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Prognostic prediction and immune landscape analysis based on m6A methylation modification and vasculogenic mimicry in cervical cancer
1Department of Gynecology, Changzhou Hospital of Traditional Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, 213003 Changzhou, Jiangsu, China
2Department of Gynecology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, 210023 Nanjing, Jiangsu, China 3Department of Liver Disease, The Third People’s Hospital of Zhenjiang, 212009 Zhenjiang, Jiangsu, China
DOI: 10.22514/ejgo.2025.063 Vol.46,Issue 5,May 2025 pp.33-45
Submitted: 26 March 2024 Accepted: 07 May 2024
Published: 15 May 2025
*Corresponding Author(s): Minmin Yu E-mail: njyy022@njucm.edu.cn
Background: N6-methyladenosine (m6A) modifications are known to play a key role in the development and progression of cancer. Vasculogenic mimicry (VM) is a unique mechanism that can contribute to tumor recurrence and metastasis. However, the specific association between m6A regulators (MAGs) and VM-related genes (VRGs) in cervical cancer (CC) have yet to be elucidated. Methods: Risk signatures were constructed by Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. The predictive performance of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves. Patients were divided into high- and low-risk groups based on the median risk score, and differences in key parameters between the two groups were assessed in terms of tumor immune landscape and somatic mutations. Results: Based on univariate Cox regression analysis and LASSO regression analyses, we constructed an eight-gene prognostic signature (termed as the mVMscore). High- and low-mVMscore groups, based on median risk scores, were associated with different clinical outcomes and biological characteristics. Survival analysis further demonstrated that patients in the low-mVMscore group had a better survival rate than those in the high-mVMscore group. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) showed that immune cells were significantly enriched in the high-mVMscore group. Immune scores, estimate scores and stromal scores were lower than those of the low-risk group. Conclusions: We constructed a novel prognostic eight-gene signature (mVMscore) based on MAGs and VRGs which exhibited significant potential to predict the need for immunotherapy in patients with cervical cancer (CC). Collectively, our findings provide a new direction for assessing the prognosis of patients with CC and designing immunotherapy strategies.
Cervical cancer; m6A modification; Vasculogenic mimicry; Prognostic signature; Immunotherapy
Lu Zhang,Yu Li,Jingrui Yang,Xinmiao Xiong,Min Kang,Minmin Yu. Prognostic prediction and immune landscape analysis based on m6A methylation modification and vasculogenic mimicry in cervical cancer. European Journal of Gynaecological Oncology. 2025. 46(5);33-45.
[1] Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians. 2021; 71: 209–249.
[2] Gennigens C, Jerusalem G, Lapaille L, De Cuypere M, Streel S, Kridelka F, et al. Recurrent or primary metastatic cervical cancer: current and future treatments. ESMO Open. 2022; 7: 100579.
[3] Mayadev JS, Ke G, Mahantshetty U, Pereira MD, Tarnawski R, Toita T. Global challenges of radiotherapy for the treatment of locally advanced cervical cancer. International Journal of Gynecological Cancer. 2022; 32: 436–445.
[4] Wang Z, Zhou J, Zhang H, Ge L, Li J, Wang H. RNA m6A methylation in cancer. Molecular Oncology. 2023; 17: 195–229.
[5] Li S, Qi Y, Yu J, Hao Y, He B, Zhang M, et al. Nuclear aurora kinase a switches m6A reader YTHDC1 to enhance an oncogenic RNA splicing of tumor suppressor RBM4. Signal Transduction and Targeted Therapy. 2022; 7: 97.
[6] Xu P, Yang J, Chen Z, Zhang X, Xia Y, Wang S, et al. N6-methyladenosine modification of CENPF mRNA facilitates gastric cancer metastasis via regulating FAK nuclear export. Cancer Communications. 2023; 43: 685–705.
[7] Wilinski D, Dus M. N6-adenosine methylation controls the translation of insulin mRNA. Nature Structural & Molecular Biology. 2023; 30: 1260–1264.
[8] Yin H, Chen L, Piao S, Wang Y, Li Z, Lin Y, et al. M6A RNA methylation-mediated RMRP stability renders proliferation and progression of non-small cell lung cancer through regulating TGFBR1/SMAD2/SMAD3 pathway. Cell Death & Differentiation. 2023; 30: 605–617.
[9] Wang JJ, Chen DX, Zhang Y, Xu X, Cai Y, Wei WQ, et al. Elevated expression of the RNA-binding protein IGF2BP1 enhances the mRNA stability of INHBA to promote the invasion and migration of esophageal squamous cancer cells. Experimental Hematology & Oncology. 2023; 12: 75.
[10] Wan W, Ao X, Chen Q, Yu Y, Ao L, Xing W, et al. METTL3/IGF2BP3 axis inhibits tumor immune surveillance by upregulating N6-methyladenosine modification of PD-L1 mRNA in breast cancer. Molecular Cancer. 2022; 21: 60.
[11] Sun B, Zhang D, Zhao N, Zhao X. Epithelial-to-endothelial transition and cancer stem cells: two cornerstones of vasculogenic mimicry in malignant tumors. Oncotarget. 2017; 8: 30502–30510.
[12] Wei X, Chen Y, Jiang X, Peng M, Liu Y, Mo Y, et al. Mechanisms of vasculogenic mimicry in hypoxic tumor microenvironments. Molecular Cancer. 2021; 20: 7.
[13] Angara K, Borin TF, Rashid MH, Lebedyeva I, Ara R, Lin PC, et al. Corrigendum to “CXCR2-expressing tumor cells drive vascular mimicry in antiangiogenic therapy-resistant glioblastoma” neoplasia, October 2018, volume 20, issue 10, pages 1070–1082. Neoplasia: An International Journal for Oncology Research. 2019; 21: 156–157.
[14] Xu Y, Li Q, Li XY, Yang QY, Xu WW, Liu GL. Short-term anti-vascular endothelial growth factor treatment elicits vasculogenic mimicry formation of tumors to accelerate metastasis. Journal of Experimental & Clinical Cancer Research. 2012; 31: 16.
[15] Cannell IG, Sawicka K, Pearsall I, Wild SA, Deighton L, Pearsall SM, et al. FOXC2 promotes vasculogenic mimicry and resistance to anti-angiogenic therapy. Cell Reports. 2023; 42: 112791.
[16] Huang S, Wang X, Zhu Y, Wang Y, Chen J, Zheng H. SOX2 promotes vasculogenic mimicry by accelerating glycolysis via the lncRNA AC005392.2-GLUT1 axis in colorectal cancer. Cell Death & Disease. 2023; 14: 791.
[17] Shi Y, Shang J, Li Y, Zhong D, Zhang Z, Yang Q, et al. ITGA5 and ITGB1 contribute to Sorafenib resistance by promoting vasculogenic mimicry formation in hepatocellular carcinoma. Cancer Medicine. 2023; 12: 3786–3796.
[18] Jung E, Lee YH, Ou S, Kim TY, Shin SY. EGR1 Regulation of vasculogenic mimicry in the MDA-MB-231 triple-negative breast cancer cell line through the upregulation of KLF4 expression. International Journal of Molecular Sciences. 2023; 24: 14375.
[19] Huang Y, Zhu C, Liu P, Ouyang F, Luo J, Lu C, et al. L1CAM promotes vasculogenic mimicry formation by miR-143-3p-induced expression of hexokinase 2 in glioma. Molecular Oncology. 2023; 17: 664–685.
[20] Provance OK, Oria VO, Tran TT, Caulfield JI, Zito CR, Aguirre-Ducler A, et al. Vascular mimicry as a facilitator of melanoma brain metastasis. Cellular and Molecular Life Sciences. 2024; 81: 188.
[21] Sabazade S, Gill V, Herrspiegel C, Stalhammar G. Vasculogenic mimicry correlates to presenting symptoms and mortality in uveal melanoma. Journal of Cancer Research and Clinical Oncology. 2022; 148: 587–597.
[22] Liu X, He H, Zhang F, Hu X, Bi F, Li K, et al. m6A methylated EphA2 and VEGFA through IGF2BP2/3 regulation promotes vasculogenic mimicry in colorectal cancer via PI3K/AKT and ERK1/2 signaling. Cell Death & Disease. 2022; 13: 483.
[23] Wu Z, Wei N. METTL3-mediated HOTAIRM1 promotes vasculogenic mimicry icontributionsn glioma via regulating IGFBP2 expression. Journal of Translational Medicine. 2023; 21: 855.
[24] Xiong J, Lian W, Zhao R, Gao K. METTL3/MALAT1/ELAVL1 axis promotes tumor growth in ovarian cancer. OncoTargets and Therapy. 2024; 17: 85–97.
[25] Di Timoteo G, Dattilo D, Centron-Broco A, Colantoni A, Guarnacci M, Rossi F, et al. Modulation of circRNA metabolism by m6A modification. Cell Reports. 2020; 31: 107641.
[26] Wang J, Xia W, Huang Y, Li H, Tang Y, Li Y, et al. A vasculogenic mimicry prognostic signature associated with immune signature in human gastric cancer. Frontiers in Immunology. 2022; 13: 1016612.
[27] Wen T, Li T, Xu Y, Zhang Y, Pan H, Wang Y. The role of m6A epigenetic modifications in tumor coding and non-coding RNA processing. Cell Communication and Signaling. 2023; 21: 355.
[28] Wilkerson MD, Hayes DN. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics. 2010; 26: 1572–1573.
[29] Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16: 284–287.
[30] Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013; 14: 7.
[31] Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nature Methods. 2015; 12: 453–457.
[32] Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nature Communications. 2013; 4: 2612.
[33] Li Y, Chen Z, Wang X, Li X, Zhou J, Zhang Y. Clinical outcomes observation in stage IIB-IIIB cervical cancer treated by adjuvant surgery following concurrent chemoradiotherapy. BMC Cancer. 2021; 21: 442.
[34] Hu Y, Gong C, Li Z, Liu J, Chen Y, Huang Y, et al. Demethylase ALKBH5 suppresses invasion of gastric cancer via PKMYT1 m6A modification. Molecular Cancer. 2022; 21: 34.
[35] Xu Y, Song M, Hong Z, Chen W, Zhang Q, Zhou J, et al. The N6-methyladenosine METTL3 regulates tumorigenesis and glycolysis by mediating m6A methylation of the tumor suppressor LATS1 in breast cancer. Journal of Experimental & Clinical Cancer Research. 2023; 42: 10.
[36] Ge H, Luo H. Overview of advances in vasculogenic mimicry—a potential target for tumor therapy. Cancer Management and Research. 2018; 10: 2429–2437.
[37] Zhang Y, Bai J, Cheng R, Zhang D, Qiu Z, Liu T, et al. TAZ promotes vasculogenic mimicry in gastric cancer through the upregulation of TEAD4. Journal of Gastroenterology and Hepatology. 2022; 37: 714–726.
[38] Liu M, Ruan X, Liu X, Dong W, Wang D, Yang C, et al. The mechanism of BUD13 m6A methylation mediated MBNL1-phosphorylation by CDK12 regulating the vasculogenic mimicry in glioblastoma cells. Cell Death & Disease. 2022; 13: 1017.
[39] Cheng B, Xie M, Zhou Y, Li T, Liu W, Yu W, et al. Vascular mimicry induced by m6A mediated IGFL2-AS1/AR axis contributes to pazopanib resistance in clear cell renal cell carcinoma. Cell Death Discovery. 2023; 9: 121.
[40] Yang JP, Liao YD, Mai DM, Xie P, Qiang YY, Zheng LS, et al. Tumor vasculogenic mimicry predicts poor prognosis in cancer patients: a meta-analysis. Angiogenesis. 2016; 19: 191–200.
[41] Liu Y, Li F, Yang YT, Xu XD, Chen JS, Chen TL, et al. IGFBP2 promotes vasculogenic mimicry formation via regulating CD144 and MMP2 expression in glioma. Oncogene. 2019; 38: 1815–1831.
[42] Qiao K, Liu Y, Xu Z, Zhang H, Zhang H, Zhang C, et al. RNA m6A methylation promotes the formation of vasculogenic mimicry in hepatocellular carcinoma via Hippo pathway. Angiogenesis. 2021; 24: 83–96.
[43] Tao M, Li X, He L, Rong X, Wang H, Pan J, et al. Decreased RNA m6A methylation enhances the process of the epithelial mesenchymal transition and vasculogenic mimicry in glioblastoma. American Journal of Cancer Research. 2022; 12: 893–906.
[44] Lin X, Wang F, Chen J, Liu J, Lin YB, Li L, et al. N6-methyladenosine modification of CENPK mRNA by ZC3H13 promotes cervical cancer stemness and chemoresistance. Military Medical Research. 2022; 9: 19.
[45] Condic M, Ralser DJ, Klumper N, Ellinger J, Qureischi M, Egger EK, et al. Comprehensive analysis of N6-Methyladenosine (m6A) writers, erasers, and readers in cervical cancer. International Journal of Molecular Sciences. 2022; 23: 7165.
[46] Zhang Q, Zhang Y, Wang SZ, Wang N, Jiang WG, Ji YH, et al. Reduced expression of tissue factor pathway inhibitor-2 contributes to apoptosis and angiogenesis in cervical cancer. Journal of Experimental & Clinical Cancer Research. 2012; 31: 1.
[47] Peng T, Lin S, Meng Y, Gao P, Wu P, Zhi W, et al. LOXL2 small molecule inhibitor restrains malignant transformation of cervical cancer cells by repressing LOXL2-induced epithelial-mesenchymal transition (EMT). Cell Cycle. 2022; 21: 1827–1841.
[48] Cardoso LP, de Sousa SO, Gusson-Zanetoni JP, de Melo Moreira Silva LL, Frigieri BM, Henrique T, et al. Piperine reduces neoplastic progression in cervical cancer cells by downregulating the cyclooxygenase 2 pathway. Pharmaceuticals. 2023; 16: 103.
[49] De Nola R, Menga A, Castegna A, Loizzi V, Ranieri G, Cicinelli E, et al. The crowded crosstalk between cancer cells and stromal microenvironment in gynecological malignancies: biological pathways and therapeutic implication. International Journal of Molecular Sciences. 2019; 20: 2401.
[50] Jianyi D, Haili G, Bo Y, Meiqin Y, Baoyou H, Haoran H, et al. Myeloid-derived suppressor cells cross-talk with B10 cells by BAFF/BAFF-R pathway to promote immunosuppression in cervical cancer. Cancer Immunology, Immunotherapy. 2023; 72: 73–85.
[51] Ni H, Zhang H, Li L, Huang H, Guo H, Zhang L, et al. T cell-intrinsic STING signaling promotes regulatory T cell induction and immunosuppression by upregulating FOXP3 transcription in cervical cancer. Journal for Immunotherapy of Cancer. 2022; 10: e005151.
[52] Shen X, Wang C, Li M, Wang S, Zhao Y, Liu Z, et al. Identification of CD8+ T cell infiltration-related genes and their prognostic values in cervical cancer. Frontiers in Oncology. 2022; 12: 1031643.
[53] Yu S, Li X, Zhang J, Wu S. Development of a novel immune infiltration-based gene signature to predict prognosis and immunotherapy response of patients with cervical cancer. Frontiers in Immunology. 2021; 12: 709493.
[54] Dong X, Liao P, Liu X, Yang Z, Wang Y, Zhong W, et al. Construction and validation of a reliable disulfidptosis-related lncrnas signature of the subtype, prognostic, and immune landscape in colon cancer. International Journal of Molecular Sciences. 2023; 24: 12915.
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