8+ T cells. J Immunother Cancer. 2022;10(5):e004605.
17. Jonsson G, Busch C, Knappskog S, Geisler J, Miletic H, Ringner M, et al. Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcomes. Clin Cancer Res. 2010;16(13):3356-67.
18. Bogunovic D, O'Neill DW, Belitskaya-Levy I, Vacic V, Yu YL, Adams S, et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. P Natl Acad Sci Usa. 2009;106(48):20429-34.
19. Cabrita R, Lauss M, Sanna A, Donia M, Skaarup LM, Mitra S, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561-5.
20. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018;175(4):984-97.
Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402(10400):485-502.
2. Liu J, Pei S, Zhang P, Jiang K, Luo B, Hou Z, et al. Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma. Bmc Cancer. 2023;23(1):388.
3. Wang JY, Wang EB, Swetter SM. What Is Melanoma? Jama-J Am Med Assoc. 2023;329(11):948.
4. Michielin O, Atkins MB, Koon HB, Dummer R, Ascierto PA. Evolving impact of long-term survival results on metastatic melanoma treatment. J Immunother Cancer. 2020;8(2):e000948.
5. Robert C, Grob JJ, Stroyakovskiy D, Karaszewska B, Hauschild A, Levchenko E, et al. Five-Year Outcomes with Dabrafenib plus Trametinib in Metastatic Melanoma. New Engl J Med. 2019;381(7):626-36.
6. Wolchok JD, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Lao CD, et al. Long-Term Outcomes With Nivolumab Plus Ipilimumab or Nivolumab Alone Versus Ipilimumab in Patients With Advanced Melanoma. J Clin Oncol. 2022;40(2):127-37.
7. Dimitriou F, Long GV, Menzies AM. Novel adjuvant options for cutaneous melanoma. Ann Oncol. 2021;32(7):854-65.
8. Tay C, Tanaka A, Sakaguchi S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy. Cancer Cell. 2023;41(3):450-65.
9. Li L, Yu R, Cai T, Chen Z, Lan M, Zou T, et al. Effects of immune cells and cytokines on inflammation and immunosuppression in the tumor microenvironment. Int Immunopharmacol. 2020;88:106939.
10. Moreno AM, Li Z, DuPage M. Treg programming and therapeutic reprogramming in cancer. Immunology. 2019;157(3):198-209.
11. Togashi Y, Shitara K, Nishikawa H. Regulatory T cells in cancer immunosuppression - implications for anticancer therapy. Nat Rev Clin Oncol. 2019;16(6):356-71.
12. Tanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27(1):109-18.
13. Shan F, Somasundaram A, Bruno TC, Workman CJ, Vignali D. Therapeutic targeting of regulatory T cells in cancer. Trends Cancer. 2022;8(11):944-61.
14. Harris RJ, Willsmore Z, Laddach R, Crescioli S, Chauhan J, Cheung A, et al. Enriched circulating and tumor-resident TGF-beta(+) regulatory B cells in patients with melanoma promote FOXP3(+) Tregs. Oncoimmunology. 2022;11(1):2104426.
15. Correll A, Tuettenberg A, Becker C, Jonuleit H. Increased regulatory T-cell frequencies in patients with advanced melanoma correlate with a generally impaired T-cell responsiveness and are restored after dendritic cell-based vaccination. Exp Dermatol. 2010;19(8):e213-21.
16. Noyes D, Bag A, Oseni S, Semidey-Hurtado J, Cen L, Sarnaik AA, et al. Tumor-associated Tregs obstruct antitumor immunity by promoting T cell dysfunction and restricting clonal diversity in tumor-infiltrating CD8+ T cells. J Immunother Cancer. 2022;10(5):e004605.
17. Jonsson G, Busch C, Knappskog S, Geisler J, Miletic H, Ringner M, et al. Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcome. Clin Cancer Res. 2010;16(13):3356-67.
18. Bogunovic D, O'Neill DW, Belitskaya-Levy I, Vacic V, Yu YL, Adams S, et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. P Natl Acad Sci Usa. 2009;106(48):20429-34.
19. Cabrita R, Lauss M, Sanna A, Donia M, Skaarup LM, Mitra S, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561-5.
20. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018;175(4):984-97.
21. Jin S, Plikus MV, Nie Q. CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics. Nat Protoc. 2024;
22. Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088.
23. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739-40.
24. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. Bmc Bioinformatics. 2008;9:559.
25. Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenetics. 2019;11(1):123.
26. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509-14.
27. Liu J, Pei S, Zhang P, Jiang K, Luo B, Hou Z, et al. Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma. Bmc Cancer. 2023;23(1):388.
28. Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22(6):bbab260.
29. Iglesias-Escudero M, Arias-Gonzalez N, Martinez-Caceres E. Regulatory cells and the effect of cancer immunotherapy. Mol Cancer. 2023;22(1):26.
30. Chen BJ, Zhao JW, Zhang DH, Zheng AH, Wu GQ. Immunotherapy of Cancer by Targeting Regulatory T cells. Int Immunopharmacol. 2022;104:108469.
31. Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Therapeut. 2021;221:107753.
32. Kalaora S, Nagler A, Wargo JA, Samuels Y. Mechanisms of immune activation and regulation: lessons from melanoma. Nat Rev Cancer. 2022;22(4):195-207.
33. Kirkwood JM, Tarhini AA, Panelli MC, Moschos SJ, Zarour HM, Butterfield LH, et al. Next generation of immunotherapy for melanoma. J Clin Oncol. 2008;26(20):3445-55.
34. Zhao S, Allis CD, Wang GG. The language of chromatin modification in human cancers. Nat Rev Cancer. 2021;21(7):413-30.
35. Chang S, Yim S, Park H. The cancer driver genes IDH1/2, JARID1C/ KDM5C, and UTX/ KDM6A: crosstalk between histone demethylation and hypoxic reprogramming in cancer metabolism. Exp Mol Med. 2019;51(6):1-17.
36. Menefee DS, McMasters A, Pan J, Li X, Xiao D, Waigel S, et al. Age-related transcriptome changes in melanoma patients with tumor-positive sentinel lymph nodes. Aging (Albany NY). 2020;12(24):24914-39.
37. Wang Q, Pan F, Li S, Huang R, Wang X, Wang S, et al. The prognostic value of the proteasome activator subunit gene family in skin cutaneous melanoma. J Cancer. 2019;10(10):2205-19.
38. Nyati KK, Zaman MM, Sharma P, Kishimoto T. Arid5a, an RNA-Binding Protein in Immune Regulation: RNA Stability, Inflammation, and Autoimmunity. Trends Immunol. 2020;41(3):255-68.
39. Kang Z, Wang J, Huang W, Liu J, Yan W. Identification of Transcriptional Heterogeneity and Construction of a Prognostic Model for Melanoma Based on Single-Cell and Bulk Transcriptome Analysis. Front Cell Dev Biol. 2022;10:874429.
40. Lu Q, Cai P, Yu Y, Liu Z, Chen G, Zeng Z. Sema4D correlates with tumour immune infiltration and is a prognostic biomarker in bladder cancer, renal clear cell carcinoma, melanoma and thymoma. Autoimmunity. 2021;54(5):294-302.
41. Liu C, Li S, Tang Y. Advances in the expression and function of Fyn in different human tumors. Clin Transl Oncol. 2023;25(10):2852-60.
42. Larriba E, de Juan RC, Garcia-Martinez A, Quintanar T, Rodriguez-Lescure A, Soto JL, et al. Identification of new targets for glioblastoma therapy based on a DNA expression microarray. Comput Biol Med. 2024;179:108833.
43. Ahmed F, Khan AA, Ansari HR, Haque A. A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer. Biology-Basel. 2022;11(12):1752.
44. Frost HR, Amos CI. Gene set selection via LASSO penalized regression (SLPR). Nucleic Acids Res. 2017;45(12):e114.
45. Kang J, Choi YJ, Kim IK, Lee HS, Kim H, Baik SH, et al. LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer. Cancer Res Treat. 2021;53(3):773-83.
46. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. Bmc Bioinformatics. 2008;9:559.
47. Horvath S, Dong J. Geometric interpretation of gene coexpression network analysis. Plos Comput Biol. 2008;4(8):e1000117.
48. Weston DJ, Gunter LE, Rogers A, Wullschleger SD. Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants. BMC Syst Biol. 2008;2:16.
49. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics. 2008;24(5):719-20.
2. Liu J, Pei S, Zhang P, Jiang K, Luo B, Hou Z, et al. Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma. Bmc Cancer. 2023;23(1):388.
3. Wang JY, Wang EB, Swetter SM. What Is Melanoma? Jama-J Am Med Assoc. 2023;329(11):948.
4. Michielin O, Atkins MB, Koon HB, Dummer R, Ascierto PA. Evolving impact of long-term survival results on metastatic melanoma treatment. J Immunother Cancer. 2020;8(2):e000948.
5. Robert C, Grob JJ, Stroyakovskiy D, Karaszewska B, Hauschild A, Levchenko E, et al. Five-Year Outcomes with Dabrafenib plus Trametinib in Metastatic Melanoma. New Engl J Med. 2019;381(7):626-36.
6. Wolchok JD, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Lao CD, et al. Long-Term Outcomes With Nivolumab Plus Ipilimumab or Nivolumab Alone Versus Ipilimumab in Patients With Advanced Melanoma. J Clin Oncol. 2022;40(2):127-37.
7. Dimitriou F, Long GV, Menzies AM. Novel adjuvant options for cutaneous melanoma. Ann Oncol. 2021;32(7):854-65.
8. Tay C, Tanaka A, Sakaguchi S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy. Cancer Cell. 2023;41(3):450-65.
9. Li L, Yu R, Cai T, Chen Z, Lan M, Zou T, et al. Effects of immune cells and cytokines on inflammation and immunosuppression in the tumor microenvironment. Int Immunopharmacol. 2020;88:106939.
10. Moreno AM, Li Z, DuPage M. Treg programming and therapeutic reprogramming in cancer. Immunology. 2019;157(3):198-209.
11. Togashi Y, Shitara K, Nishikawa H. Regulatory T cells in cancer immunosuppression - implications for anticancer therapy. Nat Rev Clin Oncol. 2019;16(6):356-71.
12. Tanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27(1):109-18.
13. Shan F, Somasundaram A, Bruno TC, Workman CJ, Vignali D. Therapeutic targeting of regulatory T cells in cancer. Trends Cancer. 2022;8(11):944-61.
14. Harris RJ, Willsmore Z, Laddach R, Crescioli S, Chauhan J, Cheung A, et al. Enriched circulating and tumor-resident TGF-beta(+) regulatory B cells in patients with melanoma promote FOXP3(+) Tregs. Oncoimmunology. 2022;11(1):2104426.
15. Correll A, Tuettenberg A, Becker C, Jonuleit H. Increased regulatory T-cell frequencies in patients with advanced melanoma correlate with a generally impaired T-cell responsiveness and are restored after dendritic cell-based vaccination. Exp Dermatol. 2010;19(8):e213-21.
16. Noyes D, Bag A, Oseni S, Semidey-Hurtado J, Cen L, Sarnaik AA, et al. Tumor-associated Tregs obstruct antitumor immunity by promoting T cell dysfunction and restricting clonal diversity in tumor-infiltrating CD8+ T cells. J Immunother Cancer. 2022;10(5):e004605.
17. Jonsson G, Busch C, Knappskog S, Geisler J, Miletic H, Ringner M, et al. Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcomes. Clin Cancer Res. 2010;16(13):3356-67.
18. Bogunovic D, O'Neill DW, Belitskaya-Levy I, Vacic V, Yu YL, Adams S, et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. P Natl Acad Sci Usa. 2009;106(48):20429-34.
19. Cabrita R, Lauss M, Sanna A, Donia M, Skaarup LM, Mitra S, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561-5.
20. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018;175(4):984-97.
Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma. Lancet. 2023;402(10400):485-502.
2. Liu J, Pei S, Zhang P, Jiang K, Luo B, Hou Z, et al. Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma. Bmc Cancer. 2023;23(1):388.
3. Wang JY, Wang EB, Swetter SM. What Is Melanoma? Jama-J Am Med Assoc. 2023;329(11):948.
4. Michielin O, Atkins MB, Koon HB, Dummer R, Ascierto PA. Evolving impact of long-term survival results on metastatic melanoma treatment. J Immunother Cancer. 2020;8(2):e000948.
5. Robert C, Grob JJ, Stroyakovskiy D, Karaszewska B, Hauschild A, Levchenko E, et al. Five-Year Outcomes with Dabrafenib plus Trametinib in Metastatic Melanoma. New Engl J Med. 2019;381(7):626-36.
6. Wolchok JD, Chiarion-Sileni V, Gonzalez R, Grob JJ, Rutkowski P, Lao CD, et al. Long-Term Outcomes With Nivolumab Plus Ipilimumab or Nivolumab Alone Versus Ipilimumab in Patients With Advanced Melanoma. J Clin Oncol. 2022;40(2):127-37.
7. Dimitriou F, Long GV, Menzies AM. Novel adjuvant options for cutaneous melanoma. Ann Oncol. 2021;32(7):854-65.
8. Tay C, Tanaka A, Sakaguchi S. Tumor-infiltrating regulatory T cells as targets of cancer immunotherapy. Cancer Cell. 2023;41(3):450-65.
9. Li L, Yu R, Cai T, Chen Z, Lan M, Zou T, et al. Effects of immune cells and cytokines on inflammation and immunosuppression in the tumor microenvironment. Int Immunopharmacol. 2020;88:106939.
10. Moreno AM, Li Z, DuPage M. Treg programming and therapeutic reprogramming in cancer. Immunology. 2019;157(3):198-209.
11. Togashi Y, Shitara K, Nishikawa H. Regulatory T cells in cancer immunosuppression - implications for anticancer therapy. Nat Rev Clin Oncol. 2019;16(6):356-71.
12. Tanaka A, Sakaguchi S. Regulatory T cells in cancer immunotherapy. Cell Res. 2017;27(1):109-18.
13. Shan F, Somasundaram A, Bruno TC, Workman CJ, Vignali D. Therapeutic targeting of regulatory T cells in cancer. Trends Cancer. 2022;8(11):944-61.
14. Harris RJ, Willsmore Z, Laddach R, Crescioli S, Chauhan J, Cheung A, et al. Enriched circulating and tumor-resident TGF-beta(+) regulatory B cells in patients with melanoma promote FOXP3(+) Tregs. Oncoimmunology. 2022;11(1):2104426.
15. Correll A, Tuettenberg A, Becker C, Jonuleit H. Increased regulatory T-cell frequencies in patients with advanced melanoma correlate with a generally impaired T-cell responsiveness and are restored after dendritic cell-based vaccination. Exp Dermatol. 2010;19(8):e213-21.
16. Noyes D, Bag A, Oseni S, Semidey-Hurtado J, Cen L, Sarnaik AA, et al. Tumor-associated Tregs obstruct antitumor immunity by promoting T cell dysfunction and restricting clonal diversity in tumor-infiltrating CD8+ T cells. J Immunother Cancer. 2022;10(5):e004605.
17. Jonsson G, Busch C, Knappskog S, Geisler J, Miletic H, Ringner M, et al. Gene expression profiling-based identification of molecular subtypes in stage IV melanomas with different clinical outcome. Clin Cancer Res. 2010;16(13):3356-67.
18. Bogunovic D, O'Neill DW, Belitskaya-Levy I, Vacic V, Yu YL, Adams S, et al. Immune profile and mitotic index of metastatic melanoma lesions enhance clinical staging in predicting patient survival. P Natl Acad Sci Usa. 2009;106(48):20429-34.
19. Cabrita R, Lauss M, Sanna A, Donia M, Skaarup LM, Mitra S, et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature. 2020;577(7791):561-5.
20. Jerby-Arnon L, Shah P, Cuoco MS, Rodman C, Su MJ, Melms JC, et al. A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade. Cell. 2018;175(4):984-97.
21. Jin S, Plikus MV, Nie Q. CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics. Nat Protoc. 2024;
22. Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun. 2021;12(1):1088.
23. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739-40.
24. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. Bmc Bioinformatics. 2008;9:559.
25. Engebretsen S, Bohlin J. Statistical predictions with glmnet. Clin Epigenetics. 2019;11(1):123.
26. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509-14.
27. Liu J, Pei S, Zhang P, Jiang K, Luo B, Hou Z, et al. Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma. Bmc Cancer. 2023;23(1):388.
28. Maeser D, Gruener RF, Huang RS. oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data. Brief Bioinform. 2021;22(6):bbab260.
29. Iglesias-Escudero M, Arias-Gonzalez N, Martinez-Caceres E. Regulatory cells and the effect of cancer immunotherapy. Mol Cancer. 2023;22(1):26.
30. Chen BJ, Zhao JW, Zhang DH, Zheng AH, Wu GQ. Immunotherapy of Cancer by Targeting Regulatory T cells. Int Immunopharmacol. 2022;104:108469.
31. Xiao Y, Yu D. Tumor microenvironment as a therapeutic target in cancer. Pharmacol Therapeut. 2021;221:107753.
32. Kalaora S, Nagler A, Wargo JA, Samuels Y. Mechanisms of immune activation and regulation: lessons from melanoma. Nat Rev Cancer. 2022;22(4):195-207.
33. Kirkwood JM, Tarhini AA, Panelli MC, Moschos SJ, Zarour HM, Butterfield LH, et al. Next generation of immunotherapy for melanoma. J Clin Oncol. 2008;26(20):3445-55.
34. Zhao S, Allis CD, Wang GG. The language of chromatin modification in human cancers. Nat Rev Cancer. 2021;21(7):413-30.
35. Chang S, Yim S, Park H. The cancer driver genes IDH1/2, JARID1C/ KDM5C, and UTX/ KDM6A: crosstalk between histone demethylation and hypoxic reprogramming in cancer metabolism. Exp Mol Med. 2019;51(6):1-17.
36. Menefee DS, McMasters A, Pan J, Li X, Xiao D, Waigel S, et al. Age-related transcriptome changes in melanoma patients with tumor-positive sentinel lymph nodes. Aging (Albany NY). 2020;12(24):24914-39.
37. Wang Q, Pan F, Li S, Huang R, Wang X, Wang S, et al. The prognostic value of the proteasome activator subunit gene family in skin cutaneous melanoma. J Cancer. 2019;10(10):2205-19.
38. Nyati KK, Zaman MM, Sharma P, Kishimoto T. Arid5a, an RNA-Binding Protein in Immune Regulation: RNA Stability, Inflammation, and Autoimmunity. Trends Immunol. 2020;41(3):255-68.
39. Kang Z, Wang J, Huang W, Liu J, Yan W. Identification of Transcriptional Heterogeneity and Construction of a Prognostic Model for Melanoma Based on Single-Cell and Bulk Transcriptome Analysis. Front Cell Dev Biol. 2022;10:874429.
40. Lu Q, Cai P, Yu Y, Liu Z, Chen G, Zeng Z. Sema4D correlates with tumour immune infiltration and is a prognostic biomarker in bladder cancer, renal clear cell carcinoma, melanoma and thymoma. Autoimmunity. 2021;54(5):294-302.
41. Liu C, Li S, Tang Y. Advances in the expression and function of Fyn in different human tumors. Clin Transl Oncol. 2023;25(10):2852-60.
42. Larriba E, de Juan RC, Garcia-Martinez A, Quintanar T, Rodriguez-Lescure A, Soto JL, et al. Identification of new targets for glioblastoma therapy based on a DNA expression microarray. Comput Biol Med. 2024;179:108833.
43. Ahmed F, Khan AA, Ansari HR, Haque A. A Systems Biology and LASSO-Based Approach to Decipher the Transcriptome-Interactome Signature for Predicting Non-Small Cell Lung Cancer. Biology-Basel. 2022;11(12):1752.
44. Frost HR, Amos CI. Gene set selection via LASSO penalized regression (SLPR). Nucleic Acids Res. 2017;45(12):e114.
45. Kang J, Choi YJ, Kim IK, Lee HS, Kim H, Baik SH, et al. LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer. Cancer Res Treat. 2021;53(3):773-83.
46. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. Bmc Bioinformatics. 2008;9:559.
47. Horvath S, Dong J. Geometric interpretation of gene coexpression network analysis. Plos Comput Biol. 2008;4(8):e1000117.
48. Weston DJ, Gunter LE, Rogers A, Wullschleger SD. Connecting genes, coexpression modules, and molecular signatures to environmental stress phenotypes in plants. BMC Syst Biol. 2008;2:16.
49. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. Bioinformatics. 2008;24(5):719-20.
| Files | ||
| Issue | Vol 24 No 2 (2025) | |
| Section | Original Article(s) | |
| Keywords | ||
| Immunotherapy Melanoma Regulatory T cell Single cell sequencing Tumor microenvironment | ||
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How to Cite
1.
Liu Y, Liu J, Jiang K, Cheng X, Di S, Tang J, Luo B. Prognostic and Immunotherapeutic Value of Regulatory T Cell Marker Gene Signature in Melanoma. Iran J Allergy Asthma Immunol. 2025;24(2):237-253.
