Urine metabolomics based prediction model approach for radiation exposure
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ABSTRACT:
SUBMITTER: Tyagi R
PROVIDER: S-EPMC7527994 | biostudies-literature | 2020 Jan
REPOSITORIES: biostudies-literature
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Project description:The acute radiation syndrome is defined in large part by radiation injury in the hematopoietic and gastrointestinal (GI) systems. To identify new pathways involved in radiation-induced GI injury, this study assessed dose- and time-dependent changes in plasma metabolites in a nonhuman primate model of whole abdominal irradiation. Male and female adult Rhesus monkeys were exposed to 6 MV photons to the abdomen at doses ranging between 8 and 14 Gy. At time points from 1 to 60 days after irradiation, plasma samples were collected and subjected to untargeted metabolomics. With the limited sample size of females, different discovery times after irradiation between males and females were observed in metabolomics pattern. Detailed analyses are restricted to only males for the discovery power. Radiation caused an increase in fatty acid oxidation and circulating levels of corticosteroids which may be an indication of physiological stress, and amino acids, indicative of a cellular repair response. The largest changes were observed at days 9 and 10 post-irradiation, with most returning to baseline at day 30. In addition, dysregulated metabolites involved in amino acid pathways, which might indicate changes in the microbiome, were detected. In conclusion, abdominal irradiation in a nonhuman primate model caused a plasma metabolome profile indicative of GI injury. These results point to pathways that may be targeted for intervention or used as early indicators of GI radiation injury. Moreover, our results suggest that effects are sex-specific and that interventions may need to be tailored accordingly.
| S-EPMC8398377 | biostudies-literature
Project description:Mass spectrometry-based metabolomics has previously demonstrated utility for identifying biomarkers of ionizing radiation exposure in cellular, mouse and rat in vivo radiation models. To provide a valuable link from small laboratory rodents to humans, ?-radiation-induced urinary biomarkers were investigated using a nonhuman primate total-body-irradiation model. Mass spectrometry-based metabolomics approaches were applied to determine whether biomarkers could be identified, as well as the previously discovered rodent biomarkers of ? radiation. Ultra-performance liquid chromatography-electrospray ionization quadrupole time-of-flight mass spectrometry analysis was carried out on a time course of clean-catch urine samples collected from nonhuman primates (n = 6 per cohort) exposed to sham, 1.0, 3.5, 6.5 or 8.5 Gy doses of (60)Co ? ray (?0.55 Gy/min) ionizing radiation. By multivariate data analysis, 13 biomarkers of radiation were discovered: N-acetyltaurine, isethionic acid, taurine, xanthine, hypoxanthine, uric acid, creatine, creatinine, tyrosol sulfate, 3-hydroxytyrosol sulfate, tyramine sulfate, N-acetylserotonin sulfate, and adipic acid. N-Acetyltaurine, isethionic acid, and taurine had previously been identified in rats, and taurine and xanthine in mice after ionizing radiation exposure. Mass spectrometry-based metabolomics has thus successfully revealed and verified urinary biomarkers of ionizing radiation exposure in the nonhuman primate for the first time, which indicates possible mechanisms for ionizing radiation injury.
| S-EPMC3498937 | biostudies-literature
Project description:Objective The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics. Materials and Methods The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD2) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models. Results Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant (p < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant (p > 0.05). Conclusion The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.
| S-EPMC9966572 | biostudies-literature
Project description:Hepatocarcinoma (HCC) is one of the deadliest cancers in the world and represents a significant disease burden. Better biomarkers are needed for early detection of HCC. Metabolomics was applied to urine samples obtained from HCC patients to discover noninvasive and reliable biomarkers for rapid diagnosis of HCC. Metabolic profiling was performed by LC-Q-TOF-MS in conjunction with multivariate data analysis, machine learning approaches, ingenuity pathway analysis and receiver-operating characteristic curves were used to select the metabolites which were used for the noninvasive diagnosis of HCC. Fifteen differential metabolites contributing to the complete separation of HCC patients from matched healthy controls were identified involving several key metabolic pathways. More importantly, five marker metabolites were effective for the diagnosis of human HCC, achieved a sensitivity of 96.5% and specificity of 83% respectively, could significantly increase the diagnostic performance of the metabolic biomarkers. Overall, these results illustrate the power of the metabolomics technology which has the potential as a non-invasive strategies and promising screening tool to evaluate the potential of the metabolites in the early diagnosis of HCC patients at high risk and provides new insight into pathophysiologic mechanisms.
| S-EPMC4726192 | biostudies-literature
Project description:NMR-based metabolomics of mouse urine was used in conjunction with the traditional staining and imaging of aortas for the characterization of disease advancement, that is, plaque formation in untreated and drug-treated apolipoprotein-E (apoE) knockout mice. The metabolomics approach with multivariate analysis was able to differentiate the captopril-treated from the untreated mice in general agreement with the staining results. Principal component analysis showed a pattern shift in both the drug-treated and untreated samples as a function of time that could possibly be explained as the effect of aging. Allantoin, a marker attributed to captopril treatment was elevated in the drug-treated mice. From partial least squares-discriminant analysis, xanthine and ascorbate were elevated in the untreated mice and were possible markers of plaque formation in the apoE knockout mice. Several additional peaks in the spectra characterizing the study endpoint were found but their respective metabolite identities were unknown.
| S-EPMC4091892 | biostudies-literature
Project description:The early detection of gastric cancer (GC) could decrease its incidence and mortality. However, there are currently no accurate noninvasive markers for GC screening. Therefore, we developed a noninvasive diagnostic approach, employing urine nuclear magnetic resonance (NMR) metabolomics, to discover putative metabolic markers associated with GC. Changes in urine metabolite levels during oncogenesis were evaluated using samples from 103 patients with GC and 100 age- and sex-matched healthy controls. Approximately 70% of the patients with GC (n = 69) had stage I GC, with the majority (n = 56) having intramucosal cancer. A multivariate statistical analysis of the urine NMR data well discriminated between the patient and control groups and revealed nine metabolites, including alanine, citrate, creatine, creatinine, glycerol, hippurate, phenylalanine, taurine, and 3-hydroxybutyrate, that contributed to the difference. A diagnostic performance test with a separate validation set exhibited a sensitivity and specificity of more than 90%, even with the intramucosal cancer samples only. In conclusion, the NMR-based urine metabolomics approach may have potential as a convenient screening method for the early detection of GC and may facilitate consequent endoscopic examination through risk stratification.
| S-EPMC7599479 | biostudies-literature
Project description:We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2?Gy ?-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.
| S-EPMC4083270 | biostudies-literature
Project description:ObjectivePatients with lung cancer are at risk of radiation pneumonia (RP) after receiving radiotherapy. We established a prediction model according to the critical indicators extracted from radiation pneumonia patients.Materials and methods74 radiation pneumonia patients were involved in the training set. Firstly, the clinical data, hematological and radiation dose parameters of the 74 patients were screened by Logistics regression univariate analysis according to the level of radiation pneumonia. Next, Stepwise regression analysis was utilized to construct the regression model. Then, the influence of continuous variables on RP was tested by smoothing function. Finally, the model was externally verified by 30 patients in validation set and visualized by R code.ResultsIn the training set, there was 40 patients suffered≥ level 2 acute radiation pneumonia. Clinical data (diabetes), blood indexes (lymphocyte percentage, basophil percentage, platelet count) and radiation dose (V15 > 40%, V20 > 30%, V35 >18%, V40 > 15%) were related to radiation pneumonia (P < 0.05). Particularly, stepwise regression analysis indicated that the history of diabetes, the basophils percentage, platelet count and V20 could be the best combination used for predicting radiation pneumonia. The column chart was obtained by fitting the regression model with the combined indicator. The receiver operating characteristic (ROC) curve showed that the AUC in the development term was 0.853, the AUC was 0.656 in the validation term. And calibration curves of both groups showed the high stability in efficiently diagnostic. Furthermore, the DCA curve showed that the model had a satisfactory positive net benefit.ConclusionThe combination of the basophils percentage, platelet count and V20 is available to build a predictive model of radiation pneumonia for patients with advanced lung cancer.
| S-EPMC8287426 | biostudies-literature
Project description:An increasingly common method for predicting gene activity is genome-wide chromatin immunoprecipitation of M-bM-^@M-^XactiveM-bM-^@M-^Y chromatin modifications followed by massively parallel sequencing (ChIP-seq). Using a novel ChIP-seq quantification method (cRPKM), we tested the power of such ChIP-seq strategies to predict relative protein and RNA levels at the pre-pro-B and pro-B differentiation stages in early B cell lymphopoiesis. Using a multi-omics approach that compares promoter chromatin status (ChIP-seq; published in GSE:21978) with ongoing active transcription (GRO-seq; published in GSE:40173), steady state mRNA (RNA-seq), inferred mRNA stability, and relative proteome abundance measurements (iTRAQ), we demonstrate that active chromatin modifications at promoters are a good indicator of transcription and steady state mRNA levels. Moreover, we found that promoters with active chromatin modifications exclusively in one of these cell states frequently predicted differentially expressed proteins. However, we found that many genes whose promoters have non-differential but active chromatin modifications also displayed changes in expression of their cognate proteins. This large class of developmentally and differentially regulated proteins that was uncoupled from chromatin status used mostly post-transcriptional mechanisms. Interestingly, the most differentially expressed protein in our B-cell development system, 2410004B18Rik, was regulated by a post-transcriptional mechanism, which further analyses indicated was mediated by an identified miRNA. These data provide a striking example of how our integrated multi-omics data set can be useful in uncovering regulatory mechanisms. Total RNA from mouse pre-pro-B and pro-B cells, depleted of rRNA and small RNAs, was sequenced using a strand specific, single end sequencing strategy.
2013-11-19 | E-GEOD-52450 | biostudies-arrayexpress
Project description:BackgroundCachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance-based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model.MethodsEighty-four cancer cachexia patients, 33 pre-cachectic patients, 105 weight-stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model.ResultsForty-six cancer cachexia patients, 22 pre-cachectic patients, 68 weight-stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre-cachectic patients, 37 weight-stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age-matched and sex-matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = -400.53 - 481.88 × log(Carnosine) -239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set.ConclusionsThis metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at-risk populations through the detection of serum metabolites.
| S-EPMC5803608 | biostudies-literature