Causal Associations between Serum Urea and Cancer: A Mendelian Randomization Study

A salivary urea sensor based on a microsieve disposable gate AlGaN/GaN high electron mobility transistor

The abundant bio-markers in saliva provide a new option for non-invasive testing. However, due to the presence of impurities in the saliva background, most of the existing saliva testing methods rely on pre-processing, which limits the application of saliva testing as a convenient means of testing in daily life. Herein, a disposable-gate AlGaN/GaN high electron mobility transistor (HEMT) biosensor integrated with a micro-sieve was introduced to solve the problem of signal interference caused by charged impurities in saliva for HEMT based biosensors, where the micro-sieve was utilized as a pre-treatment unit to remove large particles of impurities from saliva through the size effect and thus greatly improving the accuracy of detection. The experimental results showed that the HEMT based biosensor has excellent linearity (R2 = 0.9977) and a high sensitivity of 6.552 μA dec-1 for urea sensing from 1 fM to 100 mM in 0.1× PBS solution. When it comes to artificial saliva detection, compared to the HEMT sensor without the micro-sieve (sensitivity = 3.07432 μA dec-1), the sensitivity of the HEMT sensor integrated with the micro-sieve showed almost no change. Moreover, to verify that urea can be detected in actual saliva, urea is sensed directly in human saliva. The addition of the microsieve module provides a new way for biosensors to detect specific markers in saliva in real time, and the designed HEMT biosensor with the microsieve function has a wide range of application potential in rapid saliva detection.

Bioconjugate based on cisplatin and bacterial exopolysaccharide with reduced side effects: A novel proposal for cancer treatment

BACKGROUND

In the search for alternatives that attenuate the toxicity associated to oncologic treatment with cisplatin (CDDP) and considering the potential health-beneficial properties of exopolysaccharides (EPS) produced by lactic acid bacteria, it was aimed on this study to evaluate the cytotoxic, toxicologic and antitumoral efficacy of a bioconjugate based on CDDP and EPS, on the experimental tumor of sarcoma 180.

METHODS

After the synthesis of the cis-[Pt(NH3)2(Cl)2] complex and of the conjugate containing Lactobacillus fermentum exopolysaccharide was tested both in vitro and in vivo for evaluating the acute toxicity.

RESULTS

The antitumoral study was performed using mice transplanted with sarcoma 180. The bioconjugate showed low to medium cytotoxicity for the cell lines tested, as well moderated acute toxicity. After determining the LD50, the following experimental groups were established for the antitumor assay: Control (NaCl 0,9%), CDDP (1 mg/kg), EPS and bioconjugate composition (200 mg/kg). The bioconjugate promoted a 38% regression in tumor mass when compared to the control, and a regression of 41% when compared to CDDP. Liver histopathological analysis revealed discrete alterations in animals treated with (CDDP + EPS) when compared to control. The bioconjugate also minimized changes in the renal parenchyma resulting from the tumor.

CONCLUSION

Our results indicate that when CDDP is associated with EPS, this composition was more biocompatible, showing itself as a potent chemotherapeutic agent and lower tissue toxicity.

Association between serum urea concentrations and the risk of colorectal cancer, particularly in individuals with type 2 diabetes: A cohort study

Dysregulation of the urea cycle (UC) has been detected in colorectal cancer (CRC). However, the impact of the UC's end product, urea, on CRC development remains unclear. We investigated the association between serum urea and CRC risk based on the data of 348 872 participants cancer-free at recruitment from the UK Biobank. Multivariable Cox proportional hazards models were fitted to conduct risk estimates. Stratification analyses based on sex, diet pattern, metabolic factors (including body mass index [BMI], the estimated glomerular filtration rate [eGFR] and type 2 diabetes [T2D]) and genetic profiles (the polygenic risk score [PRS] of CRC) were conducted to find potential modifiers. During an average of 9.0 years of follow-up, we identified 3408 (1.0%) CRC incident cases. Serum urea showed a nonlinear relationship with CRC risk (P-nonlinear: .035). Lower serum urea levels were associated with a higher CRC risk, with a fully-adjusted hazard ratio (HR) of 1.26 (95% confidence interval [CI]: 1.13-1.41) in the first quartile (Q1) of urea, compared to the Q4. This association was largely consistent across subgroups of sex, protein diet, BMI, eGFR and CRC-PRSs (P-interaction >.05); however, it was stronger in the T2D, with an interaction between urea and T2D on both additive (synergy index: 3.32, [95% CI: 1.24-8.88]) and multiplicative scales (P-interaction: .019). Lower serum urea concentrations were associated with an increased risk of CRC, with a more pronounced effect observed in individuals with T2D. Maintaining stable levels of serum urea has important implications for CRC prevention, particularly in individuals with T2D.

Haematological and biochemical analysis of blood samples from early and late stage breast cancer patients in India

Breast cancer is the most prevalent cancer with the maximum number of cases worldwide. Early diagnosis of the cancer is necessary for an effective treatment plan. Due to a lack of awareness, diagnosis of breast cancer at an early stage is difficult. The present study aims to evaluate and compare the haematological and biochemical profiles of the early and late-stage breast cancer patient's data records. A retrospective cohort study was conducted on 56 breast cancer patients at the Institute of Medical Sciences, Banaras Hindu University India. Patient data records were obtained and haematological and biochemical parameters were arranged on an Excel sheet and analyzed. Random blood sugar (RBS), alkaline phosphates (ALP) levels, and urea levels were significantly high in patients with late-stage breast cancer (Tumor stage III and IV). At the advanced stage of breast cancer hemoglobin level falls and patients became anemic. Further large-scale studies with a greater number of patient data can help establish these parameters individually or in combination as prognostic and diagnostic markers in breast cancer staging.

Combining machine learning with Cox models to identify predictors for incident post-menopausal breast cancer in the UK Biobank

We aimed to identify potential novel predictors for breast cancer among post-menopausal women, with pre-specified interest in the role of polygenic risk scores (PRS) for risk prediction. We utilised an analysis pipeline where machine learning was used for feature selection, prior to risk prediction by classical statistical models. An "extreme gradient boosting" (XGBoost) machine with Shapley feature-importance measures were used for feature selection among [Formula: see text] 1.7 k features in 104,313 post-menopausal women from the UK Biobank. We constructed and compared the "augmented" Cox model (incorporating the two PRS, known and novel predictors) with a "baseline" Cox model (incorporating the two PRS and known predictors) for risk prediction. Both of the two PRS were significant in the augmented Cox model ([Formula: see text]). XGBoost identified 10 novel features, among which five showed significant associations with post-menopausal breast cancer: plasma urea (HR = 0.95, 95% CI 0.92-0.98, [Formula: see text]), plasma phosphate (HR = 0.68, 95% CI 0.53-0.88, [Formula: see text]), basal metabolic rate (HR = 1.17, 95% CI 1.11-1.24, [Formula: see text]), red blood cell count (HR = 1.21, 95% CI 1.08-1.35, [Formula: see text]), and creatinine in urine (HR = 1.05, 95% CI 1.01-1.09, [Formula: see text]). Risk discrimination was maintained in the augmented Cox model, yielding C-index 0.673 vs 0.667 (baseline Cox model) with the training data and 0.665 vs 0.664 with the test data. We identified blood/urine biomarkers as potential novel predictors for post-menopausal breast cancer. Our findings provide new insights to breast cancer risk. Future research should validate novel predictors, investigate using multiple PRS and more precise anthropometry measures for better breast cancer risk prediction.

Bench-to-Bedside Studies of Arginine Deprivation in Cancer

Arginine is a semi-essential amino acid which becomes wholly essential in many cancers commonly due to the functional loss of Argininosuccinate Synthetase 1 (ASS1). As arginine is vital for a plethora of cellular processes, its deprivation provides a rationale strategy for combatting arginine-dependent cancers. Here we have focused on pegylated arginine deiminase (ADI-PEG20, pegargiminase)-mediated arginine deprivation therapy from preclinical through to clinical investigation, from monotherapy to combinations with other anticancer therapeutics. The translation of ADI-PEG20 from the first in vitro studies to the first positive phase 3 trial of arginine depletion in cancer is highlighted. Finally, this review discusses how the identification of biomarkers that may denote enhanced sensitivity to ADI-PEG20 beyond ASS1 may be realized in future clinical practice, thus personalising arginine deprivation therapy for patients with cancer.

Impairment of kidney function and kidney cancer: A bidirectional Mendelian randomization study

BACKGROUND

Many observational epidemiology studies discovered that kidney cancer and impaired kidney function have a bidirectional relationship. However, it remains unclear whether these two kinds of traits are causally linked. In this study, we aimed to investigate the bidirectional causal relation between kidney cancer and kidney function biomarkers (creatinine-based estimated glomerular filtration rate (eGFRcrea), cystatin C-based estimated glomerular filtration rate (eGFRcys), blood urea nitrogen (BUN), serum urate, and urinary albumin-to-creatinine ratio (UACR)).

METHODS

For both directions, single-nucleotide polymorphisms (SNPs), as genetic instruments, for the five kidney function traits were selected from up to 1,004,040 individuals, and SNPs for kidney cancer were from 408,786 participants(1338 cases). In the main analysis, we applied two state-of-the-art MR methods, namely, contamination mixture and Robust Adjusted Profile Score to downweight the effect of weak instrument bias, pleiotropy, and extreme outliers. We additionally conducted traditional MR analyses as sensitivity analyses. Summary-level data of European ancestry were extracted from UK Biobank, Chronic Kidney Disease Genetics Consortium, and Kaiser Permanente.

RESULTS

Based on 99 SNPs, we found that the eGFRcrea had a significant negative causal effect on the risk of kidney cancer (OR = 0.007, 95% CI:2.6 × 10-4 -0.569, p = 0.041). After adjusting for body composition or diabetes, urate had a significant negative causal effect on kidney cancer (OR <1, p < 0.05). For UACR, it showed a strong causal effect on kidney cancer, after adjusting for body composition (OR = 14.503, 95% CI: 2.546-96.001, p = 0.032). Due to lacking significant signals and effect power for the reverse MR, further investigations are warranted.

CONCLUSIONS

Our study suggested a potential causal effect of damaged kidney function on kidney cancer. EGFRcrea and UACR might be causally associated with kidney cancer, especially when patients were comorbid with obesity or diabetes. We called for larger sample-size studies to further unravel the underlying causal relationship and the exact mechanism.

Additive pre-diagnostic and diagnostic value of routine blood-based biomarkers in the detection of colorectal cancer in the UK Biobank cohort

Survival rates from colorectal cancer (CRC) are drastically higher if the disease is detected and treated earlier. Current screening guidelines involve stool-based tests and colonoscopies, whose acceptability and uptake remains low. Routinely collected blood-based biomarkers may offer a low-cost alternative or aid for detecting CRC. Here we aimed to evaluate the pre-diagnostic and diagnostic value of a wide-range of multimodal biomarkers in the UK Biobank dataset, including sociodemographic, lifestyle, medical, physical, and blood and urine-based measures in detecting CRC. We performed a Cox proportional hazard and a tree-boosting model alongside feature selection methods to determine optimal combination of biomarkers. In addition to the modifiable lifestyle factors of obesity, alcohol consumption and cardiovascular health, we showed that blood-based biomarkers that capture the immune response, lipid profile, liver and kidney function are associated with CRC risk. Following feature selection, the final Cox and tree-boosting models achieved a C-index of 0.67 and an AUC of 0.76 respectively. We show that blood-based biomarkers collected in routine examinations are sensitive to preclinical and clinical CRC. They may provide an additive value and improve diagnostic accuracy of current screening tools at no additional cost and help reduce burden on the healthcare system.

Metabolomics profiling in prediction of chemo-immunotherapy efficiency in advanced non-small cell lung cancer

Background

To explore potential metabolomics biomarker in predicting the efficiency of the chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC).

Methods

A total of 83 eligible patients were assigned to receive chemo-immunotherapy. Serum samples were prospectively collected before the treatment to perform metabolomics profiling analyses under the application of gas chromatography mass spectrometry (GC-MS). The key metabolites were identified using projection to latent structures discriminant analysis (PLS-DA). The key metabolites were used for predicting the chemo-immunotherapy efficiency in advanced NSCLC patients.

Results

Seven metabolites including pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate were identified as the key metabolites to the chemo-immunotherapy response. The receiver operating characteristic curves (AUC) were 0.79 (95% CI: 0.69-0.90), 0.60 (95% CI: 0.48-0.73), 0.69 (95% CI: 0.57-0.80), 0.63 (95% CI: 0.51-0.75), 0.60 (95% CI: 0.48-0.72), 0.56 (95% CI: 0.43-0.67), and 0.67 (95% CI: 0.55-0.80) for the key metabolites, respectively. A binary logistic regression was used to construct a combined biomarker model to improve the discriminating efficiency. The AUC was 0.86 (95% CI: 0.77-0.94) for the combined biomarker model. Pathway analyses showed that urea cycle, glucose-alanine cycle, glycine and serine metabolism, alanine metabolism, and glutamate metabolism were the key metabolic pathway to the chemo-immunotherapy response in patients with advanced NSCLC.

Conclusion

Metabolomics analyses of key metabolites and pathways revealed that GC-MS could be used to predict the efficiency of chemo-immunotherapy. Pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate played a central role in the metabolic of PD patients with advanced NSCLC.

Assessment of Serum Urea, Creatinine and Uric Acid in Oral Cancer

Background: Oral squamous cell carcinoma (OSCC) is a common malignancy worldwide, leading to significant disease-associated social and financial burdens. The investigation of underlying mechanisms involved in carcinogenesis and tumor progression in OSCC might provide new therapeutic perspectives with an impact on disease control and patient survival. Our study aims to investigate the interrelation between metabolic processes, expressed through final catabolism products and clinicopathological characteristics in OSCC. Materials and methods: This is a single cancer comparative retrospective study investigating metabolic byproducts, namely serum urea, creatinine and uric acid, detected at the moment of diagnosis in patients with OSCC, in comparison to healthy controls. Clinical and paraclinical data regarding exposure to risk factors, disease staging and pathological characteristics were collected for all patients. Subjects with co-existing systemic or metabolic diseases, or with a history of malignancy, were excluded from the study. Subsequently, the metabolic byproducts revealing significant changes in OSCC patients were considered for a correlation analysis with the disease clinico-pathological characteristics. Results: Blood levels for urea, creatinine and uric acid were determined in a total of 225 subjects: 145 patients diagnosed with OSCC and 80 healthy control subjects admitted to our hospital between 2016 and 2021. The comparative analysis between groups revealed that the serum urea level was significantly lower in OSCC patients (p = 0.0344). Serum creatinine and uric acid did not reveal significant differences between groups. Furthermore, in advanced stages of the disease (stages III and IV), the blood level of urea was significantly lower compared to incipient OSCC (stages I and II) (p = 0.003). We found a negative correlation of serum urea levels with smoking (p = 0.0004) and cervical lymph node metastasis (p = 0.0070), and a positive correlation with aging (p = 0.0000). We found no significant correlation of serum urea with primary tumor size (p = 0.5061) and patient survival (p = 0.2932). Conclusions: Decreased serum urea levels are detected in patients with advanced OSCC, in correlation with lymph node metastasis. The invasive features of tumor cells in OSCC might be promoted in association with dysregulation of protein catabolism processes, facilitating aggressive behavior in OSCC.

Causal Association between Chronic Kidney Disease and Risk of 19 Site-Specific Cancers: A Mendelian Randomization Study

BACKGROUND

Results of previous observational studies examining the risk of cancer among patients with chronic kidney disease (CKD) are conflicting. We here explore the causal relationship between estimated glomerular filtration rate (eGFR) and albuminuria, two principal measurements of CKD, and 19 site-specific cancers using Mendelian randomization (MR) analysis.

METHODS

Single-nucleotide polymorphisms reported to be strongly correlated with eGFR and albuminuria in recent large genome-wide association studies were used as instrumental variables to investigate the causal relationship with cancer using summary-level statistics from several cancer-specific consortia, as well as data of 347,408 participants in the UK Biobank and 260,405 participants in the FinnGen.

RESULTS

Our data showed that impaired kidney function was associated with higher odds of leukemia [OR = 1.23; 95% confidence interval (CI), 1.06-1.43; P = 0.007], cervical cancer (OR = 1.22; 95% CI, 1.04-1.43; P = 0.017), and female renal cell carcinoma (OR = 1.4; 95% CI, 1.12-1.77; P = 0.004), per 10% decrease in eGFR. The ORs were 1.21 (95% CI, 1.07-1.36; P = 0.002) for colorectal cancer and 0.76 (95% CI, 0.62-0.92; P = 0.006) for non-Hodgkin lymphoma, per doubling odds of albuminuria. In multivariable MR, effect sizes of eGFR-cervical cancer remained strong after adjusting for confounders.

CONCLUSIONS

The current study indicates that progression of CKD contributes to carcinogenesis of renal cell carcinoma, leukemia, cervical, and colorectal cancer.

IMPACT

The potential association of kidney function and albuminuria with certain cancers warrants further investigation in order to provide appropriate recommendations regarding cancer screening among patients with CKD.

Quantitative analysis of urea in serum by synchronous modulation and demodulation fluorescence spectroscopy

High-precision spectral data is a necessary prerequisite for quantitative analysis of complex solution components. In order to improve the accuracy of spectral data, this paper proposes a method of synchronous modulation and demodulation. This article also combines the "M + N" theory, cleverly uses the excitation fluorescence of the components in the serum and its self-absorption phenomenon, collects the fluorescence spectrum of the serum sample, and then uses the partial least squares (PLS) method and the cubic optimization model method to establish a model to analyze the urea concentration of serum. At the same time, in order to verify the effectiveness of synchronous modulation and demodulation method, the unmodulated fluorescence spectrum is used to establish the regression model of urea concentration. Compared with the unmodulated fluorescence spectrum modeling results, the fluorescence spectrum modeling results after modulation and demodulation have been significantly improved. In the modeling results of fluorescence spectrum after synchronous modulation and demodulation, the Rc is 0.916753, the RMSEC is 2.05848 mmol/L, the Rp is 0.79663, and the RMSEP is 3.16812 mmol/L, the Rp-all is 0.88879, and the RMSEP-all is 2.32114 mmol/L. The results show that the method of synchronous modulation and demodulation proposed in this paper not only reduces the influence of dark current, ambient light and background noise on the signal-to-noise ratio of the spectral data, but also effectively avoids the error caused by the non-synchronization of the chopper and the spectrometer. Therefore, the method used in this paper not only improves the signal-to-noise ratio and accuracy of spectral data, but also improves the accuracy of spectral quantitative analysis of complex solutions.