1,721,050 research outputs found
Detection of Lung Cancer via Blood Plasma and <sup>1</sup>H-NMR Metabolomics: Validation by a Semi-Targeted and Quantitative Approach Using a Protein-Binding Competitor
No full textMetabolite profiling of blood plasma, by proton nuclear magnetic resonance (1H-NMR) spectroscopy, offers great potential for early cancer diagnosis and unraveling disruptions in cancer metabolism. Despite the essential attempts to standardize pre-analytical and external conditions, such as pH or temperature, the donor-intrinsic plasma protein concentration is highly overlooked. However, this is of utmost importance, since several metabolites bind to these proteins, resulting in an underestimation of signal intensities. This paper describes a novel 1H-NMR approach to avoid metabolite binding by adding 4 mM trimethylsilyl-2,2,3,3-tetradeuteropropionic acid (TSP) as a strong binding competitor. In addition, it is demonstrated, for the first time, that maleic acid is a reliable internal standard to quantify the human plasma metabolites without the need for protein precipitation. Metabolite spiking is further used to identify the peaks of 62 plasma metabolites and to divide the 1H-NMR spectrum into 237 well-defined integration regions, representing these 62 metabolites. A supervised multivariate classification model, trained using the intensities of these integration regions (areas under the peaks), was able to differentiate between lung cancer patients and healthy controls in a large patient cohort (n = 160), with a specificity, sensitivity, and area under the curve of 93%, 85%, and 0.95, respectively. The robustness of the classification model is shown by validation in an independent patient cohort (n = 72)
Renal and cardiac effects of salt loading in ambulatory heart failure patients
No full text73 p < 0.01) and, moreover, we also documented a higher sacubitril/valsartan prescription (67% vs 55%; p = 0.06) instead of lower loop diuretics use (47% vs 35%, p = 0.05). Mineralocorticoid receptor antagonist (77% vs 83%; p = 0.19) and beta blockers (94% in both groups) therapy did not change. With a total median follow-up of 19 months (Q1-Q3: 9-36), Kaplan-Meier mortality analysis was represented in Figure 1. Conclusions: In our centers, since SGLT2-i approval for HFrEF, there exists a fast joining of SGLT2-i to HFrEF therapy. It could help us to accomplish lower rates of loop diuretics prescription enabling a better titration of drugs with proven positive impact in remodelling and morbimortality. These results support in real-world the provider role of SGLT2-i, explaining a higher increase of left ventricular ejection fraction in post SGLT2-i group, with a non-statistically significant trend to lower mortality probably due to a small follow-up. Follow-up clinical visit Figure 1. Funding Acknowledgements: Type of funding sources: None. Background: Patients with hypotension have consistently been excluded from heart failure (HF) randomized controlled trials. This group of HF patients is largely unstudied. We aimed to characterize HF patients with hypotension. Methods: We retrospectively studied adult outpatients with systolic dysfunction followed in our HF clinic from January 2012 to December 2020. Patients without blood pressure measurement on the index visit (first medical visit) were excluded. We defined hypotension as systolic blood pressure (SBP) of less than 100 mmHg. The endpoint under analysis was all-cause mortality. Patients were followed until January 2023. Patients with hypotension were compared with the remaining. A Cox-regression analysis was used to assess the prognostic impact of hypotension and to study the prognostic impact of evidence-based therapy separately in HF patients with SPB < 100mmHg and those with SPB ≥ 100mmHg. Adjustments were made considering potential confounders. Results: We studied 1206 chronic ambulatory HF patients, 64.9% male, mean age 71 years, 47.4% with severe systolic dysfunction. Regarding the medication in use, 91.4% were on beta blockers (BB), 82.8% were on renin angiotensin system inhibitors (RASi), including angiotensin converting enzyme inhibitors, angiotensin receptor blockers or angiotensin receptor neprilysin inhibitors; 29.6% were on min-eralocorticoid receptor antagonists (MRA). A total of 157 patients (13.0%) presented SBP < 100mmHg on the index visit. Hypotensive patients more often presented atrial fibrillation and severe systolic dysfunction; they had lower haemoglobin values and higher brain natriuretic peptide (BNP) levels. Patients with hypotension were less medicated with RASi (70.7% vs 84.6%, p < 0.001) but more with MRA (39.6% vs 28.1%, p = 0.004) and diuretics (86.6% vs 78.6%, p = 0.02). There were no differences regarding BB use between both groups. During a median follow-up of 47 (27-85) months 645 (53.5%) patients died, 61.1% in those with hypotension and 52.3% in the remaining, p = 0.04. The use of RASi in hypotensive patients was associated with better survival (HR = 0.42 (0.26-0.69)) as in those with SPB ≥ 100mmHg (HR = 0.64 (0.51-0.80)). Contrarily to patients with SPB ≥ 100mmHg, in those with hypotension, BB use was not associated with survival benefit (HR = 0.61 (0.46-0.81) and 0.98 (0.48-1.97), respectively). MRA use showed no prognostic impact in either group. Conclusions: Hypotension was associated with poor prognosis in HF patients. In HF patients with SBP < 100mmHg, BB and MRA use did not impact prognosis, however, RASi use portended a survival benefit. Despite their exclusion from most HF therapy trials, hypotensive patients might benefit from RASi drugs. Renal and cardiac effects of salt loading in ambulatory heart failure patients Funding Acknowledgements: Type of funding sources: Public grant(s)-National budget only. Main funding source(s): Hartfalenfonds ZOL-UHasselt Limburg Sterk Merk Background: Current guidelines recommend to limit sodium intake in heart failure (HF) patients. However, stringent sodium restriction can increase neurohormonal activation, decrease quality of life and was not advantageous in recent trials. In addition, recent studies suggest that the skin can function as a sodium buffer. Purpose: To study effects and handling of an increased salt load in patients with HF and reduced ejection fraction. Methods: Eighteen patients with HF and left ventricular ejection fraction < 40% and 10 age-and sex-matched healthy volunteers without cardiovascular disease were included. HF patients with severe right ventricular dysfunction, eGFR < 30 mL/min/1.73 m 2 or severe valvular dysfunction were excluded. After 2 weeks of run-in, all study participants received 3 grams of sodium chloride (capsules of 1 g three times daily) on top of their usual diet for 4 weeks. Patients were evaluated at inclusion, at 2 weeks (end of run-in), 4 weeks (2 weeks of sodium chloride intake) and 6 weeks (4 weeks of sodium chloride intake). At each evaluation, clinical parameters, Everest congestion score, lab, echocardiography, 24-hour urine collection and bio-impedance measurements of total body water were performed. Blood volume and plasma volume were assessed using a radio-labeled red blood cells dilution technique before salt loading (at 2 weeks) and and the end of the study (at 6 weeks). At the same time points, a skin biopsy was taken at the lower leg to assess skin sodium content and glycosaminoglycan content. Results: Mean age was 66 ± 8 years, 2 (11.1%) were female, median LVEF was 35 (31-39) %, median eGFR was 68 (51-74) mL/min/1.73 m 2 and median NT-proBNP was 431 (275-961) ng/L at baseline and all patients were optimally treated medically. Salt loading did not influence weight, blood pressure, congestion score or NT-proBNP (Figure 1). There was no significant change in total body water (from 46.87 L to 44.41 L; p = 0.780), plasma volume (2735 mL vs. 2904 mL; p = 0.231) and total blood volume (4748 mL vs. 4885 mL; p = 0.327). Renal sodium excretion increased from 150 ± 55 mmol/24h to 173 ± 58 mmol/24h (p = 0.024), while plasma renin decreased from 286 (25-550) í µí¼ U/L to 88 (19-362) í µí¼ U/L (p = 0.002) (Figure 2). Salt loading did not significantly influence LVEF (from 35% to 35%; p = 0.801), leftType of funding sources: Public grant(s) – Nationalbudget only. Main funding source(s): Hartfalenfonds ZOL-UHasselt Limburg SterkMer
Cancer Metabolism - Plasma metabolites as first-line responders in lung cancer; plasma metabolite biomarkers for improved lung cancer diagnosis in patients with solitary pulmonary nodules
No full textPlasma metabolite biomarkers for improved lung cancer diagnosis in patients with solitary pulmonary nodules Solitary pulmonary nodule Screening Specificity = 96% Sensitivity = 23
Validation of 1H-NMR-based metabolomics as a tool to detect lung cancer in human blood plasma
No full textUnderstanding omics data of lung cancer patients: Correlations between metabolomics and radiomics
No full textEur J Nucl Med Mol Imaging (2021) 48 (Suppl 1): S1-S648 predicting response and survival was obtained by combining clinical data with PET and CT texture parameters (AUC 0.87). Conclusion: PET/CT derived parameters demonstrated better performances-than the clinical parameters in predicting the response and overall survival confirming the interest in considering a radiomics based approach for the optimization of therapy management in patients with head and neck cancer. References: none Aim/Introduction: Qualitative and semi-quantitative parameters of PET and CT images are used to assist decision making for cancer treatment. In initial studies PET and CT radiomic features have shown promising results in disease prognostication and treatment outcome prediction in cancer. These features are specific to the outcome and different features show association with different outcomes. Hence finding the scalability of radiomic features from one modality to another can have promising impact. In our study we have tried to check the scalability of radiomic features across the modalities, PET and CT. We have performed a study to predict CT radiomic features using PET radiomic features and vice versa. Materials and Methods: This study was approved by the institutional ethics committee as retrospective study. 104 NSCLC patients who underwent pre-treatment PET-CT scan were included in this study. Primary lung tumor was delineated by SUV cutoff (42%) method on PET images and saved as RTStructure for PET and CT series. These Images and RTStructures were used for radiomic extraction using bin-width of 25 and 5 for CT and PET respectively using pyradiomic 2.1.0 software and in-house developed python script. Subsequently, concordance correlation coefficient (CCC) was calculated between PET and CT features and top 25 correlated features (excluding shape features) were selected to develop a prediction model. Entire set of data was split into training and validation sets (70:30). For each PET radiomic feature; a set of CT features were selected and vice versa using Recursive Feature Elimination(RFE). For individual feature prediction across modalities, a multivariate linear regression model was developed using selected features. Model performance was assessed based on accuracy of prediction (C-index) on validation set. Results: Around 54% and 46 % radiomic features show positive and negative correlation across PET and CT respectively. Only 91(8.33%), 69(6.3%) and 51(4.67%) features have 0.5<CCC<0.7, 0.7≤CCC<0.9 and CCC≥0.9 respectively. Top 25 selected radiomic features had CCC equal to or more than 0.99. The average C-Index and p-value in validation set for 25 PET radiomic features prediction was found to be 0.988(±0.019) and <0.0001 respectively. Similarly, average C-Index value and p-value in validation set for 25 CT radiomic features prediction was found to be 0.987(±0.016) and <0.0001 respectively. Conclusion: As per our findings, very few radiomic features have good correlation between PET and CT. These features show excellent capability to predict features across these modalities. References: none Aim/Introduction: Treatment of lung cancer remains challenging, partly due to the late-stage diagnosis of patients. With a strong focus on non-small cell lung cancer (NSCLC), this pilot study examines the diagnostic and prognostic potential of combining specific metabolic biomarkers from blood plasma (metabolomics) with features out of medical images (radiomics). This way, metabolomics and radiomics might be at the base of developing a more personalized treatment plan for lung cancer patients. This study aims to combine a metabolomics and radiomics dataset from lung cancer patients and to unravel the underlying correlations between the techniques. Materials and Methods: The initial patient cohort consisted of 32 patients, all diagnosed with early-stage NSCLC. All patients underwent a lobectomy as part of their standard-of-care treatment plan. The PET-CT images of all the patients were collected using 18 F-FDG (Biograph Horizon camera, Siemens). The PET-CT images were then segmented using a semi-automatic tool (ACCURATE), creating specific volumes of interest (VOIs) of the lung lesions for each patient. By loading the VOIs into the second tool (RADIOMICS), 486 radiomics parameters were extracted from each VOI (Both tools developed by R.B.) Simultaneously, 238 metabolic parameters representing 62 plasma metabolites were determined from the same patients using proton nuclear magnetic resonance (1 H-NMR) spectroscopy. A correlation coefficient test was used on the total omics-dataset to find a correlation between these two sets of parameters. Results: The correlation values found between the radiomics and metabolomics parameters showed R 2 values between 0.5 and 0.65 (positive correlation) or between-0.5 and-0.65 (negative correlation). The positive correlations found in the metabolomics dataset were mainly related to the concentration of plasma glucose. The radiomics S50
Validation of 1H-NMR-based metabolomics as a tool to detect lung cancer in human blood plasma
No full textUnderstanding omics data of lung cancer patients: Correlations between metabolomics and radiomics
No full textEur J Nucl Med Mol Imaging (2021) 48 (Suppl 1): S1-S648 predicting response and survival was obtained by combining clinical data with PET and CT texture parameters (AUC 0.87). Conclusion: PET/CT derived parameters demonstrated better performances-than the clinical parameters in predicting the response and overall survival confirming the interest in considering a radiomics based approach for the optimization of therapy management in patients with head and neck cancer. References: none Aim/Introduction: Qualitative and semi-quantitative parameters of PET and CT images are used to assist decision making for cancer treatment. In initial studies PET and CT radiomic features have shown promising results in disease prognostication and treatment outcome prediction in cancer. These features are specific to the outcome and different features show association with different outcomes. Hence finding the scalability of radiomic features from one modality to another can have promising impact. In our study we have tried to check the scalability of radiomic features across the modalities, PET and CT. We have performed a study to predict CT radiomic features using PET radiomic features and vice versa. Materials and Methods: This study was approved by the institutional ethics committee as retrospective study. 104 NSCLC patients who underwent pre-treatment PET-CT scan were included in this study. Primary lung tumor was delineated by SUV cutoff (42%) method on PET images and saved as RTStructure for PET and CT series. These Images and RTStructures were used for radiomic extraction using bin-width of 25 and 5 for CT and PET respectively using pyradiomic 2.1.0 software and in-house developed python script. Subsequently, concordance correlation coefficient (CCC) was calculated between PET and CT features and top 25 correlated features (excluding shape features) were selected to develop a prediction model. Entire set of data was split into training and validation sets (70:30). For each PET radiomic feature; a set of CT features were selected and vice versa using Recursive Feature Elimination(RFE). For individual feature prediction across modalities, a multivariate linear regression model was developed using selected features. Model performance was assessed based on accuracy of prediction (C-index) on validation set. Results: Around 54% and 46 % radiomic features show positive and negative correlation across PET and CT respectively. Only 91(8.33%), 69(6.3%) and 51(4.67%) features have 0.5<CCC<0.7, 0.7≤CCC<0.9 and CCC≥0.9 respectively. Top 25 selected radiomic features had CCC equal to or more than 0.99. The average C-Index and p-value in validation set for 25 PET radiomic features prediction was found to be 0.988(±0.019) and <0.0001 respectively. Similarly, average C-Index value and p-value in validation set for 25 CT radiomic features prediction was found to be 0.987(±0.016) and <0.0001 respectively. Conclusion: As per our findings, very few radiomic features have good correlation between PET and CT. These features show excellent capability to predict features across these modalities. References: none Aim/Introduction: Treatment of lung cancer remains challenging, partly due to the late-stage diagnosis of patients. With a strong focus on non-small cell lung cancer (NSCLC), this pilot study examines the diagnostic and prognostic potential of combining specific metabolic biomarkers from blood plasma (metabolomics) with features out of medical images (radiomics). This way, metabolomics and radiomics might be at the base of developing a more personalized treatment plan for lung cancer patients. This study aims to combine a metabolomics and radiomics dataset from lung cancer patients and to unravel the underlying correlations between the techniques. Materials and Methods: The initial patient cohort consisted of 32 patients, all diagnosed with early-stage NSCLC. All patients underwent a lobectomy as part of their standard-of-care treatment plan. The PET-CT images of all the patients were collected using 18 F-FDG (Biograph Horizon camera, Siemens). The PET-CT images were then segmented using a semi-automatic tool (ACCURATE), creating specific volumes of interest (VOIs) of the lung lesions for each patient. By loading the VOIs into the second tool (RADIOMICS), 486 radiomics parameters were extracted from each VOI (Both tools developed by R.B.) Simultaneously, 238 metabolic parameters representing 62 plasma metabolites were determined from the same patients using proton nuclear magnetic resonance (1 H-NMR) spectroscopy. A correlation coefficient test was used on the total omics-dataset to find a correlation between these two sets of parameters. Results: The correlation values found between the radiomics and metabolomics parameters showed R 2 values between 0.5 and 0.65 (positive correlation) or between-0.5 and-0.65 (negative correlation). The positive correlations found in the metabolomics dataset were mainly related to the concentration of plasma glucose. The radiomics S50
Validation of 1H-NMR-based metabolomics as a tool to detect lung cancer in human blood plasma
No full textAim: Until today no effective method permits the early detection of lung cancer. Evidence has shown that disturbances in biochemical pathways which occur during the development of cancer provoke, changes in the metabolic phenotype. Recently, our research group has constructed a statistical classifier by means of multivariate orthogonal partial least squares-discriminant analysis (OPLS-DA). This classifier (constructed with 110 variables) allows to discriminate between 190 lung cancer patients (71% male, 29% female, age: 68 ± 10, BMI: 25.8 ± 4.7) and 182 controls (53% male, 47% female, age: 69 + 11, BMI: 28.1 ± 4.8) with a sensitivity of 76% and a specificity of 89%, with an AUC of 0.86. When only the 19 most discriminating variables (VIP value > 0.8) were selected to construct a classifier (i.e. glucose, lactate, myo-inositol, threonine, alanine, isoleucine and lipids signals) a sensitivity of 69%, a specificity of 83% and an AUC of 0.81 is achieved. The present study aims to examine the predictive accuracy of these statistical classifiers in an independent cohort of 50 lung cancer patients (60% male, 40% female, age: 67 ± 9, BMI: 25.6 ± 4.3) and 58 controls (64% male, 36% female, age: 63 ± 13, BMI: 26.9 ± 5.7).
Methods: The metabolic phenotype of the plasma samples from this independent cohort is determined by 1H-NMR spectroscopy. Subsequently, the constructed classifiers are used to classify the independent samples. OPLS-DA is used as discriminant statistic.
Results: By using the classifier constructed with all 110 variables, 72% of the lung cancer patients and 72% of the controls are correctly classified, with an AUC of 0.79. Moreover, when the classifier constructed with only the 19 most discriminating variables is used to classify the independent samples, a sensitivity of 82%, a specificity of 64% and an AUC of 0.79 is achieved.
Conclusions: A statistical classifier constructed with only the most discriminating variables shows already a fair predictive accuracy, similar to this of the classifier build with all variables. Future experiments will investigate whether the constructed classifier can be used as a valid screening tool
Renal and cardiac effects of salt loading in ambulatory heart failure patients
No full text73 p < 0.01) and, moreover, we also documented a higher sacubitril/valsartan prescription (67% vs 55%; p = 0.06) instead of lower loop diuretics use (47% vs 35%, p = 0.05). Mineralocorticoid receptor antagonist (77% vs 83%; p = 0.19) and beta blockers (94% in both groups) therapy did not change. With a total median follow-up of 19 months (Q1-Q3: 9-36), Kaplan-Meier mortality analysis was represented in Figure 1. Conclusions: In our centers, since SGLT2-i approval for HFrEF, there exists a fast joining of SGLT2-i to HFrEF therapy. It could help us to accomplish lower rates of loop diuretics prescription enabling a better titration of drugs with proven positive impact in remodelling and morbimortality. These results support in real-world the provider role of SGLT2-i, explaining a higher increase of left ventricular ejection fraction in post SGLT2-i group, with a non-statistically significant trend to lower mortality probably due to a small follow-up. Follow-up clinical visit Figure 1. Funding Acknowledgements: Type of funding sources: None. Background: Patients with hypotension have consistently been excluded from heart failure (HF) randomized controlled trials. This group of HF patients is largely unstudied. We aimed to characterize HF patients with hypotension. Methods: We retrospectively studied adult outpatients with systolic dysfunction followed in our HF clinic from January 2012 to December 2020. Patients without blood pressure measurement on the index visit (first medical visit) were excluded. We defined hypotension as systolic blood pressure (SBP) of less than 100 mmHg. The endpoint under analysis was all-cause mortality. Patients were followed until January 2023. Patients with hypotension were compared with the remaining. A Cox-regression analysis was used to assess the prognostic impact of hypotension and to study the prognostic impact of evidence-based therapy separately in HF patients with SPB < 100mmHg and those with SPB ≥ 100mmHg. Adjustments were made considering potential confounders. Results: We studied 1206 chronic ambulatory HF patients, 64.9% male, mean age 71 years, 47.4% with severe systolic dysfunction. Regarding the medication in use, 91.4% were on beta blockers (BB), 82.8% were on renin angiotensin system inhibitors (RASi), including angiotensin converting enzyme inhibitors, angiotensin receptor blockers or angiotensin receptor neprilysin inhibitors; 29.6% were on min-eralocorticoid receptor antagonists (MRA). A total of 157 patients (13.0%) presented SBP < 100mmHg on the index visit. Hypotensive patients more often presented atrial fibrillation and severe systolic dysfunction; they had lower haemoglobin values and higher brain natriuretic peptide (BNP) levels. Patients with hypotension were less medicated with RASi (70.7% vs 84.6%, p < 0.001) but more with MRA (39.6% vs 28.1%, p = 0.004) and diuretics (86.6% vs 78.6%, p = 0.02). There were no differences regarding BB use between both groups. During a median follow-up of 47 (27-85) months 645 (53.5%) patients died, 61.1% in those with hypotension and 52.3% in the remaining, p = 0.04. The use of RASi in hypotensive patients was associated with better survival (HR = 0.42 (0.26-0.69)) as in those with SPB ≥ 100mmHg (HR = 0.64 (0.51-0.80)). Contrarily to patients with SPB ≥ 100mmHg, in those with hypotension, BB use was not associated with survival benefit (HR = 0.61 (0.46-0.81) and 0.98 (0.48-1.97), respectively). MRA use showed no prognostic impact in either group. Conclusions: Hypotension was associated with poor prognosis in HF patients. In HF patients with SBP < 100mmHg, BB and MRA use did not impact prognosis, however, RASi use portended a survival benefit. Despite their exclusion from most HF therapy trials, hypotensive patients might benefit from RASi drugs. Renal and cardiac effects of salt loading in ambulatory heart failure patients Funding Acknowledgements: Type of funding sources: Public grant(s)-National budget only. Main funding source(s): Hartfalenfonds ZOL-UHasselt Limburg Sterk Merk Background: Current guidelines recommend to limit sodium intake in heart failure (HF) patients. However, stringent sodium restriction can increase neurohormonal activation, decrease quality of life and was not advantageous in recent trials. In addition, recent studies suggest that the skin can function as a sodium buffer. Purpose: To study effects and handling of an increased salt load in patients with HF and reduced ejection fraction. Methods: Eighteen patients with HF and left ventricular ejection fraction < 40% and 10 age-and sex-matched healthy volunteers without cardiovascular disease were included. HF patients with severe right ventricular dysfunction, eGFR < 30 mL/min/1.73 m 2 or severe valvular dysfunction were excluded. After 2 weeks of run-in, all study participants received 3 grams of sodium chloride (capsules of 1 g three times daily) on top of their usual diet for 4 weeks. Patients were evaluated at inclusion, at 2 weeks (end of run-in), 4 weeks (2 weeks of sodium chloride intake) and 6 weeks (4 weeks of sodium chloride intake). At each evaluation, clinical parameters, Everest congestion score, lab, echocardiography, 24-hour urine collection and bio-impedance measurements of total body water were performed. Blood volume and plasma volume were assessed using a radio-labeled red blood cells dilution technique before salt loading (at 2 weeks) and and the end of the study (at 6 weeks). At the same time points, a skin biopsy was taken at the lower leg to assess skin sodium content and glycosaminoglycan content. Results: Mean age was 66 ± 8 years, 2 (11.1%) were female, median LVEF was 35 (31-39) %, median eGFR was 68 (51-74) mL/min/1.73 m 2 and median NT-proBNP was 431 (275-961) ng/L at baseline and all patients were optimally treated medically. Salt loading did not influence weight, blood pressure, congestion score or NT-proBNP (Figure 1). There was no significant change in total body water (from 46.87 L to 44.41 L; p = 0.780), plasma volume (2735 mL vs. 2904 mL; p = 0.231) and total blood volume (4748 mL vs. 4885 mL; p = 0.327). Renal sodium excretion increased from 150 ± 55 mmol/24h to 173 ± 58 mmol/24h (p = 0.024), while plasma renin decreased from 286 (25-550) í µí¼ U/L to 88 (19-362) í µí¼ U/L (p = 0.002) (Figure 2). Salt loading did not significantly influence LVEF (from 35% to 35%; p = 0.801), leftType of funding sources: Public grant(s) – Nationalbudget only. Main funding source(s): Hartfalenfonds ZOL-UHasselt Limburg SterkMer
Unraveling the Rewired Metabolism in Lung Cancer Using Quantitative NMR Metabolomics
No full textLung cancer cells are well documented to rewire their metabolism and energy production networks to enable proliferation and survival in a nutrient-poor and hypoxic environment. Although metabolite profiling of blood plasma and tissue is still emerging in omics approaches, several techniques have shown potential in cancer diagnosis. In this paper, the authors describe the alterations in the metabolic phenotype of lung cancer patients. In addition, we focus on the metabolic cooperation between tumor cells and healthy tissue. Furthermore, the authors discuss how metabolomics could improve the management of lung cancer patients
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