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oncology

Evan Yi-Wen Yu, Hao Zhang, Yuanqing Fu, Ya-Ting Chen, Qiu-Yi Tang, Yu-Xiang Liu, Yan-Xi Zhang, Shi-Zhi Wang, Anke Wesselius, Wen-Chao Li, Maurice P. Zeegers, Bin Xu

8
Jul 31, 2022
Current Oncology
DOI :
10.3390/curroncol29080430
Article
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Current Oncology, Vol. 29, Pages 5442 5456: Integrative Multi Omics Analysis for the Determination of Non Muscle Invasive vs. Muscle Invasive Bladder Cancer: A Pilot Study Current Oncology doi: 10.3390/curroncol29080430 Authors: Evan Yi Wen Yu Hao Zhang Yuanqing Fu Ya Ting Chen Qiu Yi Tang Yu Xiang Liu Yan Xi Zhang Shi Zhi Wang Anke Wesselius Wen Chao Li Maurice P. Zeegers Bin Xu
Objective : 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.
Method : 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.
Result : 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.

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