Single-cell Transcriptome Analysis Reveals Immune Microenvironment Changes and Insights into the Transition from DCIS to IDC with Associated Prognostic Genes

Overview

Journal J Transl Med

Publisher Biomed Central

Specialties Biomedical Engineering
General Medicine

Date 2024 Oct 3

PMID 39363164

Authors

Yidi Sun

Zhuoyu Pan

Ziyi Wang

Haofei Wang

Leyi Wei

Feifei Cui

Quan Zou

Zilong Zhang

Affiliations

Soon will be listed here.

Abstract

Background: Ductal carcinoma in situ (DCIS) of the breast is an early stage of breast cancer, and preventing its progression to invasive ductal carcinoma (IDC) is crucial for the early detection and treatment of breast cancer. Although single-cell transcriptome analysis technology has been widely used in breast cancer research, the biological mechanisms underlying the transition from DCIS to IDC remain poorly understood.

Results: We identified eight cell types through cell annotation, finding significant differences in T cell proportions between DCIS and IDC. Using this as a basis, we performed pseudotime analysis on T cell subpopulations, revealing that differentially expressed genes primarily regulate immune cell migration and modulation. By intersecting WGCNA results of T cells highly correlated with the subtypes and the differentially expressed genes, we identified six key genes: FGFBP2, GNLY, KLRD1, TYROBP, PRF1, and NKG7. Excluding PRF1, the other five genes were significantly associated with overall survival in breast cancer, highlighting their potential as prognostic biomarkers.

Conclusions: We identified immune cells that may play a role in the progression from DCIS to IDC and uncovered five key genes that can serve as prognostic markers for breast cancer. These findings provide insights into the mechanisms underlying the transition from DCIS to IDC, offering valuable perspectives for future research. Additionally, our results contribute to a better understanding of the biological processes involved in breast cancer progression.

References

Mabbott N, Baillie J, Brown H, Freeman T, Hume D . An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics. 2013; 14:632. PMC: 3849585. DOI: 10.1186/1471-2164-14-632. View

Zhang Z, Sun Z, Gao D, Hao Y, Lin H, Liu F . Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression. IET Syst Biol. 2024; 18(6):261-270. PMC: 11665842. DOI: 10.1049/syb2.12090. View

Wang J, Chen Y, Zou Q . Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model. PLoS Genet. 2023; 19(9):e1010942. PMC: 10519590. DOI: 10.1371/journal.pgen.1010942. View

Sgroi D . Preinvasive breast cancer. Annu Rev Pathol. 2009; 5:193-221. PMC: 3918415. DOI: 10.1146/annurev.pathol.4.110807.092306. View

Su R, Wu H, Xu B, Liu X, Wei L . Developing a Multi-Dose Computational Model for Drug-Induced Hepatotoxicity Prediction Based on Toxicogenomics Data. IEEE/ACM Trans Comput Biol Bioinform. 2018; 16(4):1231-1239. DOI: 10.1109/TCBB.2018.2858756. View

Zafrakas M, Petschke B, Donner A, Fritzsche F, Kristiansen G, Knuchel R . Expression analysis of mammaglobin A (SCGB2A2) and lipophilin B (SCGB1D2) in more than 300 human tumors and matching normal tissues reveals their co-expression in gynecologic malignancies. BMC Cancer. 2006; 6:88. PMC: 1513245. DOI: 10.1186/1471-2407-6-88. View

Wang L, Lyu S, Wang S, Shen H, Niu F, Liu X . Loss of FAT1 during the progression from DCIS to IDC and predict poor clinical outcome in breast cancer. Exp Mol Pathol. 2016; 100(1):177-83. DOI: 10.1016/j.yexmp.2015.12.012. View

Guan X, Guo H, Guo Y, Han Q, Li Z, Zhang C . Perforin 1 in Cancer: Mechanisms, Therapy, and Outlook. Biomolecules. 2024; 14(8). PMC: 11352983. DOI: 10.3390/biom14080910. View

Ren L, Huang D, Liu H, Ning L, Cai P, Yu X . Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review). Oncol Lett. 2024; 27(4):152. PMC: 10885005. DOI: 10.3892/ol.2024.14285. View

10.

Chu J, Tang S, Li T, Fan H . The Role of in the Immune Microenvironment of Breast Cancer. Front Biosci (Landmark Ed). 2024; 29(2):73. DOI: 10.31083/j.fbl2902073. View

11.

Aran D, Looney A, Liu L, Wu E, Fong V, Hsu A . Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019; 20(2):163-172. PMC: 6340744. DOI: 10.1038/s41590-018-0276-y. View

12.

Milovanovic J, Todorovic-Rakovic N, Vujasinovic T, Greenman J, Mandusic V, Radulovic M . Can granulysin provide prognostic value in primary breast cancer?. Pathol Res Pract. 2022; 237:154039. DOI: 10.1016/j.prp.2022.154039. View

13.

Ben-Baruch A . The Tumor-Promoting Flow of Cells Into, Within and Out of the Tumor Site: Regulation by the Inflammatory Axis of TNFα and Chemokines. Cancer Microenviron. 2011; 5(2):151-64. PMC: 3399063. DOI: 10.1007/s12307-011-0094-3. View

14.

Su R, Liu X, Wei L . MinE-RFE: determine the optimal subset from RFE by minimizing the subset-accuracy-defined energy. Brief Bioinform. 2019; 21(2):687-698. DOI: 10.1093/bib/bbz021. View

15.

Bartoschek M, Oskolkov N, Bocci M, Lovrot J, Larsson C, Sommarin M . Spatially and functionally distinct subclasses of breast cancer-associated fibroblasts revealed by single cell RNA sequencing. Nat Commun. 2018; 9(1):5150. PMC: 6279758. DOI: 10.1038/s41467-018-07582-3. View

16.

Jiang B, Chen W, Qin H, Diao W, Li B, Cao W . TOX3 inhibits cancer cell migration and invasion via transcriptional regulation of SNAI1 and SNAI2 in clear cell renal cell carcinoma. Cancer Lett. 2019; 449:76-86. DOI: 10.1016/j.canlet.2019.02.020. View

17.

Tower H, Ruppert M, Britt K . The Immune Microenvironment of Breast Cancer Progression. Cancers (Basel). 2019; 11(9). PMC: 6769749. DOI: 10.3390/cancers11091375. View

18.

Moretta L, Moretta A . Unravelling natural killer cell function: triggering and inhibitory human NK receptors. EMBO J. 2003; 23(2):255-9. PMC: 1271745. DOI: 10.1038/sj.emboj.7600019. View

19.

Hori S, Nomura T, Sakaguchi S . Control of regulatory T cell development by the transcription factor Foxp3. Science. 2003; 299(5609):1057-61. DOI: 10.1126/science.1079490. View

20.

Duan H, Zhang Y, Qiu H, Fu X, Liu C, Zang X . Machine learning-based prediction model for distant metastasis of breast cancer. Comput Biol Med. 2024; 169:107943. DOI: 10.1016/j.compbiomed.2024.107943. View