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

Sun, Yandi; Li, Jingjia; Qu, Zihao; Yang, Zichen; Jia, Xueyao; Lin, Yindan; He, Qian; Zhang, Lihong; Luo, Yan

doi:10.3390/genes12040498

Cited by 18 publications

(12 citation statements)

References 42 publications

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“…While absolute monocytes count was identified as a potential prognostic factor for breast cancer 54 , the association of red blood cell count with breast cancer has not been studied explicitly. Plasma urea, a blood biomarker related to kidney function, was reported to have null causal relationship with breast cancer 55 , but our study suggests it may be associated with breast cancer. Aspartate aminotransferase and alkaline phosphatase are both blood biomarkers related to liver function and were not previously associated with non-metastatic breast cancer 56 .…”

Section: Discussioncontrasting

confidence: 74%

Combining Machine Learning with Cox models for identifying risk factors for incident post-menopausal breast cancer in the UK Biobank

Liu

Collister

Littlejohns

et al. 2022

Preprint

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Breast cancer is the most common cancer in women. A better understanding of risk factors plays a central role in disease prediction and prevention. We aimed to identify potential novel risk factors for breast cancer among post-menopausal women, with pre-specified interest in the role of polygenic risk scores (PRS) for risk prediction. We designed an analysis pipeline combining both machine learning (ML) and classical statistical models with emphasis on necessary statistical considerations (e.g. collinearity, missing data). Extreme gradient boosting (XGBoost) machine with Shapley (SHAP) feature importance measures were used for risk factor discovery among ~1.7k features in 104,313 post-menopausal women from the UK Biobank cohort. Cox models were constructed subsequently for in-depth investigation. Both PRS were significant risk factors when fitted simultaneously in both ML and Cox models (p<0.001). ML analyses identified 11 (excluding the two PRS) novel predictors, among which five were confirmed by the Cox models: plasma urea (HR=0.95, 95% CI 0.92-0.98, p<0.001) and plasma phosphate (HR=0.67, 95% CI 0.52-0.88, p=0.003) were inversely associated with risk of developing post-menopausal breast cancer, whereas basal metabolic rate (HR=1.15, 95% CI 1.08-1.22, p<0.001), red blood cell count (HR=1.20, 95% CI 1.08-1.34, p=0.001), and creatinine in urine (HR=1.05, 95% CI 1.01-1.09, p=0.008) were positively associated. Our final Cox model demonstrated a slight improvement in risk discrimination when adding novel features to a simpler Cox model containing PRS and the established risk factors (Harrell's C-index = 0.670 vs 0.665).

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Section: Discussioncontrasting

confidence: 74%

Combining Machine Learning with Cox models for identifying risk factors for incident post-menopausal breast cancer in the UK Biobank

Liu

Collister

Littlejohns

et al. 2022

Preprint

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“…The risk of getting colon cancer in a first-degree relative was 2.2, which drops to 1.3 and 1.2 in second and third-degree relatives 51 , suggesting a strong familial genetic component. Whereas pulse and urea had minor contributions to the risk, emphasizing the modest but key role of cardiovascular 52 and renal health 53 in CRC development 53 . Finally, self-reported poor health was a strong classifying feature, previously linked to overall cancer risk and all-cause mortality 54 , 55 .…”

Section: Discussionmentioning

confidence: 99%

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

Tanriver¹,

Kocagöncü²

2023

Sci Rep

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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.

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“…Urea is largely derived from the urea cycle reactions through hepatic detoxification of free ammonia and cleared by urination. Recently, study revealed that an intriguing link between serum urea and cancer risks ( 20 ). Previous study reported that serine/threonine kinase 2 promotes proliferation can be used to predict metastasis and poor prognosis in NSCLC ( 21 ).…”

Section: Discussionmentioning

confidence: 99%

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

Mei

Zhang

et al. 2023

Front. Oncol.

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

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

Cited by 18 publications

References 42 publications

Combining Machine Learning with Cox models for identifying risk factors for incident post-menopausal breast cancer in the UK Biobank

Combining Machine Learning with Cox models for identifying risk factors for incident post-menopausal breast cancer in the UK Biobank

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

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

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