Individual-level analysis of differential expression of genes and pathways for personalized medicine

Wang, Yan; Sun, Qiang; Zhao, Wenyuan; Qi, Lishuang; Gu, Yunyan; Li, Pengfei; Zhang, Mengmeng; Li, Yang; Liu, Shulin; Guo, Zheng

doi:10.1093/bioinformatics/btu522

Cited by 122 publications

(150 citation statements)

References 49 publications

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“…Then, we identified DE genes in each of the 72 samples of rectal cancer collected from two datasets (GSE35452 and GSE53781) with RankComp through comparing with the stable REO normal landscape of rectal tissue21 (see Methods). Averagely, 1596 DE genes per sample were identified, among which 186 genes had significantly different deregulation frequencies between the non-responders and responders (Fisher exact test, FDR < 0.05).…”

Section: Resultsmentioning

confidence: 99%

“…Then, we applied the RankComp algorithm21 to identify differentially expressed genes (DE genes) at the individual level. Briefly, to determine whether a given gene X is differentially expressed in a given disease sample, Fisher’s exact test is used to test the null hypothesis that the frequency of reversal gene pairs supporting its upregulation and the frequency of reversal gene pairs supporting its downregulation are not significantly different.…”

Section: Methodsmentioning

confidence: 99%

“…Although there are some available data of normal rectal samples generated by different laboratories, they cannot be compared directly due to experimental batch effects1819 and current methods for adjusting batch effects could distort biological signals20. Recently, we have reported an interesting biological phenomenon that the within-sample relative expression orderings (REOs) of genes within each of a particular type of normal tissues are highly stable but widely disrupted in the corresponding cancer tissues21. Based on that, RankComp was proposed to detect the deregulated genes in a disease sample through comparing the REOs in this sample with the stable REOs in normal human tissues21.…”

mentioning

confidence: 99%

“…Recently, we have reported an interesting biological phenomenon that the within-sample relative expression orderings (REOs) of genes within each of a particular type of normal tissues are highly stable but widely disrupted in the corresponding cancer tissues21. Based on that, RankComp was proposed to detect the deregulated genes in a disease sample through comparing the REOs in this sample with the stable REOs in normal human tissues21. Because all comparisons of REOs occur within individual samples, which obviates the need of data normalization, we will be able to utilize multiple datasets for the rectal normal samples to identify deregulated genes in cancer samples at the individual level.…”

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confidence: 99%

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Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers

Guo

Cheng

et al. 2016

Sci Rep

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For patients with locally advanced rectal cancer (LARC) treated with preoperation chemoradiation (pCRT), identifying differentially expressed (DE) genes between non-responders and responders is a common approach for investigating mechanisms of chemoradiation resistance. However, some of such DE genes might be irrelevant to cancer itself but simply reflect the pharmacokinetic differences of the normal tissues. In this study, we adopted the RankComp algorithm to identify DE genes for each of LARC sample compared with its own normal state. Then, we identified genes with significantly different deregulation frequencies between the non-responders and responders, defined as cancer-related pCRT-response genes. Pathway enrichment and protein-protein interaction analyses showed that these genes specifically and intensively interacted with currently known effective genes of pCRT, involving in DNA replication, cell cycle and DNA repair. In contrast, after excluding the cancer-related pCRT-response genes, the other DE genes between non-responders and responders were enriched in many pathways of drug and protein metabolisms and transports, and interacted with both the known effective genes and pharmacokinetic genes. Hence, these two types of DE genes should be distinguished for investigating mechanisms of pCRT response in LARCs.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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confidence: 99%

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Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers

Guo

Cheng

et al. 2016

Sci Rep

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“…We adapted the gene ranking method from Wang et al [26], which is described in detail in Section 3.2. The rationale behind this ranking is that relative ordering of gene expression is generally stable in a particular type of normal human tissue but is widely disturbed in a diseased tissue.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Classifying Gene Expression Data using Topic Modeling

Kho

Yalamanchili

Raymer

et al. 2017

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics

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Understanding the role of differential gene expression in cancer etiology and cellular process is a complex problem that continues to pose a challenge due to sheer number of genes and inter-related biological processes involved. In this paper, we employ an unsupervised topic model, Latent Dirichlet Allocation (LDA) to mitigate overfitting of high-dimensionality gene expression data and to facilitate understanding of the associated pathways. LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. Here, we proposed to use LDA in clustering as well as in classification of cancer and healthy tissues using lung cancer and breast cancer messenger RNA (mRNA) sequencing data. We describe our study in three phases: clustering, classification, and gene interpretation. First, LDA is used as a clustering algorithm to group the data in an unsupervised manner. Next we developed a novel LDA-based classification approach to classify unknown samples based on similarity of co-expression patterns. Evaluation to assess the effectiveness of this approach shows that LDA can achieve high accuracy compared to alternative approaches. Lastly, we present a functional analysis of the genes identified using a novel topic profile matrix formulation. This analysis identified several genes and pathways that could potentially be involved in differentiating tumor samples from normal. Overall, our results project LDA as a promising approach for classification of tissue types based on gene expression data in cancer studies.

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An individualized gene expression signature for prediction of lung adenocarcinoma metastases

Shi

et al. 2017

Molecular Oncology

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Our laboratory previously reported an individual‐level signature consisting of nine gene pairs, named 9‐GPS. This signature was developed by training on microarray expression data and validated using three independent integrated microarray data sets, with samples of stage I non‐small‐cell lung cancer after complete surgical resection. In this study, we first validated the cross‐platform robustness of 9‐GPS by demonstrating that 9‐GPS could significantly stratify the overall survival of 213 stage I lung adenocarcinoma (LUAD) patients detected with RNA‐sequencing platform in The Cancer Genome Atlas (TCGA; log‐rank P = 0.0318, C‐index = 0.55). Applying 9‐GPS to all the 423 stage I‐IV LUAD samples in TCGA, the predicted high‐risk samples were significantly enriched with clinically diagnosed metastatic samples (Fisher's exact test, P = 0.0015). We further modified the voting rule of 9‐GPS and found that the modified 9‐GPS had a better performance in predicting metastasis states (Fisher's exact test, P < 0.0001). With the aid of the modified 9‐GPS for reclassifying the metastasis states of patients with LUAD, the reclassified metastatic samples presented clearer transcriptional and genomic characteristics compared to the reclassified nonmetastatic samples. Finally, regulator network analysis identified TP53 and IRF1 with frequent genomic aberrations in the reclassified metastatic samples, indicating their key roles in driving tumor metastasis. In conclusion, 9‐GPS is a robust signature for identifying early‐stage LUAD patients with potential occult metastasis. This occult metastasis prediction was associated with clear transcriptional and genomic characteristics as well as the clinical diagnoses.

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Individual-level analysis of differential expression of genes and pathways for personalized medicine

Abstract: RankComp is implemented in R script that is freely available from Supplementary Materials.

Cited by 122 publications

References 49 publications

Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers

Discriminating cancer-related and cancer-unrelated chemoradiation-response genes for locally advanced rectal cancers

A Novel Approach for Classifying Gene Expression Data using Topic Modeling

An individualized gene expression signature for prediction of lung adenocarcinoma metastases

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