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Molecular Diagnostics

Identification of diagnostic biomarkers in prostate cancer-related fatigue by construction of predictive models and experimental validation

British Journal of Cancer volume 132, pages 283–294 (2025)Cite this article

Subjects

Abstract

Background

Cancer-related fatigue (CRF) is a prominent cancer-related complication occurring in Prostate cancer (PCa) patients, profoundly affecting prognosis. The lack of diagnostic criteria and biomarkers hampers the management of CRF.

Methods

The CRF-related data and PCa single-cell data were retrieved from the GEO database and clinical data was downloaded from the TCGA database. The univariate logistic/Cox regression analysis were used to construct the prediction models. The predictive value of models was analyzed using the ROC curve and Kaplan-Meier survival. The hub genes were screened by an intersection analysis of DEGs. The mice model of PCa and PCa-related fatigue were established, and fatigue-like behaviors of mice were detected. The expression of selected hub genes was validated by RT-PCR and IHC analysis.

Results

The diagnosis and risk models showed great predictive value both in the training and validation dataset. Five genes (Baiap2l2, Cacng4, Sytl2, Sec31b and Ms4a1) that enriched the CXCL signaling were identified as hub genes. Among all hub genes, the MS4A1 expression is the most significant in PCa-related fatigue mice.

Conclusions

We identified MS4A1 as a promising biomarker for the diagnosis of PCa-related fatigue. Our findings would lay a foundation for revealing the pathogenesis and developing therapies for PCa-related fatigue.

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Fig. 1: Screening of DEGs associated with CRF and identification of immune cell-related genes.
Fig. 2: Construction of a diagnostic prediction model and analysis of molecular and immunological characteristics for CRF.
Fig. 3: Construction of a prognostic risk model and analysis of clinical features and immune infiltration for PCa.
Fig. 4: Single-cell analysis of the hub gene following integration from the CRF diagnostic prediction model and the PCa prognostic risk model.
Fig. 5: The fatigue-like behavior of chemotherapy-treated PCa-bearing mice.
Fig. 6: The validation of hub-genes and the enriched signaling in the PCa-related fatigue.

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Data availability

The data generated in the present study may be found in the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) under accession number GSE30174, GSE14786, GSE84437, and GSE143791. We sourced the gene expression matrix and associated clinical data for PCa from the TCGA database (https://portal.gdc.cancer.gov/). The single cell sequencing data and results were acquired from the TISCH2 public data platform (http://tisch.comp-genomics.org/).

Code availability

The relevant R codes utilised in this study can be accessed through the following GitHub repositories: https://github.com/cherishCM/CRF-code.git.

References

  1. James ND, Tannock I, N’Dow J, Feng F, Gillessen S, Ali SA, et al. The Lancet Commission on prostate cancer: planning for the surge in cases. Lancet. 2024;403:1683–722.

    PubMed  Google Scholar 

  2. Cheng KK, Lee DT. Effects of pain, fatigue, insomnia, and mood disturbance on functional status and quality of life of elderly patients with cancer. Crit Rev Oncol Hematol. 2011;78:127–37.

    PubMed  Google Scholar 

  3. Thong MSY, van Noorden CJF, Steindorf K, Arndt V. Cancer-related fatigue: causes and current treatment options. Curr Treat Options Oncol. 2020;21:17.

    PubMed  PubMed Central  Google Scholar 

  4. Baguley BJ, Bolam KA, Wright ORL, Skinner TL. The effect of nutrition therapy and exercise on cancer-related fatigue and quality of life in men with prostate cancer: a systematic review. Nutrients. 2017;8:623–9.

    Google Scholar 

  5. Nazha B, Bilen MA. Circulating interleukin 6, androgen deprivation therapy, and fatigue in prostate cancer: Is inflammation the link? Cancer. 2021;127:1371–3.

    CAS  PubMed  Google Scholar 

  6. Gavin AT, Drummond FJ, Donnelly C, O’Leary E, Sharp L, Kinnear HR. Patient-reported ‘ever had’ and ‘current’ long-term physical symptoms after prostate cancer treatments. BJU Int. 2015;116:397–406.

    PubMed  PubMed Central  Google Scholar 

  7. Yang S, Chu S, Gao Y, Ai Q, Liu Y, Li X, et al. A Narrative Review of Cancer-Related Fatigue (CRF) and Its Possible Pathogenesis. Cells. 2019;8:738.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang Y, Huang X, Feng S, Chen C, Guo D, Fang L. Platinum accumulation and cancer-related fatigue, correlation with IL-8, TNF-alpha and Hemocytes. Front Pharm. 2021;12:658792.

    CAS  Google Scholar 

  9. Bower JE, Ganz PA, Irwin MR, Crespi CM, Petersen L, Asher A, et al. Type I interferons, inflammation, and fatigue in a longitudinal RNA study of women with breast cancer. Brain Behav Immun. 2024;118:312–7.

    CAS  PubMed  Google Scholar 

  10. Kaushik D, Shah PK, Mukherjee N, Ji N, Dursun F, Kumar AP, et al. Effects of yoga in men with prostate cancer on quality of life and immune response: a pilot randomized controlled trial. Prostate Cancer Prostatic Dis. 2022;25:531–8.

    CAS  PubMed  Google Scholar 

  11. Saligan LN, Hsiao CP, Wang D, Wang XM, St John L, Kaushal A, et al. Upregulation of α-synuclein during localized radiation therapy signals the association of cancer-related fatigue with the activation of inflammatory and neuroprotective pathways. Brain Behav Immun. 2013;27:63–70.

    CAS  PubMed  Google Scholar 

  12. Landmark-Høyvik H, Reinertsen KV, Loge JH, Fosså SD, Børresen-Dale AL, Dumeaux V. Alterations of gene expression in blood cells associated with chronic fatigue in breast cancer survivors. Pharmacogenomics J. 2009;9:333–40.

    PubMed  Google Scholar 

  13. Yoon SJ, Park J, Shin Y, Choi Y, Park SW, Kang SG, et al. Deconvolution of diffuse gastric cancer and the suppression of CD34 on the BALB/c nude mice model. BMC Cancer. 2020;20:314.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Kfoury Y, Baryawno N, Severe N, Mei S, Gustafsson K, Hirz T, et al. Human prostate cancer bone metastases have an actionable immunosuppressive microenvironment. Cancer Cell. 2021;39:1464–1478.e1468.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 2016;44:e71.

    PubMed  Google Scholar 

  16. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018;554:544–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498–504.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12:453–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Xu F, Zhan X, Zheng X, Xu H, Li Y, Huang X, et al. A signature of immune-related gene pairs predicts oncologic outcomes and response to immunotherapy in lung adenocarcinoma. Genomics. 2020;112:4675–83.

    CAS  PubMed  Google Scholar 

  20. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41:D955–961.

    CAS  PubMed  Google Scholar 

  21. Cheng J, Yang L, Kumar V, Agarwal P. Systematic evaluation of connectivity map for disease indications. Genome Med. 2014;6:540.

    PubMed  Google Scholar 

  22. Han Y, Wang Y, Dong X, Sun D, Liu Z, Yue J, et al. TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res. 2023;51:D1425–d1431.

    PubMed  Google Scholar 

  23. Sun D, Wang J, Han Y, Dong X, Ge J, Zheng R, et al. TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment. Nucleic Acids Res. 2021;49:D1420–d1430.

    CAS  PubMed  Google Scholar 

  24. Abulizi A, Hu L, Ma A, Shao FY, Zhu HZ, Lin SM, et al. Ganoderic acid alleviates chemotherapy-induced fatigue in mice bearing colon tumor. Acta Pharm Sin. 2021;42:1703–13.

    CAS  Google Scholar 

  25. Grossberg AJ, Vichaya EG, Christian DL, Molkentine JM, Vermeer DW, Gross PS, et al. Tumor-Associated Fatigue in Cancer Patients Develops Independently of IL1 Signaling. Cancer Res. 2018;78:695–705.

    CAS  PubMed  Google Scholar 

  26. Chelette B, Chidomere CL, Dantzer R. The GDF15-GFRAL axis mediates chemotherapy-induced fatigue in mice. Brain Behav Immun. 2023;108:45–54.

    CAS  PubMed  Google Scholar 

  27. Yang F, Zhou L, Song J, WangJinMei A, Yang Y, Tang ZW, et al. Liver CEBPbeta Modulates the Kynurenine Metabolism and Mediates the Motility for Hypoxia-Induced Central Fatigue in Mice. Front Physiol. 2019;10:243.

    PubMed  PubMed Central  Google Scholar 

  28. Zhang X, Jing S, Lin H, Sun W, Jiang W, Yu C, et al. Anti-fatigue effect of anwulignan via the NRF2 and PGC-1alpha signaling pathway in mice. Food Funct. 2019;10:7755–66.

    CAS  PubMed  Google Scholar 

  29. NCCN, Clinical Practice Guidelines in Oncology. Cancer-Related Fatigue Version 1 2021. (https://www.nccn.org/professionals/physician_gls/pdf/fatigue.pdf.) (2020).

  30. Arring NM, Barton DL, Brooks T, Zick SM. Integrative therapies for cancer-related fatigue. Cancer J. 2019;25:349–56.

    PubMed  PubMed Central  Google Scholar 

  31. David A, Hausner D, Frenkel M. Cancer-related fatigue-is there a role for complementary and integrative medicine? Curr Oncol Rep. 2021;23:145.

    PubMed  Google Scholar 

  32. Bower JE. The role of neuro-immune interactions in cancer-related fatigue: Biobehavioral risk factors and mechanisms. Cancer. 2019;125:353–64.

    PubMed  Google Scholar 

  33. Heidegger I, Fotakis G, Offermann A, Goveia J, Daum S, Salcher S, et al. Comprehensive characterization of the prostate tumor microenvironment identifies CXCR4/CXCL12 crosstalk as a novel antiangiogenic therapeutic target in prostate cancer. Mol Cancer. 2022;21:132.

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Lopes F, Vicentini FA, Cluny NL, Mathews AJ, Lee BH, Almishri WA, et al. Brain TNF drives post-inflammation depression-like behavior and persistent pain in experimental arthritis. Brain Behav Immun. 2020;89:224–32.

    CAS  PubMed  Google Scholar 

  35. Guo H, Peng J, Hu J, Chang S, Liu H, Luo H, et al. BAIAP2L2 promotes the proliferation, migration and invasion of osteosarcoma associated with the Wnt/beta-catenin pathway. J Bone Oncol. 2021;31:100393.

    PubMed  PubMed Central  Google Scholar 

  36. Song Y, Zhuang G, Li J, Zhang M. BAIAP2L2 facilitates the malignancy of prostate cancer (PCa) via VEGF and apoptosis signaling pathways. Genes Genomics. 2021;43:421–32.

    CAS  PubMed  Google Scholar 

  37. Kanwar N, Carmine-Simmen K, Nair R, Wang C, Moghadas-Jafari S, Blaser H, et al. Amplification of a calcium channel subunit CACNG4 increases breast cancer metastasis. EBioMedicine. 2020;52:102646.

    PubMed  PubMed Central  Google Scholar 

  38. Li Z, Tao Y, Gao Z, Peng S, Lai Y, Li K, et al. SYTL2 promotes metastasis of prostate cancer cells by enhancing FSCN1-mediated pseudopodia formation and invasion. J Transl Med. 2023;21:303.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Stankewich MC, Stabach PR, Morrow JS. Human Sec31B: a family of new mammalian orthologues of yeast Sec31p that associate with the COPII coat. J Cell Sci. 2006;119:958–69.

    CAS  PubMed  Google Scholar 

  40. Mattiola I, Mantovani A, Locati M. The tetraspan MS4A family in homeostasis, immunity, and disease. Trends Immunol. 2021;42:764–81.

    CAS  PubMed  Google Scholar 

  41. Pavlasova G, Mraz M. The regulation and function of CD20: an “enigma” of B-cell biology and targeted therapy. Haematologica. 2020;105:1494–506.

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Pavlasova G, Borsky M, Svobodova V, Oppelt J, Cerna K, Novotna J, et al. Rituximab primarily targets an intra-clonal BCR signaling proficient CLL subpopulation characterized by high CD20 levels. Leukemia. 2018;32:2028–31.

    PubMed  Google Scholar 

  43. Hsiao CP, Reddy SY, Chen MK, Saligan LN. Genomic profile of fatigued men receiving localized radiation therapy. Biol Res Nurs. 2016;18:281–9.

    CAS  PubMed  Google Scholar 

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Acknowledgements

We acknowledge the GEO and TCGA database for providing datasets used in this study. We also thank the help of the Medical Research Center for structural biology and Prof. Yaping Fu for assistance with animal behavior experiments.

Funding

This work was supported by the by the National Natural Science Foundation of China (82304550 and 82401028).

Author information

Author notes

  1. These authors contributed equally: Ming Chen, Siqi Zhou.

Authors and Affiliations

  1. Department of Pharmacy, Renmin Hospital of Wuhan University, Wuhan, 430060, China

    Ming Chen & Haiyan Wen

  2. Department of Psycho-oncology, Peking University Cancer Hospital & Institute, Beijing, 100142, China

    Ming Chen

  3. Key Laboratory of Carcinogenesis and Translational Research, Beijing, 100142, China

    Ming Chen

  4. Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, 430060, China

    Siqi Zhou & Xiongwei He

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Contributions

All authors read and approved the final version of the manuscript. M. Chen.: conceptualization, methodology, formal analysis, supervision and writing- original draft; Sq. Z.: formal analysis, validation, data curation, investigation and writing- original draft; Xw He: visualization, formal analysis and methodology; Hy. W.: conceptualization, investigation, validation, methodology and writing-review & editing.

Corresponding author

Correspondence to Haiyan Wen.

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All animal procedures were performed according to the Guidelines for the Care and Use of Laboratory Animals of the Chinese Animal Welfare Committee and approved by the Institutional Animal Care and Use Committee (IACUC) of Wuhan University of Renmin Hospital.

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Chen, M., Zhou, S., He, X. et al. Identification of diagnostic biomarkers in prostate cancer-related fatigue by construction of predictive models and experimental validation. Br J Cancer 132, 283–294 (2025). https://doi.org/10.1038/s41416-024-02922-1

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