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Page 85 Comertpay et al. J Transl Genet Genom 2022;6:84-94 https://dx.doi.org/10.20517/jtgg.2021.44

Conclusion: These genes may be used for risk analysis of the disease progression of obese patients with breast
cancer. Corresponding genes and pathways should be validated via experimental studies.

Keywords: Obesity, breast cancer, machine learning, penalty regression models

INTRODUCTION
Breast cancer is the second largest cause of mortality from cancer among women; however, detection at an
[1]
early stage and treatment could significantly improve outcomes . The World Health Organization (WHO)
stated that last year breast cancer was diagnosed in 2.3 million women worldwide and resulted in 685,000
[2]
deaths . It has a complex etiology that involves various genetic, physiological, and lifestyle-related risk
factors (alcohol/smoking, excessive body weight, etc.) . Particularly, several studies have demonstrated the
[3,4]
association between obesity status and breast cancer, highlighting the potential of an increase in personal
health behaviors to reduce the burden of disease . In the WHO report, overweight and obesity are
[4]
determined as a surplus fat aggregation that may harm to health. Body mass index (BMI) is a basic height-
2
weight index mostly used to categorize overweight and obesity in adults (BMI > 30 kg/m ). According to the
most recent WHO case report, currently, more than 1.9 billion adults and 650 million people worldwide can
be categorized as overweight or obese, respectively, and these rates are predicted to increase more rapidly in
the coming decades .
[2]

Numerous studies have examined the association between obesity and cancer development in various
cancer types, such as esophagus, pancreas, prostate, colorectal, and breast cancer . Although there is
[5]
substantial evidence indicating a high BMI is linked to a growing risk of breast cancer in postmenopausal
women and poorer clinical outcomes in people of all ages, the specific nature of the exposure is unknown.

This uncertainty is mirrored in the variety of methodologies used in the research to characterize or define
[6]
body composition: BMI, body weight, body composition, metabolic state, and nutritional condition .

Obesity is linked to a higher incidence of postmenopausal estrogen receptor-positive breast cancer and
poorer cancer-associated results across the board . The obesity-cancer relationship is thought to be
[7]
influenced by significant quantities of circulating and local estrogens, changed concentrations of
adipokines (adiponectin and leptin), disrupted insulin/IGF signaling, changes in the microbiome, and
[8]
local and systemic inflammatory effects (e.g., WAT) . The latest studies indicate that obesity-associated
[7]
insulin/insulin-like growth factor-1 axis, adipokines, inflammatory cytokines and leptin, sex hormones ,
[9]
[8]
[11]
[10]
adiponectin , ORPS , and HER2 proteins play a significant role in breast cancer-related pathways. On
the other hand, CD68 immunohistochemistry (CD68 + CLS-B) expression has been related to insulin
[12]
resistance and negative prognosis in obesity-associated breast cancer . According to another study,
vitamin D supplementation may have varied impacts on gene expression in breast and adipose tissue during
weight loss .
[13]
Obesity affects various aspects of breast cancer treatment, including surgery, chemotherapy, endocrine
therapy, and radiotherapy. In addition, breast cancer risk and recurrence are affected by anti-inflammatory
drugs, metformin, diet, and physical activity [7,14] . Surgery, radiation, and chemotherapy problems are more
common in obesity-associated breast cancer patients. Furthermore, obese patients have a higher chance of
local recurrence than women of normal weight. Mechanistically driven approaches, involving biomarker
development, are essential for the prevention and treatment of obesity-related malignancies, much as they
are for tumor-directed pharmacologic therapy in oncology .
[14]