Effect of Serum Lipid Profile on the Risk of Breast Cancer: Systematic Review and Meta-Analysis of 1,628,871 Women

A prognostic model based on CLEC6A predicts clinical outcome of breast cancer patients

CLEC6A, (C-type lectin domain family 6, member A), plays a prominent role in regulating innate immunity and adaptive immunity. CLEC6A has shown great potential as a target for cancer immunotherapy. This study aims to explore the prognostic value of CLEC6A, and analyze the relationship associated with the common hematological parameters in breast cancer patients. We performed a retrospective analysis on 183 breast cancer patients data in hospital information system from January 2013 to December 2015. The expression of CLEC6A was recorded via semiquantitative immunohistochemistry in breast cancer. The association between expression of CLEC6A and relative parameters were performed by Chi-square test and Fisher's exact test. Kaplan-Meier assay and Log-rank test were performed to evaluate the survival time. The Cox proportional hazards regression analysis was applied to identify prognostic factors. Nomograms were conducted to predict 1-, 3-, and 5-year disease free survival (DFS) and overall survival (OS) for breast cancer, which could be a good reference in clinical practice. The nomogram model was estimated by calibration curve analysis for its function of discrimination. The accuracy and benefit of the nomogram model were appraised by comparing it to only CLEC6A via decision curve analysis (DCA). The prediction accuracy of CLEC6A was also determined by time-dependent receiver operating characteristics (TDROC) curves, and the area under the curve (AUC) for different survival time. There were 94 cases in the CLEC6A low-expression group and 89 cases in CLEC6A high-expression group. Compared to CLEC6A low-expression group, the CLEC6A high-expression group had better survival (DFS: 56.95 vs. 70.81 months, P = 0.0078 and OS: 67.98 vs. 79.05 months, P = 0.0089). The CLEC6A was a potential prognostic factor in multivariate analysis (DFS: P = 0.023, hazard ratio (HR): 0.454, 95 % confidence interval (CI): 0.229-0.898; OS: P = 0.020, HR: 0.504, 95 %CI: 0.284-0.897). The nomogram in accordance with these potential prognostic factors was constructed to predict survival and the calibration curve analysis had indicated that the predicted line was well-matched with reference line in 1-, 3-, and 5-year DFS and OS category. The 1-, 3-, and 5-year DCA curves have revealed that nomogram model yielded larger net benefits than CLEC6A alone. Finally, the TDROC curve indicated that CLEC6A could better predict 1-year DFS and OS than others. Furthermore, we combined these potential independent prognostic factors to analyze the relationship among these hematologic index and oxidative stress indicators, and indicated that higher CLEC6A level, higher CO2 level or low CHOL level or high HDL-CHO level would have survived longer and better prognosis. In breast cancer, high expression of CLEC6A can independently predict better survival. Our nomogram consisted of CLEC6A and other indicators has good predictive performance and can facilitate clinical decision-making.

Metabolites Associated with Polygenic Risk of Breast Cancer

While hundreds of germline genetic variants have been associated with breast cancer risk, the mechanisms underlying the impacts of most of these variants on breast cancer remain uncertain. Metabolomics may offer valuable insights into the mechanisms underlying genetic risks of breast cancer. Among 143 cancer-free female participants, we used linear regression analyses to explore associations between the genetic risk of breast cancer, as determined by a previously developed polygenic risk score (PRS) that included 266 single-nucleotide polymorphisms (SNPs), and 223 measures of metabolites obtained from blood samples using nuclear magnetic resonance (NMR). A false discovery rate of 10% was applied to account for multiple comparisons. PRS was statistically significantly associated with 45 metabolite measures. These were primarily measures of very low-density lipoproteins (VLDLs) and high-density lipoproteins (HDLs), including triglycerides, cholesterol, and phospholipids. For example, the strongest effect was observed with the percent ratio of medium VLDL triglycerides to total lipids (0.53 unit increase in mean-standardized ln-transformed percent ratio per unit increase in PRS; q = 0.1). While larger-scale studies are needed to confirm these results, this exploratory study presents biologically plausible findings that are consistent with previously reported associations between lipids and breast cancer risk. If confirmed, these lipids could be targeted for lifestyle and pharmaceutical interventions among women at increased genetic risk of breast cancer.

Cardiovascular disease and cancer: shared risk factors and mechanisms

Cardiovascular disease (CVD) and cancer are among the leading causes of morbidity and mortality globally, and these conditions are increasingly recognized to be fundamentally interconnected. In this Review, we present the current epidemiological data for each of the modifiable risk factors shared by the two diseases, including hypertension, hyperlipidaemia, diabetes mellitus, obesity, smoking, diet, physical activity and the social determinants of health. We then review the epidemiological data demonstrating the increased risk of CVD in patients with cancer, as well as the increased risk of cancer in patients with CVD. We also discuss the shared mechanisms implicated in the development of these conditions, highlighting their inherent bidirectional relationship. We conclude with a perspective on future research directions for the field of cardio-oncology to advance the care of patients with CVD and cancer.

Blood Lipid Metabolic Profiles and Causal Links to Site-Specific Cancer Risks: A Mendelian Randomization Study

Observational and Mendelian randomization (MR) studies have established links between dyslipidemia and select cancer susceptibilities. However, there is a lack of comprehensive exploration of causal relationships spanning diverse cancer types. Here, we conducted a two-sample MR analysis to elucidate the causative connections between 9 blood lipid metabolic profiles (namely, adiponectin, leptin, lipoprotein A, apolipoprotein A1, apolipoprotein B, cholesterol, triglycerides, LDL-cholesterol, and HDL-cholesterol) and 21 site-specific cancer risks. Our findings reveal genetically predicted adiponectin levels to be associated with a reduced ovarian cancer risk, while genetically determined leptin increases bladder cancer risk but decreases prostate cancer risk. Lipoprotein A elevates risk of prostate cancer while diminishing risk of endometrial cancer, while apolipoprotein A1 heightens risks of breast and cervical cancers. Furthermore, elevated levels of cholesterol are positively correlated with kidney cancer, and triglycerides demonstrate a positive association with non-melanoma skin cancer but a negative association with breast cancer. Protective effects of genetically predicted LDL-cholesterol on endometrial cancer and adverse effects of HDL-cholesterol on breast cancer are also observed. Our study conclusively establishes that blood lipid metabolic profiles exert causal effects on cancer susceptibility, providing more robust evidence for cancer prevention and prompting contemplation regarding the future health of the human populace.

Temporal relationships between BMI and obesity-related predictors of cardiometabolic and breast cancer risk in a longitudinal cohort

Prospective inter-relationships among biomarkers were unexplored, which may provide mechanistic insights into diseases. We investigated the longitudinal associations of BMI change with trajectories of biomarkers related to cardiometabolic or breast cancer risk. A longitudinal study was conducted among 444 healthy women between 2019 to 2021. Cross‑lagged path analysis was used to examine the temporal relationships among BMI, cardiometabolic risk score (CRS), and obesity‑related proteins score (OPS) of breast cancer. Linear mixed-effect models were applied to investigate associations of time-varying BMI with biomarker-based risk score trajectories. Baseline BMI was associated with subsequent change of breast cancer predictors (P = 0.03), and baseline CRS were positively associated with OPS change (P < 0.001) but not vice versa. After fully adjustment of confounders, we found a 0.058 (95%CI = 0.009-0.107, P = 0.020) units increase of CRS and a 1.021 (95%CI = 0.041-1.995, P = 0.040) units increase of OPS as BMI increased 1 kg/m² per year in postmenopausal women. OPS increased 0.784 (95%CI = 0.053-1.512, P = 0.035) units as CRS increased 1 unit per year. However, among premenopausal women, BMI only significantly affected CRS (β = 0.057, 95%CI = 0.007 to 0.107, P = 0.025). No significant change of OPS with time-varying CRS was found. Higher increase rates of BMI were associated with worse trajectories of biomarker-based risk of cardiometabolic and breast cancer. The longitudinal impact of CRS on OPS is unidirectional. Recommendations such as weight control for the reduction of cardiometabolic risk factors may benefit breast cancer prevention, especially in postmenopausal women.

Integrating Proteomics and Lipidomics for Evaluating the Risk of Breast Cancer Progression: A Pilot Study

Metastasis is a serious and often life-threatening condition, representing the leading cause of death among women with breast cancer (BC). Although the current clinical classification of BC is well-established, the addition of minimally invasive laboratory tests based on peripheral blood biomarkers that reflect pathological changes in the body is of utmost importance. In the current study, the serum proteome and lipidome profiles for 50 BC patients with (25) and without (25) metastasis were studied. Targeted proteomic analysis for concertation measurements of 125 proteins in the serum was performed via liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) using the BAK 125 kit (MRM Proteomics Inc., Victoria, BC, Canada). Untargeted label-free lipidomic analysis was performed using liquid chromatography coupled to tandem mass-spectrometry (LC-MS/MS), in both positive and negative ion modes. Finally, 87 serum proteins and 295 lipids were quantified and showed a moderate correlation with tumor grade, histological and biological subtypes, and the number of lymph node metastases. Two highly accurate classifiers that enabled distinguishing between metastatic and non-metastatic BC were developed based on proteomic (accuracy 90%) and lipidomic (accuracy 80%) features. The best classifier (91% sensitivity, 89% specificity, AUC = 0.92) for BC metastasis diagnostics was based on logistic regression and the serum levels of 11 proteins: alpha-2-macroglobulin, coagulation factor XII, adiponectin, leucine-rich alpha-2-glycoprotein, alpha-2-HS-glycoprotein, Ig mu chain C region, apolipoprotein C-IV, carbonic anhydrase 1, apolipoprotein A-II, apolipoprotein C-II and alpha-1-acid glycoprotein 1.

Laparoscopic versus Robotic Hepatectomy: A Systematic Review and Meta-Analysis

This study aimed to assess the surgical outcomes of robotic compared to laparoscopic hepatectomy, with a special focus on the meta-analysis method. Original studies were collected from three Chinese databases, PubMed, EMBASE, and Cochrane Library databases. Our systematic review was conducted on 682 patients with robotic liver resection, and 1101 patients were operated by laparoscopic platform. Robotic surgery has a long surgical duration (MD = 43.99, 95% CI: 23.45-64.53, p = 0.0001), while there is no significant difference in length of hospital stay (MD = 0.10, 95% CI: -0.38-0.58, p = 0.69), blood loss (MD = -20, 95% CI: -64.90-23.34, p = 0.36), the incidence of conversion (OR = 0.84, 95% CI: 0.41-1.69, p = 0.62), and tumor size (MD = 0.30, 95% CI: -0-0.60, p = 0.05); the subgroup analysis of major and minor hepatectomy on operation time is (MD = -7.08, 95% CI: -15.22-0.07, p = 0.09) and (MD = 39.87, 95% CI: -1.70-81.44, p = 0.06), respectively. However, despite the deficiencies of robotic hepatectomy in terms of extended operation time compared to laparoscopic hepatectomy, robotic hepatectomy is still effective and equivalent to laparoscopic hepatectomy in outcomes. Scientific evaluation and research on one portion of the liver may produce more efficacity and more precise results. Therefore, more clinical trials are needed to evaluate the clinical outcomes of robotic compared to laparoscopic hepatectomy.