Li Deng

Cancer stem cells (CSCs) can adapt their metabolism, which limits the effectiveness of therapies targeting metabolic pathways. Here we show that high glucose conditions promote metformin resistance in CD133⁺ colorectal CSCs by driving a switch from glucose dependence to lipid use. Mechanistically, metformin increases LDHA-dependent lactate production in these cells under high glucose conditions. Lactate accumulation enhances histone H3 lysine 18 lactylation (H3K18la), which activates c-JUN and induces CD36 expression. CD36 promotes free fatty acid (FFA) uptake and lipid droplet (LD) formation, providing fuel for fatty acid β-oxidation. This process generates ATP and NADPH, reduces energy stress and reactive oxygen species, and thereby supports CSC survival during metformin treatment. In vitro and in vivo, inhibition of either LDHA or CD36 restores metformin sensitivity in CD133⁺ colorectal CSCs. These findings identify a lactate-driven epigenetic and metabolic pathway underlying metformin resistance and suggest LDHA and CD36 as potential therapeutic targets.

Triple-negative breast cancer (TNBC) is correlated to a poor prognosis, especially in the context of obesity. The interaction between adipocytes and TNBC cellsplay a key role in the progression of TNBC. This study aims to investigate the mechanisms underlying the cross-talk and progression between adipocytes and TNBC cells. We established a co-culture model involving mature adipose cells (hADSC and 3T3-L1) and TNBC cells. Cell invasion abilities were assessed using wound healing and Transwell assays. Gene and protein expression levels were examined using RT-PCR, western blotting, and immunostaining. Adipocytokine and chemokine levels were measured using ELISA. Additionally, we developed a fat mouse model induced by a high-fat diet and a tumor-bearing model of TNBC cells in vivo. The results indicated a significant enhancement in the invasion abilities of TNBC cells after co-culture. Mature adipose tissue co-cultured with TNBC cells increased the expression and secretion of C-X-C motif chemokine ligand 1 (CXCL1) and upregulated matrix metalloproteinase 7 (MMP7) and MMP9 in TNBC cells by activating the signal transducer and activator of transcription 3 (STAT3) /nuclear factor-κB p65 (NF-κB p65) pathway. Additionally, co-culture activated the STAT3/NF-κB p65 pathway, increasing the expression and secretion of IL6 in adipocytes. Based on the mouse obesity model, our experiments on orthotopic breast fat pad xenoimplantation showed consistent results in vivo. Our findings suggest a cross-talk between TNBC cells and adipocytes, activating the STAT/NF-κB p65 pathway through the production and secretion of CXCL1 and IL6, respectively, thereby promoting TNBC progression. These results propose a potential strategy for developing individualized treatments for patients with TNBC in clinical practice.

Resistance to anti-PD-1 therapy remains a significant challenge in gastric cancer (GC) treatment. Here, we revealed that the USP14-IMP2-CXCL2 axis in tumor-associated macrophages (TAMs) drove resistance by recruiting myeloid-derived suppressor cells (MDSCs). Endoscopic biopsy samples were obtained from patients with inoperable GC who were candidates for anti-PD-1 therapy. Single-cell RNA sequencing (scRNA-seq) analysis showed a higher prevalence of USP14⁺ TAMs in therapy-resistant cases, where USP14 was linked to the immunosuppressive phenotype of TAMs. Clinically, GC samples with elevated USP14⁺ TAM infiltration exhibited decreased CD8⁺ T cell presence and increased MDSC infiltration. In vivo experiments further confirmed that USP14⁺ TAMs facilitated resistance to anti-PD-1 therapy in GC, reduced the infiltration of CD8⁺ T cells, and significantly increased the infiltration of MDSCs. In particular, USP14⁺ TAMs markedly enhanced the recruitment of MDSCs into the GC microenvironment through the secretion of CXCL2. Mechanistically, USP14 stabilized the m6A reader IMP2 through deubiquitination, thus enhancing CXCL2 expression and secretion. Conversely, the E3 ligase RNF40 facilitated IMP2 degradation via increasing its ubiquitination, with USP14 and RNF40 dynamically balancing IMP2’s protein abundance. Furthermore, animal experiments demonstrated that targeted intervention of USP14 markedly enhanced the sensitivity of GC to anti-PD-1 therapy. This study provided a comprehensive exploration of USP14’s oncogenic roles in TAMs, suggesting a novel strategy to enhance the efficacy of anti-PD-1 therapy by inhibiting the USP14/IMP2/CXCL2 signaling axis in GC. The sc-RNA analysis revealed that infiltration of USP14⁺ TAM was significantly higher in anti-PD-1 resistant GC compared to anti-PD-1 sensitive GC (top). USP14 stabilized IMP2 protein in tumor-associated macrophages (middle-left), leading to recruitment of MDSCs into GC microenvironment through secretion of CXCL2 (middle-right), thus reducing the infiltration of CD8⁺ T cells and facilitating the resistance to anti-PD-1 therapy in GC (bottom). Comment: Dear editors, in formally published articles, figure legends could be placed beneath their corresponding figures. In this version, is this arrangement adopted for the sake of convenience during the proofreading process? If so, we understand and accept this layout.

Background: Obesity is as an important risk factor for chronic diseases. Metabolically healthy obesity (MHO) is considered a benign state. The association between metabolic health and obesity categories and cancer risk remains unclear. This study aimed to investigate the relationship between metabolic health status combined with obesity phenotypes and the risk of cancer.Methods: Data from 91,834 participants in the Kailuan cohort were analyzed, excluding individuals with a body mass index (BMI) < 18.5 kg/m² and those with a history of cancer. Obesity phenotypes were classified based on BMI and waist circumference (WC) combined with metabolic health status, resulting in six phenotypes. Cox proportional hazard regression models were used to assess the association between metabolic health and obesity phenotypes with cancer risk and all-cause mortality.Results: The prevalence of metabolically healthy obesity and metabolically unhealthy obesity defined by BMI was 6.86% and 12.18%, while that defined by WC was 20.79% and 25.76%, respectively. Compared to metabolically healthy participants, individuals with an unhealthy metabolic status had a significantly higher risk of cancer (HR, 1.09; 95% CI, 1.03–1.15; p=0.004). The hazard ratios for cancer were 1.19, 1.23, 1.20, and 1.55 for individuals with one, two, three, and four metabolic disorders, respectively. Among those classified as metabolically unhealthy, both overweight and obesity were associated with a protective effect on cancer risk (HR, 0.88; 95% CI, 0.80–0.96; p=0.006 for overweight; HR, 0.87; 95% CI, 0.78–0.97; p=0.010 for obesity). However, abdominal obesity significantly increased cancer risk in both metabolically healthy and unhealthy participants. In subgroup analysis, simple obesity showed a protective trend against cancer in those with respiratory cancers, while abdominal obesity consistently posed a risk for various cancer types.Conclusion: Metabolically unhealthy status and abdominal obesity are risk factors for cancer and all-cause mortality, whereas simple obesity offers protective effects against cancer and all-cause mortality in metabolically unhealthy individuals. These findings suggest that maintaining metabolic health and reducing the metabolic risks associated with abdominal obesity should be key targets for cancer prevention.

Background Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge. Objective This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia. Methods We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process. Results Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy. Conclusion We developed two models for identifying precachexia and cachexia, which will help clinicians detect and diagnose precachexia.

BackgroundAging is an inevitable biological process. Accelerated aging renders adults more susceptible to chronic diseases and increases their mortality rates. Previous studies have reported the relationship between lifestyle factors and phenotypic aging. However, the relationship between intrinsic factors, such as reproductive factors, and phenotypic aging remains unclear.MethodsThis study utilized data from the National Health and Nutrition Examination Survey (NHANES), spanning from 1999 to 2010 and 2015-2018, with 14,736 adult women. Random forest imputation was used to handle missing covariate values in the final cohort. Weighted linear regression was utilized to analyze the relationship between women-specific reproductive factors and PhenoAgeAccel. Considering the potential impact of menopausal status on the results, additional analyses were conducted on premenopausal and postmenopausal participants. Additionally, the Life's Essential 8 (LE8) was used to investigate the impact of healthy lifestyle and other factors on the relationship between women-specific reproductive factors and PhenoAgeAccel. Stratified analyses were conducted based on significant interaction p-values.ResultsIn the fully adjusted models, delayed menarche and gynecological surgery were associated with increased PhenoAgeAccel, whereas pregnancy history were associated with a decrease. Additionally, early or late ages of menopause, first live birth, and last live birth can all negatively impact PhenoAgeAccel. The relationship between women-specific reproductive factors and PhenoAgeAccel differs between premenopausal and postmenopausal women. High LE8 scores positively impacted the relationship between certain reproductive factors (age at menarche, age at menopause, age at first live birth, and age at last live birth) and phenotypic age acceleration. Stratified analysis showed significant interactions for the following variables: BMI with age at menarche, pregnancy history, and age at menopause; ethnicity with age at menopause, age at first live birth, and parity; smoking status with use of contraceptive pills and gynecologic surgery; hypertension with use of contraceptive pills, pregnancy history, and age at menopause.ConclusionDelayed menarche, gynecological surgery, and early or late ages of menopause, first live birth, and last live birth are associated with accelerated phenotypic aging. High LE8 score may alleviate the adverse effects of reproductive factors on phenotypic aging.

The characteristics of early-onset (onset age <50 years) and later-onset (onset age ≽ 50 years) cancers differ significantly. Identifying novel risk factors for both types of cancer is crucial for increasing awareness of cancer prevention and for reducing its burden. This study aimed to analyze the trends in incidence and risk factors for early-onset and late-onset cancers. We conducted a prospective study by drawing data from the Kailuan Study. This study included 6,741 participants with cancer (624 with early-onset cancer and 6,117 with later-onset cancer) and 6,780 matched controls among the 186,249 participants who underwent Kailuan health examinations from 2006 to 2019. The primary outcomes were cancer incidence rates, and associated risk factors for early- and later-onset cancer. Weighted Cox regression was used to calculate hazard ratios and 95% confidence intervals of each exposure factor for early- and later-onset cancer by cancer type. Population-attributable risk proportions were used to estimate the number of cases that could be prevented by eliminating a risk factor from the population. Except for liver cancer, incidence rates for nearly all types of cancer increased during the study period. Smoking, alcohol consumption, lipid metabolism disorders, hypertension, diabetes mellitus, fatty liver, and inflammation were associated with a significantly increased risk of cancer at multiple sites, but risk factors for cancer incidence differed by site. Smoking, alcohol consumption, inflammation, and hypertension were the major contributors to preventable cancer. The incidence of several different types of cancer, including early-onset cancer, is increasing in northeastern China. Differences in risk factors between early-onset and later-onset malignancies may contribute to the divergence in the observed changes in incidence trends between these two specific types of cancer.

Approximately 19.3 million new cancer cases and 10 million cancer-related deaths occur worldwide each year [1]. Cancer is a complex disease that poses a substantial threat to public health, which makes understanding its characteristics and the underlying developmental mechanisms necessary [2]. Recent studies have demonstrated a close relationship between cancer, inflammation, and metabolic disorders, with […]