LncRNAs related key pathways and genes in ischemic stroke by weighted gene co-expression network analysis (WGCNA)

Unfurling the functional association between long intergenic noncoding RNAs (lincRNAs) and HPV16-related cervical cancer pathogenesis through weighted gene co-expression network analysis of differentially expressed lincRNAs and coding genes

Long intergenic noncoding RNAs (lincRNAs) do not overlap annotated coding genes and are located in intergenic regions, as opposed to antisense and sense-intronic lncRNAs, located in genic regions. LincRNAs influence gene expression profiles and are thereby key to disease pathogenesis. In this study, we assessed the association between lincRNAs and HPV16-positive cervical cancer (CaCx) pathogenesis using weighted gene co-expression network analysis (WGCNA) with coding genes, comparing differentially expressed lincRNA and coding genes (DElincGs and DEcGs, respectively) in HPV16-positive patients with CaCx (n = 44) with those in HPV-negative healthy individuals (n = 34). Our analysis revealed five DElincG modules, co-expressing and correlating with DEcGs. We validated a substantial number of such module-specific correlations in the HPV16-positive cancer TCGA-CESC dataset. Four such modules, displayed significant correlations with patient traits, such as HPV16 physical status, lymph node involvement and overall survival (OS), highlighting a collaborative effect of all genes within specific modules on traits. Using the DAVID bioinformatics knowledgebase, we identified the underlying biological processes associated with these modules as cancer development and progression-associated pathways. Next, we identified the top 10 DElincGs with the highest connectivity within each functional module. Focusing on the prognostic module hub genes, downregulated CTD-2619J13.13 expression was associated with poor patient OS. This lincRNA gene interacted with 25 coding genes of its module and was associated with such biological processes as keratinization loss and keratinocyte differentiation, reflecting severe disease phenotypes. This study has translational relevance in fighting various cancers with high mortality rates in underdeveloped countries.

Targeting MAPK14 in microglial cells: neuroimmune implications of Panax ginseng in post-stroke inflammation

AIM

This study investigates the molecular mechanisms through which Panax ginseng and Panax notoginseng saponin (PNS) mitigate neuroinflammatory damage and promote neural repair postischemic stroke, utilizing bioinformatics, and experimental approaches.

BACKGROUND

Cerebral infarction significantly contributes to disability worldwide, with chronic neuroinflammation worsening cognitive impairments and leading to neurodegenerative diseases. Addressing neuroimmune interactions is crucial for slowing disease progression and enhancing patient recovery, highlighting the need for advanced research in neuroimmune regulatory mechanisms and therapeutic strategies.

OBJECTIVE

To elucidate the effects of the traditional Chinese medicine components Panax ginseng and PNS on neuroinflammatory damage following ischemic stroke, focusing on the molecular pathways involved in mitigating inflammation and facilitating neural repair.

METHODS

The study employs single-cell sequencing and transcriptomic analysis to investigate gene expression changes associated with cerebral infarction. Gene set enrichment analysis and weighted gene co-expression network analysis are used to identify key molecular markers and core genes. Furthermore, pharmacological profiling, including functional assays, assesses the impact of Ginsenoside-Rc, a PNS derivative, on microglial cell viability, cytokine production, and reactive oxygen species (ROS) levels.

RESULTS

Our analysis revealed that MAPK14 is a critical mediator in the neuroinflammatory response to ischemic stroke. Ginsenoside-Rc potentially targets and modulates MAPK14 activity to suppress inflammation. Experimental validation showed that Ginsenoside-Rc treatment, combined with MAPK14 silencing, significantly alters MAPK14 expression and mitigates neuroinflammatory damage, evidenced by reduced microglial cell death, inflammatory factor secretion, and ROS production.

CONCLUSION

Ginsenoside-Rc's modulation of MAPK14 offers a promising therapeutic strategy for reducing neuroinflammation and potentially improving cognitive recovery post-ischemic stroke. This supports the therapeutic application of the traditional Chinese medicine Sanqi in ischemic stroke care, providing a theoretical and experimental foundation for its use.

OTHERS

Future work will focus on extending these findings through clinical trials to evaluate the efficacy and safety of Ginsenoside-Rc in human subjects, aiming to translate these promising preclinical results into practical therapeutic interventions for ischemic stroke recovery.

Identification of immunogenic cell death-related genes involved in Alzheimer's disease

Alzheimer's disease (AD) is the leading cause of dementia worldwide, with recent studies highlighting the potential role of immunogenic cell death (ICD) in the pathogenesis of this neurodegenerative disorder. A total of 52 healthy controls and 64 patients with AD were included. Compared to the controls, the patients with AD exhibited 2392 differentially expressed genes (DEGs), of which 1015 and 1377 were upregulated and downregulated genes, respectively. Among them, nine common genes were identified by intersecting the AD-related module genes with the DEGs and ICD-associated genes. Gene ontology (GO)analysis further revealed "positive regulation of cytokine production" as the most significant term. Moreover, the enriched molecular functions were primarily related to the inflammatory body complex, while the overlapping genes were significantly enriched in lipopolysaccharide binding. Kyoto encyclopedia of genes and genomes (KEGG) analysis also indicated that these overlapping genes were mainly enriched in immunity, inflammation, and lipid metabolism pathways. Furthermore, the following four hub genes were detected using machine learning algorithms: P2RX7, HSP90AA1, NT5E, and NLRP3. These genes demonstrated significant differences in expression between the AD and healthy control groups (P < 0.05). Additionally, the area under the curve values of these four genes were all > 0.7, indicating their potential diagnostic value for AD. We further validated the protein levels of these four genes in the hippocampus of 3xTg-AD and C57BL/6J mice, showing P2RX7 and HSP90AA1 expression levels consistent with the previously analyzed trends. Finally, the single-sample gene set enrichment analysis (ssGSEA) algorithm provided additional evidence by demonstrating the crucial role of immune cell infiltration and its link with the hub genes in AD progression. Our study results suggest that ICD-mediated elevation of HSP90AA1 and P2RX7 levels and the resulting induction of tau hyperphosphorylation and neuroinflammation are vital in the AD pathogenic mechanism.

Identification of hub genes and their correlation with immune infiltrating cells in membranous nephropathy: an integrated bioinformatics analysis

BACKGROUND

Membranous nephropathy (MN) is a chronic glomerular disease that leads to nephrotic syndrome in adults. The aim of this study was to identify novel biomarkers and immune-related mechanisms in the progression of MN through an integrated bioinformatics approach.

METHODS

The microarray data were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between MN and normal samples were identified and analyzed by the Gene Ontology analysis, the Kyoto Encyclopedia of Genes and Genomes analysis and the Gene Set Enrichment Analysis (GSEA) enrichment. Hub The hub genes were screened and identified by the weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) algorithm. The receiver operating characteristic (ROC) curves evaluated the diagnostic value of hub genes. The single-sample GSEA analyzed the infiltration degree of several immune cells and their correlation with the hub genes.

RESULTS

We identified a total of 574 DEGs. The enrichment analysis showed that metabolic and immune-related functions and pathways were significantly enriched. Four co-expression modules were obtained using WGCNA. The candidate signature genes were intersected with DEGs and then subjected to the LASSO analysis, obtaining a total of 6 hub genes. The ROC curves indicated that the hub genes were associated with a high diagnostic value. The CD4+ T cells, CD8+ T cells and B cells significantly infiltrated in MN samples and correlated with the hub genes.

CONCLUSIONS

We identified six hub genes (ZYX, CD151, N4BP2L2-IT2, TAPBP, FRAS1 and SCARNA9) as novel biomarkers for MN, providing potential targets for the diagnosis and treatment.

Identification of a novel competing endogenous RNA network and candidate drugs associated with ferroptosis in aldosterone-producing adenomas

Aldosterone-producing adenoma (APA), characterized by unilaterally excessive aldosterone production, is a common cause of primary aldosteronism. Ferroptosis, a recently raised iron-dependent mode of programmed cell death, has been involved in the development and therapy of various diseases. This study obtained datasets of the mRNA and lncRNA expression profiles for APA and adjacent adrenal gland (AAG) from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and lncRNAs (DE lncRNAs) associated with ferroptosis were identified. Enrichment analyses indicated 89 ferroptosis-related DEGs were primarily enriched in ROS related processes and ferroptosis. Two physical cores, and one combined core were identified in the protein-protein interaction (PPI). DEGs and clinical traits were used in conjunction to screen eight hub genes from two hub modules and 89 DEGs. A competitive endogenous RNA (ceRNA) network was constructed via co-express analysis. Thereafter, molecular docking was used to identify potential targets. Two active compounds, QL-X-138 and MK-1775, bound to AURKA and DUOX1, respectively, with the lowest binding energies. Molecular dynamics simulation verified the stability of the two complexes. In summary, our studies identified eight hub genes and a novel ceRNA regulatory network associated with ferroptosis, wherein QL-X-138 and MK-1775 were considered to be potential drugs for treating APA.

Novel Molecular Subtyping Scheme Based on In Silico Analysis of Cuproptosis Regulator Gene Patterns Optimizes Survival Prediction and Treatment of Hepatocellular Carcinoma

BACKGROUND

The liver plays an important role in maintaining copper homeostasis. Copper ion accumulation was elevated in HCC tissue samples. Copper homeostasis is implicated in cancer cell proliferation and angiogenesis. The potential of copper homeostasis as a new theranostic biomarker for molecular imaging and the targeted therapy of HCC has been demonstrated. Recent studies have reported a novel copper-dependent nonapoptotic form of cell death called cuproptosis, strikingly different from other known forms of cell death. The correlation between cuproptosis and hepatocellular carcinoma (HCC) is not fully understood.

MATERIALS AND METHODS

The transcriptomic data of patients with HCC were retrieved from the Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) and were used as a discovery cohort to construct the prognosis model. The gene expression data of patients with HCC retrieved from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases were used as the validation cohort. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to construct the prognosis model. A principal component analysis (PCA) was used to evaluate the overall characteristics of cuproptosis regulator genes and obtain the PC1 and PC2 scores. Unsupervised clustering was performed using the ConsensusClusterPlus R package to identify the molecular subtypes of HCC. Cox regression analysis was performed to identify cuproptosis regulator genes that could predict the prognosis of patients with HCC. The receiver operating characteristics curve and Kaplan-Meier survival analysis were used to understand the role of hub genes in predicting the diagnosis and prognosis of patients, as well as the prognosis risk model. A weighted gene co-expression network analysis (WGCNA) was used for screening the cuproptosis subtype-related hub genes. The functional enrichment analysis was performed using Metascape. The 'glmnet' R package was used to perform the LASSO regression analysis, and the randomForest algorithm was performed using the 'randomForest' R package. The 'pRRophetic' R package was used to estimate the anticancer drug sensitivity based on the data retrieved from the Genomics of Drug Sensitivity in Cancer database. The nomogram was constructed using the 'rms' R package. Pearson's correlation analysis was used to analyze the correlations.

RESULTS

We constructed a six-gene signature prognosis model and a nomogram to predict the prognosis of patients with HCC. The Kaplan-Meier survival analysis revealed that patients with a high-risk score, which was predicted by the six-gene signature model, had poor prognoses (log-rank test p < 0.001; HR = 1.83). The patients with HCC were grouped into three distinct cuproptosis subtypes (Cu-clusters A, B, and C) based on the expression pattern of cuproptosis regulator genes. The patients in Cu-cluster B had poor prognosis (log-rank test p < 0.001), high genomic instability, and were not sensitive to conventional chemotherapeutic treatment compared to the patients in the other subtypes. Cancer cells in Cu-cluster B exhibited a higher degree of the senescence-associated secretory phenotype (SASP), a marker of cellular senescence. Three representative genes, CDCA8, MCM6, and NCAPG2, were identified in patients in Cu-cluster B using WGCNA and the "randomForest" algorithm. A nomogram was constructed to screen patients in the Cu-cluster B subtype based on three genes: CDCA8, MCM6, and NCAPG2.

CONCLUSION

Publicly available databases and various bioinformatics tools were used to study the heterogeneity of cuproptosis in patients with HCC. Three HCC subtypes were identified, with differences in the survival outcomes, genomic instability, senescence environment, and response to anticancer drugs. Further, three cuproptosis-related genes were identified, which could be used to design personalized therapeutic strategies for HCC.

Serum Uric Acid Levels and Recurrence Rate of Ischemic Stroke: A Meta-Analysis

The role of serum uric acid as a factor in the recurrence of ischemic stroke stays unclear. Several studies have examined the relationship between serum uric acid and recurrence of acute ischemic stroke, with various results. Therefore, we carried out a meta-analysis to have a look at the relationship between serum uric acid levels and the potential danger of stroke recurrence in patients with ischemic stroke. Relevant experiments have been recognized via looking out the electronic databases and conference sessions. This present study included a case-control study of the impact of uric acid on the recurrence of ischemic stroke. After the assessment of eligibility, this meta-analysis included four articles in which 2452 patients with ischemic stroke were tested for their level of serum uric acid. The results obtained from this meta-analysis confirmed that improved uric acid concentrations were extensively and independently related to an accelerated and higher risk of recurrent stroke. The pooled OR (95% CI) was 1.80 (1.47, 2.20) (p<0.001). Overall, this meta-analysis shows a relationship between uric acid concentration and stroke recurrence rate. Furthermore, high uric acid levels could enhance the recurrence rate of ischemic stroke.

Circulating miR-107 as a diagnostic biomarker of osteoporotic vertebral compression fracture increases bone formation in vitro and in vivo

AIMS

This study aimed to examine the key circulating miRNAs in the plasma of patients with osteoporotic vertebral compression fracture and assess their potential role as diagnostic biomarkers and explore their function in vitro and in vivo.

METHODS

Weighted gene co-expression network analysis (WGCNA) was applied to identify hub miRNAs for subsequent analysis. The candidate miRNAs were tested using plasma from 144 patients and the results were applied to construct receiver operating characteristic (ROC) curves to assess their diagnostic value. In addition, the function of the target microRNA was validated in MC3T3-E1 cells, human bone marrow-derived mesenchymal stromal cells (BMSCs), and an ovariectomized (OVX) mouse model.

KEY FINDINGS

Seven modules were obtained by WGCNA analysis. The expression levels of circulating miR-107 in the red module were significantly lower in osteoporotic patients than in healthy controls. In addition, miR-107 provided discrimination with an AUC > 85 % by ROC analyses to differentiate women osteoporosis patients from healthy controls and differentiate women osteoporotic patients with vertebral compression fractures from osteoporotic patients without vertebral compression fractures. In vitro experiments revealed that miR-107 levels were increased in osteogenically induced MC3T3-E1 cells and BMSCs and transfection with synthetic miR-107 could promote bone formation. Lastly, the bone parameters were improved by miR-107 upregulation in OVX mice.

SIGNIFICANCE

Our findings show that circulating miR-107 plays an essential role in facilitating osteogenesis and may be a useful diagnostic biomarker and therapeutic target in osteoporosis.

Machine learning algorithms assisted identification of post-stroke depression associated biological features

Objectives

Post-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD.

Methods

Expression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed.

Results

Functional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD (r = 0.653, P &lt; 0.001) and FERM3 (r = 0.728, P &lt; 0.001) were positively related to the Hamilton Depression Rating Scale 17-item (HAMD) score.

Conclusion

A total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.

Mir-421 and mir-550a-1 are potential prognostic markers in esophageal adenocarcinoma

OBJECTIVE

To identify the prognostic indicators of esophageal adenocarcinoma (EAC) for future EAC diagnosis and treatment.

METHODS

The EAC dataset from The Cancer Genome Atlas was screened for differentially expressed microRNAs (miRNAs) and mRNAs associated with EAC. Weighted gene coexpression network analysis was performed to cluster miRNAs or mRNA with similar expression patterns to identify the miRNAs or mRNA that are highly associated with EAC. Prognostic miRNAs for overall survival (OS) were identified using Cox proportional-hazards regression analysis and least absolute shrinkage and selection operator based on survival duration and status. Two types of miRNAs were selected to develop a prognostic signature model for EAC using multiple Cox regression analysis. Furthermore, the signature was validated using internal validation sets 1 and 2. The receiver operating characteristic curve and concordance index were used to evaluate the accuracy of the signature and validation sets. The expression of miR-421, miR-550a-3p, and miR-550a-5p was assessed using quantitative polymerase chain reaction (qPCR). The proliferation, invasion, and migration of EAC cells were assessed using CCK8 and transwell assays. The OS of target mRNAs was assessed using Kaplan-Meier analysis. Functional enrichment analysis of the target mRNAs was performed using Metascape.

RESULTS

The prognostic signature and validation sets comprising mir-421 and mir-550a-1 had favorable predictive power in OS. Compared with the patients with EAC in the high-expression group, those assigned to the low-expression group displayed increased OS according to survival analysis. Differential and qPCR analysis showed that miR-421, miR-550a-3p, and miR-550a-5p were highly expressed in the EAC tissues and cell lines. Moreover, the downregulation of miR-421 and miR-550a-3p with inhibitor markedly suppressed the proliferation, invasion, and migration in OE33 cells compared with the negative control. A total of 20 target mRNAs of three miRNAs were predicted, among which seven target mRNAs-ASAP3, BCL2L2, LMF1, PPM1L, PTPN21, SLC18A2, and NR3C2-had prognostic value; PRKACB, PDCD4, RPS6KA5, and BCL2L2 were enriched in the miRNA cancer pathway.

CONCLUSION

Prognostic indicators of EAC may be useful in future EAC diagnosis and treatment.

Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs

Tuberculosis (TB) is a common infectious disease linked to host genetics and the innate immune response. It is vital to investigate new molecular mechanisms and efficient biomarkers for Tuberculosis because the pathophysiology of the disease is still unclear, and there aren't any precise diagnostic tools. This study downloaded three blood datasets from the GEO database, two of which (GSE19435 and 83456) were used to build a weighted gene co-expression network for searching hub genes associated with macrophage M1 by the CIBERSORT and WGCNA algorithms. Furthermore, 994 differentially expressed genes (DEGs) were extracted from healthy and TB samples, four of which were associated with macrophage M1, naming RTP4, CXCL10, CD38, and IFI44. They were confirmed as upregulation in TB samples by external dataset validation (GSE34608) and quantitative real-time PCR analysis (qRT-PCR). CMap was used to predict potential therapeutic compounds for tuberculosis using 300 differentially expressed genes (150 downregulated and 150 upregulated genes), and six small molecules (RWJ-21757, phenamil, benzanthrone, TG-101348, metyrapone, and WT-161) with a higher confidence value were extracted. We used in-depth bioinformatics analysis to investigate significant macrophage M1-related genes and promising anti-Tuberculosis therapeutic compounds. However, more clinical trials were necessary to determine their effect on Tuberculosis.

Microplastics existence intensified bloom of antibiotic resistance in livestock feces transformed by black soldier fly

Efficient degradation of residual antibiotics in livestock and poultry feces by black soldier flies (BSFs) has been widely reported. Nevertheless, the effects of widely detected microplastics in feces on the dynamic reduction of antibiotics and the transfer of gut bacterial resistome remain unclear. In this study, red fluorescence-labeled microplastics are observed to be abundantly distributed in BSFs gut, which caused epithelial cell damage along with gut peristalsis and friction, thereby releasing reactive oxygen species and activating the antioxidant enzyme system. In addition, they result in not only in inflammatory cytokine release to induce gut inflammation, but fecal hardening because of mucus released from the BSFs, thereby hindering organic mineralization and antibiotic degradation. Besides, the gut pathogenic bacteria easily obtain growth energy and crowded out ecological niches by reducing nitrate produced by inflammatory host cells to nitrite with nitrate reductase. Consequently, linear discriminant analysis effect size and detrended correspondence analysis found that microplastic intake significantly reshape the microbial community structure and cause the significant reduction of several important organic-decomposing bacteria and probiotics (e.g., Pseudomonadales, Coriobacteriales, Lachnospirales, and Ruminococcaceae). In addition, a large number of pathogenic bacteria (e.g., Enterococcaceae, Hungateiclostridiaceae, and Clostridia) are enriched in feces and BSFs gut. Weighted correlation network analysis and bubble diagram analysis indicate that microplastic intake intensified gut colonization of pathogenic bacteria carrying antibiotic-resistant genes/mobile genetic elements, driving the bloom of antibiotic resistance in transformed fecal piles. Therefore, microplastics in feces should be isolated as much as possible before insect transformation.

A Comprehensive Weighted Gene Co-expression Network Analysis Uncovers Potential Targets in Diabetic Kidney Disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman's correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Proteome Network Analysis Identifies Potential Biomarkers for Brain Aging

BACKGROUND

Alzheimer's disease and related dementias (ADRD) involve biological processes that begin years to decades before onset of clinical symptoms. The plasma proteome can offer insight into brain aging and risk of incident dementia among cognitively healthy adults.

OBJECTIVE

To identify biomarkers and biological pathways associated with neuroimaging measures and incident dementia in two large community-based cohorts by applying a correlation-based network analysis to the plasma proteome.

METHODS

Weighted co-expression network analysis of 1,305 plasma proteins identified four modules of co-expressed proteins, which were related to MRI brain volumes and risk of incident dementia over a median 20-year follow-up in Framingham Heart Study (FHS) Offspring cohort participants (n = 1,861). Analyses were replicated in the Cardiovascular Health Study (CHS) (n = 2,117, mean 6-year follow-up).

RESULTS

Two proteomic modules, one related to protein clearance and synaptic maintenance (M2) and a second to inflammation (M4), were associated with total brain volume in FHS (M2: p = 0.014; M4: p = 4.2×10-5). These modules were not significantly associated with hippocampal volume, white matter hyperintensities, or incident all-cause or AD dementia. Associations with TCBV did not replicate in CHS, an older cohort with a greater burden of comorbidities.

CONCLUSIONS

Proteome networks implicate an early role for biological pathways involving inflammation and synaptic function in preclinical brain atrophy, with implications for clinical dementia.

RNA sequencing uncover crucial genes mediating progression of large-artery atherosclerotic and small-artery occlusion ischemic stroke

PURPOSE

The goal of our study is to uncover the pathogenesis of large-artery atherosclerotic ischemic stroke (LAAIS) and small-artery occlusion ischemic stroke (SAOIS) and analyze their difference using RNA sequencing.

METHODS

RNA sequencing was used to filtrate differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) in LAAIS and SAOIS. Specific DEmRNAs and DElncRNAs in LAAIS and SAOIS were further found. Functional annotation and DElncRNA-DEmRNA co-expression network were built to reveal biological function of DEmRNAs.

RESULTS

A total of 832 DEmRNAs and 96 DElncRNAs were identified in LAAIS vs normal controls. 587 DEmRNAs and 105 DElncRNAs were identified in SAOIS vs normal controls. In LAAIS vs SAOIS, 636 DEmRNAs and 112 DElncRNAs were identified. Among which, 571 DEmRNAs and 61 DElncRNAs were LAAIS specific DEmRNAs and DElncRNAs, respectively. 325 DEmRNAs and 66 DElncRNAs were respectively SAOIS specific DEmRNAs and DElncRNAs. We also obtained 3086 LAAIS specific DElncRNA-DEmRNA co-expression pairs and 661 SAOIS specific DElncRNA-DEmRNA co-expression pairs. Oxidative phosphorylation and Alzheimer's disease were significantly enriched pathways in both LAAIS specific DEmRNAs and DEmRNAs in LAAIS specific DElncRNA-DEmRNA co-expression network. ECM-receptor interaction, hypertrophic cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in both SAOIS specific DEmRNAs and DEmRNAs in SAOIS specific DElncRNA-DEmRNA co-expression network.

CONCLUSION

This finding may help to understand the mechanisms of LAAIS and SAOIS and offer novel clues for finding specific biomarkers for LAAIS and SAOIS.

NetTDP: permutation-based true discovery proportions for differential co-expression network analysis

Existing methods for differential network analysis could only infer whether two networks of interest have differences between two groups of samples, but could not quantify and localize network differences. In this work, a novel method, permutation-based Network True Discovery Proportions (NetTDP), is proposed to quantify the number of edges (correlations) or nodes (genes) for which the co-expression networks are different. In the NetTDP method, we propose an edge-level statistic and a node-level statistic, and detect true discoveries of edges and nodes in the sense of differential co-expression network, respectively, by the permutation-based sumSome method. Furthermore, the NetTDP method could further localize the differences by inferring the TDPs for edge or gene subsets of interest, which can be selected post hoc. Our NetTDP method allows inference on data-driven modules or biology-driven gene sets, and remains valid even when these sub-networks are optimized using the same data. Experimental results on both simulation data sets and five real data sets show the effectiveness of the proposed method in inferring the quantification and localization of differential co-expression networks. The R code is available at https://github.com/LiminLi-xjtu/NetTDP.

A comprehensive weighted gene co-expression network analysis uncovers potential targets in diabetic kidney disease

Background and Objectives

Diabetic kidney disease (DKD) is one of the most common microvascular complications of diabetes. It has always been difficult to explore novel biomarkers and therapeutic targets of DKD. We aimed to identify new biomarkers and further explore their functions in DKD.

Methods

The weighted gene co-expression network analysis (WGCNA) method was used to analyze the expression profile data of DKD, obtain key modules related to the clinical traits of DKD, and perform gene enrichment analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the mRNA expression of the hub genes in DKD. Spearman’s correlation coefficients were used to determine the relationship between gene expression and clinical indicators.

Results

Fifteen gene modules were obtained via WGCNA analysis, among which the green module had the most significant correlation with DKD. Gene enrichment analysis revealed that the genes in this module were mainly involved in sugar and lipid metabolism, regulation of small guanosine triphosphatase (GTPase) mediated signal transduction, G protein-coupled receptor signaling pathway, peroxisome proliferator-activated receptor (PPAR) molecular signaling pathway, Rho protein signal transduction, and oxidoreductase activity. The qRT-PCR results showed that the relative expression of nuclear pore complex-interacting protein family member A2 (NPIPA2) and ankyrin repeat domain 36 (ANKRD36) was notably increased in DKD compared to the control. NPIPA2 was positively correlated with the urine albumin/creatinine ratio (ACR) and serum creatinine (Scr) but negatively correlated with albumin (ALB) and hemoglobin (Hb) levels. ANKRD36 was positively correlated with the triglyceride (TG) level and white blood cell (WBC) count.

Conclusion

NPIPA2 expression is closely related to the disease condition of DKD, whereas ANKRD36 may be involved in the progression of DKD through lipid metabolism and inflammation, providing an experimental basis to further explore the pathogenesis of DKD.

Human microRNA-4433 (hsa-miR-4443) Targets 18 Genes to be a Risk Factor of Neurodegenerative Diseases

BACKGROUND

Neurodegenerative diseases, such as Alzheimer's disease patients (AD), Huntington's disease (HD) and Parkinson's disease (PD), are common causes of morbidity, mortality, and cognitive impairment in older adults.

OBJECTIVE

We aimed to understand the transcriptome characteristics of the cortex of neurodegenerative diseases and to provide an insight into the target genes of differently expressed microRNAs in the occurrence and development of neurodegenerative diseases.

METHODS

The Limma package of R software was used to analyze GSE33000, GSE157239, GSE64977 and GSE72962 datasets to identify the differentially expressed genes (DEGs) and microRNAs in the cortex of neurodegenerative diseases. Bioinformatics methods, such as GO enrichment analysis, KEGG enrichment analysis and gene interaction network analysis, were used to explore the biological functions of DEGs. Weighted gene co-expression network analysis (WGCNA) was used to cluster DEGs into modules. RNA22, miRDB, miRNet 2.0 and TargetScan7 databases were performed to predict the target genes of microRNAs.

RESULTS

Among 310 Alzheimer's disease (AD) patients, 157 Huntington's disease (HD) patients and 157 non-demented control (Con) individuals, 214 co-DEGs were identified. Those co-DEGs were filtered into 2 different interaction network complexes, representing immune-related genes and synapserelated genes. The WGCNA results identified five modules: yellow, blue, green, turquoise, and brown. Most of the co-DEGs were clustered into the turquoise module and blue module, which respectively regulated synapse-related function and immune-related function. In addition, human microRNA-4433 (hsa-miR-4443), which targets 18 co-DEGs, was the only 1 co-up-regulated microRNA identified in the cortex of neurodegenerative diseases.

CONCLUSION

214 DEGs and 5 modules regulate the immune-related and synapse-related function of the cortex in neurodegenerative diseases. Hsa-miR-4443 targets 18 co-DEGs and may be a potential molecular mechanism in neurodegenerative diseases' occurrence and development.

Integration of lncRNAs, Protein-Coding Genes and Pathology Images for Detecting Metastatic Melanoma

Melanoma is a lethal skin disease that develops from moles. This study aimed to integrate multimodal data to predict metastatic melanoma, which is highly aggressive and difficult to treat. The proposed EnsembleSKCM method evaluated the prediction performances of long noncoding RNAs (lncRNAs), protein-coding messenger genes (mRNAs) and pathology images (images) for metastatic melanoma. Feature selection was used to screen for metastatic biomarkers in the lncRNA and mRNA datasets. The integrated EnsembleSKCM model was built based on the weighted results of the lncRNA-, mRNA- and image-based models. EnsembleSKCM achieved 0.9444 in the prediction accuracy of metastatic melanoma and outperformed the single-modal prediction models based on the lncRNA, mRNA and image data. The experimental data suggest the importance of integrating the complementary information from the three data modalities. WGCNA was used to analyze the relationship of molecular-level features and image features, and the results show connections between them. Another cohort was used to validate our prediction.

Identification of mRNA expression biomarkers associated with epilepsy and response to valproate with co-expression analysis

Purpose

Valproate (VPA) resistance was reported to be an important predictor of intractable epilepsy. We conducted this study to identify candidate biomarkers in peripheral blood correlated with VPA resistance.

Methods

The microarray dataset (GSE143272) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed to construct co-expression modules and obtain the most prominent module associated with VPA resistance. Differentially expressed genes (DEGs) between VPA-responsive and VPA-resistant patients were obtained using the “Limma” package in R. The intersections between the most prominent module and DEGs were identified as target genes. Metascape was performed to discover the possible involved pathways of the target genes. GeneCards database was used to know the function of each target gene.

Results

All genes in the GSE143272 were divided into 24 different modules. Among these modules, the darkred module showed a pivotal correlation with VPA resistance. A total of 70 DEGs between VPA-responsive and VPA-resistant patients were identified. After taking the intersection, 25 target genes were obtained. The 25 target genes were significantly enriched in T cell receptor recognition, T cell receptor signaling pathway, regulation of T cell activation, cytokine–cytokine receptor interaction, and in utero embryonic development. Half of the target genes (CD3D, CD3G, CXCR3, CXCR6, GATA3, GZMK, IL7R, LIME1, SIRPG, THEMIS, TRAT1, and ZNF683) were directly involved in the T cell development, migration, and activation signaling pathway.

Conclusion

We identified 25 target genes prominently associated with VPA resistance, which could be potential candidate biomarkers for epilepsy resistance in peripheral blood. The peripheral blood T cells may play a crucial role in VPA resistance. Those genes and pathways might become therapeutic targets with clinical usefulness in the future.

Long non-coding RNA RPL34-AS1 ameliorates oxygen-glucose deprivation-induced neuronal injury via modulating miR-223-3p/IGF1R axis

Ribosomal protein L34-antisense RNA 1 (RPL34-AS1), one of the long non-coding RNAs (lncRNAs), plays an important function in regulating diverse human malignant tumors. Nevertheless, the functions of RPL34-AS1 in ischemic stroke remain unclear. The present work focused on determining the candidate targets of RPL34-AS1 and its related mechanism in ischemic injury. The oxygen-glucose deprivation (OGD/R) in vitro cell model and middle cerebral artery occlusion (MCAO) in vivo rat model were utilized to simulate the pathological process of ischemic stroke. Additionally, the CCK8, WB (detecting Bcl-2 and Bax protein levels), and caspase-3 activity assays were done to investigate the anti-apoptotic functions of RPL34-AS1. The relationship among RPL34-AS1, insulin-like growth factor 1 receptor (IGF1R), and microRNA-223-3p (miR-223-3p) was determined through luciferase reporter assay. In this study, RPL34-AS1 expression was reduced in patients suffering from ischemic stroke. The overexpression of RPL34-AS1 reduced ischemic brain damage. However, the cell viability and glucose uptake were increased, and the apoptosis rate was decreased in the OGD/R-induced neurons. Further, miR-223-3p resulted in the decreased cell viability and glucose uptake and the increased cell apoptosis to cause ischemic brain damage. Besides, the neuroprotective effects of RPL34-AS1 on OGD/R injury were partly reversed by miR-223-3p. Mechanistically, lncRNA RPL34-AS1 could function as the competing endogenous RNA (ceRNA) of miR-223-3p to regulate IGF1R. Collectively, our study demonstrated that lncRNA RPL34-AS1 attenuated OGD/R-induced neuronal injury by mediating miR-223-3p/IGF1R axis. This discovery might serve as the candidate therapeutic target for ischemic stroke.

Weighted Gene Co-expression Network Analysis Revealed That CircMARK3 Is a Potential CircRNA Affects Fat Deposition in Buffalo

Background

Buffalo meat is increasingly widely accepted for consumption as it shares several quality attributes with cattle meat (beef). Hence, there is a huge opportunity for growth in the buffalo meat industry. However, buffalo meat has relatively low intramuscular fat (IMF) content, affecting its flavor, tenderness and juiciness. As there is a dearth of information on factors that control fat deposition, this study was undertaken to provide new candidate factor associated with buffalo fat deposition. Circular RNA (circRNA) is a novel class of non-coding RNA with a closed-loop structure, and play an important role in fat deposition.

Methods

In this study, weighted gene co-expression network analysis (WGCNA) was used to construct a circRNA co-expression network and revealed a candidate circRNA that may affect the IMF deposition of buffalo as determined by RT-qPCR, semiquantitative PCR and gain-of-function experiments.

Results

Herein, WGCNA determined that one module (turquoise module) is significantly associated with the growth and development stages of buffalo. Further analysis revealed a total of 191 overlapping circRNAs among differentially expressed (DE) circRNAs and the co-expression module. A candidate circRNA was found, 21:6969877|69753491 (circRNA_ID), with a reported involvement in lipid metabolism. This circRNA is stably expressed and originates from the MARK3 gene, hence the name circMARK3. circMARK3 is highly expressed in adipose tissue and mature adipocytes and is located in the cytoplasm. Gain-of-function experiments demonstrated that circMARK3 promoted adipogenic differentiation of buffalo adipocytes and 3T3-L1 cells by up-regulating the expression levels of adipogenic marker genes PPARG, C/EBPα and FABP4.

Conclusion

These results indicate that circMARK3 is a potential factor that promotes fat deposition by regulating adipocyte differentiation and adipogenesis in buffalo.

Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis

The aims of this work were to explore the use of weighted gene coexpression network analysis (WGCNA) for identifying the key genes in severe burns and to provide a reference for finding therapeutic targets for burn wounds. The GSE8056 dataset was selected from the gene expression database of the US National Center for Biotechnology Information for analysis, and a WGCNA network was constructed to screen differentially expressed genes (DEGs). Gene Ontology and pathway enrichment of DGEs were analyzed, and protein interaction network was constructed. A burn mouse model was constructed, and the burn tissue was taken to identify the expression levels of differentially expressed genes. The results showed that the optimal soft threshold for constructing the WGCNA network was 9. 10 coexpressed gene modules were identified, among which the green, brown, and gray modules had the largest number of burn-related genes. The DEGs were mainly related to immune cell activation, inflammatory response, and immune response, and they were enriched in PD-1/PD-L1, Toll-like receptor, p53, and nuclear factor-kappa B (NF-κB) signaling pathways. 5 DEGs were screened and identified, namely, Jun protooncogene (JUN), signal transducer and activator of transcription 1 (STAT1), BCL2 apoptosis regulator (Bcl2), matrix metallopeptidase 9 (MMP9), and Toll-like receptor 2 (TLR2). Compared with skin tissue of normal mouse, the messenger ribose nucleic acid (mRNA) and protein expression levels (PEL) of STAT1 and Bcl2 in burn tissue were greatly decreased, while those of JUN, MMP9, and TLR2 were increased obviously (p < 0.05). In conclusion, STAT1, Bcl2, JUN, MMP9, and TLR2 can be potential biological targets for the treatment of severe burn wounds.

Effects of Different Doses of Clopidogrel plus Early Rehabilitation Therapy on Motor Function and Inflammatory Factors in Patients with Ischemic Stroke

This prospective randomized controlled study was intended to assess the effects of different doses of clopidogrel plus early rehabilitation therapy on motor function and inflammatory factors in patients with ischemic stroke. Between August 2018 and October 2020, 90 cases of ischemic stroke treated in the Second People's Hospital of Yibin were randomized at a ratio of 1 : 1 to receive either oral 50 mg/d clopidogrel plus early rehabilitation therapy (low-dose group) or oral 75 mg/d clopidogrel plus early rehabilitation therapy (high-dose group), with 45 cases in each group. The outcome measures including the Barthel Index (BI), National Institutes of Health Stroke Scale (NIHSS), Fugl-Meyer simplified scale, hypersensitive C-reactive protein (hs-CRP), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and occurrence of adverse events were collected. After treatment, the high-dose group had higher BI results than the low-dose group. All eligible patients showed significantly declined NIHSS scores, and the high-dose group had markedly lower results (P < 0.05). After treatment, the Fugl-Meyer scores of both upper and lower extremities of the high-dose group were significantly higher than those in the low-dose group. The high-dose group achieved a greater decrease in inflammatory factor levels after treatment versus the low-dose group. The two groups showed a similar incidence of adverse events. High-dose clopidogrel plus early rehabilitation outperforms the low-dose treatment for patients with ischemic stroke by effectively mitigating the inflammatory response in the body, promoting the restoration of neurological function, improving the level of motor function, and enhancing the patient's quality of life, with manageable safety.

ARG1 as a promising biomarker for sepsis diagnosis and prognosis: evidence from WGCNA and PPI network

Background

Sepsis is a life-threatening multi-organ dysfunction caused by the dysregulated host response to infection. Sepsis remains a major global concern with high mortality and morbidity, while management of sepsis patients relies heavily on early recognition and rapid stratification. This study aims to identify the crucial genes and biomarkers for sepsis which could guide clinicians to make rapid diagnosis and prognostication.

Methods

Preliminary analysis of multiple global datasets, including 170 samples from patients with sepsis and 110 healthy control samples, revealed common differentially expressed genes (DEGs) in peripheral blood of patients with sepsis. After Gene Oncology (GO) and pathway analysis, the Weighted Gene Correlation Network Analysis (WGCNA) was used to screen for genes most related with clinical diagnosis. Also, the Protein-Protein Interaction Network (PPI Network) was constructed based on the DEGs and the hub genes were found. The results of WGCNA and PPI network were compared and one shared gene was discovered. Then more datasets of 728 experimental samples and 355 control samples were used to prove the diagnostic and prognostic value of this gene. Last, we used real-time PCR to confirm the bioinformatic results.

Results

Four hundred forty-four common differentially expressed genes in the blood of sepsis patients from different ethnicities were identified. Fifteen genes most related with clinical diagnosis were found by WGCNA, and 24 hub genes with most node degrees were identified by PPI network. ARG1 turned out to be the unique overlapped gene. Further analysis using more datasets showed that ARG1 was not only sharply up-regulated in sepsis than in healthy controls, but also significantly high-expressed in septic shock than in non-septic shock, significantly high-expressed in severe or lethal sepsis than in uncomplicated sepsis, and significantly high-expressed in non-responders than in responders upon early treatment. These all demonstrate the performance of ARG1 as a key biomarker. Last, the up-regulation of ARG1 in the blood was confirmed experimentally.

Conclusions

We identified crucial genes that may play significant roles in sepsis by WGCNA and PPI network. ARG1 was the only overlapped gene in both results and could be used to make an accurate diagnosis, discriminate the severity and predict the treatment response of sepsis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-022-00240-1.

Weighted Gene Co-Expression Network Analysis (WGCNA) Reveals the Functions of Syndecan-1 to Regulate Immune Infiltration by Influenced T Cells in Glioma

Our previous studies shown that syndecan-1 (SDC1) may be a novel class of biomarkers for the diagnosis and treatment of glioma, but its specific roles and the in-depth molecular mechanism remain elusive. Here, we used Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithms and single-sample Gene Set Enrichment Analysis (ssGSEA) algorithms to evaluate the immune score of tumor samples and quantify the relative infiltration of immune cells in the tumor microenvironment (TME), respectively, in different data sets obtained from the Chinese Glioma Genome Atlas and The Cancer Gene Atlas. Next, we calculate the correlation of the immune score and immune cells with SDC1, respectively. To identify the specific process regulated by SDC1, the differentially expressed genes (DEGs) analysis between the high and low expression of SDC1 of glioma samples were used to discover the hub genes through Weighted Gene Coexpression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed cardinal biological processes and pathways involved in genes and tumor grade correlation and survival analysis verified its significance in glioma. The results show that SDC1 is associated with the immune infiltration of glioma in the TME, especially activated CD4+T cells and CD8+T cells. The three data sets filter 8,887 DEGs, the genes in the blue modules were selected as hub genes in WGCNA. GO and KEGG analysis found eight genes in the blue modules involved in antigen processing and presentation in T cells in glioma. Kaplan–Meier estimator and log-rank test statistic determined that the introduced genes are associated with poor prognosis in glioma. Protein–protein network interaction analysis showed that SDC1 may regulate antigen processing and presentation through CTSL or CD4 in glioma. Finally, this study provided insights and clues for the next research direction of SDC1 and identified the key pathways and genes that might participate in the immune escape of glioma. These results might provide a new insight on the study of immune infiltration of glioma in the future.

Weighted Gene Co-Expression Network Analysis to Identify Potential Biological Processes and Key Genes in COVID-19-Related Stroke

The purpose of this research was to explore the underlying biological processes causing coronavirus disease 2019- (COVID-19-) related stroke. The Gene Expression Omnibus (GEO) database was utilized to obtain four COVID-19 datasets and two stroke datasets. Thereafter, we identified key modules via weighted gene co-expression network analysis, following which COVID-19- and stroke-related crucial modules were crossed to identify the common genes of COVID-19-related stroke. The common genes were intersected with the stroke-related hub genes screened via Cytoscape software to discover the critical genes associated with COVID-19-related stroke. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for common genes associated with COVID-19-related stroke, and the Reactome database was used to annotate and visualize the pathways involved in the key genes. Two COVID-19-related crucial modules and one stroke-related crucial module were identified. Subsequently, the top five genes were screened as hub genes after visualizing the genes of stroke-related critical module using Cytoscape. By intersecting the COVID-19- and stroke-related crucial modules, 28 common genes for COVID-19-related stroke were identified. ITGA2B and ITGB3 have been further identified as crucial genes of COVID-19-related stroke. Functional enrichment analysis indicated that both ITGA2B and ITGB3 were involved in integrin signaling and the response to elevated platelet cytosolic Ca²⁺, thus regulating platelet activation, extracellular matrix- (ECM-) receptor interaction, the PI3K-Akt signaling pathway, and hematopoietic cell lineage. Therefore, platelet activation, ECM-receptor interaction, PI3K-Akt signaling pathway, and hematopoietic cell lineage may represent the potential biological processes associated with COVID-19-related stroke, and ITGA2B and ITGB3 may be potential intervention targets for COVID-19-related stroke.

Pan-cancer analysis of the DNA methylation patterns of long non-coding RNA

Long non-coding RNA (lncRNA) regulated by abnormal DNA methylation (ADM-lncRNA) emerges as a biomarker for cancer diagnosis and treatment. This study comprehensively described the methylation patterns of lncRNA in pan-cancer using the cancer data set in The Cancer Genome Atlas (TCGA). Based on the cancer heterogeneity of ADM-lncRNA in pan-cancer, we constructed a co-expression network of pan-cancer ADM-lncRNA (pADM-lncRNA) in 10 cancers, highlighting the combined action mode of abnormal DNA methylation, and indicating the internal connection among different cancers. Functional analysis revealed the pan-carcinogenic pathway of pADM-lncRNA and suggested potential factors for cancer heterogeneity and tumor immune microenvironment changes. Survival analysis showed the potential of pADM-lncRNA-mRNA co-expression pair as cancer biomarkers. Revealing the action mode of lncRNA and DNA methylation in cancer may help understand the key molecular mechanisms of cell carcinogenesis.

Evaluating the cytotoxicity and pathogenicity of multi-walled carbon nanotube through weighted gene co-expression network analysis: a nanotoxicogenomics study

Background

Multi-walled carbon nanotube (MWCNT) is one of the most momentous carbonaceous nanoparticles which is widely used for various applications such as electronics, vehicles, and therapeutics. However, their possible toxicity and adverse effects convert them into a major health threat for humans and animals.

Results

In this study, we employed weighted gene co-expression network analysis (WGCNA) to identify the co-expressed gene groups and dysregulated pathways due to the MWCNT exposure. For this purpose, three weighted gene co-expression networks for the microarray gene expression profiles of the mouse after 1, 6, and 12-month post-exposure to MWCNT were constructed. The module-trait analysis specified the significant modules related to different doses (1, 10, 40, and 80 µg) of MWCNT. Afterward, common genes between co-regulated and differentially expressed genes were determined. The further pathway analysis highlighted the enrichment of genes including Actb, Ube2b, Psme3, Ezh2, Alas2, S100a10, Ypel5, Rhoa, Rac1, Ube2l6, Prdx2, Ctsb, Bnip3l, Gp6, Myh9, Ube2k, Mbnl1, Kbtbd8, Riok3, Itgb1, Rap1a, and Atp5h in immune-, inflammation-, and protein metabolism-related pathways.

Conclusions

This study discloses the genotoxicity and cytotoxicity effects of various doses of MWCNT which also affect the metabolism system. The identified genes can serve as potential biomarkers and therapeutic candidates. However, further studies should be performed to validate them in human cells.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01031-3.

The SLC27A1 Gene and Its Enriched PPAR Pathway Are Involved in the Regulation of Flavor Compound Hexanal Content in Chinese Native Chickens

The role of hexanal in flavor as an indicator of the degree of oxidation of meat products is undeniable. However, the genes and pathways of hexanal formation have not been characterized in detail. In this study, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) on groups of Tiannong partridge chickens with different relative hexanal content in order to find the genes involved in the formation of hexanal and the specific pathways of hexanal formation. Then we confirmed the relationship of these candidate genes with hexanal using Jingxing Yellow chicken and Wenchang chicken. In this study, WGCNA revealed a module of co-expressed genes that were highly associated with the volatile organic compound hexanal. We also compared transcriptome gene expression data of samples from chicken groups with high and low relative contents of hexanal and identified a total of 651 differentially expressed genes (DEGs). Among them, 356 genes were up regulated, and 295 genes were downregulated. The different biological functions associated with the DEGs, hub genes and hexanal were identified by functional analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations. Among all the hub genes in the significant module identified by WGCNA, more were enriched in the PPAR signaling pathway, the proteasome pathway, etc. Additionally, we found that DEGs and hub genes, including SLC27A1, ACOX3, NR4A1, VEGFA, JUN, EGR1, CACNB1, GADD45A and DUSP1, were co-enriched in the peroxisome proliferator-activated receptor (PPAR) signaling pathway, p53 signaling pathway and mitogen-activated protein kinases (MAPK) signaling pathway, etc. Transcriptome results of the Jingxing Yellow chicken population showed that the SLC27A1 gene was significantly associated with hexanal and enriched in the PPAR pathway. Our study provides a comprehensive insight into the key genes related to hexanal content, and can be further explored by functional and molecular studies.

Identification of Up-Regulated ANXA3 Resulting in Fracture Non-Union in Patients With T2DM

Diabetes mellitus is a metabolic disorder that increases fracture risk and interferes with bone formation and impairs fracture healing. Genomic studies on diabetes and fracture healing are lacking. We used a weighted co-expression network analysis (WGCNA) method to identify susceptibility modules and hub genes associated with T2DM and fracture healing. First, we downloaded the GSE95849, GSE93213, GSE93215, and GSE142786 data from the Gene Expression Omnibus (GEO) website, analyzed differential expression genes and constructed a WGCNA network. Second, we screened out 30 hub genes, which were found to be enriched in neutrophil activation, translational initiation, RAGE receptor binding, propanoate metabolism, and other pathways through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analyses. Third, we searched for genes related to bone metabolism and fracture healing in the published genome-wide single nucleotide polymorphism (SNP) data, built a protein-protein interaction (PPI) network with hub genes, and found that they were associated with metabolic process, blood vessel development, and extracellular matrix organization. ANXA3 was identified as the biomarker based on gene expression and correlation analysis. And the AUC value of it was 0.947. Fourth, we explored that ANXA3 was associated with neutrophils in fracture healing process by single-cell RNA sequencing analysis. Finally, we collected clinical patient samples and verified the expression of ANXA3 by qRT-PCR in patents with T2DM and fracture non-union. In conclusion, this is the first genomics study on the effect of T2DM on fracture healing. Our study identified some characteristic modules and hub genes in the etiology of T2DM-associated fracture non-union, which may help to further investigate the molecular mechanisms. Up-regulated ANXA3 potentially contributed to fracture non-union in T2DM by mediating neutrophils. It can be a prognostic biomarker and potential therapeutic target.

Protective Properties of the Extract of Chrysanthemum on Patients with Ischemic Stroke

Investigation of the protective effect of chrysanthemum extract in ischemic strokes patients is among the challenging issues with the traditional hospital system in general and smart technology-based hospitals in particular. In this study, we have evaluated the protective effect of chrysanthemum extract on patients with ischemic stroke by detecting the severity of stroke, neuronal indexes, and oxidative stress biomarkers. For this purpose, forty-six patients with ischemic stroke were randomly divided into the control group (n = 30) and chrysanthemum group (n = 30). The control group received standard stroke treatment, and the chrysanthemum group was treated with chrysanthemum extract 400 mg/day (200 mg/day, twice/day) on the basis of standard treatment. The groups were compared the effect of saffron capsules using the National Institute of Health Stoke Scale (NIHSS), serum neuron specific enolase (NSE), S100, brain-derived neurotrophic factor (BDNF), malondialdehyde (MDA), Su-peroxide dismutase (SOD), and total antioxidant capacity (TAC ) levels, at the time of first day and fourth day after treatment. On the first day after treatment, there was no significant difference in the NIHSS score, serum NSE, S100, BDNF, MDA, SOD, and TAC levels between the chrysanthemum group and the control group (P > 0.05). On the fourth day after treatment, the NIHSS, serum NSE, S100, and MDA levels were significantly reduced in the chrysanthemum group compared to the control group, while the BDNF, SOD, and TAC levels were higher (P < 0.05). In addition, compared to the levels on the first day, the NIHSS, serum NSE, S100, and MDA levels were significantly reduced, and the BDNF, SOD, and TAC levels were increased in the chrysanthemum group on the fourth day (P < 0.05). Chrysanthemum extract has the effects of scavenging oxygen free radicals and antioxidation and has a neuroprotective effect on ischemic stroke patients.

DNA Hypomethylation of DOCK1 Leading to High Expression Correlates with Neurologic Deterioration and Poor Function Outcomes after Spontaneous Intracerebral Hemorrhage

Objective

Spontaneous intracerebral hemorrhage (ICH) is a blood clot arising in the brain parenchyma in the absence of trauma or surgery and accounts for 10% to 15% of all strokes, leading to higher rates of mortality and morbidity than either ischemic stroke or subarachnoid hemorrhage. We sought to investigate the potential association of DOCK1 with neurological deficits and outcomes in patients with spontaneous ICH.

Methods

Identification of methylation-regulated differentially expressed genes (MeDEGs) between ICH patients and matched controls was performed by analyzing the raw data from the GSE179759 and GSE125512 datasets deposited in the Gene Expression Omnibus. A total of 114 patients who were admitted to our hospital for spontaneous ICH were retrospectively analyzed, with 108 healthy volunteers who had received physical examinations at the same period as controls. The mRNA expression of DOCK1 was determined by quantitative real-time polymerase chain reaction (qRT-PCR). The hematoma volume was calculated according to the Coniglobus formula. The severity of neurological deficits was evaluated using National Institutes of Health Stroke Scale (NIHSS) scores and function outcomes were evaluated by modified Rankin Scale (mRS) scores.

Results

A total of 15 MeDEGs between ICH patients and matched controls were identified. The mRNA expression of DOCK1 was remarkably higher in the serum samples of patients with spontaneous ICH than in the healthy controls. According to hematoma volume after ICH attack, small (<10 mL), medium (10 to 30 mL), and large (>30 mL) groups were arranged. The proportions of male patients and patients aged ≥60 years were significantly higher in the large group than in the small and medium groups (P < 0.05). The mRNA expression of DOCK1 was significantly higher in the large group than in the small and medium groups (P < 0.05). According to NIHSS scores, mild (NIHSS scores ≤15), moderate (NIHSS scores from 16 to 30), and severe (NIHSS scores from 31 to 45) groups were classified. It was observed that the severe group had higher proportions of male patients and patients aged ≥60 years than the mild and moderate groups (P < 0.05). The severe group exhibited a higher mRNA expression of DOCK1 than the mild and moderate groups (P < 0.05). According to mRS scores, higher proportions of male patients and patients aged ≥60 years were observed in the unfavorable group than the favorable group (P < 0.05). The patients in the unfavorable group showed an elevated DOCK1 mRNA expression compared to those in the favorable group (P < 0.05).

Conclusion

The study provided evidence that male gender, older age, and higher DOCK1 mRNA expression were related to higher admission hematoma volume, neurologic deterioration, and poor function outcomes in patients with spontaneous ICH.

Bioinformatic analysis for potential biological processes and key targets of heart failure-related stroke

This study aimed to uncover underlying mechanisms and promising intervention targets of heart failure (HF)-related stroke. HF-related dataset GSE42955 and stroke-related dataset GSE58294 were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted to identify key modules and hub genes. Gene Ontology (GO) and pathway enrichment analyses were performed on genes in the key modules. Genes in HF- and stroke-related key modules were intersected to obtain common genes for HF-related stroke, which were further intersected with hub genes of stroke-related key modules to obtain key genes in HF-related stroke. Key genes were functionally annotated through GO in the Reactome and Cytoscape databases. Finally, key genes were validated in these two datasets and other datasets. HF- and stroke-related datasets each identified two key modules. Functional enrichment analysis indicated that protein ubiquitination, Wnt signaling, and exosomes were involved in both HF- and stroke-related key modules. Additionally, ten hub genes were identified in stroke-related key modules and 155 genes were identified as common genes in HF-related stroke. OTU deubiquitinase with linear linkage specificity(OTULIN) and nuclear factor interleukin 3-regulated(NFIL3) were determined to be the key genes in HF-related stroke. Through functional annotation, OTULIN was involved in protein ubiquitination and Wnt signaling, and NFIL3 was involved in DNA binding and transcription. Importantly, OTULIN and NFIL3 were also validated to be differentially expressed in all HF and stroke groups. Protein ubiquitination, Wnt signaling, and exosomes were involved in HF-related stroke. OTULIN and NFIL3 may play a key role in HF-related stroke through regulating these processes, and thus serve as promising intervention targets.

Glucotoxicity Activation of IL6 and IL11 and Subsequent Induction of Fibrosis May Be Involved in the Pathogenesis of Islet Dysfunction

Background: Islet dysfunction is the main pathological process of type 2 diabetes mellitus (T2DM). Fibrosis causes islet dysfunction, but the current mechanism is still unclear. Here, bioinformatics analysis identified gene clusters closely related to T2DM and differentially expressed genes related to fibrosis, and animal models verified the roles of these genes.

Methods: Human islet transcriptomic datasets were obtained from the Gene Expression Omnibus (GEO), and weighted gene coexpression network analysis (WGCNA) was applied to screen the key gene modules related to T2DM and analyze the correlations between the modules and clinical characteristics. Enrichment analysis was performed to identify the functions and pathways of the key module genes. WGCNA, protein-protein interaction (PPI) analysis and receiver operating characteristic (ROC) curve analysis were used to screen the hub genes. The hub genes were verified in another GEO dataset, the islets of high-fat diet (HFD)-fed Sprague-Dawley rats were observed by H&E and Masson’s trichrome staining, the fibrotic proteins were verified by immunofluorescence, and the hub genes were tested by immunohistochemistry.

Results: The top 5,000 genes were selected according to the median absolute deviation, and 18 modules were analyzed. The yellow module was highly associated with T2DM, and its positive correlation with glycated hemoglobin (HbA1c) was significantly stronger than that with body mass index (BMI). Enrichment analysis revealed that extracellular matrix organization, the collagen-containing extracellular matrix and cytokine−cytokine receptor interaction might influence T2DM progression. The top three hub genes, interleukin 6 (IL6), IL11 and prostaglandin-endoperoxide synthase 2 (PTGS2), showed upregulated expression in T2DM. In the validation dataset, IL6, IL11, and PTGS2 levels were upregulated in T2DM, and IL6 and PTGS2 expression was positively correlated with HbA1c and BMI; however, IL11 was positively correlated only with HbA1c. In HFD-fed Sprague-Dawley rats, the positive of IL6 and IL11 in islets was stronger, but PTGS2 expression was not significantly altered. The extent of fibrosis, irregular cellular arrangement and positive actin alpha 2 (ACTA2) staining in islets was significantly greater in HFD-fed rats than in normal diet-fed rats.

Conclusion: Glucotoxicity is a major factor leading to increased IL6 and IL11 expression, and IL6-and IL11-induced fibrosis might be involved in islet dysfunction.

The Performance Comparison of Gene Co-expression Networks of Breast and Prostate Cancer using Different Selection Criteria

Gene co-expression networks (GCN) present undirected relations between genes to understand molecular structures behind the diseases, including cancer. The utilization of various biological datasets and gene network inference (GNI) algorithms can reveal meaningful gene-gene interactions of GCNs. This study applies three GNI algorithms on mRNA gene expression, RNA-Seq, and miRNA-target genes datasets to infer GCNs of breast and prostate cancers. To evaluate the performance of the GCNs, we utilize overlap analysis via literature data, topological assessment, and Gene Ontology-based biological assessment. The results emphasize how the selection of biological datasets and GNI algorithms affect the performance results on different evaluation criteria. GCNs on microarray gene expression data slightly outperform in overlap analysis. Also, GCNs on RNA-Seq and gene expression datasets follow scale-free topology. The biological assessment results are close to each other on all biological datasets. C3NET algorithm-based GCNs did not contain any biological assessment modules; therefore, it is not optimal for biological assessment. GNI algorithms' selection did not change the overlap analysis and topological assessment results. Our primary objective is to compare the performance results of biological datasets and GNI algorithms based on different evaluation criteria. For this purpose, we developed the GNIAP R package that enables users to select different GNI algorithms to infer GCNs. The GNIAP R package also provides literature-based overlap analysis, and topological and biological analyses on GCNs. Users can access the GNIAP R package via https://github.com/ozgurcingiz/GNIAP .

Serum proteomics reveals disorder of lipoprotein metabolism in sepsis

This study illustrated that lipoprotein and lipid metabolism might play a significant role in patients with sepsis and that complement activation was significantly enriched in patients with sepsis-associated encephalopathy.

Sepsis is defined as an organ dysfunction syndrome and it has high mortality worldwide. This study analysed the proteome of serum from patients with sepsis to characterize the pathological mechanism and pathways involved in sepsis. A total of 59 patients with sepsis were enrolled for quantitative proteomic analysis. Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network specific to sepsis. Key regulatory modules that were detected were highly correlated with sepsis patients and related to multiple functional groups, including plasma lipoprotein particle remodeling, inflammatory response, and wound healing. Complement activation was significantly associated with sepsis-associated encephalopathy. Triglyceride/cholesterol homeostasis was found to be related to sepsis-associated acute kidney injury. Twelve hub proteins were identified, which might be predictive biomarkers of sepsis. External validation of the hub proteins showed their significantly differential expression in sepsis patients. This study identified that plasma lipoprotein processes played a crucial role in sepsis patients, that complement activation contributed to sepsis-associated encephalopathy, and that triglyceride/cholesterol homeostasis was associated with sepsis-associated acute kidney injury.

Assessment of Weighted Gene Co-Expression Network Analysis to Explore Key Pathways and Novel Biomarkers in Muscular Dystrophy

Purpose

This study aimed to explore the key molecular pathways involved in Duchenne muscular dystrophy (DMD) and Becker muscular dystrophy (BMD) and thereby identify hub genes to be potentially used as novel biomarkers using a bioinformatics approach.

Methods

Raw GSE109178 data were collected from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was conducted on the top 50% of altered genes. The key modules associated with the clinical features of DMD and BMD were identified. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the DAVID website. A protein-protein interaction (PPI) network was constructed using the STRING website. MCODE, together with the Cytohubba plug-ins of Cytoscape, screened out the potential hub genes, which were subsequently verified via receiver operating characteristic (ROC) curves in other datasets.

Results

Among the 11 modules obtained, the black module was predominantly associated with pathology and DMD, whereas the light-green module was primarily related to age and BMD. Functional enrichment assessments indicated that the genes in the black module were primarily clustered in “immune response” and “phagosome,” whereas the ones in the light-green module were chiefly enriched in “protein polyubiquitination”. Eleven essential genes were eventually identified, including VCAM1, TYROBP, CD44, ITGB2, CSF1R, LCP2, C3AR1, CCL2, and ITGAM for DMD, along with UBA5 and UBR2 for BMD.

Conclusion

Overall, our findings may be useful for investigating the mechanisms underlying DMD and BMD. In addition, the hub genes discovered might serve as novel molecular markers correlated with dystrophinopathies.

Functional annotation of lncRNA in high-throughput screening

Recent efforts on the characterization of long non-coding RNAs (lncRNAs) revealed their functional roles in modulating diverse cellular processes. These include pluripotency maintenance, lineage commitment, carcinogenesis, and pathogenesis of various diseases. By interacting with DNA, RNA and protein, lncRNAs mediate multifaceted mechanisms to regulate transcription, RNA processing, RNA interference and translation. Of more than 173000 discovered lncRNAs, the majority remain functionally unknown. The cell type-specific expression and localization of the lncRNA also suggest potential distinct functions of lncRNAs across different cell types. This highlights the niche of identifying functional lncRNAs in different biological processes and diseases through high-throughput (HTP) screening. This review summarizes the current work performed and perspectives on HTP screening of functional lncRNAs where different technologies, platforms, cellular responses and the downstream analyses are discussed. We hope to provide a better picture in applying different technologies to facilitate functional annotation of lncRNA efficiently.

Global transcriptome changes of elongating internode of sugarcane in response to mepiquat chloride

Background

Mepiquat chloride (DPC) is a chemical that is extensively used to control internode growth and create compact canopies in cultured plants. Previous studies have suggested that DPC could also inhibit gibberellin biosynthesis in sugarcane. Unfortunately, the molecular mechanism underlying the suppressive effects of DPC on plant growth is still largely unknown.

Results

In the present study, we first obtained high-quality long transcripts from the internodes of sugarcane using the PacBio Sequel System. A total of 72,671 isoforms, with N50 at 3073, were generated. These long isoforms were used as a reference for the subsequent RNA-seq. Afterwards, short reads generated from the Illumina HiSeq 4000 platform were used to compare the differentially expressed genes in both the DPC and the control groups. Transcriptome profiling showed that most significant gene changes occurred after six days post DPC treatment. These genes were related to plant hormone signal transduction and biosynthesis of several metabolites, indicating that DPC affected multiple pathways, in addition to suppressing gibberellin biosynthesis. The network of DPC on the key stage was illustrated by weighted gene co-expression network analysis (WGCNA). Among the 36 constructed modules, the top positive correlated module, at the stage of six days post spraying DPC, was sienna3. Notably, Stf0 sulfotransferase, cyclin-like F-box, and HOX12 were the hub genes in sienna3 that had high correlation with other genes in this module. Furthermore, the qPCR validated the high accuracy of the RNA-seq results.

Conclusion

Taken together, we have demonstrated the key role of these genes in DPC-induced growth inhibition in sugarcane.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-020-07352-w.

Identification of IL10RA by Weighted Correlation Network Analysis and in vitro Validation of Its Association With Prognosis of Metastatic Melanoma

Skin cutaneous melanoma (SKCM) is the major cause of death for skin cancer patients, its high metastasis often leads to poor prognosis of patients with malignant melanoma. However, the molecular mechanisms underlying metastatic melanoma remain to be elucidated. In this study we aim to identify and validate prognostic biomarkers associated with metastatic melanoma. We first construct a co-expression network using large-scale public gene expression profiles from GEO, from which candidate genes are screened out using weighted gene co-expression network analysis (WGCNA). A total of eight modules are established via the average linkage hierarchical clustering, and 111 hub genes are identified from the clinically significant modules. Next, two other datasets from GEO and TCGA are used for further screening of biomarker genes related to prognosis of metastatic melanoma, and identified 11 key genes via survival analysis. We find that IL10RA has the highest correlation with clinically important modules among all identified biomarker genes. Further in vitro biochemical experiments, including CCK8 assays, wound-healing assays and transwell assays, have verified that IL10RA can significantly inhibit the proliferation, migration and invasion of melanoma cells. Furthermore, gene set enrichment analysis shows that PI3K-AKT signaling pathway is significantly enriched in metastatic melanoma with highly expressed IL10RA, indicating that IL10RA mediates in metastatic melanoma via PI3K-AKT pathway.

Identification of crucial genes mediating abdominal aortic aneurysm pathogenesis based on gene expression profiling of perivascular adipose tissue by WGCNA

Background

With a mortality rate of 65–85%, a ruptured abdominal aortic aneurysm (AAA) can have catastrophic consequences for patients. However, few effective pharmaceutical treatments are available to treat this condition. Therefore, elucidating the pathogenesis of AAA and finding the potential molecular targets for medical therapies are vital lines of research.

Methods

An mRNA microarray dataset of perivascular adipose tissue (PVAT) in AAA patients was downloaded and differentially expressed gene (DEG) screening was performed. Weighted gene co-expression networks for dilated and non-dilated PVAT samples were constructed via weighted correlation network analysis (WGCNA) and used to detect gene modules. Functional annotation analysis was performed for the DEGs and gene modules. We identified the hub genes of the modules and created a DEG co-expression network. We then mined crucial genes based on this network using Molecular Complex Detection (MCODE) in Cytoscape. Crucial genes with top-6 degree in the crucial gene cluster were visualized, and their potential clinical significance was determined.

Results

Of the 173 DEGs screened, 99 were upregulated and 74 were downregulated. Co-expression networks were built and we detected 6 and 5 modules for dilated and non-dilated PVAT samples, respectively. The turquoise and black modules for dilated PVAT samples were related to inflammation and immune response. MAP4K1 and PROK2 were the hub genes of these 2 modules, respectively. Then a DEG co-expression network with 112 nodes and 953 edges was created. PLAU was the crucial gene with the highest connectivity and showed potential clinical significance.

Conclusions

Using WGCNA, gene modules were detected and hub genes and crucial genes were identified. These crucial genes might be potential targets for pharmaceutic therapies and have potential clinical significance. Future in vitro and in vivo experiments are required to more comprehensively explore the biological mechanisms by which these genes affect AAA pathogenesis

Hub gene identification and prognostic model construction for isocitrate dehydrogenase mutation in glioma

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We identified ten hub genes which were driving IDH status in GBM and LGG.

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We constructed a prognostic model for IDH-mutant patients.

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Our findings have important clinical implications for accurate treatment in glioma.

Our study attempted to identify hub genes related to isocitrate dehydrogenase (IDH) mutation in glioma and develop a prognostic model for IDH-mutant glioma patients. In a first step, ten hub genes significantly associated with the IDH status were identified by weighted gene coexpression analysis (WGCNA). The functional enrichment analysis demonstrated that the most enriched terms of these hub genes were cadherin binding and glutathione metabolism. Three of these hub genes were significantly linked with the survival of glioma patients. 328 samples of IDH-mutant glioma were separated into two datasets: a training set (N = 228) and a test set (N = 100). Based on the training set, we identified two IDH-mutant subtypes with significantly different pathological features by using consensus clustering. A 31 gene-signature was identified by the least absolute shrinkage and selection operator (LASSO) algorithm and used for establishing a differential prognostic model for IDH-mutant patients. In addition, the test set was employed for validating the prognostic model, and the model was proven to be of high value in classifying prognostic information of samples. The functional annotation revealed that the genes related to the model were mainly enriched in nuclear division, DNA replication, and cell cycle. Collectively, this study provided novel insights into the molecular mechanism of IDH mutation in glioma, and constructed a prognostic model which can be effective for predicting prognosis of glioma patients with IDH-mutation, which might promote the development of IDH target agents in glioma therapies and contribute to accurate prognostication and management in IDH-mutant glioma patients.

Integrated analysis of transcriptomic and metabolomic profiling reveal the p53 associated pathways underlying the response to ionizing radiation in HBE cells

Background

Radiation damage to normal tissues is a serious concern. P53 is a well-known transcription factor which is closely associated with radiation-induced cell damage. Increasing evidence has indicated that regulation of metabolism by p53 represents a reviving mechanism vital to protect cell survival. We aimed to explore the interactions of radiation-induced transcripts with the cellular metabolism regulated by p53.

Methods

Human bronchial epithelial (HBE) cell line was used to knockout p53 using CRISPR/cas9. Transcriptomic analysis was conducted by microarray and metabolomic analysis was conducted by GC–MS. Integrative omics was performed using MetaboAnalyst.

Results

326 mRNAs showed significantly altered expression in HBE p53-/- cells post-radiation, of which 269 were upregulated and 57 were downregulated. A total of 147 metabolites were altered, including 45 that increased and 102 that decreased. By integrated analysis of both omic data, we found that in response to radiation insult, nitrogen metabolism, glutathione metabolism, arachidonic acid metabolism, and glycolysis or gluconeogenesis may be dysregulated due to p53.

Conclusions

Our study provided a pilot comprehensive view of the metabolism regulated by p53 in response to radiation exposure. Detailed evaluation of these important p53-regulated metabolic pathways, including their roles in the response to radiation of cells, is essential to elucidate the molecular mechanisms of radiation-induced damage.