Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer

Abstract Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature...

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Autores principales:	Neha Shree Maurya, Sandeep Kushwaha, Aakash Chawade, Ashutosh Mani
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2259edeee46d4068928014e90a777c6b
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spelling	oai:doaj.org-article:2259edeee46d4068928014e90a777c6b2021-12-02T16:08:06ZTranscriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer10.1038/s41598-021-92692-02045-2322https://doaj.org/article/2259edeee46d4068928014e90a777c6b2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92692-0https://doaj.org/toc/2045-2322Abstract Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis.Neha Shree MauryaSandeep KushwahaAakash ChawadeAshutosh ManiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Neha Shree Maurya Sandeep Kushwaha Aakash Chawade Ashutosh Mani Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer
description	Abstract Colorectal cancer (CRC) is a common cause of cancer-related deaths worldwide. The CRC mRNA gene expression dataset containing 644 CRC tumor and 51 normal samples from the cancer genome atlas (TCGA) was pre-processed to identify the significant differentially expressed genes (DEGs). Feature selection techniques Least absolute shrinkage and selection operator (LASSO) and Relief were used along with class balancing for obtaining features (genes) of high importance. The classification of the CRC dataset was done by ML algorithms namely, random forest (RF), K-nearest neighbour (KNN), and artificial neural networks (ANN). The significant DEGs were 2933, having 1832 upregulated and 1101 downregulated genes. The CRC gene expression dataset had 23,186 features. LASSO had performed better than Relief for classifying tumor and normal samples through ML algorithms namely RF, KNN, and ANN with an accuracy of 100%, while Relief had given 79.5%, 85.05%, and 100% respectively. Common features between LASSO and DEGs were 38, from them only 5 common genes namely, VSTM2A, NR5A2, TMEM236, GDLN, and ETFDH had shown statistically significant survival analysis. Functional review and analysis of the selected genes helped in downsizing the 5 genes to 2, which are VSTM2A and TMEM236. Differential expression of TMEM236 was statistically significant and was markedly reduced in the dataset which solicits appreciation for assessment as a novel biomarker for CRC diagnosis.
format	article
author	Neha Shree Maurya Sandeep Kushwaha Aakash Chawade Ashutosh Mani
author_facet	Neha Shree Maurya Sandeep Kushwaha Aakash Chawade Ashutosh Mani
author_sort	Neha Shree Maurya
title	Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer
title_short	Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer
title_full	Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer
title_fullStr	Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer
title_full_unstemmed	Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer
title_sort	transcriptome profiling by combined machine learning and statistical r analysis identifies tmem236 as a potential novel diagnostic biomarker for colorectal cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2259edeee46d4068928014e90a777c6b
work_keys_str_mv	AT nehashreemaurya transcriptomeprofilingbycombinedmachinelearningandstatisticalranalysisidentifiestmem236asapotentialnoveldiagnosticbiomarkerforcolorectalcancer AT sandeepkushwaha transcriptomeprofilingbycombinedmachinelearningandstatisticalranalysisidentifiestmem236asapotentialnoveldiagnosticbiomarkerforcolorectalcancer AT aakashchawade transcriptomeprofilingbycombinedmachinelearningandstatisticalranalysisidentifiestmem236asapotentialnoveldiagnosticbiomarkerforcolorectalcancer AT ashutoshmani transcriptomeprofilingbycombinedmachinelearningandstatisticalranalysisidentifiestmem236asapotentialnoveldiagnosticbiomarkerforcolorectalcancer
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Transcriptome profiling by combined machine learning and statistical R analysis identifies TMEM236 as a potential novel diagnostic biomarker for colorectal cancer

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