Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Alaimo, Salvatore; Giugno, Rosalba; Acunzo, Mario; Veneziano, Dario; Ferro, Alfredo; Pulvirenti, Alfredo

doi:10.18632/oncotarget.9788

Cited by 42 publications

(63 citation statements)

References 49 publications

(76 reference statements)

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“…The ratio of (LDHA+LDHB)/(PDHA1+PDHA2) for each lineage and statistical differences (two-sample t-test) were calculated in MATLAB. MITHrIL pathway analysis: The MITHrIL algorithm was used as described previously (Alaimo et al 2016). We used the combined Log2FC values of the differentially expressed genes (DEGs) and altered metabolites identified from BrM2 and LM2 samples.…”

Section: Star Methodsmentioning

confidence: 99%

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Divergent use of metabolic fluxes in breast cancer metastasis

Mathur

Liao

et al. 2020

Preprint

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Breast cancers can metastasize to many organs. But how do the cells disseminated from a primary tumor adapt to fit distal tissues? Here we combined metabolomics, flux measurements and mathematical modeling to study the metabolic fluxes in human breast cancer cells adapted to home to different organs. We found that lung-homing cells maintain a high glycolytic flux despite low levels of glycolytic intermediates, by constitutively activating a pathway sink into lactate. Their distinct behavior - a strong Warburg effect - has a gene expression signature: a high ratio of lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) gene expression, which also correlates with lung metastases in patients with breast cancer. Surprisingly, a strong Warburg effect does not necessarily increase cellular growth rate, suggesting that lactate secretion may be a trait under selection in breast cancer lung metastasis.

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Section: Star Methodsmentioning

confidence: 99%

“…We sought to integrate the transcriptomic and metabolomic data and investigate the pathways most affected throughout the entire metabolic network. For this we used MITHrIL (Mirna enrIched paTHway Impact anaLysis) (Alaimo et al 2017) (Alaimo et al 2016) ( Supplementary Fig. 2A).…”

Section: Metabolomic and Transcriptomic Profiles Show Differences Inmentioning

confidence: 99%

Divergent use of metabolic fluxes in breast cancer metastasis

Mathur

Liao

et al. 2020

Preprint

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“…Recently, a new paradigm has emerged. Indeed, considering both gene expression and their interaction network can lead to more accurate results (Tarca et al, 2008 ; Alaimo et al, 2016 , 2017 ). These tools have great potential for microbial activity analysis and quantifying microbial interactions with the host, through its pathways.…”

Section: Computational Methods For Microbiome Analysismentioning

confidence: 99%

Current Knowledge and Computational Techniques for Grapevine Meta-Omics Analysis

et al. 2018

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Growing grapevine (Vitis vinifera) is a key contribution to the economy of many countries. Tools provided by genomics and bioinformatics did help researchers in obtaining biological knowledge about the different cultivars. Several genetic markers for common diseases were identified. Recently, the impact of microbiome has been proved to be of fundamental importance both in humans and in plants for its ability to confer protection or induce diseases. In this review we report current knowledge about grapevine microbiome, together with a description of the available computational methodologies for meta-omics analysis.

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“…If any related literature evidence or any related KEGG pathway or GO-term or both indicating the involvement between the disease and each participating gene belonging to any marker were found, the marker was treated as “existing”; whereas, if no literature evidence or pathway or GO-term supporting the association between the disease and any participating gene of a marker was obtained, the marker was treated as “novel”. Of note, two important articles related to disease-specific pathway extraction are [ 34 , 35 ]. Finally, the steps of the proposed algorithm are represented in Figure 2 for better visualization.…”

Section: Methodsmentioning

confidence: 99%

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

Mallik

Zhao

2017

Genes

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For transcriptomic analysis, there are numerous microarray-based genomic data, especially those generated for cancer research. The typical analysis measures the difference between a cancer sample-group and a matched control group for each transcript or gene. Association rule mining is used to discover interesting item sets through rule-based methodology. Thus, it has advantages to find causal effect relationships between the transcripts. In this work, we introduce two new rule-based similarity measures—weighted rank-based Jaccard and Cosine measures—and then propose a novel computational framework to detect condensed gene co-expression modules (ConGEMs) through the association rule-based learning system and the weighted similarity scores. In practice, the list of evolved condensed markers that consists of both singular and complex markers in nature depends on the corresponding condensed gene sets in either antecedent or consequent of the rules of the resultant modules. In our evaluation, these markers could be supported by literature evidence, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway and Gene Ontology annotations. Specifically, we preliminarily identified differentially expressed genes using an empirical Bayes test. A recently developed algorithm—RANWAR—was then utilized to determine the association rules from these genes. Based on that, we computed the integrated similarity scores of these rule-based similarity measures between each rule-pair, and the resultant scores were used for clustering to identify the co-expressed rule-modules. We applied our method to a gene expression dataset for lung squamous cell carcinoma and a genome methylation dataset for uterine cervical carcinogenesis. Our proposed module discovery method produced better results than the traditional gene-module discovery measures. In summary, our proposed rule-based method is useful for exploring biomarker modules from transcriptomic data.

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Post-transcriptional knowledge in pathway analysis increases the accuracy of phenotypes classification

Cited by 42 publications

References 49 publications

Divergent use of metabolic fluxes in breast cancer metastasis

Divergent use of metabolic fluxes in breast cancer metastasis

Current Knowledge and Computational Techniques for Grapevine Meta-Omics Analysis

ConGEMs: Condensed Gene Co-Expression Module Discovery Through Rule-Based Clustering and Its Application to Carcinogenesis

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