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Article

Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types

1
School of Life Sciences and Technology, Tongji University, Shanghai 200092, China
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Department of Medicine Library, Tongji University Library, Tongji University, Shanghai 200092, China
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Department of Laboratory Medicine, Eastern Hepatobiliary Surgery Institute, Second Military Medical University, Shanghai 200438, China
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Wolfson College, Oxford University, Oxford OX2 6UD, UK
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Institute of Biomedical and Environmental Science & Technology, University of Bedfordshire, Luton LU1 3JU, UK
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School of Life Sciences, Shanxi University, Taiyuan 030006, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Stefania Bortoluzzi
Genes 2022, 13(2), 365; https://doi.org/10.3390/genes13020365
Received: 28 December 2021 / Revised: 12 February 2022 / Accepted: 14 February 2022 / Published: 17 February 2022
(This article belongs to the Special Issue Bioinformatic Analysis of NGS Data)
Tumor mutational burden (TMB) is considered a potential biomarker for predicting the response and effect of immune checkpoint inhibitors (ICIs). However, there are still inconsistent standards of gene panels using next-generation sequencing and poor correlation between the TMB genes, immune cell infiltrating, and prognosis. We applied text-mining technology to construct specific TMB-associated gene panels cross various cancer types. As a case exploration, Pearson’s correlation between TMB genes and immune cell infiltrating was further analyzed in colorectal cancer. We then performed LASSO Cox regression to construct a prognosis predictive model and calculated the risk score of each sample for receiver operating characteristic (ROC) analysis. The results showed that the assessment of TMB gene panels performed well with fewer than 500 genes, highly mutated genes, and the inclusion of synonymous mutations and immune regulatory and drug-target genes. Moreover, the analysis of TMB differentially expressed genes (DEGs) suggested that JAKMIP1 was strongly correlated with the gene expression level of CD8+ T cell markers in colorectal cancer. Additionally, the prognosis predictive model based on 19 TMB DEGs reached AUCs of 0.836, 0.818, and 0.787 in 1-, 3-, and 5-year OS models, respectively (C-index: 0.810). In summary, the gene panel performed well and TMB DEGs showed great potential value in immune cell infiltration and in predicting survival. View Full-Text
Keywords: tumor mutational burden; next-generation sequencing gene panel; omics data mining; tumor-infiltrating lymphocytes tumor mutational burden; next-generation sequencing gene panel; omics data mining; tumor-infiltrating lymphocytes
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MDPI and ACS Style

Wang, S.; Tong, Y.; Zong, H.; Xu, X.; Crabbe, M.J.C.; Wang, Y.; Zhang, X. Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types. Genes 2022, 13, 365. https://doi.org/10.3390/genes13020365

AMA Style

Wang S, Tong Y, Zong H, Xu X, Crabbe MJC, Wang Y, Zhang X. Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types. Genes. 2022; 13(2):365. https://doi.org/10.3390/genes13020365

Chicago/Turabian Style

Wang, Shuangkuai, Yuantao Tong, Hui Zong, Xuewen Xu, M. James C. Crabbe, Ying Wang, and Xiaoyan Zhang. 2022. "Multi-Level Analysis and Identification of Tumor Mutational Burden Genes across Cancer Types" Genes 13, no. 2: 365. https://doi.org/10.3390/genes13020365

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