Applications and prospects of spatial transcriptomics in prostate cancer research: A narrative review

Yiling Jin , Zhiming Bai , Gang Wang , Yu Zhang , Jing Chen

Current Urology ›› 2025, Vol. 19 ›› Issue (5) : 303 -308.

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Current Urology ›› 2025, Vol. 19 ›› Issue (5) :303 -308. DOI: 10.1097/CU9.0000000000000288
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Applications and prospects of spatial transcriptomics in prostate cancer research: A narrative review
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Abstract

Spatial transcriptomics, an increasingly prominent technique, has been extensively utilized to examine tumors within the digestive tract (such as liver and colorectal cancers) and the nervous system. However, its application in prostate cancer research remains comparatively limited. This article provides a detailed overview of the principles and features of spatial transcriptomics, particularly highlighting its applications in studying the tumor microenvironment, heterogeneity, and clinical implications in prostate cancer. Through a systematic review and analysis of current literature, we identify the main focus areas and limitations of existing research on spatial transcriptomics and suggest potential future research directions.

Keywords

Prostate cancer / Spatial transcriptomics / Cancer research

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Yiling Jin, Zhiming Bai, Gang Wang, Yu Zhang, Jing Chen. Applications and prospects of spatial transcriptomics in prostate cancer research: A narrative review. Current Urology, 2025, 19(5): 303-308 DOI:10.1097/CU9.0000000000000288

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Acknowledgments

None.

Statement of ethics

Not applicable.

Conflict of interest statement

No conflict of interest has been declared by the authors.

Funding source

This work was supported by funding from the Key R&D Projects of Hainan Province (grant ZDYF2021SHFZ243) and (grant ZDYF2021SHFZ094).

Author contributions

YJ, JC: Conceptualization, writing—original draft;

ZB, GW, YZ: Supervision, writing—review and editing.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

References

[1]

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; 74(3):229-263.

[2]

Rebello RJ, Oing C, Knudsen KE, et al. Prostate cancer. Nat Rev Dis Primers 2021; 7(1):9.

[3]

Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015. CA Cancer J Clin 2016; 66(2):115-132.

[4]

Bergengren O, Pekala KR, Matsoukas K, et al. 2022 update on prostate cancer epidemiology and risk factors—A systematic review. Eur Urol 2023; 84(2):191-206.

[5]

Pernar CH, Ebot EM, Wilson KM, Mucci LA. The epidemiology of prostate cancer. Cold Spring Harb Perspect Med 2018; 8(12):a030361.

[6]

McNeal JE. The zonal anatomy of the prostate. Prostate 1981; 2(1):35-49.

[7]

Mohanty SK, Lobo A, Cheng L. The 2022 revision of the World Health Organization classification of tumors of the urinary system and male genital organs: Advances and challenges. Hum Pathol 2023;136:123-143.

[8]

Andreoiu M, Cheng L. Multifocal prostate cancer: Biologic, prognostic, and therapeutic implications. Hum Pathol 2010; 41(6):781-793.

[9]

Ul-Hassan A, Hassan G, Shafi M, Bhat M. Changes in the normal cellular architecture in the prostatic tissue with the increasing age. Int J Health Sci (Qassim) 2008; 2(2):171-178.

[10]

Ali A, Du Feu A, Oliveira P, et al. Prostate zones and cancer: Lost in transition? Nat Rev Urol 2022; 19(2):101-115.

[11]

Liu J, Dong B, Qu W, et al. Using clinical parameters to predict prostate cancer and reduce unnecessary biopsy among patients with PSA in the gray zone. Sci Rep 2020; 10(1):5157.

[12]

Kasivisvanathan V, Rannikko AS, Borghi M, et al. MRI-targeted or standard biopsy for prostate cancer diagnosis. N Engl J Med 2018; 378(19):1767-1777.

[13]

Sekhoacha M, Riet K, Motloung P, et al. Prostate cancer review: Genetics, diagnosis, treatment options, and alternative approaches. Molecules 2022; 27(17):5730.

[14]

Fang YQ, Zhou XF. Interpretation of the updated points of the 2020 edition of the European Society of Urology guidelines for the diagnosis and treatment of prostate cancer [in Chinese]. Chin J Endourol (Electron Ed) 2020; 14(6):401-404.

[15]

Cai M, Song XL, Li XA, et al. Current therapy and drug resistance in metastatic castration-resistant prostate cancer. Drug Resist Updat 2023;68:100962.

[16]

Lei Y, Tang R, Xu J, et al. Applications of single-cell sequencing in cancer research: Progress and perspectives. J Hematol Oncol 2021; 14(1):91.

[17]

Yu Q, Jiang M, Wu L. Spatial transcriptomics technology in cancer research. Front Oncol 2022;12:1019111.

[18]

Quan Y, Zhang H, Wang M, Ping H. Visium spatial transcriptomics reveals intratumor heterogeneity and profiles of Gleason score progression in prostate cancer. iScience 2023; 26(12):108429.

[19]

Du J, Yang YC, An ZJ, et al. Advances in spatial transcriptomics and related data analysis strategies. J Transl Med 2023; 21(1):330.

[20]

Liao J, Lu X, Shao X, Zhu L, Fan X. Uncovering an organ's molecular architecture at single-cell resolution by spatially resolved transcriptomics. Trends Biotechnol 2021; 39(1):43-58.

[21]

Couñago F, López-Campos F, Díaz-Gavela AA, et al. Clinical applications of molecular biomarkers in prostate cancer. Cancers (Basel) 2020; 12(6):1550.

[22]

Park HE, Jo SH, Lee RH, et al. Spatial transcriptomics: Technical aspects of recent developments and their applications in neuroscience and cancer research. Adv Sci (Weinh) 2023; 10(16):e2206939.

[23]

Marx V. Method of the year: Spatially resolved transcriptomics. Nat Methods 2021; 18(1):9-14.

[24]

Ren L, Huang D, Liu H, et al. Applications of single-cell omics and spatial transcriptomics technologies in gastric cancer (review). Oncol Lett 2024; 27(4):152.

[25]

Singer RH, Ward DC. Actin gene expression visualized in chicken muscle tissue culture by using in situ hybridization with a biotinated nucleotide analog. Proc Natl Acad Sci U S A 1982; 79(23):7331-7335.

[26]

Raj A, van den Bogaard P, Rifkin SA, van Oudenaarden A, Tyagi S. Imaging individual mRNA molecules using multiple singly labeled probes. Nat Methods 2008; 5(10):877-879.

[27]

Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X. RNA imaging. RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 2015; 348(6233):aaa6090.

[28]

Shah S, Takei Y, Zhou W, et al. Dynamics and spatial genomics of the nascent transcriptome by intron seqFISH. Cell 2018; 174(2):363-376.e16.

[29]

Ke R, Mignardi M, Pacureanu A, et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat Methods 2013; 10(9):857-860.

[30]

Weibrecht I, Lundin E, Kiflemariam S, et al. In situ detection of individual mRNA molecules and protein complexes or post-translational modifications using padlock probes combined with the in situ proximity ligation assay. Nat Protoc 2013; 8(2):355-372.

[31]

Lee JH, Daugharthy ER, Scheiman J, et al. Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues. Nat Protoc 2015; 10(3):442-458.

[32]

Alon S, Goodwin DR, Sinha A, et al. Expansion sequencing: Spatially precise in situ transcriptomics in intact biological systems. Science 2021; 371(6528):eaax2656.

[33]

Ståhl PL, Salmén F, Vickovic S, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 2016; 353(6294):78-82.

[34]

Rodriques SG, Stickels RR, Goeva A, et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 2019; 363(6434):1463-1467.

[35]

Vickovic S, Eraslan G, Salmén F, et al. High-definition spatial transcriptomics for in situ tissue profiling. Nat Methods 2019; 16(10):987-990.

[36]

Emmert-Buck MR, Bonner RF, Smith PD, et al. Laser capture microdissection. Science 1996; 274(5289):998-1001.

[37]

Junker JP, Noël ES, Guryev V, et al. Genome-wide RNA tomography in the zebrafish embryo. Cell 2014; 159(3):662-675.

[38]

Medaglia C, Giladi A, Stoler-Barak L, et al. Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science 2017; 358(6370):1622-1626.

[39]

Chen J, Suo S, Tam PP, Han JJ, Peng G, Jing N. Spatial transcriptomic analysis of cryosectioned tissue samples with GEO-seq. Nat Protoc 2017; 12(3):566-580.

[40]

Bilotta MT, Antignani A, Fitzgerald DJ. Managing the TME to improve the efficacy of cancer therapy. Front Immunol 2022;13:954992.

[41]

Hirz T, Mei S, Sarkar H, et al. Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses. Nat Commun 2023; 14(1):663.

[42]

Bian X, Wang W, Abudurexiti M, et al. Integration analysis of single-cell multi-omics reveals prostate cancer heterogeneity. Adv Sci (Weinh) 2024; 11(18):e2305724.

[43]

Siltari A, Syvälä H, Lou YR, Gao Y, Murtola TJ. Role of lipids and lipid metabolism in prostate cancer progression and the tumor's immune environment. Cancers (Basel) 2022; 14(17):4293.

[44]

Tan WP, Lin C, Chen M, Deane LA. Periprostatic fat: A risk factor for prostate cancer? Urology 2016;98:107-112.

[45]

Finley DS, Calvert VS, Inokuchi J, et al. Periprostatic adipose tissue as a modulator of prostate cancer aggressiveness. J Urol 2009; 182(4):1621-1627.

[46]

Dahran N, Szewczyk-Bieda M, Wei C, Vinnicombe S, Nabi G. Normalized periprostatic fat MRI measurements can predict prostate cancer aggressiveness in men undergoing radical prostatectomy for clinically localised disease. Sci Rep 2017; 7(1):4630.

[47]

Álvarez-Artime A, García-Soler B, Sainz RM, Mayo JC. Emerging roles for browning of white adipose tissue in prostate cancer malignant behaviour. Int J Mol Sci 2021; 22(11):5560.

[48]

Sacca PA, Calvo JC. Periprostatic adipose tissue microenvironment: Metabolic and hormonal pathways during prostate cancer progression. Front Endocrinol (Lausanne) 2022;13:863027.

[49]

Chiarugi P, Paoli P, Cirri P. Tumor microenvironment and metabolism in prostate cancer. Semin Oncol 2014; 41(2):267-280.

[50]

Cancel M, Pouillot W, Mahéo K, et al. Interplay between prostate cancer and adipose microenvironment: A complex and flexible scenario. Int J Mol Sci 2022; 23(18):10762.

[51]

Brady L, Kriner M, Coleman I, et al. Inter- and intra-tumor heterogeneity of metastatic prostate cancer determined by digital spatial gene expression profiling. Nat Commun 2021; 12(1):1426.

[52]

Salachan PV, Rasmussen M, Ulhøi BP, et al. Spatial whole transcriptome profiling of primary tumor from patients with metastatic prostate cancer. Int J Cancer 2023; 153(12):2055-2067.

[53]

Feng DC, Zhu WZ, Wang J, et al. The implications of single-cell RNA-seq analysis in prostate cancer: Unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy. Mil Med Res 2024; 11(1):21.

[54]

Aggarwal RR, Quigley DA, Huang J, et al. Whole-genome and transcriptional analysis of treatment-emergent small-cell neuroendocrine prostate cancer demonstrates intraclass heterogeneity. Mol Cancer Res 2019; 17(6):1235-1240.

[55]

Wang Y, Ma S, Ruzzo WL. Spatial modeling of prostate cancer metabolic gene expression reveals extensive heterogeneity and selective vulnerabilities. Sci Rep 2020; 10(1):3490.

[56]

Eickelschulte S, Riediger AL, Angeles AK, et al. Biomarkers for the detection and risk stratification of aggressive prostate cancer. Cancers (Basel) 2022; 14(24):6094.

[57]

Berglund E, Maaskola J, Schultz N, et al. Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity. Nat Commun 2018; 9(1):2419.

[58]

Ehsani M, David FO, Baniahmad A. Androgen receptor-dependent mechanisms mediating drug resistance in prostate cancer. Cancers (Basel) 2021; 13(7):1534.

[59]

Marklund M, Schultz N, Friedrich S, et al. Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones. Nat Commun 2022; 13(1):5475.

[60]

Kumar V, Randhawa P, Bilodeau R, et al. Spatial profiling of the prostate cancer tumor microenvironment reveals multiple differences in gene expression and correlation with recurrence risk. Cancers (Basel) 2022; 14(19):4923.

[61]

Zou C, Li W, Zhang Y, et al. Identification of an anaplastic subtype of prostate cancer amenable to therapies targeting SP1 or translation elongation. Sci Adv 2024; 10(14):eadm7098.

[62]

Zhuang J, Li M, Zhang X, et al. Construction of bone metastasis-specific regulation network based on prognostic stemness-related signatures in prostate cancer. Dis Markers 2022;2022:8495923.

[63]

Stultz J, Fong L. How to turn up the heat on the cold immune microenvironment of metastatic prostate cancer. Prostate Cancer Prostatic Dis 2021; 24(3):697-717.

[64]

Wu X, Zhu Y, Hu C, et al. Extracellular vesicles related gene HSPH1 exerts anti-tumor effects in prostate cancer via promoting the stress response of CD8+ T cells. Cell Oncol (Dordr) 2024; 47(3):1059-1064.

[65]

Keam SP, Halse H, Nguyen T, et al. High dose-rate brachytherapy of localized prostate cancer converts tumors from cold to hot. J Immunother Cancer 2020; 8(1):e000792.

[66]

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; 23(1):87-98.

[67]

Mangiola S, McCoy P, Modrak M, et al. Transcriptome sequencing and multi-plex imaging of prostate cancer microenvironment reveals a dominant role for monocytic cells in progression. BMC Cancer 2021; 21(1):846.

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