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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.12.528144v1?rss=1
Authors: Prakash, O., Khan, F.
Abstract: Background: Gene signatures represents set of molecular modulations in disease genomes or in cells at specific conditions, and are frequently used to classify samples into different groups for better research or clinical treatment. Multiple methods and applications are available in the literature, but powerful ones that can account for early detection of cancer are still lacking. Method: In this article, gene-signatures identified through new in-house algorithm (NCT method) by processing transcriptome data (DEGs extracted from RNA-seq dataset) from population. NCT-Method utilized for processing population dataset, from 28 different human cancer from TCGA & GTEx databases, as empirical background. NCT-score used for optimal clustering of gene-set. The identified gene clusters evaluated through survival analysis. Gene-sets with disease-vs-normal survival plot with logPvalue less than 0.05 represented as reliable gene signatures. Results: We applied NCT algorithm to the 28 different cancers, and identified novel gene-signatures as well as inter-relation between different cancers in reference of identified signatures. Conclusions: The algorithm uses population data, and provides validated gene signatures with reliable capacity to discriminate the cancer and normal samples with higher classification performance. The algorithm will be useful to find signature for any RNA-seq data.
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