CancerCSP: Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer

This study focuses on identifying gene expression-based biomarkers capable of distinguishing early-stage and late-stage clear cell renal cell carcinoma (ccRCC). The authors analyzed RNA-Seq gene expression data from 523 ccRCC patients obtained from The Cancer Genome Atlas (TCGA) database and developed multiple machine learning and threshold-based classification models. The work aimed to reduce the large gene feature space into a compact biomarker panel that can accurately classify cancer stages while maintaining biological relevance. Different feature selection strategies, including threshold-based selection, Support Vector Machine (SVM)-based selection, and Weka-based correlation feature selection, were applied to identify highly discriminative genes. The study identified several important biomarkers such as NR3C2, ENAM, DNASE1L3, FRMPD2, PLEKHA9, MAP6D1, SMPD4, and C11orf73 that demonstrated significant differential expression between early and late stages of ccRCC. Key findings include: Single-gene threshold models achieved up to 71.12% accuracy using NR3C2. An 8-gene biomarker panel achieved 74.04% validation accuracy with ROC of 0.80. Weka-selected 64-gene models achieved 72.64% validation accuracy with ROC of 0.81. Gender-specific models showed improved predictive performance, suggesting differential genomic regulation between male and female ccRCC patients. A publicly accessible web server named CancerCSP was developed for stage prediction using RNA-Seq expression data. The study demonstrates the importance of compact biomarker signatures and machine learning approaches in cancer stage prediction and prognosis. webserver : https://webs.iiitd.edu.in/raghava/cancercsp/ Bhalla S, Chaudhary K, Kumar R, Sehgal M, Kaur H, Sharma S, Raghava GPS. Gene expression-based biomarkers for discriminating early and late stage of clear cell renal cancer. Scientific Reports. 2017;7:44997. DOI: https://doi.org/10.1038/srep44997 Key Features RNA-Seq based biomarker discovery Threshold-based and machine learning classification models Support Vector Machine (SVM), Random Forest (RF), Naive Bayes, and J48 classifiers Weka-based feature selection Cancer hallmark GO-term enrichment analysis Gender-specific stage classification models Protein-protein interaction network analysis Web server implementation: CancerCSP