Dataset Information

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

ABSTRACT: Purpose: Clear cell renal cell carcinoma (ccRCC) is the most common renal cancer. Thirty percent of patients with localized ccRCC develop metastases during follow-up. Although current scoring methods correctly identify patients at low progression risk, a small subgroup still experiences metastatization. We therefore aimed to identify ccRCC progression biomarkers in “low-risk” patients, potentially eligible for adjuvant treatments. Methods: We performed next-generation sequencing of RNA from formalin-fixed samples obtained at initial surgery from 8 “low-risk” patients with progressing tumours and 16 patients with similar Leibovich score, tumour stage and size, creatinine levels and surgical treatment, not progressing to recurrence with metastasis. Key results were confirmed with qPCR, immunohistochemistry and in external data. Results: Principal component analysis indicates that systematic transcriptomic differences are detectable at the time of initial surgery. 1167 genes, related to cancer and immune-related pathways, were differentially expressed between progressors and non-progressors. Search for a classifier revealed that overexpression of AGAP2-AS1, an antisense long non-coding RNA, alone, correctly classified 23 of 24 samples without requiring larger gene panels. AGAP2-AS1 gene overexpression was confirmed by qPCR (p <0.05) and correlated with shorter progression-free survival (p: <0.0005). Immunohistochemistry confirmed upregulation at the protein level of AGAP2. Conclusion: AGAP2-AS1 may represent a novel biomarker identifying high risk patients currently classified as “low risk” at the time of surgery.

ORGANISM(S): Homo sapiens

PROVIDER: GSE171955 | GEO | 2021/12/29

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:Background: In a previous study of patients with assumed benign IgA nephropathy (IgAN), we have shown that 18.6% develop a progressive clinical course, which was not predicted by any known clinical nor histopathological parameters (Knoop 2017). We aim to differentiate between future progressors and non-progressors with assumed benign IgAN by using glomerular transcriptomics from diagnostic kidney biopsies and to investigate whether this can identify drugs to be used in those patients. Methods: We included adult progressors (n=15) and patients with clinical remission (non-progressors, n=21) from our previously reported IgAN cohort. Glomerular cross-sections were obtained through laser-capture microdissection from 10µm archival kidney biopsy sections for mRNA sequencing, using the SMARTer Stranded Total RNA-Seq kit – Pico Input Mammalian (Clontech Laboratories, California, USA). Key results were validated in external cohorts and on the protein level with immunohistochemistry in the discovery cohort and an internal validation cohort. We also compared our transcriptomic findings with an external genome-wide association study (GWAS) dataset (Kiryluk 2021). Results: For the statistical analyses, we studied 8 progressors and 9 non-progressors. Based on the 1,240 differentially expressed glomerular genes from the initial kidney biopsies, we were able to identify future IgAN progressors and non-progressors using a two-component classifier. The classifier predicted disease progression with 88% accuracy, 75% sensitivity and 100% specificity (AUC=0.875), more accurate than the recommended International IgAN Prediction Tool in this specific patient cohort. Among our differentially expressed genes, 10 were also found in the GWAS dataset (Kiryluk 2021). We identified 45 drug targets in the DrugBank database, including angiotensinogen, a target of sparsentan, currently investigated as therapy in IgAN (NCT03762850 and NCT04663204). The findings on upregulation of angiotensinogen, which was independent of blood pressure, were validated in an internal IgAN validation cohort. Interestingly, complement activation did not play a major role in our data. 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.

Dataset Information

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

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets