Papillary renal cell carcinoma (PRCC) is the second most common subtype of kidney cancer, accounting for approximately 10-15% of all RCC cases, but its molecular landscape is less well-characterized than clear cell RCC.
PRCC encompasses at least two histological subtypes (type 1 and type 2) with distinct clinical behaviors, genetic alterations, and prognoses, making a one-size-fits-all approach to diagnosis and treatment inadequate.
Identifying robust molecular biomarkers for PRCC is essential for improving early detection, risk stratification, and the development of targeted therapies that address PRCC-specific biology.
This study uses an integrative bioinformatics approach across multiple public datasets to identify consistently dysregulated genes and hub biomarkers in PRCC compared to normal kidney tissue.
Three publicly available GEO datasets were analyzed: GSE7023, GSE48352, and GSE15641, comprising a combined 70 PRCC tumor samples and 43 normal kidney tissue samples.
Differentially expressed genes (DEGs) were identified in each dataset independently, and only genes that were consistently dysregulated across all three datasets were retained for downstream analysis.
This cross-dataset intersection approach reduces false positives from dataset-specific technical artifacts and identifies a robust consensus signature of PRCC-associated gene expression changes.
Protein-protein interaction (PPI) network analysis was then used to identify hub genes within the DEG set, and survival associations were validated using TCGA-KIRP data.
The cross-dataset analysis identified 214 overlapping DEGs between PRCC and normal kidney tissue, representing the reproducible transcriptomic signature of PRCC across three independent cohorts.
Of these, 205 genes were downregulated and only 9 were upregulated in PRCC, suggesting that PRCC is predominantly characterized by loss of normal kidney gene expression rather than gain of new oncogenic programs.
This pattern of widespread downregulation may reflect loss of differentiated renal tubular cell identity in PRCC, as cells de-differentiate toward a more proliferative phenotype.
Pathway enrichment analysis of the downregulated genes highlighted disruption of metabolic, vascular, and transport functions that are normally active in healthy proximal tubular epithelial cells.
PPI network analysis identified 17 hub genes: ALB, EGF, KDR, CXCL12, REN, PLG, PECAM1, KNG1, CDH5, AQP2, C3, THY1, WT1, MGAM, PLAU, AGTR1, and DCN.
Of these, 10 hub genes were significantly associated with overall survival (OS) and/or recurrence-free survival (RFS) in TCGA-KIRP data, confirming their prognostic relevance.
The hub genes span diverse biological functions including angiogenesis (KDR, CDH5, PECAM1), immune signaling (C3, CXCL12), and kidney-specific physiology (AQP2, REN, AGTR1), reflecting the multi-system disruption in PRCC.
Their consistent downregulation across datasets and association with survival outcomes makes these genes strong candidates for further functional validation and potential therapeutic targeting.
PECAM1 (also known as CD31) and PLAU (urokinase-type plasminogen activator) were identified as the most critical hub genes through key module analysis of the PPI network.
Both genes were significantly downregulated in PRCC compared to normal kidney tissue, with PECAM1 most strongly downregulated in stage 1 Caucasian type 1 PRCC patients.
PLAU showed the most pronounced downregulation in Asian patients with stage 4 CIMP-type PRCC, suggesting that PLAU expression may vary by patient subgroup and disease stage.
PECAM1 is a vascular adhesion molecule critical for endothelial integrity, while PLAU drives extracellular matrix remodeling -- both functions are central to tumor growth and metastasis.
This study provides a comprehensive bioinformatics characterization of PRCC using a multi-dataset approach that yields more reliable results than single-cohort analyses.
The 17 hub genes identified represent a prioritized set of targets for functional validation, biomarker development, and exploration as potential therapeutic vulnerabilities in PRCC.
PECAM1 and PLAU, as key seed genes with prognostic associations, warrant further investigation as tissue-based or circulating biomarkers for PRCC diagnosis and risk stratification.
Future experimental studies confirming causal roles for these genes in PRCC biology will be critical for translating these bioinformatic findings into clinical applications.