Integrative Multi-Omics Analysis for the Determination of Non-Muscle Invasive vs. Muscle Invasive Bladder Cancer: A Pilot Study

Abstract

Objectives: The molecular landscape of non-muscle-invasive (NMIBC) and muscle-invasive (MIBC) bladder cancer based on molecular characteristics is essential but poorly understood. In this pilot study we aimed to identify a multi-omics signature that can distinguish MIBC from NMIBC. Such a signature can assist in finding potential mechanistic biomarkers and druggable targets.

Methods: Patients diagnosed with NMIBC (n = 15) and MIBC (n = 11) were recruited at a tertiary-care hospital in Nanjing from 1 April 2021, and 31 July 2021. Blood, urine and stool samples per participant were collected, in which the serum metabolome, urine metabolome, gut microbiome, and serum extracellular vesicles (EV) proteome were quantified. The differences of the global profiles and individual omics measure between NMIBC vs. MIBC were assessed by permutational multivariate analysis and the Mann-Whitney test, respectively. Logistic regression analysis was used to assess the association of each identified analyte with NMIBC vs. MIBC, and the Spearman correlation was used to investigate the correlations between identified analytes, where both were adjusted for age, sex and smoking status.

Results: Among 3168 multi-omics measures that passed the quality control, 159 were identified to be differentiated in NMIBC vs. MIBC. Of these, 46 analytes were associated with bladder cancer progression. In addition, the global profiles showed significantly different urine metabolome (p = 0.029), gut microbiome (p = 0.036), and serum EV (extracellular vesicles) proteome (p = 0.039) but not serum metabolome (p = 0.059). We also observed 17 (35%) analytes that had been developed as drug targets. Multiple interactions were obtained between the identified analytes, whereas for the majority (61%), the number of interactions was at 11-20. Moreover, unconjugated bilirubin (p = 0.009) and white blood cell count (p = 0.006) were also shown to be different in NMIBC and MIBC, and associated with 11 identified omics analytes.

Conclusions: The pilot study has shown promising to monitor the progression of bladder cancer by integrating multi-omics data and deserves further investigations.

Keywords: bladder cancer; molecular epidemiology; multi-omics; progression of disease.

Figure 1

Design and work-flow of the current study. The NMIBC and MIBC patients were recruited with specimen collection for blood, urine and stool. Abbreviations: NMIBC, non-muscle-invasive bladder cancer; MIBC, muscle-invasive bladder cancer.

Figure 2

Global profile between NMIBC and MIBC based on multi-omics. To distinguish the molecular characteristics between NMIBC and MIBC, we measured the serum metabolome, urine metabolome, gut microbiome and serum EV proteome. We also profiled the clinical information of each participant. PCoA analysis was used, with a permutational multivariate analysis of variance, to perform the difference of global profiles. Statistical difference between two groups (NMIBC vs. MIBC) was assessed with the Mann–Whitney test. Abbreviations: NMIBC, non-muscle-invasive bladder cancer; MIBC, muscle-invasive bladder cancer; PCoA, Principal coordinates.

Figure 3

Identification of analytes of multi-omics and functional annotation. (A) Identified multi-omics analytes with annotated mechanistic pathways; (B) Venn plot of identified metabolites in NMIBC and MIBC; (C) Comparison of Shannon diversity between NMIBC and MIBC; (D) Established druggable targets from the identified analytes. Abbreviations: EV, extracellular vesicles; GM, gut microbiome; His-leu, Histidine-Leucine; Gly-Ile, Glycyl-Isoleucine; FFA (9:0), Nonanoic acids; SM(d18:1/18:0), Sphingomyelin; FFA (15:0), Pentadecylic acid; DAPP1, Dual adapter for phosphotyrosine and 3-phosphotyrosine and 3-phosphoinositide; PHPT1, 14 kDa phosphohistidine phosphatase; PON3, Serum paraoxonase/lactonase 3; GNG10, Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-10; ADD2, Beta-adducin; ADD1, Alpha-adducin; TGM2, Protein-glutamine gamma-glutamyltransferase 2; ORM2, Alpha-1-acid glycoprotein 2; ANK1, Ankyrin-1 OS = Homo sapiens; SPTB, Spectrin beta chain non-erythrocytic 1; EPB41, Protein 4.1 OS = Homo sapiens; GYPC, Glycophorin-C OS = Homo sapiens; ORM1, Alpha-1-acid glycoprotein 1; SLC4A1, Band 3 anion transport protein; SPTA1, Spectrin alpha chain erythrocytic 1; HLAB, HLA class I histocompatibility antigen B alpha chain; Metalloproteinase inhibitor 1.

Figure 4

Interactive analysis of identified multi-omics biomarkers. (A) Correlation of identified analytes with unconjugated bilirubin and white blood cell count; (B) Correlation between each pair of identified analytes; (C) The number of correlations of each analyte to others. Abbreviations: UBC, unconjugated bilirubin; WBC, white blood cell count; His-leu, Histidine-Leucine; Gly-Ile, Glycyl-Isoleucine; FFA (9:0), Nonanoic acids; SM (d18:1/18:0), Sphingomyelin; FFA (15:0), Pentadecylic acid; DAPP1, Dual adapter for phosphotyrosine and 3-phosphotyrosine and 3-phosphoinositide; PHPT1, 14 kDa phosphohistidine phosphatase; PON3, Serum paraoxonase/lactonase 3; GNG10, Guanine nucleotide-binding protein G(I)/G(S)/G(O) subunit gamma-10; ADD2, Beta-adducin; ADD1, Alpha-adducin; TGM2, Protein-glutamine gamma-glutamyltransferase 2; ORM2, Alpha-1-acid glycoprotein 2; ANK1, Ankyrin-1 OS = Homo sapiens; SPTB, Spectrin beta chain non-erythrocytic 1; EPB41, Protein 4.1 OS = Homo sapiens; GYPC, Glycophorin-C OS = Homo sapiens; ORM1, Alpha-1-acid glycoprotein 1; SLC4A1, Band 3 anion transport protein; SPTA1, Spectrin alpha chain erythrocytic 1; HLAB, HLA class I histocompatibility antigen B alpha chain; Metalloproteinase inhibitor 1. * p < 0.05.