Abstract
Purpose
The aim of the study was to construct a risk score model based on m6A-related targets to predict overall survival and immunotherapy response in ovarian cancer.
Methods
The gene expression profiles of 24 m6A regulators were extracted. Survival analysis screened 9 prognostic m6A regulators. Next, consensus clustering analysis was applied to identify clusters of ovarian cancer patients. Furthermore, 47 phenotype-related differentially expressed genes, strongly correlated with 9 prognostic m6A regulators, were screened and subjected to univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression. Ultimately, a nomogram was constructed which presented a strong ability to predict overall survival in ovarian cancer.
Results
CBLL1, FTO, HNRNPC, METTL3, METTL14, WTAP, ZC3H13, RBM15B and YTHDC2 were associated with worse overall survival (OS) in ovarian cancer. Three m6A clusters were identified, which were highly consistent with the three immune phenotypes. What is more, a risk model based on seven m6A-related targets was constructed with distinct prognosis. In addition, the low-risk group is the best candidate population for immunotherapy.
Conclusion
We comprehensively analyzed the m6A modification landscape of ovarian cancer and detected seven m6A-related targets as an independent prognostic biomarker for predicting survival. Furthermore, we divided patients into high- and low-risk groups with distinct prognosis and select the optimum population which may benefit from immunotherapy and constructed a nomogram to precisely predict ovarian cancer patients’ survival time and visualize the prediction results.
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Data availability
Publicly available datasets were analyzed in this study. The datasets generated and/or analyzed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE140082), TCGA datasets (https://portal.gdc.cancer.gov/legacy-archive/search/f) and GTEx (https://commonfund.nih.gov/GTEx/). Additional data not presented in the manuscript can be obtained by contacting the authors.
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Acknowledgements
We sincerely thank the data provided by TCGA and GEO databases, the equipment and experimental guidance of Renmin Hospital Central Laboratory, and thank all the staff of obstetrics and gynecology for their support during the study.
Funding
This work was supported by Key Research and Development Program of Hubei Province (grant number 2020BCB023); the National Natural Science Foundation of China (grant number 82071655, 81860276); Young Teacher Qualification Project of the Fundamental Research Funds for the Central Universities (2042020kf0088); China Medical Association Clinical Medical Research Special Fund Project (grant number 17020310700); the Fundamental Research Funds for the Central Universities (grant number 2042020kf1013); Educational and Teaching Reform Research Project (grant number 413200095) and Graduate credit course projects (grant number 413000206).
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F-FD and S-YL collected and initially screened the data. WH, F-FD, L-BS and Y-XC guided the research ideas of the full text. WT performed a visual analysis of the data and was the main contributor to the manuscript. Z-MD improved the writing style and addressed grammatical errors. All the authors read and approved the final manuscript.
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432_2022_4162_MOESM1_ESM.pdf
Supplementary file1 The expression and genetic alteration of m6A regulators in ovarian cancer. A The heatmap of 24 m6A regulators in 738 normal controls (n = 88) and ovarian cancer (n = 379). B The mutation frequency of 24 m6A regulators in ovarian cancer from the TCGA cohort. The upper bar plot represents TMB. The right bar plot represents the proportion of each variant type. The stacked bar plot below represents the fraction of conversions in each sample. C The expression of 24 m6A regulators between normal tissues and ovarian cancers. Tumor, red; normal, blue. D The copy number variation (CNV) frequency of 24 m6A regulators in the TCGA dataset. The height of the column represents the alteration frequency, and the color represents gains or losses. The deletion, green dot; The amplification, red dot. E The location of CNV alteration of m6A regulators on 23 chromosomes. (PDF 15845 KB)
432_2022_4162_MOESM2_ESM.pdf
Supplementary file2 Correlation analysis and unsupervised clustering of m6A regulators. A The PPI network analysis of the 24 m6A regulators. B The correlation analysis of 24 m6A regulators. C Consensus matrices of the TCGA and GSE140082 cohort for k = 2–5. D PCA of the total RNA expression profile. (PDF 2131 KB)
432_2022_4162_MOESM3_ESM.pdf
Supplementary file3 Relationship between diverse CNV patterns and mRNA expression of m6A regulators in the TCGA dataset. The mRNA expression of CBLL1 (A), FTO (B), HNRNPC (C), METTL3 (D), METTL14 (E), RBM15B (F), WTAP (G), YTHDC2 (H), and ZC3H13 (I)was higher in Copy number gains (A–I). (PDF 305 KB)
432_2022_4162_MOESM4_ESM.pdf
Supplementary file4 GSVA 757 enrichment analysis in distinct clusters. A M6Acluster B and C. B m6Acluster A and B. (PDF 715 KB)
432_2022_4162_MOESM5_ESM.pdf
Supplementary file5 Functional enrichment of m6A-related targets. A The venn diagram of 357 m6A phenotype-related genes. B correlation analysis of nine prognostic m6A and 48 overlapped genes. C Gene Ontology analysis of 357 DEGs. D The network between m6A and m6A related targets. (PDF 912 KB)
432_2022_4162_MOESM6_ESM.pdf
Supplementary file6 The relationship of risk score and clinical characteristics in ovarian cancer. A–C Expression of risk scores based on the patients’ grade (A), stage (B), and age (C). D–F Survival analysis of high- and low-risk groups in age >65 (p =0.016), grade III–IV (p <0.001) and stage III–IV (p <0.001). G The distribution and cutoff value of the risk score in ovarian cancer set. (PDF 564 KB)
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Tan, W., Liu, S., Deng, Z. et al. Gene signature of m6A-related targets to predict prognosis and immunotherapy response in ovarian cancer. J Cancer Res Clin Oncol 149, 593–608 (2023). https://doi.org/10.1007/s00432-022-04162-3
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DOI: https://doi.org/10.1007/s00432-022-04162-3