Recent technological advances in molecular biology have led to great progress in the knowledge of structure and function of cells and their main constituents. In this setting, ‘omics’ is standing out in order to significantly improve the understanding of etiopathogenetic mechanisms of disease and contribute to the development of new biochemical diagnostics and therapeutic tools. ‘Omics’ indicates the scientific branches investigating every aspect of cell’s biology, including structures, functions and dynamics pathways. The main ‘omics’ are genomics, epigenomics, proteomics, transcriptomics, metabolomics and radiomics. Their diffusion, success and proliferation, addressed to many research fields, has led to many important acquisitions, even in Urology. Aim of this narrative review is to define the state of art of ‘omics’ application in Urology, describing the most recent and relevant findings, in both oncological and non-oncological diseases, focusing the attention on urinary tract infectious, interstitial cystitis, urolithiasis, prostate cancer, bladder cancer and renal cell carcinoma. In Urology the majority of ‘omics’ applications regard the pathogenesis and diagnosis of the investigated diseases. In future, its role should be implemented in order to develop specific predictors and tailored treatments.
TaylorMJLukowskiJKAndertonCR. Spatially resolved mass spectrometry at the single cell: recent innovations in proteomics and metabolomics. J Am Soc Mass Spectrom2021; 32(4): 872–894.
2.
MenyhártOGyőrffyB. Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput Struct Biotechnol J2021; 19: 949–960.
3.
AlyassATurcotteMMeyreD. From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics2015; 8(1): 1–12.
4.
HasinYSeldinMLusisA. Multi-omics approaches to disease. Genome Biol2017; 18(1): 1–15.
5.
MayerhoeferMEMaterkaALangsG, et al. Introduction to radiomics. J Nucl Med2020; 61(4): 488–495.
6.
ChakrabortyS, et al. Onco-multi-OMICS approach: a new frontier in cancer research. Biomed Res Int2018; 2018: 1–14.
7.
WeinsteinRAGaynesREdwardsJR. Overview of nosocomial infections caused by gram-negative bacilli. Clin Infect Dis2005; 41(6): 848–854.
8.
TandogduZWagenlehnerFME. Global epidemiology of urinary tract infections. Curr Opin Infect Dis2016; 29(1): 73–79.
9.
FoxmanB. The epidemiology of urinary tract infection. Nat Rev Urol2010; 7(12): 653–660.
10.
Flores-MirelesALWalkerJNCaparonM, et al. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol2015; 13(5): 269–284.
11.
LoAWMorielDGPhanMD, et al. ‘Omic’ approaches to study uropathogenic Escherichia coli virulence. Trends Microbiol2017; 25(9): 729–740.
12.
BienJSokolovaOBozkoP. Role of uropathogenic Escherichia coli virulence factors in development of urinary tract infection and kidney damage. Int J Nephrol2012; 2012: 1–15.
WelchRABurlandVPlunkettG, et al. Extensive mosaic structure revealed by the complete genome sequence of uropathogenic Escherichia coli. Proc Natl Acad Sci U S A2002; 99(26): 17020–17024.
15.
BlumGOttMLischewskiA, et al. Excision of large DNA regions termed pathogenicity islands from tRNA-specific loci in the chromosome of an Escherichia coli wild-type pathogen. Infect Immun1994; 62(2): 606–614.
GuptaKHootonTMNaberKG, et al. International clinical practice guidelines for the treatment of acute uncomplicated cystitis and pyelonephritis in women: a 2010 update by the Infectious Diseases Society of America and the European Society for Microbiology and Infectious Diseases. Clin Infect Dis2011; 52(5): e103–e120.
18.
LugtenbergMBurgersJSZegers-van SchaickJM, et al. Guidelines on uncomplicated urinary tract infections are difficult to follow: perceived barriers and suggested interventions. BMC Fam Pract2010; 11(1): 1–8.
19.
LussuMCamboniTPirasC, et al. 1H NMR spectroscopy-based metabolomics analysis for the diagnosis of symptomatic E. coli-associated urinary tract infection (UTI). BMC Microbiol2017; 17(1): 1–8.
20.
LamC-WLawCYSzeKH, et al. Quantitative metabolomics of urine for rapid etiological diagnosis of urinary tract infection: evaluation of a microbial–mammalian co-metabolite as a diagnostic biomarker. Clin Chim Acta2015; 438: 24–28.
21.
YuYSikorskiPBowman-GholstonC, et al. Diagnosing inflammation and infection in the urinary system via proteomics. J Transl Med2015; 13(1): 1–14.
22.
PettyNKBen ZakourNLStanton-CookM, et al. Global dissemination of a multidrug resistant Escherichia coli clone. Proc Natl Acad Sci U S A2014; 111(15): 5694–5699.
23.
ThanertRReskeKHinkT, et al. 2847. Comparative genomics and clonal tracking of multi-drug-resistant uropathogens implicates the fecal microbiome as a potential reservoir for recurrent urinary tract infections. Open Forum Infect Dis2019; 6(Suppl. 2): S70–S71.
24.
BirderLA. Pathophysiology of interstitial cystitis. Internet J Urol2019; 26(S1): 12–15.
25.
AkiyamaYHannoP. Phenotyping of interstitial cystitis/bladder pain syndrome. Internet J Urol2019; 26(S1): 17–19.
26.
YouSYangWAngerJT, et al. ‘Omics’ approaches to understanding interstitial cystitis/painful bladder syndrome/bladder pain syndrome. Int Neurourol J2012; 16(4): 159–168.
27.
KeaySKZhangCOShoenfeltJ, et al. Sensitivity and specificity of antiproliferative factor, heparin-binding epidermal growth factor–like growth factor, and epidermal growth factor as urine markers for interstitial cystitis. Urology2001; 57(6 Suppl. 1): 9–14.
28.
KeaySKSzekelyZConradsTP, et al. An antiproliferative factor from interstitial cystitis patients is a frizzled 8 protein-related sialoglycopeptide. Proc Natl Acad Sci U S A2004; 101(32): 11803–11808.
29.
YangWGoo ChungYKimY, et al. Quantitative proteomics identifies a β-Catenin Network as an element of the signaling response to Frizzled-8 protein-related antiproliferative factor. Mol Cell Proteomics2011; 10(6): M110.007492.
30.
van de MerweJPYamadaTSakamotoY. Systemic aspects of interstitial cystitis, immunology and linkage with autoimmune disorders. Internet J Urol2003; 10: S35–S38.
31.
SugayaKNishijimaSYamadaT, et al. Molecular analysis of adrenergic receptor genes and interleukin-4/interleukin-4 receptor genes in patients with interstitial cystitis. J Urol2002; 168(6): 2668–2671.
32.
CanterMPGrahamCAHeitMH, et al. Proteomic techniques identify urine proteins that differentiate patients with interstitial cystitis from asymptomatic control subjects. Am J Obstet Gynecol2008; 198(5): 553.e1–553.e6.
33.
GooYATsaiYSLiuAY, et al. Urinary proteomics evaluation in interstitial cystitis/painful bladder syndrome: a pilot study. Int Braz J Urol2010; 36(4): 464–479. discussion 478-9, 479.
34.
FukuiYKatoMInoueY, et al. A metabonomic approach identifies human urinary phenylacetylglutamine as a novel marker of interstitial cystitis. J Chromatogr B Analyt Technol Biomed Life Sci2009; 877(30): 3806–3812.
35.
ParkerKSCrowleyJRStephens-ShieldsAJ, et al. Urinary metabolomics identifies a molecular correlate of interstitial cystitis/bladder pain syndrome in a Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network Cohort. EBioMedicine2016; 7: 167–174.
36.
YasuiT. Pathophysiology-based treatment of urolithiasis. Int J Urol2017; 24(1): 32–38.
37.
MerchantMLCumminsTDWilkeyDW, et al. Proteomic analysis of renal calculi indicates an important role for inflammatory processes in calcium stone formation. Am J Physiol Renal Physiol2008; 295(4): F1254–F1258.
DuanXZhangTOuL, et al. 1H NMR-based metabolomic study of metabolic profiling for the urine of kidney stone patients. Urolithiasis2020; 48(1): 27–35.
40.
PearleMSGoldfarbDSAssimosDG, et al. Medical management of kidney stones: AUA guideline. J Urol2014; 192(2): 316–324.
41.
RamaswamyKKillileaDWKapahiP, et al. The elementome of calcium-based urinary stones and its role in urolithiasis. Nat Rev Urol2015; 12(10): 543–557.
42.
SiegelRLMillerKDJemalA. Cancer statistics, 2020. CA Cancer J Clin2020; 70(1): 7–30.
43.
AdashekJJJainRKZhangJ. Clinical development of PARP inhibitors in treating metastatic castration-resistant prostate cancer. Cells2019; 8(8): 860.
44.
KingslandCRSteerCVPampiglioneJS, et al. Outcome of triplet pregnancies resulting from IVF at Bourn Hallam 1984–1987. Eur J Obstet Gynecol Reprod Biol1990; 34(3): 197–203.
45.
MateoJPortaNBianchiniD, et al. Olaparib in patients with metastatic castration-resistant prostate cancer with DNA repair gene aberrations (TOPARP-B): a multicentre, open-label, randomised, phase 2 trial. Lancet Oncol2020; 21(1): 162–174.
46.
AbidaWCampbellDPatnaikA, et al. Non-BRCA DNA damage repair gene alterations and response to the PARP inhibitor rucaparib in metastatic castration-resistant prostate cancer: analysis from the phase II TRITON2 study. Clin Cancer Res2020; 26(11): 2487–2496.
47.
JafariSMolaviOKahrobaH, et al. Clinical application of immune checkpoints in targeted immunotherapy of prostate cancer. Cell Mol Life Sci2020; 77(19): 3693–3710.
48.
HandyCEAntonarakisES. Sipuleucel-T for the treatment of prostate cancer: novel insights and future directions. Future Oncol2018; 14(10): 907–917.
49.
TwardowskiPWongJYCPalSK, et al. Randomized phase II trial of sipuleucel-T immunotherapy preceded by sensitizing radiation therapy and sipuleucel-T alone in patients with metastatic castrate resistant prostate cancer. Cancer Treat Res Commun2019; 19: 100116.
50.
LimaARBastosMDLCarvalhoM, et al. Biomarker discovery in human prostate cancer: an update in metabolomics studies. Transl Oncol2016; 9(4): 357–370.
51.
LucarelliGFanelliMLaroccaAMV, et al. Serum sarcosine increases the accuracy of prostate cancer detection in patients with total serum PSA less than 4.0 ng/ml. Prostate2012; 72(15): 1611–1621.
52.
VittrantBBergeronAMolinaOE, et al. Immune-focused multi-omics analysis of prostate cancer: leukocyte Ig-like receptors are associated with disease progression. OncoImmunology2020; 9(1): 1851950.
53.
BurgerMCattoJWFDalbagniG, et al. Epidemiology and risk factors of urothelial bladder cancer. Eur Urol2013; 63(2): 234–241.
54.
KamatAMHahnNMEfstathiouJA, et al. Bladder cancer. Lancet2016; 388(10061): 2796–2810.
55.
RuppenIGrauLOrenes-PiñeroE, et al. Differential protein expression profiling by iTRAQ-two-dimensional LC-MS/MS of human bladder cancer EJ138 cells transfected with the metastasis suppressor KiSS-1 gene. Mol Cell Proteomics2010; 9(10): 2276–2291.
56.
OhtakiTShintaniYHondaS, et al. Metastasis suppressor gene KiSS-1 encodes peptide ligand of a G-protein-coupled receptor. Nature2001; 411(6837): 613–617.
57.
TaberAChristensenELamyP, et al. Molecular correlates of cisplatin-based chemotherapy response in muscle invasive bladder cancer by integrated multi-omics analysis. Nat Commun2020; 11(1): 1–15.
58.
TafuriASmithDDCacciamaniGE, et al. Programmed death 1 and programmed death ligand 1 inhibitors in advanced and recurrent urothelial carcinoma: meta-analysis of single-agent studies. Clin Genitourin Cancer2020; 18(5): 351–360.e3.
59.
SatheGGeorgeIADebB, et al. Urinary glycoproteomic profiling of non-muscle invasive and muscle invasive bladder carcinoma patients reveals distinct N-glycosylation pattern of CD44, MGAM, and GINM1. Oncotarget2020; 11(34): 3244–3255.
60.
VantakuVDongJAmbatiCR, et al. Multi-omics integration analysis robustly predicts high-grade patient survival and identifies CPT1B effect on fatty acid metabolism in bladder cancer. Clin Cancer Res2019; 25(12): 3689–3701.
61.
LinPWenDYChenL, et al. A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma. Eur Radiol2020; 30(1): 547–557.
62.
WuSZhengJLiY, et al. A radiomics nomogram for the preoperative prediction of lymph node metastasis in bladder cancer. Clin Cancer Res2017; 23(22): 6904–6911.
63.
ZhangXXuXTianQ, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging2017; 46(5): 1281–1288.
64.
SiracusanoSRizzettoRPorcaroAB. Bladder cancer genomics. Urologia2020; 87(2): 49–56.
65.
InamuraK. Renal cell tumors: understanding their molecular pathological epidemiology and the 2016 WHO classification. Int J Mol Sci2017; 18(10): 2195.
66.
ShuchBAminAArmstrongAJ, et al. Understanding pathologic variants of renal cell carcinoma: distilling therapeutic opportunities from biologic complexity. Eur Urol2015; 67(1): 85–97.
67.
MooreLENickersonMLBrennanP, et al. Von Hippel-Lindau (VHL) inactivation in sporadic clear cell renal cancer: associations with germline VHL polymorphisms and etiologic risk factors. PLoS Genet2011; 7(10): e1002312.
68.
BrugarolasJ. PBRM1 and BAP1 as novel targets for renal cell carcinoma. Cancer J2013; 19(4): 324–332.
69.
IbragimovaIMaradeoMEDulaimiE, et al. Aberrant promoter hypermethylation of PBRM1, BAP1, SETD2, KDM6A and other chromatin-modifying genes is absent or rare in clear cell RCC. Epigenetics2013; 8(5): 486–493.
70.
KabirAUSubramanianMLeeDH, et al. Dual role of endothelial Myct1 in tumor angiogenesis and tumor immunity. Sci Transl Med2021; 13(583): eabb6731.
71.
MotzerRJBanchereauRHamidiH, et al. Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade. Cancer Cell2020; 38(6): 803–817.e4.
72.
GolshayanARGeorgeSHengDY, et al. Metastatic sarcomatoid renal cell carcinoma treated with vascular endothelial growth factor-targeted therapy. J Clin Oncol2009; 27(2): 235–241.
73.
TillmannUViolaGKayserB, et al. cDNA clones of the auxin-binding protein from corn coleoptiles (Zea mays L.): isolation and characterization by immunological methods. EMBO J1989; 8(9): 2463–2467.
74.
SatoTKawasakiYMaekawaM, et al. Value of global metabolomics in association with diagnosis and clinicopathological factors of renal cell carcinoma. Int J Cancer2019; 145(2): 484–493.
