Divergent Flowering Time Responses to Increasing Temperatures Are Associated With Transcriptome Plasticity and Epigenetic Modification Differences at FLC Promoter Region of Arabidopsis thaliana.

Understanding the genetic, and transcriptomic changes that drive the phenotypic plasticity of fitness traits is a central question in evolutionary biology. In this study, we utilised 152 natural Swedish Arabidopsis thaliana accessions with re-sequenced genomes, transcriptomes and methylomes and measured flowering times (FTs) under two temperature conditions (10°C and 16°C) to address this question. We revealed that the northern accessions exhibited advanced flowering in response to decreased temperature, whereas the southern accessions delayed their flowering, indicating a divergent flowering response. This contrast in flowering responses was associated with the isothermality of their native ranges, which potentially enables the northern accessions to complete their life cycle more rapidly in years with shorter growth seasons. At the transcriptome level, we observed extensive rewiring of gene co-expression networks, with the expression of 25 core genes being associated with the mean FT and its plastic variation. Notably, variations in FLC expression sensitivity between northern and southern accessions were found to be associated with the divergence FT response. Further analysis suggests that FLC expression sensitivity is associated with differences in CG, CHG and CHH methylation at the promoter region. Overall, our study revealed the association between transcriptome plasticity and flowering time plasticity among different accessions, providing evidence for its relevance in ecological adaptation. These findings offer deeper insights into the genetics of rapid responses to environmental changes and ecological adaptation.

Gene co-expression networks reveal sex-biased differences in musculoskeletal ageing.

Aging is a universal and progressive process involving the deterioration of physiological functions and the accumulation of cellular damage. Gene regulation programs influence how phenotypes respond to environmental and intrinsic changes during aging. Although several factors, including sex, are known to impact this process, the underlying mechanisms remain incompletely understood. Here, we investigate the functional organization patterns of skeletal muscle genes across different sexes and ages using gene co-expression networks (GCNs) to explore their influence on aging. We constructed GCNs for three different age groups for male and female samples, analyzed topological similarities and differences, inferred significant associated processes for each network, and constructed null models to provide statistically robust results. We found that each network is topologically and functionally distinct, with young women having the most associated processes, likely due to reproductive tasks. The functional organization and modularity of genes decline with age, starting from middle age, potentially leading to age-related deterioration. Women maintain better gene functional organization throughout life compared to men, especially in processes like macroautophagy and sarcomere organization. The study suggests that the loss of gene co-expression could be a universal aging marker. This research offers insights into how gene organization changes with age and sex, providing a complementary method to analyze aging.

Unveiling functionality and conducting two-sample mendelian randomization on WGCNA-identified oxidative stress-related hub genes in metabolic dysfunction-associated fatty liver disease.

Metabolic dysfunction-associated fatty liver disease (MAFLD) shows accelerated development under the impact of oxidative stress (OS). There is an imperative to identify OS-related biomarkers in MAFLD and explore their potential mechanistic insights. The objective of this study was to identify OS-related biomarkers in MAFLD and explore their potential mechanisms. DEG analysis was performed using GSE17470 and GSE24807 datasets. An investigative exploration utilizing WGCNA was executed to elucidate hub OS-related genes. The intersection of OS-related hub genes identified by WGCNA and DEGs was systematically employed for thorough analyses. A mendelian randomization (MR) study examined the causal effect of C-reactive protein (CRP) on MAFLD. 59 OS-related DEGs were identified in MAFLD. WGCNA revealed 100 OS-related hub genes in MAFLD. Sixteen OS-related genes have been delineated as critical components in MAFLD. Enrichment analyses, employing GO and KEGG pathways, revealed pathways enriched with these genes. Following PPI analyses, the highest-ranking ten hub genes demonstrating abnormal expression were determined. Ultimately, a two-sample MR analysis demonstrated a causal link between the hub gene CRP and the occurrence of MAFLD. In this study, we harnessed WGCNA to formulate a co-expression network and identified hub OS-related DEGs in MAFLD. Additionally, the hub gene CRP exhibited a significant correlation with the predisposition to MAFLD. These findings offer innovative perspectives on the applications of OS-associated genes in individuals afflicted with MAFLD.

Novel insights into the circadian modulation of lipid metabolism in chicken livers revealed by RNA sequencing and weighted gene co-expression network analysis.

The circadian clock is crucial for maintaining lipid metabolism homeostasis in mammals. Despite the economic importance of fat content in poultry, research on the regulatory effects and molecular mechanisms of the circadian clock on avian hepatic lipid metabolism has been limited. In this study, we observed significant diurnal variations (P<0.05) in triglyceride (TG), free fatty acids (FFA), fatty acid synthase (FAS), and total cholesterol (TC) levels in the chicken embryonic liver under 12-h light/12-h dark incubation conditions, with TG, FFA, and TC concentrations showing significant cosine rhythmic oscillations (P<0.05). However, such rhythmic variations were not observed under complete darkness incubation conditions. Using transcriptome sequencing technology, we identified 157 genes significantly upregulated at night and 313 genes significantly upregulated during the 12-h light/12-h dark cycle. These circadian differential genes are involved in processes and pathways such as lipid catabolic process regulation, meiotic cell cycle, circadian rhythm regulation, positive regulation of the MAPK cascade, and glycerolipid metabolism. Weighted gene co-expression network analysis (WGCNA) revealed 3 modules-green, blue, and red-that significantly correlate with FFA, FAS, and TG, respectively. Genes within these modules were enriched in processes and pathways including the cell cycle, light stimulus response, circadian rhythm regulation, phosphorylation, positive regulation of the MAPK cascade, and lipid biosynthesis. Notably, we identified ten hub genes, including protein kinase C delta (PRKCD), polo like kinase 4 (PLK4), clock circadian regulator (CLOCK), steroid 5 alpha-reductase 3 (SRD5A3), BUB1 mitotic checkpoint serine/threonine kinase (BUB1B), shugoshin 1 (SGO1), NDC80 kinetochore complex component (NDC80), NIMA related kinase 2 (NEK2), minichromosome maintenance complex component 4 (MCM4), polo like kinase 1 (PLK1), potentially link circadian regulation with lipid metabolic homeostasis. These findings demonstrate the regulatory role of the circadian clock in chicken liver lipid metabolism homeostasis and provide a theoretical basis and molecular targets for optimizing the circadian clock to reduce excessive fat deposition in chickens, which is significant for the healthy development of the poultry industry.

Leveraging gene correlations in single cell transcriptomic data.

BACKGROUND: Many approaches have been developed to overcome technical noise in single cell RNA-sequencing (scRNAseq). As researchers dig deeper into data-looking for rare cell types, subtleties of cell states, and details of gene regulatory networks-there is a growing need for algorithms with controllable accuracy and fewer ad hoc parameters and thresholds. Impeding this goal is the fact that an appropriate null distribution for scRNAseq cannot simply be extracted from data in which ground truth about biological variation is unknown (i.e., usually). RESULTS: We approach this problem analytically, assuming that scRNAseq data reflect only cell heterogeneity (what we seek to characterize), transcriptional noise (temporal fluctuations randomly distributed across cells), and sampling error (i.e., Poisson noise). We analyze scRNAseq data without normalization-a step that skews distributions, particularly for sparse data-and calculate p values associated with key statistics. We develop an improved method for selecting features for cell clustering and identifying gene-gene correlations, both positive and negative. Using simulated data, we show that this method, which we call BigSur (Basic Informatics and Gene Statistics from Unnormalized Reads), captures even weak yet significant correlation structures in scRNAseq data. Applying BigSur to data from a clonal human melanoma cell line, we identify thousands of correlations that, when clustered without supervision into gene communities, align with known cellular components and biological processes, and highlight potentially novel cell biological relationships. CONCLUSIONS: New insights into functionally relevant gene regulatory networks can be obtained using a statistically grounded approach to the identification of gene-gene correlations.

Combined Analysis of Human and Experimental Rat Samples Identified Biomarkers for Ischemic Stroke.

The genetic transcription profile and underlying molecular mechanisms of ischemic stroke (IS) remain elusive. To address this issue, four mRNA and one miRNA expression profile of rats with middle cerebral artery occlusion (MCAO) were acquired from the Gene Expression Omnibus (GEO) database. A total of 780 differentially expressed genes (DEGs) and 56 miRNAs (DEMs) were screened. Gene set and functional enrichment analysis revealed that a substantial number of immune-inflammation-related pathways were abnormally activated in IS. Through weighted gene co-expression network analysis, the turquoise module was identified as meaningful. By taking the intersection of the turquoise module genes, DEM-target genes, and all DEGs, 354 genes were subsequently obtained as key IS-related genes. Among them, six characteristic genes were identified using the least absolute shrinkage and selection operator. After validation with three external datasets, transforming growth factor beta 1 (Tgfb1) was selected as the hub gene. This finding was further confirmed by gene expression pattern analysis in both the MCAO model rats and clinical IS patients. Moreover, the expression of the hub genes exhibited a negative correlation with the modified Rankin scale score (P < 0.05). Collectively, these results expand our knowledge of the genetic profile and molecular mechanisms involved in IS and suggest that the Tgfb1 gene is a potential biomarker of this disease.

Identification of susceptibility modules and characteristic genes to osteoarthritis by WGCNA.

The susceptibility modules and characteristic genes of patients with osteoarthritis (OA) were determined by weighted gene co-expression network analysis (WGCNA), and the role of immune cells in OA related microenvironment was analyzed. GSE98918 and GSE117999 data sets are from GEO database. R language was used to conduct difference analysis for the new data set after merging. The formation of gene co-expression network, screening of susceptibility modules and screening of core genes are all through WGCNA. GO and KEGG enrichment analyses were used for Hub genes. The characteristic genes of the disease were obtained by Lasso regression screening. SSGSEA was used to estimate immune cell abundance in sample and a series of correlation analyses were performed. WGCNA was used to form 6 gene co-expression modules. The yellow-green module is identified as the susceptible module of OA. 202 genes were identified as core genes. Finally, RHOT2, FNBP4 and NARF were identified as the characteristic genes of OA. The results showed that the characteristic genes of OA were positively correlated with plasmacytoid dendritic cells, NKT cells and immature dendritic cells, but negatively correlated with active B cells. MDSC were the most abundant immune cells in cartilage. This study identified the Hippo signaling pathway, mTOR signaling pathway, and three characteristic genes (RHOT2, FNBP4, NARF) as being associated with osteoarthritis (OA). These three genes are downregulated in the cartilage of OA patients and may serve as biomarkers for early diagnosis and targeted therapy. Proper regulation of immune cells may aid in the treatment of OA. Future research should focus on developing tools to detect these genes and exploring their therapeutic applications.

Relationship between the rumen microbiome and liver transcriptome in beef cattle divergent for feed efficiency.

BACKGROUND: Feed costs account for a high proportion of the variable cost of beef production, ultimately impacting overall profitability. Thus, improving feed efficiency of beef cattle, by way of determining the underlying genomic control and selecting for feed efficient cattle provides a method through which feed input costs may be reduced whilst also contributing to the environmental sustainability of beef production. The rumen microbiome dictates the feed degradation capacity and consequent nutrient supply in ruminants, thus potentially impacted by feed efficiency phenotype. Equally, liver tissue has been shown to be responsive to feed efficiency phenotype as well as dietary intake. However, although both the rumen microbiome and liver transcriptome have been shown to be impacted by host feed efficiency phenotype, knowledge of the interaction between the rumen microbiome and other peripheral tissues within the body, including the liver is lacking. Thus, the objective of this study was to compare two contrasting breed types (Charolais and Holstein-Friesian) divergent for residual feed intake (RFI) over contrasting dietary phases (zero-grazed grass and high-concentrate), based on gene co-expression network analysis of liver transcriptome data and microbe co-abundance network of rumen microbiome data. Traits including RFI, dry matter intake (DMI) and growth rate (ADG), as well as rumen concentrations of volatile fatty acids were also included within the network analysis. RESULTS: Overall, DMI had the greatest number of connections followed by RFI, with ADG displaying the fewest number of significant connections. Hepatic genes related to lipid metabolism were correlated to both RFI and DMI phenotypes, whilst genes related to immune response were correlated to DMI. Despite the known relationship between RFI and DMI, the same microbes were not directly connected to these phenotypes, the Succiniclasticum genus was however, negatively connected to both RFI and ADG. Additionally, a stepwise regression analysis revealed significant roles for both Succiniclasticum genus and Roseburia.faecis sp. in predicting RFI, DMI and ADG. CONCLUSIONS: Results from this study highlight the interactive relationships between rumen microbiome and hepatic transcriptome data of cattle divergent for RFI, whilst also increasing our understanding of the underlying biology of both DMI and ADG in beef cattle.

Patterns of Fitness and Gene Expression Epistasis Generated by Beneficial Mutations in the rho and rpoB Genes of Escherichia coli during High-Temperature Adaptation.

Epistasis is caused by genetic interactions among mutations that affect fitness. To characterize properties and potential mechanisms of epistasis, we engineered eight double mutants that combined mutations from the rho and rpoB genes of Escherichia coli. The two genes encode essential functions for transcription, and the mutations in each gene were chosen because they were beneficial for adaptation to thermal stress (42.2 °C). The double mutants exhibited patterns of fitness epistasis that included diminishing returns epistasis at 42.2 °C, stronger diminishing returns between mutations with larger beneficial effects and both negative and positive (sign) epistasis across environments (20.0 °C and 37.0 °C). By assessing gene expression between single and double mutants, we detected hundreds of genes with gene expression epistasis. Previous work postulated that highly connected hub genes in coexpression networks have low epistasis, but we found the opposite: hub genes had high epistasis values in both coexpression and protein-protein interaction networks. We hypothesized that elevated epistasis in hub genes reflected that they were enriched for targets of Rho termination but that was not the case. Altogether, gene expression and coexpression analyses revealed that thermal adaptation occurred in modules, through modulation of ribonucleotide biosynthetic processes and ribosome assembly, the attenuation of expression in genes related to heat shock and stress responses, and with an overall trend toward restoring gene expression toward the unstressed state.

Identification of Immune-Related Genes as Potential Biomarkers in Early Septic Shock.

INTRODUCTION: Septic shock, a severe manifestation of infection-induced systemic immune response, poses a critical threat resulting in life-threatening multi-organ failure. Early diagnosis and intervention are imperative due to the potential for irreversible organ damage. However, specific and sensitive detection tools for the diagnosis of septic shock are still lacking. METHODS: Gene expression files of early septic shock were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT analysis was used to evaluate immune cell infiltration. Genes related to immunity and disease progression were identified using weighted gene co-expression network analysis (WGCNA), followed by enrichment analysis. CytoHubba was then employed to identify hub genes, and their relationships with immune cells were explored through correlation analysis. Blood samples from healthy controls and patients with early septic shock were collected to validate the expression of hub genes, and an external dataset was used to validate their diagnostic efficacy. RESULTS: Twelve immune cells showed significant infiltration differences in early septic shock compared to control, such as neutrophils, M0 macrophages, and natural killer cells. The identified immune and disease-related genes were mainly enriched in immune, cell signaling, and metabolism pathways. In addition, six hub genes were identified (PECAM1, F11R, ITGAL, ICAM3, HK3, and MCEMP1), all significantly associated with M0 macrophages and exhibiting an area under curve of over 0.7. These genes exhibited abnormal expression in patients with early septic shock. External datasets and real-time qPCR validation supported the robustness of these findings. CONCLUSION: Six immune-related hub genes may be potential biomarkers for early septic shock.

Identification of fatty acid metabolism signature genes in patients with pulmonary arterial hypertension using WGCNA and machine learning.

OBJECTIVE: To investigate the signature genes of fatty acid metabolism and their association with immune cells in pulmonary arterial hypertension (PAH). METHODS: Fatty acid metabolism-related genes were obtained from the GeneCards database. In this retrospective study, a PAH-related dataset was downloaded from the Gene Expression Omnibus database and analyzed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify the signature genes. Diagnostic efficiency was assessed by receiver operating characteristic (ROC) curve analysis and a nomogram. Immune cell infiltration was subsequently classified using CIBERSORT. RESULTS: In total, 817 DEGs were screened from the GSE33463 dataset. The data were clustered into six modules via WGCNA, and the MEdarkred module was significantly related to PAH. The LASSO and random forest algorithms identified five signature genes: ARV1, KCNJ2, PEX11B, PITPNC1, and SCO1. The areas under the ROC curves of these signature genes were 0.917, 0.934, 0.947, 0.963, and 0.940, respectively. CIBERSORT suggested these signature genes may participate in immune cell infiltration. CONCLUSIONS: ARV1, KCNJ2, PEX11B, PITPNC1, and SCO1 show remarkable diagnostic performance in PAH and are involved in immune cell infiltration.

Transcriptomic and coexpression network analyses revealed the regulatory mechanism of Cydia pomonella infestation on the synthesis of phytohormones in walnut husks.

The codling moth (Cydia pomonella) has a major effect on the quality and yield of walnut fruit. Plant defences respond to insect infestation by activating hormonal signalling and the flavonoid biosynthetic pathway. However, little is known about the role of walnut husk hormones and flavonoid biosynthesis in response to C. pomonella infestation. The phytohormone content assay revealed that the contents of salicylic acid (SA), abscisic acid (ABA), jasmonic acid (JA), jasmonic acid-isoleucine conjugate (JA-ILE), jasmonic acid-valine (JA-Val) and methyl jasmonate (MeJA) increased after feeding at different time points (0, 12, 24, 36, 48, and 72 h) of walnut husk. RNA-seq analysis of walnut husks following C. pomonella feeding revealed a temporal pattern in differentially expressed genes (DEGs), with the number increasing from 3,988 at 12 h to 5,929 at 72 h postfeeding compared with the control at 0 h postfeeding. Walnut husks exhibited significant upregulation of genes involved in various defence pathways, including flavonoid biosynthesis (PAL, CYP73A, 4CL, CHS, CHI, F3H, ANS, and LAR), SA (PAL), ABA (ZEP and ABA2), and JA (AOS, AOC, OPR, JAZ, and MYC2) pathways. Three gene coexpression networks that had a significant positive association with these hormonal changes were constructed based on the basis of weighted gene coexpression network analysis (WGCNA). We identified several hub transcription factors, including the turquoise module (AIL6, MYB4, PRE6, WRKY71, WRKY31, ERF003, and WRKY75), the green module (bHLH79, PCL1, APRR5, ABI5, and ILR3), and the magenta module (ERF27, bHLH35, bHLH18, TIFY5A, WRKY31, and MYB44). Taken together, these findings provide useful genetic resources for exploring the defence response mediated by phytohormones in walnut husks.

Brief research report: WGCNA-driven identification of histone modification genes as potential biomarkers in AQP4-Associated optic neuritis.

Introduction: Neuromyelitis Optica spectrum disorder (NMOSD) is an autoimmune disease characterized by anti-aquaporin-4 (AQP4) auto-antibodies. The discovery of antibodies AQP4 and myelin oligodendrocyte glycoprotein (MOG) has expanded our understanding of the pathogenesis of neuromyelitis optica. However, the molecular mechanisms underlying the disease, particularly AQP4-associated optic neuritis (AQP4-ON), remain to be fully elucidated. Methods: In this study, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to investigate the transcriptomic profiles of peripheral blood samples from patients with AQP4-ON and MOG-positive optic neuritis (MOG-ON), compared to healthy controls. Results: WGCNA revealed a brown module (ME brown) strongly associated with AQP4-ON, which correlated positively with post-onset visual acuity decline. A total of 132 critical genes were identified, mainly involved in histone modification and microtubule dynamics. Notably, genes HDAC4, HDAC7, KDM6A, and KDM5C demonstrated high AUC values in ROC analysis, indicating their potential as biomarkers for AQP4-ON. Conclusion: Our findings provide novel insights into the molecular signature of AQP4-ON and highlight the potential of systems biology approaches in identifying biomarkers for NMOSD. The identified histone modification genes warrant further investigation for their role in disease pathogenesis and as therapeutic targets.

T-cell exhaustion-related genes in Graves' disease: a comprehensive genome mapping analysis.

Background: T-cell exhaustion (Tex) can be beneficial in autoimmune diseases, but its role in Graves' disease (GD), an autoimmune disorder of the thyroid, remains unknown. This study investigated Tex-related gene expression in GD patients to discern the potential contributions of these genes to GD pathogenesis and immune regulation. Methods: Through gene landscape analysis, a protein-protein interaction network of 40 Tex-related genes was constructed. mRNA expression levels were compared between GD patients and healthy control (HCs). Unsupervised clustering categorized GD cases into subtypes, revealing distinctions in gene expression, immune cell infiltration, and immune responses. Weighted gene co-expression network analysis and differential gene expression profiling identified potential therapeutic targets. RT-qPCR validation of candidate gene expression was performed using blood samples from 112 GD patients. Correlations between Tex-related gene expression and clinical indicators were analyzed. Results: Extensive Tex-related gene interactions were observed, with six genes displaying aberrant expression in GD patients. This was associated with atypical immune cell infiltration and regulation. Cluster analysis delineated two GD subtypes, revealing notable variations in gene expression and immune responses. Screening efforts identified diverse drug candidates for GD treatment. The Tex-related gene CBL was identified for further validation and showed reduced mRNA expression in GD patients, especially in cases of relapse. CBL mRNA expression was significantly lower in patients with moderate-to-severe thyroid enlargement than in those without such enlargement. Additionally, CBL mRNA expression was negatively correlated with the disease-specific indicator thyrotropin receptor antibodies. Conclusion: Tex-related genes modulate GD pathogenesis, and their grouping aids subtype differentiation and exploration of therapeutic targets. CBL represents a potential marker for GD recurrence.

MS4A1-PTGS2 axis induces taurine metabolic reprogramming to exacerbate abdominal aortic aneurysm progression.

This study unveils the pivotal roles of taurine metabolic reprogramming and its implications in the development and progression of Abdominal Aortic Aneurysm (AAA). Leveraging an integrated approach that combines single-cell RNA sequencing (scRNA-seq) and Weighted Gene Co-expression Network Analysis (WGCNA), our research investigates the intricate transcriptional and gene expression dynamics crucial to AAA. Our findings uniquely link metabolic shifts to the integrity of the extracellular matrix (ECM) and the functionality of smooth muscle cells (SMCs), key elements in the pathology of AAA. Utilizing scRNA-seq data from a mouse model (GSE152583 dataset), we identified critical alterations in cellular composition during AAA progression, particularly highlighting shifts in fibroblasts and inflammatory cells. Concurrently, WGCNA of human AAA tissue samples has outlined distinct gene expression patterns correlated with disease severity and progression, offering comprehensive insights into both molecular and cellular disease mechanisms. Moreover, this study introduces innovative metabolic profiling techniques to identify differential metabolites in AAA, integrating extensive metabolomic analyses with pathway enrichment strategies. This novel approach has pinpointed potential biomarkers and therapeutic targets, notably within taurine metabolism pathways, crucial for crafting non-surgical interventions. By merging state-of-the-art bioinformatics with thorough molecular analysis, our study not only enhances the understanding of AAA's complex pathophysiology but also catalyzes the development of targeted therapeutic strategies. This research represents a significant advancement in the molecular characterization of AAA, with substantial implications for its future diagnosis and treatment strategies.

Co-expression network and survival analysis of breast cancer inflammation and immune system hallmark genes.

The tertiary lymphoid structure (TLS) plays a central role in cancer immune response, and its gene expression pattern, called the TLS signature, has shown prognostic value in breast cancer. The formation of TLS and tumor-associated high endothelial venules (TA-HEVs), responsible for lymphocytic infiltration within the TLS, is associated with the expression of cancer hallmark genes (CHGs) related to immunity and inflammation. In this study, we performed co-expression network analysis of immune- and inflammation-related CHGs to identify predictive genes for breast cancer. In total, 382 immune- and inflammation-related CHGs with high expression variance were extracted from the GSE86166 microarray dataset of patients with breast cancer. CHGs were classified into five modules by applying weighted gene co-expression network analysis. The survival analysis results for each module showed that one module comprising 45 genes was statistically significant for relapse-free and overall survival. Four network properties identified key genes in this module with high prognostic prediction abilities: CD34, CXCL12, F2RL2, JAM2, PROS1, RAPGEF3, and SELP. The prognostic accuracy of the seven genes in breast cancer was synergistic and exceeded that of other predictors in both small and large public datasets. Enrichment analysis predicted that these genes had functions related to leukocyte infiltration of TA-HEVs. There was a positive correlation between key gene expression and the TLS signature, suggesting that gene expression levels are associated with TLS density. Co-expression network analysis of inflammation- and immune-related CHGs allowed us to identify genes that share a standard function in cancer immunity and have a high prognostic predictive value. This analytical approach may contribute to the identification of prognostic genes in TLS.

Identification of SNHG11 as a Therapeutic Target in Pulmonary Hypertension.

Pulmonary hypertension (PH) is a life-threatening condition characterized by pulmonary vascular remodeling and endothelial dysfunction. Current therapies primarily target vasoactive imbalances but often fail to address adverse vascular remodeling. Long non-coding RNA (lncRNA), which are key regulators of various cellular processes, remain underexplored in the context of PH. To investigate the role of lncRNA in PH, we performed a comprehensive analysis using Weighted Gene Co-expression Network Analysis (WGCNA) on the GSE113439 dataset, comprising human lung tissue samples from different PH subtypes. Our analysis identified the lncRNA SNHG11 as consistently downregulated in PH. Functional assays in human pulmonary artery endothelial cells (HPAECs) demonstrated that SNHG11 plays a critical role in modulating inflammation, cell proliferation, apoptosis, and the JAK/STAT and MAPK signaling pathways. Mechanistically, SNHG11 influences the stability of PRPF8, a crucial mRNA spliceosome component, thereby affecting multiple cellular functions beyond splicing. In vivo experiments using a hypoxic rat model showed that knockdown of SNHG11 alleviates PH development and improves right ventricular function. These findings highlight SNHG11 as a key regulator in PH pathogenesis and suggest it as a potential therapeutic target.

A useful mTORC1 signaling-related RiskScore model for the personalized treatment of osteosarcoma patients by using the bulk RNA-seq analysis.

AIMS: This research developed a prognostic model for OS patients based on the Mechanistic Target of Rapamycin Complex 1 (mTORC1) signature. BACKGROUND: The mTORC1 signaling pathway has a critical role in the maintenance of cellular homeostasis and tumorigenesis and development through the regulation of cell growth, metabolism and autophagy. However, the mechanism of action of this signaling pathway in Osteosarcoma (OS) remains unclear. OBJECTIVE: The datasets including the TARGET-OS and GSE39058, and 200 mTORC1 genes were collected. METHODS: The mTORC1 signaling-related genes were obtained based on the Molecular Signatures Database (MSigDB) database, and the single sample gene set enrichment analysis (ssGSEA) algorithm was utilized in order to calculate the mTORC1 score. Then, the WGCNA were performed for the mTORC1-correlated gene module, the un/multivariate and lasso Cox regression analysis were conducted for the RiskScore model. The immune infiltration analysis was performed by using the ssGSEA method, ESTIMATE tool and MCP-Count algorithm. KM survival and Receiver Operating Characteristic (ROC) Curve analysis were performed by using the survival and timeROC package. RESULTS: The mTORC1 score and WGCNA with ß = 5 screened the mTORC1 positively correlated skyblue2 module that included 67 genes, which are also associated with the metabolism and hypoxia pathways. Further narrowing of candidate genes and calculating the regression coefficient, we developed a useful and reliable RiskScore model, which can classify the patients in the training and validation set into high and low-risk groups based on the median value of RiskScore as an independent and robust prognostic factor. High-risk patients had a significantly poor prognosis, lower immune infiltration level of multiple immune cells and prone to cancer metastasis. Finally, we a nomogram model incorporating the metastasis features and RiskScore showed excellent prediction accuracy and clinical practicability. CONCLUSION: We developed a useful and reliable risk prognosis model based on the mTORC1 signaling signature.

Exploring the shared biomarkers between cardioembolic stroke and atrial fibrillation by WGCNA and machine learning.

Background: Cardioembolic Stroke (CS) and Atrial Fibrillation (AF) are prevalent diseases that significantly impact the quality of life and impose considerable financial burdens on society. Despite increasing evidence of a significant association between the two diseases, their complex interactions remain inadequately understood. We conducted bioinformatics analysis and employed machine learning techniques to investigate potential shared biomarkers between CS and AF. Methods: We retrieved the CS and AF datasets from the Gene Expression Omnibus (GEO) database and applied Weighted Gene Co-Expression Network Analysis (WGCNA) to develop co-expression networks aimed at identifying pivotal modules. Next, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the shared genes within the modules related to CS and AF. The STRING database was used to build a protein-protein interaction (PPI) network, facilitating the discovery of hub genes within the network. Finally, several common used machine learning approaches were applied to construct the clinical predictive model of CS and AF. ROC curve analysis to evaluate the diagnostic value of the identified biomarkers for AF and CS. Results: Functional enrichment analysis indicated that pathways intrinsic to the immune response may be significantly involved in CS and AF. PPI network analysis identified a potential association of 4 key genes with both CS and AF, specifically PIK3R1, ITGAM, FOS, and TLR4. Conclusion: In our study, we utilized WGCNA, PPI network analysis, and machine learning to identify four hub genes significantly associated with CS and AF. Functional annotation outcomes revealed that inherent pathways related to the immune response connected to the recognized genes might could pave the way for further research on the etiological mechanisms and therapeutic targets for CS and AF.

Identification of key genes in diabetic nephropathy based on lipid metabolism.

Diabetic nephropathy (DN) is a common systemic microvascular complication of diabetes with a high incidence rate. Notably, the disturbance of lipid metabolism is associated with DN progression. The present study aimed to identify lipid metabolism-related hub genes associated with DN for improved diagnosis of DN. The gene expression profile data of DN and healthy samples (GSE142153) were obtained from the Gene Expression Omnibus database, and the lipid metabolism-related genes were obtained from the Molecular Signatures Database. Differentially expressed genes (DEGs) between DN and healthy samples were analyzed. The weighted gene co-expression network analysis (WGCNA) was performed to examine the relationship between genes and clinical traits to identify the key module genes associated with DN. Next, the Venn Diagram R package was used to identify the lipid metabolism-related genes associated with DN and their protein-protein interaction (PPI) network was constructed. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The hub genes were identified using machine-learning algorithms. The Gene Set Enrichment Analysis (GSEA) was used to analyze the functions of the hub genes. The present study also investigated the immune infiltration discrepancies between DN and healthy samples, and assessed the correlation between the immune cells and hub genes. Finally, the expression levels of key genes were verified by reverse transcription-quantitative (RT-q)PCR. The present study determined 1,445 DEGs in DN samples. In addition, 694 DN-related genes in MEyellow and MEturquoise modules were identified by WGCNA. Next, the Venn Diagram R package was used to identify 17 lipid metabolism-related genes and to construct a PPI network. GO analysis revealed that these 17 genes were markedly associated with 'phospholipid biosynthetic process' and 'cholesterol biosynthetic process', while the KEGG analysis showed that they were enriched in 'glycerophospholipid metabolism' and 'fatty acid degradation'. In addition, SAMD8 and CYP51A1 were identified through the intersections of two machine-learning algorithms. The results of GSEA revealed that the 'mitochondrial matrix' and 'GTPase activity' were the markedly enriched GO terms in both SAMD8 and CYP51A1. Their KEGG pathways were mainly concentrated in the 'pathways of neurodegeneration-multiple diseases'. Immune infiltration analysis showed that nine types of immune cells had different expression levels in DN (diseased) and healthy samples. Notably, SAMD8 and CYP51A1 were both markedly associated with activated B cells and effector memory CD8 T cells. Finally, RT-qPCR confirmed the high expression of SAMD8 and CYP51A1 in DN. In conclusion, lipid metabolism-related genes SAMD8 and CYP51A1 may play key roles in DN. The present study provides fundamental information on lipid metabolism that may aid the diagnosis and treatment of DN.