Identification and characterization of genes with absolute mRNA abundances changes in tumor cells with varied transcriptome sizes

Hao Cai¹, Xiangyu Li², Jun He², Wenbin Zhou³, Kai Song³, You Guo¹, Huaping Liu¹, Qingzhou Guan², Haidan Yan², Xianlong Wang⁴, Zheng Guo^{5

6}

Affiliations

¹ Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, Jiangxi, China.
² Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China.
³ Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, Fujian, China.
⁴ Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China. wang.xianlong@139.com.
⁵ Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China. guoz@ems.hrbmu.edu.cn.
⁶ Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, Fujian, China. guoz@ems.hrbmu.edu.cn.

PMID: 30760197
PMCID: PMC6374894
DOI: 10.1186/s12864-019-5502-y

Identification and characterization of genes with absolute mRNA abundances changes in tumor cells with varied transcriptome sizes

Hao Cai et al. BMC Genomics. 2019.

. 2019 Feb 13;20(1):134.

doi: 10.1186/s12864-019-5502-y.

Authors

Hao Cai¹, Xiangyu Li², Jun He², Wenbin Zhou³, Kai Song³, You Guo¹, Huaping Liu¹, Qingzhou Guan², Haidan Yan², Xianlong Wang⁴, Zheng Guo^{5

6}

Affiliations

¹ Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000, Jiangxi, China.
² Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China.
³ Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, Fujian, China.
⁴ Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China. wang.xianlong@139.com.
⁵ Department of Bioinformatics, Fujian Key Laboratory of Medical Bioinformatics, Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Fujian Medical University, Fuzhou, 350122, Fujian, China. guoz@ems.hrbmu.edu.cn.
⁶ Department of Systems Biology, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150086, Fujian, China. guoz@ems.hrbmu.edu.cn.

PMID: 30760197
PMCID: PMC6374894
DOI: 10.1186/s12864-019-5502-y

Abstract

Background: The amount of RNA per cell, namely the transcriptome size, may vary under many biological conditions including tumor. If the transcriptome size of two cells is different, direct comparison of the expression measurements on the same amount of total RNA for two samples can only identify genes with changes in the relative mRNA abundances, i.e., cellular mRNA concentration, rather than genes with changes in the absolute mRNA abundances.

Results: Our recently proposed RankCompV2 algorithm identify differentially expressed genes (DEGs) through comparing the relative expression orderings (REOs) of disease samples with that of normal samples. We reasoned that both the mRNA concentration and the absolute abundances of these DEGs must have changes in disease samples. In simulation experiments, this method showed excellent performance for identifying DEGs between normal and disease samples with different transcriptome sizes. Through analyzing data for ten cancer types, we found that a significantly higher proportion of the DEGs with absolute mRNA abundance changes overlapped or directly interacted with known cancer driver genes and anti-cancer drug targets than that of the DEGs only with mRNA concentration changes alone identified by the traditional methods. The DEGs with increased absolute mRNA abundances were enriched in DNA damage-related pathways, while DEGs with decreased absolute mRNA abundances were enriched in immune and metabolism associated pathways.

Conclusions: Both the mRNA concentration and the absolute abundances of the DEGs identified through REOs comparison change in disease samples in comparison with normal samples. In cancers these genes might play more important upstream roles in carcinogenesis.

Keywords: Absolute mRNA abundances; Cellular mRNA concentration; Differentially expressed gene; Relative expression ordering.

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Figures

**Fig. 1**
Reproducibility evaluation of DEGs identified by RankCompV2. a Comparison of DEGs identified by RankCompV2 with DEGs identified by SAM or edgeR in the same dataset. POG₁₂ (or POG₂₁) denotes the ratio of consistently detected DEGs by the two methods to DEGs identified by RankCompV2 (or by SAM or edgeR) for the same dataset. b Comparison of RankCompV2 DEGs identified from different datasets. POG₁₂ (or POG₂₁) denotes the ratio of consistently detected DEGs from two datasets to DEGs detected from the first dataset (or the second dataset) for each cancer type. The concordance score denotes the percentage of consistently detected DEGs that display the same dysregulation directions to the overlapped DEGs between two DEG lists

**Fig. 2**
Association between the identified absolute and relative DEGs with known cancer driver genes or drug targets. a 616 cancer driver genes from COSMIC; b cancer driver genes from DriverDBv2; c anti-cancer drug targets. Statistically significant differences (p < 0.05) were found in all the ratios between absolute DEGs and relative DEGs

**Fig. 3**
The pathways significantly enriched with absolute and relative DEGs, respectively, in each cancer type and commonly enriched in at least five cancer types

See this image and copyright information in PMC

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