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Current status of potential magnetic resonance imaging markers in the neural microenvironment in prostate cancer patients
QI Dong  QI Xuan  YANG Hongkai  DU Bing  ZHAI Chengfeng  HE Yongsheng 

Cite this article as: QI D, QI X, YANG H K, et al. Current status of potential magnetic resonance imaging markers in the neural microenvironment in prostate cancer patients[J]. Chin J Magn Reson Imaging, 2024, 15(8): 194-200. DOI:10.12015/issn.1674-8034.2024.08.031.


[Abstract] Prostate cancer (PCa) is the most prevalent and second deadliest cancer among men worldwide. The neural microenvironment of PCa is closely related to tumor progression, surgical curative degree, and postoperative recurrence, but the specific mechanism is not yet clear. The neural density (ND), perineural invasion (PNI), and neuroendocrine features (NEF) in the neural microenvironment are closely related to the expression of TMPRSS2 ERG gene, monoamine oxidase A (MAOA), nuclear factor kappa B, neurotrophic factors, and neuropeptide Y (NPY). Exploring imaging biomarkers related to genomics and proteomics can early identify the PCa neural microenvironment and affect clinical diagnosis and treatment plans. Based on the imaging omics features of multi-parameter magnetic resonance imaging (mp-MRI), potential imaging biomarkers for PNI and NEF can be identified. Neural visualization can be performed based on magnetic particle imaging (MPI) and deep neural network (DNN) image classification models. Emerging neuroimaging technologies such as diffusion tensor imaging (DTI), diffusion spectrum imaging (DSI), neurite orientation diffusion and density imaging (NODDI), and the design, synthesis, and neuroimaging of near-infrared fluorophores based on phenoxazine also hold unique value in displaying and predicting ND, PNI, and NEF. This article reviews the current research status of potential imaging biomarkers in the neural microenvironment of PCa patients, in order to further reveal the neurophysiological mechanisms of the PCa neural microenvironment and provide imaging evidence for subsequent diagnosis and treatment processes and improving patient prognosis.
[Keywords] prostate cancer；neuro microenvironment；image markers；magnetic resonance imaging；multiparameter magnetic resonance imaging；neurovisualization；magnetic resonance diffusion imaging

QI Dong1, 3   QI Xuan1   YANG Hongkai1   DU Bing1, 2   ZHAI Chengfeng1, 3   HE Yongsheng1*  

1 Department of Radiology, Ma'anshan People's Hospital, Ma'anshan 243000, China

2 Wannan Medical College, Wuhu 241002, China

3 Ma'anshan Clinical College (Ma'anshan People's Hospital), The Fifth Clinical Medical College of Anhui Medical University, Ma'anshan 243000, China

Corresponding author: HE Y S, E-mail: heyongsheng881@163.com

Conflicts of interest   None.

Received  2024-02-20
Accepted  2024-08-07
DOI: 10.12015/issn.1674-8034.2024.08.031
Cite this article as: QI D, QI X, YANG H K, et al. Current status of potential magnetic resonance imaging markers in the neural microenvironment in prostate cancer patients[J]. Chin J Magn Reson Imaging, 2024, 15(8): 194-200. DOI:10.12015/issn.1674-8034.2024.08.031.

[1]
SIEGEL R L, MILLER K D, FUCHS H E, et al. Cancer statistics, 2022[J]. CA Cancer J Clin, 2022, 72(1): 7-33. DOI: 10.3322/caac.21708.
[2]
HAN S J, ZHANG S W, CHEN W Q, et al. Analysis of the status and trends of prostate cancer incidence in China[J]. Chin Clin Oncol, 2013, 18(4): 330-334. DOI: 10.3969/j.issn.1009-0460.2013.04.009.
[3]
SIGORSKI D, GULCZYŃSKI J, SEJDA A, et al. Investigation of neural microenvironment in prostate cancer in context of neural density, perineural invasion, and neuroendocrine profile of tumors[J/OL]. Front Oncol, 2021, 11: 710899 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8281889/. DOI: 10.3389/fonc.2021.710899.
[4]
ZHU X H, SHAO L Z, LIU Z Y, et al. MRI-derived radiomics models for diagnosis, aggressiveness, and prognosis evaluation in prostate cancer[J]. J Zhejiang Univ Sci B, 2023, 24(8): 663-681. DOI: 10.1631/jzus.B2200619.
[5]
FU Y F, WU X S, WU W J, et al. Research progress of neuroimaging markers for predicting the curative effect of schizophrenia[J]. Radiol Pract, 2021, 36(4): 556-559. DOI: 10.13609/j.cnki.1000-0313.2021.04.026.
[6]
NIELSEN M M, TOLBOD L P, BORRE M, et al. The relationship between tumor aggressiveness and cholinergic PET imaging in prostate cancer tissue. A proof-of-concept study[J]. Am J Nucl Med Mol Imaging, 2019, 9(3): 185-192.
[7]
ROWE C W, DILL T, GRIFFIN N, et al. Innervation of papillary thyroid cancer and its association with extra-thyroidal invasion[J/OL]. Sci Rep, 2020, 10(1): 1539 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/32001748/. DOI: 10.1038/s41598-020-58425-5.
[8]
YOU H J, SHANG W T, MIN X D, et al. Sight and switch off: nerve density visualization for interventions targeting nerves in prostate cancer[J/OL]. Sci Adv, 2020, 6(6): eaax6040 [2024-05-06]. https://pubmed.ncbi.nlm.nih.gov/32076639/. DOI: 10.1126/sciadv.aax6040.
[9]
IWASAKI T, HIRAOKA N, INO Y, et al. Reduction of intrapancreatic neural density in cancer tissue predicts poorer outcome in pancreatic ductal carcinoma[J]. Cancer Sci, 2019, 110(4): 1491-1502. DOI: 10.1111/cas.13975.
[10]
HÄNZE J, REXIN P, JAKUBOWSKI P, et al. Prostate cancer tissues with positive TMPRSS2-ERG-gene-fusion status may display enhanced nerve density[J/OL]. Urol Oncol, 2020, 38(1): 3.e7-3.e15 [2024-05-06]. https://pubmed.ncbi.nlm.nih.gov/30241953/. DOI: 10.1016/j.urolonc.2018.07.019.
[11]
REEVES F A, BATTYE S, ROTH H, et al. Prostatic nerve subtypes independently predict biochemical recurrence in prostate cancer[J]. J Clin Neurosci, 2019, 63: 213-219. DOI: 10.1016/j.jocn.2019.01.052.
[12]
MAGNON C, HALL S J, LIN J, et al. Autonomic nerve development contributes to prostate cancer progression[J/OL]. Science, 2013, 341(6142): 1236361 [2024-05-06]. https://pubmed.ncbi.nlm.nih.gov/23846904/. DOI: 10.1126/science.1236361.
[13]
XIE X L, ZHAI J, ZHOU X Y, et al. Magnetic particle imaging: from tracer design to biomedical applications in vasculature abnormality[J/OL]. Adv Mater, 2024, 36(17): e2306450 [2024-05-06]. https://doi.org/10.1002/adma.202306450. DOI: 10.1002/adma.202306450.
[14]
REZAEI B, TAY Z W, MOSTUFA S, et al. Magnetic nanoparticles for magnetic particle imaging (MPI): design and applications[J]. Nanoscale, 2024, 16(25): 11802-11824. DOI: 10.1039/d4nr01195c.
[15]
LEI S A, HE J, GAO P L, et al. Magnetic particle imaging-guided hyperthermia for precise treatment of cancer: review, challenges, and prospects[J]. Mol Imaging Biol, 2023, 25(6): 1020-1033. DOI: 10.1007/s11307-023-01856-z.
[16]
WANG W S, LI F F, LI S B, et al. M2 macrophage-targeted iron oxide nanoparticles for magnetic resonance image-guided magnetic hyperthermia therapy[J]. J Mater Sci Technol, 2021, 81: 77-87. DOI: 10.1016/j.jmst.2020.11.058.
[17]
ZHANG W J, LIANG X L, ZHU L, et al. Optical magnetic multimodality imaging of plectin-1-targeted imaging agent for the precise detection of orthotopic pancreatic ductal adenocarcinoma in mice[J/OL]. EBioMedicine, 2022, 80: 104040 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/35525203/. DOI: 10.1016/j.ebiom.2022.104040.
[18]
WANG G R, LI W Z, SHI G Y, et al. Sensitive and specific detection of breast cancer lymph node metastasis through dual-modality magnetic particle imaging and fluorescence molecular imaging: a preclinical evaluation[J]. Eur J Nucl Med Mol Imaging, 2022, 49(8): 2723-2734. DOI: 10.1007/s00259-022-05834-5.
[19]
AMIT M, ERAN A, BILLAN S, et al. Perineural spread in noncutaneous head and neck cancer: new insights into an old problem[J]. J Neurol Surg B Skull Base, 2016, 77(2): 86-95. DOI: 10.1055/s-0036-1571834.
[20]
BAKST R L, WONG R J. Mechanisms of perineural invasion[J]. J Neurol Surg B Skull Base, 2016, 77(2): 96-106. DOI: 10.1055/s-0036-1571835.
[21]
CHEN S H, ZHANG B Y, ZHOU B, et al. Perineural invasion of cancer: a complex crosstalk between cells and molecules in the perineural niche[J]. Am J Cancer Res, 2019, 9(1): 1-21.
[22]
SILVERMAN D A, MARTINEZ V K, DOUGHERTY P M, et al. Cancer-associated neurogenesis and nerve-cancer cross-talk[J]. Cancer Res, 2021, 81(6): 1431-1440. DOI: 10.1158/0008-5472.CAN-20-2793.
[23]
HE S B, CHEN C H, CHERNICHENKO N, et al. GFRα1 released by nerves enhances cancer cell perineural invasion through GDNF-RET signaling[J/OL]. Proc Natl Acad Sci USA, 2014, 111(19): E2008-E2017 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24778213/. DOI: 10.1073/pnas.1402944111.
[24]
MENG Y, LIAO Y B, XU P, et al. Perineural invasion is an independent predictor of biochemical recurrence of prostate cancer after local treatment: a meta-analysis[J]. Int J Clin Exp Med, 2015, 8(8): 13267-13274.
[25]
TRUONG M, RAIS-BAHRAMI S, NIX J W, et al. Perineural invasion by prostate cancer on MR/US fusion targeted biopsy is associated with extraprostatic extension and early biochemical recurrence after radical prostatectomy[J]. Hum Pathol, 2017, 66: 206-211. DOI: 10.1016/j.humpath.2017.06.017.
[26]
KARSIYAKALI N, BULENT OZGEN M, OZVEREN B, et al. The association between perineural invasion in mpMRI-targeted and/or systematic prostate biopsy and adverse pathological outcomes in robot-assisted radical prostatectomy[J]. Actas Urol Esp, 2022, 46(6): 377-384. DOI: 10.1016/j.acuroe.2022.02.004.
[27]
CHEN C H, WU B J. Monoamine oxidase A: an emerging therapeutic target in prostate cancer[J/OL]. Front Oncol, 2023, 13: 1137050 [2024-05-06]. https://pubmed.ncbi.nlm.nih.gov/36860320/. DOI: 10.3389/fonc.2023.1137050.
[28]
HAN H, LI H, MA Y F, et al. Monoamine oxidase A (MAOA): a promising target for prostate cancer therapy[J/OL]. Cancer Lett, 2023, 563: 216188 [2024-05-06]. https://pubmed.ncbi.nlm.nih.gov/37076041/. DOI: 10.1016/j.canlet.2023.216188.
[29]
YIN L J, LI J J, WANG J, et al. MAOA promotes prostate cancer cell perineural invasion through SEMA3C/PlexinA2/NRP1-cMET signaling[J]. Oncogene, 2021, 40(7): 1362-1374. DOI: 10.1038/s41388-020-01615-2.
[30]
OLAR A, HE D D, FLORENTIN D, et al. Biological correlates of prostate cancer perineural invasion diameter[J]. Hum Pathol, 2014, 45(7): 1365-1369. DOI: 10.1016/j.humpath.2014.02.011.
[31]
BAI J B, TAO N, AN H Q. Research status of nerve invasion of prostate cancer[J]. J Mod Urol, 2021, 26(1): 83-87. DOI: 10.3969/j.issn.1009-8291.2021.01.020.
[32]
DONATO M D, GIOVANNELLI P, MIGLIACCIO A, et al. The nerve growth factor-delivered signals in prostate cancer and its associated microenvironment: when the dialogue replaces the monologue[J/OL]. Cell Biosci, 2023, 13(1): 60 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/36941697/. DOI: 10.1186/s13578-023-01008-4.
[33]
ZHANG W, ZHANG W T, LI X, et al. Predicting tumor perineural invasion status in high-grade prostate cancer based on a clinical-radiomics model incorporating T2-weighted and diffusion-weighted magnetic resonance images[J/OL]. Cancers, 2022, 15(1): 86 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9817925/. DOI: 10.3390/cancers15010086.
[34]
FAN X H, XIE N, CHEN J W, et al. Multiparametric MRI and machine learning based radiomic models for preoperative prediction of multiple biological characteristics in prostate cancer[J/OL]. Front Oncol, 2022, 12: 839621 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/35198452/. DOI: 10.3389/fonc.2022.839621.
[35]
ZHANG Y, PENG J X, LIU J, et al. Preoperative prediction of perineural invasion status of rectal cancer based on radiomics nomogram of multiparametric magnetic resonance imaging[J/OL]. Front Oncol, 2022, 12: 828904 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/35480114/. DOI: 10.3389/fonc.2022.828904.
[36]
MENG J L, LU X F, JIN C, et al. Integrated multi-omics data reveals the molecular subtypes and guides the androgen receptor signalling inhibitor treatment of prostate cancer[J/OL]. Clin Transl Med, 2021, 11(12): e655 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/34936729/. DOI: 10.1002/ctm2.655.
[37]
LU X F, MENG J L, ZHOU Y J, et al. MOVICS: an R package for multi-omics integration and visualization in cancer subtyping[J]. Bioinformatics, 2021, 36(22/23): 5539-5541. DOI: 10.1093/bioinformatics/btaa1018.
[38]
WANG B D, LI M, LI R S. Identification and verification of prognostic cancer subtype based on multi-omics analysis for kidney renal papillary cell carcinoma[J/OL]. Front Oncol, 2023, 13: 1169395 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/37091151/. DOI: 10.3389/fonc.2023.1169395.
[39]
ZOU Y M, CAO C L, WANG Y L, et al. Multi-omics consensus portfolio to refine the classification of lung adenocarcinoma with prognostic stratification, tumor microenvironment, and unique sensitivity to first-line therapies[J]. Transl Lung Cancer Res, 2022, 11(11): 2243-2260. DOI: 10.21037/tlcr-22-775.
[40]
GUAN Y, YUE S Y, CHEN Y D, et al. Molecular cluster mining of adrenocortical carcinoma via multi-omics data analysis aids precise clinical therapy[J/OL]. Cells, 2022, 11(23): 3784 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/36497046/. DOI: 10.3390/cells11233784.
[41]
PEREZ-JOHNSTON R, ARAUJO-FILHO J A, CONNOLLY J G, et al. CT-based radiogenomic analysis of clinical stage I lung adenocarcinoma with histopathologic features and oncologic outcomes[J]. Radiology, 2022, 303(3): 664-672. DOI: 10.1148/radiol.211582.
[42]
ASHRAF A B, DAYE D, GAVENONIS S, et al. Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles[J]. Radiology, 2014, 272(2): 374-384. DOI: 10.1148/radiol.14131375.
[43]
CHALISE P, FRIDLEY B L. Integrative clustering of multi-level 'omic data based on non-negative matrix factorization algorithm[J/OL]. PLoS One, 2017, 12(5): e0176278 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28459819/. DOI: 10.1371/journal.pone.0176278.
[44]
TIBSHIRANI R, WALTHER G, HASTIE T. Estimating the number of clusters in a data set via the gap statistic[J]. J R Stat Soc Ser B Stat Methodol, 2001, 63(2): 411-423. DOI: 10.1111/1467-9868.00293.
[45]
PANEBIANCO V, BARCHETTI F, SCIARRA A, et al. In vivo 3D neuroanatomical evaluation of periprostatic nerve plexus with 3T-MR Diffusion Tensor Imaging[J]. Eur J Radiol, 2013, 82(10): 1677-1682. DOI: 10.1016/j.ejrad.2013.05.013.
[46]
TIAN W Z, ZHANG J, TIAN F Z, et al. Correlation of diffusion tensor imaging parameters and Gleason scores of prostate cancer[J]. Exp Ther Med, 2018, 15(1): 351-356. DOI: 10.3892/etm.2017.5363.
[47]
DI TRANI M G, NEZZO M, CAPORALE A S, et al. Performance of diffusion kurtosis imaging versus diffusion tensor imaging in discriminating between benign tissue, low and high gleason grade prostate cancer[J]. Acad Radiol, 2019, 26(10): 1328-1337. DOI: 10.1016/j.acra.2018.11.015.
[48]
KARTASALO K, STRÖM P, RUUSUVUORI P, et al. Detection of perineural invasion in prostate needle biopsies with deep neural networks[J]. Virchows Arch, 2022, 481(1): 73-82. DOI: 10.1007/s00428-022-03326-3.
[49]
WU C L, KIM M, WU S L, et al. Transperineal multiparametric magnetic resonance imaging-ultrasound fusion-targeted prostate biopsy combined with standard template improves perineural invasion detection[J]. Hum Pathol, 2021, 117: 101-107. DOI: 10.1016/j.humpath.2021.08.008.
[50]
JIANG C, YUAN Y, GU B, et al. Preoperative prediction of microvascular invasion and perineural invasion in pancreatic ductal adenocarcinoma with 18F-FDG PET/CT radiomics analysis[J]. Clin Radiol, 2023, 78(9): 687-696. DOI: 10.1016/j.crad.2023.05.007.
[51]
WANG L G, MONTAÑO A R, MASILLATI A M, et al. Nerve Visualization using Phenoxazine-Based Near-Infrared Fluorophores to Guide Prostatectomy[J/OL]. Adv Mater, 2024, 36(16): e2304724 [2024-05-06]. https://onlinelibrary.wiley.com/doi/10.1002/adma.202304724. DOI: 10.1002/adma.202304724.
[52]
SEJDA A, SIGORSKI D, GULCZYŃSKI J, et al. Complexity of neural component of tumor microenvironment in prostate cancer[J]. Pathobiology, 2020, 87(2): 87-99. DOI: 10.1159/000505437.
[53]
KRISTENSEN G, RØDER M A, BERG K D, et al. Predictive value of combined analysis of pro-NPY and ERG in localized prostate cancer[J]. APMIS, 2018, 126(10): 804-813. DOI: 10.1111/apm.12886.
[54]
ADAMO P, LADOMERY M R. The oncogene ERG: a key factor in prostate cancer[J]. Oncogene, 2016, 35(4): 403-414. DOI: 10.1038/onc.2015.109.
[55]
SCARAVILLI M, KOIVUKOSKI S, LATONEN L. Androgen-driven fusion genes and chimeric transcripts in prostate cancer[J/OL]. Front Cell Dev Biol, 2021, 9: 623809 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/33634124/. DOI: 10.3389/fcell.2021.623809.
[56]
FARACH A, DING Y, LEE M, et al. Neuronal trans-differentiation in prostate cancer cells[J]. Prostate, 2016, 76(14): 1312-1325. DOI: 10.1002/pros.23221.
[57]
LI Q, ZHANG C S, ZHANG Y F. Molecular aspects of prostate cancer with neuroendocrine differentiation[J]. Chin J Cancer Res, 2016, 28(1): 122-129. DOI: 10.3978/j.issn.1000-9604.2016.01.02.
[58]
DING Y, LEE M, GAO Y, et al. Neuropeptide Y nerve paracrine regulation of prostate cancer oncogenesis and therapy resistance[J]. Prostate, 2021, 81(1): 58-71. DOI: 10.1002/pros.24081.
[59]
SIGORSKI D, WESOŁOWSKI W, GRUSZECKA A, et al. Neuropeptide Y and its receptors in prostate cancer: associations with cancer invasiveness and perineural spread[J]. J Cancer Res Clin Oncol, 2023, 149(9): 5803-5822. DOI: 10.1007/s00432-022-04540-x.
[60]
FU X W, CHEN Y Y, NI H Y. Principle and clinical application of various new MR diffusion imaging based on different models[J]. Chin J Radiol, 2017, 51(11): 883-886. DOI: 10.3760/cma.j.issn.1005?1201.2017.11.019.
[61]
ZHANG M Z, YUAN H S. Research progress on magnetic resonance imaging of cervical spondylosis myelopathy[J]. Chin J Magn Reson Imag, 2019, 10(12): 944-946. DOI: 10.12015/issn.1674-8034.2019.12.016.
[62]
JU C, WANG M, WANG H. Research progress of neurite direction diffusion and density imaging for glioma classification[J]. Chin J Magn Reson Imag, 2021, 12(4): 100-102, 110. DOI: 10.12015/issn.1674-8034.2021.04.025.
[63]
DELIGIANNI F, CARMICHAEL D W, ZHANG G H, et al. NODDI and tensor-based microstructural indices as predictors of functional connectivity[J/OL]. PLoS One, 2016, 11(4): e0153404 [2024-05-06]. https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/27078862/. DOI: 10.1371/journal.pone.0153404.
[64]
MERLUZZI A P, DEAN D C, ADLURU N, et al. Age-dependent differences in brain tissue microstructure assessed with neurite orientation dispersion and density imaging[J]. Neurobiol Aging, 2016, 43: 79-88. DOI: 10.1016/j.neurobiolaging.2016.03.026.

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