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In-Depth Mapping of the Urinary N-Glycoproteome: Distinct Signatures of ccRCC-related Progression.

ABSTRACT: Protein N-glycosylation is one of the most important post-translational modifications and is involved in many biological processes, with aberrant changes in protein N-glycosylation patterns being closely associated with several diseases, including the progression and spreading of tumours. In light of this, identifying these aberrant protein glycoforms in tumours could be useful for understanding the molecular mechanism of this multifactorial disease, developing specific biomarkers and finding novel therapeutic targets. We investigated the urinary N-glycoproteome of clear cell renal cell carcinoma (ccRCC) patients at different stages (n = 15 at pT1 and n = 15 at pT3), and of non-ccRCC subjects (n = 15), using an N-glyco-FASP-based method. Using label-free nLC-ESI MS/MS, we identified and quantified several N-glycoproteins with altered expression and abnormal changes affecting the occupancy of the glycosylation site in the urine of RCC patients compared to control. In particular, nine of them had a specific trend that was directly related to the stage progression: CD97, COCH and P3IP1 were up-expressed whilst APOB, FINC, CERU, CFAH, HPT and PLTP were down-expressed in ccRCC patients. Overall, these results expand our knowledge related to the role of this post-translational modification in ccRCC and translation of this information into pre-clinical studies could have a significant impact on the discovery of novel biomarkers and therapeutic target in kidney cancer.

SUBMITTER: Santorelli L

PROVIDER: S-EPMC7016614 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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In-Depth Mapping of the Urinary N-Glycoproteome: Distinct Signatures of ccRCC-related Progression.

Santorelli Lucia L Capitoli Giulia G Chinello Clizia C Piga Isabella I Clerici Francesca F Denti Vanna V Smith Andrew A Grasso Angelica A Raimondo Francesca F Grasso Marco M Magni Fulvio F

Cancers 20200118 1

Protein N-glycosylation is one of the most important post-translational modifications and is involved in many biological processes, with aberrant changes in protein N-glycosylation patterns being closely associated with several diseases, including the progression and spreading of tumours. In light of this, identifying these aberrant protein glycoforms in tumours could be useful for understanding the molecular mechanism of this multifactorial disease, developing specific biomarkers ...[more]

PMID: 31963743

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Project description:BackgroundCancer metastasis and recurrence remain major challenges in renal carcinoma patient management. There are limited biomarkers to predict the metastatic probability of renal cancer, especially in the early-stage subgroup. Here, our study applied robust machine-learning algorithms to identify metastatic and recurrence-related signatures across multiple renal cancer cohorts, which reached high accuracy in both training and testing cohorts.MethodsClear cell renal cell carcinoma (ccRCC) patients with primary or metastatic site sequencing information from eight cohorts, including one out-house cohort, were enrolled in this study. Three robust machine-learning algorithms were applied to identify metastatic signatures. Then, two distinct metastatic-related subtypes were identified and verified; matrix remodeling associated 5 (MXRA5), as a promising diagnostic and therapeutic target, was investigated in vivo and in vitro.ResultsWe identified five stable metastasis-related signatures (renin, integrin subunit beta-like 1, MXRA5, mesenchyme homeobox 2, and anoctamin 3) from multicenter cohorts. Additionally, we verified the specificity and sensibility of these signatures in external and out-house cohorts, which displayed a satisfactory consistency. According to these metastatic signatures, patients were grouped into two distinct and heterogeneous ccRCC subtypes named metastatic cancer subtype 1 (MTCS1) and type 2 (MTCS2). MTCS2 exhibited poorer clinical outcomes and metastatic tendencies than MTCS1. In addition, MTCS2 showed higher immune cell infiltration and immune signature expression but a lower response rate to immune blockade therapy than MTCS1. The MTCS2 subgroup was more sensitive to saracatinib, sunitinib, and several molecular targeted drugs. In addition, MTCS2 displayed a higher genome mutation burden and instability. Furthermore, we constructed a prognosis model based on subtype biomarkers, which performed well in training and validation cohorts. Finally, MXRA5, as a promising biomarker, significantly suppressed malignant ability, including the cell migration and proliferation of ccRCC cell lines in vitro and in vivo.ConclusionsThis study identified five robust metastatic signatures and proposed two metastatic probability clusters with stratified prognoses, multiomics landscapes, and treatment options. The current work not only provided new insight into the heterogeneity of renal cancer but also shed light on optimizing decision-making in immunotherapy and chemotherapy.

Dataset Information

In-Depth Mapping of the Urinary N-Glycoproteome: Distinct Signatures of ccRCC-related Progression.

Publications

In-Depth Mapping of the Urinary <i>N</i>-Glycoproteome: Distinct Signatures of ccRCC-related Progression.

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