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Original Research

Open Access

Application of a high-dimensional gene co-expression network to identify metal ion transport-associated epithelial cells with diagnostic function for prostate cancer

  • Jian Sun1,†
  • Xueqi Zhu2,†
  • Kai Li1
  • Ke Zhang1
  • Fei Wang1,*,

1Department of Urology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, 215000 Suzhou, Jiangsu, China

2Department of ICU, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, 215000 Suzhou, Jiangsu, China

DOI: 10.22514/jomh.2024.052 Vol.20,Issue 4,April 2024 pp.39-53

Submitted: 14 September 2023 Accepted: 05 December 2023

Published: 30 April 2024

*Corresponding Author(s): Fei Wang E-mail:

† These authors contributed equally.


Prostate cancer is a prevalent malignancy and leading cause of male mortality worldwide, thus highlighting the need for precision medicine and a combined single-cell and bulk transcriptome-based diagnostic assessment model. First, we used single-cell RNA sequencing data and the high dimensional weighted gene co-expression network analysis (hdWGCNA) method to identify specific cell types in tumor tissues. We identified higher proportions of epithelial cells and increased intra-tissue heterogeneity in prostate cancer; most of these epithelial cells were closely associated with metal ion transport functions. Lasso regression identified diagnostic-related genes that were incorporated into a sample evaluation model using multiple machine learning algorithms. Thus, we established a diagnostic model that combined immune cells, diagnostic genes and deep learning methods. The Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) models exhibited the highest diagnostic efficacy for Lasso genes. Neural network models, combining immune microenvironment assessment and diagnostic gene expression, also showed good diagnostic efficacy. Our study highlights the potential of machine learning and convolutional neural networks for the diagnostic assessment of prostate cancer, thus providing support for personalized treatment decisions, and identifying areas for future research.


Prostate cancer; Diagnosis; Machine learning

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Jian Sun,Xueqi Zhu,Kai Li,Ke Zhang,Fei Wang. Application of a high-dimensional gene co-expression network to identify metal ion transport-associated epithelial cells with diagnostic function for prostate cancer. Journal of Men's Health. 2024. 20(4);39-53.


[1] Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent global patterns in prostate cancer incidence and mortality rates. European Urology. 2020; 77: 38–52.

[2] Roberts MJ, Maurer T, Perera M, Eiber M, Hope TA, Ost P, et al. Using PSMA imaging for prognostication in localized and advanced prostate cancer. Nature Reviews Urology. 2023; 20: 23–47.

[3] Epstein JI, Egevad L, Amin MB, Delahunt B, Srigley JR, Humphrey PA. The 2014 international society of urological pathology (ISUP) consensus conference on Gleason grading of prostatic carcinoma. American Journal of Surgical Pathology. 2016; 40: 244–252.

[4] Avanzo M, Wei L, Stancanello J, Vallières M, Rao A, Morin O, et al. Machine and deep learning methods for radiomics. Medical Physics. 2020; 47: e185–e202.

[5] Trujillo B, Wu A, Wetterskog D, Attard G. Blood-based liquid biopsies for prostate cancer: clinical opportunities and challenges. British Journal of Cancer. 2022; 127: 1394–1402.

[6] Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology. Anesthesiology. 2020; 132: 379–394.

[7] Petegrosso R, Li Z, Kuang R. Machine learning and statistical methods for clustering single-cell RNA-sequencing data. Briefings in Bioinformatics. 2020; 21: 1209–1223.

[8] García Garzón JR, de Arcocha Torres M, Delgado-Bolton R, Ceci F, Alvarez Ruiz S, Orcajo Rincón J, et al. 68Ga-PSMA PET/CT in prostate cancer. Revista EspañOla De Medicina Nuclear E Imagen Molecular. 2018; 37: 130–138.

[9] Wang X, An P, Gu Z, Luo Y, Luo J. Mitochondrial metal ion transport in cell metabolism and disease. International Journal of Molecular Sciences. 2021; 22: 7525.

[10] Bozzi AT, Gaudet R. Molecular mechanism of Nramp-family transition metal transport. Journal of Molecular Biology. 2021; 433: 166991.

[11] To PK, Do MH, Cho J-H, Jung C. Growth modulatory role of zinc in prostate cancer and application to cancer therapeutics. International Journal of Molecular Sciences. 2020; 21: 2991.

[12] Ghafoor S, Burger IA, Vargas AH. Multimodality imaging of prostate cancer. Journal of Nuclear Medicine. 2019; 60: 1350–1358.

[13] Papalexi E, Satija R. Single-cell RNA sequencing to explore immune cell heterogeneity. Nature Reviews Immunology. 2018; 18: 35–45.

[14] Chen X, Sun Y, Guan N, Qu J, Huang Z, Zhu Z, et al. Computational models for lncRNA function prediction and functional similarity calculation. Briefings in Functional Genomics. 2019; 18: 58–82.

[15] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015; 521: 436–444.

[16] Bao J-h, Lu W-c, Duan H, Ye YQ, Li JB, Liao WT, et al. Identification of a novel cuproptosis-related gene signature and integrative analyses in patients with lower-grade gliomas. Frontiers in Immunology. 2022; 13: 933973.

[17] Adamidi ES, Mitsis K, Nikita KS. Artificial intelligence in clinical care amidst COVID-19 pandemic: a systematic review. Computational and Structural Biotechnology Journal. 2021; 19: 2833–2850.

[18] Benítez-Parejo N, Rodríguez del Águila MM, Pérez-Vicente S. Survival analysis and Cox regression. Allergologia Et Immunopathologia. 2011; 39: 362–373.

[19] Wong HY, Sheng Q, Hesterberg AB, Croessmann S, Rios BL, Giri K, et al. Single cell analysis of cribriform prostate cancer reveals cell intrinsic and tumor microenvironmental pathways of aggressive disease. Nature Communications. 2022; 13: 6036.

[20] Henry GH, Malewska A, Joseph DB, Malladi VS, Lee J, Torrealba J, et al. A cellular anatomy of the normal adult human prostate and prostatic urethra. Cell Reports. 2018; 25: 3530–3542.e5.

[21] Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell. 2021; 184: 3573–3587.e29.

[22] 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. Nature Methods. 2019; 16: 1289–1296.

[23] Aran D, Looney AP, Liu L, Wu E, Fong V, Hsu A, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nature Immunology. 2019; 20: 163–172.

[24] Jin S, Guerrero-Juarez CF, Zhang L, Chang I, Ramos R, Kuan CH, et al. Inference and analysis of cell-cell communication using CellChat. Nature Communications. 2021; 12: 1088.

[25] Qiu X, Mao Q, Tang Y, Wang L, Chawla R, Pliner HA, et al. Reversed graph embedding resolves complex single-cell trajectories. Nature Methods. 2017; 14: 979–982.

[26] Morabito S, Reese F, Rahimzadeh N, Miyoshi E, Swarup V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Reports Methods. 2023; 3: 100498.

[27] Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nature Communications. 2019; 10: 1523.

[28] Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013; 14: 7.

[29] Zeng D, Ye Z, Shen R, Yu G, Wu J, Xiong Y, et al. IOBR: multi-omics immuno-oncology biological research to decode tumor microenvironment and signatures. Frontiers in Immunology. 2021; 12: 687975.

[30] Gaujoux R, Seoighe C. A flexible R package for nonnegative matrix factorization. BMC Bioinformatics. 2010; 11: 367.

[31] Mohammad N, Muad AM, Ahmad R, Yusof MYPM. Accuracy of advanced deep learning with tensorflow and keras for classifying teeth developmental stages in digital panoramic imaging. BMC Medical Imaging. 2022; 22: 66.

[32] Buddingh’ BC, Elzinga J, van Hest JC. Intercellular communication between artificial cells by allosteric amplification of a molecular signal. Nature Communications. 2020; 11: 1652.

[33] Noël F, Massenet-Regad L, Carmi-Levy I, Cappuccio A, Grandclaudon M, Trichot C, et al. Dissection of intercellular communication using the transcriptome-based framework ICELLNET. Nature Communications. 2021; 12: 1089.

[34] Hou R, Denisenko E, Ong HT, Ramilowski JA, Forrest ARR. Predicting cell-to-cell communication networks using NATMI. Nature Communications. 2020; 11: 5011.

[35] Zhang Y, Wang D, Peng M, Tang L, Ouyang J, Xiong F, et al. Single‐cell RNA sequencing in cancer research. Journal of Experimental & Clinical Cancer Research. 2021; 40: 81.

[36] Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Experimental & Molecular Medicine. 2018; 50: 1–14.

[37] Song H, Weinstein HN, Allegakoen P, Wadsworth MH 2nd, Xie J, Yang H, et al. Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states. Nature Communications. 2022; 13: 141.

[38] Masetti M, Carriero R, Portale F, Marelli G, Morina N, Pandini M, et al. Lipid-loaded tumor-associated macrophages sustain tumor growth and invasiveness in prostate cancer. Journal of Experimental Medicine. 2022; 219: e20210564.

[39] Chen Y, Zhang P, Liao J, Cheng J, Zhang Q, Li T, et al. Single-cell transcriptomics reveals cell type diversity of human prostate. Journal of Genetics and Genomics. 2022; 49: 1002–1015.

[40] Han H, Lee HH, Choi K, Moon YJ, Heo JE, Ham WS, et al. Prostate epithelial genes define therapy-relevant prostate cancer molecular subtype. Prostate Cancer and Prostatic Diseases. 2021; 24: 1080–1092.

[41] Sekhoacha M, Riet K, Motloung P, Gumenku L, Adegoke A, Mashele S. Prostate cancer review: genetics, diagnosis, treatment options, and alternative approaches. Molecules. 2022; 27: 5730.

[42] Andl T, Ganapathy K, Bossan A, Chakrabarti R. MicroRNAs as guardians of the prostate: those who stand before cancer. What do we really know about the role of microRNAs in prostate biology? International Journal of Molecular Sciences. 2020; 21: 4796.

[43] Gonzalez-Avila G, Sommer B, García-Hernandez AA, Ramos C, Flores-Soto E. Nanotechnology and matrix metalloproteinases in cancer diagnosis and treatment. Frontiers in Molecular Biosciences. 2022; 9: 918789.

[44] Cabral-Pacheco GA, Garza-Veloz I, Castruita-De la Rosa C, Ramirez-Acuña JM, Perez-Romero BA, Guerrero-Rodriguez JF, et al. The roles of matrix metalloproteinases and their inhibitors in human diseases. International Journal of Molecular Sciences. 2020; 21: 9739.

[45] Geng X, Chen C, Huang Y, Hou J. The prognostic value and potential mechanism of matrix metalloproteinases among prostate cancer. International Journal of Medical Sciences. 2020; 17: 1550–1560.

[46] Niland S, Riscanevo AX, Eble JA. Matrix metalloproteinases shape the tumor microenvironment in cancer progression. International Journal of Molecular Sciences. 2021; 23: 146.

[47] Carruba G, Stefano R, Cocciadiferro L, Saladino F, Di Cristina A, Tokar E, et al. Intercellular communication and human prostate carcinogenesis. Annals of the New York Academy of Sciences. 2002; 963: 156–168.

[48] Wu Y, Clark KC, Niranjan B, Chüeh AC, Horvath LG, Taylor RA, et al. Integrative characterisation of secreted factors involved in intercellular communication between prostate epithelial or cancer cells and fibroblasts. Molecular Oncology. 2023; 17: 469–486.

[49] Erdogan S, Doganlar ZB, Doganlar O, Turkekul K, Serttas R. Inhibition of midkine suppresses prostate cancer CD133 + stem cell growth and migration. The American Journal of the Medical Sciences. 2017; 354: 299–309.

[50] Meyer-Siegler K, Hudson PB. Enhanced expression of macrophage migration inhibitory factor in prostatic adenocarcinoma metastases. Urology. 1996; 48: 448–452.

[51] Rafiei S, Gui B, Wu J, Liu XS, Kibel AS, Jia L. Targeting the MIF/CXCR7/AKT signaling pathway in castration-resistant prostate cancer. Molecular Cancer Research. 2019; 17: 263–276.

[52] Bokobza SM, Ye L, Kynaston H, Jiang WG. Growth and differentiation factor 9 (GDF-9) induces epithelial–mesenchymal transition in prostate cancer cells. Molecular and Cellular Biochemistry. 2011; 349: 33–40.

[53] Bokobza SM, Ye L, Kynaston HG, Jiang WG. GDF‐9 promotes the growth of prostate cancer cells by protecting them from apoptosis. Journal of Cellular Physiology. 2010; 225: 529–536.

[54] Nordin A, Wang W, Welén K, Damber J. Midkine is associated with neuroendocrine differentiation in castration‐resistant prostate cancer. the Prostate. 2013; 73: 657–667.

[55] Zhou Q, Yang C, Mou Z, Wu S, Dai X, Chen X, et al. Identification and validation of a poor clinical outcome subtype of primary prostate cancer with Midkine abundance. Cancer Science. 2022; 113: 3698–3709.

[56] Hilscher M, Røder A, Helgstrand JT, Klemann N, Brasso K, Vickers AJ, et al. Risk of prostate cancer and death after benign transurethral resection of the prostate—a 20‐year population‐based analysis. Cancer. 2022; 128: 3674–3680.

[57] Oczkowski M, Dziendzikowska K, Pasternak-Winiarska A, Włodarek D, Gromadzka-Ostrowska J. Dietary factors and prostate cancer development, progression, and reduction. Nutrients. 2021; 13: 496.

[58] Al-Fayez S, El-Metwally A. Cigarette smoking and prostate cancer: a systematic review and meta-analysis of prospective cohort studies. Tobacco Induced Diseases. 2023; 21: 19.

[59] Storck WK, May AM, Westbrook TC, Duan Z, Morrissey C, Yates JA, et al. The role of epigenetic change in therapy-induced neuroendocrine prostate cancer lineage plasticity. Frontiers in Endocrinology. 2022; 13: 926585.

[60] Merkens L, Sailer V, Lessel D, Janzen E, Greimeier S, Kirfel J, et al. Aggressive variants of prostate cancer: underlying mechanisms of neuroendocrine transdifferentiation. Journal of Experimental & Clinical Cancer Research. 2022; 41: 46.

[61] Lee D-H, Olson AW, Wang J, Kim WK, Mi J, Zeng H, et al. Androgen action in cell fate and communication during prostate development at single-cell resolution. Development. 2021; 148: dev196048.

[62] Safi R, Nelson ER, Chitneni SK, Franz KJ, George DJ, Zalutsky MR, et al. Copper signaling axis as a target for prostate cancer therapeutics. Cancer Research. 2014; 74: 5819–5831.

[63] Daragó A, Klimczak M, Stragierowicz J, Jobczyk M, Kilanowicz A. Age-related changes in zinc, copper and selenium levels in the human prostate. Nutrients. 2021; 13: 1403.

[64] Yu Y, Liu S, Ren B, Nelson J, Jarrard D, Brooks JD, et al. Fusion gene detection in prostate cancer samples enhances the prediction of prostate cancer clinical outcomes from radical prostatectomy through machine learning in a multi-institutional analysis. The American Journal of Pathology. 2023; 193: 392–403.

[65] Blatti C, de la Fuente J, Gao H, Marín-Goñi I, Chen Z, Zhao SD, et al. Bayesian machine learning enables identification of transcriptional network disruptions associated with drug-resistant prostate cancer. Cancer Research. 2023; 83: 1361–1380.

[66] Wang Y, Wu S, Su H, Zhao X. Role of tumor-associated immune cells in prostate cancer: angel or devil? Asian Journal of Andrology. 2019; 21: 433.

[67] Meng J, Zhou Y, Lu X, Bian Z, Chen Y, Zhou J, et al. Immune response drives outcomes in prostate cancer: implications for immunotherapy. Molecular Oncology. 2021; 15: 1358–1375.

[68] Runcie KD, Dallos MC. Prostate cancer immunotherapy—finally in from the cold? Current Oncology Reports. 2021; 23: 88.

[69] Tran KA, Kondrashova O, Bradley A, Williams ED, Pearson JV, Waddell N. Deep learning in cancer diagnosis, prognosis and treatment selection. Genome Medicine. 2021; 13: 152.

[70] Naik N, Tokas T, Shetty DK, Hameed BMZ, Shastri S, Shah MJ, et al. Role of deep learning in prostate cancer management: past, present and future based on a comprehensive literature review. Journal of Clinical Medicine. 2022; 11: 3575.

[71] Elmarakeby HA, Hwang J, Arafeh R, Crowdis J, Gang S, Liu D, et al. Biologically informed deep neural network for prostate cancer discovery. Nature. 2021; 598: 348–352.

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