The datasets analysed in the manuscript "Inferring phenotypes of single cells based on the expression profiles of phenotype-associated marker genes in bulks and single cells".

Recent reports have suggested an inverse relationship between Alzheimer's disease (AD) and cancer, although the underlying mechanism remains unclear. We performed an epidemiological meta-analysis to assess cancer likelihood in AD patients and vice versa and explored the role of APOE in tumor immunity across 33 The Cancer Genome Atlas (TCGA) cancer types. Our analysis revealed that people with AD are epidemiologically less likely to develop cancer than individuals without AD (RR: 0.53), and cancer patients are less likely to develop AD than non-cancer patients (RR: 0.61). Notably, APOE expression was positively associated with anti-tumor immune signatures and prevalent in early-stage tumors. This research reveals that AD patients are less likely to develop cancer and vice versa, pinpoints APOE gene as a risk factor for AD with anti-tumor activity, and provides new insight into the epidemiologically observed inverse relationship between both diseases.

Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq) data. To overcome this limitation, we proposed a pathway-based clustering method for single cells (scPathClus). scPathClus first transforms the single-cell gene expression matrix into a pathway enrichment matrix and generates its latent feature matrix. Based on the latent feature matrix, scPathClus clusters single cells using the method of community detection. Applying scPathClus to pancreatic ductal adenocarcinoma (PDAC) scRNA-seq datasets, we identified two types of cancer-associated fibroblasts (CAFs), termed csCAFs and gapCAFs, which highly expressed complement system and gap junction-related pathways, respectively. Spatial transcriptome analysis revealed that gapCAFs and csCAFs are located at cancer and non-cancer regions, respectively. Pseudotime analysis suggested a potential differentiation trajectory from csCAFs to gapCAFs. Bulk transcriptome analysis showed that gapCAFs-enriched tumors are more endowed with tumor-promoting characteristics and worse clinical outcomes, while csCAFs-enriched tumors confront stronger antitumor immune responses. Compared to established CAF subtyping methods, this method displays better prognostic relevance.

Previous studies have identified B cell subpopulations with pro- and anti-tumoral activities, while the clinical relevance of B cell subpopulations-specific markers in pan-cancer remains understudied. We integrated 14 scRNA-seq datasets (102,504 cells from 424 patients, 15 cancer types) to identify B cell subpopulations via unsupervised clustering. We characterized their functional dynamics and prognostic relevance through analyzing single-cell, bulk and spatial transcriptomic data. Moreover, using B cell subpopulations-specific gene signatures, we constructed models for predicting cancer prognosis and immunotherapy response. We identified eight B cell subpopulations (b00-b07) which were classified into naive, plasma, memory, germinal center (GC), and cycling B cells. Trajectory analysis revealed b02-naive and b04-GC cells in early phases, evolving into b01- and b03-plasma/b05- and b06-memory/b07-cycling and b05-memory subpopulations. Anti-tumor responses were activated in early pseudotime, complement/immunoglobulin pathways peaked in mid-pseudotime, and energy metabolism increased in late-pseudotime. The enrichment of b07-cycling and b04-GC was negatively correlated with cancer prognosis, while b02-naive had a positive correlation. Spatial transcriptomic analysis showed clustered b00-b06 versus dispersed b07 cells, with b04-GC and b07-cycling cells distant from tertiary lymphoid structure cores. Based on the expression profiles of 1,047 B cell subpopulations-specific signatures, we identified three pan-cancer subtypes with distinct clinical and molecular characteristics. Using 13 B cell subpopulations-specific signatures, we constructed models to accurately predict cancer survival outcomes and immunotherapy response. Our study delineates eight B cell subpopulations with distinct prognostic relevance. Signature-based stratification and models underscore their clinical relevance in cancer outcomes and therapy response, advancing understanding of B cell heterogeneity in cancer.

2,2',4,4'-Tetrabromodiphenyl ether (BDE-47), an emerging contaminant (EC), is widely used in the production of brominated flame retardants and is biotoxic to marine organisms. However, our understanding of the mechanism of polybrominated diphenyl ethers (PBDEs)-induced toxicity remains incomplete. In this study, BDE-47 cytotoxicity after short-term exposure was investigated in PCK cells. BDE-47 significantly decreased cell viability, and morphological alterations were observed. Moreover, BDE-47 exposure induced apoptosis and ferroptosis, a newly described form of iron-mediated cell death, as demonstrated by transcriptomic analysis and physiological/biochemical tests. The observed cell death was associated with mitochondrial damage and a decrease in ATP production. Pharmacological intervention of cytotoxicity via 5-aminoimidazole-4-carboxamide ribonucleotide (AICAR), an activator of the adenosine monophosphate-activated protein kinase (AMPK) protein, a regulator of energy, strongly confirmed the causal relationship between cell death and energy metabolism dysfunction. Furthermore, lipidomic analysis revealed lipid metabolism disorders resulting from the accumulation of triglycerides (TGs) and glycerophospholipids (GPs) and the suppression of β-oxidation, ultimately inhibiting ATP synthesis. Molecular docking analysis revealed the binding potential of BDE-47 with energy metabolism checkpoints AMPK and Carnitine Palmitoyltransferase 1 (CPT1). Thus, our study broadens the understanding of the toxicity of BDE-47 and provides a new potential cellular and molecular mechanism.

BACKGROUND & AIMS:Hepatocellular carcinoma (HCC), a leading cause of cancer-related mortality, is closely linked to liver fibrosis, yet effective preventive therapies remain elusive. Autophagy is a critical cellular process that maintains hepatic homeostasis, and its disruption is implicated in the progression of fibrosis and HCC. This study aimed to evaluate the efficacy of oroxyloside (OAG), a flavonoid derived from Scutellaria baicalensis, in preventing liver fibrosis associated with cancer. METHODS:Mouse models of liver fibrosis and hepatocarcinogenesis were developed using carbon tetrachloride (CCl4) alone or a combined with diethylnitrosamine (DEN), with or without OAG treatment. Hepatocyte-specific Atg5 knockout mice (Atg5Hep-/-) and In vitro models with silenced Atg5 or PPARγ were used to investigate autophagy's role in OAG's therapeutic effects. To analyze the correlation between autophagy and hepatic fibrosis or cancer we use the TCGA and GSE database. Patient tissue samples (79 pairs) associated HCC was investigated by immunohistochemistry. RESULTS:This study demonstrates that OAG restores autophagic flux through the AMPK-ULK1 pathway in a PPARγ-dependent manner, reducing oxidative stress, DNA damage, and inflammatory cytokine IL-6 production. These mechanisms culminate in the inhibiting the activation of hepatic stellate cell activation and fibrosis progression. OAG also significantly attenuated liver tumor burden and improved survival in a chronic liver injury model. Importantly, the therapeutic effects of OAG were diminished in Atg5-deficient hepatocytes, highlighting its reliance on autophagy. This mechanistic insight differentiates OAG from existing anti-fibrotic or HCC therapies by targeting the interplay between autophagy and inflammation. CONCLUSION:OAG represents an innovative therapeutic approach to liver fibrosis and HCC, acting through autophagy-dependent pathways to inhibit inflammation and oxidative stress. Its dual anti-fibrotic and anti-carcinogenic effects position OAG as a promising candidate for addressing the unmet clinical needs in chronic liver disease.

ABSTRACT Viral infections contribute to a higher incidence of cancer than any other individual risk factor. This study aimed to compare the clinical and molecular features of four viral‐associated cancers: stomach adenocarcinoma (STAD), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), and cervical squamous cell carcinoma (CESC). Patients were categorized based on viral infection status, as provided in the clinical data, into virus‐associated and non‐virus‐associated groups, followed by a comprehensive comparison of clinical and molecular features. Our analysis disclosed that viral infections confer unique clinical and molecular signatures to their associated tumors. Specifically, human papillomavirus‐associated (HPV+) HNSC and hepatitis B virus‐associated (HBV+) LIHC patients were predominantly male, younger, and exhibited better clinical prognoses. Virus‐associated tumors displayed enhanced immune microenvironments and high DNA damage response scores, while non‐virus‐associated tumors were enriched in stromal signatures. HPV+ HNSC and Epstein‐Barr virus‐associated (EBV+) STAD showed similarities across multi‐omics features, including better responses to immunotherapy, lower TP53 mutation rates, tumor mutation burden (TMB), and copy number alteration (CNA). Conversely, HBV+, Hepatitis C virus‐associated (HCV+) LIHCs and HPV+ CESC were more genomically unstable due to high TP53 mutation rates, TMB, and CNA. At the protein level, Caspase‐7 and Syk were upregulated in HPV+ HNSC and EBV+ STAD, and positively correlated with the enrichment levels of CD8 + T cell, PD‐L1, and cytolytic activity. Patient stratification based on infection status has significant clinical implications, particularly for patient prognosis and drug response.

Acute myeloid leukemia (AML) is the most common type of acute leukemia in adults and the second most common in children. Despite the introduction of targeted therapies, AML survival rates have shown limited improvement, particularly among older patients. This study explored personalized treatment strategies for AML by proposing a novel subtyping method. Through unsupervised clustering based on the enrichment scores of 14 pathways related to metabolism, immunity, DNA repair, and oncogenic signaling, we identified three AML subtypes: DNA repair (DR), immune-enriched (ImE), and immune-deprived (ImD), consistent in four independent datasets. DR is marked by high expression of DNA repair and metabolic pathways, high stemness and proliferation potential, as well as high sensitivity to chemotherapy. ImD is characterized by low expression of immune and oncogenic pathways, favorable survival prognosis, low mutation rates of RUNX1 and TP53, high homeostasis, and low migration potential. ImE exhibits high enrichment of immune and oncogenic pathways, low stemness and proliferation capacity, low homeostasis, high migration potential, and low sensitivity to chemotherapy. Our pathway enrichment-based subtyping approach would offer a promising framework for understanding the molecular heterogeneity of AML and guiding personalized treatment of this disease.

PURPOSE:Immunity, stemness, and DNA damage repair (DDR) are crucial for cancer development and therapy resistance. With advancements in multiomics technology, the exploration of cancers related to immunity, stemness, and the DDR has triggered interest, but the combination of these levels for analyzing multiple cancers remains insufficient. METHODS:In this study, 9906 solid tumor samples from 31 TCGA cancer types were clustered on the basis of the enrichment levels of 13 gene sets associated with stemness, immunity, and DDR. Moreover, a soft ensemble model was constructed on the basis of the enrichment levels of these 13 gene sets to predict cancer subtypes via other omics data. RESULTS:We identified four pancancer subtypes, termed C1, C2, C3, and C4, which presented distinct molecular and clinical features, including the immune microenvironment, stemness, genome instability, intratumor heterogeneity, methylation levels, tumor progression, sensitivity to chemotherapy and immunotherapy, and survival prognosis. The soft ensemble model validated this subtyping method in two breast cancer datasets (gene expression level), a pancancer proteomic dataset (protein expression level), and a pancancer cell line dataset (cell line gene expression level). CONCLUSION:Our findings indicate that immune, stemness, and DDR signature-based subtyping offers new perspectives on cancer biology and holds promise for improving the clinical management of cancers.

Pathways-based clustering methods have been proposed to explore tumor heterogeneity. However, such methods are currently disadvantageous in that specific pathways need to be explicitly claimed. We developed the PathClustNet algorithm, a pathway-based clustering method designed to identify cancer subtypes. This method first detects gene clusters and identifies overrepresented pathways associated with them. Based on the pathway enrichment scores, it reveals cancer subtypes by clustering analysis. We applied the method to TCGA pan-cancer data and identified four pan-cancer subtypes, termed C1, C2, C3 and C4. C1 exhibited high metabolic activity, favorable survival, and the lowest TP53 mutation rate. C2 had high immune, developmental, and stromal pathway activities, the lowest tumor purity, and intratumor heterogeneity. C3, which overexpressed cell cycle and DNA repair pathways, was the most genomically unstable and had the highest TP53 mutation rate. C4 overrepresented neuronal pathways, with the lowest response rate to chemotherapy, but the highest tumor purity and genomic stability. Furthermore, age showed positive correlations with most pathways but a negative correlation with neuronal pathways. Smoking, viral infections, and alcohol use were found to affect the activities of neuron, cell cycle, immune, stromal, developmental, and metabolic pathway in varying degrees. The PathClustNet algorithm unveils a novel classification of pan-cancer based on metabolic, immune, stromal, developmental, cell cycle, and neuronal pathways. These subtypes display different molecular and clinical features to warrant the investigation of precision oncology.

BackgroundBrain tumours are known to have a high mortality and morbidity rate due to their localised and frequent invasive growth. The concept that glioma resistance could originate from the dissimilarity in the vulnerability of clonogenic glial stem cells to chemotherapeutic drugs and radiation has driven the scientific community to reexamine the comprehension of glioma growth and strategies that target these cells or modify their stemness.MethodsBased on the enrichment scores of 12 stemness signatures, we identified glioma subtypes in both tumour bulks and single cells by clustering analysis. Furthermore, we comprehensively compared molecular and clinical features among the glioma subtypes.ResultsConsistently, in seven different datasets, hierarchical clustering uncovered three subtypes of glioma, termed Stem-H, Stem-M, and Stem-L, with high, medium, and low stemness signatures, respectively. Stem-H and Stem-L exhibited the most unfavourable and favourable overall and disease-free survival, respectively. Stem-H showed the highest enrichment scores of the EMT, invasion, proliferation, differentiation, and metastasis processes signatures, while Stem-L displayed the lowest. Stem-H harboured a greater proportion of late-stage tumours compared to Stem-L. Moreover, Stem-H manifested higher tumour mutation burden, DNA damage repair and cell cycle activity, intratumour heterogeneity, and a more frequent incidence of TP53 and EGFR mutations than Stem-L. In contrast, Stem-L had higher O6-Methylguanine-DNA Methyltransferase (MGMT) methylation levels.ConclusionThe classification of glioma based on stemness may offer new insights into the biology of the tumour, as well as more accurate clinical management of the disease.

Existing algorithms for assessing tumor purity are limited to a single omics data, such as gene expression, somatic copy number variations, somatic mutations, and DNA methylation. Here we proposed the machine learning Multi-omics Tumor Purity prediction (MoTP) algorithm to estimate tumor purity based on multiple types of omics data. MoTP utilizes the Bayesian Regularized Neural Networks as the prediction algorithm, and Consensus Tumor Purity Estimates as labels. We trained MoTP using multi-omics data (mRNA, microRNA, long non-coding RNA, and DNA methylation) across 21 TCGA solid cancer types. By testing MoTP in TCGA validation sets, TCGA test sets, and eight datasets outside the TCGA cancer cohorts, we showed that although MoTP could achieve excellent performance in predicting tumor purity based on a single omics data type, the integration of multiple single omics data-based predictions can enhance the prediction performance. Moreover, we demonstrated the robustness of MoTP by testing it in datasets with Gaussian noise and feature missing. Benchmark analysis showed that MoTP outperformed most established tumor purity prediction algorithms, and that it required less running time and computational resource to fulfill the predictive task. Thus, MoTP would be an attractive option for computational tumor purity inference.

BackgroundRegulated cell death (RCD) pathways play significant roles in tumorigenesis. However, systematic investigation into correlations between RCD and various molecular and clinical features, particularly anti-tumor immunity and immunotherapy response in pan-cancer remains lacking.MethodsUsing the single-sample gene set enrichment analysis, we quantified the activities of six RCD pathways (apoptosis, autophagy, ferroptosis, cuproptosis, necroptosis, and pyroptosis) in each cancer specimen. Then, we explored associations of these six RCD pathways with tumor immunity, genomic instability, tumor phenotypes and clinical features, and responses to immunotherapy and targeted therapies in pan-cancer by statistical analyses.ResultsOur results showed that the RCD (except autophagy) activities were oncogenic signatures, as evidenced by their hyperactivation in late stage or metastatic cancer patients, positive correlations with tumor proliferation, stemness, genomic instability and intratumor heterogeneity, and correlation with worse survival outcomes in cancer. In contrast, autophagy was a tumor suppressive signature as its associations with molecular and clinical features in cancer shows an opposite pattern compared to the other RCD pathways. Furthermore, heightened RCD (except cuproptosis) activities were correlated with increased sensitivity to immune checkpoint inhibitors. Additionally, elevated activities of pyroptosis, autophagy, cuproptosis and necroptosis were associated with increased drug sensitivity in a broad spectrum of anti-tumor targeted therapies, while the elevated activity of ferroptosis was correlated with decreased sensitivity to numerous targeted therapies.ConclusionRCD (except autophagy) activities correlate with unfavorable cancer prognosis, while the autophagy activity correlate with favorable clinical outcomes. RCD (except cuproptosis) activities are positive biomarkers for anti-tumor immunity and immunotherapy response.

Gliomas are highly heterogeneous in molecular, histology, and microenvironment. However, a classification of gliomas by integrating different tumor microenvironment (TME) components remains unexplored. Based on the enrichment scores of 17 pathways involved in immune, stromal, DNA repair, and nervous system signatures in diffuse gliomas, we performed consensus clustering to uncover novel subtypes of gliomas. Consistently in three glioma datasets (TCGA-glioma, CGGA325, and CGGA301), we identified three subtypes: Stromal-enriched (Str-G), Nerve-enriched (Ner-G), and mixed (Mix-G). Ner-G was charactered by low immune infiltration levels, stromal contents, tumor mutation burden, copy number alterations, DNA repair activity, cell proliferation, epithelial-mesenchymal transformation, stemness, intratumor heterogeneity, androgen receptor expression and EGFR, PTEN, NF1 and MUC16 mutation rates, while high enrichment of neurons and nervous system pathways, and high tumor purity, estrogen receptor expression, IDH1 and CIC mutation rates, temozolomide response rate and overall and disease-free survival rates. In contrast, Str-G displayed contrastive characteristics to Ner-G. Our analysis indicates that the heterogeneity between glioma cells and neurons is lower than that between glioma cells and immune and stromal cells. Furthermore, the abundance of neurons is positively associated with clinical outcomes in gliomas, while the enrichment of immune and stromal cells has a negative association with them. Our classification method provides new insights into the tumor biology of gliomas, as well as clinical implications for the precise management of this disease.

Abstract Background TMPRSS2, a key molecule for SARS-CoV-2 invading human host cells, has an association with cancer. However, its association with lung cancer remains insufficiently unexplored. Methods In five bulk transcriptomics datasets, one single‐cell RNA sequencing (scRNA-seq) dataset and one proteomics dataset for lung adenocarcinoma (LUAD), we explored associations between TMPRSS2 expression and immune signatures, tumor progression phenotypes, genomic features, and clinical prognosis in LUAD by the bioinformatics approach. Furthermore, we performed experimental validation of the bioinformatics findings. Results TMPRSS2 expression levels correlated negatively with the enrichment levels of both immune-stimulatory and immune-inhibitory signatures, while they correlated positively with the ratios of immune-stimulatory/immune-inhibitory signatures. It indicated that TMPRSS2 levels had a stronger negative correlation with immune-inhibitory than with immune-stimulatory signatures. TMPRSS2 downregulation correlated with increased proliferation, stemness, genomic instability, tumor progression, and worse survival in LUAD. We further validated that TMPRSS2 was downregulated with tumor progression in the LUAD cohort we collected from Jiangsu Cancer Hospital, China. In vitro and in vivo experiments verified the association of TMPRSS2 deficiency with increased tumor cell proliferation and invasion and antitumor immunity in LUAD. Moreover, in vivo experiments demonstrated that TMPRSS2-knockdown tumors were more sensitive to BMS-1, an inhibitor of PD-1/PD-L1. Conclusions TMPRSS2 is a tumor suppressor, while its downregulation is a positive biomarker of immunotherapy in LUAD. Our data provide a potential link between lung cancer and pneumonia caused by SARS-CoV-2 infection.

Background: Non-small cell lung cancer (NSCLC) accounts for 85 % of all lung cancers, the leading cause of cancer-related deaths worldwide. Despite recent advancements in NSCLC treatment, such as targeted therapy and immunotherapy, a large proportion of NSCLC patients develop drug resistance. Thus, development of new effective treatments for NSCLC is urgently needed. Traditional Chinese medicine may provide sources for developing new anti-cancer drugs. Although Scutellaria baicalensis (SB) has been proven to have anti-cancer effects, its action mechanism on NSCLC is not yet fully understood. Here we used network pharmacology to investigate the potential mechanism of SB treating NSCLC. Methods: We identified the active constituents of SB from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and predicted their potential targets using the SwissTargetPrediction database. Next, therapeutic targets for NSCLC were screened using five databases: GeneCards, DisGeNET, DrugBank, Online Mendelian Inheritance in Man (OMIM), and Therapeutic Target Database (TTD). A protein-protein interaction (PPI) network of the common targets between SB and NSCLC was constructed using STRING were visualized using Cytoscape. We also performed enrichment analysis of these targets to identify gene ontology (GO) and KEGG pathways associated with them. Finally, we conducted molecular docking analysis using Autodock Vina and PyMOL to investigate the interactions between active SB components and NSCLC targets. Furthermore, we performed in vitro experimental validations of the findings by bioinformatics analysis. Results: We identified 35 active compounds of SB and their 463 potential targets, and 1,073 NSCLC-associated targets. A total of 140 targets overlapped between the SB and NSCLC targets. PPI network analysis and drug-ingredient-target network analysis identified SRC, HSP90AA1, STAT3, AKT1, and MAPK3 as the core target proteins, and baicalein, 5,7,2,5-tetrahydroxy-8,6-dimethoxyflavone, skullcapflavone II, tenaxin I, and rivularin as critical active compounds of SB. GO and pathway enrichment analysis uncovered pathways significantly associated with the common targets between SB and NSCLC, including PI3K-Akt signaling, VEGF, and EGFR tyrosine kinase inhibitor resistance. Molecular docking revealed a stable binding between the main active components and core targets. In vitro experiments showed that baicalein suppressed A549 cell proliferation in a concentration-dependent manner, induced apoptosis, and caused cell cycle arrest in the S phase. Moreover, baicalein inhibited A549 cell migration and activation of the PI3K/Akt pathway. Conclusions: SB has a potential to treat NSCLC through multiple targets and pathways. This study afforded potential targets for further experimental research to explore the mechanism of SB in the treatment of NSCLC.

PurposeBecause only a subset of cancer patients can benefit from immunotherapy, identifying predictive biomarkers of ICI therapy response is of utmost importance.MethodsWe analyzed the association between hemoglobin (HGB) levels and clinical outcomes in 1,479 ICIs-treated patients across 16 cancer types. We explored the dose-dependent associations between HGB levels and survival and immunotherapy response using the spline-based cox regression analysis. Furthermore, we investigated the associations across subgroups of patients with different clinicopathological characteristics, treatment programs and cancer types using the bootstrap resampling method.ResultsHGB levels correlated positively with clinical outcomes in cancer patients receiving immunotherapy but not in those without immunotherapy. Moreover, this association was independent of other clinicopathological characteristics (such as sex, age, tumor stage and tumor mutation burden (TMB)), treatment program and cancer type. Also, this association was independent of the established biomarkers of immunotherapy response, including TMB, PD-L1 expression and microsatellite instability. The combination of TMB and HGB level are more powerful in predicting immunotherapy response than TMB alone. Multi-omics analysis showed that HGB levels correlated positively with antitumor immune signatures and negatively with tumor properties directing antitumor immunosuppression, such as homologous recombination defect, stemness and intratumor heterogeneity.ConclusionThe HGB measure has the potential clinical value as a novel biomarker of immunotherapy response that is easily accessible from clinically routine examination. The combination of TMB and HGB measures have better predictive performance for immunotherapy response than TMB.

Hepatitis B virus (HBV) infection is a major etiology of hepatocellular carcinoma (HCC). An interesting question is how different are the molecular and phenotypic profiles between HBV-infected (HBV+) and non-HBV-infected (HBV-) HCCs? Based on the publicly available multi-omics data for HCC, including bulk and single-cell data, and the data we collected and sequenced, we performed a comprehensive comparison of molecular and phenotypic features between HBV+ and HBV- HCCs. Our analysis showed that compared to HBV- HCCs, HBV+ HCCs had significantly better clinical outcomes, higher degree of genomic instability, higher enrichment of DNA repair and immune-related pathways, lower enrichment of stromal and oncogenic signaling pathways, and better response to immunotherapy. Furthermore, in vitro experiments confirmed that HBV+ HCCs had higher immunity, PD-L1 expression and activation of DNA damage response pathways. This study may provide insights into the profiles of HBV+ and HBV- HCCs, and guide rational therapeutic interventions for HCC patients.

Objective To investigate the associations between dynapenic obesity and the risk of dementia, and the modifying effects of age, sex, and the APOE gene, using a large population-based cohort. Methods 279,884 participants aged 55 and above from the UK Biobank were included. The participants were classified into four categories based on body mass index and hand grip strength: healthy, obesity, dynapenia, and dynapenic obesity. The incident dementia was identified based on linked hospital records and death register data. Cox proportional hazards regression models were used to estimate the associations, followed by age-, sex-, and apolipoprotein E (APOE) gene-stratified analyses. Results During the median follow-up of 12.4 years, 5,170 (1.8%) participants developed dementia. Compared with the healthy group, participants with dynapenic obesity had 67% higher dementia risk (hazard ratio [HR]: 1.67, 95% confidence interval [CI]: 1.44–1.94). Compared with the healthy group, higher risks of dementia in participants with dynapenic obesity were respectively observed in male (HR: 2.03, 95% CI: 1.65–2.50), younger (<65 years, HR: 1.97, 95% CI: 1.55–2.50), and non-ε4-carrier (HR: 1.97, 95% CI: 1.60–2.44) (all P for interaction <0.05). In participants under 65 years and non-ε4-carrier, those with dynapenic obesity had the highest risk of dementia (HR: 2.63, 95% CI: 1.91–3.62), compared with the healthy group (P for second order interaction = 0.026). Conclusions Dynapenic obesity is associated with increased risks of dementia, especially in participants under 65 years and non-ε4-carrier, suggesting the importance of managing dynapenic obesity in the prevention of cognition-related disorders.

IntroductionSarcopenia is a progressive deterioration of skeletal muscle mass strength and function.MethodsTo uncover the underlying cellular and biological mechanisms, we studied the association between sarcopenia's three stages and the patient's ethnicity, identified a gene regulatory network based on motif enrichment in the upregulated gene set of sarcopenia, and compared the immunological landscape among sarcopenia stages.ResultsWe found that sarcopenia (S) was associated with GnRH, neurotrophin, Rap1, Ras, and p53 signaling pathways. Low muscle mass (LMM) patients showed activated pathways of VEGF signaling, B-cell receptor signaling, ErbB signaling, and T-cell receptor signaling. Low muscle mass and physical performance (LMM_LP) patients showed lower enrichment scores in B-cell receptor signaling, apoptosis, HIF-1 signaling, and the adaptive immune response pathways. Five common genes among DEGs and the elastic net regression model, TTC39DP, SLURP1, LCE1C, PTCD2P1, and OR7E109P, were expressed between S patients and healthy controls. SLURP1 and LCE1C showed the highest expression levels among sarcopenic Chinese descent than Caucasians and Afro-Caribbeans. Gene regulatory analysis of top upregulated genes in S patients yielded a top-scoring regulon containing GATA1, GATA2, and GATA3 as master regulators and nine predicted direct target genes. Two genes were associated with locomotion: POSTN and SLURP1. TTC39DP upregulation was associated with a better prognosis and stronger immune profile in S patients. The upregulation of SLURP1 and LCE1C was associated with a worse prognosis and weaker immune profile.ConclusionThis study provides new insight into sarcopenia's cellular and immunological prospects and evaluates the age and sarcopenia-related modifications of skeletal muscle.