Glomerular proteomic profiling reveals early differences between preexisting and de novo type 2 diabetes in human renal allografts.

BACKGROUND: Diabetes mellitus (DM), either preexisting or developing after transplantation, remains a crucial clinical problem in kidney transplantation. To obtain insights into the molecular mechanisms underlying PTDM development and early glomerular damage before the development of histologically visible diabetic kidney disease, we comparatively analysed the proteome of histologically normal glomeruli from patients with PTDM and normoglycaemic (NG) transplant recipients. Moreover, to assess specificities inherent in PTDM, we also comparatively evaluated glomerular proteomes from transplant recipients with preexisting type 2 DM (T2DM). METHODS: Protocol biopsies were obtained from adult NG, PTDM and T2DM patients one year after kidney transplantation. Biopsies were formalin-fixed and embedded in paraffin, and glomerular cross-sections were microdissected. A total of 4 NG, 7 PTDM and 6 T2DM kidney biopsies were used for the analysis. The proteome was determined by liquid chromatography-tandem mass spectrometry. Relative differences in protein abundance and significantly dysregulated pathways were analysed. RESULTS: Proteins involved in cell adhesion, immune response, leukocyte transendothelial filtration, and cell localization and organization were less abundant in glomeruli from PTDM patients than in those from NG patients, and proteins associated with supramolecular fibre organization and protein-containing complex binding were more abundant in PTDM patients. Overall, proteins related to adherens and tight junctions and those related to the immune system, including leukocyte transendothelial migration, were more abundant in NG patients than in transplanted patients with DM, irrespective of the timing of its development. However, proteins included in cell‒cell junctions and adhesion, insulin resistance, and vesicle-mediated transport were all less abundant in PTDM patients than in T2DM patients. CONCLUSIONS: The glomerular proteome profile differentiates PTDM from NG and T2DM, suggesting specific pathogenetic mechanisms. Further studies are warranted to validate these results, potentially leading to an improved understanding of PTDM kidney transplant pathophysiology and to the identification of novel biomarkers.

AGAP2-AS1 as a prognostic biomarker in low-risk clear cell renal cell carcinoma patients with progressing disease.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer and one of the most common cancers. While survival for localized ccRCC is good, the survival of metastatic disease is not, and thirty percent of patients with ccRCC develop metastases during follow-up. Although current scoring methods accurately identify patients at low progression risk, a small subgroup of those patients still experience metastasis. We therefore aimed to identify ccRCC progression biomarkers in "low-risk" patients who were potentially eligible for adjuvant treatments or more intensive follow-up. METHODS: We assembled a cohort of ccRCC patients (n = 443) and identified all "low-risk" patients who later developed progressing tumors (n = 8). Subsequently, we performed genome-wide expression profiling from formalin-fixed samples obtained at initial surgery from these "low-risk" patients and 16 matched patients not progressing to recurrence with metastasis. The patients were matched for Leibovich sore, creatinine, age, sex, tumor size and tumor stage. Key results were confirmed with qPCR and external data from The Cancer Genome Atlas. RESULTS: Principal component analysis indicated that systematic transcriptomic differences were already detectable at the time of initial surgery. One thousand one hundred sixty-seven genes, mainly associated with cancer and immune-related pathways, were differentially expressed between progressors and nonprogressors. A search for a classifier revealed that overexpression of AGAP2-AS1, an antisense long noncoding RNA, correctly classified 23 of 24 samples, years (4.5 years average) in advance of the discovery of metastasis and without requiring larger gene panels. Subsequently, we confirmed AGAP2-AS1 gene overexpression by qPCR in the same samples (p < 0.05). Additionally, in external data from The Cancer Genome Atlas, overexpression of AGAP2-AS1 is correlated with overall unfavorable survival outcome in ccRCC, irrespective of other prognostic predictors (p = 2.44E-7). CONCLUSION: AGAP2-AS1 may represent a novel biomarker identifying high-risk ccRCC patients currently classified as "low risk" at the time of surgery.

A multiomics disease progression signature of low-risk ccRCC.

Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Identification of ccRCC likely to progress, despite an apparent low risk at the time of surgery, represents a key clinical issue. From a cohort of adult ccRCC patients (n = 443), we selected low-risk tumors progressing within a 5-years average follow-up (progressors: P, n = 8) and non-progressing (NP) tumors (n = 16). Transcriptome sequencing, miRNA sequencing and proteomics were performed on tissues obtained at surgery. We identified 151 proteins, 1167 mRNAs and 63 miRNAs differentially expressed in P compared to NP low-risk tumors. Pathway analysis demonstrated overrepresentation of proteins related to "LXR/RXR and FXR/RXR Activation", "Acute Phase Response Signaling" in NP compared to P samples. Integrating mRNA, miRNA and proteomic data, we developed a 10-component classifier including two proteins, three genes and five miRNAs, effectively differentiating P and NP ccRCC and capturing underlying biological differences, potentially useful to identify "low-risk" patients requiring closer surveillance and treatment adjustments. Key results were validated by immunohistochemistry, qPCR and data from publicly available databases. Our work suggests that LXR, FXR and macrophage activation pathways could be critically involved in the inhibition of the progression of low-risk ccRCC. Furthermore, a 10-component classifier could support an early identification of apparently low-risk ccRCC patients.