Research Papers:

Differential expression analysis for individual cancer samples based on robust within-sample relative gene expression orderings across multiple profiling platforms

Qingzhou Guan, Rou Chen, Haidan Yan, Hao Cai, You Guo, Mengyao Li, Xiangyu Li, Mengsha Tong, Lu Ao, Hongdong Li, Guini Hong, Zheng Guo _

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Oncotarget. 2016; 7:68909-68920. https://doi.org/10.18632/oncotarget.11996

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Qingzhou Guan1, Rou Chen1, Haidan Yan1, Hao Cai1, You Guo1,2, Mengyao Li1, Xiangyu Li1, Mengsha Tong1, Lu Ao1, Hongdong Li1, Guini Hong1, Zheng Guo1

1Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, Fujian Medical University, Fuzhou, 350001, China

2Department of Preventive Medicine, School of Basic Medicine Sciences, Gannan Medical University, Ganzhou, 341000, China

Correspondence to:

Zheng Guo, email: guoz@ems.hrbmu.edu.cn

Guini Hong, email: hongguini08@gmail.com

Keywords: gene expression profiling, multiple platforms, differentially expressed genes, heterogeneity of cancer, individual level

Received: February 23, 2016     Accepted: August 09, 2016     Published: September 13, 2016


The highly stable within-sample relative expression orderings (REOs) of gene pairs in a particular type of human normal tissue are widely reversed in the cancer condition. Based on this finding, we have recently proposed an algorithm named RankComp to detect differentially expressed genes (DEGs) for individual disease samples measured by a particular platform. In this paper, with 461 normal lung tissue samples separately measured by four commonly used platforms, we demonstrated that tens of millions of gene pairs with significantly stable REOs in normal lung tissue can be consistently detected in samples measured by different platforms. However, about 20% of stable REOs commonly detected by two different platforms (e.g., Affymetrix and Illumina platforms) showed inconsistent REO patterns due to the differences in probe design principles. Based on the significantly stable REOs (FDR<0.01) for normal lung tissue consistently detected by the four platforms, which tended to have large rank differences, RankComp detected averagely 1184, 1335 and 1116 DEGs per sample with averagely 96.51%, 95.95% and 94.78% precisions in three evaluation datasets with 25, 57 and 58 paired lung cancer and normal samples, respectively. Individualized pathway analysis revealed some common and subtype-specific functional mechanisms of lung cancer. Similar results were observed for colorectal cancer. In conclusion, based on the cross-platform significantly stable REOs for a particular normal tissue, differentially expressed genes and pathways in any disease sample measured by any of the platforms can be readily and accurately detected, which could be further exploited for dissecting the heterogeneity of cancer.

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