Siegel, R.L., Kratzer, T.B., Giaquinto, A.N., et al., Cancer statistics, 2025, Ca-Cancer J. Clin., 2025, vol. 75, no. 1, pp. 10—45. https://doi.org/10.3322/caac.21871
Article PubMed PubMed Central Google Scholar
Bray, F., Laversanne, M., Sung, H., et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, Ca-Cancer J. Clin., 2024, vol. 74, no. 3, pp. 229—263. https://doi.org/10.3322/caac.21834
Abeshouse, A., Ahn, J., Akbani, R., et al., The molecular taxonomy of primary prostate cancer, Cell, 2015, vol. 163, no. 4, pp. 1011—1025. https://doi.org/10.1016/j.cell.2015.10.025
Abida, W., Cyrta, J., Heller, G., et al., Genomic correlates of clinical outcome in advanced prostate cancer, Proc. Natl. Acad. Sci. U. S. A., 2019, vol. 116, no. 23, pp. 11428—11436. https://doi.org/10.1073/pnas.1902651116
Article CAS PubMed PubMed Central Google Scholar
Chen, S., Zhu, G., Yang, Y., et al., Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression, Nat. Cell. Biol., 2021, vol. 23, no. 1, pp. 87—98. https://doi.org/10.1038/s41556-020-00613-6
Article CAS PubMed Google Scholar
Tran, H.T.N., Ang, K.S., Chevrier, M., et al., A benchmark of batch-effect correction methods for single-cell RNA sequencing data, Genome Biol., 2020, vol. 21, no. 1, p. 12. https://doi.org/10.1186/s13059-019-1850-9
Article CAS PubMed PubMed Central Google Scholar
Johnson, W.E., Li, C., and Rabinovic, A., Adjusting batch effects in microarray expression data using empirical Bayes methods, Biostatistics, 2007, vol. 8, no. 1, pp. 118—127. https://doi.org/10.1093/biostatistics/kxj037
Lazar, C., Meganck, S., Taminau, J., et al., Batch effect removal methods for microarray gene expression data integration: a survey, Brief Bioinf., 2013, vol. 14, no. 4, pp. 469—484. https://doi.org/10.1093/bib/bbs037
Leek, J.T., Johnson, W.E., Parker, H.S., et al., The sva package for removing batch effects and other unwanted variation in high-throughput experiments, Bioinformatics, 2012, vol. 28, no. 6, pp. 882—883. https://doi.org/10.1093/bioinformatics/bts034
Article CAS PubMed PubMed Central Google Scholar
Patel, H., Garcia, M.U., Talbot, A., et al., nf-core/fetchngs: nf-core/fetchngs v1.12.0—Titanium Platypus (1.12.0), Zenodo, 2024. https://doi.org/10.5281/zenodo.10728509
Ewels, P.A., Peltzer, A., Fillinger, S., et al., The nf-core framework for community-curated bioinformatics pipelines, Nat. Biotechnol., 2020, vol. 38, no. 3, pp. 276—278. https://doi.org/10.1038/s41587-020-0439-x
Article CAS PubMed Google Scholar
Patel, H., Manning, J., Ewels, P., et al., nf-core/rnaseq: nf-core/rnaseq v3.19.0—Tungsten Turtle, Zenodo, 2025. https://doi.org/10.5281/ZENODO.15631172
Homo sapiens—ensemble genome browser 114. https://www.ensembl.org/Homo_sapiens/Info/Index. Accessed July 7, 2025.
Aaltonen, L.A., Abascal, F., Abeshouse, A., et al., Pan-cancer analysis of whole genomes, Nature, 2020, vol. 578, no. 7793, pp. 82—93. https://doi.org/10.1038/s41586-020-1969-6
Armenia, J., Wankowicz, S.A.M., Liu, D., et al., The long tail of oncogenic drivers in prostate cancer, Nat. Genet., 2018, vol. 50, no. 5, pp. 645—651. https://doi.org/10.1038/s41588-018-0078-z
Article CAS PubMed PubMed Central Google Scholar
Berglund, E., Maaskola, J., Schultz, N., et al., Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity, Nat. Commun., 2018, vol. 9, no. 1, p. 2419. https://doi.org/10.1038/s41467-018-04724-5
Article CAS PubMed PubMed Central Google Scholar
Karthaus, W.R., Hofree, M., Choi, D., et al., Regenerative potential of prostate luminal cells revealed by single-cell analysis, Science, 2020, vol. 368, no. 6490, pp. 497—505. https://doi.org/10.1126/science.aay0267
Article CAS PubMed PubMed Central Google Scholar
Netzel-Arnett, S., Hooper, J.D., Szabo, R., et al., Membrane anchored serine proteases: a rapidly expanding group of cell surface proteolytic enzymes with potential roles in cancer, Cancer Metastasis Rev., 2003, vol. 22, nos. 2—3, pp. 237—258. https://doi.org/10.1023/a:1023003616848
Article CAS PubMed Google Scholar
Lasham, A., Print, C.G., Woolley, A.G., et al., YB-1: oncoprotein, prognostic marker and therapeutic target, Biochem. J., 2013, vol. 449, no. 1, pp. 11—23. https://doi.org/10.1042/BJ20121323
Article CAS PubMed Google Scholar
Maurya, P.K., Mishra, A., Yadav, B.S., et al., Role of Y box protein-1 in cancer: as potential biomarker and novel therapeutic target, J. Cancer, 2017, vol. 8, no. 10, pp. 1900—1907. https://doi.org/10.7150/jca.17689
Article CAS PubMed PubMed Central Google Scholar
Rivas-Fuentes, S., Salgado-Aguayo, A., Arratia-Quijada, J., et al., Regulation and biological functions of the CX3CL1-CX3CR1 axis and its relevance in solid cancer: a mini-review, J. Cancer, 2021, vol. 12, no. 2, pp. 571—583. https://doi.org/10.7150/jca.47022
Article CAS PubMed PubMed Central Google Scholar
Shulby, S.A., Dolloff, N.G., Stearns, M.E., et al., CX3CR1-fractalkine expression regulates cellular mechanisms involved in adhesion, migration, and survival of human prostate cancer cells, Cancer Res., 2004, vol. 64, no. 14, pp. 4693—4698. https://doi.org/10.1158/0008-5472.CAN-03-3437
Article CAS PubMed Google Scholar
Zhou, Y.-H., Wu, X., Tan, F., et al., PAX6 suppresses growth of human glioblastoma cells, J. Neurooncol., 2005, vol. 71, pp. 223—229. https://doi.org/10.1007/s11060-004-1720-4
Article CAS PubMed Google Scholar
Li, C.G. and Eccles, M.R., PAX genes in cancer; friends or foes?, Front. Genet., 2012, vol. 3, p. 6. https://doi.org/10.3389/fgene.2012.00006
Article PubMed PubMed Central Google Scholar
Hou, Z., Abudureheman, A., Wang, L., et al., Expression, prognosis and functional role of Thsd7a in esophageal squamous cell carcinoma of Kazakh patients, Xinjiang, Oncotarget, 2017, vol. 8, no. 36, pp. 60539—60557. https://doi.org/10.18632/oncotarget.16966
Article PubMed PubMed Central Google Scholar
Ayala, G.E., Wheeler, T.M., Shine, H.D., et al., In vitro dorsal root ganglia and human prostate cell line interaction: redefining perineural invasion in prostate cancer, Prostate, 2001, vol. 49, no. 3, pp. 213—223. https://doi.org/10.1002/pros.1137
Article CAS PubMed Google Scholar
Lu, P., Weaver, V.M., and Werb, Z., The extracellular matrix: a dynamic niche in cancer progression, J. Cell Biol., 2012, vol. 196, no. 4, pp. 395—406. https://doi.org/10.1083/jcb.201102147
Article CAS PubMed PubMed Central Google Scholar
Karthaus, W.R., Hofree, M., Choi, D., et al., Regenerative potential of prostate luminal cells revealed by single-cell analysis, Science, 2020, vol. 368, no. 6490, pp. 497—505. https://doi.org/10.1126/science.aay0267
Article CAS PubMed PubMed Central Google Scholar
Nygaard, V., Rødland, E.A., and Hovig, E., Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses, Biostatistics, 2016, vol. 17, no. 1, pp. 29—39. https://doi.org/10.1093/biostatistics/kxv027
Franzén, O., Gan, L.-M., and Björkegren, J.L.M., PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data, Database, 2019, vol. 2019, p baz046. https://doi.org/10.1093/database/baz046
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