Overweight and obesity are identified by a high body mass index (BMI) and carry significant health risks due to associated comorbidities. Although epidemiologic data connect overweight/obesity with 13 cancer types, a better understanding of the molecular mechanisms underlying this correlation is 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. 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 (DEG) 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 patients with cancer. 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 patients with cancer. Significance: Elucidation of the complex interplay between body weight and the molecular landscape of cancer could potentially guide tailored therapies and improve patient management amid the global obesity crisis.

V(D)J recombination generates antigen receptor diversity in developing B and T cells. Long-read transcriptome technologies (e.g., PacBio Iso-Seq, Nanopore RNA/cDNA) capture full-length transcripts and thus resolve V(D)J events more accurately than short-read platforms. However, existing short-read tools are not applicable to or optimized for long-read data. We developed VDJcraft, the first integrated pipeline designed for V(D)J recombination analysis using long-read transcriptome sequencing data. The workflow uses a two-pass alignment strategy: global alignment to the GENCODE reference with minimap2, followed by local realignment and annotation using the international ImMunoGeneTics information system (IMGT). A customized module enhances D-gene detection sensitivity and positional precision. Sequencing errors are reduced through consensus-based correction toward the predominant subclass. Antigen-binding regions are annotated using IMGT-defined motifs to characterize CDRs and binding site composition. VDJcraft was validated on simulated and Human Genome Structural Variation Consortium (HGSVC) datasets and applied to disease datasets. It accurately recovered full-length V(D)J-C sequences and outperformed existing methods in gene detection and recombination accuracy. Long-read calls also showed significantly higher concordance with high-confidence short-read calls (Mann-Whitney U test, p = 1.55 × 10⁻ ⁴ ). Additionally, we identified 31 putative novel gene subclasses absent from the IMGT database from HGSVC datasets. Analyses of longitudinal blood samples from a COVID-19 patient revealed distinct V(D)J recombination patterns and segment enrichment, characterized by increased IGHV1-2 usage, enrichment of the IGHV3-7/IGHD6-9/IGHJ5_02 rearranged clonotype, and a transient peak in IgG2 levels at day 4 followed by a gradual return to baseline. In conclusion, VDJcraft provides a robust framework for long-read V(D)J characterization and enables the discovery of disease-associated immune signatures.

Alzheimer’s disease (AD) is a leading cause of dementia in the elderly, with late-onset AD (LOAD) accounting for 95% of the cases. The etiology underlying LOAD, however, remains unclear. Using a humanized mouse model, we showed previously that exposure to ozone (O₃), a potential environment risk factor, in a cyclic exposure protocol that mimics a human exposure scenario, accelerated AD-like neuropathophysiology in old humanized male ApoE3 (E3) but not ApoE4 (E4) mice. Using RNA sequencing (RNA-seq) techniques, we further demonstrate here that the ApoE genotype has the greatest influence on transcriptional changes, followed by age and O₃ exposure. Notably, AD-related genes were expressed even at baseline and in young mice, but the differences in the expression levels are obvious in old age. Importantly, although both E3 and E4 mice exhibited some AD-related transcriptomic alterations, old E3 mice exposed to O₃, which showed memory impairment, experienced more pronounced disruptions in the expression of genes related to redox balance, neurogenesis, neuroinflammation, and cellular senescence in the hippocampus, compared with O₃-exposed old E4 mice. These results provide new insights into the molecular mechanisms underlying memory loss in O₃-exposed old E3 male mice and emphasize the complexity of interactions between gene, environment, and aging in AD pathophysiology.

Supplementary Figures and Tables

Supplementary Figures and Tables

Overweight and obesity are identified by a high body mass index (BMI) and carry significant health risks due to associated comorbidities. Although epidemiologic data connect overweight/obesity with 13 cancer types, a better understanding of the molecular mechanisms underlying this correlation is 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. 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 (DEG) 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 patients with cancer. 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 patients with cancer.Significance:Elucidation of the complex interplay between body weight and the molecular landscape of cancer could potentially guide tailored therapies and improve patient management amid the global obesity crisis.