Download PDF
Horm Metab Res
DOI: 10.1055/a-2330-3696
Original Article: Endocrine Care

Construction and Validation of a Prognostic Model Based on Mitochondrial Genes in Prostate Cancer

Dan Wang
1   Radiology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
,
Hui Pan
2   Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
,
Shaoping Cheng
2   Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
,
Zhigang Huang
2   Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
,
Zhenlei Shi
2   Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
,
Hao Deng
2   Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
,
Junwu Yang
2   Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
,
Chenghua Jin
2   Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
,
Jin Dai 
2   Urology, The First Affiliated Hospital of Yangtze University, Jingzhou, China (Ringgold ID: RIN117936)
› Author Affiliations
› Further Information
Also available at
   Permissions and Reprints

Abstract

This study attempted to build a prostate cancer (PC) prognostic risk model with mitochondrial feature genes. PC-related MTGs were screened for Cox regression analyses, followed by establishing a prognostic model. Model validity was analyzed via survival analysis and receiver operating characteristic (ROC) curves, and model accuracy was validated in the GEO dataset. Combining risk score with clinical factors, the independence of the risk score was verified by using Cox analysis, followed by generating a nomogram. The Gleason score, microsatellite instability (MSI), immune microenvironment, and tumor mutation burden were analyzed in two risk groups. Finally, the prognostic feature genes were verified through a q-PCR test. Ten PC-associated MTGs were screened, and a prognostic model was built. Survival analysis and ROC curves illustrated that the model was a good predictor for the risk of PC. Cox regression analysis revealed that risk score acted as an independent prognostic factor. The Gleason score and MSI in the high-risk group were substantially higher than in the low-risk group. Levels of ESTIMATE Score, Immune Score, Stromal Score, immune cells, immune function, immune checkpoint, and immunopheno score of partial immune checkpoints in the high-risk group were significantly lower than in the low-risk group. Genes with the highest mutation frequencies in the two groups were SPOP, TTN, and TP53. The q-PCR results of the feature genes were consistent with the gene expression results in the database. The 10-gene model based on MTGs could accurately predict the prognosis of PC patients and their responses to immunotherapy.

Keywords

mitochondrial genes - prostate cancer - immunity - tumor mutation burden - prognosis

Supplementary Material



Publication History

Received: 29 January 2024

Accepted after revision: 11 May 2024

Article published online:
13 June 2024

© 2024. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Sung H, Ferlay J, Siegel RL. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71: 209-249
    CrossrefPubMedGoogle Scholar
  • 2 Matsushita M, Fujita K, Nonomura N. Influence of diet and nutrition on prostate cancer. Int J Mol Sci 2020; 21: 1447
    CrossrefPubMedGoogle Scholar
  • 3 Testa U, Castelli G, Pelosi E. Cellular and molecular mechanisms underlying prostate cancer development: therapeutic implications. Medicines (Basel) 2019; 6: 82
    CrossrefPubMedGoogle Scholar
  • 4 Jeong SH, Kwak C. Immunotherapy for prostate cancer: requirements for a successful regime transfer. Investig Clin Urol 2022; 63: 3-13
    CrossrefPubMedGoogle Scholar
  • 5 Thomas TS, Pachynski RK. Treatment of advanced prostate cancer. Mo Med 2018; 115: 156-161
    PubMedGoogle Scholar
  • 6 Tilokani L, Nagashima S, Paupe V. et al. Mitochondrial dynamics: overview of molecular mechanisms. Essays Biochem 2018; 62: 341-360
    CrossrefPubMedGoogle Scholar
  • 7 Jia D, Park JH, Jung KH. et al. Elucidating the metabolic plasticity of cancer: mitochondrial reprogramming and hybrid metabolic states. Cells 2018; 7: 21
    CrossrefPubMedGoogle Scholar
  • 8 Chang JC, Chang HS, Wu YC. et al. Mitochondrial transplantation regulates antitumour activity, chemoresistance and mitochondrial dynamics in breast cancer. J Exp Clin Cancer Res 2019; 38: 30
    CrossrefPubMedGoogle Scholar
  • 9 Kloss-Brandstätter A, Schäfer G, Erhart G. et al. Somatic mutations throughout the entire mitochondrial genome are associated with elevated PSA levels in prostate cancer patients. Am J Hum Genet 2010; 87: 802-812
    CrossrefPubMedGoogle Scholar
  • 10 Hopkins JF, Sabelnykova VY, Weischenfeldt J. et al. Mitochondrial mutations drive prostate cancer aggression. Nat Commun 2017; 8: 656
    CrossrefPubMedGoogle Scholar
  • 11 Buck MD, Sowell RT, Kaech SM. et al. Metabolic instruction of immunity. Cell 2017; 169: 570-586
    CrossrefPubMedGoogle Scholar
  • 12 Jin P, Jiang J, Zhou L. et al. Mitochondrial adaptation in cancer drug resistance: prevalence, mechanisms, and management. J Hematol Oncol 2022; 15: 97
    CrossrefPubMedGoogle Scholar
  • 13 Robinson MD, McCarthy DJ, Smyth GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics (Oxford, England) 2010; 26: 139-140
    PubMedGoogle Scholar
  • 14 Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw 2010; 33: 1-22
    CrossrefPubMedGoogle Scholar
  • 15 Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 2013; 32: 5381-5397
    CrossrefPubMedGoogle Scholar
  • 16 Ritchie ME, Phipson B, Wu D. et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015; 43: e47
    CrossrefPubMedGoogle Scholar
  • 17 Huang C, Liu Z, Xiao L. et al. Clinical significance of serum CA125, CA19-9, CA72-4, and fibrinogen-to-lymphocyte ratio in gastric cancer with peritoneal dissemination. Front Oncol 2019; 9: 1159
    CrossrefPubMedGoogle Scholar
  • 18 Yu G, Wang LG, Han Y. et al. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 2012; 16: 284-287
    CrossrefPubMedGoogle Scholar
  • 19 Yoshihara K, Shahmoradgoli M, Martínez E. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013; 4: 2612
    CrossrefPubMedGoogle Scholar
  • 20 Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 2013; 14: 7
    CrossrefPubMedGoogle Scholar
  • 21 Lorenc T, Klimczyk K, Michalczewska I. et al. Exosomes in prostate cancer diagnosis, prognosis and therapy. Int J Mol Sci 2020; 21: 2118
    CrossrefPubMedGoogle Scholar
  • 22 Kretschmer A, Tilki D. Biomarkers in prostate cancer - current clinical utility and future perspectives. Crit Rev Oncol Hematol 2017; 120: 180-193
    CrossrefPubMedGoogle Scholar
  • 23 Vasan K, Werner M, Chandel NS. Mitochondrial metabolism as a target for cancer therapy. Cell Metab 2020; 32: 341-352
    CrossrefPubMedGoogle Scholar
  • 24 Li J, Li Y, Chen L. et al. p53/PGC‑1α‑mediated mitochondrial dysfunction promotes PC3 prostate cancer cell apoptosis. Mol Med Rep 2020; 22: 155-164
    CrossrefPubMedGoogle Scholar
  • 25 Pecinová A, Alán L, Brázdová A. et al. Role of mitochondrial glycerol-3-phosphate dehydrogenase in metabolic adaptations of prostate cancer. Cells 2020; 9: 1764
    CrossrefPubMedGoogle Scholar
  • 26 Yoshimura M, Kohzaki M, Nakamura J. et al. Vertebrate POLQ and POLbeta cooperate in base excision repair of oxidative DNA damage. Mol Cell 2006; 24: 115-125
    CrossrefPubMedGoogle Scholar
  • 27 Kawamura K, Bahar R, Seimiya M. et al. DNA polymerase theta is preferentially expressed in lymphoid tissues and upregulated in human cancers. Int J Cancer 2004; 109: 9-16
    CrossrefPubMedGoogle Scholar
  • 28 Kuei CH, Lin HY, Lin MH. et al. DNA polymerase theta repression enhances the docetaxel responsiveness in metastatic castration-resistant prostate cancer. Biochim Biophys Acta Mol Basis Dis 2020; 1866: 165954
    CrossrefPubMedGoogle Scholar
  • 29 Dubrez L, Causse S, Borges Bonan N. et al. Heat-shock proteins: chaperoning DNA repair. Oncogene 2020; 39: 516-529
    CrossrefPubMedGoogle Scholar
  • 30 Wu J, Liu T, Rios Z. et al. Heat shock proteins and cancer. Trends Pharmacol Sci 2017; 38: 226-256
    CrossrefPubMedGoogle Scholar
  • 31 Cho SY, Kang S, Kim DS. et al. HSP27, ALDH6A1 and prohibitin act as a trio-biomarker to predict survival in late metastatic prostate cancer. Anticancer Res 2018; 38: 6551-6560
    CrossrefPubMedGoogle Scholar
  • 32 Lin YC, Papadopoulos V. Neurosteroidogenic enzymes: CYP11A1 in the central nervous system. Front Neuroendocrinol 2021; 62: 100925
    CrossrefPubMedGoogle Scholar
  • 33 Maksymchuk OV, Kashuba VI. Altered expression of cytochrome P450 enzymes involved in metabolism of androgens and vitamin D in the prostate as a risk factor for prostate cancer. Pharmacol Rep 2020; 72: 1161-1172
    CrossrefPubMedGoogle Scholar
  • 34 Zhang H, Shao Y, Chen W. et al. Identifying mitochondrial-related genes NDUFA10 and NDUFV2 as prognostic markers for prostate cancer through biclustering. Biomed Res Int. 2021 5512624. DOI: 10.1155/2021/5512624
    CrossrefPubMedGoogle Scholar
  • 35 Karatas OF, Guzel E, Duz MB. et al. The role of ATP-binding cassette transporter genes in the progression of prostate cancer. Prostate 2016; 76: 434-444
    CrossrefPubMedGoogle Scholar
  • 36 Antony F, Deantonio C, Cotella D. et al. High-throughput assessment of the antibody profile in ovarian cancer ascitic fluids. Oncoimmunology 2019; 8: e1614856
    CrossrefPubMedGoogle Scholar
  • 37 Zhang H, Wang Q, Gu J. et al. Elevated mitochondrial SLC25A29 in cancer modulates metabolic status by increasing mitochondria-derived nitric oxide. Oncogene 2018; 37: 2545-2558
    CrossrefPubMedGoogle Scholar
  • 38 Zhao Z, Zhan Y, Jing L. et al. KLF10 upregulates ACSM3 via the PI3K/Akt signaling pathway to inhibit the malignant progression of melanoma. Oncol Lett 2022; 23: 175
    CrossrefPubMedGoogle Scholar
  • 39 Sakaguchi S, Yamaguchi T, Nomura T. et al. Regulatory T cells and immune tolerance. Cell 2008; 133: 775-787
    CrossrefPubMedGoogle Scholar
  • 40 Ohue Y, Nishikawa H. Regulatory T (Treg) cells in cancer: can treg cells be a new therapeutic target?. Cancer Sci 2019; 110: 2080-2089
    CrossrefPubMedGoogle Scholar
  • 41 Liu ZZ, Han ZD, Liang YK. et al. TRIB1 induces macrophages to M2 phenotype by inhibiting IKB-zeta in prostate cancer. Cell Signal 2019; 59: 152-162
    CrossrefPubMedGoogle Scholar
  • 42 Li Y, Huang Q, Zhou Y. et al. The clinicopathologic and prognostic significance of programmed cell death ligand 1 (PD-L1) expression in patients with prostate cancer: a systematic review and meta-analysis. Front Pharmacol 2018; 9: 1494
    CrossrefPubMedGoogle Scholar
  • 43 Herbst RS, Baas P, Kim DW. et al. Pembrolizumab versus docetaxel for previously treated, PD-L1-positive, advanced non-small-cell lung cancer (KEYNOTE-010): a randomised controlled trial. Lancet 2016; 387: 1540-1550
    CrossrefPubMedGoogle Scholar
  • 44 Rowshanravan B, Halliday N, Sansom DM. CTLA-4: a moving target in immunotherapy. Blood 2018; 131: 58-67
    CrossrefPubMedGoogle Scholar
  • 45 Waitz R, Solomon SB, Petre EN. et al. Potent induction of tumor immunity by combining tumor cryoablation with anti-CTLA-4 therapy. Cancer Res 2012; 72: 430-439
    CrossrefPubMedGoogle Scholar