Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 15:16:1590363.
doi: 10.3389/fphar.2025.1590363. eCollection 2025.

Integrative analysis of m6A-SNPs and single-cell RNA sequencing reveals key drivers of endocrine combined with CDK4/6 inhibitor therapy resistance in ER+ breast cancer

Ruijie Ming #  1   2 , Han Zhang #  1 , Huan Wu  1 , Fangbiao Zhan  2   3 , Xiaoping Huang  1   2 , Huawen Liu  1   2
Affiliations

Integrative analysis of m6A-SNPs and single-cell RNA sequencing reveals key drivers of endocrine combined with CDK4/6 inhibitor therapy resistance in ER+ breast cancer

Ruijie Ming et al. Front Pharmacol. .

Abstract

Background: Endocrine therapy combined with CDK4/6 inhibitors remains a standard treatment for ER+ breast cancer, yet resistance is a prevalent challenge. This study explores the role of N6-methyladenosine (m6A) modifications, influenced by m6A-SNPs, in shaping therapy resistance, utilizing single-cell RNA sequencing to delineate the underlying molecular mechanisms.

Methods: We integrated genome-wide association study data with single-cell transcriptomic profiles from ER+ breast cancer patients, focusing on differences between resistant and sensitive responses to CDK4/6 inhibitors. m6A-SNPs were identified and analyzed for their impact on gene expression and interactions with RNA-binding proteins, with a particular focus on their roles within key cellular pathways.

Results: The study identified crucial m6A-SNPs associated with therapy resistance. Notably, changes in the expression of FILIP1L and TOM1L1, related to these SNPs, were mapped using pseudotime trajectory analysis, which traced the evolution from sensitive to resistant cellular states. FILIP1L and TOM1L1 exhibited dynamic expression changes along the trajectory, correlating with significant shifts in cell fate decisions. These findings underscore their potential roles as mediators in the development of resistance, particularly through their involvement in the PI3K-Akt and Wnt signaling pathways, critical in cancer progression and drug resistance.

Conclusion: Our findings emphasize the importance of m6A-SNPs in influencing resistance to therapy in ER+ breast cancer. The dynamic regulation of FILIP1L and TOM1L1 along the developmental trajectory of tumor cells from sensitivity to resistance provides insights into the molecular complexity of therapy resistance. These results pave the way for developing targeted therapies that modify m6A-driven pathways, offering new strategies to counteract resistance and improve patient outcomes.

Keywords: CDK4/6 inhibitor; ER+ breast cancer; m6A methylation; single nucleotide polymorphism; therapy resistance.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Flowchart of the study.
FIGURE 2
FIGURE 2
Manhattan plot of ER+ breast cancer-associated m6A-SNPs.
FIGURE 3
FIGURE 3
Functional enrichment analysis of genes related to m6A-SNPs associated with ER+ breast cancer.
FIGURE 4
FIGURE 4
Differential expression and m6A modification sites of m6A-SNPs related genes. Differentially expressed genes between ER+ breast cancer and normal tissues in TCGA-BRCA cohort (A). M6A modifications near the rs1017968 (FILIP1L, (B), rs1802212 and rs4829 (TOM1L1, (C), and rs2267372 and rs9610915 (MAFF, (D). In B-D, the x-axis represents the nucleotide sequence containing the target SNP position, while the y-axis indicates the score assigned by SRAMP for the presence of an m6A peak. In the plot, taller black vertical lines denote a higher predicted probability of m6A modification at that specific sequence position.
FIGURE 5
FIGURE 5
Interaction of m6A-SNPs, rs1017968 (A), rs1802212 (B), rs4829 (C), rs2267372 (D) and rs9610915 (E), and RNA-binding proteins. The x-axis represents the genomic coordinates. The y-axis, from top to bottom, displays gene expression profiles across 54 tissues from the GTEx database, the distribution of RNA-binding proteins, transcription levels in various cell lines, DNase I hypersensitive sites, and SNPs located within the genomic region. The yellow vertical line indicates the genomic position of the target SNP.
FIGURE 6
FIGURE 6
Identification and characterization of resistant subpopulations in tumor cells. (A) UMAP Clustering of Tumor Cell Subpopulations. (B) Distribution of Resistant and Non-resistant Cells in Clusters. (C) IC50 Values for Ribociclib across Tumor Subpopulations. (D) Differential Gene Expression in Tumor_Resistant Cells. (E) Pathway Analysis of Upregulated Genes. (F) Kaplan-Meier Survival Curves for Resistant and Sensitive Subgroups. (G) Multivariable Cox regression analysis of overall survival was performed based on three factors: the score (High vs Low), age, and clinical stage (III/IV vs I/II).
FIGURE 7
FIGURE 7
Metabolic features of cell subpopulations. Metabolic profile differences between Tumor_Resistant and Tumor_Sensitive groups through GSVA (A) and scFEA (B). (C,D) Correlation Between Ribociclib IC50 and Caffeine Metabolism.
FIGURE 8
FIGURE 8
Pseudotime trajectory analysis of tumor cell differentiation. (A) Developmental trajectory of tumor cells. (B) BEAM analysis at node 1. (C) Expression patterns of FILIP1L, MAFF and TOM1L1.
FIGURE 9
FIGURE 9
Gene enrichment analysis in cell fate clusters. Functional enrichment in cluster 1 (A), cluster 2 (B) and cluster 3 (C).
FIGURE 10
FIGURE 10
Analysis of Protein-Protein Interactions and Correlations Between Resistant Subgroup Characteristic Genes and m6A-SNPs Related Genes. (A) Protein-Protein Interaction Network. (B) Correlation Analysis of m6A-SNPs Related and Resistant Subgroup Characteristic Genes.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Alghamdi N., Chang W., Dang P., Lu X., Wan C., Gampala S., et al. (2021). A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq data. Genome Res. 31 (10), 1867–1884. 10.1101/gr.271205.120 - DOI - PMC - PubMed
    1. Alves C. L., Ditzel H. J. (2023). Drugging the PI3K/AKT/mTOR pathway in ER+ breast cancer. Int. J. Mol. Sci. 24 (5), 4522. 10.3390/ijms24054522 - DOI - PMC - PubMed
    1. Blakely B., Shin S., Jin K. (2023). Overview of the therapeutic strategies for ER positive breast cancer. Biochem. Pharmacol. 212, 115552. 10.1016/j.bcp.2023.115552 - DOI - PMC - PubMed
    1. Chen D., Gu X., Nurzat Y., Xu L., Li X., Wu L., et al. (2024). Writers, readers, and erasers RNA modifications and drug resistance in cancer. Mol. Cancer 23 (1), 178. 10.1186/s12943-024-02089-6 - DOI - PMC - PubMed
    1. Chevalier C., Collin G., Descamps S., Touaitahuata H., Simon V., Reymond N., et al. (2016a). TOM1L1 drives membrane delivery of MT1-MMP to promote ERBB2-induced breast cancer cell invasion. Nat. Commun. 7, 10765. 10.1038/ncomms10765 - DOI - PMC - PubMed

LinkOut - more resources