DOI: 10.5281/zenodo.18600365
title: Validation of ITPR2, DPF3, EPAS1 and PVT1 -associated SNPs as biomarkers for RCC in an independent case-control cohort. Datasets generated and employed for statistical analyses

description: Important: The datasets are completely anonymised, generating their own identifier for each one according to the order in which the observations are found. Therefore, observation P1 from the first dataset will only be valid for that single dataset.

Dataset 1: RCC_genotype_clin_survival.xlsx

The file RCC_genotype_clin_survival.xlsx contains a dataset comprising 155 observations (155 RCC patients) and 20 variables, including genotyping data together with clinical, sociodemographic, and survival-related variables.



Genotyping variables.Qualitative data describing the alleles detected in each sample for the following single nucleotide polymorphisms (SNPs): ITPR2-rs1049380, ITPR2-rs10771279, DPF3-rs4903064, EPAS1-rs7579899, and PVT1-rs35252396. Alleles are reported in their original form, and dominant and recessive genetic models were subsequently generated through value aggregation.

Clinical variables.Variables related to RCC staging and disease progression, including RCC stage (I–IV), T status (pT1a, pT1b, pT2, pT2a, pT3a, pT3b, pT3c, pT4), N status (N0, NX, N1), aggregated T status (T_agg; 1: pT1a, pT1b, pT2, pT2a; 2: pT3a, pT3b, pT3c, pT4), metastatic status at diagnosis (0: absence, 1: presence), and post-treatment metastatic status (MTC_POST; 0: no metastasis, 1: metastasis).

Survival variables.Variables used to compute overall survival (OS), including recalculation of 5-year OS based on the original data. These variables include CLASS (survival status: 0 = censored or death due to causes unrelated to RCC; 1 = death due to RCC), and OS, defined as the time (in months) from RCC diagnosis to death or censoring.

Sociodemographic variables.Patient sex (M: male; F: female) and age group (0: <50 years; 1: ≥50 years).


Dataset 2: RCC_genotype_expression_HvT.xlsx

The file RCC_genotype_expression_HvT.xlsx includes genotyping and gene expression data from 31 RCC patients with paired FFPE tumor tissue and adjacent non-tumoral FFPE tissue samples.



Genotyping variables.Qualitative allele data for the SNPs ITPR2-rs1049380, ITPR2-rs10771279, DPF3-rs4903064, EPAS1-rs7579899, and PVT1-rs35252396. As in Dataset 1, dominant and recessive genetic models were derived a posteriori by aggregating allele values.

Gene expression variables.Two expression variables per gene quantified by RT-qPCR are provided: one corresponding to tumor tissue and one to adjacent healthy tissue. Expression levels were measured in FFPE biopsy samples for the genes ITPR2, MYC, PVT1, and ZEB. Gene expression is reported as relative quantification (RQ) values, calculated using the 2⁻ΔΔCt method, normalized to an endogenous housekeeping gene and expressed as fold change relative to the matched healthy tissue. Raw Ct (Cq) values were used to compute ΔCt and ΔΔCt metrics prior to RQ estimation. For gene expression analyses, expression levels were quantified by RT-qPCR and reported as relative quantification (RQ) values, expressed as fold change (2⁻ΔΔCt) after normalization to the mean of two endogenous controls (HPRT1 and GAPDH).


Dataset 3: RCC_genotype_clin_PT_HC.xlsx

The file RCC_genotype_clin_PT_HC.xlsx contains clinical and genotyping data from 215 RCC patients and 212 healthy controls. The dataset includes the same genotyping, clinical, and sociodemographic variables described in RCC_genotype_clin_survival.xlsx, with the additional variable condition, indicating group membership (Patient or Control).

 

 

 
description: Abstract

Introduction: Renal cell carcinoma (RCC) is a heterogeneous malignancy influenced by genetic and environmental factors. Previous genome-wide association studies (GWAS) have identified risk single nucleotide polymorphisms (SNPs) associated with RCC susceptibility, particularly within genes such as ITPR2, DPF3, EPAS1, PVT1, and MYC. These SNPs are in regions implicated in key cellular processes like calcium signaling, chromatin remodeling, hypoxia response and oncogenesis. These pathways are highly relevant to RCC pathogenesis, although the functional significance of these genetic variations in sporadic RCC remains insufficiently characterized.

Methods: This study analyzed five GWAS-identified SNPs—rs1049380 and rs10771279 (ITPR2), rs4903064 (DPF3), rs7579899 (EPAS1), and rs35252396 (PVT1/MYC)—in a Spanish case-control cohort comprising 168 RCC patients and 259 healthy controls. Genotyping was performed from buccal swabs, and gene expression levels were assessed in 33 paired formalin-fixed paraffin-embedded (FFPE) tumor and adjacent normal kidney tissue samples. Associations between SNPs, overall survival, and expression of quantitative trait loci (eQTLs) were evaluated in relation to RCC risk and RCC progression in the case of survival curves.

Results: The C/C genotype of ITPR2 rs10771279 was nominally associated with a protective effect (OR:0.41), with higher ITPR2 expression observed in healthy tissues than in RCC. The C/C genotype of DPF3 rs4903064 was nominally correlated with increased RCC risk (OR: 2.21) and higher DPF3 expression, potentially linked to hypoxia-inducible pathways. Similarly, EPAS1 rs7579899 A/A genotype was nominally associated with RCC risk (OR:1.78) While PVT1/MYC rs35252396 did not show susceptibility relation, both genes showed upregulated expression in RCC tissue.In survival analyses, the G allele of rs1049380 (ITPR2) was significantly associated with reduced  5-year survival in metastic and non-metastatic patients. Additionally, the AC genotype of rs35252396 showed nominal associations with  highest risk in 5-year survival models.

Conclusion:  This study provides independent evidence supporting the biological relevance of GWAS-identified loci in RCC. While several variants showed nominal associations with disease risk, ITPR2 rs1049380 emerged as a variant of potential prognostic relevance for five-year overall survival. Overall, these findings highlight the differential contribution of genetic variants to RCC susceptibility and progression and should be considered hypothesis-generating, warranting validation in larger, independent cohorts.

Keywords: renal cell carcinoma, SNPs, biomarkers, ITPR2, EPAS1, DPF3