Data mining of nutrigenomics experiments: Identification of a cancer protective gene signature

2018 ◽  
Vol 42 ◽  
pp. 380-386 ◽  
Author(s):  
Roberto Martín-Hernández ◽  
Guillermo Reglero ◽  
Alberto Dávalos
Keyword(s):  
Data Mining ◽  
Gene Signature ◽  
Protective Gene

scholarly journals Cellular acidosis triggers human MondoA transcriptional activity by driving mitochondrial ATP production

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Blake R Wilde ◽  
Zhizhou Ye ◽  
Tian-Yeh Lim ◽  
Donald E Ayer
Keyword(s):  
Transcriptional Activity ◽  
Energy Charge ◽  
Transcriptional Response ◽  
Gene Signature ◽  
High Energy ◽  
Low Ph ◽  
Outer Mitochondrial Membrane ◽  
Atp Production ◽  
Preferential Binding ◽  
Protective Gene

Human MondoA requires glucose as well as other modulatory signals to function in transcription. One such signal is acidosis, which increases MondoA activity and also drives a protective gene signature in breast cancer. How low pH controls MondoA transcriptional activity is unknown. We found that low pH medium increases mitochondrial ATP (mtATP), which is subsequently exported from the mitochondrial matrix. Mitochondria-bound hexokinase transfers a phosphate from mtATP to cytoplasmic glucose to generate glucose-6-phosphate (G6P), which is an established MondoA activator. The outer mitochondrial membrane localization of MondoA suggests that it is positioned to coordinate the adaptive transcriptional response to a cell’s most abundant energy sources, cytoplasmic glucose and mtATP. In response to acidosis, MondoA shows preferential binding to just two targets, TXNIP and its paralog ARRDC4. Because these transcriptional targets are suppressors of glucose uptake, we propose that MondoA is critical for restoring metabolic homeostasis in response to high energy charge.

scholarly journals Identifying novel cell glycolysis related gene signature predictive of overall survival in bladder urothelial carcinoma

2020 ◽  
Author(s):  
Xin Zhao ◽  
Jia Li ◽  
Jiafeng Li ◽  
Wenjun Xiong ◽  
Rui Jiang
Keyword(s):  
Data Mining ◽  
Overall Survival ◽  
High Risk ◽  
Urothelial Carcinoma ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Bladder Urothelial Carcinoma

Abstract Background: Bladder urothelial carcinoma (BLCA) is the most common pathological type of bladder cancer and featured by a high risk for relapse and metastasis. Although many biomarkers have been developed by data mining and experimental studies to predict the prognosis of BLCA, a single-gene biomarker usually has poor specificity and sensitivity, leading to unsatisfactory prediction. Therefore, novel gene signatures are needed to more accurately predict the prognosis of BLCA.Methods: Data mining was performed for expression profile analysis of 433 mRNA expression data from the TCGA BLCA patients (n=412). Gene Set Enrichment Analysis (GSEA) was used to interpret the glycolysis-related gene sets. Gene signature related to the prognosis of BLCA was identified by univariate and multivariate Cox regression. A risk score was computed based on three genes by linear regression model and its relation with overall survival was investigated by Kaplan-Meier analysis.Results: Three genes (CHPF, AK3, NUP188) were found to be significantly correlated to the overall survival of BLCA patients. Based on the signature composed of these three genes, 412 BLCA patients were divided into high-risk and low-risk groups. The survival time of the high-risk group was significantly shorter than that of the low-risk group, indicating a worse prognosis.Conclusion: A signature composed of three glycolysis-related genes was developed as biomarkers to predict the prognosis of BLCA and to provide a meaningful reference for the clinical treatment of BLCA.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Heart Rate Variability, Emotions, and Music

2010 ◽  
Vol 24 (2) ◽  
pp. 112-119 ◽  
Author(s):  
F. Riganello ◽  
A. Candelieri ◽  
M. Quintieri ◽  
G. Dolce
Keyword(s):  
Data Mining ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Low Frequency ◽  
Emotional Reactions ◽  
Heart Beat ◽  
Healthy Controls ◽  
Frequency Spectra ◽  
Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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