Integrated Transcriptomic and Regulatory Network Analysis Reveals Distinct Molecular Subtypes with Prognostic Significance in Clear Cell Renal Cell Carcinoma
- 1Department of Pharmaceutical Sciences, College of Pharmacy, QU Health, Qatar University, Doha P.O. Box 2713, Qatar
Abstract
Background: Clear cell renal cell carcinoma (ccRCC) shows molecular heterogeneity that complicates prognosis and selection of therapy. Integration of upstream pathway activity and transcription factor (TF) regulatory networks with gene expression to define molecular subtypes remains underexplored. Methods: We harmonized integrative transcriptomics and regulatory data from 483 primary ccRCC tumors from TCGA, integrating PARADIGM pathway activity Z-scores, RABIT TF activity scores, and normalized mRNA expression. Molecular subtypes were identified using consensus clustering. Clusters were characterized using GSEA with MSigDB Hallmark gene sets, differential pathway and expression analyses, and multivariate Cox regression (adjusted for age, gender, and stage). External validation was performed using a survival-guided Random Forest classifier. Results: Consensus clustering identified two molecular subtypes (K = 2, stability = 0.957). Cluster 0 (proliferative-metabolic) (n = 154, 31.9%) was enriched for E2F Targets (NES = 2.140, FDR < 0.001), G2-M Checkpoint (NES = 1.548), and Cholesterol Homeostasis (NES = 1.645), with SREBF1 as the dominant TF driver (t = 11.53). Cluster 1 (angiogenic-immune) (n = 329, 68.1%) was enriched for Angiogenesis (NES = −1.816, FDR < 0.001), Inflammatory Response (NES = −1.623), and EMT (NES = −1.767), governed by IKZF1 (t = −13.15), SPI1 (t = −13.12), and FOS (t = −12.80). Differential expression identified 880 significant genes (FDR < 0.05, |Log2FC| > 1). Multivariate Cox regression confirmed cluster assignment as an independent prognostic factor (HR = 0.70, 95% CI 0.51–0.96, p = 0.026), with Cluster 1 associated with improved overall survival. External validation reproduced significant survival stratification (Log-Rank p = 1.66 × 10−2). Conclusions: Two biologically distinct ccRCC subtypes were defined by integrating regulatory networks with gene expression data, a proliferative-metabolic subtype with worse prognosis and an angiogenic-immune subtype with improved survival. This provides a network-informed transcriptomic framework for molecularly stratified decision-making for prognosis and therapeutics.
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BibTeX
@article{zahid2026integrated,
title = {{Integrated Transcriptomic and Regulatory Network Analysis Reveals Distinct Molecular Subtypes with Prognostic Significance in Clear Cell Renal Cell Carcinoma}},
author = {Muhammad Ammar Zahid and Walid Kouidri and Abbas Khan and Abdelali Agouni},
journal = {Cancers},
year = {2026},
month = oct,
doi = {10.3390/cancers18213485},
url = {https://www.mdpi.com/2072-6694/18/21/3485},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Integrated Transcriptomic and Regulatory Network Analysis Reveals Distinct Molecular Subtypes with Prognostic Significance in Clear Cell Renal Cell Carcinoma
AU - Muhammad Ammar Zahid
AU - Walid Kouidri
AU - Abbas Khan
AU - Abdelali Agouni
PY - 2026
DA - 2026/10/29
JO - Cancers
DO - 10.3390/cancers18213485
UR - https://www.mdpi.com/2072-6694/18/21/3485
AB - Background: Clear cell renal cell carcinoma (ccRCC) shows molecular heterogeneity that complicates prognosis and selection of therapy. Integration of upstream pathway activity and transcription factor (TF) regulatory networks with gene expression to define molecular subtypes remains underexplored. Methods: We harmonized integrative transcriptomics and regulatory data from 483 primary ccRCC tumors from TCGA, integrating PARADIGM pathway activity Z-scores, RABIT TF activity scores, and normalized mRNA expression. Molecular subtypes were identified using consensus clustering. Clusters were characterized using GSEA with MSigDB Hallmark gene sets, differential pathway and expression analyses, and multivariate Cox regression (adjusted for age, gender, and stage). External validation was performed using a survival-guided Random Forest classifier. Results: Consensus clustering identified two molecular subtypes (K = 2, stability = 0.957). Cluster 0 (proliferative-metabolic) (n = 154, 31.9%) was enriched for E2F Targets (NES = 2.140, FDR < 0.001), G2-M Checkpoint (NES = 1.548), and Cholesterol Homeostasis (NES = 1.645), with SREBF1 as the dominant TF driver (t = 11.53). Cluster 1 (angiogenic-immune) (n = 329, 68.1%) was enriched for Angiogenesis (NES = −1.816, FDR < 0.001), Inflammatory Response (NES = −1.623), and EMT (NES = −1.767), governed by IKZF1 (t = −13.15), SPI1 (t = −13.12), and FOS (t = −12.80). Differential expression identified 880 significant genes (FDR < 0.05, |Log2FC| > 1). Multivariate Cox regression confirmed cluster assignment as an independent prognostic factor (HR = 0.70, 95% CI 0.51–0.96, p = 0.026), with Cluster 1 associated with improved overall survival. External validation reproduced significant survival stratification (Log-Rank p = 1.66 × 10−2). Conclusions: Two biologically distinct ccRCC subtypes were defined by integrating regulatory networks with gene expression data, a proliferative-metabolic subtype with worse prognosis and an angiogenic-immune subtype with improved survival. This provides a network-informed transcriptomic framework for molecularly stratified decision-making for prognosis and therapeutics.
SN - 2072-6694
ER -