Genetic and epigenetic modifications of adiponectin gene: Potential association with breast cancer risk

Epigenetic modifications in obesity-associated diseases

The global prevalence of obesity has reached epidemic levels, significantly elevating the susceptibility to various cardiometabolic conditions and certain types of cancer. In addition to causing metabolic abnormalities such as insulin resistance (IR), elevated blood glucose and lipids, and ectopic fat deposition, obesity can also damage pancreatic islet cells, endothelial cells, and cardiomyocytes through chronic inflammation, and even promote the development of a microenvironment conducive to cancer initiation. Improper dietary habits and lack of physical exercise are important behavioral factors that increase the risk of obesity, which can affect gene expression through epigenetic modifications. Epigenetic alterations can occur in early stage of obesity, some of which are reversible, while others persist over time and lead to obesity-related complications. Therefore, the dynamic adjustability of epigenetic modifications can be leveraged to reverse the development of obesity-associated diseases through behavioral interventions, drugs, and bariatric surgery. This review provides a comprehensive summary of the impact of epigenetic regulation on the initiation and development of obesity-associated cancers, type 2 diabetes, and cardiovascular diseases, establishing a theoretical basis for prevention, diagnosis, and treatment of these conditions.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

Genetic alterations in LEP and ADIPOQ genes and risk for breast cancer: a meta-analysis

Introduction

Breast cancer has a strong genetic predisposition, and its genetic architecture is not fully understood thus far. In this study, we aimed to perform a meta-analysis to evaluate the association of genetic alterations in LEP and ADIPOQ genes, as well as their receptor-encoded genes with risk for breast cancer.

Methods

Only published studies conducted in humans and written in English were identified by searching PubMed, SCOPUS, CINAHIL and Embase from their inception to October 2022. Eligibility assessment and data collection were completed independently by two researchers. Statistical analyses were done using the STATA software.

Results

After literature search, 33 publications were eligible for inclusion. Overall, LEP gene rs7799039-G allele (odds ratio [OR]: 0.78, 95% confidence interval [CI]: 0.62 to 0.98) and ADIPOQ gene rs1501299-T allele (OR: 1.41, 95% CI: 1.06 to 1.88) were associated with the significant risk of breast cancer. In subgroup analyses, differences in menopausal status, obesity, race, study design, diagnosis of breast cancer, genotyping method and sample size might account for the divergent observations of individual studies. Circulating leptin levels were comparable across genotypes of LEP gene rs7799039, as well as that of LEPR gene rs1137101 (P>0.05). Begg's funnel plots seemed symmetrical, with the exception of LEPR gene rs1137100 and ADIPOQ gene rs1501299.

Discussion

Taken together, we found, in this meta-analysis, that LEP gene rs7799039 and ADIPOQ gene rs1501299 were two promising candidate loci in predisposition to breast cancer risk.

Classification Prediction of Breast Cancer Based on Machine Learning

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively.

The impact of obesity and adipokines on breast and gynecologic malignancies

The link between obesity and multiple disease comorbidities is well established. In 2003, Calle and colleagues presented the relationship between obesity and several cancer types, including breast, ovarian, and endometrial malignancies. Nearly, 20% of cancer-related deaths in females can be accounted for by obesity. Identifying obesity as a risk factor for cancer led to a focus on the role of fat-secreted cytokines, known as adipokines, on carcinogenesis and tumor progression. Early studies indicated that the adipokine leptin increases cell proliferation, invasion, and inhibition of apoptosis in multiple cancer types. As a greater appreciation of the obesity-cancer link has amassed, we now know that additional adipokines can impact tumorigenesis. A deeper understanding of the adipokine-activated signaling in cancer may identify new treatment strategies irrespective of obesity. Moreover, adipokines may serve as disease biomarkers, harnessing the potential of obesity-associated factors to serve as indicators of treatment response and disease prognosis. As studies investigating obesity and women's cancers continue to expand, it has become evident that breast, ovarian, and uterine cancers are distinctly impacted by adipokines. While complex, these distinct interactions may provide insight into cancer progression in these organs and new opportunities for targeted therapies. This review aims to organize and present the literature from the last 5 years investigating the mechanisms and implications of adipokine signaling in breast, endometrial, and ovarian cancers with a special focus on leptin and adiponectin.

Associations between ADIPOQ rs2241766 SNP and breast cancer risk: a systematic review and a meta-analysis

Purpose

We aimed to conduct a meta-analysis to accurately evaluate the potential association between ADIPOQ rs2241766 gene SNP and breast cancer risk.

Methods

A systematic literature search on Cochrane Library, PubMed, Embase, Web of Science and China National Knowledge Infrastructure (CNKI) identified 8 articles with 1692 cases and 1890 controls. Strength of association was evaluated by pooled odds ratio (OR), 95 % confidence interval (CI) and p value. Funnel plots and Begger’s regression test were applied for testing the publication bias. Statistical analysis of all data was performed by Stata 12.0.

Results

The meta-analysis results indicated that the ADIPOQ rs2241766 gene polymorphism did not significantly associated with the risk of breast cancer for these genetic models (TT vs. TG + GG: OR = 1.20, 95 % CI = 0.77–1.89, p=0.417; TT + TG vs. GG: OR = 1.05, 95 % CI = 0.71–1.56, p=0.805; T vs. G: OR =1.17, 95 % CI = 0.79–1.74, p=0.437).

Conclusions

This study indicated that no significant relationship between the ADIPOQ rs2241766 SNP and breast cancer. Further large-scale and well-designed studies will be indispensable to confirm our result.

Interactions Between Adiponectin-Pathway Polymorphisms and Obesity on Postmenopausal Breast Cancer Risk Among African American Women: The WHI SHARe Study

BACKGROUND

A decreased level of serum adiponectin is associated with obesity and an increased risk of breast cancer among postmenopausal women. Yet, the interplay between genetic variants associated with adiponectin phenotype, obesity, and breast cancer risk is unclear in African American (AA) women.

METHODS

We examined 32 single-nucleotide polymorphisms (SNPs) previously identified in genome-wide association and replication studies of serum adiponectin levels using data from 7,991 AA postmenopausal women in the Women's Health Initiative SNP Health Association Resource.

RESULTS

Stratifying by obesity status, we identified 18 adiponectin-related SNPs that were associated with breast cancer risk. Among women with BMI ≥ 30 kg/m2, the minor TT genotype of FER rs10447248 had an elevated breast cancer risk. Interaction was observed between obesity and the CT genotype of ADIPOQ rs6773957 on the additive scale for breast cancer risk (relative excess risk due to interaction, 0.62; 95% CI, 0.32-0.92). The joint effect of BMI ≥ 30 kg/m2 and the TC genotype of OR8S1 rs11168618 was larger than the sum of the independent effects on breast cancer risk.

CONCLUSIONS

We demonstrated that obesity plays a significant role as an effect modifier in an increased effect of the SNPs on breast cancer risk using one of the most extensive data on postmenopausal AA women.

IMPACT

The results suggest the potential use of adiponectin genetic variants as obesity-associated biomarkers for informing AA women who are at greater risk for breast cancer and also for promoting behavioral interventions, such as weight control, to those with risk genotypes.

Current Landscape: The Mechanism and Therapeutic Impact of Obesity for Breast Cancer

Obesity is defined as a chronic disease induced by an imbalance of energy homeostasis. Obesity is a widespread health problem with increasing prevalence worldwide. Breast cancer (BC) has already been the most common cancer and one of the leading causes of cancer death in women worldwide. Nowadays, the impact of the rising prevalence of obesity has been recognized as a nonnegligible issue for BC development, outcome, and management. Adipokines, insulin and insulin-like growth factor, sex hormone and the chronic inflammation state play critical roles in the vicious crosstalk between obesity and BC. Furthermore, obesity can affect the efficacy and side effects of multiple therapies such as surgery, radiotherapy, chemotherapy, endocrine therapy, immunotherapy and weight management of BC. In this review, we focus on the current landscape of the mechanisms of obesity in fueling BC and the impact of obesity on diverse therapeutic interventions. An in-depth exploration of the underlying mechanisms linking obesity and BC will improve the efficiency of the existing treatments and even provide novel treatment strategies for BC treatment.

Postmenopausal estrogen receptor positive breast cancer and obesity associated gene variants

Obesity is one of the most important health risks in postmenopausal women. Molecular pathways that are connected with obesity are believed to interact with the pathogenesis of breast cancer (BC). The aim of this research was to study the polymorphisms of two obesity-associated genes ADIPOQ and FTO that are also related to the pathogenesis of BC. Obesity-associated gene polymorphisms ADIPOQ rs1501299 and rs2241766, and FTO rs1477196, rs7206790, rs8047395, and rs9939609 were studied in 101 Turkish postmenopausal estrogen receptor-positive BC patients and 100 healthy control individuals. ADIPOQ rs1501299 was detected to be associated with protection against BC. The ADIPOQ rs1501299 TT genotype, the rs2241766 GT genotype and the G allele were found to be significantly higher in the control group. In addition, ADIPOQ rs1501299 polymorphism was protective in the recessive model and rs2241766 polymorphism was protective in the dominant model. While none of the FTO gene polymorphisms were found to be associated with BC, the frequencies of rs9939609 A allele and rs7206790 G allele were correlated with body mass index (BMI) in BC patients. ADIPOQ rs1501299 TT genotype, rs2241766 GT genotype, and G allele might be protective against BC in the Turkish population but this conclusion needs to be further verified.

Diagnostic and prognostic value of FOXD1 expression in head and neck squamous cell carcinoma

FOXD1 has been reported to function as an oncogene in several types of cancer. This study evaluated the expression of FOXD1 and its role in head and neck squamous cell carcinoma (HNSCC). We mined the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases for expression profiles, clinical significance, and potential mechanisms of FOXD1in HNSCC. Our validation cohort consisted of FOXD1 mRNA expression in 162 paired HNSCC and adjacent normal tissues, as determined using quantitative real-time polymerase chain reaction. FOXD1 expression was upregulated in HNSCC in the public databases and in the validation cohort. The expression level of FOXD1 was associated with DNA amplification and methylation level. The areas under the curves (AUC) of TCGA cohort and the validation cohort were 0.855 and 0.843, respectively. Furthermore, higher FOXD1 expression was significantly associated with worse overall survival (hazard ratio [HR]: 1.849, 95% confidence interval [CI]: 1.280-2.670, P = 0.001) and a lower rate of recurrence-free survival (HR: 1.650, 95% CI: 1.058-2.575, P = 0.027) in patients with HNSCC. Moreover, gene set enrichment analysis showed that cases of HNSCC with FOXD1 overexpression were enriched in bladder cancer, cell cycle, DNA replication, glycosaminoglycan biosynthesis chondroitin sulfate, homologous recombination, glycan biosynthesis, nucleotide excision repair, p53 signaling pathway, pyrimidine metabolism, and spliceosome pathways. In summary, FOXD1 was significantly upregulated in HNSCC and was a good diagnostic biomarker and an independent predictor of poor survival and low rate of recurrence-free survival in patients with HNSCC.