WGCNA: an R package for weighted correlation network analysis

Revealing the role of natural killer cells in ankylosing spondylitis: identifying diagnostic biomarkers and therapeutic targets

BACKGROUND

Ankylosing spondylitis (AS) is a chronic autoimmune disease that primarily affects the axial joints. Immune cells play a key role in the pathogenesis of AS. This study integrated bioinformatics methods with experimental validation to explore the role of natural killer (NK) cells in AS.

METHODS

Two microarray datasets, GSE25101 and GSE73754, were selected, and the scRNA-seq data were obtained from GSE194315 and Liu's research. Differentially expressed genes (DEGs) and functional enrichment analysis were performed respectively. Weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules of co-expressed genes and genes involved in NK cell function. The diagnostic value of the identified key genes was evaluated using ROC curves, logistic regression analysis, and a nomogram. Real-time PCR (RT-PCR) was used to quantified the expression of genes. Statistical analysis was conducted using the R software package, and a p-value of less than 0.05 was considered statistically significant.

RESULTS

Pathways enrichment analysis revealed the involvement of NK cell-mediated immune pathways and regulation of the innate immune response, indicating the crucial role of innate immunity, especially NK cells, in AS pathogenesis. The construction of a co-expression network revealed that the MElightyellow module was most relevant to the NK cell-mediated immune pathway. IL2RB, CD247, PLEKHF1, EOMES, S1PR5, FGFBP2 from the MElightyellow module were identified as key genes involved in NK cell-mediated immune response and served as potential diagnostic biomarkers for AS, with moderate to high diagnostic values based on AUC values. Further analysis using scRNA-seq profiling revealed the higher expression level of IL2RB, CD247, PLEKHF1, S1PR5, FGFBP2 in NK cells compared to that in other cell types. CD247, PLEKHF1, EOMES, S1PR5, and FGFBP2 were reduced expressed in AS patients as compare to control group verified by scRNA-seq data, CD247, EOMES, FGFBP2, IL2RB and S1PR5 were reduced expressed verified by RT-PCR, and PLEKHF1, S1PR5, and FGFBP2 was upregulated after TNF-α blocker therapy.

CONCLUSION

The study revealed the potential role of NK cells and identified IL2RB, CD247, PLEKHF1, EOMES, S1PR5, and FGFBP2 as key genes associated with NK cells in the pathogenesis of AS.

Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) is a fatal disease, and radioresistance is an important factor leading to treatment failure and disease progression. The objective of this research was to detect radioresistance-related genes (RRRGs) with prognostic value in NSCLC.

METHODS

The weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) analysis were performed to identify RRRGs using expression profiles from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO) regression and random survival forest (RSF) were used to screen for prognostically relevant RRRGs. Multivariate Cox regression was used to construct a risk score model. Then, Immune landscape and drug sensitivity were evaluated. The biological functions exerted by the key gene LBH were verified by in vitro experiments.

RESULTS

Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2 and TSPAN2) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. The immune landscape and sensitivity to anti-tumour drugs showed significant disparities between patients categorized into different risk score subgroups. In vitro experiments indicated that overexpression of LBH enhanced the radiosensitivity of A549 cells, and knockdown LBH reversed the cytotoxicity induced by X-rays.

CONCLUSION

Our study developed an eight-gene risk score model with potential clinical value that can be adopted for choice of drug treatment and prognostic prediction. Its clinical routine use may assist clinicians in selecting more rational practices for individuals, which is important for improving the prognosis of NSCLC patients. These findings also provide references for the development of potential therapeutic targets.

Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study

OBJECTIVE

Asherman's syndrome (AS) is a significant cause of subfertility in women from developing countries. Over 80% of AS cases in these regions are linked to dilation and curettage (D&C) procedures following pregnancy. The incidence of AS in patients with infertility and recurrent miscarriage can be as high as 10%, while the pregnancy rate in cases of moderate to severe adhesions can be as low as 34%. We aimed to establish a hysteroscopic artificial intelligence system using image-deep-learning algorithms for fertility assessment.

METHODS

This diagnostic study included 555 cases with 4922 hysteroscopic images from a Chinese intrauterine adhesions cohort clinical database (NCT05381376). The study evaluated two image-deep-learning algorithms' effectiveness in predicting pregnancy within one year, using AUCs and decision curve analysis. The models' performance was evaluated for two-year prediction via concordance index and cumulative time-dependent ROC. A quantifiable visualization panel of the system was established.

RESULTS

The proportional hazard CNN system accurately predicted conception, with AUCs of 0.982, 0.992, and 0.990 in three randomly assigned datasets, superior to the InceptionV3 framework, and achieved a net benefit of 69.4% for subfertility assessment. The system fitted well with c-indexes of 0.920-0.940 and was time-stable. The quantifiable visualization panel displayed four intrauterine pathologies intuitively. The performance was comparable to senior hysteroscopists, with a kappa value of 0.84-0.89.

CONCLUSIONS

The CNN based on the proportional hazard approach accurately assesses fertility postoperatively. The quantifiable visualization panel could assist in intrauterine pathologies assessment, optimize treatment strategies, and achieve individualized and cost-efficient practices.

Integration of transcriptome and Mendelian randomization analyses in exploring the extracellular vesicle-related biomarkers of diabetic kidney disease

BACKGROUND

Diabetic Kidney Disease (DKD) is a common complication in patients with diabetes, and its pathogenesis remains incompletely understood. Recent studies have suggested that extracellular vesicles (EVs) may play a significant role in the initiation and progression of DKD. This study aimed to identify biomarkers associated with EVs in DKD through bioinformatics and Mendelian randomization (MR) analysis.

METHODS

This study utilized two DKD-related datasets, GSE96804 and GSE30528, alongside 121 exosome-related genes (ERGs) and 200 inflammation-related genes (IRGs). Differential analysis, co-expression network construction, and MR analysis were conducted to identify candidate genes. Machine learning techniques and expression validation were then employed to determine biomarkers. Finally, the potential mechanisms of action of these biomarkers were explored through Immunohistochemistry (IHC) staining, enrichment analysis, immune infiltration analysis, and regulatory network construction.

RESULTS

A total of 22 candidate genes were identified as causally linked to DKD. CMAS and RGS10 were identified as biomarkers, with both showing reduced expression in DKD. IHC confirmed low RGS10 expression, providing new insights into DKD management. CMAS was involved primarily in mitochondria-related pathways, while RGS10 was enriched in the extracellular matrix and associated pathways. Significant differences were observed in neutrophils and M2 macrophages between DKD and normal groups, correlating strongly with the biomarkers.

CONCLUSION

This study identified two EV-associated biomarkers, CMAS and RGS10, linked to DKD and elucidated their potential roles in disease progression. These results offer valuable insights for further exploration of DKD pathogenesis and the development of new therapeutic targets.

Gut microbiome shifts in adolescents after sleeve gastrectomy with increased oral-associated taxa and pro-inflammatory potential

Bariatric surgery is highly effective in achieving weight loss in children and adolescents with severe obesity, however the underlying mechanisms are incompletely understood, and gut microbiome changes are unknown. Here, we show that adolescents exhibit significant gut microbiome and metabolome shifts several months after laparoscopic vertical sleeve gastrectomy (VSG), with increased alpha diversity and notably with enrichment of oral-associated taxa. To assess causality of the microbiome/metabolome changes in phenotype, pre-VSG and post-VSG stool was transplanted into germ-free mice. Post-VSG stool was not associated with any beneficial outcomes such as adiposity reduction compared pre-VSG stool. However, post-VSG stool exhibited a potentially inflammatory phenotype with increased intestinal Th17 and decreased regulatory T cells. Concomitantly, we found elevated fecal calprotectin and an enrichment of proinflammatory pathways in a subset of adolescents post-VSG. We show that in some adolescents, microbiome changes post-VSG may have inflammatory potential, which may be of importance considering the increased incidence of inflammatory bowel disease post-VSG.

Identification and validation of susceptibility modules and hub genes in polyarticular juvenile idiopathic arthritis using WGCNA and machine learning

BACKGROUND

Juvenile idiopathic arthritis (JIA), superseding juvenile rheumatoid arthritis (JRA), is a chronic autoimmune disease affecting children and characterized by various types of childhood arthritis. JIA manifests clinically with joint inflammation, swelling, pain, and limited mobility, potentially leading to long-term joint damage if untreated. This study aimed to identify genes associated with the progression and prognosis of JIA polyarticular to enhance clinical diagnosis and treatment.

METHODS

We analyzed the gene expression omnibus (GEO) dataset GSE1402 to screen for differentially expressed genes (DEGs) in peripheral blood single nucleated cells (PBMCs) of JIA polyarticular patients. Weighted gene co-expression network analysis (WGCNA) was applied to identify key gene modules, and protein-protein interaction networks (PPIs) were constructed to select hub genes. The random forest model was employed for biomarker gene screening. Functional enrichment analysis was conducted using David's online database, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis to annotate and identify potential JIA pathways. Hub genes were validated using the receiver operating characteristic (ROC) curve.

RESULTS

PHLDA1, EGR3, CXCL2, and PF4V1 were identified as significantly associated with the progression and prognosis of JIA polyarticular phenotype, demonstrating high diagnostic and prognostic assessment value.

CONCLUSION

These genes can be utilized as potential molecular biomarkers, offering valuable insights for the early diagnosis and personalized treatment of JIA polyarticular patients.

Unveiling the ageing-related genes in diagnosing osteoarthritis with metabolic syndrome by integrated bioinformatics analysis and machine learning

Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.

Faecal metaproteomics analysis reveals a high cardiovascular risk profile across healthy individuals and heart failure patients

The gut microbiota is a crucial link between diet and cardiovascular disease (CVD). Using fecal metaproteomics, a method that concurrently captures human gut and microbiome proteins, we determined the crosstalk between gut microbiome, diet, gut health, and CVD. Traditional CVD risk factors (age, BMI, sex, blood pressure) explained < 10% of the proteome variance. However, unsupervised human protein-based clustering analysis revealed two distinct CVD risk clusters (low-risk and high-risk) with different blood pressure (by 9 mmHg) and sex-dependent dietary potassium and fiber intake. In the human proteome, the low-risk group had lower angiotensin-converting enzymes, inflammatory proteins associated with neutrophil extracellular trap formation and auto-immune diseases. In the microbial proteome, the low-risk group had higher expression of phosphate acetyltransferase that produces SCFAs, particularly in fiber-fermenting bacteria. This model identified severity across phenotypes in heart failure patients and long-term risk of cardiovascular events in a large population-based cohort. These findings underscore multifactorial gut-to-host mechanisms that may underlie risk factors for CVD.

Genetic variation in gut microbe as a key regulator of host social behavior in C. elegans

Gut microbiota have been shown to influence the social behaviors of their hosts, while variations in host genetics can affect the composition of the microbiome. Nonetheless, the degree to which genetic variations in microbial populations impact host behavior, as well as any potential transgenerational effects, remains inadequately understood. Utilizing C. elegans as a model organism, we identified 77 strains of E. coli from a total of 3,983 mutants that significantly enhanced aggregation behavior through various neurobehavioral pathways. This discovery underscores a collaborative regulatory mechanism between microbial genetics and host behavior. Notably, we observed that some mutant bacteria might affect social behavior via the mitochondrial pathway. Additionally, the modulation of social behavior has been identified as a heritable trait in offspring. Our results provide a novel perspective on the regulatory role of microbial genetic variation in host behavior, which may have significant implications for human studies and the development of genetically engineered probiotics aimed at enhancing well-being across generations.

Integrative analysis of mitochondrial and immune pathways in diabetic kidney disease: identification of AASS and CASP3 as key predictors and therapeutic targets

OBJECTIVES

Diabetic kidney disease (DKD) is driven by mitochondrial dysfunction and immune dysregulation, yet the mechanistic interplay remains poorly defined. This study aimed to identify key molecular networks linking mitochondrial and immune pathways to DKD progression, with a focus on uncovering biomarkers and therapeutic targets.

METHODS

We conducted an integrative analysis of human DKD cohorts (GSE30122, GSE96804) using weighted gene co-expression network analysis (WGCNA) to identify gene modules enriched for immune response genes and mitochondrial pathways (from MitoCarta3.0). Machine learning algorithms were employed to prioritize key biomarkers for further investigation. Experimental validation was performed using a DKD rat model.

RESULTS

WGCNA revealed significant gene modules associated with immune responses and mitochondrial functions. Machine learning analysis highlighted two central biomarkers: aminoadipate-semialdehyde synthase (AASS) and caspase-3 (CASP3). In the DKD rat model, elevated levels of AASS and CASP3 were found to correlate with increased oxidative stress. Mechanistically, AASS was shown to drive mitochondrial damage via lysine metabolism, while CASP3 amplified inflammatory apoptosis pathways.

CONCLUSIONS

Our findings establish AASS and CASP3 as dual biomarkers and therapeutic targets, bridging mitochondrial-immune crosstalk to DKD pathogenesis. This multi-omics framework provides actionable insights for targeting kidney damage in diabetes.

ITGA3-MET interaction promotes papillary thyroid cancer progression via ERK and PI3K/AKT pathways

BACKGROUND

Studies have examined the role of integrin α3 (ITGA3) in papillary thyroid carcinoma (PTC). However, the functional and molecular mechanism by which ITGA3 is involved in the progression of PTC remains poorly understood.

METHODS

To investigate the role of ITGA3 in PTC, raw PTC transcriptome data underwent comprehensive bioinformatics analyses, including differential expression, co-expression network, and enrichment analyses. ITGA3 expression was validated via immunohistochemistry and western blotting in PTC tissues. Cell functional assays and xenograft models assessed PTC cell behaviour. The potential mechanisms of ITGA3 were elucidated using bioinformatics analyses, western blotting, co-immunoprecipitation, and immunofluorescence. Finally, integration of ITGA3 expression with clinical parameters enabled nomogram construction for precise prediction of cervical lymph node metastasis (CLNM) in PTC.

RESULTS

ITGA3 was upregulated in PTC and associated strongly with CLNM (79.5% vs. 53.84%, p = 0.016). ITGA3 expression enhanced PTC proliferation and migration in vitro and in vivo via cooperating with the MET protein tyrosine kinase, followed by phosphorylation of MET at Tyr1234/1235, and activation of ERK and PI3K/AKT signaling pathways. Furthermore, upregulation ITGA3 reduced phosphorylation at FAK-Tyr397 and Src-Tyr416 in PTC cells. Finally, a nomogram combining ITGA3 expression and clinical parameters for predicting CLNM was constructed and validated, achieving a ROC curve AUC of 0.719, suggesting potential application for PTC diagnosis.

CONCLUSIONS

ITGA3 promotes PTC cell proliferation and migration by cooperating with MET to activate MET-ERK and MET-PI3K-AKT signalling. ITGA3-MET cooperation may serve as a potential therapeutic target.

An immunocompetent mouse model revealed that congenital Zika virus infection disrupted hippocampal function by activating autophagy

Congenital Zika virus (ZIKV) infection significantly affects neurological development in infants and subsequently induces neurodevelopmental abnormality symptoms; however, the potential mechanism is still unknown. Therefore, in order to effectively intervene in neurodevelopmental abnormalities in infected infants, it is necessary to identify the main brain regions affected by congenital infection. In this study, we constructed a congenital ZIKV-infected murine model using immunocompetent human STAT2 knock-in mice, which presented long-term neurodevelopmental abnormalities with abnormal neurodevelopmental symptoms. We found that the hippocampus, which regulates cognitive behaviour and processes spatial information and navigation, was the main brain region affected by congenital infection and that hippocampal cells were more prone to autophagy during the growth period of these mice at the transcriptional and pathological levels. These findings highlighted that congenital ZIKV infection could interrupt hippocampal function by activating autophagy, thus providing a theoretical basis for the clinical treatment of congenital ZIKV-infected infants.

The potential mechanisms by which Xiaoyao Powder may exert therapeutic effects on thyroid cancer were examined at various levels

BACKGROUND

Thyroid cancer (TC) is the most prevalent endocrine malignancy, with a rising incidence necessitating safer treatment strategies to reduce overtreatment and its side effects. Xiaoyao Powder (XYP), a widely used herbal formula, shows promise in treating TC. This study aims to investigate the mechanisms by which XYP may affect TC.

METHODS

The components of XYP were identified through database retrieval, and targets related to TC were collected to construct a target network for key screening. GEO dataset samples analyzed immune cells and identified significantly differentially expressed core genes (SDECGs). Based on SDECG expression and clustering, samples were classified for comparison. WGCNA was employed to identify gene modules linked to clinical characteristics. ML models screened characteristic genes and constructed a nomogram validated using another GEO dataset. MR methods explored causal relationships between genes and TC.

RESULTS

The top ten active components of XYP were identified, along with 27 SDECGs that exhibited significant differences in immune cell infiltration between TC patients and normal controls. The nomogram effectively predicted TC risk, validated through ROC curves. Key characteristic genes included SMIM1, PPP1R16A, KIAA1462, DNAJC22, and EFNA5.

CONCLUSION

XYP may treat TC by regulating SMIM1, PPP1R16A, KIAA1462, DNAJC22, EFNA5, and associated immune pathways; this provides theoretical support for its potential mechanisms.

Exploring the relationship and diagnostic targets of seborrheic dermatitis and scalp psoriasis based on multi-omics integration analysis

Scalp seborrheic dermatitis (SD) and psoriasis (PSO) are prevalent chronic skin conditions with overlapping clinical manifestations, especially when confined to the scalp, which complicates differential diagnosis. This study integrates transcriptomic profiling and genetic association analyses to investigate potential causal links between SD and scalp PSO. Through RNA sequencing on lesional and non-lesional scalp samples from patients with SD and scalp PSO, as well as healthy controls, we found that seborrheic dermatitis may represent a risk factor for the progression to scalp PSO. Furthermore, TGM1 and IL36RN were identified as key potential molecular targets that could differentiate seborrheic dermatitis from scalp PSO.

Identification and analysis of extracellular matrix and epithelial-mesenchymal transition-related genes in idiopathic pulmonary fibrosis by bioinformatics analysis and experimental validation

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disorder that is characterized by the disruption of lung architecture and respiratory failure. Notwithstanding the advent of novel therapeutic agents such as pirfenidone and nintedanib, there remains a pressing need for the development of innovative diagnostic and therapeutic strategies. Next-generation sequencing allows for the analysis of gene expression and the discovery of biomarkers. The objective of our study was to identify IPF-specific gene signatures, construct a diagnostic nomogram, and explore the role of the extracellular matrix (ECM) and epithelial-to-mesenchymal transition (EMT) in IPF pathogenesis. Utilizing data from the Gene Expression Omnibus (GEO) database, we identified differentially expressed genes (DEGs), performed weighted correlation network analysis (WGCNA), and constructed a nomogram. The present study has identified a group of key genes that are associated with IPF. The identified genes include GREM1, ITLN2, MAP3K15, RGS9BP, and SLCO1A2. The results of the immunohistochemical analysis indicated a significant correlation between these central genes and immune cell infiltration. Furthermore, Gene Set Enrichment Analysis (GSEA) revealed that these genes play a critical role in the pathogenesis of IPF. To validate the diagnostic potential of these core genes, we performed confirmatory analyses in independent Gene Expression Omnibus (GEO) datasets. We observed a significant upregulation of GREM1 expression in IPF animal and cellular models. These findings provide new insights into the molecular mechanisms of IPF and suggest potential targets for future diagnostic and therapeutic strategies.

Perilipin-2 mediates ferroptosis in oligodendrocyte progenitor cells and myelin injury after ischemic stroke

JOURNAL/nrgr/04.03/01300535-202507000-00024/figure1/v/2024-09-09T124005Z/r/image-tiff Differentiation of oligodendrocyte progenitor cells into mature myelin-forming oligodendrocytes contributes to remyelination. Failure of remyelination due to oligodendrocyte progenitor cell death can result in severe nerve damage. Ferroptosis is an iron-dependent form of regulated cell death caused by membrane rupture induced by lipid peroxidation, and plays an important role in the pathological process of ischemic stroke. However, there are few studies on oligodendrocyte progenitor cell ferroptosis. We analyzed transcriptome sequencing data from GEO databases and identified a role of ferroptosis in oligodendrocyte progenitor cell death and myelin injury after cerebral ischemia. Bioinformatics analysis suggested that perilipin-2 (PLIN2) was involved in oligodendrocyte progenitor cell ferroptosis. PLIN2 is a lipid storage protein and a marker of hypoxia-sensitive lipid droplet accumulation. For further investigation, we established a mouse model of cerebral ischemia/reperfusion. We found significant myelin damage after cerebral ischemia, as well as oligodendrocyte progenitor cell death and increased lipid peroxidation levels around the infarct area. The ferroptosis inhibitor, ferrostatin-1, rescued oligodendrocyte progenitor cell death and subsequent myelin injury. We also found increased PLIN2 levels in the peri-infarct area that co-localized with oligodendrocyte progenitor cells. Plin2 knockdown rescued demyelination and improved neurological deficits. Our findings suggest that targeting PLIN2 to regulate oligodendrocyte progenitor cell ferroptosis may be a potential therapeutic strategy for rescuing myelin damage after cerebral ischemia.

Akt mitigates ER stress-instigated cardiac dysfunction via regulation of ferroptosis and mitochondrial integrity in a DHODH-dependent manner

ER stress evokes various types of cell death and myocardial dysfunction. This study aimed to discern the involvement of ferroptosis in chronic Akt activation-offered benefit, if any, against ER stress-triggered cardiac remodeling and contractile anomalies. Cardiac-selective expression of active mutant of Akt (AktOE) and wild-type (WT) mice were challenged with the ER stress instigator tunicamycin (1 mg/kg, 48 h) prior to assessment of cardiac morphology and function. Tunicamycin insult prompted cardiac remodeling (interstitial fibrosis), deranged echocardiographic (higher LVESD, dropped ejection fraction and fractional shortening), cardiomyocyte mechanical and intracellular Ca2+ features alongside mitochondrial injury (collapsed mitochondrial membrane potential and ultrastructural change), oxidative stress, compromised Akt-GSK3β signaling, ER stress (upregulated GRP78 and Gadd153), carbonyl formation, apoptosis and ferroptosis (decreased GPX4, SLC7A11). Intriguingly, tunicamycin-evoked anomalies (except GRP78 and Gadd153) were abrogated by Akt activation. Chronic Akt activation negated tunicamycin-induced downregulation of ferric flavin enzyme dihydroorotate dehydrogenase (DHODH), which catalyzes the fourth step of pyrimidine ab initio biosynthesis, and conversion of dihydroorotic acid to orotate. ER stress-induced myocardial anomalies were reversed by the newly identified PI3K activator triptolide, DHODH activator menaquinone-4 and pyrimidine booster coenzyme Q. In vitro experiment revealed that Akt activation- or triptolide-evoked beneficial responses against tunicamycin-induced cardiomyocyte anomalies were cancelled off by DHODH inhibitor BAY2402234 or ferroptosis inducer erastin. These findings support that chronic Akt activation rescues ER stress-evoked myocardial derangements through DHODH-dependent control of ferroptosis and mitochondrial homeostasis.

Mechanism of fatty acid synthesis metabolism during tuber swelling period of Chinese yam

Chinese yam is well-known for its nutritional value, specifically for its low fat content, which is rich in unsaturated fatty acids, thus providing high-quality and essential fats. This study used GC-MS analysis to identify 28 metabolites, predominantly composed of unsaturated fatty acids such as linoleic, linolenic, and oleic acids, which play a crucial role in maintaining membrane stability. Unsaturated fatty acids improve fluidity under low-temperature conditions, shield cells from oxidative stress, and maintain cell integrity. Through weighted gene co-expression network analysis, this study discovered 12 potential regulatory factors, including Acetyl CoA carboxylase, lipoxygenase, and fatty acid desaturase, that contribute to the synthesis of highly unsaturated fatty acids in Chinese yam. Additionally, variations in fatty acid biosynthesis pathway metabolite accumulation significantly affects the cold tolerance of different Chinese yam varieties. This study enhances our understanding of the regulatory network of fatty acid metabolites and offers new insights into the genetic enhancement of Chinese yam varieties, particularly in terms of low-temperature tolerance.

Co-occurrence and co-expression of antibiotic, biocide, and metal resistance genes with mobile genetic elements in microbial communities subjected to long-term antibiotic pressure: Novel insights from metagenomics and metatranscriptomics

The burgeoning of antibiotic resistance has emerged as a pressing global challenge. To gain a deeper understanding of the interactions between antibiotic resistance genes (ARGs), biocide and metal resistance genes (BRGs&MRGs), and mobile genetic elements (MGEs), this study utilized metagenomics and metatranscriptomics to investigate their co-occurrence and co-expression in two consortia subjected to long-term exposure to chloramphenicol and lincomycin. Long-term exposure to these antibiotics resulted in significant disparities in resistance profiles: ConsortiumCAP harbored 130 ARGs and 150 BRGs&MRGs, while ConsortiumLIN contained 57 ARGs and 32 BRGs&MRGs. Horizontal gene transfer (HGT) events were predicted at 125 and 300 instances in ConsortiumCAP and ConsortiumLIN, respectively, facilitating the emergence of multidrug-resistant bacteria, such as Caballeronia (10 ARGs, 2 BRGs&MRGs), Cupriavidus (2 ARGs, 10 BRGs&MRGs), and Bacillus (14 ARGs, 21 BRGs&MRGs). Chloramphenicol exposure significantly enriched genes linked to phenicol resistance (floR, capO) and co-expressed ARGs and BRGs&MRGs, while lincomycin exerted narrower effects on resistance genes. Additionally, both antibiotics modulated the expression of degradation genes and virulence factors, highlighting their role in altering bacterial substrate utilization and pathogenic traits. This study provides quantitative insights into the impact of antibiotics on microbial resistance profiles and functions at both DNA and RNA levels, highlighting the importance of reducing antibiotic pollution and limiting the spread of resistance genes in the environment.

Exploring the latent space of transcriptomic data with topic modeling

The availability of high-dimensional transcriptomic datasets is increasing at a tremendous pace, together with the need for suitable computational tools. Clustering and dimensionality reduction methods are popular go-to methods to identify basic structures in these datasets. At the same time, different topic modeling techniques have been developed to organize the deluge of available data of natural language using their latent topical structure. This paper leverages the statistical analogies between text and transcriptomic datasets to compare different topic modeling methods when applied to gene expression data. Specifically, we test their accuracy in the specific task of discovering and reconstructing the tissue structure of the human transcriptome and distinguishing healthy from cancerous tissues. We examine the properties of the latent space recovered by different methods, highlight their differences, and their pros and cons across different tasks. We focus in particular on how different statistical priors can affect the results and their interpretability. Finally, we show that the latent topic space can be a useful low-dimensional embedding space, where a basic neural network classifier can annotate transcriptomic profiles with high accuracy.

BCL2A1‑ and G0S2‑driven neutrophil extracellular traps: A protective mechanism linking preeclampsia to reduced breast cancer risk

Preeclampsia has been associated with a reduced risk of breast cancer (BC), but the mechanisms underlying this relationship remain unclear. It has been suggested that neutrophil extracellular traps (NETs), which are released upon neutrophil activation, play a key role in both preeclampsia and BC. To investigate this link, the single‑cell RNA sequencing dataset GSE173193 was analyzed and upregulated genes BCL2A1 and G0/G1 switch gene 2 (G0S2) were identified in neutrophils from preeclamptic placentas. These findings were validated using reverse transcription‑quantitative PCR and western blotting. Combined analyses of preeclampsia and BC tissues, from Gene Expression Omnibus (GSE24129) and The Cancer Genome Atlas databases respectively, identified 2,040 upregulated differentially expressed genes, including BCL2A1 and G0S2. Furthermore, these genes showed clinical relevance to BC, as demonstrated by Receiver Operating Characteristic curve, survival analyses and weighted gene co‑expression network analysis. Functional experiments revealed that overexpression of BCL2A1 and G0S2 increased NET release and inhibited BC cell proliferation, invasion and migration. The present study provides novel insights into the shared molecular pathways of preeclampsia and BC, emphasizing NETs as a potential protective mechanism as increased NET production in preeclampsia may contribute to a reduced BC risk by influencing tumor progression and offer avenues for further research into therapeutic interventions.

DDR2 alleviates retinal vaso-obliteration and pathological neovascularization by modulating microglia M1/M2 phenotypic polarization in a mouse model of proliferative retinopathy

Retinopathy of prematurity (ROP), a leading cause of blindness in premature infants, is characterized by retinal vaso-obliteration during hyperoxia and pathological neovascularization (NV) in relative hypoxia phase. Current treatments, which focus on the late stages of pathological neovascularization, are associated with numerous side effects. Studies demonstrated that discoidin domain receptor 2 (DDR2), a collagen-binding receptor tyrosine kinase, inhibits the experimental choroidal neovascularization and participates in tumor angiogenesis. However, the role of DDR2 in ROP and underlying mechanisms is unclear. In this study, we initially found that DDR2 expressed during mouse physiological retinal vascular development and significantly decreased in vaso-obliteration phase followed by increase during pathological neovascularization phase in mouse oxygen-induced retinopathy (OIR) model. Early upregulation of DDR2 before hyperoxia attenuates oxygen-induced vaso-obliteration, reduces pathological neovascularization, and promotes retinal vascular maturation. Additionally, DDR2 upregulation increased the number of microglia around retinal blood vessels and induced anti-inflammatory M2 polarization. Furthermore, the STAT6/TGF-β signaling pathway suppressed during hyperoxia was activated after DDR2 upregulation. In conclusion, DDR2 attenuated vaso-obliteration and inhibited pathological neovascularization by switching the microglia polarization from M1 to M2 phenotype via the STAT6/TGF-β signaling pathway in OIR. This suggests that DDR2 could be a novel target for the early treatment of ROP.

Sex differences in proteomics of cardiovascular disease - Results from the Yale-CMD registry

Aims This study assessed sex-specific proteomic profiles by cardiovascular disease (CVD) phenotype (coronary artery disease [CAD] vs coronary microvascular dysfunction [CMD]) and describe their role in sex-specific pathways.

METHODS

In a secondary biobank analysis of the Yale-CMD registry, adults with ischemic symptoms who underwent cardiac positron emission test/computed tomography were categorized as a) controls (normal coronary flow reserve (CFR) > 2 without perfusion defect or coronary calcification), b) having CMD (CFR < 2 without defect or calcification), or c) having CAD (known CAD or new perfusion defect). Using proximity extension assays (Olink® Explore 3072), we examined 2944 proteins. Differential protein expression was assessed using linear regression models, adjusting for age, race, body mass index, diabetes, dyslipidemia, hypertension, or smoking.

RESULTS

Of 190 patients, 91 provided blood samples (mean age, 56 years; 66 %, females; 48 %, controls; 24 %, CAD; 27 %, CMD). Among controls, 15 proteins showed sex differences (5 proteins upregulated in females, 10 in males; false discovery rate [FDR < 0.05]). Upregulated in CAD patients were FSHB in females and INSL3 and EDDM3B in males (FDR < 0.05). Among CMD patients, SCGB3A1 and HGFAC were higher in females; INSL3, SPINT3, EDDM3B, and KLK3 were higher in males (FDR < 0.05). Per pathway analysis, females showed upregulation of immune pathways in CAD and lipid and glucose metabolism pathways in CMD. Males showed upregulated endothelial regulation of blood flow in CAD and increased angiogenesis in CMD.

CONCLUSIONS

Sex differences exist in the proteomic profiles of CAD and CMD patients, highlighting a need for precision medicine.

Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases

The combined analysis of dual diseases can provide new insights into pathogenic mechanisms, identify novel biomarkers, and develop targeted therapeutic strategies. Polycythemia vera (PV) is a chronic myeloproliferative neoplasm associated with a risk of acute myeloid leukemia (AML) transformation. However, the chronic nature of disease transformation complicates longitudinal high-throughput sequencing studies of patients with PV before and after AML transformation. This study aimed to develop a diagnostic model for malignant transformation of chronic proliferative diseases, addressing the challenges of early detection and intervention. Integrated public datasets of PV and AML were analyzed to identify differentially expressed genes (DEGs) and construct a weighted correlation network. Machine-learning algorithms screen genes for potential biomarkers, leading to the development of diagnostic models. Clinical specimens were collected to validate gene expression. cMAP and molecular docking predicted potential drugs. In vitro experiments were performed to assess drug efficacy in PV and AML cells. CIBERSORT and single-cell RNA-sequencing (scRNA-seq) analyses were used to explore the impact of hub genes on the tumor microenvironment. We identified 24 genes shared between PV and AML, which were enriched in immune-related pathways. Lactoferrin (LTF) and G protein-coupled receptor 65 (GPR65) were integrated into a nomogram with a robust predictive power. The predicted drug vemurafenib inhibited proliferation and increased apoptosis in PV and AML cells. TME analysis has linked these biomarkers to macrophages. Clinical samples were used to confirm LTF and GPR65 expression levels. We identified shared genes between PV and AML and developed a diagnostic nomogram that offers a novel avenue for the diagnosis and clinical management of AML-related PV.

Machine learning unravels the mysteries of glioma typing and treatment

Gliomas, which are complex primary malignant brain tumors known for their heterogeneous and invasive nature, present substantial challenges for both treatment and prognosis. Recent advancements in whole-genome studies have opened new avenues for investigating glioma mechanisms and therapies. Through single-cell analysis, we identified a specific cluster of cancer cell-related genes within gliomas. By leveraging diverse datasets and employing non-negative matrix factorization (NMF), we developed a glioma subtyping method grounded in this identified gene set. Our exploration delved into the clinical implications and underlying regulatory frameworks of the newly defined subtype classification, revealing its intimate ties to glioma malignancy and prognostic outcomes. Comparative assessments between the identified subtypes revealed differences in clinical features, immune modulation, and the tumor microenvironment (TME). Using tools such as the limma R package, weighted gene co-expression network analysis (WGCNA), machine learning methodologies, survival analyses, and protein-protein interaction (PPI) networks, we identified key driver genes influencing subtype differentiation while quantifying associated outcomes. This study not only sheds light on the biological mechanisms within gliomas but also paves the way for precise molecular targeted therapies within this intricate disease landscape.

Roles of Adam8 in Neuroinflammation in experimental ischemic Stroke: Insights from single-cell and ribosome-bound mRNA sequencing

Stroke remains a leading cause of global mortality, with neuroinflammation significantly exacerbating clinical outcomes. Microglia serve as key mediators of post-stroke neuroinflammation, though the mechanisms driving their migration to injury sites remain poorly understood. In this study, using publicly available single-cell sequencing data (GSE234052), we identified a migration-associated microglial subtype in a murine model of distal middle cerebral artery occlusion (dMCAO). Additionally, ribosome-bound mRNA sequencing data (GSE225110) from microglia isolated from peri-infarct cortical tissue uncovered dMCAO-induced alterations in microglial mRNA translation. By integrating these datasets, we identified A Disintegrin And Metalloproteinase 8 (Adam8) as a key gene upregulated at both the transcriptional and translational levels post-dMCAO. Protein analysis revealed that both the precursor and active forms of Adam8 were predominantly expressed in microglia and significantly upregulated in peri-infarct regions following dMCAO. Notably, Adam8 inhibition with BK-1361 significantly reduced Adam8 cleavage, M1 microglial migration, inflammation, infarct size, and improved neurological outcomes. Bioinformatics analysis further identified Myo1e as a potential interacting partner of Adam8, a finding validated through immunofluorescence co-localization. These findings highlight Adam8 as a promising therapeutic target for mitigating post-stroke neuroinflammation and offer new insights into the mechanisms of microglial migration.

Harnessing machine learning in contemporary tobacco research

Machine learning (ML) has the potential to transform tobacco research and address the urgent public health crisis posed by tobacco use. Despite the well-documented health risks, cessation rates remain low. ML techniques offer innovative solutions by analyzing vast datasets to uncover patterns in smoking behavior, genetic predispositions, and effective cessation strategies. ML can predict smoking-induced non-communicable diseases (SiNCDs) like lung cancer and postmenopausal osteoporosis by identifying biomarkers and genetic profiles, generating personalized predictions, and guiding interventions. It also improves prediction of infant tobacco smoke exposure, distinguishes secondhand and thirdhand smoke, and enhances protection strategies for children. Data-driven, personalized approaches using ML track real-time data for personalized feedback and offer timely interventions, continuously improving cessation strategies. Overall, ML provides sophisticated predictive models, enhances understanding of complex biological mechanisms, and enables personalized interventions, demonstrating significant potential in the fight against the tobacco epidemic.

Genetic and transcriptomic analysis of lentil seed imbibition and dormancy in relation to its domestication

Seed dormancy is an adaptation that delays germination to prevent the start of this process during unsuitable conditions. It is crucial in wild species but its loss was selected during crop domestication to ensure a fast and uniform germination. Water uptake, or imbibition, is the first step of germination. In the Fabaceae family, seeds have physical dormancy, in which seed coats are impermeable to water. We used an interspecific cross between an elite lentil line (Lens culinaris) and a wild lentil (L. orientalis) to investigate the genetic basis of imbibition capacity through quantitative trait locus (QTL) mapping and by using RNA from embryos and seed coats at different development stages, and phenotypic data of seed coat thickness (SCT) and proportion of imbibed seeds (PIS). Both characteristics were consistent throughout different years and locations, suggesting a hereditary component. QTL results suggest that they are each controlled by relatively few loci. Differentially expressed genes (DEGs) within the QTL were considered candidate genes. Two glycosyl-hydrolase genes (a β-glucosidase and a β-galactosidase), which degrade complex polysaccharides in the cell wall, were found among the candidate genes, and one of them had a positive correlation (β-glucosidase) between gene expression and imbibition capacity, and the other gene (β-galactosidase) presented a negative correlation between gene expression and SCT.

TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT) enables automated assessment of global gene regulation linked to disparate expression in user defined genes and gene sets

Publicly accessible expression data produced by large consortium projects like TCGA and GTEx are increasing in number and size at an unprecedented rate. Their utility cannot be underestimated given the diversity of valuable tools widely used to interrogate these data and the many discoveries of biological and clinical significance already garnered from these datasets. However, there remain undiscovered ways to mine these rich resources and a continuing need to provide researchers with easily accessible and user-friendly applications for complex or bespoke analyses. We introduce TRanscriptome ANalysis of StratifiEd CohorTs (TRANSECT), a bioinformatics application automating the stratification and subsequent differential expression analysis of cohort data to provide further insights into gene regulation. TRANSECT works by defining two groups within a cohort based on disparate expression of a gene or a gene set and subsequently compares the groups for differences in global expression. Akin to reverse genetics minus the inherent requirement of in vitro or in vivo perturbations, cell lines or model organisms and all the while working within natural physiological limits of expression, TRANSECT compiles information about global transcriptomic change and functional outcomes. TRANSECT is freely available as a command line application or online at https://transect.au.

Role and mechanism of mitochondrial dysfunction‑related gene biomarkers in the progression of type 2 diabetes mellitus

The present study aimed to elucidate the roles and mechanisms of gene biomarkers associated with mitochondrial dysfunction in the progression of Type 2 diabetes mellitus (T2DM). It conducted an analysis of differentially expressed genes related to mitochondrial dysfunction in T2DM and employed bioinformatics approaches to predict potential target drugs for key biomarkers. Additionally, the present study used the EPIC algorithm to examine immune cell infiltration in T2DM. Furthermore, the single‑cell RNA sequencing dataset GSE221156 was analyzed to identify specific cell types involved in T2DM. The expression of biomarkers was investigated through cellular experiments to assess the effect of marker genes on macrophage polarization. A total of five biomarker genes associated with T2DM were identified, namely ERAP2, HLA‑DQB1, HLA‑DRB5, MAP1B and OAS3. The combined detection of these genes yielded a risk‑predictive area under the curve value of 0.833 for T2DM. These five marker genes may serve as potential targets for valproic acid (VPA). During the progression of T2DM, there is an increase in macrophage numbers, with these genes being highly expressed in macrophages. In a high glucose‑induced RAW264.7 macrophage model, the expressions of MAP1B and OAS3 were upregulated. Notably, the knockdown of OAS3 markedly reduced M1 macrophage polarization, indicating OAS3 facilitates M1 macrophage polarization in a high‑glucose environment. The downregulation of OAS3 expression attenuated M1 macrophage polarization by inhibiting mTORC activation. In conclusion, five candidate biomarkers for T2DM were identified that may serve as therapeutic targets for VPA and are associated with immune infiltration in T2DM. Among these, OAS3 enhances M1 macrophage polarization in a high‑glucose environment by regulating the mTORC1 pathway.

Comparative analyses of morphology and temporal floral organ transcriptome provide insights into the development of staminodes in Globba racemosa (Zingiberaceae)

Staminode, the most conspicuous floral organ in Zingiberaceae, which greatly contributes to the ornamental value of flowers in this family. Meanwhile, staminode is a key innovation in Zingiberaceae, which is hypothesized to have originated from the fertile stamen. Previous morphological and gene expression analyses have provided evidence for this hypothesis. However, in Zingiberaceae, little is known about the gene expression dynamics of the staminode compared to other floral organs at transcriptomic level, and the molecular mechanisms underlying identity specification of the staminodes remain unresolved. In this study, by using G. racemosa, an ornamental plant in Zingiberaceae, we first traced the flower development of G. racemosa, with special attention to the development of the two types of staminodes (the labellum and the outer androecial member), to explore the morphological differences between staminodes and the fertile stamen. Then, by combining a full-length transcriptome and comparative transcriptome data from seven types of floral organs at four developmental stages, we identified candidate genes that are specifically, preferentially, or differentially expressed in the labellum and the outer androecial member compared to other floral organs. Using weighted gene co-expression network analysis (WGCNA), we further identified several modules that are significantly correlated with the labellum and the outer androecial member. Lastly, by examining the expression patterns of four well-known gene regulatory networks, which, according to previous studies, are presumed to be involved in the identity specification and morphogenesis of staminodes in Zingiberaceae, we found other potential regulators for the development of staminodes of G. racemosa. Notably, we found that on the one hand, the labellum and the outer androecial member shared some genes with the fertile stamen, providing evidence for the stamen origin of staminodes in Zingiberaceae; on the other hand, the labellum, outer androecial member, and petal also share many genes, explaining the morphological similarity among labellum, outer androecial member, and petal. Thus, in terms of regulatory mechanisms, the staminodes in G. racemosa may represent a complex of stamen and petal characteristics. In summary, our results offer valuable resources for further research on gene functions and lay the foundation for future analyses of the molecular mechanisms underlying staminode development in Zingiberaceae.

GALR1 and PENK serve as potential biomarkers in invasive non-functional pituitary neuroendocrine tumours

BACKGROUND

Some nonfunctioning pituitary neuroendocrine tumor (NFPitNET) can show invasive growth, which increases the difficulty of surgery and indicates a poor prognosis. However, the molecular mechanism related to invasiveness remains to be further studied. This study is to screen and identify the characteristic biomarkers of invasive NFPitNETs.

METHODS

Based on the data of 73 NFPitNETs microarray chips in the GSE169498 dataset, this study used weighted gene co-expression network (WGCNA), differential expression analysis, protein-protein interaction (PPI) network analysis and various machine learning methods (XGBOOST, LASSO regression, random forest, support vector machine) to screen candidate biomarkers for invasive NFPitNET. Then, using gene set enrichment analysis (GSEA) to explore the differences in biological activities and signaling pathways between invasive NFPitNET and non-invasive NFPitNET. Single-sample GSEA (ssGSEA) was used to analyze key biomarkers-related signaling pathways. Finally, the expression and function of the key biomarkers were verified by q-RT PCR, immunohistochemical (IHC) experiments and in vitro experiments.

RESULTS

Combined with WGCNA and differential expression analysis, 128 high-expression and 85 low-expression candidate biomarkers were preliminarily obtained. PPI analysis and four machine learning algorithms further identified GALR1, PENK and HOXD9. The receiver operating characteristic (ROC) curve results showed that the three biomarkers had good predictive ability of invasiveness. After combining the validation set data, GALR1 and PENK were the final key biomarkers. Finally, PCR and IHC results verified the decreased expression of GALR1 and PENK in invasive NFPitNET and promotes proliferation and invasive ablity of pituitary tumor cells.

CONCLUSION

This study confirmed that the reduced expression of GALR1 and PENK is an important molecular feature of invasive NFPitNETs, which may play an important role in inhibiting the development of NFPitNET.

Involvement of disulfidptosis in the pathophysiology of autism spectrum disorder

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder, with oxidative stress recognized as a key pathogenic mechanisms. Oxidative stress disrupts intracellular dynamic- thiol/disulfide homeostasis (DTDH), potentially leading to disulfidptosis, a newly identified cell death mechanism. While studies suggest a link between DTDH and ASD, direct evidence implicating disulfidptosis in ASD pathogenesis remains limited. In this study, Mendelian randomization analysis revealed a significant causal association between disulfidptosis-related sulfhydryl oxidase 1 and 2 and ASD (OR1 = 0.883, OR2 = 0.924, p < 0.05). A positive correlation between protein disulfide-isomerase and cognitive performance (OR = 1.021, p < 0.01) further supported the role of disulfidptosis in ASD. Seven disulfidptosis-related genes (TIMP1, STAT3, VWA1, ADA, IL5, PF4, and TXNDC12) were identified and linked to immune cell alterations. A TF-miRNA-mRNA regulatory network and a predictive model (AUC = 0.759) were constructed and external validation datasets (AUC = 0.811). Immune infiltration analysis demonstrated altered expression of naive B cells and three other types of immune cells in ASD children. Animal experiments further validated the differential expression of key genes, highlighting their relevance to ASD pathogenesis. Animal experiments found that BTBR mice exhibit glucose starvation and NADPH depletion, with the specific indicator Slc7a11 being highly expressed. Silencing Slc7a11 can improve core ASD impairments in BTBR mice. CONCLUSION: This study establishes the first mechanistic link between disulfidptosis and ASD, identifies seven key genes and their regulatory network, and develops a predictive model with clinical utility. Animal experiments further confirmed the strong association between disulfidpotosis and ASD phenotypes. These findings offer novel therapeutic targets for modulating oxidative stress in ASD.

The characterization and validation of regulated cell death-related genes in chronic rhinosinusitis with nasal polyps

BACKGROUND

Regulated cell death (RCD), a genetically controlled process mediated by specialized molecular pathways (commonly termed programmed cell death), plays pivotal roles in diverse pathophysiological processes. However, the landscape and functional implications of RCD subtypes in chronic rhinosinusitis with nasal polyps (CRSwNP) remain poorly characterized. This study aimed to systematically investigate the involvement of RCD mechanisms in the pathogenesis and progression of CRSwNP.

METHODS

Transcriptomic datasets (GSE136825, GSE23552, GSE198950, GSE196169, GSE156285) related to CRSwNP were retrieved from the Gene Expression Omnibus (GEO) database. A comprehensive panel of 18 RCD-associated gene sets was compiled through a systematic literature review. Gene set variation analysis (GSVA) was employed to profile RCD activation patterns in CRSwNP. Integrative bioinformatics approaches including weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) regression were implemented to identify hub RCD-related genes and construct a cell death index (CDI). Single-cell RNA sequencing (scRNA-seq) data were analyzed to map RCD dynamics across cellular subpopulations. Clinical validation was performed using qRT-PCR quantification of key genes in nasal polyp/inferior turbinate tissues, with the concurrent assessment of symptom severity via visual analogue scale (VAS) scores.

RESULTS

GSVA revealed significant upregulation of 8 RCD subtypes in CRSwNP: apoptosis, ferroptosis, necroptosis, entotic cell death, lysosome-dependent cell death, NETosis, immunogenic cell death, and anoikis. Pathway enrichment analysis demonstrated that RCD-related differentially expressed genes were predominantly involved in epithelial-mesenchymal transition (EMT) and immune-inflammatory regulation. Furthermore, the WGCNA algorithm and LASSO analysis identified 8 key cell death genes (PTHLH, GRINA, S100A9, SCG2, HMOX1, RNF183, TYROBP, SEMA7A), which were utilized to construct the cell death-related index (CDI). In training and validation cohorts, the CDI was significantly elevated in CRSwNP compared to control and exhibited high diagnostic performance, with elevated scores correlating with enhanced immune cell infiltration. Single-cell resolution analysis uncovered cell type-specific RCD activation patterns. Clinical validation confirmed significantly higher expression of S100A9, PTHLH, and HMOX1 in eosinophilic versus non-eosinophilic polyps. Notably, expression levels of PTHLH, S100A9, HMOX1, GRINA, and TYROBP showed strong positive correlations with VAS scores.

CONCLUSIONS

Our investigation delineates an RCD activation signature in CRSwNP pathogenesis, characterized by 8 key cell death modalities and their regulatory genes. The novel CDI exhibits promising diagnostic potential, while mechanistic insights suggest RCD pathways may drive disease progression through EMT potentiation and inflammatory cascade amplification. These findings provide a framework for developing RCD-targeted therapeutic strategies in CRSwNP.

Metabolic-associated fatty liver disease (MAFLD) promotes the progression of hepatocellular carcinoma by enhancing KIF20A expression

BACKGROUND

Compared to other HCC, those related to MAFLD exhibit distinct prognostic differences. This article aims to elucidate the impact of MAFLD on HCC prognosis through the lens of KIF20A, thereby providing a theoretical foundation for targeted therapies in MAFLD-related HCC.

METHODS

We employed the Weighted gene co-expression network analysis (WGCNA) method alongside the Mime package to identify key genes associated with MAFLD-related HCC. Subsequently, we utilized OCLR and CytoTRACE algorithms to evaluate the relationship between these genes and HCC stemness. The R package was employed to conduct immunological analyses on both mRNA sequencing and single-cell data. We validated the effects of core genes on HCC through experimental approaches, including cell culture, Transwell assays, Western Blot, and proliferation assays. Finally, we predicted potential therapeutic drugs using the OncoPredict software package.

RESULTS

WGCNA identified the cyan module associated with MAFLD in GSE135251 and the blue module linked to HCC in TCGA. Further analysis identified KIF20A as the core gene in MAFLD-related HCC. Utilizing the OCLR and CytoTRACE algorithms, KIF20A was found to correlate with mRNA stemness index (mRNAsi). Analysis of public databases revealed that KIF20A promotes immune tolerance through the SPP1-CD44 pathway and drives HCC progression via the G2M checkpoint. Experimental results demonstrated that lipotoxic damage in HCC cells and small extracellular vesicles (sEVs) derived from these cells upregulate KIF20A, thereby accelerating HCC progression. Finally, OncoPredict and AutoDock were employed to predict drugs targeting KIF20A.

CONCLUSION

MAFLD-related HCC can elevate KIF20A levels and promote tumor proliferation and migration.

Molecular crosstalk and potential causal mechanisms of rheumatoid arthritis and sarcopenia co-morbidity: A gene integration analysis

INTRODUCTION

Rheumatoid arthritis (RA) promotes the onset and progression of sarcopenia, yet mechanisms of co-morbidity between RA and sarcopenia are under-explored. Therefore, this study integrated Gene Expression Omnibus (GEO) and Genome-wide association studies (GWAS) data to comprehensively identify shared genes, associated mechanisms, and biological pathways in RA and sarcopenia.

METHODS

Utilizing two GEO datasets-GSE226151, which includes 60 RNA-seq samples of skeletal muscle from healthy aged, pre-sarcopenia, and sarcopenia individuals, and GSE55235, with 20 RNA-seq samples of synovial tissue from healthy and RA joints-we performed differentially expressed genes analysis, weighted gene co-expression network analysis to identify crosstalk genes in RA and sarcopenia, and enrichment analysis for these genes. Using relevant GWAS datasets, SMR analyses and cis-eQTL analyses were performed. We further validated and identified key crosstalk genes and explored potential causal associations between key crosstalk genes and RA and sarcopenia-related traits.

RESULTS

We identified 25 crosstalk genes shared between RA and sarcopenia, which are involved in immune-inflammatory response pathways, including neutrophil extracellular trap formation and Fc gamma receptor-mediated phagocytosis. SMR analysis further identified six core crosstalk genes: NCF1, FCGR2A, FCGR3A, SORL1, FCGR3B, and ITGAX (PSMR < 0.05). cis-eQTL analysis showed that FCGR2A might have a negative causal association with appendicular lean mass, whole body fat-free mass, and a positive causal association with RA (P < 0.05).

CONCLUSION

Overall, this study is the first to reveal the molecular crosstalk between RA and sarcopenia, identifying 25 shared genes and key immune-inflammatory response-related pathways. Further SMR and cis-eQTL analyses were conducted to validate six core genes, with FCGR2A emerging as a potential drug target for RA-associated sarcopenia. These findings provide new insights into the comorbid mechanisms of RA and sarcopenia, offering potential therapeutic targets for both conditions.

Integration analysis of PLAUR as a sunitinib resistance and macrophage related biomarker in ccRCC, an in silicon and experimental study

Sunitinib remains the preferred systemic treatment option for specific patients with advanced RCC who are ineligible for immune therapy. However, it's essential to recognize that Sunitinib fails to elicit a favourable response in all patients. Moreover, most patients eventually develop resistance to Sunitinib. Therefore, identifying new targets associated with Sunitinib resistance is crucial. Utilizing multiple datasets from public cohorts, we conducted an exhaustive analysis and identified a total of 8 microRNAs and 112 mRNAs displaying significant expression differences between Sunitinib responsive and resistant groups. A particular set of six genes, specifically NIPSNAP1, STK40, SDC4, NEU1, TBC1D9, and PLAUR, were identified as highly significant via WGCNA. To delve deeper into the resistance mechanisms, we performed additional investigations using cell, molecular, and flow cytometry tests. These studies confirmed PLAUR's pivotal role in fostering Sunitinib resistance, both in vitro and in vivo. Our findings suggest that PLAUR could be a promising therapeutic target across various cancer types. In conclusion, this investigation not only uncovers vital genes and microRNAs associated with Sunitinib resistance in RCC but also introduces PLAUR as a prospective therapeutic target for diverse cancers. The outcomes contribute to advancing personalized healthcare and developing superior therapeutic strategies.

Age-related loss of large dendritic spines in the precuneus is statistically mediated by proteins which are predicted targets of existing drugs

Preservation of dendritic spines is a putative mechanism of protection against cognitive impairment despite development of Alzheimer Disease (AD)-related pathologies. Aging, the chief late-onset AD risk factor, is associated with dendritic spine loss in select brain areas. However, no study to our knowledge has observed this effect in precuneus, an area selectively vulnerable to early accumulation of AD-related pathology. We therefore quantified dendritic spine density in precuneus from 98 subjects without evidence of neurocognitive decline, spanning ages 20-96, and found a significant negative correlation between age and large dendritic spine density. In these same subjects, we conducted liquid chromatography-tandem mass spectrometry of >5000 proteins and identified 203 proteins which statistically mediate the effect of age on large dendritic spine density. Using computational pharmacology, we identified ten drugs which are predicted to target these mediators, informing future studies designed to test their effects on age-related dendritic spine loss and cognitive decline.

Changes in leaf economic trait relationships across a precipitation gradient are related to differential gene expression in a C4 perennial grass

The leaf economics spectrum (LES) describes a suite of functional traits that consistently covary at large spatial and taxonomic scales. Despite its importance at these larger scales, few studies have examined the major drivers of intraspecific variation in the LES - phenotypic plasticity and standing genetic variation. Using experimental precipitation manipulations, we examined whether covariation among leaf economics traits and selection on leaf economics traits and trait combinations change as diverse genotypes of the widespread perennial grass Panicum virgatum are exposed to differences in precipitation. We also used RNA-Seq to examine whether groups of co-expressed genes that align with leaf economics traits function in processes hypothesized to underlie the LES. Water availability impacted leaf economics trait covariation in important ways - covariation between leaf economics traits and selection on covariation between traits (i.e. correlational selection) tended to be strongest when water availability was high. Additionally, many genes associated with leaf economics traits functioned in processes that may explain how the LES originates, such as chloroplasts, cell walls, and nitrogen metabolism. Water availability is likely an important modulator of selection and evolution of the LES in P. virgatum that can be better understood by examining gene expression.

Comparative Analysis of Human Brain RNA-seq Reveals the Combined Effects of Ferroptosis and Autophagy on Alzheimer's Disease in Multiple Brain Regions

Ferroptosis and autophagy are closely associated with Alzheimer's disease (AD). Elevated ferric ion levels can induce oxidative stress and chronic inflammatory responses, resulting in brain tissue damage and further neurological cell damage. Autophagy in Alzheimer's has a dual role. On one hand, it protects neurons by removing β-amyloid and cellular damage products caused by oxidative stress and inflammation. On the other hand, abnormal autophagy is linked to neuronal apoptosis and neurodegeneration. However, the intricate interplay between ferroptosis and autophagy in AD remains insufficiently explored. This study focuses on the roles of ferroptosis and autophagy in AD and their interconnection through bioinformatics analysis, shedding light on the disease. Ferroptosis and autophagy significantly correlate with the development and course of AD. Using PPI network analysis and unsupervised consistency clustering analysis, we uncovered a complex network of interactions between ferroptosis and autophagy during disease progression, demonstrating a significant congruence in their modification patterns. Functional analyses further demonstrated that ferroptosis and autophagy together affect the immunological status and synaptic regulation in hippocampal regions in patients with AD, which significantly impacts the start and progression of the disease.

Genome-Wide Network Analysis of DRG-Sciatic Nerve Network-Inferred Cellular Senescence and Senescence Phenotype in Peripheral Sensory Neurons

Accumulation of senescent neurons in the dorsal root ganglion (DRG) is an important tissue phenotype that causes age-related degeneration of peripheral sensory nerves. Senescent neurons are neurons with arrested cell cycle that have undergone cellular senescence but remain in the tissue and play various biological roles. To understand the accumulation of senescent neurons in the DRG during aging, we aimed to elucidate the mechanism that induces cellular senescence in DRG neurons and the role of senescent DRG neurons. We integrated multiple public transcriptome datasets for DRGs, which include cell bodies in neurons, and the sciatic nerve, which includes axons in neurons, using network medicine-based bioinformatics analysis. We thus inferred the molecular mechanisms involved in cellular senescence of DRG neurons, from molecular responses to senescence, in the DRG-sciatic nerve network. Network medicine-based bioinformatics analysis revealed that age-related Mapk3 decline leads to impaired cholesterol metabolism and biosynthetic function in axons, resulting in compensatory upregulation of Srebf1, a transcription factor involved in lipid and cholesterol metabolism. This in turn leads to CDKN2A-mediated cellular senescence. Furthermore, our analysis revealed that senescent DRG neurons develop a senescence phenotype characterized by activation of antigen-presenting cells via upregulation of Ctss as a hub gene. B cells were inferred as antigen-presenting cells activated by Ctss, and CD8-positive T cells were inferred as cells that receive antigen presentation from B cells.

Altered immune response is associated with sex difference in vulnerability to Alzheimer's disease in human prefrontal cortex

Alzheimer's disease (AD) is a neurodegenerative disorder with a higher risk incidence in females than in males, and there are also differences in AD pathophysiology between sexes. The role of sex in the pathogenesis of AD may be crucial, yet the cellular and molecular basis remains unclear. Here, we performed a comprehensive analysis using four public transcriptome datasets of AD patients and age-matched control individuals in prefrontal cortex, including bulk transcriptome (295 females and 402 males) and single-nucleus RNA sequencing (snRNA-seq) data (224 females and 219 males). We found that the transcriptomic profile in female control was similar to those in AD. To characterize the key features associated with both the pathogenesis of AD and sex difference, we identified a co-expressed gene module that positively correlated with AD, sex, and aging, and was also enriched with immune-associated pathways. Using snRNA-seq datasets, we found that microglia (MG), a resident immune cell in the brain, demonstrated substantial differences in several aspects between sexes, such as an elevated proportion of activated MG, altered transcriptomic profile and cell-cell interaction between MG and other brain cell types in female control. Additionally, genes upregulated in female MG, such as TLR2, MERTK, SPP1, SLA, ACSL1, and FKBP5, had high confidence to be identified as biomarkers to distinguish AD status, and these genes also interacted with some approved drugs for treatment of AD. These findings underscore the altered immune response in female is associated with sex difference in susceptibility to AD, and the necessity of considering sex factors when developing AD biomarkers and therapeutic strategies, providing a scientific basis for further in-depth studies on sex differences in AD.

In a nutshell: pistachio genome and kernel development

Pistachio is a sustainable nut crop with exceptional climate resilience and nutritional value. However, the molecular processes underlying pistachio nut development and nutritional traits are largely unknown, compounded by limited genomic and molecular resources. To advance pistachios as a future food source and a model system for hard-shelled fruits, we generated a chromosome-scale reference genome of the most widely grown pistachio cultivar (Pistacia vera 'Kerman') and a spatiotemporal study of nut development. We integrated tissue-level physiological data from thousands of nuts over three growing seasons with transcriptomic data encompassing 14 developmental time points of the hull, shell, and kernel to assemble gene modules associated with physiological changes. Our study defined four distinct stages of pistachio nut growth and maturation. We then focused on the kernel to identify transcriptional and metabolic changes in molecular pathways governing nutritional quality, such as the accumulation of unsaturated fatty acids, which are vital for shelf life and dietary value. These findings revealed key candidate conserved regulatory genes, such as PvAP2-WRI1 and PvNFYB-LEC1, likely involved in oil accumulation in kernels. This work yields new knowledge and resources that will inform other woody crops and facilitate further improvement of pistachio as a globally significant, sustainable, and nutritious crop.

Discovery of core genes and intercellular communication role in osteosarcoma

Osteosarcoma is a primary malignant bone tumor that affects children and young adults. Understanding the molecular mechanisms underlying osteosarcoma is critical to develop effective treatments. This study aimed to identify core genes and explore the role of intercellular communication in osteosarcoma. We used GSE87437 and GSE152048 dataset to conduct a weighted correlation network analysis (WGCNA) and identify co-expression modules. The enriched biological processes and cellular components of the genes in the steelblue module were analyzed. Next, we explored the expression, diagnostic value, correlation, and association with immune infiltrate of CCSER1 and LOC101929154. Finally, we utilized CIBERSORT algorithm to predict the infiltrated immune cells in osteosarcoma tissues. Our results identified 44 co-expression modules, and the steelblue module was mainly associated with axon development, axonogenesis, and innervation. CCSER1 and LOC101929154 were significantly upregulated in osteosarcoma tissues with poor response to preoperative chemotherapy. Moreover, the expressions of CCSER1 and LOC101929154 were positively correlated. The area under the receiver operating characteristic curve of CCSER1 and LOC101929154 was 0.800 and 0.773, respectively. The expression of CCSER1 was negatively correlated with follicular helper T cells and positively correlated with M0 macrophages, while LOC101929154 was negatively correlated with activated mast cells. Besides, CD4 memory-activated T cells were observed at lower levels in patients who responded well to chemotherapy. Our study identified core genes CCSER1 and LOC101929154 and provided insight into the intercellular communication profile in osteosarcoma. Our results suggested that targeting CCSER1, LOC101929154, and CD4 memory-activated T cells may be a promising strategy for the treatment of osteosarcoma.

Insecticide-induced sublethal effect in the fall armyworm is mediated by miR-9993/miR-2a-3p - FPPS/JHAMT - JH molecular module

The sublethal effect of insecticides can affect the population dynamics of pests by changing the physiological or behavioral changes, which poses a serious threat to the sustainable control of crop pests in the field. However, the molecular regulation mechanism that mediates the sublethal effect of insecticides on crop pests remains unsolved. Here, we show that the sublethal effect of spinetoram and cyantraniliprole on Spodoptera frugiperda is mediated by the molecular module of microRNA-9993/microRNA-2a-3p - farnesyl diphosphate synthase gene (FPPS)/juvenile hormone (JH) acid methyltransferase gene (JHAMT) - JH. Spinetoram prolonged the duration of larvae and pupae, decreased the weight of pupae, while cyantraniliprole prolonged the duration of larvae and decreased the emergence rate. Similarly, injection of the juvenile hormone analogue (JHA) methoprene significantly prolonged the developmental duration of larvae and pupae, decreased the pupal weight and emergence rate. This sublethal phenotypic change was due to the upregulation of key JH synthesis genes, including FPPS and JHAMT, mediated by spinetoram and cyantraniliprole, resulting in an increase in JH titer. Furthermore, it was confirmed by small RNA sequencing, dual luciferase analysis and agomir-miRNA injection, miR-9993 and miR-2a-3p that it could bind FPPS and JHAMT respectively, and regulated the expression level of FPPS and JHAMT to affect the titer of JH, thus changing the phenotype of S. frugiperda. Collectively, these results provide insights into the mechanism of insecticide regulation of sublethal effects of pests, expand our understanding of development-related miRNAs, and reveal key factors involved in JH signaling pathways that support sublethal effects of insecticides.

Optimized RNA sequencing deconvolution illustrates the impact of obesity and weight loss on cell composition of human adipose tissue

OBJECTIVE

Cellular heterogeneity of human adipose tissue is linked to the pathophysiology of obesity and may impact the response to energy restriction and changes in fat mass. Herein, we provide an optimized pipeline to estimate cellular composition in human abdominal subcutaneous adipose tissue (ASAT) bulk RNA sequencing (RNA-seq) datasets using a single-nuclei RNA-seq signature matrix.

METHODS

A deconvolution pipeline for ASAT was optimized by benchmarking publicly available algorithms using a signature matrix derived from ASAT single-nuclei RNA-seq data from 20 adults and then applied to estimate ASAT cell-type proportions in publicly available obesity and weight loss studies.

RESULTS

Individuals with obesity had greater proportions of macrophages and lower proportions of adipocyte subpopulations and vascular cells compared with lean individuals. Two months of diet-induced weight loss increased the estimated proportions of macrophages; however, 2 years of diet-induced weight loss reduced the estimated proportions of macrophages, thereby suggesting a biphasic nature of cellular remodeling of ASAT during weight loss.

CONCLUSIONS

Our optimized high-throughput pipeline facilitates the assessment of composition changes of highly characterized cell types in large numbers of ASAT samples using low-cost bulk RNA-seq. Our data reveal novel changes in cellular heterogeneity and its association with cardiometabolic health in humans with obesity and following weight loss.

Interdisciplinary integration strategy reveals the anti-inflammatory efficacy and potential mechanism of Jianpi Qingre Tongluo prescription in rheumatoid arthritis

BACKGROUND

Rheumatoid arthritis (RA) is an autoimmune disease characterized by chronic synovitis and associated with high rates of disability and systemic damage. Jianpi Qingre Tongluo prescription (Huangqin Qingre Chubi Capsule, HQC), an herbal formula with abundant clinical applications, has played a definite role in both clinical and experimental studies of RA. However, the specific mechanisms by which HQC relieves inflammation in RA have not been fully elucidated.

OBJECTIVE

This study aimed to elucidate the anti-inflammatory efficacy and potential molecular mechanisms of HQC in RA and provide new targets and strategies for its clinical treatment.

METHODS

An adjuvant-induced arthritis with damp-heat pattern rat model was established to observe the in vivo effects of HQC. Hematoxylin-eosin and toluidine blue staining, and enzyme-linked immunosorbent assay were used to assess potential efficacy. Bioinformatics methods and molecular docking were used to predict potential targets and intervention pathways in which HQC might act on RA. Clinical samples, overexpressed / silenced genes, and pathway agonists were selected to investigate the clinical relevance and regulatory relationships of the pathways. The regulatory mechanism of HQC was confirmed in an in vitro co-culture of neutrophils and fibroblast-like synoviocytes (FLSs) and an in vivo model.

RESULTS

HQC dose-dependently reversed synovial pathological injury and systemic inflammatory responses in rats in vivo. Integrated bioinformatics and molecular docking identified the p38 mitogen-activated protein kinase (MAPK) signaling pathway and neutrophil extracellular trap (NET) formation as the key mechanisms by which HQC exerts anti-inflammatory effects on RA. Subsequently, a high correlation between circ0005732, p38 MAPK, and clinical features of RA was confirmed in clinical samples. In vitro experiments demonstrated that HQC alleviated the proliferation and inflammatory response of FLSs by regulating circ0005732 expression to inhibit NET formation driven by the p38 MAPK signaling pathway. Finally, RT-qPCR and western blotting confirmed that HQC modulated circ0005732, p38 MAPK pathway, and NET formation to alleviate RA in vivo.

CONCLUSION

HQC exerts therapeutic effects against RA by modulating circ0005732 to inhibit p38 MAPK signaling pathway-mediated NET generation and inflammation progression.

Normative aging results in degradation of gene networks in a zebra finch basal ganglia nucleus dedicated to vocal behavior

Aging increases brain susceptibility to neurodegenerative diseases, but the mechanisms are not clear. Vocal behavior provides an accessible, reliable, and sensitive biomarker to address this because voice changes in middle age can be early indicators of neurodegenerative diseases. The adult male zebra finch is an excellent model organism for these studies due to well-characterized vocal brain circuitry and strong homology to human brain centers. We performed RNA sequencing of song-dedicated basal ganglia nucleus Area X followed by weighted gene co-expression network analyses to examine changes in gene patterns across younger adult, middle, and older ages. Song-correlated gene networks degrade with age, with modules losing their coherence and migrating to different sets of genes, and changes in connection strength particularly for hub genes including those associated with human speech, Parkinson's, and Alzheimer's diseases. Gene pathway enrichment analyses reveal a lack of ongoing metabolic and biogenic processes in older finches. Our findings provide a robust platform for targeting network hubs in the treatment of neurologically driven human vocal disorders.

METTL7B-induced histone lactylation prevents heart failure by ameliorating cardiac remodelling

INTRODUCTION

Lactylation is important for a variety of biological activities. It is reported that Class I histone deacetylases (HDAC1-3) are histone lysine delactylases. However, the role of lactylation in cardiac remodelling remains uncertain.

OBJECTIVES

To explore a novel regulator of lactylation and elucidate their functional mechanisms in cardiac remodelling and heart failure.

METHODS

GSE36961, GSE141910 and GSE174691 related to HCM (hypertrophic cardiomyopathy) were separately acquired from Gene expression Omnibus. Candidate genes related to both HCM and histone lactylation were determined by the intersection of DEGs (differentially expressed genes) and module genes sifted by WGCNA (Weighted Gene Co-Expression Network Analysis). METTL7B was screened out and its expression in hypertrophic myocardium was measured by qRT-PCR and western blotting. Furthermore, immunofluorescence, immunoprecipitation, and RNA pull-down assays were utilized to identify the biological functions of METTL7B. The myocardial biopsy of HCM and transverse aortic constriction (TAC) mouse model were performed to analyze the effects of METTL7B on cardiac remodelling in vivo.

RESULTS

We observed that the expression of METTL7B was down-regulated in hypertrophic myocardium, and the lactylation level was increased during the early stage and falling rapidly in the process of cardiac remodelling. Furthermore, we demonstrated that sodium lactate (NALA) administration fulfil a protective role on cardiac remodelling, and METTL7B alleviates cardiac remodelling and improves heart function by maintaining the activation of histone lactylation possibly at the later stage. Impressively, METTL7B suppressed the expression of USP38 via m6A dependent mRNA degradation, resulting in increasing ubiquitylation of HDAC3, which is a proven histone lysine delactylases.

CONCLUSION

We identifed METTL7B as a potential therapeutic target for myocardial remodelling and showed that it played a critical role in the promotion of myocardial lactylation, which is beneficial for improvement of cardiac function and attenuation of cardiac remodelling.

Identification of a novel FOXO3‑associated prognostic model in hepatocellular carcinoma

Although numerous molecular classifications are available to predict the prognosis of patients with hepatocellular carcinoma (HCC), they are still unsatisfactory. Forkhead box O3 (FOXO3) has been widely reported as a transcription factor involved in human cancers, but its role in HCC remains controversial. The present study aimed to explore the role of FOXO3 in HCC, as well as to identify biomarkers and construct prognostic models based on FOXO3. FOXO3 was highly expressed in HCC and was closely associated with poor prognosis in The Cancer Genome Atlas (the training set) and International Cancer Genome Consortium (the validation set). Subsequently, a co-expression network indicated that the red modules were closely related to FOXO3. Five key FOXO3-related genes [DEAD-box helicase 55 (DDX55), RAB10, member RAS oncogene family (RAB10), RAB7A, TATA-box binding protein associated factor, RNA polymerase I subunit B (TAF1B) and TAF3] were obtained using Cox-least absolute shrinkage and selection operator analyses. The 5-gene signature successfully predicted the prognosis of patients with HCC in both the training and validation sets. Enrichment analysis suggested marked differences in AKT and cell cycle-related (E2F targets and G2/M checkpoints) pathways between HCC subgroups. Furthermore, the tumor microenvironment analysis suggested that the difference in the distribution of M2 macrophages among various subgroups may contribute to the poor prognosis using the CIBERSORTx framework. Furthermore, the mRNA and protein expressions of DDX55, RAB10, RAB7A, TAF1B and TAF3 were found to be higher in HCC tissues compared with paracancerous tissues using RT-qPCR and western blotting. Additionally, knockdown of RAB10, RAB7A and TAF3 inhibited proliferation of Huh7 cells, assessed by a Cell Counting Kit-8 assay. In conclusion, a novel FOXO3-related model was constructed and revealed that RAB10, RAB7A and TAF3 may be potential molecular targets or biomarkers for HCC.