Abstract
Next-generation sequencing (NGS), which allows the simultaneous sequencing of billions of DNA fragments simultaneously, has revolutionized how we study genomics and molecular biology by generating genome-wide molecular maps of molecules of interest. However, the amount of information produced by NGS has made it difficult for researchers to choose the optimal set of genes. We have sought to resolve this issue by developing a neural network-based feature (gene) selection algorithm called Wx. The Wx algorithm ranks genes based on the discriminative index (DI) score that represents the classification power for distinguishing given groups. With a gene list ranked by DI score, researchers can institutively select the optimal set of genes from the highest-ranking ones. We applied the Wx algorithm to a TCGA pan-cancer gene-expression cohort to identify an optimal set of gene-expression biomarker candidates that can distinguish cancer samples from normal samples for 12 different types of cancer. The 14 gene-expression biomarker candidates identified by Wx were comparable to or outperformed previously reported universal gene expression biomarkers, highlighting the usefulness of the Wx algorithm for next-generation sequencing data. Thus, we anticipate that the Wx algorithm can complement current state-of-the-art analytical applications for the identification of biomarker candidates as an alternative method. The stand-alone and web versions of the Wx algorithm are available at https://github.com/deargen/DearWXpub and https://wx.deargendev.me/, respectively.
Highlights
Advances in science and technology often lead to paradigm shifts
Further validation of the identified gene signature with three independent studies confirmed that the 14-gene signature identified by the Wx algorithm accurately classified cancer samples from normal samples compared to other methods[14,15]
We applied our Wx method, which is based on the Discriminative Index (DI) algorithm, into a pan-cancer cohort from the cancer genome atlas (TCGA) RNA-seq data consisting of 12 different types of cancer and normal samples (Table 1)
Summary
Advances in science and technology often lead to paradigm shifts. In biology and biomedical fields, high-throughput screening (HTS) techniques such as microarray and next-generation sequencing (NGS) have changed how we identify measurable biological indicators (called biomarkers) for various diseases. With a full list of genes (up to 190,000 transcripts in the human genome; https://www.gencodegenes.org/), researchers can narrow down biomarker candidates via downstream analyses such as unsupervised clustering[3], gene ontology (GO) analysis[4], regression analysis[5], and/ or differentially expression gene (DEG) analysis[6,7]. We tested the algorithm’s usefulness by attempting to identify universal gene-expression cancer biomarker candidates that could potentially distinguish various types of cancer from normal samples in the pan-cancer data set of the cancer genome atlas (TCGA) project. Our algorithm successfully identified 14 key genes as a conceptual set of universal biomarkers, accurately distinguishing 12 types of cancer from normal tissue samples. We expect that the Wx algorithm can complement differentially expressed gene (DEG) analysis as an alternative method for the identification of biomarker candidates
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