Multivariate search for differentially expressed gene combinations

Xiao, Yongshuang; Frisina, Robert D.; Gordon, Alexander Y.; Klebanov, Lev B.; Yakovlev, Andrei

doi:10.1186/1471-2105-5-164

Cited by 40 publications

(4 citation statements)

References 28 publications

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“…Feature selection algorithms search for the best set of genes with the highest potential for accurate prediction. Because gene expression is not independent, feature selection algorithms must identify groups of genes that act in concert (85). However, feature selection is also subject to the curse of dimensionality.…”

Section: Box 1: the “Curse Of Dimensionality” And Its Knowledge-basedmentioning

confidence: 99%

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Phan

Moffitt

Stokes

et al. 2009

Trends in Biotechnology

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Recent advances in biomarker discovery, biocomputing, and nanotechnology have raised new opportunities for the emerging field of personalized medicine in which disease detection, diagnosis, and therapy are tailored to each individual’s molecular profile, and also for predictive medicine that uses genetic/molecular information to predict disease development, progression, and clinical outcome. Here we discuss advanced biocomputing tools for cancer biomarker discovery and multiplexed nanoparticle probes for cancer biomarker profiling, together with prospects and challenges in correlating biomolecular signatures with clinical outcome. This bio-nano-info convergence holds great promise for molecular diagnosis and individualized therapy of cancer and other human diseases.

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Section: Box 1: the “Curse Of Dimensionality” And Its Knowledge-basedmentioning

confidence: 99%

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Phan

Moffitt

Stokes

et al. 2009

Trends in Biotechnology

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“…Then in 2005, a correlation-based score was developed that highlights genes where correlations are particularly different between groups(Dettling et al, 2005 ). In parallel, in a series of papers by Yakovlev and colleagues (Szabo et al, 2003 ; Xiao et al, 2004 ), a non-parametric measure of variability in correlations was developed; this approach includes clustering to find subsets of genes showing variability. Their measure is built on Fisher-transformed correlations, considered both within and between groups, and was extended in 2009 (Hu R. et al, 2009 ) to allow comparisons between two groups.…”

Section: Introductionmentioning

confidence: 99%

Gene Coexpression Analyses Differentiate Networks Associated with Diverse Cancers Harboring TP53 Missense or Null Mutations

et al. 2016

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In a variety of solid cancers, missense mutations in the well-established TP53 tumor suppressor gene may lead to the presence of a partially-functioning protein molecule, whereas mutations affecting the protein encoding reading frame, often referred to as null mutations, result in the absence of p53 protein. Both types of mutations have been observed in the same cancer type. As the resulting tumor biology may be quite different between these two groups, we used RNA-sequencing data from The Cancer Genome Atlas (TCGA) from four different cancers with poor prognosis, namely ovarian, breast, lung and skin cancers, to compare the patterns of coexpression of genes in tumors grouped according to their TP53 missense or null mutation status. We used Weighted Gene Coexpression Network analysis (WGCNA) and a new test statistic built on differences between groups in the measures of gene connectivity. For each cancer, our analysis identified a set of genes showing differential coexpression patterns between the TP53 missense- and null mutation-carrying groups that was robust to the choice of the tuning parameter in WGCNA. After comparing these sets of genes across the four cancers, one gene (KIR3DL2) consistently showed differential coexpression patterns between the null and missense groups. KIR3DL2 is known to play an important role in regulating the immune response, which is consistent with our observation that this gene's strongly-correlated partners implicated many immune-related pathways. Examining mutation-type-related changes in correlations between sets of genes may provide new insight into tumor biology.

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“…This analysis, first proposed more than a decade ago 8, 9 , identifies the pairs of genes that have their interaction changed during such transition. Several later publications have suggested different algorithms and statistics to determine differential gene co-expression 10– 27 .…”

Section: Introductionmentioning

confidence: 99%

Differentially correlated genes in co-expression networks control phenotype transitions

et al. 2016

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Background: Co-expression networks are a tool widely used for analysis of “Big Data” in biology that can range from transcriptomes to proteomes, metabolomes and more recently even microbiomes. Several methods were proposed to answer biological questions interrogating these networks. Differential co-expression analysis is a recent approach that measures how gene interactions change when a biological system transitions from one state to another. Although the importance of differentially co-expressed genes to identify dysregulated pathways has been noted, their role in gene regulation is not well studied. Herein we investigated differentially co-expressed genes in a relatively simple mono-causal process (B lymphocyte deficiency) and in a complex multi-causal system (cervical cancer). Methods: Co-expression networks of B cell deficiency (Control and BcKO) were reconstructed using Pearson correlation coefficient for two mus musculus datasets: B10.A strain (12 normal, 12 BcKO) and BALB/c strain (10 normal, 10 BcKO). Co-expression networks of cervical cancer (normal and cancer) were reconstructed using local partial correlation method for five datasets (total of 64 normal, 148 cancer). Differentially correlated pairs were identified along with the location of their genes in BcKO and in cancer networks. Minimum Shortest Path and Bi-partite Betweenness Centrality where statistically evaluated for differentially co-expressed genes in corresponding networks. Results: We show that in B cell deficiency the differentially co-expressed genes are highly enriched with immunoglobulin genes (causal genes). In cancer we found that differentially co-expressed genes act as “bottlenecks” rather than causal drivers with most flows that come from the key driver genes to the peripheral genes passing through differentially co-expressed genes. Using in vitro knockdown experiments for two out of 14 differentially co-expressed genes found in cervical cancer (FGFR2 and CACYBP), we showed that they play regulatory roles in cancer cell growth. Conclusion: Identifying differentially co-expressed genes in co-expression networks is an important tool in detecting regulatory genes involved in alterations of phenotype.

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Multivariate search for differentially expressed gene combinations

Cited by 40 publications

References 28 publications

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Convergence of biomarkers, bioinformatics and nanotechnology for individualized cancer treatment

Gene Coexpression Analyses Differentiate Networks Associated with Diverse Cancers Harboring TP53 Missense or Null Mutations

Differentially correlated genes in co-expression networks control phenotype transitions

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