Circulating gamma-glutamyl transferase and development of specific breast cancer subtypes: findings from the Apolipoprotein Mortality Risk (AMORIS) cohort

Shackshaft, Lydia; Hemelrijck, Mieke Van; Garmo, Hans; Malmström, Håkan; Lambe, Mats; Hammar, Niklas; Walldius, Göran; Jungner, Ingmar; Wulaningsih, Wahyu

doi:10.1186/s13058-017-0816-7

Cited by 11 publications

(5 citation statements)

References 33 publications

(43 reference statements)

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“…Moreover, evidence summarised in a meta-analysis indicates that increasing levels of GGT and ALT may also be related to incident cancer risk 16. However, the number of included studies for site-specific cancers was limited and only a few further prospective studies on GGT and cancer risks were conducted since then 17–19…”

Section: Introductionmentioning

confidence: 99%

Circulating liver enzymes and risks of chronic diseases and mortality in the prospective EPIC-Heidelberg case-cohort study

et al. 2020

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ObjectivesElevated liver enzyme concentrations in blood are indicative of liver diseases and may provide an early signal for being at risk for other chronic diseases. Our study aimed to assess the relationships of alkaline phosphatase (ALP), gamma-glutamyltransferase (GGT), alanine aminotransferase (ALT), aspartate transaminase (AST) and the De Ritis ratio (AST/ALT) with incidence and mortality of cardiovascular diseases (CVD) and the four most common cancers, that is, breast, prostate, colorectal and lung.Setting, participants and outcome measuresWe analysed a case-cohort sample of the prospective European Prospective Investigation into Cancer and Nutrition-Heidelberg cohort, including cancer (n=1632), cancer mortality (n=761), CVD (n=1070), CVD mortality (n=381) and a random subcohort (n=2739) with an average follow-up duration of 15.6 years. Concentrations of liver enzymes were measured in prediagnostic blood samples and Prentice-weighted Cox regression models were used to estimate HRs with 95% CIs.ResultsHigh ALP levels were associated with increased risk for lung cancer and all-cause mortality (highest vs lowest quartile, multivariable adjusted HR=2.39 (95% CI 1.30 to 4.39), HR=1.31 (95% CI 1.02 to 1.67)), high AST levels with all-cause mortality (HR=1.45 (95% CI 1.15 to 1.82)), and a high De Ritis ratio with prostate cancer risk, all-cause and cancer mortality (HR=1.61 (95% CI 1.10 to 2.36), HR=1.60 (95% CI 1.25 to 2.04), HR=1.67 (95% CI 1.26 to 2.23)). Using cut-points for liver enzyme levels above normal, we observed positive associations for all-cause mortality with ALP, GGT and AST, and assigning a combined risk score resulted in positive associations with all-cause and cause-specific mortality.ConclusionsMeasurements of serum liver enzymes, as routinely performed in health check-ups, may support the identification of individuals at increased risk for all-cause mortality. Further prospective studies are needed to verify our first results on individual cancers and on a combined risk score.

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Section: Introductionmentioning

confidence: 99%

Circulating liver enzymes and risks of chronic diseases and mortality in the prospective EPIC-Heidelberg case-cohort study

et al. 2020

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“…Aspartate aminotransferase and alkaline phosphatase are both blood biomarkers related to liver function and were not previously associated with non-metastatic breast cancer 59 . In contrast, gamma glutamyltransferase was observed to have positive association with subtypes of breast cancer in the Apolipoprotein Mortality Risk (AMORIS) cohort 60 , and C-reactive protein, a marker of systemic inflammation, was reported to be associated with breast cancer via meta-analysis 61 , 62 . To our knowledge, no previous studies have reported the association of plasma phosphate, sodium in urine or creatinine in urine with breast cancer.…”

Section: Discussionmentioning

confidence: 98%

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

Liu

Morelli

Littlejohns

et al. 2023

Sci Rep

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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 $$\approx$$ ≈ 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 ($$p<0.001$$ p < 0.001 ). 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, $$p<0.001$$ p < 0.001 ), plasma phosphate (HR = 0.68, 95% CI 0.53–0.88, $$p=0.003$$ p = 0.003 ), basal metabolic rate (HR = 1.17, 95% CI 1.11–1.24, $$p<0.001$$ p < 0.001 ), red blood cell count (HR = 1.21, 95% CI 1.08–1.35, $$p<0.001$$ p < 0.001 ), and creatinine in urine (HR = 1.05, 95% CI 1.01–1.09, $$p=0.006$$ p = 0.006 ). 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.

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“…GGT is an enzyme located on the cell membrane, primarily involved in glutathione metabolism and amino acid transport ( 24 ). GGT may act as an oxidative stress amplifier, and persistent oxidative stress stimulation can lead to the destabilization of gene stability, disrupting the balance between cell proliferation and apoptosis, thereby affecting tumor formation and progression ( 25 , 26 ). Moreover, the elevation of GGT may indicate increased invasiveness in HCC as it is involved in the survival, proliferation, and migration of tumor cells, thereby promoting tumor spread and growth.…”

Section: Discussionmentioning

confidence: 99%

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy

Zhang,

Sheng,

Qiao

et al. 2024

Front. Oncol.

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IntroductionThis study aimed to assess factors affecting the prognosis of early-stage hepatocellular carcinoma (HCC) patients undergoing ablation therapy and create a nomogram for predicting their 3-, 5-, and 8-year overall survival (OS).MethodsThe research included 881 early-stage HCC patients treated at Beijing You’an Hospital, affiliated with Capital Medical University, from 2014 to 2022. A nomogram was developed using independent prognostic factors identified by Lasso and multivariate Cox regression analyses. Its predictive performance was evaluated with concordance index (C-index), receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).ResultsThe study identified age, tumor number, tumor size, gamma-glutamyl transpeptidase (GGT), international normalized ratio (INR), and prealbumin (Palb) as independent prognostic risk factors. The nomogram achieved C-indices of 0.683 (primary cohort) and 0.652 (validation cohort), with Area Under the Curve (AUC) values of 0.776, 0.779, and 0.822 (3-year, 5-year, and 8-year OS, primary cohort) and 0.658, 0.724, and 0.792 (validation cohort), indicating that the nomogram possessed strong discriminative ability. Calibration and DCA curves further confirmed the nomogram’s predictive accuracy and clinical utility. The nomogram can effectively stratify patients into low-, intermediate-, and high-risk groups, particularly identifying high-risk patients.ConclusionsThe established nomogram in our study can provide precise prognostic information for HCC patients following ablation treatment and enable physicians to accurately identify high-risk individuals and facilitate timely intervention.

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Circulating gamma-glutamyl transferase and development of specific breast cancer subtypes: findings from the Apolipoprotein Mortality Risk (AMORIS) cohort

Cited by 11 publications

References 33 publications

Circulating liver enzymes and risks of chronic diseases and mortality in the prospective EPIC-Heidelberg case-cohort study

Circulating liver enzymes and risks of chronic diseases and mortality in the prospective EPIC-Heidelberg case-cohort study

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

A novel nomogram to predict the overall survival of early-stage hepatocellular carcinoma patients following ablation therapy

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