Single-cell Sequencing and Multi-omics Integration Reveals a Lipid Metabolism-Based Prognostic Model for 5-Fluorouracil Resistance in Breast Cancer
DOI:
https://doi.org/10.71321/xdm8ef82Keywords:
Breast cancer, 5-Fluorouracil, Lipid metabolism, Prognostic model, Chemotherapy resistance, Single-cell sequencingAbstract
Background:Breast cancer is a major malignancy among women worldwide. Despite therapeutic advances, resistance to 5-fluorouracil (5-Fu) limits treatment efficacy. Lipid metabolism reprogramming may play a critical role in this resistance, but its mechanisms remain unclear.
Methods:We integrated single-cell sequencing data and multi-omics analysis to explore molecular characteristics associated with 5-Fu resistance. Differential gene expression analysis and Cox regression were used to construct a prognostic risk model, validated in independent cohorts.
Results:We developed a three-gene prognostic model (PDLIM4, SDC1, EMP1) with robust predictive performance. High-risk scores were associated with elevated lipid metabolism and distinct immune microenvironment features.
Conclusion:Lipid metabolism reprogramming contributes to 5-Fu resistance in breast cancer. Our model offers a tool for risk assessment and a potential basis for therapeutic strategies targeting lipid metabolism.
References
[1] F. Bray, M. Laversanne, H. Sung, J. Ferlay, R.L. Siegel, I. Soerjomataram, A. Jemal, Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA. Cancer J. Clin. 74 (2024) 229–263. https://doi.org/10.3322/caac.21834.
[2] B. Han, R. Zheng, H. Zeng, S. Wang, K. Sun, R. Chen, L. Li, W. Wei, J. He, Cancer incidence and mortality in China, 2022, J. Natl. Cancer Cent. 4 (2024) 47–53. https://doi.org/10.1016/j.jncc.2024.01.006.
[3] R. Guo, J. Si, J. Xue, Y. Su, M. Mo, B. Yang, Q. Zhang, W. Chi, Y. Chi, J. Wu, Changing patterns and survival improvements of young breast cancer in China and SEER database, 1999-2017, Chin. J. Cancer Res. Chung-Kuo Yen Cheng Yen Chiu 31 (2019) 653–662. https://doi.org/10.21147/j.issn.1000-9604.2019.04.09.
[4] N. Sathiakumar, E. Delzell, M.A. Morrisey, C. Falkson, M. Yong, V. Chia, J. Blackburn, T. Arora, I. Brill, M.L. Kilgore, Mortality following bone metastasis and skeletal-related events among women with breast cancer: a population-based analysis of U.S. Medicare beneficiaries, 1999-2006, Breast Cancer Res. Treat. 131 (2012) 231–238. https://doi.org/10.1007/s10549-011-1721-x.
[5] D.B. Longley, D.P. Harkin, P.G. Johnston, 5-fluorouracil: mechanisms of action and clinical strategies, Nat. Rev. Cancer 3 (2003) 330–338. https://doi.org/10.1038/nrc1074.
[6] K. Bukowski, M. Kciuk, R. Kontek, Mechanisms of Multidrug Resistance in Cancer Chemotherapy, Int. J. Mol. Sci. 21 (2020) 3233. https://doi.org/10.3390/ijms21093233.
[7] A. Wong, S. Chen, L.K. Yang, Y. Kanagasundaram, K. Crasta, Lipid accumulation facilitates mitotic slippage-induced adaptation to anti-mitotic drug treatment, Cell Death Discov. 4 (2018) 109. https://doi.org/10.1038/s41420-018-0127-5.
[8] J.H. Jung, K. Taniguchi, H.M. Lee, M.Y. Lee, R. Bandu, K. Komura, K.Y. Lee, Y. Akao, K.P. Kim, Comparative lipidomics of 5-Fluorouracil-sensitive and -resistant colorectal cancer cells reveals altered sphingomyelin and ceramide controlled by acid sphingomyelinase (SMPD1), Sci. Rep. 10 (2020) 6124. https://doi.org/10.1038/s41598-020-62823-0.
[9] Y. Fu, G. Yang, F. Zhu, C. Peng, W. Li, H. Li, H.-G. Kim, A.M. Bode, Z. Dong, Z. Dong, Antioxidants decrease the apoptotic effect of 5-Fu in colon cancer by regulating Src-dependent caspase-7 phosphorylation, Cell Death Dis. 5 (2014) e983. https://doi.org/10.1038/cddis.2013.509.
[10] T. Barrett, S.E. Wilhite, P. Ledoux, C. Evangelista, I.F. Kim, M. Tomashevsky, K.A. Marshall, K.H. Phillippy, P.M. Sherman, M. Holko, A. Yefanov, H. Lee, N. Zhang, C.L. Robertson, N. Serova, S. Davis, A. Soboleva, NCBI GEO: archive for functional genomics data sets--update, Nucleic Acids Res. 41 (2013) D991-995. https://doi.org/10.1093/nar/gks1193.
[11] G. Stelzer, N. Rosen, I. Plaschkes, S. Zimmerman, M. Twik, S. Fishilevich, T.I. Stein, R. Nudel, I. Lieder, Y. Mazor, S. Kaplan, D. Dahary, D. Warshawsky, Y. Guan-Golan, A. Kohn, N. Rappaport, M. Safran, D. Lancet, The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses, Curr. Protoc. Bioinforma. 54 (2016) 1.30.1-1.30.33. https://doi.org/10.1002/cpbi.5.
[12] D. Sun, X. Guan, A.E. Moran, L.-Y. Wu, D.Z. Qian, P. Schedin, M.-S. Dai, A.V. Danilov, J.J. Alumkal, A.C. Adey, P.T. Spellman, Z. Xia, Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data, Nat. Biotechnol. 40 (2022) 527–538. https://doi.org/10.1038/s41587-021-01091-3.
[13] D. Szklarczyk, R. Kirsch, M. Koutrouli, K. Nastou, F. Mehryary, R. Hachilif, A.L. Gable, T. Fang, N.T. Doncheva, S. Pyysalo, P. Bork, L.J. Jensen, C. von Mering, The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest, Nucleic Acids Res. 51 (2023) D638–D646. https://doi.org/10.1093/nar/gkac1000.
[14] D. Zeng, Z. Ye, R. Shen, G. Yu, J. Wu, Y. Xiong, R. Zhou, W. Qiu, N. Huang, L. Sun, X. Li, J. Bin, Y. Liao, M. Shi, W. Liao, IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures, Front. Immunol. 12 (2021) 687975. https://doi.org/10.3389/fimmu.2021.687975.
[15] S. Mostafavi, D. Ray, D. Warde-Farley, C. Grouios, Q. Morris, GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function, Genome Biol. 9 Suppl 1 (2008) S4. https://doi.org/10.1186/gb-2008-9-s1-s4.
[16] P. Huang, D.-J. Ouyang, S. Chang, M.-Y. Li, L. Li, Q.-Y. Li, R. Zeng, Q.-Y. Zou, J. Su, P. Zhao, L. Pei, W.-J. Yi, Chemotherapy-driven increases in the CDKN1A/PTN/PTPRZ1 axis promote chemoresistance by activating the NF-κB pathway in breast cancer cells, Cell Commun. Signal. CCS 16 (2018) 92. https://doi.org/10.1186/s12964-018-0304-4.
[17] T. Saha Detroja, R. Detroja, S. Mukherjee, A.O. Samson, Identifying Hub Genes Associated with Neoadjuvant Chemotherapy Resistance in Breast Cancer and Potential Drug Repurposing for the Development of Precision Medicine, Int. J. Mol. Sci. 23 (2022) 12628. https://doi.org/10.3390/ijms232012628.
[18] Y. Du, Y. Han, X. Wang, H. Wang, Y. Qu, K. Guo, W. Ma, L. Fu, Identification of Immune-Related Breast Cancer Chemotherapy Resistance Genes via Bioinformatics Approaches, Front. Oncol. 12 (2022) 772723. https://doi.org/10.3389/fonc.2022.772723.
[19] S. Liutkauskiene, S. Grizas, K. Jureniene, J. Suipyte, A. Statnickaite, E. Juozaityte, Retrospective analysis of the impact of anthracycline dose reduction and chemotherapy delays on the outcomes of early breast cancer molecular subtypes, BMC Cancer 18 (2018) 453. https://doi.org/10.1186/s12885-018-4365-y.
[20] Y. Matsushita, H. Nakagawa, K. Koike, Lipid Metabolism in Oncology: Why It Matters, How to Research, and How to Treat, Cancers 13 (2021) 474. https://doi.org/10.3390/cancers13030474.
[21] C. Peetla, R. Bhave, S. Vijayaraghavalu, A. Stine, E. Kooijman, V. Labhasetwar, Drug resistance in breast cancer cells: biophysical characterization of and doxorubicin interactions with membrane lipids, Mol. Pharm. 7 (2010) 2334–2348. https://doi.org/10.1021/mp100308n.
[22] D.S. Kravchenko, E.I. Frolova, J.E. Kravchenko, S.P. Chumakov, [Role of PDLIM4 and c-Src in Breast Cancer Progression], Mol. Biol. (Mosk.) 50 (2016) 69–79. https://doi.org/10.7868/S0026898416010092.
[23] J. Jelinek, V. Gharibyan, M.R.H. Estecio, K. Kondo, R. He, W. Chung, Y. Lu, N. Zhang, S. Liang, H.M. Kantarjian, J.E. Cortes, J.-P.J. Issa, Aberrant DNA methylation is associated with disease progression, resistance to imatinib and shortened survival in chronic myelogenous leukemia, PloS One 6 (2011) e22110. https://doi.org/10.1371/journal.pone.0022110.
[24] M. He, D.K. Vanaja, R.J. Karnes, C.Y.F. Young, Epigenetic regulation of Myc on retinoic acid receptor beta and PDLIM4 in RWPE1 cells, The Prostate 69 (2009) 1643–1650. https://doi.org/10.1002/pros.21013.
[25] D.S. Kravchenko, A.E. Ivanova, E.S. Podshivalova, S.P. Chumakov, PDLIM4/RIL-mediated regulation of Src and malignant properties of breast cancer cells, Oncotarget 11 (2020) 22–30. https://doi.org/10.18632/oncotarget.27410.
[26] S. Liao, C. Liu, G. Zhu, K. Wang, Y. Yang, C. Wang, Relationship between SDC1 and cadherin signalling activation in cancer, Pathol. Res. Pract. 216 (2020) 152756. https://doi.org/10.1016/j.prp.2019.152756.
[27] V. Nikolova, C.-Y. Koo, S.A. Ibrahim, Z. Wang, D. Spillmann, R. Dreier, R. Kelsch, J. Fischgräbe, M. Smollich, L.H. Rossi, W. Sibrowski, P. Wülfing, L. Kiesel, G.W. Yip, M. Götte, Differential roles for membrane-bound and soluble syndecan-1 (CD138) in breast cancer progression, Carcinogenesis 30 (2009) 397–407. https://doi.org/10.1093/carcin/bgp001.
[28] Y. Yang, X. Tao, C.-B. Li, C.-M. Wang, MicroRNA-494 acts as a tumor suppressor in pancreatic cancer, inhibiting epithelial-mesenchymal transition, migration and invasion by binding to SDC1, Int. J. Oncol. 53 (2018) 1204–1214. https://doi.org/10.3892/ijo.2018.4445.
[29] W. Tang, D.R. Morgan, M.O. Meyers, R.L. Dominguez, E. Martinez, K. Kakudo, P.F. Kuan, N. Banet, H. Muallem, K. Woodward, O. Speck, M.L. Gulley, Epstein-barr virus infected gastric adenocarcinoma expresses latent and lytic viral transcripts and has a distinct human gene expression profile, Infect. Agent. Cancer 7 (2012) 21. https://doi.org/10.1186/1750-9378-7-21.
[30] Z. Pap, Z. Pávai, L. Dénes, I. Kovalszky, J. Jung, An immunohistochemical study of colon adenomas and carcinomas: E-cadherin, Syndecan-1, Ets-1, Pathol. Oncol. Res. POR 15 (2009) 579–587. https://doi.org/10.1007/s12253-009-9157-x.
[31] Y.-W. Wang, H.-L. Cheng, Y.-R. Ding, L.-H. Chou, N.-H. Chow, EMP1, EMP 2, and EMP3 as novel therapeutic targets in human cancer, Biochim. Biophys. Acta Rev. Cancer 1868 (2017) 199–211. https://doi.org/10.1016/j.bbcan.2017.04.004.
[32] Q. Wang, D. Li, H. Ma, Z. Li, J. Wu, J. Qiao, J. Liu, J. Zhao, R. Ma, L. Tian, L. Zhang, J. Yang, J. Wang, S. Qin, Z. Su, Tumor cell-derived EMP1 is essential for cancer-associated fibroblast infiltration in tumor microenvironment of triple-negative breast cancer, Cell Death Dis. 16 (2025) 143. https://doi.org/10.1038/s41419-025-07464-9.
[33] A. Kumari, D. Pal Pathak, S. Asthana, Bile acids mediated potential functional interaction between FXR and FATP5 in the regulation of Lipid Metabolism, Int. J. Biol. Sci. 16 (2020) 2308–2322. https://doi.org/10.7150/ijbs.44774.
[34] J. Zhang, L.-Z. Gao, Y.-J. Chen, P.-P. Zhu, S.-S. Yin, M.-M. Su, Y. Ni, J. Miao, W.-L. Wu, H. Chen, K.L.R. Brouwer, C.-X. Liu, L. Xu, W. Jia, K. Lan, Continuum of Host-Gut Microbial Co-metabolism: Host CYP3A4/3A7 are Responsible for Tertiary Oxidations of Deoxycholate Species, Drug Metab. Dispos. Biol. Fate Chem. 47 (2019) 283–294. https://doi.org/10.1124/dmd.118.085670.
[35] J. Heo, R. Eki, T. Abbas, Deregulation of F-box proteins and its consequence on cancer development, progression and metastasis, Semin. Cancer Biol. 36 (2016) 33–51. https://doi.org/10.1016/j.semcancer.2015.09.015.
[36] R.S. Alli, A. Khar, Dendritic cells as natural adjuvants and modulators of immune response in cancer immunotherapy, Indian J. Biochem. Biophys. 39 (2002) 363–367.
[37] J. Liu, H.-J. Li, Y.-L. Luo, Y.-F. Chen, Y.-N. Fan, J.-Z. Du, J. Wang, Programmable Delivery of Immune Adjuvant to Tumor-Infiltrating Dendritic Cells for Cancer Immunotherapy, Nano Lett. 20 (2020) 4882–4889. https://doi.org/10.1021/acs.nanolett.0c00893.
[38] Y. Lu, W. Zhu, G.X. Zhang, J.C. Chen, Q.L. Wang, M.Y. Mao, S.C. Deng, L.P. Jin, H. Liu, Y.H. Kuang, Adenosine A2A receptor activation regulates the M1 macrophages activation to initiate innate and adaptive immunity in psoriasis, Clin. Immunol. Orlando Fla 266 (2024) 110309. https://doi.org/10.1016/j.clim.2024.110309.
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