Is obesity always a risk factor for all breast cancer patients? c-erbB2 expression is significantly lower in obese patients with early stage breast cancer

Postdiagnosis body fatness, weight change and breast cancer prognosis: Global Cancer Update Program (CUP global) systematic literature review and meta-analysis

Previous evidence on postdiagnosis body fatness and mortality after breast cancer was graded as limited-suggestive. To evaluate the evidence on body mass index (BMI), waist circumference, waist-hip-ratio and weight change in relation to breast cancer prognosis, an updated systematic review was conducted. PubMed and Embase were searched for relevant studies published up to 31 October, 2021. Random-effects meta-analyses were conducted to estimate summary relative risks (RRs). The evidence was judged by an independent Expert Panel using pre-defined grading criteria. One randomized controlled trial and 225 observational studies were reviewed (220 publications). There was strong evidence (likelihood of causality: probable) that higher postdiagnosis BMI was associated with increased all-cause mortality (64 studies, 32 507 deaths), breast cancer-specific mortality (39 studies, 14 106 deaths) and second primary breast cancer (11 studies, 5248 events). The respective summary RRs and 95% confidence intervals per 5 kg/m2 BMI were 1.07 (1.05-1.10), 1.10 (1.06-1.14) and 1.14 (1.04-1.26), with high between-study heterogeneity (I2  = 56%, 60%, 66%), but generally consistent positive associations. Positive associations were also observed for waist circumference, waist-hip-ratio and all-cause and breast cancer-specific mortality. There was limited-suggestive evidence that postdiagnosis BMI was associated with higher risk of recurrence, nonbreast cancer deaths and cardiovascular deaths. The evidence for postdiagnosis (unexplained) weight or BMI change and all outcomes was graded as limited-no conclusion. The RCT showed potential beneficial effect of intentional weight loss on disease-free-survival, but more intervention trials and well-designed observational studies in diverse populations are needed to elucidate the impact of body composition and their changes on breast cancer outcomes.

Bioinformatics and Next-Generation Data Analysis for Identification of Genes and Molecular Pathways Involved in Subjects with Diabetes and Obesity

Background and Objectives: A subject with diabetes and obesity is a class of the metabolic disorder. The current investigation aimed to elucidate the potential biomarker and prognostic targets in subjects with diabetes and obesity. Materials and Methods: The next-generation sequencing (NGS) data of GSE132831 was downloaded from Gene Expression Omnibus (GEO) database. Functional enrichment analysis of DEGs was conducted with ToppGene. The protein-protein interactions network, module analysis, target gene-miRNA regulatory network and target gene-TF regulatory network were constructed and analyzed. Furthermore, hub genes were validated by receiver operating characteristic (ROC) analysis. A total of 872 DEGs, including 439 up-regulated genes and 433 down-regulated genes were observed. Results: Second, functional enrichment analysis showed that these DEGs are mainly involved in the axon guidance, neutrophil degranulation, plasma membrane bounded cell projection organization and cell activation. The top ten hub genes (MYH9, FLNA, DCTN1, CLTC, ERBB2, TCF4, VIM, LRRK2, IFI16 and CAV1) could be utilized as potential diagnostic indicators for subjects with diabetes and obesity. The hub genes were validated in subjects with diabetes and obesity. Conclusion: This investigation found effective and reliable molecular biomarkers for diagnosis and prognosis by integrated bioinformatics analysis, suggesting new and key therapeutic targets for subjects with diabetes and obesity.

The significances and clinical implications of cholesterol components in human breast cancer

Breast cancer is one the most common malignancies and leading cause of cancer-related mortality in women. Recent studies suggested that hypercholesterolemia may be the potential modifiable risk factors for breast cancer. Cholesterol was well-known for its strong association with cardiovascular disease for long. Moreover, solid evidence has been provided by different studies to illustrate the correlation between lipid and incidence in multiple cancers. Although the conclusion remains controversial or sometimes contrary, which may be due to the multifactorial nature of the disease and the disparity of ethnic population, it is critical to elucidate the relationship between specific cholesterol components in certain population and the exact underlying mechanism of the lipid-associated signaling pathway in breast cancer. The implications of dysregulated lipoproteins as therapeutic targets or options for breast cancer provide novel strategies for us in combating with this malignant disease, which may be achieved by manipulating lipid levels with pharmacological compounds.

Identification of Key Pathways and Genes in Obesity Using Bioinformatics Analysis and Molecular Docking Studies

Obesity is an excess accumulation of body fat. Its progression rate has remained high in recent years. Therefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. The gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Functional enrichment analysis was performed. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then protein-protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. The module analysis was performed based on the whole PPI network. We finally filtered out STAT3, CORO1C, SERPINH1, MVP, ITGB5, PCM1, SIRT1, EEF1G, PTEN and RPS2 hub genes. Hub genes were validated by ICH analysis, receiver operating curve (ROC) analysis and RT-PCR. Finally a molecular docking study was performed to find small drug molecules. The robust DEGs linked with the development of obesity were screened through the expression profile, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data

PURPOSE

The purpose of OncoMX¹ knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidimensional data types.

METHODS

Cancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database.

RESULTS

OncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs.

CONCLUSION

OncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.

Impact of Serum Lipid on Breast Cancer Recurrence

The association between serum lipid level and prognosis of breast cancer is controversial. The purpose of this study was to evaluate the impact of serum lipid level in breast cancer recurrence. We analyzed a total of 4190 patients with operable breast cancer who had baseline serum lipid profiles; total cholesterol (TC), triglycerides (TG), low density lipoprotein-cholesterol (LDL-C), high density lipoprotein-cholesterol (HDL-C), apolipoprotein A-1, and apolipoprotein B. Recurrence-free survival is defined as the elapsed time from the date of curative surgery to the detection of any recurrence, and recurrence includes locoregional recurrence, distant metastasis, or both local and distant metastasis. Cox-proportional hazard analysis was used to estimate hazard ratios with 95% confidence intervals (CI) for study outcomes comparing the three lowest quartiles of each lipid parameter to the highest quartile adjusting for age, body mass index (BMI), and pathologic stage, estrogen receptor (ER), progesterone receptor (PR), comorbidities (hypertension, diabetes, or vascular event) at time of breast cancer diagnosis. Patients with dyslipidemia (high bad cholesterol and low good cholesterol level) had worse prognostic factors (i.e., negative hormone receptor status, positive human epidermal growth factor receptor 2 (HER2) expression, higher nuclear grade). After adjusting for these poor prognostic factors, the patients with dyslipidemia showed good prognosis for breast cancer recurrence. Our study showed that baseline high lipid level could be a good prognostic factor of breast cancer. This study indicates that desirable changes in lipid profile for cardiovascular disease risk are not always beneficial for patients with breast cancer. However, as proper control of lipid level has advantages for cardiovascular disease, these findings require careful interpretation.

Body mass index and survival in women with breast cancer-systematic literature review and meta-analysis of 82 follow-up studies

BACKGROUND

Positive association between obesity and survival after breast cancer was demonstrated in previous meta-analyses of published data, but only the results for the comparison of obese versus non-obese was summarised.

METHODS

We systematically searched in MEDLINE and EMBASE for follow-up studies of breast cancer survivors with body mass index (BMI) before and after diagnosis, and total and cause-specific mortality until June 2013, as part of the World Cancer Research Fund Continuous Update Project. Random-effects meta-analyses were conducted to explore the magnitude and the shape of the associations.

RESULTS

Eighty-two studies, including 213 075 breast cancer survivors with 41 477 deaths (23 182 from breast cancer) were identified. For BMI before diagnosis, compared with normal weight women, the summary relative risks (RRs) of total mortality were 1.41 [95% confidence interval (CI) 1.29-1.53] for obese (BMI >30.0), 1.07 (95 CI 1.02-1.12) for overweight (BMI 25.0-<30.0) and 1.10 (95% CI 0.92-1.31) for underweight (BMI <18.5) women. For obese women, the summary RRs were 1.75 (95% CI 1.26-2.41) for pre-menopausal and 1.34 (95% CI 1.18-1.53) for post-menopausal breast cancer. For each 5 kg/m(2) increment of BMI before, <12 months after, and ≥12 months after diagnosis, increased risks of 17%, 11%, and 8% for total mortality, and 18%, 14%, and 29% for breast cancer mortality were observed, respectively.

CONCLUSIONS

Obesity is associated with poorer overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Being overweight is also related to a higher risk of mortality. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer.