Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power

Graudenzi, Alex; Maspero, Davide; Filippo, Marzia Di; Gnugnoli, Marco; Isella, Claudio; Mauri, Giancarlo; Medico, Enzo; Antoniotti, Marco; Damiani, C.

doi:10.1016/j.jbi.2018.09.010

Cited by 29 publications

(50 citation statements)

References 48 publications

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“…Linoleic acid metabolism and arachidonic acid metabolism are two acid metabolism-related pathways that were also significantly activated in KRT High OSCC tumor groups. Acid metabolism have been extensively proved to be associated with cancer initiation and progression [28][29][30] by previously studies. Linoleic acid and arachidonic acid were the two acids that specifically identified in the present study, whose metabolism might be dysregulated by aberrant KRT84 expression, so this should also be potential pathways by which KRT84 could regulate OSCC progression.…”

Section: Discussionmentioning

confidence: 99%

KRT84 is a potential tumor suppressor and good prognosis signature of oral squamous cell carcinoma

Liu

Ren

2020

Bioscience Reports

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Aims: Oral squamous cell carcinoma (OSCC) is a common oral cancer; however, current therapeutic approaches still show limited efficacy. Our research aims to explore effective biomarkers related to OSCC. Main methods: Gene expression profiles of paired OSCC tumor and paracancerous samples from The Cancer Genome Atlas (TCGA) were analyzed. mRNA and protein levels of KRT84 in OSCC cell line HSC-3 were measured by real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot. KRT84 protein levels in OSCC tumor samples of different stages were determined by immunohistochemistry. Overall survival (OS) of OSCC samples was evaluated and association of multiple factors with OS was assessed. Key findings: Compared with paracancerous samples, 4642 DEGs were identified in OSCC tumor samples. Among them, KRT84 expression level in OSCC tumor tissues was obviously decreased, which was validated in HSC-3 cells. KRT84 expression level showed decreasing tendency with the increase of tumor grade and stage. Patients with low KRT84 expression level had inferior OS independently of multiple factors. Besides, antigen processing and presentation pathway were significantly activated in OSCC samples with high KRT84 expression. Elevated KRT84 mRNA as well as protein levels were confirmed by RT-qPCR and Western blot in OSCC and normal cell lines, and immunohistochemistry in OSCC tumor and paracancerous tissues. Significance: Our study suggests KRT84 as a tumor suppressor and good prognostic indicator for OSCC, which might be significant for OSCC diagnosis and treatment.

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Section: Discussionmentioning

confidence: 99%

KRT84 is a potential tumor suppressor and good prognosis signature of oral squamous cell carcinoma

Liu

Ren

2020

Bioscience Reports

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“…Dysregulation of cellular metabolism promotes tumor aggressiveness by sustaining the activity of key growth, invasion, and survival pathways. Previous studies have identified several key metabolic pathways (glycolysis, glutamine metabolism, oxidative phosphorylation, et al) and the metabolic genes involved in these pathways as potential therapeutic targets [23][24][25]. Cancer cells are reprogrammed to consume large amounts of glucose to support anabolic biosynthetic pathways.…”

Section: Discussionmentioning

confidence: 99%

Transcriptome analysis reveals gender-specific differences in overall metabolic response of male and female patients in lung adenocarcinoma

et al. 2020

PLoS ONE

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Background Evidence from multiple studies suggests metabolic abnormalities play an important role in lung cancer. Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer. The present study aimed to explore differences in the global metabolic response between male and female patients in LUAD and to identify the metabolic genes associated with lung cancer susceptibility. Methods Transcriptome and clinical LUAD data were acquired from The Cancer Genome Atlas (TCGA) database. Information on metabolic genes and metabolic subsystems were collected from the Recon3D human metabolic model. Two validation datasets (GSE68465 and GSE72094) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis, gene set enrichment analysis and protein-protein interaction networks were used to identified key metabolic pathways and genes. Functional experiments were used to verify the effects of genes on proliferation, migration, and invasion in lung cancer cells in vitro. Results Samples of tumors and adjacent non-tumor tissue from both male and female patients exhibited distinct global patterns of gene expression. In addition, we found large differences in methionine and cysteine metabolism, pyruvate metabolism, cholesterol metabolism, nicotinamide adenine dinucleotide (NAD) metabolism, and nuclear transport between male and female LUAD patients. We identified 34 metabolic genes associated with lung cancer

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“…Specifically, we distribute the total (bulk) possible flux of each reaction proportionally to the activity score of that reaction in each cell. To compute a score for reactions that involve many genes, similarly to other approaches [36–38, 48], we assume that enzyme isoforms contribute additively to the overall activity of a given reaction, whereas enzyme subunits limit its activity, by requiring all the components to be present for the reaction to occur. Alternative but similar approaches, such as [30], consider the maximum value (instead of the sum) of isoform values.…”

Section: Methodsmentioning

confidence: 99%

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

et al. 2019

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Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

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Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power

Cited by 29 publications

References 48 publications

KRT84 is a potential tumor suppressor and good prognosis signature of oral squamous cell carcinoma

KRT84 is a potential tumor suppressor and good prognosis signature of oral squamous cell carcinoma

Transcriptome analysis reveals gender-specific differences in overall metabolic response of male and female patients in lung adenocarcinoma

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism

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