Top

Journal of Experimental & Clinical Cancer Research

Published in:

Open Access 01-12-2021 | Glioblastoma | Review

What are the applications of single-cell RNA sequencing in cancer research: a systematic review

Authors: Lvyuan Li, Fang Xiong, Yumin Wang, Shanshan Zhang, Zhaojian Gong, Xiayu Li, Yi He, Lei Shi, Fuyan Wang, Qianjin Liao, Bo Xiang, Ming Zhou, Xiaoling Li, Yong Li, Guiyuan Li, Zhaoyang Zeng, Wei Xiong, Can Guo

Published in: Journal of Experimental & Clinical Cancer Research | Issue 1/2021

Abstract

Single-cell RNA sequencing (scRNA-seq) is a tool for studying gene expression at the single-cell level that has been widely used due to its unprecedented high resolution. In the present review, we outline the preparation process and sequencing platforms for the scRNA-seq analysis of solid tumor specimens and discuss the main steps and methods used during data analysis, including quality control, batch-effect correction, normalization, cell cycle phase assignment, clustering, cell trajectory and pseudo-time reconstruction, differential expression analysis and gene set enrichment analysis, as well as gene regulatory network inference. Traditional bulk RNA sequencing does not address the heterogeneity within and between tumors, and since the development of the first scRNA-seq technique, this approach has been widely used in cancer research to better understand cancer cell biology and pathogenetic mechanisms. ScRNA-seq has been of great significance for the development of targeted therapy and immunotherapy. In the second part of this review, we focus on the application of scRNA-seq in solid tumors, and summarize the findings and achievements in tumor research afforded by its use. ScRNA-seq holds promise for improving our understanding of the molecular characteristics of cancer, and potentially contributing to improved diagnosis, prognosis, and therapeutics.

Available only for authorised users

Williams MJ, Werner B, Heide T, et al. Quantification of subclonal selection in cancer from bulk sequencing data. Nat Genet. 2018;50(6):895–903. https://doi.org/10.1038/s41588-018-0128-6.

Tang F, Barbacioru C, Wang Y, et al. mRNA-seq whole-transcriptome analysis of a single cell. Nat Methods. 2009;6(5):377–82. https://doi.org/10.1038/nmeth.1315.

Suva ML, Tirosh I. Single-cell RNA sequencing in cancer: lessons learned and emerging challenges. Mol Cell. 2019;75(1):7–12. https://doi.org/10.1016/j.molcel.2019.05.003.

Tirosh I, Izar B, Prakadan SM, Wadsworth MH, Treacy D, Trombetta JJ, et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352(6282):189–96. https://doi.org/10.1126/science.aad0501.

Shalek AK, Satija R, Shuga J, et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature. 2014;510(7505):363–9. https://doi.org/10.1038/nature13437.

Haque A, Engel J, Teichmann SA, Lonnberg T. A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Med. 2017;9(1):75. https://doi.org/10.1186/s13073-017-0467-4.

Zhao J, Guo C, Xiong F, Yu J, Ge J, Wang H, et al. Single cell RNA-seq reveals the landscape of tumor and infiltrating immune cells in nasopharyngeal carcinoma. Cancer Lett. 2020;477:131–43. https://doi.org/10.1016/j.canlet.2020.02.010.

Zheng C, Zheng L, Yoo JK, Guo H, Zhang Y, Guo X, et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell. 2017;169(7):1342–56 e1316. https://doi.org/10.1016/j.cell.2017.05.035.

Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K, Gillespie S, et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell. 2017;171(7):1611–24 e1624. https://doi.org/10.1016/j.cell.2017.10.044.

10.

Zhang X, Li T, Liu F, Chen Y, Yao J, Li Z, et al. Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems. Mol Cell. 2019;73(1):130–42 e135. https://doi.org/10.1016/j.molcel.2018.10.020.

11.

Natarajan KN, Miao Z, Jiang M, Huang X, Zhou H, Xie J, et al. Comparative analysis of sequencing technologies for single-cell transcriptomics. Genome Biol. 2019;20(1):70. https://doi.org/10.1186/s13059-019-1676-5.

12.

Jiang P, Thomson JA, Stewart R. Quality control of single-cell RNA-seq by SinQC. Bioinformatics. 2016;32(16):2514–6. https://doi.org/10.1093/bioinformatics/btw176.

13.

Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 2016;17(1):29. https://doi.org/10.1186/s13059-016-0888-1.

14.

McCarthy DJ, Campbell KR, Lun ATL, Wills QF. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics. 2017;33(8):1179–86. https://doi.org/10.1093/bioinformatics/btw777.

15.

Osorio D, Cai JJ. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA sequencing data quality control. Bioinformatics. 2020. https://doi.org/10.1093/bioinformatics/btaa751.

16.

Zhao Q, Wang J, Levichkin IV, Stasinopoulos S, Ryan MT, Hoogenraad NJ. A mitochondrial specific stress response in mammalian cells. EMBO J. 2002;21(17):4411–9. https://doi.org/10.1093/emboj/cdf445.

17.

Li X, Nair A, Wang S, Wang L. Quality control of RNA-seq experiments. Methods Mol Biol (Clifton, NJ). 2015;1269:137–46. https://doi.org/10.1007/978-1-4939-2291-8_8.

18.

Liu Q, Sheng Q, Ping J, et al. scRNABatchQC: multi-samples quality control for single cell RNA-seq data. Bioinformatics. 2019;35(24):5306–8. https://doi.org/10.1093/bioinformatics/btz601.

19.

Etherington GJ, Soranzo N, Mohammed S, Haerty W, Davey RP, Palma FD. A Galaxy-based training resource for single-cell RNA-sequencing quality control and analyses. Gigascience. 2019;8(12):giz144. https://doi.org/10.1093/gigascience/giz144.

20.

Hicks SC, Townes FW, Teng M, Irizarry RA. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics. 2018;19(4):562–78. https://doi.org/10.1093/biostatistics/kxx053.

21.

Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888–1902.e1821. https://doi.org/10.1016/j.cell.2019.05.031.

22.

Shaham U, Stanton KP, Zhao J, et al. Removal of batch effects using distribution-matching residual networks. Bioinformatics. 2017;33(16):2539–46. https://doi.org/10.1093/bioinformatics/btx196.

23.

Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods. 2019;16(12):1289–96. https://doi.org/10.1038/s41592-019-0619-0.

24.

Hie B, Bryson B, Berger B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat Biotechnol. 2019;37(6):685–91. https://doi.org/10.1038/s41587-019-0113-3.

25.

Welch J, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko E. Integrative inference of brain cell similarities and differences from single-cell genomics. bioRxiv. 2018:459891. https://doi.org/10.1101/459891.

26.

Lin Y, Ghazanfar S, Wang KYX, et al. scMerge leverages factor analysis, stable expression, and pseudoreplication to merge multiple single-cell RNA-seq datasets. Proc Natl Acad Sci U S A. 2019;116(20):9775–84. https://doi.org/10.1073/pnas.1820006116.

27.

Risso D, Perraudeau F, Gribkova S, Dudoit S, Vert JP. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat Commun. 2018;9(1):284. https://doi.org/10.1038/s41467-017-02554-5.

28.

Tran HTN, Ang KS, Chevrier M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020;21(1):12. https://doi.org/10.1186/s13059-019-1850-9.

29.

Fei T, Yu T. scBatch: batch effect correction of RNA-seq data through sample distance matrix adjustment. Bioinformatics. 2020;36(10):3115–23. https://doi.org/10.1093/bioinformatics/btaa097.

30.

Lun AT, Bach K, Marioni JC. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 2016;17:75. https://doi.org/10.1186/s13059-016-0947-7.

31.

Vallejos CA, Marioni JC, Richardson S. BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput Biol. 2015;11(6):e1004333. https://doi.org/10.1371/journal.pcbi.1004333.

32.

Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25. https://doi.org/10.1186/gb-2010-11-3-r25.

33.

Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106. https://doi.org/10.1186/gb-2010-11-10-r106.

34.

Cole MB, Risso D, Wagner A, et al. Performance assessment and selection of normalization procedures for single-cell RNA-seq. Cell Syst. 2019;8(4):315–328.e318. https://doi.org/10.1016/j.cels.2019.03.010.

35.

Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 2019;20(1):296. https://doi.org/10.1186/s13059-019-1874-1.

36.

Lytal N, Ran D, An L. Normalization methods on single-cell RNA-seq data: an empirical survey. Front Genet. 2020;11:41. https://doi.org/10.3389/fgene.2020.00041.

37.

Kastan MB, Bartek J. Cell-cycle checkpoints and cancer. Nature. 2004;432(7015):316–23. https://doi.org/10.1038/nature03097.

38.

Bar-Joseph Z, Siegfried Z, Brandeis M, et al. Genome-wide transcriptional analysis of the human cell cycle identifies genes differentially regulated in normal and cancer cells. Proc Natl Acad Sci U S A. 2008;105(3):955–60. https://doi.org/10.1073/pnas.0704723105.

39.

Wang D, Zeng Z, Zhang S, et al. Epstein-Barr virus-encoded miR-BART6-3p inhibits cancer cell proliferation through the LOC553103-STMN1 axis. FASEB J. 2020;34(6):8012–27. https://doi.org/10.1096/fj.202000039rr.

40.

Wu P, Mo Y, Peng M, Tang T, Zhong Y, Deng X, et al. Emerging role of tumor-related functional peptides encoded by lncRNA and circRNA. Mol Cancer. 2020;19(1):22. https://doi.org/10.1186/s12943-020-1147-3.

41.

Jin K, Wang S, Zhang Y, et al. Long non-coding RNA PVT1 interacts with MYC and its downstream molecules to synergistically promote tumorigenesis. Cell Mol Life Sci. 2019;76(21):4275–89. https://doi.org/10.1007/s00018-019-03222-1.

42.

Pauklin S, Vallier L. The cell-cycle state of stem cells determines cell fate propensity. Cell. 2013;155(1):135–47. https://doi.org/10.1016/j.cell.2013.08.031.

43.

Singh AM, Chappell J, Trost R, et al. Cell-cycle control of developmentally regulated transcription factors accounts for heterogeneity in human pluripotent cells. Stem Cell Rep. 2013;1(6):532–44. https://doi.org/10.1016/j.stemcr.2013.10.009.

44.

Scialdone A, Natarajan KN, Saraiva LR, et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods (San Diego, Calif). 2015;85:54–61. https://doi.org/10.1016/j.ymeth.2015.06.021.

45.

Buettner F, Natarajan KN, Casale FP, et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat Biotechnol. 2015;33(2):155–60.https://doi.org/10.1038/nbt.3102.

46.

Hsiao CJ, Tung P, Blischak JD, et al. Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis. Genome Res. 2020;30(4):611–21. https://doi.org/10.1101/gr.247759.118.

47.

Dey KK, Hsiao CJ, Stephens M. Visualizing the structure of RNA-seq expression data using grade of membership models. PLoS Genet. 2017;13(3):e1006599. https://doi.org/10.1371/journal.pgen.1006599.

48.

Lin P, Troup M, Ho JW. CIDR: ultrafast and accurate clustering through imputation for single-cell RNA-seq data. Genome Biol. 2017;18(1):59. https://doi.org/10.1186/s13059-017-1188-0.

49.

Wang B, Zhu J, Pierson E, Ramazzotti D, Batzoglou S. Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning. Nat Methods. 2017;14(4):414–6. https://doi.org/10.1038/nmeth.4207.

50.

Yang Y, Huh R, Culpepper HW, Lin Y, Love MI, Li Y. SAFE-clustering: single-cell aggregated (from ensemble) clustering for single-cell RNA-seq data. Bioinformatics. 2019;35(8):1269–77. https://doi.org/10.1093/bioinformatics/bty793.

51.

Freytag S, Tian L, Lönnstedt I, Ng M, Bahlo M. Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data. F1000Research. 2018;7:1297. https://doi.org/10.12688/f1000research.15809.2.

52.

Duò A, Robinson MD, Soneson C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Research. 2018;7:1141. https://doi.org/10.12688/f1000research.15666.3.

53.

Kim T, Chen IR, Lin Y, Wang AY, Yang JYH, Yang P. Impact of similarity metrics on single-cell RNA-seq data clustering. Brief Bioinform. 2019;20(6):2316–26. https://doi.org/10.1093/bib/bby076.

54.

Peng L, Tian X, Tian G, et al. Single-cell RNA-seq clustering: datasets, models, and algorithms. RNA Biol. 2020;17(6):765–83. https://doi.org/10.1080/15476286.2020.1728961.

55.

Geddes TA, Kim T, Nan L, Burchfield JG, Yang JYH, Tao D, et al. Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis. BMC Bioinformatics. 2019;20(Suppl 19):660. https://doi.org/10.1186/s12859-019-3179-5.

56.

Fang Q, Su D, Ng W, Feng J. An effective biclustering-based framework for identifying cell subpopulations from scRNA-seq data. IEEE/ACM Trans Comput Biol Bioinform. 2020. https://doi.org/10.1109/tcbb.2020.2979717.

57.

Huh R, Yang Y, Jiang Y, Shen Y, Li Y. SAME-clustering: single-cell aggregated clustering via mixture model ensemble. Nucleic Acids Res. 2020;48(1):86–95. https://doi.org/10.1093/nar/gkz959.

58.

Tsia KK, So HKH, Ho JWK, et al. PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells. Bioinformatics. 2020;36(9):2778–86. https://doi.org/10.1093/bioinformatics/btaa042.

59.

Shao X, Liao J, Lu X, Xue R, Ai N, Fan X. scCATCH: automatic annotation on cell types of clusters from single-cell RNA sequencing data. iScience. 2020;23(3):100882. https://doi.org/10.1016/j.isci.2020.100882.

60.

Chen G, Ning B, Shi T. Single-cell RNA-seq technologies and related computational data analysis. Front Genet. 2019;10:317. https://doi.org/10.3389/fgene.2019.00317.

61.

Qiu X, Mao Q, Tang Y, et al. Reversed graph embedding resolves complex single-cell trajectories. Nat Methods. 2017;14(10):979–82. https://doi.org/10.1038/nmeth.4402.

62.

Cao J, Spielmann M, Qiu X, et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature. 2019;566(7745):496–502. https://doi.org/10.1038/s41586-019-0969-x.

63.

Ji Z, Ji H. TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 2016;44(13):e117. https://doi.org/10.1093/nar/gkw430.

64.

Street K, Risso D, Fletcher RB, Das D, Ngai J, Yosef N, et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics. 2018;19(1):477. https://doi.org/10.1186/s12864-018-4772-0.

65.

Guo M, Bao EL, Wagner M, Whitsett JA, Xu Y. SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res. 2017;45(7):e54. https://doi.org/10.1093/nar/gkw1278.

66.

Chen Y, Zhang Y, Ouyang Z. LISA: accurate reconstruction of cell trajectory and pseudo-time for massive single cell RNA-seq data. Pac Symp Biocomput. 2019;24:338–49. https://doi.org/10.1142/9789813279827_0031.

67.

Herring CA, Banerjee A, McKinley ET, et al. Unsupervised trajectory analysis of single-cell RNA-seq and imaging data reveals alternative tuft cell origins in the gut. Cell Syst. 2018;6(1):37–51.e39. https://doi.org/10.1016/j.cels.2017.10.012.

68.

Schiebinger G, Shu J, Tabaka M, et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell. 2019;176(4):928–943 e922. https://doi.org/10.1016/j.cell.2019.01.006.

69.

Saelens W, Cannoodt R, Todorov H, Saeys Y. A comparison of single-cell trajectory inference methods: towards more accurate and robust tools. bioRxiv. 2018:276907. https://doi.org/10.1101/276907.

70.

Delmans M, Hemberg M. Discrete distributional differential expression (D3E)--a tool for gene expression analysis of single-cell RNA-seq data. BMC Bioinformatics. 2016;17:110. https://doi.org/10.1186/s12859-016-0944-6.

71.

Miao Z, Deng K, Wang X, Zhang X. DEsingle for detecting three types of differential expression in single-cell RNA-seq data. Bioinformatics. 2018;34(18):3223–4. https://doi.org/10.1093/bioinformatics/bty332.

72.

Ye C, Speed TP, Salim A. DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data. Bioinformatics. 2019;35(24):5155–62. https://doi.org/10.1093/bioinformatics/btz453.

73.

Zhang W, Wei Y, Zhang D, Xu EY. ZIAQ: a quantile regression method for differential expression analysis of single-cell RNA-seq data. Bioinformatics. 2020;36(10):3124–30. https://doi.org/10.1093/bioinformatics/btaa098.

74.

Luecken MD, Theis FJ. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol Syst Biol. 2019;15(6):e8746. https://doi.org/10.15252/msb.20188746.

75.

Huang DW, Sherman BT, Tan Q, et al. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 2007;35(Web Server issue):W169–75. https://doi.org/10.1093/nar/gkm415.

76.

Kim SY, Volsky DJ. PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics. 2005;6:144. https://doi.org/10.1186/1471-2105-6-144.

77.

Wu D, Smyth GK. Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Res. 2012;40(17):e133. https://doi.org/10.1093/nar/gks461.

78.

Khatri P, Sirota M, Butte AJ. Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol. 2012;8(2):e1002375. https://doi.org/10.1371/journal.pcbi.1002375.

79.

Ma Y, Sun S, Shang X, Keller ET, Chen M, Zhou X. Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studies. Nat Commun. 2020;11(1):1585. https://doi.org/10.1038/s41467-020-15298-6.

80.

Woodhouse S, Piterman N, Wintersteiger CM, Göttgens B, Fisher J. SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data. BMC Syst Biol. 2018;12(1):59. https://doi.org/10.1186/s12918-018-0581-y.

81.

Lim CY, Wang H, Woodhouse S, et al. BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinformatics. 2016;17(1):355. https://doi.org/10.1186/s12859-016-1235-y.

82.

Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. SINCERA: a pipeline for single-cell RNA-Seq profiling analysis. PLoS Comput Biol. 2015;11(11):e1004575. https://doi.org/10.1371/journal.pcbi.1004575.

83.

Matsumoto H, Kiryu H, Furusawa C, et al. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics. 2017;33(15):2314–21. https://doi.org/10.1093/bioinformatics/btx194.

84.

Ocone A, Haghverdi L, Mueller NS, Theis FJ. Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics. 2015;31(12):i89–96. https://doi.org/10.1093/bioinformatics/btv257.

85.

Moerman T, Aibar Santos S, Bravo González-Blas C, et al. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics. 2019;35(12):2159–61. https://doi.org/10.1093/bioinformatics/bty916.

86.

[No authors listed]. What happened to personalized medicine? Nat Biotechnol. 2012;30(1):1. https://doi.org/10.1038/nbt.2096.

87.

Benson M. Clinical implications of omics and systems medicine: focus on predictive and individualized treatment. J Intern Med. 2016;279(3):229–40. https://doi.org/10.1111/joim.12412.

88.

Shalek AK, Benson M. Single-cell analyses to tailor treatments. Sci Transl Med. 2017;9(408):eaan4730. https://doi.org/10.1126/scitranslmed.aan4730.

89.

Wei J, Wu C, Meng H, et al. The biogenesis and roles of extrachromosomal oncogene involved in carcinogenesis and evolution. Am J Cancer Res. 2020;10(11):3532–50.

90.

Stupp R, Mason WP, van den Bent MJ, et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med. 2005;352(10):987–96. https://doi.org/10.1056/nejmoa043330.

91.

Sottoriva A, Spiteri I, Piccirillo SG, et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci U S A. 2013;110(10):4009–14. https://doi.org/10.1073/pnas.1219747110.

92.

Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM, Wakimoto H, et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science. 2014;344(6190):1396–401. https://doi.org/10.1126/science.1254257.

93.

Yuan J, Levitin HM, Frattini V, et al. Single-cell transcriptome analysis of lineage diversity in high-grade glioma. Genome Med. 2018;10(1):57. https://doi.org/10.1186/s13073-018-0567-9.

94.

Namikawa K, Yamazaki N. Targeted therapy and immunotherapy for melanoma in Japan. Curr Treat Options in Oncol. 2019;20(1):7. https://doi.org/10.1007/s11864-019-0607-8.

95.

Gerber T, Willscher E, Loeffler-Wirth H, et al. Mapping heterogeneity in patient-derived melanoma cultures by single-cell RNA-seq. Oncotarget. 2017;8(1):846–62. https://doi.org/10.18632/oncotarget.13666.

96.

Fan C, Wang J, Tang Y, Zhang S, Xiong F, Guo C, et al. Upregulation of long non-coding RNA LOC284454 may serve as a new serum diagnostic biomarker for head and neck cancers. BMC Cancer. 2020;20(1):917. https://doi.org/10.1186/s12885-020-07408-w.

97.

Ban Y, Tan P, Cai J, et al. LNCAROD is stabilized by m6A methylation and promotes cancer progression via forming a ternary complex with HSPA1A and YBX1 in head and neck squamous cell carcinoma. Mol Oncol. 2020;14(6):1282–96. https://doi.org/10.1002/1878-0261.12676.

98.

Yi M, Tan Y, Wang L, et al. TP63 links chromatin remodeling and enhancer reprogramming to epidermal differentiation and squamous cell carcinoma development. Cell Mol Life Sci. 2020;77(21):4325–46. https://doi.org/10.1007/s00018-020-03539-2.

99.

Deng X, Xiong W, Jiang X, Zhang S, Li Z, Zhou Y, et al. LncRNA LINC00472 regulates cell stiffness and inhibits the migration and invasion of lung adenocarcinoma by binding to YBX1. Cell Death Dis. 2020;11(11):945. https://doi.org/10.1038/s41419-020-03147-9.

100.

Lambrechts D, Wauters E, Boeckx B, et al. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat Med. 2018;24(8):1277–89. https://doi.org/10.1038/s41591-018-0096-5.

101.

van Galen P, Hovestadt V, Wadsworth Ii MH, et al. Single-cell RNA-Seq reveals AML hierarchies relevant to disease progression and immunity. Cell. 2019;176(6):1265–1281 e1224. https://doi.org/10.1016/j.cell.2019.01.031.

102.

Fan C, Qu H, Xiong F, Tang Y, Tang T, Zhang L, et al. CircARHGAP12 promotes nasopharyngeal carcinoma migration and invasion via ezrin-mediated cytoskeletal remodeling. Cancer Lett. 2021;496:41–56. https://doi.org/10.1016/j.canlet.2020.09.006.

103.

Tang L, Xiong W, Zhang L, et al. circSETD3 regulates MAPRE1 through miR-615-5p and miR-1538 sponges to promote migration and invasion in nasopharyngeal carcinoma. Oncogene. 2021;40(2):307–21. https://doi.org/10.1038/s41388-020-01531-5.

104.

Wu Y, Wang D, Wei F, et al. EBV-miR-BART12 accelerates migration and invasion in EBV-associated cancer cells by targeting tubulin polymerization-promoting protein 1. FASEB J. 2020;34(12):16205–23. https://doi.org/10.1096/fj.202001508r.

105.

Tang T, Yang L, Cao Y, et al. LncRNA AATBC regulates Pinin to promote metastasis in nasopharyngeal carcinoma. Mol Oncol. 2020;14(9):2251–70. https://doi.org/10.1002/1878-0261.12703.

106.

Wu C, Li M, Meng H, Liu Y, Niu W, Zhou Y, et al. Analysis of status and countermeasures of cancer incidence and mortality in China. Sci China Life Sci. 2019;62(5):640–7. https://doi.org/10.1007/s11427-018-9461-5.

107.

Ting David T, Wittner Ben S, Ligorio M, et al. Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep. 2014;8(6):1905–18. https://doi.org/10.1016/j.celrep.2014.08.029.

108.

Mermer G, Turk M. Assessment of the effects of breast cancer training on women between the ages of 50 and 70 in Kemalpasa, Turkey. Asian Pac J Cancer Prev. 2014;15(24):10749–55. https://doi.org/10.7314/apjcp.2014.15.24.10749.

109.

Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–86. https://doi.org/10.1002/ijc.29210.

110.

Chung W, Eum HH, Lee H-O, et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat Commun. 2017;8(1):15081. https://doi.org/10.1038/ncomms15081.

111.

Brigle K, Rogers B. Pathobiology and diagnosis of multiple myeloma. Semin Oncol Nurs. 2017;33(3):225–36. https://doi.org/10.1016/j.soncn.2017.05.012.

112.

Geng S, Wang J, Zhang X, et al. Single-cell RNA sequencing reveals chemokine self-feeding of myeloma cells promotes extramedullary metastasis. FEBS Lett. 2019;594(3):452–65. https://doi.org/10.1002/1873-3468.13623.

113.

Lee MC, Lopez-Diaz FJ, Khan SY, et al. Single-cell analyses of transcriptional heterogeneity during drug tolerance transition in cancer cells by RNA sequencing. Proc Natl Acad Sci U S A. 2014;111(44):E4726–35. https://doi.org/10.1073/pnas.1404656111.

114.

Kim KT, Lee HW, Lee HO, et al. Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells. Genome Biol. 2015;16:127. https://doi.org/10.1186/s13059-015-0692-3.

115.

Chen X, Wen Q, Stucky A, et al. Relapse pathway of glioblastoma revealed by single-cell molecular analysis. Carcinogenesis. 2018;39(7):931–6. https://doi.org/10.1093/carcin/bgy052.

116.

Millard NE, De Braganca KC. Medulloblastoma. J Child Neurol. 2016;31(12):1341–53. https://doi.org/10.1177/0883073815600866.

117.

Smoll NR, Drummond KJ. The incidence of medulloblastomas and primitive neurectodermal tumours in adults and children. J Clin Neurosci. 2012;19(11):1541–4. https://doi.org/10.1016/j.jocn.2012.04.009.

118.

Ocasio J, Babcock B, Malawsky D, et al. scRNA-seq in medulloblastoma shows cellular heterogeneity and lineage expansion support resistance to SHH inhibitor therapy. Nat Commun. 2019;10(1):5829. https://doi.org/10.1038/s41467-019-13657-6.

119.

Wei X, Chen Y, Jiang X, et al. Mechanisms of vasculogenic mimicry in hypoxic tumor microenvironments. Mol Cancer. 2021;20(1):7. https://doi.org/10.1186/s12943-020-01288-1.

120.

Jiang X, Wang J, Deng X, Xiong F, Zhang S, Gong Z, et al. The role of microenvironment in tumor angiogenesis. J Exp Clin Cancer Research. 2020;39(1):204. https://doi.org/10.1186/s13046-020-01709-5.

121.

Fan C, Zhang S, Gong Z, et al. Emerging role of metabolic reprogramming in tumor immune evasion and immunotherapy. Sci China Life Sci. 2021;64(4):534-547. https://doi.org/10.1007/s11427-019-1735-4.

122.

Zhu K, Li P, Mo Y, Wang J, Jiang X, Ge J, et al. Neutrophils: accomplices in metastasis. Cancer Lett. 2020;492:11–20. https://doi.org/10.1016/j.canlet.2020.07.028.

123.

Wei F, Wang D, Wei J, et al. Metabolic crosstalk in the tumor microenvironment regulates antitumor immunosuppression and immunotherapy resisitance. Cell Mol Life Sci. 2021;78(1):173–93. https://doi.org/10.1007/s00018-020-03581-0.

124.

Jiang X, Wang J, Deng X, Xiong F, Ge J, Xiang B, et al. Role of the tumor microenvironment in PD-L1/PD-1-mediated tumor immune escape. Mol Cancer. 2019;18(1):10. https://doi.org/10.1186/s12943-018-0928-4.

125.

Wang YA, Li XL, Mo YZ, Fan CM, Tang L, Xiong F, et al. Effects of tumor metabolic microenvironment on regulatory T cells. Mol Cancer. 2018;17(1):168. https://doi.org/10.1186/s12943-018-0913-y.

126.

Duan S, Guo W, Xu Z, et al. Natural killer group 2D receptor and its ligands in cancer immune escape. Mol Cancer. 2019;18(1):29. https://doi.org/10.1186/s12943-019-0956-8.

127.

Peng M, Mo Y, Wang Y, et al. Neoantigen vaccine: an emerging tumor immunotherapy. Mol Cancer. 2019;18(1):128. https://doi.org/10.1186/s12943-019-1055-6.

128.

Ren D, Hua Y, Yu B, et al. Predictive biomarkers and mechanisms underlying resistance to PD1/PD-L1 blockade cancer immunotherapy. Mol Cancer. 2020;19(1):19. https://doi.org/10.1186/s12943-020-1144-6.

129.

Marengo A, Rosso C, Bugianesi E. Liver cancer: connections with obesity, fatty liver, and cirrhosis. Annu Rev Med. 2016;67(1):103–17. https://doi.org/10.1146/annurev-med-090514-013832.

130.

Muller S, Kohanbash G, Liu SJ, et al. Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment. Genome Biol. 2017;18(1):234. https://doi.org/10.1186/s13059-017-1362-4.

131.

Lavin Y, Kobayashi S, Leader A, et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell. 2017;169(4):750–765 e717. https://doi.org/10.1016/j.cell.2017.04.014.

132.

Azizi E, Carr AJ, Plitas G, et al. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell. 2018;174(5):1293–1308 e1236. https://doi.org/10.1016/j.cell.2018.05.060.

133.

Bo H, Fan L, Li J, et al. High expression of lncRNA AFAP1-AS1 promotes the progression of colon cancer and predicts poor prognosis. J Cancer. 2018;9(24):4677–83. https://doi.org/10.7150/jca.26461.

134.

Zhang L, Yu X, Zheng L, Zhang Y, Li Y, Fang Q, et al. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature. 2018;564(7735):268–72. https://doi.org/10.1038/s41586-018-0694-x.

135.

Johnson TS, Abrams ZB, Mo X, Zhang Y, Huang K. Lack of human cytomegalovirus expression in single cells from glioblastoma tumors and cell lines. J Neuro-Oncol. 2017;23(5):671–8. https://doi.org/10.1007/s13365-017-0543-y.

136.

Tirosh I, Venteicher AS, Hebert C, et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature. 2016;539(7628):309–13. https://doi.org/10.1038/nature20123.

137.

Venteicher AS, Tirosh I, Hebert C, et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science. 2017;355(6332):eaai8478. https://doi.org/10.1126/science.aai8478.

138.

Saurty-Seerunghen MS, Bellenger L, El-Habr EA, et al. Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells. Acta Neuropathol Commun. 2019;7(1):155. https://doi.org/10.1186/s40478-019-0819-y.

139.

Hovestadt V, Smith KS, Bihannic L, et al. Resolving medulloblastoma cellular architecture by single-cell genomics. Nature. 2019;572(7767):74–9. https://doi.org/10.1038/s41586-019-1434-6.

140.

Weng Q, Wang J, Wang J, et al. Single-cell transcriptomics uncovers glial progenitor diversity and cell fate determinants during development and gliomagenesis. Cell Stem Cell. 2019;24(5):707–723 e708. https://doi.org/10.1016/j.stem.2019.03.006.

141.

Chen CCL, Deshmukh S, Jessa S, Hadjadj D, Lisi V, Andrade AF, et al. Histone H3.3G34-mutant interneuron progenitors co-opt PDGFRA for gliomagenesis. Cell. 2020;183(6):1617–1633 e1622. https://doi.org/10.1016/j.cell.2020.11.012.

142.

Gupta K, Miller JD, Li JZ, Russell MW, Charbonneau C. Epidemiologic and socioeconomic burden of metastatic renal cell carcinoma (mRCC): a literature review. Cancer Treat Rev. 2008;34(3):193–205. https://doi.org/10.1016/j.ctrv.2007.12.001.

143.

Kim KT, Lee HW, Lee HO, et al. Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma. Genome Biol. 2016;17:80. https://doi.org/10.1186/s13059-016-0945-9.

144.

Zhang J, Guan M, Wang Q, Zhang J, Zhou T, Sun X. Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas. Brief Bioinform. 2019; 21(3):1080-1097. https://doi.org/10.1093/bib/bbz040.

145.

Darmanis S, Sloan SA, Croote D, et al. Single-cell RNA-Seq analysis of infiltrating neoplastic cells at the migrating front of human glioblastoma. Cell Rep. 2017;21(5):1399–410. https://doi.org/10.1016/j.celrep.2017.10.030.

146.

Filbin MG, Tirosh I, Hovestadt V, Shaw MKL, Escalante LE, Mathewson ND, et al. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science. 2018;360(6386):331–5. https://doi.org/10.1126/science.aao4750.

147.

Wang Q, Tan Y, Fang C, et al. Single-cell RNA-seq reveals RAD51AP1 as a potent mediator of EGFRvIII in human glioblastomas. Aging. 2019;11(18):7707–22. https://doi.org/10.18632/aging.102282.

148.

Cai J, Chen S, Yi M, et al. ΔNp63α is a super enhancer-enriched master factor controlling the basal-to-luminal differentiation transcriptional program and gene regulatory networks in nasopharyngeal carcinoma. Carcinogenesis. 2020;41(9):1282–93. https://doi.org/10.1093/carcin/bgz203.

149.

Chen S, Youhong T, Tan Y, et al. EGFR-PKM2 signaling promotes the metastatic potential of nasopharyngeal carcinoma through induction of FOSL1 and ANTXR2. Carcinogenesis. 2020;41(6):723–33. https://doi.org/10.1093/carcin/bgz180.

150.

Fan C, Tu C, Qi P, et al. GPC6 promotes cell proliferation, migration, and invasion in nasopharyngeal carcinoma. J Cancer. 2019;10(17):3926–32. https://doi.org/10.7150/jca.31345.

151.

Mo Y, Wang Y, Xiong F, Ge X, Li Z, Li X, et al. Proteomic analysis of the molecular mechanism of lovastatin inhibiting the growth of nasopharyngeal carcinoma cells. J Cancer. 2019;10(10):2342–9. https://doi.org/10.7150/jca.30454.

152.

Xiong F, Deng S, Huang HB, et al. Effects and mechanisms of innate immune molecules on inhibiting nasopharyngeal carcinoma. Chin Med J. 2019;132(6):749–52. https://doi.org/10.1097/cm9.0000000000000132.

153.

Zhang Y, Wang D, Peng M, et al. Single-cell RNA sequencing in cancer research. J Exp Clin Cancer Res. 2021;40(1):81. https://doi.org/10.1186/s13046-021-01874-1.

154.

Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14(9):865–8. https://doi.org/10.1038/nmeth.4380.

Title: What are the applications of single-cell RNA sequencing in cancer research: a systematic review
Authors: Lvyuan Li
Fang Xiong
Yumin Wang
Shanshan Zhang
Zhaojian Gong
Xiayu Li
Yi He
Lei Shi
Fuyan Wang
Qianjin Liao
Bo Xiang
Ming Zhou
Xiaoling Li
Yong Li
Guiyuan Li
Zhaoyang Zeng
Wei Xiong
Can Guo
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Glioblastoma
Glioblastoma
Glioblastoma
Published in: Journal of Experimental & Clinical Cancer Research / Issue 1/2021
Electronic ISSN: 1756-9966
DOI: https://doi.org/10.1186/s13046-021-01955-1

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 1/2021

Immuno-genomic classification of colorectal cancer organoids reveals cancer cells with intrinsic immunogenic properties associated with patient survival

The immunomodulatory effects of endocrine therapy in breast cancer

Deciphering the temporal heterogeneity of cancer-associated fibroblast subpopulations in breast cancer

Overexpressed WDR3 induces the activation of Hippo pathway by interacting with GATA4 in pancreatic cancer

Novel approaches in cancer treatment: preclinical and clinical development of small non-coding RNA therapeutics

Distinct profile of CD34+ cells and plasma-derived extracellular vesicles from triple-negative patients with Myelofibrosis reveals potential markers of aggressive disease

Keynote webinar | Spotlight on antibody–drug conjugates in cancer