Multiple algorithms highlight key brain genes driven by multiple anesthetics.

Anesthesia serves as a pivotal tool in modern medicine, creating a transient state of sensory deprivation to ensure a pain-free surgical or medical intervention. While proficient in alleviating pain, anesthesia significantly modulates brain dynamics, metabolic processes, and neural signaling, thereby impairing typical cognitive functions. Furthermore, anesthesia can induce notable impacts such as memory impairment, decreased cognitive function, and diminished intelligence, emphasizing the imperative need to explore the concealed repercussions of anesthesia on individuals. In this investigation, we aggregated gene expression profiles (GSE64617, GSE141242, GSE161322, GSE175894, and GSE178995) from public repositories following second-generation sequencing analysis of various anesthetics. Through scrutinizing post-anesthesia brain tissue gene expression utilizing Gene Set Enrichment Analysis (GSEA), Robust Rank Aggregation (RRA), and Weighted Gene Co-expression Network Analysis (WGCNA), this research aims to pinpoint pivotal genes, pathways, and regulatory networks linked to anesthesia. This undertaking not only enhances comprehension of the physiological changes brought about by anesthesia but also lays the groundwork for future investigations, cultivating new insights and innovative perspectives in medical practice.

Transcriptome meta-analysis reveals the hair genetic rules in six animal breeds and genes associated with wool fineness.

Wool plays an irreplaceable role in the lives of livestock and the textile industry. The variety of hair quality and shape leads to the diversity of its functions and applications, and the finer wool has a higher economic value. In this study, 10 coarse and 10 fine ordos fine wool sheep skin samples were collected for RNA-seq, and coarse and fine skin/hair follicle RNA-seq datasets of other five animal breeds were obtained from NCBI. Weighted gene co-expression network analysis showed that the common genes were clustered into eight modules. Similar gene expression patterns in sheep and rabbits with the same wool types, different gene expression patterns in animal species with different hair types, and brown modules were significantly correlated with species and breeds. GO and KEGG enrichment analyses showed that, most genes in the brown module associated with hair follicle development. Hence, gene expression patterns in skin tissues may determine hair morphology in animal. The analysis of differentially expressed genes revealed that 32 highly expressed candidate genes associated with the wool fineness of Ordos fine wool sheep. Among them, KAZALD1 (grey module), MYOC (brown module), C1QTNF6 (brown module), FOS (tan module), ITGAM, MX2, MX1, and IFI6 genes have been reported to be involved in the regulation of the hair follicle cycle or hair loss. Additionally, 12 genes, including KAZALD1, MYOC, C1QTNF6, and FOS, are differentially expressed across various animal breeds and species. The above results suggest that different sheep breeds share a similar molecular regulatory basis of wool fineness. Finally, the study provides a theoretical reference for molecular breeding of sheep breeds as well as for the investigation of the origin and evolution of animal hair.

Characterizing defective lipid metabolism in the lateral septum of mice treated with olanzapine: implications for its side effects.

Schizophrenia significantly impacts cognitive and behavioral functions and is primarily treated with second-generation antipsychotics (SGAs) such as olanzapine. Despite their efficacy, these drugs are linked to serious metabolic side effects which can diminish patient compliance, worsen psychiatric symptoms and increase cardiovascular disease risk. This study explores the hypothesis that SGAs affect the molecular determinants of synaptic plasticity and brain activity, particularly focusing on the lateral septum (LS) and its interactions within hypothalamic circuits that regulate feeding and energy expenditure. Utilizing functional ultrasound imaging, RNA sequencing, and weighted gene co-expression network analysis, we identified significant alterations in the functional connection between the hypothalamus and LS, along with changes in gene expression in the LS of mice following prolonged olanzapine exposure. Our analysis revealed a module closely linked to increases in body weight and adiposity, featuring genes primarily involved in lipid metabolism pathways, notably Apoa1, Apoc3, and Apoh. These findings suggest that olanzapine may influence body weight and adiposity through its impact on lipid metabolism-related genes in the LS. Therefore, the neural circuits connecting the LS and LH, along with the accompanying alterations in lipid metabolism, are likely crucial factors contributing to the weight gain and metabolic side effects associated with olanzapine treatment.

The Prognostic Hub Gene POLE2 Promotes BLCA Cell Growth via the PI3K/AKT Signaling Pathway.

BACKGROUND: BLCA is a common urothelial malignancy characterized by a high recurrence rate. Despite its prevalence, the molecular mechanisms underlying its development remain unclear. AIMS: This study aimed to explore new prognostic biomarkers and investigate the underlying mechanism of bladder cancer (BLCA). OBJECTIVE: The objective of this study is to identify key prognostic biomarkers for BLCA and to elucidate their roles in the disease. METHODS: We first collected the overlapping DEGs from GSE42089 and TCGA-BLCA samples for the subsequent weighted gene co-expression network analysis (WGCNA) to find a key module. Then, key module genes were analyzed by the MCODE algorithm, prognostic risk model, expression and immunohistochemical staining to identify the prognostic hub gene. Finally, the hub gene was subjected to clinical feature analysis, as well as cellular function assays. RESULTS: In WGCNA on 1037 overlapping genes, the blue module was the key module. After a series of bioinformatics analyses, POLE2 was identified as a prognostic hub gene in BLCA from potential genes (TROAP, POLE2, ANLN, and E2F8). POLE2 level was increased in BLCA and related to different clinical features of BLCA patients. Cellular assays showed that si-POLE2 inhibited BLCA proliferation, and si-POLE2+ 740Y-P in BLCA cells up-regulated the PI3K and AKT protein levels. CONCLUSION: In conclusion, POLE2 was identified to be a promising prognostic biomarker as an oncogene in BLCA. It was also found that POLE2 exerts a promoting function by the PI3K/AKT signaling pathway in BLCA.

SGCP: a spectral self-learning method for clustering genes in co-expression networks.

BACKGROUND: A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. RESULTS: We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. CONCLUSION: Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis.

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.

Mendelian randomization based on genome-wide association studies and expression quantitative trait loci, predicting gene targets for the complexity of osteoarthritis as well as the clinical prognosis of the condition.

Background: Osteoarthritis (OA) entails a prevalent chronic ailment, marked by the widespread involvement of entire joints. Prolonged low-grade synovial inflammation serves as the key instigator for a cascade of pathological alterations in the joints. Objective: The study seeks to explore potential therapeutic targets for OA and investigate the associated mechanistic pathways. Methods: Summary-level data for OA were downloaded from the genome-wide association studies (GWAS) database, expression quantitative trait loci (eQTL) data were acquired from the eQTLGen consortium, and synovial chip data for OA were obtained from the GEO database. Following the integration of data and subsequent Mendelian randomization analysis, differential analysis, and weighted gene co-expression network analysis (WGCNA) analysis, core genes that exhibit a significant causal relationship with OA traits were pinpointed. Subsequently, by employing three machine learning algorithms, additional identification of gene targets for the complexity of OA was achieved. Additionally, corresponding ROC curves and nomogram models were established for the assessment of clinical prognosis in patients. Finally, western blotting analysis and ELISA methodology were employed for the initial validation of marker genes and their linked pathways. Results: Twenty-two core genes with a significant causal relationship to OA traits were obtained. Through the application of distinct machine learning algorithms, MAT2A and RBM6 emerged as diagnostic marker genes. ROC curves and nomogram models were utilized for evaluating both the effectiveness of the two identified marker genes associated with OA in diagnosis. MAT2A governs the synthesis of SAM within synovial cells, thereby thwarting synovial fibrosis induced by the TGF-ß1-activated Smad3/4 signaling pathway. Conclusion: The first evidence that MAT2A and RBM6 serve as robust diagnostic for OA is presented in this study. MAT2A, through its involvement in regulating the synthesis of SAM, inhibits the activation of the TGF-ß1-induced Smad3/4 signaling pathway, thereby effectively averting the possibility of synovial fibrosis. Concurrently, the development of a prognostic risk model facilitates early OA diagnosis, functional recovery evaluation, and offers direction for further therapy.

DNA methylation patterns in the frontal lobe white matter of multiple system atrophy, Parkinson's disease, and progressive supranuclear palsy: a cross-comparative investigation.

Multiple system atrophy (MSA) is a rare neurodegenerative disease characterized by neuronal loss and gliosis, with oligodendroglial cytoplasmic inclusions (GCIs) containing α-synuclein being the primary pathological hallmark. Clinical presentations of MSA overlap with other parkinsonian disorders, such as Parkinson's disease (PD), dementia with Lewy bodies (DLB), and progressive supranuclear palsy (PSP), posing challenges in early diagnosis. Numerous studies have reported alterations in DNA methylation in neurodegenerative diseases, with candidate loci being identified in various parkinsonian disorders including MSA, PD, and PSP. Although MSA and PSP present with substantial white matter pathology, alterations in white matter have also been reported in PD. However, studies comparing the DNA methylation architectures of white matter in these diseases are lacking. We therefore aimed to investigate genome-wide DNA methylation patterns in the frontal lobe white matter of individuals with MSA (n = 17), PD (n = 17), and PSP (n = 16) along with controls (n = 15) using the Illumina EPIC array, to identify shared and disease-specific DNA methylation alterations. Genome-wide DNA methylation profiling of frontal lobe white matter in the three parkinsonian disorders revealed substantial commonalities in DNA methylation alterations in MSA, PD, and PSP. We further used weighted gene correlation network analysis to identify disease-associated co-methylation signatures and identified dysregulation in processes relating to Wnt signaling, signal transduction, endoplasmic reticulum stress, mitochondrial processes, RNA interference, and endosomal transport to be shared between these parkinsonian disorders. Our overall analysis points toward more similarities in DNA methylation patterns between MSA and PD, both synucleinopathies, compared to that between MSA and PD with PSP, which is a tauopathy. Our results also highlight several shared DNA methylation changes and pathways indicative of converging molecular mechanisms in the white matter contributing toward neurodegeneration in all three parkinsonian disorders.

Identification of differentially expressed genes of blood leukocytes for Schizophrenia.

Background: Schizophrenia (SCZ) is a severe neurodevelopmental disorder with brain dysfunction. This study aimed to use bioinformatic analysis to identify candidate blood biomarkers for SCZ. Methods: The study collected peripheral blood leukocyte samples of 9 SCZ patients and 20 healthy controls for RNA sequencing analysis. Bioinformatic analyses included differentially expressed genes (DEGs) analysis, pathway enrichment analysis, and weighted gene co-expression network analysis (WGCNA). Results: This study identified 1,205 statistically significant DEGs, of which 623 genes were upregulated and 582 genes were downregulated. Functional enrichment analysis showed that DEGs were mainly enriched in cell chemotaxis, cell surface, and serine peptidase activity, as well as involved in Natural killer cell-mediated cytotoxicity. WGCNA identified 16 gene co-expression modules, and five modules were significantly correlated with SCZ (p < 0.05). There were 106 upregulated genes and 90 downregulated genes in the five modules. The top ten genes sorted by the Degree algorithm were RPS28, BRD4, FUS, PABPC1, PCBP1, PCBP2, RPL27A, RPS21, RAG1, and RPL27. RAG1 and the other nine genes belonged to the turquoise and pink module respectively. Pathway enrichment analysis indicated that these 10 genes were mainly involved in processes such as Ribosome, cytoplasmic translation, RNA binding, and protein binding. Conclusion: This study finds that the gene functions in key modules and related enrichment pathways may help to elucidate the molecular pathogenesis of SCZ, and the potential of key genes to become blood biomarkers for SCZ warrants further validation.

ScHGSC-IGDC: Identifying genes with differential correlations of high-grade serous ovarian cancer based on single-cell RNA sequencing analysis.

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an in silico framework for identifying core genes in the development of HGSC. We detected thirty-four modules in the network. Scores of new genes with opposite correlations with others such as NDUFS5, TMSB4X, SERPINE2 and ITPR2 were identified. Further survival and literature validation emphasized their great values in the HGSC management. Meanwhile, RT-qPCR verified expression pattern of NDUFS5, TMSB4X, SERPINE2 and ITPR2 in human OC cell lines and tissues. Our research offered novel perspectives on the gene modulatory mechanisms from single cell resolution, guiding network based algorithms in cancer etiology field.

The identification of hub genes associated with pure ground glass nodules using weighted gene co-expression network analysis.

BACKGROUND: Whether there are invasive components in pure ground glass nodules(pGGNs) in the lungs is still a huge challenge to forecast. The objective of our study is to investigate and identify the potential biomarker genes for pure ground glass nodule(pGGN) based on the method of bioinformatics analysis. METHODS: To investigate differentially expressed genes (DEGs), firstly the data obtained from the gene expression omnibus (GEO) database was used.Next Weighted gene co-expression network analysis (WGCNA) investigate the co-expression network of DEGs. The black key module was chosen as the key one in correlation with pGGN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were done. Then STRING was uesd to create a protein-protein interaction (PPI) network, and the chosen module genes were analyzed by Cytoscape software.In addition the polymerase chain reaction (PCR) was used to evaluate the value of these hub genes in pGGN patients' tumor tissues compared to controls. RESULTS: A total of 4475 DEGs were screened out from GSE193725, then 225 DEGs were identified in black key module, which were found to be enriched for various functions and pathways, such as extracellular exosome, vesicle, ribosome and so on. Among these DEGs, 6 overlapped hub genes with high degrees of stress method were selected. These hub genes include RPL4, RPL8, RPLP0, RPS16, RPS2 and CCT3.At last relative expression levels of CCT3 and RPL8 mRNA were both regulated in pGGN patients' tumor tissues compared to controls. CONCLUSIONS: To summarize, the determined DEGs, pathways, modules, and overlapped hub genes can throw light on the potential molecular mechanisms of pGGN.

Identification of biomarkers associated with ferroptosis in diabetic retinopathy based on WGCNA and machine learning.

Objective: Diabetic retinopathy (DR) is a chronic progressive eye disease that affects millions of diabetic patients worldwide, and ferroptosis may contribute to the underlying mechanisms of DR. The main objective of this work is to explore key genes associated with ferroptosis in DR and to determine their feasibility as diagnostic markers. Methods: WGCNA identify the most relevant signature modules in DR. Machine learning methods were used to de-screen the feature genes. ssGSEA calculated the scoring of immune cells in the DR versus control samples and compared the associations with the core genes by Spearman correlation. Results: We identified 2,897 differential genes in DR versus normal samples. WGCNA found tan module to have the highest correlation with DR patients. Finally, 20 intersecting genes were obtained from differential genes, tan module and iron death genes, which were screened by LASSO and SVM-RFE method, and together identified 6 genes as potential diagnostic markers. qPCR verified the expression and ROC curves confirmed the diagnostic accuracy of the 6 genes. In addition, our ssGSEA scoring identified these 6 core genes as closely associated with immune infiltrating cells. Conclusion: In conclusion, we analyzed for the first time the potential link of iron death in the pathogenesis of DR. This has important implications for future studies of iron death-mediated pro-inflammatory immune mechanisms.

Identification of potential biomarkers and pathways for asthenozoospermia by bioinformatics analysis and experiments.

Background: Asthenozoospermia, a type of male infertility, is primarily caused by dysfunctional sperm mitochondria. Despite previous bioinformatics analysis identifying potential key lncRNAs, miRNAs, hub genes, and pathways associated with asthenospermia, there is still a need to explore additional molecular mechanisms and potential biomarkers for this condition. Methods: We integrated data from Gene Expression Omnibus (GEO) (GSE22331, GSE34514, and GSE160749) and performed bioinformatics analysis to identify differentially expressed genes (DEGs) between normozoospermia and asthenozoospermia. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to gain insights into biological processes and signaling pathways. Weighted Gene Co-expression Network Analysis (WGCNA) identified gene modules associated with asthenozoospermia. Expression levels of key genes were assessed using datasets and experimental data. Gene Set Enrichment Analysis (GSEA) and correlation analysis identified pathways associated with the hub gene and explore the relationship between the ZNF764 and COQ9 and mitochondrial autophagy-related genes. Competitive endogenous RNA (ceRNA) networks were constructed, and in vitro experiments using exosome samples were conducted to validate this finding. Results: COQ9 was identified as a marker gene in asthenozoospermia, involved in autophagy, ATP-dependent chromatin remodeling, endocytosis, and cell cycle, etc. The ceRNA regulatory network (LINC00893/miR-125a-5p/COQ9) was constructed, and PCR demonstrated that LINC00893 and COQ9 were downregulated in asthenozoospermia, while miR-125a-5p and m6A methylation level of LINC00893 were upregulated in asthenozoospermia compared to normozoospermic individuals. Conclusion: The ceRNA regulatory network (LINC00893/miR-125a-5p/COQ9) likely plays a crucial role in the mechanism of asthenozoospermia. However, further functional experiments are needed to fully understand its significance.

Identification the role of necroptosis in rheumatoid arthritis by WGCNA network.

Rheumatoid arthritis (RA) is the predominant manifestation of inflammatory arthritis, distinguished by an increasing burden of morbidity and mortality. The intricate interplay of genes and signalling pathways involved in synovial inflammation in patients with RA remains inadequately comprehended. This study aimed to ascertain the role of necroptosis in RA, as along with their associations with immune cell infiltration. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify central genes for RA. In this study, identified total of 28 differentially expressed genes (DEGs) were identified in RA. Utilising WGCNA, two co-expression modules were generated, with one module demonstrating the strongest correlation with RA. Through the integration of differential gene expression analysis, a total of 5 intersecting genes were discovered. These 5 hub genes, namely fused in sarcoma (FUS), transformer 2 beta homolog (TRA2B), eukaryotic translation elongation factor 2 (EEF2), cleavage and polyadenylation specific factor 6 (CPSF6) and signal transducer and activator of transcription 3 (STAT3) were found to possess significant diagnostic value as determined by receiver operating characteristic (ROC) curve analysis. The close association between the concentrations of various immune cells is anticipated to contribute to the diagnosis and treatment of RA. Furthermore, the infiltration of immune cells mentioned earlier is likely to exert a substantial influence on the initiation of this disease.

Clinically relevant immune subtypes based on alternative splicing landscape of immune-related genes for lung cancer advanced PPPM approach.

Background: Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer. Methods: The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC. Results: Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells. Conclusions: This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

Unveiling the role of copper metabolism and STEAP2 in idiopathic pulmonary fibrosis molecular landscape.

Idiopathic pulmonary fibrosis (IPF) is a debilitating interstitial lung disease characterized by progressive fibrosis and poor prognosis. Despite advancements in treatment, the pathophysiological mechanisms of IPF remain elusive. Herein, we conducted an integrated bioinformatics analysis combining clinical data and carried out experimental validations to unveil the intricate molecular mechanism of IPF. Leveraging three IPF datasets, we identified 817 upregulated and 560 downregulated differentially expressed genes (DEGs). Of these, 14 DEGs associated with copper metabolism were identified, shedding light on the potential involvement of disrupted copper metabolism in IPF progression. Immune infiltration analysis revealed dysregulated immune cell infiltration in IPF, with a notable correlation between copper metabolism-related genes and immune cells. Weighted gene co-expression network analysis (WGCNA) identified a central module correlated with IPF-associated genes, among which STEAP2 emerged as a key hub gene. Subsequent in vivo and in vitro studies confirmed the upregulation of STEAP2 in IPF model. Knockdown of STEAP2 using siRNA alleviated fibrosis in vitro, suggesting potential pathway related to copper metabolism in the pathophysiological progression of IPF. Our study established a novel link between immune cell infiltration and dysregulated copper metabolism. The revelation of intracellular copper overload and upregulated STEAP2 unravelled a potential therapeutic option. These findings offer valuable insights for future research and therapeutic interventions targeting STEAP2 and associated pathways in IPF.

Promotion of apoptosis in melanoma cells by taxifolin through the PI3K/AKT signaling pathway: Screening of natural products using WGCNA and CMAP platforms.

Melanoma is a skin cancer originating from melanocytes. The global incidence rate of melanoma is rapidly increasing, posing significant public health challenges. Identifying effective therapeutic agents is crucial in addressing this growing problem. Natural products have demonstrated promising anti-tumor activity. In this study, a plant flavonoid, taxifolin, was screened using Weighted Correlation Network Analysis (WGCNA) in combination with the Connectivity Map (CMAP) platform. Taxifolin was confirmed to inhibit the proliferation, migration, and invasion ability of melanoma A375 and MV-3 cells by promoting apoptosis. Additionally, it suppressed the Epithelial-Mesenchymal Transition (EMT) process of melanoma cells. Cyber pharmacological analysis revealed that taxifolin exerts its inhibitory effect on melanoma through the PI3K/AKT signaling pathway, specifically by downregulating the protein expression of p-PI3K and p-AKT. Notably, the addition of SC-79, an activator of the PI3K/AKT signaling pathway, reversed the effects of taxifolin on cell migration and apoptosis. Furthermore, in vivo experiments demonstrated that taxifolin treatment slowed tumor growth in mice without significant toxic effects. Based on these findings, taxifolin holds promise as a potential drug for melanoma treatment.

Enhancing cowpea wilt resistance: insights from gene coexpression network analysis with exogenous melatonin treatment.

BACKGROUND: Cowpea wilt is a harmful disease caused by Fusarium oxysporum, leading to substantial losses in cowpea production. Melatonin reportedly regulates plant immunity to pathogens; however the specific regulatory mechanism underlying the protective effect of melatonin pretreated of cowpea against Fusarium oxysporum remains known. Accordingly, the study sought to evaluate changes in the physiological and biochemical indices of cowpea following melatonin treated to facilitate Fusarium oxysporum resistance and elucidate the associated molecular mechanism using a weighted gene coexpression network. RESULTS: Treatment with 100 µM melatonin was effective in increasing cowpea resistance to Fusarium oxysporum. Glutathione peroxidase (GSH-PX), catalase (CAT), and salicylic acid (SA) levels were significantly upregulated, and hydrogen peroxide (H2O2) levels were significantly downregulated in melatonin treated samples in roots. Weighted gene coexpression network analysis of melatonin- and Fusarium oxysporum-treated samples identified six expression modules comprising 2266 genes; the number of genes per module ranged from 9 to 895. In particular, 17 redox genes and 32 transcription factors within the blue module formed a complex interconnected expression network. KEGG analysis revealed that the associated pathways were enriched in secondary metabolism, peroxisomes, phenylalanine metabolism, flavonoids, and flavonol biosynthesis. More specifically, genes involved in lignin synthesis, catalase, superoxide dismutase, and peroxidase were upregulated. Additionally, exogenous melatonin induced activation of transcription factors, such as WRKY and MYB. CONCLUSIONS: The study elucidated changes in the expression of genes associated with the response of cowpea to Fusarium oxysporum under melatonin treated. Specifically, multiple defence mechanisms were initiated to improve cowpea resistance to Fusarium oxysporum.

Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases.

Gynecological diseases are triggered by aberrant molecular pathways that alter gene expression, hormonal balance, and cellular signaling pathways, which may lead to long-term physiological consequences. This study was able to identify highly preserved modules and key hub genes that are mainly associated with gynecological diseases, represented by endometriosis (EM), ovarian cancer (OC), cervical cancer (CC), and endometrial cancer (EC), through the weighted gene co-expression network analysis (WGCNA) of microarray datasets sourced from the Gene Expression Omnibus (GEO) database. Five highly preserved modules were observed across the EM (GSE51981), OC (GSE63885), CC (GSE63514), and EC (GSE17025) datasets. The functional annotation and pathway enrichment analysis revealed that the highly preserved modules were heavily involved in several inflammatory pathways that are associated with transcription dysregulation, such as NF-kB signaling, JAK-STAT signaling, MAPK-ERK signaling, and mTOR signaling pathways. Furthermore, the results also include pathways that are relevant in gynecological disease prognosis through viral infections. Mutations in the ESR1 gene that encodes for ERα, which were shown to also affect signaling pathways involved in inflammation, further indicate its importance in gynecological disease prognosis. Potential drugs were screened through the Drug Repurposing Encyclopedia (DRE) based on the up-and downregulated hub genes, wherein a bacterial ribosomal subunit inhibitor and a benzodiazepine receptor agonist were the top candidates. Other drug candidates include a dihydrofolate reductase inhibitor, glucocorticoid receptor agonists, cholinergic receptor agonists, selective serotonin reuptake inhibitors, sterol demethylase inhibitors, a bacterial antifolate, and serotonin receptor antagonist drugs which have known anti-inflammatory effects, demonstrating that the gene network highlights specific inflammatory pathways as a therapeutic avenue in designing drug candidates for gynecological diseases.