Integrated multi-omics profiling of immune microenvironment and drug resistance signatures for precision prognosis in prostate cancer

Abstract

Introduction: Prostate cancer (PCa) continues to be a significant cause of mortality among men, with treatment resistance often influenced by the complexity of the tumor microenvironment (TME). This study aims to develop an immune-centric prognostic model that correlates TME dynamics, genomic instability, and the heterogeneity of drug resistance in PCa.

Methods: Multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were integrated, encompassing transcriptomic profiles of 554 TCGA-PRAD samples and 329 external validation samples. Immune cell infiltration was assessed using CIBERSORT and ESTIMATE. Weighted gene co-expression network analysis (WGCNA) was employed to identify immune-related modules. Single-cell RNA sequencing (ScRNA-seq) of 835 PCa cells uncovered subtype-specific resistance patterns. Prognostic models were constructed using least absolute shrinkage and selection operator (LASSO) regression and subsequently validated experimentally in PCa cell lines.

Results: Two immune subtypes were identified: high-risk subgroups displayed TP53 mutations, increased tumor mutation burden (TMB), and enriched energy metabolism pathways. ScRNA-seq delineated three PCa cell clusters, with high-risk subtypes being sensitive to bendamustine/dacomitinib and resistant to apalutamide/neratinib. A 10-gene prognostic model (e.g., MUC5B, TREM1) categorized patients into high/low-risk groups with distinct survival outcomes (log-rank P < 0.0001). Validation in external datasets confirmed the robust predictive accuracy (AUC: 0.854-0.889). Experimental assays verified subtype-specific drug responses and dysregulation of key model genes.

Discussion: This study establishes a TME-driven prognostic framework that connects immune heterogeneity, genomic instability, and therapeutic resistance in PCa. By pinpointing metabolic dependencies and subtype-specific vulnerabilities, our findings provide actionable strategies to circumvent treatment failure, such as targeting energy metabolism or tailoring therapies based on resistance signatures.

Keywords

Tumor microenvironment / prostate cancer / drug resistance / immunotherapy / prognostic model

References

[1] Siegel RL,Wagle NS.Cancer statistics, 2023.CA Cancer J Clin2023;73:17-48

[2] Binnewies M,Kersten K.Understanding the tumor immune microenvironment (TIME) for effective therapy.Nat Med2018;24:541-50 PMCID:PMC5998822

[3] Chen S,Yang Y.Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression.Nat Cell Biol2021;23:87-98

[4] Yu W,Shang Z.Unveiling novel insights in prostate cancer through single-cell RNA sequencing.Front Oncol2023;13:1224913 PMCID:PMC10514910

[5] Zhong S,Fan X.A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex.Nature2018;555:524-8

[6] Peng YL,Zhou ZH.Single-cell transcriptomics reveals a low CD8⁺ T cell infiltrating state mediated by fibroblasts in recurrent renal cell carcinoma.J Immunother Cancer2022;10:e004206 PMCID:PMC8819783

[7] Sridaran D,DeSelm C,Mahajan K.Prostate cancer immunotherapy: improving clinical outcomes with a multi-pronged approach.Cell Rep Med2023;4:101199 PMCID:PMC10591038

[8] Tomczak K,Wiznerowicz M.The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge.Contemp Oncol2015;19:A68-77 PMCID:PMC4322527

[9] Barrett T,Ledoux P.NCBI GEO: archive for functional genomics data sets - update.Nucleic Acids Res2013;41:D991-5 PMCID:PMC3531084

[10] Mortensen MM,Lynnerup AS.Expression profiling of prostate cancer tissue delineates genes associated with recurrence after prostatectomy.Sci Rep2015;5:16018 PMCID:PMC4629186

[11] Ross-Adams H, Lamb AD, Dunning MJ, et al; CamCaP Study Group. Integration of copy number and transcriptomics provides risk stratification in prostate cancer: a discovery and validation cohort study. EBioMedicine. 2015;2:1133-44. PMCID:PMC4588396

[12] Jain S,Walker SM.Validation of a Metastatic Assay using biopsies to improve risk stratification in patients with prostate cancer treated with radical radiation therapy.Ann Oncol2018;29:215-22 PMCID:PMC5834121

[13] He MX,Crowdis J.Transcriptional mediators of treatment resistance in lethal prostate cancer.Nat Med2021;27:426-33 PMCID:PMC7960507

[14] Yoshihara K,Martínez E.Inferring tumour purity and stromal and immune cell admixture from expression data.Nat Commun2013;4:2612 PMCID:PMC3826632

[15] Newman AM,Liu CL.Determining cell type abundance and expression from bulk tissues with digital cytometry.Nat Biotechnol2019;37:773-82 PMCID:PMC6610714

[16] Kassambara A. factoextra: extract and visualize the results of multivariate data analyses. 2017. Available from: https://rpkgs.datanovia.com/factoextra/. [Last accessed on 23 Jun 2025]

[17] Villanueva RAM.ggplot2: elegant graphics for data analysis (2nd ed.). 2019;17:160-7.

[18] Kassambara A. ggpubr: ‘ggplot2’ Based Publication Ready Plots. 2020. Available from: https://rpkgs.datanovia.com/ggpubr/. [Last accessed on 23 Jun 2025]

[19] Wei T,Levy M. Package ‘corrplot’. Statistician of a correlation matrix. Available from: https://github.com/taiyun/corrplot. [Last accessed on 23 Jun 2025]

[20] Love MI,Anders S.Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.Genome Biol2014;15:550 PMCID:PMC4302049

[21] Kolde R. pheatmap: Pretty heatmaps. Version 1.0.13. 2025. Available from: https://cran.r-project.org/web/packages/pheatmap/index.html. [Last accessed on 23 Jun 2025]

[22] Zhang X,Chen T.Characterization of the immune cell infiltration landscape in head and neck squamous cell carcinoma to aid immunotherapy.Mol Ther Nucleic Acids2020;22:298-309 PMCID:PMC7522342

[23] Langfelder P.WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics2008;9:559 PMCID:PMC2631488

[24] Zhang Q,Yan Y,Zhang Y.CYB5R1 is a potential biomarker that correlates with stemness and drug resistance in gastric cancer.Transl Oncol2024;39:101766 PMCID:PMC10587760

[25] Ashburner M,Blake JA.Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat Genet2000;25:25-9 PMCID:PMC3037419

[26] Kanehisa M.KEGG: kyoto encyclopedia of genes and genomes.Nucleic Acids Res2000;28:27-30 PMCID:PMC102409

[27] Yu G,Han Y.clusterProfiler: an R package for comparing biological themes among gene clusters.OMICS2012;16:284-7 PMCID:PMC3339379

[28] Wu T,Xu S.clusterProfiler 4.0: a universal enrichment tool for interpreting omics data.Innovation2021;2:100141 PMCID:PMC8454663

[29] Therneau TM. Package ‘survival’. R Top Doc. Available from: https://github.com/therneau/survival. [Last accessed on 23 Jun 2025]

[30] Friedman J,Tibshirani R. Lasso and elastic-net regularized generalized linear models. Available from: https://glmnet.stanford.edu/. [Last accessed on 23 Jun 2025]

[31] Kassambara A,Biecek P. survminer: Survival analysis and visualization. Available from: https://rpkgs.datanovia.com/survminer/index.html. [Last accessed on 23 Jun 2025]

[32] Blanche P,Jacqmin-Gadda H.Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks.Stat Med2013;32:5381-97

[33] Mayakonda A,Assenov Y,Koeffler HP.Maftools: efficient and comprehensive analysis of somatic variants in cancer.Genome Res2018;28:1747-56 PMCID:PMC6211645

[34] Hao Y,Andersen-Nissen E.Integrated analysis of multimodal single-cell data.Cell2021;184:3573-87.e29 PMCID:PMC8238499

[35] Abdi H.Principal component analysis.Wiley Interdiscip Rev Comput Stat2010;2:433-59

[36] Mcinnes L,Melville J. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv 2018; arXiv:1802.03426. Available from: https://doi.org/10.48550/arXiv.1802.03426. [accessed 23 Jun 2025]

[37] van der Maaten L, Hinton G. Visualizing high-dimensional data using t-SNE. J Mach Learn Res 2008;9:2579-605. Available from: https://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf?fbcl. [Last accessed on 23 Jun 2025]

[38] Subramanian A,Mootha VK.Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A2005;102:15545-50 PMCID:PMC1239896

[39] Maeser D,Huang RS.oncoPredict: an R package for predicting in vivo or cancer patient drug response and biomarkers from cell line screening data.Brief Bioinform2021;22:bbab260 PMCID:PMC8574972

[40] Yang W,Greninger P.Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.Nucleic Acids Res2013;41:D955-61 PMCID:PMC3531057

[41] Zhao R,Zhang M.Screening of potential therapy targets for prostate cancer using integrated analysis of two gene expression profiles.Oncol Lett2017;14:5361-9 PMCID:PMC5662906

[42] Shalapour S,Di Caro G.Immunosuppressive plasma cells impede T-cell-dependent immunogenic chemotherapy.Nature2015;521:94-8 PMCID:PMC4501632

[43] Woo JR,Muldong MT.Tumor infiltrating B-cells are increased in prostate cancer tissue.J Transl Med2014;12:30 PMCID:PMC3914187

[44] Wang B,Zhang J,Wu H.Fructose-1,6-bisphosphatase loss modulates STAT3-dependent expression of PD-L1 and cancer immunity.Theranostics2020;10:1033-45 PMCID:PMC6956820

[45] Ke ZB,Chen JY.A radiation resistance related index for biochemical recurrence and tumor immune environment in prostate cancer patients.Comput Biol Med2022;146:105711

[46] Wang H.MUC5B regulates alterations in the immune microenvironment in nasopharyngeal carcinoma via the Wnt/β-catenin signaling pathway.Discov Oncol2025;16:27 PMCID:PMC11717746

[47] Juric V,Binnewies M.TREM1 activation of myeloid cells promotes antitumor immunity.Sci Transl Med2023;15:eadd9990

[48] Feng D,Shi X.Identification of senescence-related lncRNA prognostic index correlating with prognosis and radiosensitivity in prostate cancer patients.Aging2023;15:9358-76 PMCID:PMC10564441

[49] Huang X,Zou X.BAG2 drives chemoresistance of breast cancer by exacerbating mutant p53 aggregate.Theranostics2023;13:339-54 PMCID:PMC9800719

[50] Tong D.Unravelling the molecular mechanisms of prostate cancer evolution from genotype to phenotype.Crit Rev Oncol Hematol2021;163:103370

[51] Robey RW,Hall MD,Bates SE.Revisiting the role of ABC transporters in multidrug-resistant cancer.Nat Rev Cancer2018;18:452-64 PMCID:PMC6622180