Skip to main content
Top
Published in: European Journal of Medical Research 1/2023

Open Access 01-12-2023 | Prostate Cancer | Research

A novel tumor purity and immune infiltration-related model for predicting distant metastasis-free survival in prostate cancer

Authors: Qiang Su, Yongbei Zhu, Bingxi He, Bin Dai, Wei Mu, Jie Tian

Published in: European Journal of Medical Research | Issue 1/2023

Login to get access

Abstract

Background

umor cells, immune cells and stromal cells jointly modify tumor development and progression. We aim to explore the potential effects of tumor purity on the immune microenvironment, genetic landscape and prognosis in prostate cancer (PCa).

Methods

Tumor purity of prostate cancer patients was extracted from The cancer genome atlas (TCGA). Immune cellular proportions were calculated by the CIBERSORT. To identify critical modules related to tumor purity, we used weighted gene co-expression network analysis (WGCNA). Using STRING and Cytoscape, protein–protein interaction (PPI) networks were constructed and analyzed. A Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Disease Ontology (DO), and Gene Set Enrichment Analysis (GSEA) enrichment analysis of identified modules was conducted. To identify the expression of key genes at protein levels, we used the Human Protein Atlas (HPA) platform.

Results

A model of tumor purity score (TPS) was constructed in the gene expression omnibus series (GSE) 116,918 cohort. TCGA cohort served as a validation set and was employed to validate the TPS. TPS model, as an independent prognostic factor of distant metastasis‐free survival (DMFS) in PCa. Patients had higher tumor purity and better prognosis in the low-TPS group. Tumor purity was related to the infiltration of mast cells and macrophage cells positively, whereas related to the infiltration of dendritic cells, T cells and B cells negatively in PCa. The nomogram based on TPS, Age, Gleason score and T stage had a good predictive value and could evaluate the prognosis of PCa metastasis. GO and KEGG enrichment analyses showed that hub genes mainly participate in T cell activation and T-helper lymphocytes (TH) differentiation. Hub genes were mainly enriched in primary immunodeficiency disease, according to DO analysis. SLAMF8 was identified as the most critical gene by Cytoscape and HPA analysis.

Conclusions

Dynamic changes in the immune microenvironment associated with tumor purity could correlate with a poor DMFS of low-purity PCa. The TPS can predict the DMFS of PCa. In addition, prostate cancer metastases may be related to immunosuppression caused by a disorder of the immune microenvironment.
Appendix
Available only for authorised users
Literature
1.
go back to reference Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.CrossRefPubMed Sung H, Ferlay J, Siegel R, Laversanne M, Soerjomataram I, Jemal A, Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.CrossRefPubMed
2.
go back to reference Cao W, Chen H, Yu Y, Li N, Chen W. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J. 2021;134(7):783–91.CrossRefPubMedPubMedCentral Cao W, Chen H, Yu Y, Li N, Chen W. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J. 2021;134(7):783–91.CrossRefPubMedPubMedCentral
3.
go back to reference Taitt H. Global trends and prostate cancer: a review of incidence, detection, and mortality as influenced by race, ethnicity, and geographic location. Am J Mens Health. 2018;12(6):1807–23.CrossRefPubMedPubMedCentral Taitt H. Global trends and prostate cancer: a review of incidence, detection, and mortality as influenced by race, ethnicity, and geographic location. Am J Mens Health. 2018;12(6):1807–23.CrossRefPubMedPubMedCentral
4.
go back to reference Aran D, Sirota M, Butte A. Systematic pan-cancer analysis of tumour purity. Nat Commun. 2015;6:8971.CrossRefPubMed Aran D, Sirota M, Butte A. Systematic pan-cancer analysis of tumour purity. Nat Commun. 2015;6:8971.CrossRefPubMed
6.
go back to reference Mao Y, Feng Q, Zheng P, Yang L, Liu T, Xu Y, Zhu D, Chang W, Ji M, Ren L, et al. Low tumor purity is associated with poor prognosis, heavy mutation burden, and intense immune phenotype in colon cancer. Cancer Manag Res. 2018;10:3569–77.CrossRefPubMedPubMedCentral Mao Y, Feng Q, Zheng P, Yang L, Liu T, Xu Y, Zhu D, Chang W, Ji M, Ren L, et al. Low tumor purity is associated with poor prognosis, heavy mutation burden, and intense immune phenotype in colon cancer. Cancer Manag Res. 2018;10:3569–77.CrossRefPubMedPubMedCentral
7.
go back to reference Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird P, Levine D, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.CrossRefPubMed Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird P, Levine D, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.CrossRefPubMed
9.
go back to reference Edgar R, Domrachev M, Lash A. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.CrossRefPubMedPubMedCentral Edgar R, Domrachev M, Lash A. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.CrossRefPubMedPubMedCentral
10.
go back to reference Jain S, Lyons C, Walker S, McQuaid S, Hynes S, Mitchell D, Pang B, Logan G, McCavigan A, O’Rourke D, et al. Validation of a metastatic assay using biopsies to improve risk stratification in patients with prostate cancer treated with radical radiation therapy. Ann Oncol. 2018;29(1):215–22.CrossRefPubMed Jain S, Lyons C, Walker S, McQuaid S, Hynes S, Mitchell D, Pang B, Logan G, McCavigan A, O’Rourke D, et al. Validation of a metastatic assay using biopsies to improve risk stratification in patients with prostate cancer treated with radical radiation therapy. Ann Oncol. 2018;29(1):215–22.CrossRefPubMed
11.
go back to reference Wang Z, Jensen M, Zenklusen J. A practical guide to The Cancer Genome Atlas (TCGA). Methods Mol Biol (Clifton, NJ). 2016;1418:111–41.CrossRef Wang Z, Jensen M, Zenklusen J. A practical guide to The Cancer Genome Atlas (TCGA). Methods Mol Biol (Clifton, NJ). 2016;1418:111–41.CrossRef
13.
go back to reference Newman A, Liu C, Green M, Gentles A, Feng W, Xu Y, Hoang C, Diehn M, Alizadeh A. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.CrossRefPubMedPubMedCentral Newman A, Liu C, Green M, Gentles A, Feng W, Xu Y, Hoang C, Diehn M, Alizadeh A. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453–7.CrossRefPubMedPubMedCentral
14.
go back to reference Ritchie M, Phipson B, Wu D, Hu Y, Law C, Shi W, Smyth G. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7): e47.CrossRefPubMedPubMedCentral Ritchie M, Phipson B, Wu D, Hu Y, Law C, Shi W, Smyth G. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7): e47.CrossRefPubMedPubMedCentral
15.
16.
go back to reference Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for cox’s proportional hazards model via coordinate descent. J Stat Softw. 2011;39(5):1–13.CrossRefPubMedPubMedCentral Simon N, Friedman J, Hastie T, Tibshirani R. Regularization paths for cox’s proportional hazards model via coordinate descent. J Stat Softw. 2011;39(5):1–13.CrossRefPubMedPubMedCentral
17.
go back to reference Heagerty P, Lumley T, Pepe M. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000;56(2):337–44.CrossRefPubMed Heagerty P, Lumley T, Pepe M. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics. 2000;56(2):337–44.CrossRefPubMed
18.
go back to reference Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou K, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–52.CrossRefPubMed Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou K, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43:D447–52.CrossRefPubMed
19.
go back to reference Shannon P, Markiel A, Ozier O, Baliga N, Wang J, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.CrossRefPubMedPubMedCentral Shannon P, Markiel A, Ozier O, Baliga N, Wang J, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.CrossRefPubMedPubMedCentral
21.
go back to reference Yu G, Wang L, Yan G, He Q. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics (Oxford, England). 2015;31(4):608–9.PubMed Yu G, Wang L, Yan G, He Q. DOSE: an R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics (Oxford, England). 2015;31(4):608–9.PubMed
22.
go back to reference Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141.PubMedPubMedCentral Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, et al. clusterProfiler 4.0: a universal enrichment tool for interpreting omics data. Innovation (Camb). 2021;2(3):100141.PubMedPubMedCentral
23.
go back to reference Zhang C, Cheng W, Ren X, Wang Z, Liu X, Li G, Han S, Jiang T, Wu A. Tumor purity as an underlying key factor in glioma. Clin Cancer Res. 2017;23(20):6279–91.CrossRefPubMed Zhang C, Cheng W, Ren X, Wang Z, Liu X, Li G, Han S, Jiang T, Wu A. Tumor purity as an underlying key factor in glioma. Clin Cancer Res. 2017;23(20):6279–91.CrossRefPubMed
24.
go back to reference Shao L, Yan Y, Liu Z, Ye X, Xia H, Zhu X, Zhang Y, Zhang Z, Chen H, He W, et al. Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy. Theranostics. 2020;10(22):10200–12.CrossRefPubMedPubMedCentral Shao L, Yan Y, Liu Z, Ye X, Xia H, Zhu X, Zhang Y, Zhang Z, Chen H, He W, et al. Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy. Theranostics. 2020;10(22):10200–12.CrossRefPubMedPubMedCentral
25.
go back to reference Su Q, Liu Z, Chen C, Gao H, Zhu Y, Wang L, Pan M, Liu J, Yang X, Tian J. Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy. Cancer Med. 2021;10(18):6492–502.CrossRefPubMedPubMedCentral Su Q, Liu Z, Chen C, Gao H, Zhu Y, Wang L, Pan M, Liu J, Yang X, Tian J. Gene signatures predict biochemical recurrence-free survival in primary prostate cancer patients after radical therapy. Cancer Med. 2021;10(18):6492–502.CrossRefPubMedPubMedCentral
26.
go back to reference Su Q, Dai B, Zhang S. Construction of miRNA-mRNA network and a nomogram model of prognostic analysis for prostate cancer. Transl Cancer Res. 2022;11(8):2562–71.CrossRefPubMedPubMedCentral Su Q, Dai B, Zhang S. Construction of miRNA-mRNA network and a nomogram model of prognostic analysis for prostate cancer. Transl Cancer Res. 2022;11(8):2562–71.CrossRefPubMedPubMedCentral
27.
go back to reference Su Q, Dai B, Zhang H, Zhang S. Discovering gene signature shared by prostate cancer and neurodegenerative diseases based on the bioinformatics approach. Comput Math Methods Med. 2022;2022:8430485.CrossRefPubMedPubMedCentral Su Q, Dai B, Zhang H, Zhang S. Discovering gene signature shared by prostate cancer and neurodegenerative diseases based on the bioinformatics approach. Comput Math Methods Med. 2022;2022:8430485.CrossRefPubMedPubMedCentral
28.
go back to reference Shao L, Liu Z, Yan Y, Liu J, Ye X, Xia H, Zhu X, Zhang Y, Zhang Z, Chen H, et al. Patient-level prediction of multi-classification task at prostate MRI based on end-to-end framework learning from diagnostic logic of radiologists. IEEE Trans Biomed Eng. 2021;68(12):3690–700.CrossRefPubMed Shao L, Liu Z, Yan Y, Liu J, Ye X, Xia H, Zhu X, Zhang Y, Zhang Z, Chen H, et al. Patient-level prediction of multi-classification task at prostate MRI based on end-to-end framework learning from diagnostic logic of radiologists. IEEE Trans Biomed Eng. 2021;68(12):3690–700.CrossRefPubMed
29.
go back to reference Woo J, Liss M, Muldong M, Palazzi K, Strasner A, Ammirante M, Varki N, Shabaik A, Howell S, Kane C, et al. Tumor infiltrating B-cells are increased in prostate cancer tissue. J Transl Med. 2014;12:30.CrossRefPubMedPubMedCentral Woo J, Liss M, Muldong M, Palazzi K, Strasner A, Ammirante M, Varki N, Shabaik A, Howell S, Kane C, et al. Tumor infiltrating B-cells are increased in prostate cancer tissue. J Transl Med. 2014;12:30.CrossRefPubMedPubMedCentral
30.
go back to reference Ihle C, Provera M, Straign D, Smith E, Edgerton S, Van Bokhoven A, Lucia M, Owens P. Distinct tumor microenvironments of lytic and blastic bone metastases in prostate cancer patients. J Immunother Cancer. 2019;7(1):293.CrossRefPubMedPubMedCentral Ihle C, Provera M, Straign D, Smith E, Edgerton S, Van Bokhoven A, Lucia M, Owens P. Distinct tumor microenvironments of lytic and blastic bone metastases in prostate cancer patients. J Immunother Cancer. 2019;7(1):293.CrossRefPubMedPubMedCentral
31.
go back to reference Huen N, Pang A, Tucker J, Lee T, Vergati M, Jochems C, Intrivici C, Cereda V, Chan W, Rennert O, et al. Up-regulation of proliferative and migratory genes in regulatory T cells from patients with metastatic castration-resistant prostate cancer. Int J Cancer. 2013;133(2):373–82.CrossRefPubMedPubMedCentral Huen N, Pang A, Tucker J, Lee T, Vergati M, Jochems C, Intrivici C, Cereda V, Chan W, Rennert O, et al. Up-regulation of proliferative and migratory genes in regulatory T cells from patients with metastatic castration-resistant prostate cancer. Int J Cancer. 2013;133(2):373–82.CrossRefPubMedPubMedCentral
32.
go back to reference Hayashi T, Fujita K, Matsushita M, Nonomura N. Main inflammatory cells and potentials of anti-inflammatory agents in prostate cancer. Cancers. 2019;11(8):1153.CrossRefPubMedPubMedCentral Hayashi T, Fujita K, Matsushita M, Nonomura N. Main inflammatory cells and potentials of anti-inflammatory agents in prostate cancer. Cancers. 2019;11(8):1153.CrossRefPubMedPubMedCentral
33.
go back to reference Zhao S, Lehrer J, Chang S, Das R, Erho N, Liu Y, Sjöström M, Den R, Freedland S, Klein E, et al. The immune landscape of prostate cancer and nomination of PD-L2 as a potential therapeutic target. J Natl Cancer Inst. 2019;111(3):301–10.CrossRefPubMed Zhao S, Lehrer J, Chang S, Das R, Erho N, Liu Y, Sjöström M, Den R, Freedland S, Klein E, et al. The immune landscape of prostate cancer and nomination of PD-L2 as a potential therapeutic target. J Natl Cancer Inst. 2019;111(3):301–10.CrossRefPubMed
34.
go back to reference Hirsch H, Iliopoulos D, Joshi A, Zhang Y, Jaeger S, Bulyk M, Tsichlis P, Shirley Liu X, Struhl K. A transcriptional signature and common gene networks link cancer with lipid metabolism and diverse human diseases. Cancer Cell. 2010;17(4):348–61.CrossRefPubMedPubMedCentral Hirsch H, Iliopoulos D, Joshi A, Zhang Y, Jaeger S, Bulyk M, Tsichlis P, Shirley Liu X, Struhl K. A transcriptional signature and common gene networks link cancer with lipid metabolism and diverse human diseases. Cancer Cell. 2010;17(4):348–61.CrossRefPubMedPubMedCentral
35.
go back to reference Zhao X, Hu D, Li J, Zhao G, Tang W, Cheng H. Database mining of genes of prognostic value for the prostate adenocarcinoma microenvironment using the cancer gene atlas. Biomed Res Int. 2020;2020:5019793.PubMedPubMedCentral Zhao X, Hu D, Li J, Zhao G, Tang W, Cheng H. Database mining of genes of prognostic value for the prostate adenocarcinoma microenvironment using the cancer gene atlas. Biomed Res Int. 2020;2020:5019793.PubMedPubMedCentral
36.
go back to reference Hwang J, Joung J, Shin S, Choi M, Kim J, Kim Y, Park W, Lee S, Lee K. Ad5/35E1aPSESE4: a novel approach to marking circulating prostate tumor cells with a replication competent adenovirus controlled by PSA/PSMA transcription regulatory elements. Cancer Lett. 2016;372(1):57–64.CrossRefPubMed Hwang J, Joung J, Shin S, Choi M, Kim J, Kim Y, Park W, Lee S, Lee K. Ad5/35E1aPSESE4: a novel approach to marking circulating prostate tumor cells with a replication competent adenovirus controlled by PSA/PSMA transcription regulatory elements. Cancer Lett. 2016;372(1):57–64.CrossRefPubMed
37.
go back to reference Lee J, Green M, Huppert L, Chow C, Pierce R, Daud A. The liver-immunity nexus and cancer immunotherapy. Clinical cancer Res. 2022;28(1):5–12.CrossRef Lee J, Green M, Huppert L, Chow C, Pierce R, Daud A. The liver-immunity nexus and cancer immunotherapy. Clinical cancer Res. 2022;28(1):5–12.CrossRef
38.
go back to reference Li Z, Zhao S, Zhu S, Fan Y. MicroRNA-153-5p promotes the proliferation and metastasis of renal cell carcinoma via direct targeting of AGO1. Cell Death Dis. 2021;12(1):33.CrossRefPubMedPubMedCentral Li Z, Zhao S, Zhu S, Fan Y. MicroRNA-153-5p promotes the proliferation and metastasis of renal cell carcinoma via direct targeting of AGO1. Cell Death Dis. 2021;12(1):33.CrossRefPubMedPubMedCentral
39.
go back to reference Lee W, Reuben A, Hu X, McGranahan N, Chen R, Jalali A, Negrao M, Hubert S, Tang C, Wu C, et al. Multiomics profiling of primary lung cancers and distant metastases reveals immunosuppression as a common characteristic of tumor cells with metastatic plasticity. Genome Biol. 2020;21(1):271.CrossRefPubMedPubMedCentral Lee W, Reuben A, Hu X, McGranahan N, Chen R, Jalali A, Negrao M, Hubert S, Tang C, Wu C, et al. Multiomics profiling of primary lung cancers and distant metastases reveals immunosuppression as a common characteristic of tumor cells with metastatic plasticity. Genome Biol. 2020;21(1):271.CrossRefPubMedPubMedCentral
Metadata
Title
A novel tumor purity and immune infiltration-related model for predicting distant metastasis-free survival in prostate cancer
Authors
Qiang Su
Yongbei Zhu
Bingxi He
Bin Dai
Wei Mu
Jie Tian
Publication date
01-12-2023
Publisher
BioMed Central
Published in
European Journal of Medical Research / Issue 1/2023
Electronic ISSN: 2047-783X
DOI
https://doi.org/10.1186/s40001-023-01522-8

Other articles of this Issue 1/2023

European Journal of Medical Research 1/2023 Go to the issue