ArticleScientific reports2017
Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
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- CPSM: an R package for cancer patient survival risk model using transcriptomics and clinical data.GigaScience · 2026Article
- Integrated Multiomics Analysis Reveals a Migrasome-Related Signature for Prognosis and Immunotherapy Response in Lung Adenocarcinoma.Human mutation · 2026Article
- AI-based pathomics in kidney diseases: progress and application.Renal failure · 2025Review
- Prediction of exosomal miRNA-based biomarkers for liquid biopsy.Scientific reports · 2025Article
- Deep Reinforcement Learning for CT-Based Non-Invasive Prediction of SOX9 Expression in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2025Article
- High expression of SMPD4 promotes liver cancer and is associated with poor prognosis.BMC research notes · 2025Article
- SiRCle (Signature Regulatory Clustering) model integration reveals mechanisms of phenotype regulation in renal cancer.Genome medicine · 2024Article
- Enhancing cancer stage prediction through hybrid deep neural networks: a comparative study.Frontiers in big data · 2024Article
- Comprehensive analysis of the expression, prognosis and biological significance of FSCN family in clear cell renal cell carcinoma.Oncology letters · 2023Article
- Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives.Diagnostics (Basel, Switzerland) · 2023Review
- Molecular differences in renal cell carcinoma between males and females.World journal of urology · 2023Review
- Role of HIKESHI on Hyperthermia for Castration-Resistant Prostate Cancer and Application of a Novel Magnetic Nanoparticle with Carbon Nanohorn for Magnetic Hyperthermia.Pharmaceutics · 2023Article
- An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS).Journal of pathology informatics · 2023Review
- DNASE1L3 inhibits proliferation, invasion and metastasis of hepatocellular carcinoma by interacting with β-catenin to promote its ubiquitin degradation pathway.Cell proliferation · 2022Article
- Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2022Review
- Diagnostic classification of cancers using DNA methylation of paracancerous tissues.Scientific reports · 2022Article
- The potential value of cuprotosis (copper-induced cell death) in the therapy of clear cell renal cell carcinoma.American journal of cancer research · 2022Article
- Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response.Computational and mathematical methods in medicine · 2022Article
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7 authors.
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Abstract
In this study, an attempt has been made to identify expression-based gene biomarkers that can discriminate early and late stage of clear cell renal cell carcinoma (ccRCC) patients. We have analyzed the gene expression of 523 samples to identify genes that are differentially expressed in the early and late stage of ccRCC. First, a threshold-based method has been developed, which attained a maximum accuracy of 71.12% with ROC 0.67 using single gene NR3C2. To improve the performance of threshold-based method, we combined two or more genes and achieved maximum accuracy of 70.19% with ROC of 0.74 using eight genes on the validation dataset. These eight genes include four underexpressed (NR3C2, ENAM, DNASE1L3, FRMPD2) and four overexpressed (PLEKHA9, MAP6D1, SMPD4, C11orf73) genes in the late stage of ccRCC. Second, models were developed using state-of-art techniques and achieved maximum accuracy of 72.64% and 0.81 ROC using 64 genes on validation dataset. Similar accuracy was obtained on 38 genes selected from subset of genes, involved in cancer hallmark biological processes. Our analysis further implied a need to develop gender-specific models for stage classification. A web server, CancerCSP, has been developed to predict stage of ccRCC using gene expression data derived from RNAseq experiments.
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