Fengyuan Huang, Peng Xu, Zongliang Yue, Yuwei Song, Kaili Hu, Xinyang Zhao, Min Gao, Zechen Chong

Body Weight Correlates with Molecular Variances in Cancer Patients

  • Cancer Research
  • Oncology

Abstract Overweight and obesity are identified by a high body mass index (BMI) and carry significant health risks due to associated co-morbidities. Although epidemiological data connects overweight/obesity with 13 cancer types, a better understanding of the molecular mechanisms underlying this correlation are needed to improve prevention and treatment strategies. In this study, we conducted a comprehensive analysis of molecular differences between overweight or obese patients and normal-weight patients across 14 different cancer types from The Cancer Genome Atlas (TCGA). Using the propensity score weighting algorithm to control for confounding factors, obesity-specific mutational features were identified, such as higher mutation burden in rectal cancer and biased mutational signatures in other cancers. Differentially expressed genes (DEGs) in tumors from patients with overweight/obesity were predominantly upregulated and enriched in inflammatory and hormone-related pathways. These DEGs were significantly associated with survival rates in various cancer types, highlighting the impact of elevated body fat on gene expression profiles and clinical outcomes in cancer patients. Interestingly, while high BMI seemed to have a negative impact on most cancer types, the normal weight biased mutational and gene expression patterns indicated overweight/obesity may be beneficial in endometrial cancer, suggesting the presence of an "obesity paradox" in this context. Body fat also significantly impacted the tumor microenvironment by modulating immune cell infiltration, underscoring the importance of understanding the interplay between weight and immune response in cancer progression. Together, this study systematically elucidates the molecular differences corresponding to body weight in multiple cancer types, offering potentially critical insights for developing precision therapy for cancer patients.

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