Extensive Decoupling of Metabolic Genes in Cancer

Reznik, Ed; Sander, Chris

doi:10.1101/008946

Cited by 8 publications

(19 citation statements)

References 49 publications

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“…Consistent with other cancer types, for ESCC, the TCN also displayed a reduced connectivity compared with the NCN (Figure 1A‐C). In the NCN, there were 11 482 PCGs and 3942 lncRNAs that were linked by 3 236 238 edges.…”

Section: Resultsmentioning

confidence: 99%

“…To test whether the difference between the PCC of a gene pair in normal samples and that in tumour samples was significant, the PCCs were transformed into z scores by using the Fisher transformation:

z = \frac{1}{2} \log (\frac{1 + r}{1 - r})

where r is the PCC, and z is the Fisher‐transformed PCC. Then, z is approximately normally distributed with variance

σ^{2} = 1 true/ \sqrt{n - 3}

, where n is the number of the samples . The PCCs

r_{ij}^{N}

and

r_{ij}^{T}

of a gene pair ( i , j ) in the normal and tumour samples are transformed into

z_{ij}^{N}

and

z_{ij}^{T}

by using Equation (), respectively.…”

Section: Methodsmentioning

confidence: 99%

“…As co‐expressed gene pairs are more likely to have putative interactions, dependencies or coordinated activities in a given biological state, changes in co‐expression patterns between two conditions may reveal disease‐associated dysregulated mechanisms and indicate key driver genes . For human cancer, the gene co‐expression relationships in normal samples are extensively lost in matched tumour samples, such as breast cancer, colorectal cancer, lung cancer and gastric cancer . Many studies have focused on this discrepancy to identify genes or gene modules that are dysfunctional in tumour samples by differential co‐expression analysis.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Anglani et al showed that differential co‐expression analysis was complementary to differential expression to unveil novel candidate cancer genes and improve the classic pathway enrichment analysis . In clear cell renal cell carcinoma, an HNF4A ‐associated module was found to be functional in normal tissues but disrupted in tumour tissues, which could promote cell proliferation . Furthermore, differential co‐expression analysis has been successfully used to identify differentially co‐expressed modules, a group of genes significantly correlated under one condition but not the other, which may reflect dynamic changes in gene interaction networks .…”

Section: Introductionmentioning

confidence: 99%

“…In clear cell renal cell carcinoma, an HNF4A ‐associated module was found to be functional in normal tissues but disrupted in tumour tissues, which could promote cell proliferation . Furthermore, differential co‐expression analysis has been successfully used to identify differentially co‐expressed modules, a group of genes significantly correlated under one condition but not the other, which may reflect dynamic changes in gene interaction networks . However, for ESCC, the differential co‐expression patterns of genes have not been investigated.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Identification of lncRNA‐associated differential subnetworks in oesophageal squamous cell carcinoma by differential co‐expression analysis

Liu

Gan

Wang

et al. 2020

J Cellular Molecular Medi

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Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co-expression of lncRNAs and protein-coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA-PCG differential co-expression network (DCN). DCN was characterized as a scale-free, small-world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA-PCG subnetworks were identified from the DCN by integrating both differential expression and differential co-expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers. K E Y W O R D Sdifferential co-expression, differential subnetwork, lncRNA, oesophageal squamous cell carcinoma | 4805 LIU et aL.

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Section: Resultsmentioning

confidence: 99%

z = \frac{1}{2} \log (\frac{1 + r}{1 - r})

where r is the PCC, and z is the Fisher‐transformed PCC. Then, z is approximately normally distributed with variance

σ^{2} = 1 true/ \sqrt{n - 3}

, where n is the number of the samples . The PCCs

r_{ij}^{N}

and

r_{ij}^{T}

of a gene pair ( i , j ) in the normal and tumour samples are transformed into

z_{ij}^{N}

and

z_{ij}^{T}

by using Equation (), respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Identification of lncRNA‐associated differential subnetworks in oesophageal squamous cell carcinoma by differential co‐expression analysis

Liu

Gan

Wang

et al. 2020

J Cellular Molecular Medi

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In search for symmetries in the metabolism of cancer

Gatto

Nielsen

2015

WIREs Mechanisms of Disease

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Even though aerobic glycolysis, or the Warburg effect, is arguably the most common trait of metabolic reprogramming in cancer, it is unobserved in certain tumor types. Systems biology advocates a global view on metabolism to dissect which traits are consistently reprogrammed in cancer, and hence likely to constitute an obligate step for the evolution of cancer cells. We refer to such traits as symmetric. Here, we review early systems biology studies that attempted to reveal symmetric traits in the metabolic reprogramming of cancer, discuss the symmetry of reprogramming of nucleotide metabolism, and outline the current limitations that, if unlocked, could elucidate whether symmetries in cancer metabolism may be claimed.

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A Landscape of Metabolic Variation across Tumor Types

et al. 2018

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Tumor metabolism is reorganized to support proliferation in the face of growth-related stress. Unlike the widespread profiling of changes to metabolic enzyme levels in cancer, comparatively less attention has been paid to the substrates/products of enzyme-catalyzed reactions, small-molecule metabolites. We developed an informatic pipeline to concurrently analyze metabolomics data from over 900 tissue samples spanning seven cancer types, revealing extensive heterogeneity in metabolic changes relative to normal tissue across cancers of different tissues of origin. Despite this heterogeneity, a number of metabolites were recurrently differentially abundant across many cancers, such as lactate and acyl-carnitine species. Through joint analysis of metabolomic data alongside clinical features of patient samples, we also identified a small number of metabolites, including several polyamines and kynurenine, which were associated with aggressive tumors across several tumor types. Our findings offer a glimpse onto common patterns of metabolic reprogramming across cancers, and the work serves as a large-scale resource accessible via a web application (http://www.sanderlab.org/pancanmet).

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Extensive Decoupling of Metabolic Genes in Cancer

Cited by 8 publications

References 49 publications

Identification of lncRNA‐associated differential subnetworks in oesophageal squamous cell carcinoma by differential co‐expression analysis

Identification of lncRNA‐associated differential subnetworks in oesophageal squamous cell carcinoma by differential co‐expression analysis

In search for symmetries in the metabolism of cancer

A Landscape of Metabolic Variation across Tumor Types

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