Obesity is a multifaceted metabolic disorder characterized by excessive accumulation of adipose tissue. It is a well-established risk factor for the development and progression of breast cancer. Adipose tissue, which was once regarded solely as a passive energy storage depot, is now acknowledged as an active endocrine organ producing a plethora of bioactive molecules known as adipokines that contribute to the elevation of proinflammatory cytokines and estrogen production due to enhanced aromatase activity. In the context of breast cancer, the crosstalk between adipocytes and cancer cells within the adipose microenvironment exerts profound effects on tumor initiation, progression, and therapeutic resistance. Moreover, adipocytes can engage in direct interactions with breast cancer cells through physical contact and paracrine signaling, thereby facilitating cancer cell survival and invasion. This review endeavors to summarize the current understanding of the intricate interplay between adipocyte-associated factors and breast cancer progression. Furthermore, by discussing the different aspects of breast cancer that can be adversely affected by obesity, this review aims to shed light on potential avenues for new and novel therapeutic interventions.
Background: Extensive research has been conducted on the correlation between adipose tissue and the risk of malignant lymphoma. Despite numerous observational studies exploring this connection, uncertainty remains regarding a causal relationship between adipose tissue and malignant lymphoma.
Methods: The increase or decrease in adipose tissue was represented by the height of BMI. The BMI and malignant lymphoma genome-wide association studies (GWAS) used a summary dataset from the OPEN GWAS website. Single-nucleotide polymorphisms (SNPs) that met the criteria of P <5e–8 and LD of r2 = 0.001 in the BMI GWAS were chosen as genetic instrumental variants (IVs). Proxy SNPs with LD of r2 > 0.8 were identified, while palindromic and outlier SNPs were excluded. Mendelian randomization (MR) analysis used five methods, including inverse-variance weighted (IVW) model, weighted median (WM), MR-Egger, simple mode, and weighted mode. Sensitivity assessments included Cochran’s Q test, MR-Egger intercept test, and leave-one-out analysis. Participants randomly selected by the National Center for Health Statistics (NHANSE) and newly diagnosed HL patients at Fujian Medical University Union Hospital were used for external validation.
Results: The results of the MR analysis strongly supported the causal link between BMI and Hodgkin’s lymphoma (HL). The research demonstrated that individuals with lower BMI face a significantly increased risk of developing HL, with a 91.65% higher risk (ORIVW = 0.0835, 95% CI 0.0147 – 0.4733, P = 0.005). No signs of horizontal or directional pleiotropy were observed in the MR studies. The validation results aligned with the results from the MR analysis (OR = 0.871, 95% CI 0.826 – 0.918, P< 0.001). And there was no causal relationship between BMI and non-Hodgkin’s lymphoma (NHL).
Conclusions: The MR analysis study demonstrated a direct correlation between lower BMI and HL. This suggested that a decrease in adipose tissue increases the risk of developing HL. Nevertheless, further research is essential to grasp the underlying mechanism of this causal association comprehensively.
Background: Prostate cancer is one of the leading causes of cancer-related mortality among men in the United States. We examined the role of neighborhood obesogenic attributes on prostate cancer risk and mortality in the Southern Community Cohort Study (SCCS).
Methods: From the total of 34,166 SCCS male participants, 28,356 were included in the analysis. We assessed the relationship between neighborhood obesogenic factors [neighborhood socioeconomic status (nSES) and neighborhood obesogenic environment indices including the restaurant environment index, the retail food environment index, parks, recreational facilities, and businesses] and prostate cancer risk and mortality by controlling for individual-level factors using a multivariable Cox proportional hazards model. We further stratified prostate cancer risk analysis by race and body mass index (BMI).
Results: Median follow-up time was 133 months [interquartile range (IQR): 103, 152], and the mean age was 51.62 (SD: ± 8.42) years. There were 1,524 (5.37%) prostate cancer diagnoses and 98 (6.43%) prostate cancer deaths during follow-up. Compared to participants residing in the wealthiest quintile, those residing in the poorest quintile had a higher risk of prostate cancer (aHR = 1.32, 95% CI 1.12–1.57, p = 0.001), particularly among non-obese men with a BMI < 30 (aHR = 1.46, 95% CI 1.07–1.98, p = 0.016). The restaurant environment index was associated with a higher prostate cancer risk in overweight (BMI ≥ 25) White men (aHR = 3.37, 95% CI 1.04–10.94, p = 0.043, quintile 1 vs. None). Obese Black individuals without any neighborhood recreational facilities had a 42% higher risk (aHR = 1.42, 95% CI 1.04–1.94, p = 0.026) compared to those with any access. Compared to residents in the wealthiest quintile and most walkable area, those residing within the poorest quintile (aHR = 3.43, 95% CI 1.54–7.64, p = 0.003) or the least walkable area (aHR = 3.45, 95% CI 1.22–9.78, p = 0.020) had a higher risk of prostate cancer death.
Conclusion: Living in a lower-nSES area was associated with a higher prostate cancer risk, particularly among Black men. Restaurant and retail food environment indices were also associated with a higher prostate cancer risk, with stronger associations within overweight White individuals. Finally, residing in a low-SES neighborhood or the least walkable areas were associated with a higher risk of prostate cancer mortality.
Frontiers in Cell and Developmental Biology
Tipping the Balance: DNA Replication and Repair Vulnerabilities in Cancer