Weighted Correlation Network Analysis Analysis Research Articles

Transcriptional networks revealed late embryogenesis abundant genes regulating drought mitigation in aromatic Keteki Joha rice.

Climate change has become increasingly intertwined with the occurrence and severity of droughts. As global temperatures rise due to greenhouse gas emissions, weather patterns are altered, leading to shifts in precipitation levels and distribution. These exacerbate the risk of drought in many regions, with potentially devastating consequences. A comprehensive transcriptome analysis was performed on Keteki Joha, an aromatic rice from North East India, with the aim of elucidating molecular responses to drought. Numerous genes linked to drought were activated, with both ABA-dependent and ABA-independent pathways playing crucial roles. Upregulated genes were enriched with gene ontology terms with response to abscisic acid and abscisic acid-activated signalling pathway, suggesting the existence of an ABA-dependent pathway for drought mitigation. The upregulated genes were also enriched with responses to stress, water, heat, jasmonic acid, and hydrogen peroxide, indicating the presence of an ABA-independent pathway alongside the ABA-dependent mechanism. Weighted Correlation Network Analysis (WGCNA) identified 267 genes that specifically govern drought mitigation in Keteki Joha. The late embryogenesis abundant (LEA) gene family emerges as the most overrepresented in both RNA sequencing data and WGCNA analysis, suggesting their dominant role in mitigating drought. Notably, 31 LEA genes were induced in seedlings and 32 in mature stages under drought stress. The LEA3-1, LEA14/WSI18, RAB16A, RAB16B, DHN1, DHN6, LEA1, LEA3, LEA17, and LEA33 exhibited and established co-expression with numerous other drought stress-related genes, indicating their inseparable role in alleviating drought. Consequently, LEA genes have been proposed to be primary and crucial responders to drought in Keteki Joha.

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.

Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant therapeutic challenge. In order to provide new ideas for the diagnosis of AF and HF, it is necessary to carry out biomarker related studies. The training set and validation set data of AF and HF patient samples were downloaded from the GEO database, 'limma' was used to compare the differences in gene expression levels between the disease group and the normal group to screen for differentially expressed genes (DEGs). Weighted correlation network analysis (WGCNA) identified the modules with the highest positive correlation with AF and HF. Functional enrichment and PPI network construction of key genes were carried out. Biomarkers were screened by machine learning. The infiltration of immune cells in AF and HF groups was evaluated by R-packet 'CIBERSORT'. The miRNA network was constructed and potential therapeutic agents for biomarker genes were predicted through the drugbank database. Through WGCNA analysis, it was found that the modules most positively correlated with AF and HF were MEturquoise (r=0.21, P value=0.09) and MEbrown (r=0.62, P value=8e-12), respectively. We screened 25 genes that were highly correlated with both AF and HF. Lasso regression analysis results showed 7 and 20 core genes in AF and HF groups, respectively. The top 20 important genes in AF and HF groups were obtained as core genes by RF model analysis. Four biomarkers were obtained after the intersection of core genes in four groups, namely, GLUL, NCF2, S100A12, and SRGN. The diagnostic efficacy of four genes in AF validation sets was good (AUC: GLUL 0.76, NCF2 0.64, S100A12 0.68, and SRGN 0.76), as well as in the HF validation set (AUC: GLUL 0.76, NCF2 0.84, S100A12 0.92, and SRGN 0.68). The highest correlation with neutrophils was observed for GLUL, NCF2, and S100A12, while SRGN exhibited the strongest correlation with T cells CD4 memory resting in the AF group. GLUL, NCF2, S100A12, and SRGN were most associated with neutrophils in the HF group. A total of 101 miRNAs were predicted by four genes, and GLUL, NCF2, and S100A12 predicted a total of 10 potential therapeutic agents. We identified four biological markers that are highly correlated with AF and HF, namely, GLUL, NCF2, S100A12, and SRGN. Our findings provide theoretical basis for the clinical diagnosis and treatment of AF and HF.

Machine Learning-Based MRI Radiogenomics for Evaluation of Response to Induction Chemotherapy in Head and Neck Squamous Cell Carcinoma

To develop and validate a radiogenomics model integrating clinical data, radiomics-based machine learning (RBML) classifiers, and transcriptomics data for predicting the response to induction chemotherapy (IC) in patients with head and neck squamous cell carcinoma (HNSCC). Radiomics features derived from T2-weighted, pre- and post-contrast-enhanced T1-weighted MRI sequences, clinical data, and RNA sequencing data of 150 patients with HNSCC were included in the study. Analysis of variance or recursive feature elimination was used to reduce radiomics features. Three RBML classifiers were developed to distinguish non-responders from responders. Weighted correlation network analysis (WGCNA) was performed to identify the correlation between clinical data or radiomics features and molecular features; subsequently, protein interaction and functional enrichment analyses were performed. The predictive performance of the radiogenomics model integrating significant clinical variables, RBML classifiers, and molecular features was evaluated using receiver operating characteristic curve analysis. Five radiomics features and two conventional MRI findings significantly stratified HNSCC patients into responders and non-responders. On WGCNA analysis, 809 genes showed a significant correlation with two radiomics features. Functional enrichment analysis suggested that our proposed radiomics features could reflect the T cell-mediated immune response and immune infiltration of HNSCC. The radiogenomics model showed the highest area under the curve (0.88[95%CI 0.75-0.96]) for predicting IC response, which was better than MRI findings(p=0.0407) or molecular features(p=0.004) alone, but showed no significant difference with that of RBML model (p=0.2254) in test cohort. Merging imaging phenotypes with transcriptomic data improved the prediction of IC response in HNSCC.

Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma

ObjectiveCancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism. MethodsLUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells. ResultsA total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122. ConclusionOur study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.

Identification of ACKR4 as an immune checkpoint in pulmonary arterial hypertension.

Inflammation is recognized as a contributor in the development of pulmonary arterial hypertension (PAH), and the recruitment and functional capacity of immune cells are well-orchestrated by chemokines and their receptors. This study is aimed at identification of critical chemokines in the progression of PAH via transcriptomic analysis. Differentially expressed genes (DEGs) from lungs of PAH patients were achieved compared to controls based on Gene Expression Omnibus (GEO) database. Gene set enrichment analysis (GSEA) was applied for functional annotation and pathway enrichement. The abundance of immune cells was estimated by the xCell algorithm. Weighted correlation network analysis (WGCNA) was used to construct a gene expression network, based on which a diagnostic model was generated to determine its accuracy to distinguish PAH from control subjects. Target genes were then validated in lung of hypoxia-induce pulmonary hypertension (PH) mouse model. ACKR4 (atypical chemokine receptor 4) was downregulated in PAH lung tissues in multiple datasets. PAH relevant biological functions and pathways were enriched in patients with low-ACKR4 level according to GSEA enrichment analysis. Immuno-infiltration analysis revealed a negative correlation of activated dendritic cells, Th1 and macrophage infiltration with ACKR4 expression. Three gene modules were associated with PAH via WGCNA analysis, and a model for PAH diagnosis was generated using CXCL12, COL18A1 and TSHZ2, all of which correlated with ACKR4. The ACKR4 expression was also downregulated in lung tissues of our experimental PH mice compared to that of controls. The reduction of ACKR4 in lung tissues of human PAH based on transcriptomic data is consistent with the alteration observed in our rodent PH. The correlation with immune cell infiltration and functional annotation indicated that ACKR4 might serve as a protective immune checkpoint for PAH.

Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods

ObjectiveThe aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning.MethodThe pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets.ResultsNine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result.ConclusionsThe XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients.

Identification of genetic mechanisms underlying lipid metabolism-mediated tumor immunity in head and neck squamous cell carcinoma

ObjectiveTo identify the genetic mechanisms underlying lipid metabolism-mediated tumor immunity in head and neck squamous carcinoma (HNSC).Materials and methodsRNA sequencing data and clinical characteristics of HNSC patients were procured from The Cancer Genome Atlas (TCGA) database. Lipid metabolism-related genes were collected from KEGG and MSigDB databases. Immune cells and immune-related genes were obtained from the TISIDB database. The differentially expressed genes (DEGs) in HNSC were identified and weighted correlation network analysis (WGCNA) was performed to identify the significant gene modules. Lasso regression analysis was performed to identify hub genes. The differential gene expression pattern, diagnostic values, relationships with clinical features, prognostic values, relationships with tumor mutation burden (TMB), and signaling pathways involved, were each investigated.ResultsOne thousand six hundred sixty-eight DEGs were identified as dysregulated between HNSC tumor samples and healthy control head and neck samples. WGCNA analysis and Lasso regression analysis identified 8 hub genes, including 3 immune-related genes (PLA2G2D, TNFAIP8L2 and CYP27A1) and 5 lipid metabolism-related genes (FOXP3, IL21R, ITGAL, TRAF1 and WIPF1). Except CYP27A1, the other hub genes were upregulated in HNSC as compared with healthy control samples, and a low expression of these hub genes indicated a higher risk of death in HNSC. Except PLA2G2D, all other hub genes were significantly and negatively related with TMB in HNSC. The hub genes were implicated in several immune-related signaling pathways including T cell receptor signaling, Th17 cell differentiation, and natural killer (NK) cell mediated cytotoxicity.ConclusionThree immune genes (PLA2G2D, TNFAIP8L2, and CYP27A1) and immune-related pathways (T cell receptor signaling, Th17 cell differentiation, and natural killer (NK) cell mediated cytotoxicity) were predicted to play significant roles in the lipid metabolism-mediated tumor immunity in HNSC.

Use of machine learning-based integration to develop an immune-related signature for improving prognosis in patients with gastric cancer

Gastric cancer is one of the most common malignancies. Although some patients benefit from immunotherapy, the majority of patients have unsatisfactory immunotherapy outcomes, and the clinical significance of immune-related genes in gastric cancer remains unknown. We used the single-sample gene set enrichment analysis (ssGSEA) method to evaluate the immune cell content of gastric cancer patients from TCGA and clustered patients based on immune cell scores. The Weighted Correlation Network Analysis (WGCNA) algorithm was used to identify immune subtype-related genes. The patients in TCGA were randomly divided into test 1 and test 2 in a 1:1 ratio, and a machine learning integration process was used to determine the best prognostic signatures in the total cohort. The signatures were then validated in the test 1 and the test 2 cohort. Based on a literature search, we selected 93 previously published prognostic signatures for gastric cancer and compared them with our prognostic signatures. At the single-cell level, the algorithms "Seurat," "SCEVAN", "scissor", and "Cellchat" were used to demonstrate the cell communication disturbance of high-risk cells. WGCNA and univariate Cox regression analysis identified 52 prognosis-related genes, which were subjected to 98 machine-learning integration processes. A prognostic signature consisting of 24 genes was identified using the StepCox[backward] and Enet[alpha = 0.7] machine learning algorithms. This signature demonstrated the best prognostic performance in the overall, test1 and test2 cohort, and outperformed 93 previously published prognostic signatures. Interaction perturbations in cellular communication of high-risk T cells were identified at the single-cell level, which may promote disease progression in patients with gastric cancer. We developed an immune-related prognostic signature with reliable validity and high accuracy for clinical use for predicting the prognosis of patients with gastric cancer.

Abstract P2-23-06: Estrogen receptor-negative progesterone receptor-positive breast cancer is a molecularly distinct group characterized by the down-regulation of genes controlled by ESR1 and SUZ12

Abstract Background: Single hormone receptor-positive breast cancers (BCs) display two distinct phenotypes: ER+/PgR– and ER–/PgR+ further stratified by their HER2 status. Their molecular features are not well defined. Our study aimed to identify differentially expressed genes in ER–/PgR+ BCs compared to other phenotypes. Methods: Our cohort comprised 15 ER+/PgR–/HER2–, 11 ER+/PgR–/HER2+, 17 ER–/PgR+/HER2–, 9 ER–/PgR+/HER2+, 5 ER+/PgR+/HER2–, and 5 ER–/PgR–/HER2– invasive BCs collected from 9 Polish and 2 Hungarian centers. The cases were selected from a larger cohort after being matched according to grade, HER2 status, lymph nodes, and distant metastasis status. ER–/PgR+ group was thoroughly validated via immunohistochemistry [Kunc et al. 2022]. The expression of 776 genes was profiled with nCounter® Breast Cancer 360™ Panel in archival formalin-fixed paraffin-embedded tissue samples. A gene was defined as differentially expressed between groups if it met the following criteria: the log2 fold-change in the expression of >1 or ←1 and the p-value < 0.05 (Mann-Whitney U test). Additionally, weighted correlation network analysis (WGCNA) was performed to identify modules of at least 15 highly correlated genes. Subsequently, the association between gene modules and PgR status in ER– subgroup was performed. Identified mRNAs were subjected to functional annotation analysis to determine the top enriched pathways. Results: ER–/PgR+ BCs were characterized by significantly lower expression of ESR1 compared to double-positive (p< 0.001) and ER+/PgR– tumors (p< 0.001), whereas PGR expression was higher compared to ER+/PgR– (p< 0.001), and no significantly different from ER+/PgR+ BCs (p=0.14). Triple-negative BCs had no detectable PGR mRNA. Four genes (MIA, ID4, FOXC1, CDC20) were consistently up-regulated and six genes (FAM214A, MLPH, NFKBIZ, FOS, SLC44A4, SPDEF) were down-regulated in ER–/PgR+/HER2– tumors compared to other HER2– subgroups. Compared to ER+/HER2– BCs, ER–/PgR+/HER2– cases showed up-regulation of 15 genes associated with response to vitamin D, response to ketone, and regulation of transcription, and downregulation of 33 genes involved in response to estrogen, negative regulation of cell population proliferation, regulation of epithelial-mesenchymal transition, and controlled by ESR1 and SUZ12. In WGCNA analysis of the ER– subgroup, PgR status was negatively correlated with 4 gene modules and positively correlated with 1 gene module. In line with differential gene expression analysis, genes negatively correlated with ER–/PgR+ status are regulated by ESR1 and SUZ12 and are involved in the regulation of cell proliferation, extracellular matrix organization, and NOTCH1 signaling. Genes positively correlated with ER–/PgR+ status are regulated by E2F4, FOXM1, SIN3A, NFYB, E2F1, FOS, IRF1, ZMIZ1, and UBTF and participate in cell cycle, regulation of mitosis, and microtubule cytoskeleton regulation. Conclusions: ER–/PgR+ BCs display a distinct mRNA expression profile characterized by the down-regulation of genes controlled by ESR1 and SUZ12. The latter as a part of Polycomb Repressive Complex 2 contributes to chromatin silencing, and some previous studies suggested its role in the regulation of steroid hormone receptors expression. Additionally, ER–/PgR+ BCs overexpress FOXC1 which is linked to more aggressive, high-grade, and treatment-resistant breast cancers. Our data indicate the need to unravel the mechanism of epigenetic regulation of PGR expression, especially its methylation status, in ER–/PgR+ breast cancer. Citation Format: Michał Kunc, Marta Popęda, Michał Bieńkowski, Marcin Braun, Aleksandra Łacko, Barbara Radecka, Joanna Pikiel, Maria Litwiniuk, Katarzyna Pogoda, Magdalena Niemira, Anna Szałkowska, Ewa Iżycka-Świeszewska, Gabor Cserni, Wojciech Biernat, Elżbieta Senkus. Estrogen receptor-negative progesterone receptor-positive breast cancer is a molecularly distinct group characterized by the down-regulation of genes controlled by ESR1 and SUZ12 [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-23-06.

Identification of novel targets and pathways to distinguish suicide dependent or independent on depression diagnosis

In recent years, postmortem brain studies have revealed that some molecular, cellular, and circuit changes associated with suicide, have an independent or additive effect on depression. The aim of the present study is to identify potential phenotypic, tissue, and sex-specific novel targets and pathways to distinguish depression or suicide from major depressive disorder (MDD) comorbid with suicide. The mRNA expression profiling datasets from two previous independent postmortem brain studies of suicide and depression (GSE102556 and GSE101521) were retrieved from the GEO database. Machine learning analysis was used to differentiate three regrouped gene expression profiles, i.e., MDD with suicide, MDD without suicide, and suicide without depression. Weighted correlation network analysis (WGCNA) was further conducted to identify the key modules and hub genes significantly associated with each of these three sub-phenotypes. TissueEnrich approaches were used to find the essential brain tissues and the difference of tissue enriched genes between depression with or without suicide. Dysregulated gene expression cross two variables, including phenotypes and tissues, were determined by global analysis with Vegan. RRHO analysis was applied to examine the difference in global expression pattern between male and female groups. Using the optimized machine learning model, several ncRNAs and mRNAs with higher AUC and MeanDecreaseGini, including GCNT1P1 and AC092745.1, etc., were identified as potential molecular targets to distinguish suicide with, or without MDD and depression without suicide. WGCNA analysis identified some key modules significantly associated with these three phenotypes, and the gene biological functions of the key modules mainly relate to ncRNA and miRNA processing, as well as oxidoreductase and dehydrogenase activity. Hub genes such as RP11-349A22.5, C20orf196, MAPK8IP3 and RP11-697N18.2 were found in these key modules. TissueEnrich analysis showed that nucleus accumbens and subiculum were significantly changed among the 6 brain regions studied. Global analysis with Vegan and RRHO identified PRS26, ARNT and SYN3 as the most significantly differentially expressed genes across phenotype and tissues, and there was little overlap between the male and female groups. In this study, we have identified novel gene targets, as well as annotated functions of co-expression patterns and hub genes that are significantly distinctive between depression with suicide, depression without suicide, and suicide without depression. Moreover, global analysis across three phenotypes and tissues confirmed the evidence of sex difference in mood disorders.

Identification of ferroptosis-related genes in ulcerative colitis: a diagnostic model with machine learning.

Ulcerative colitis (UC) is an idiopathic, chronic disorder characterized by inflammation, injury, and disruption of the colonic mucosa. However, there are still many difficulties in the diagnosis and differential diagnosis of UC. An increasing amount of research has shown a connection between ferroptosis and the etiology of UC. Therefore, our study aimed to identify the key genes related to ferroptosis in UC to provide new ideas for diagnosis UC. Gene expression profiles of normal and UC samples were extracted from the Gene Expression Omnibus (GEO) database. By combining differentially expressed genes (DEGs), Weighted correlation network analysis (WGCNA) genes, and ferroptosis-related genes, hub genes were identified and then screened using Lasso regression. Based on the key genes, gene ontology (GO) and gene set enrichment analysis (GSEA) analyses were performed. We used NaiveBeyas, Logistic, IBk, and RandomForest algorithms to build a disease diagnosis model using the hub genes. The model was validated using GSE87473 as the validation set. Gene expression matrices of GSE87466 and GSE75214 were downloaded from the GEO database, including 184 UC patients and 43 control samples. A total of 699 DEGs were obtained. From FerrDb, 565 genes related to ferroptosis were identified. The 1,513 genes with the highest absolute correlation coefficient value in the MEblue module were obtained from WGCNA analysis. Five hub genes (LCN2, MUC1, PARP8, PLIN2, and TIMP1) were identified using the Lasso regression algorithm based on the overlapped DEGs, WGCNA-identified genes, and ferroptosis-related genes. GO and GSEA analyses revealed that 5 hub genes were identified as being involved in the negative regulation of transcription by competitive promoter binding, cellular response to citrate cycle_tca_cycle, cytosolic_dna_sensing pathway, UV-A, and beta-alanine metabolism. The logistic algorithm's values of the area under the curve (AUC)were 1.000 and 0.995 for training and validation cohorts, and sensitivity is 0.962, specificity is 1.000, respectively, as determined by comparing various methods. The previously described hub genes were identified as being intimately related to ferroptosis in UC and capable of distinguishing UC patients from controls. By detecting the expression of several genes, this model may aid in diagnosing UC and understanding the etiology and treatment of the disease.

P014 Behaviour in mice with chronic DSS colitis mimics fatigue in IBD and is associated with neuroinflammation

Abstract Background Inflammatory bowel diseases (IBD) are often associated with psychological comorbidities such as fatigue. Up to 47% of patients reports fatigue despite disease remission. The aim of the current study was to assess neurobehavioural changes in a mouse model of extinguished chronic colitis and to explore associated changes along the gut-brain axis. Methods Repeated administration of 2% dextran sulphate sodium (DSS) was used to induce chronic colitis in C57BL/6 mice, followed by a recovery period of 3 weeks to mimic quiescent IBD (Figure 1). Behavioural testing was performed at baseline and after the recovery period and compared with control mice. RNA sequencing was performed of the distal colon and choroid plexus and weighted correlation network analysis (WGCNA) was applied. Next, quantitative polymerase chain reaction (qPCR) was used for the confirmation of the expression levels of genes identified in the WGCNA analysis. The number of microglia and their morphology were assessed by Iba1 immunofluorescence in a second experiment, also including acute colitis (aDSS), which was induced by 1 week of 2% DSS, with sampling at day 7. Results Chronic DSS (cDSS) treatment resulted in chronic inflammation with minimal residual intestinal symptoms (control: median disease activity index (DAI) of 0 IQR [0-0], cDSS: median DAI of 0.5 [0.5-0.88], P <0.0001) after 3 weeks of recovery, which mimics quiescent IBD. At the time of sampling, the mean weight and haematological parameters (including red and white blood cells) in cDSS were comparable to those in control mice. cDSS mice exhibited significantly reduced spontaneous activity compared with their baseline behaviour (P = 0.0084) and with the controls (P = 0.0186). WGCNA on the colon and the choroid plexus expression data revealed a consensus module of 123 co-expressed genes that differed significantly between the control and cDSS mice (P = 0.00001721). These genes were implied in pathways of neutrophil and complement activation and qPCR on different brain regions (frontal cortex, hippocampus and choroid plexus) confirmed upregulation of inflammatory genes Lcn2, TNFa, S100A8 and S100A9 (Figure 2). Acute colitis was associated with activation of microglia in the prefrontal cortex and hippocampus, persisting in the prefrontal cortex of mice that recovered from chronic colitis (Figures 3 and 4). Conclusion In this mouse model of chronic extinguished colitis reduced spontaneous activity was seen, indicating fatigue-like behaviour as observed in patients with IBD. Moreover, mice that recovered from colitis exhibited persisting neuroinflammation.

Identification of hub genes in AR-induced tamoxifen resistance in breast cancer based on weighted gene co-expression network analysis.

Approximately 30% of patients with oestrogen receptor (ER)-positive breast cancer (BC) exhibit intrinsic or recurrent resistance to tamoxifen (TAM) adjuvant endocrine therapy. The androgen receptor (AR) is expressed in about 90% of ER-positive patients. Our previous studies found that BC patients with an AR:ER expression ratio ≥ 2.0 are more susceptible to TAM resistance. However, the specific mechanism by which a high AR:ER ratio promotes TAM resistance remains unknown. RNA sequencing was performed on 10 cases of BC tissues with AR:ER ratios ≥ 2.0 and 3 cases with AR:ER ratios < 2.0. We then compared our data with the screened TAM-resistant and TAM-sensitive cases from the TCGA BC database. Bioinformatics methods were used to screen differentially expressed genes (DEGs) and to perform gene enrichment analysis. Weighted correlation network analysis (WGCNA) was used to screen hub genes in the AR-induced TAM resistance process. PAM50 analysis showed that the molecular phenotype of BC patients with AR:ER ratios ≥ 2.0 was similar to that of triple-negative breast cancer (TNBC), whereas the BC samples with AR:ER ratios < 2.0 were classified as the luminal subtype. Among the AR:ER ratio ≥ 2.0 and AR:ER < 2.0 BC tumours, 1855 DEGs were identified. Gene enrichment analysis showed that DEGs were enriched mainly in proliferation-related molecular pathways, such as the cell cycle, necroptosis, metabolic pathways and DNA replication. WGCNA analysis showed that SEC14L2, RIIAD1, STC2 and MAGEA6 served as hub genes in AR-induced TAM resistance and were associated with BC survival prognosis in the TCGA cohort. A high AR:ER expression ratio is a biomarker for patients who might develop TAM resistance, and AR expression seems to be a possible mechanism of resistance to endocrine therapy.

WGCNA analysis revealing molecular mechanism that bio-organic fertilizer improves pear fruit quality by increasing sucrose accumulation and reducing citric acid metabolism.

It’s been long known that the application of organic fertilizer (OF) and bio-organic fertilizer (BF) which containing beneficial microorganisms to pear trees can both significantly improve fruit quality and yield. In order to reveal the mechanism of BF and OF regulating fruit growth and quality in pear, the effects of BF and OF on the photosynthetic characteristics and the accumulation of major sugars and organic acids of the pear fruit were quantified compared with chemical fertilizer (CF). Additionally, the molecular mechanisms regulating pear fruit development and quality were studied through transcriptome analysis. The three treatments were conducted based on the same amounts of nitrogen supply. The results showed that compared with CF, BF and OF treatments increased the fruit yield, and also significantly improved the photosynthesis efficiency in pear. BF and OF both significantly increased the sucrose content but significantly decreased the fructose and glucose content within the pear fruit. The amount of malic acid was significantly higher in OF treatment. Compared with CF and OF, BF significantly increased the sugar-acid ratio and thus improved the fruit quality. Transcriptome analysis and weighted correlation network analysis (WGCNA) revealed that the sugar metabolism of fruits applied with the BF was enhanced compared with those applied with CF or OF. More specifically, the expression of SDH (Sorbitol dehydrogenase) was higher in BF, which converts sorbitol into fructose. For both of the OF and BF, the transcript abundance of sugar transporter genes was significantly increased, such as SOT (Sorbitol transporter), SUT14 (Sugar transport 14), UDP-GLUT4 (UDP-glucose transporter 4), UDP-SUT (UDP-sugar transporter), SUC4 (Sucrose transport 4), SUT7 (Sugar transporter 7), SWEET10 and SWEET15 (Bidirectional sugar transporter), which ensures sugar transportation. The genes involved in organic acid metabolism showed decreased transcripts abundance in both BF and OF treatments, such as VAP (Vesicle-associated protein) and cyACO (Cytosolic aconitase), which reduce the conversion from succinate to citric acid, and decrease the conversion from citric acid to malic acid in the TCA cycle (Tricarboxylic Acid cycle) through Pept6 (Oligopeptide transporter). In conclusion, the application of BF and OF improved fruit quality by regulating the expression of sugar and organic acid metabolism-related genes and thus altering the sugar acid metabolism. Both BF and OF promote sucrose accumulation and citric acid degradation in fruits, which may be an important reason for improving pear fruit quality. The possible mechanism of bio-organic fertilizer to improve fruit quality was discussed.

Identification of key monocytes/macrophages related gene set of the early-stage abdominal aortic aneurysm by integrated bioinformatics analysis and experimental validation.

ObjectiveAbdominal aortic aneurysm (AAA) is a lethal peripheral vascular disease. Inflammatory immune cell infiltration is a central part of the pathogenesis of AAA. It’s critical to investigate the molecular mechanisms underlying immune infiltration in early-stage AAA and look for a viable AAA marker.MethodsIn this study, we download several mRNA expression datasets and scRNA-seq datasets of the early-stage AAA models from the NCBI-GEO database. mMCP-counter and CIBERSORT were used to assess immune infiltration in early-stage experimental AAA. The scRNA-seq datasets were then utilized to analyze AAA-related gene modules of monocytes/macrophages infiltrated into the early-stage AAA by Weighted Correlation Network analysis (WGCNA). After that, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis for the module genes was performed by ClusterProfiler. The STRING database was used to create the protein-protein interaction (PPI) network. The Differentially Expressed Genes (DEGs) of the monocytes/macrophages were explored by Limma-Voom and the key gene set were identified. Then We further examined the expression of key genes in the human AAA dataset and built a logistic diagnostic model for distinguishing AAA patients and healthy people. Finally, real-time quantitative polymerase chain reaction (RT-qPCR) and Enzyme Linked Immunosorbent Assay (ELISA) were performed to validate the gene expression and serum protein level between the AAA and healthy donor samples in our cohort.ResultsMonocytes/macrophages were identified as the major immune cells infiltrating the early-stage experimental AAA. After pseudocell construction of monocytes/macrophages from scRNA-seq datasets and WGCNA analysis, four gene modules from two datasets were identified positively related to AAA, mainly enriched in Myeloid Leukocyte Migration, Collagen-Containing Extracellular matrix, and PI3K-Akt signaling pathway by functional enrichment analysis. Thbs1, Clec4e, and Il1b were identified as key genes among the hub genes in the modules, and the high expression of Clec4e, Il1b, and Thbs1 was confirmed in the other datasets. Then, in human AAA transcriptome datasets, the high expression of CLEC4E, IL1B was confirmed and a logistic regression model based on the two gene expressions was built, with an AUC of 0.9 in the train set and 0.79 in the validated set. Additionally, in our cohort, we confirmed the increased serum protein levels of IL-1β and CLEC4E in AAA patients as well as the increased expression of these two genes in AAA aorta samples.ConclusionThis study identified monocytes/macrophages as the main immune cells infiltrated into the early-stage AAA and constructed a logistic regression model based on monocytes/macrophages related gene set. This study could aid in the early diagnostic of AAA.

Novel lncRNAs with diagnostic or prognostic value screened out from breast cancer via bioinformatics analyses.

BackgroundRecent studies have shown that long non-coding RNAs (lncRNAs) may play key regulatory roles in many malignant tumors. This study investigated the use of novel lncRNA biomarkers in the diagnosis and prognosis of breast cancer.Materials and MethodsThe database subsets of The Cancer Genome Atlas (TCGA) by RNA-seq for comparing analysis of tissue samples between breast cancer and normal control groups were downloaded. Additionally, anticoagulant peripheral blood samples were collected and used in this cohort study. The extracellular vesicles (EVs) from the plasma were extracted and sequenced, then analyzed to determine the expressive profiles of the lncRNAs, and the cancer-related differentially expressed lncRNAs were screened out. The expressive profiles and associated downstream-mRNAs were assessed using bioinformatics (such as weighted correlation network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichments, Receiver-Operating Characteristic (ROC) curve and survival analysis, etc.) to investigate the diagnostic and prognostic values of these EV lncRNAs and their effectors.ResultsIn this study, 41 breast cancer-related lncRNAs were screen out from two datasets of tissue and fresh collected plasma samples of breast cancer via the transcriptomic and bioinformatics techniques. A total of 19 gene modules were identified with WGCNA analysis, of which five modules were significantly correlated with the clinical stage of breast cancer, including 28 lncRNA candidates. The ROC curves of these lncRNAs revealed that the area under the curve (AUC) of all candidates were great than 70%. However, eight lncRNAs had an AUC >70%, indicating that the combined one has a good diagnostic value. In addition, the results of survival analysis suggested that two lncRNAs with low expressive levels may indicate the poor prognosis of breast cancer. By tissue sample verification, C15orf54, AL157935.1, LINC01117, and SNHG3 were determined to have good diagnostic ability in breast cancer lesions, however, there was no significant difference in the plasma EVs of patients. Moreover, survival analysis data also showed that AL355974.2 may serve as an independent prognostic factor and as a protective factor.ConclusionA total of five lncRNAs found in this study could be developed as biomarkers for breast cancer patients, including four diagnostic markers (C15orf54, AL157935.1, LINC01117, and SNHG3) and a potential prognostic marker (AL355974.2).

WGCNA Analysis Identifies the Hub Genes Related to Heat Stress in Seedling of Rice (Oryza sativa L.).

Frequent high temperature weather affects the growth and development of rice, resulting in the decline of seed–setting rate, deterioration of rice quality and reduction of yield. Although some high temperature tolerance genes have been cloned, there is still little success in solving the effects of high temperature stress in rice (Oryza sativa L.). Based on the transcriptional data of seven time points, the weighted correlation network analysis (WGCNA) method was used to construct a co–expression network of differentially expressed genes (DEGs) between the rice genotypes IR64 (tolerant to heat stress) and Koshihikari (susceptible to heat stress). There were four modules in both genotypes that were highly correlated with the time points after heat stress in the seedling. We further identified candidate hub genes through clustering and analysis of protein interaction network with known–core genes. The results showed that the ribosome and protein processing in the endoplasmic reticulum were the common pathways in response to heat stress between the two genotypes. The changes of starch and sucrose metabolism and the biosynthesis of secondary metabolites pathways are possible reasons for the sensitivity to heat stress for Koshihikari. Our findings provide an important reference for the understanding of high temperature response mechanisms and the cultivation of high temperature resistant materials.

Identification of the Key Genes and Potential Therapeutic Compounds for Abdominal Aortic Aneurysm Based on a Weighted Correlation Network Analysis.

Background: There is still an unmet need for therapeutic drugs for patients with an abdominal aortic aneurysm (AAA), especially for candidates unsuitable for surgical or interventional repair. Therefore, the purpose of this in silico study is to identify significant genes and regulatory mechanisms in AAA patients to predicate the potential therapeutic compounds for significant genes. Methods: The GSE57691 dataset was obtained from Gene Expression Omnibus (GEO) and used to identify the differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA). The biological function of DEGs was determined using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). AAA-related genes were obtained from the Comparative Toxicogenomics Database (CTD) using the keywords: aortic aneurysm and abdominal. The hub genes in AAA were obtained by overlapping DEGs, WGCNA-based hub genes, and CTD-based genes. The diagnostic values of hub genes were determined using ROC curve analysis. Hereby, a TF-miRNA-hub gene network was constructed based on the miRnet database. Using these data, potential therapeutic compounds for the therapy of AAA were predicted based on the Drug Gene Interaction Database (DGIdb). Results: A total of 218 DEGs (17 upregulated and 201 downregulated) and their biological function were explored; 4093 AAA-related genes were derived by text mining. Three hub modules and 144 hub genes were identified by WGCNA. asparagine synthetase (ASNS), axin-related protein 2 (AXIN2), melanoma cell adhesion molecule (MCAM), and the testis-specific Y-encoded-like protein 1 (TSPYL1) were obtained as intersecting hub genes and the diagnostic values were confirmed with ROC curves. As potential compounds targeting the hub genes, asparaginase was identified as the target compound for ASNS. Prednisolone and abiraterone were identified as compounds targeting TSPYL1. For MCAM and TSPYL1, no potential therapeutic compound could be predicted. Conclusion: Using WGCNA analysis and text mining, pre-existing gene expression data were used to provide novel insight into potential AAA-related protein targets. For two of these targets, compounds could be predicted.

Scale Development-Related Genes Identified by Transcriptome Analysis

Scales, as key structures of fish skin, play an important role in physiological function. The study of fish scale development mechanisms provides a basis for exploring the molecular-level developmental differences between scaled and non-scaled fishes. In this study, alizarin red staining was used to divide the different stages of zebrafish (Danio rerio) scale development. Four developmental stages, namely stage I (~17 dpf, scales have not started to grow), stage II (~33 dpf, the point at which scales start to grow), stage III (~41 dpf, the period in which the scales almost cover the whole body), and stage IV (~3 mpf, scales cover the whole body), were determined and used for subsequent transcriptome analysis. WGCNA (weighted correlation network analysis) and DEG (differentially expressed gene) analysis were used for screening the key genes. Based on the comparison between stage II and stage I, 54 hub-genes were identified by WGCNA analysis. Key genes including the Scpp family (Scpp7, Scpp6, Scpp5, and Scpp8), the Fgf family (Fgfr1b and Fgfr3), Tcf7, Wnt10b, Runx2b, and Il2rb were identified by DEG analysis, which indicated that these genes played important roles in the key nodes of scale development signal pathways. Combined with this analysis, the TGF-β, Wnt/β-catenin, and FGF signaling pathways were suggested to be the most important signal pathways for scales starting to grow. This study laid a foundation for exploring the scale development mechanism of other fishes. The scale development candidate genes identified in the current study will facilitate functional gene identifications in the future.

Susceptibility Modules and Genes in Hypertrophic Cardiomyopathy by WGCNA and ceRNA Network Analysis.

Background: We attempted to identify a regulatory competing endogenous RNA (ceRNA) network and a hub gene of Hypertrophic Cardiomyopathy (HCM). Methods: Microarray datasets of HCM tissue were obtained from NCBI Gene Expression Omnibus (GEO) database. The R package “limma” was used to identify differentially expressed genes. Online search databases were utilized to match the relation among differentially expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs. Weighted correlation network analysis (WGCNA) was used to identify the correlations between key modules and HCM. STRING database was applied to construct PPI networks. Gene Set Enrichment Analysis (GSEA) was used to perform functional annotations and verified the hub genes. Results: A total of 269 DE-lncRNAs, 63 DE-miRNAs and 879 DE-mRNAs were identified in myocardial tissues from microarray datasets GSE130036, GSE36946 and GSE36961, respectively. According to online databases, we found 1 upregulated miRNA hsa-miR-184 that was targeted by 2 downregulated lncRNAs (SNHG9, AC010980.2), potentially targeted 2 downregulated mRNAs (LRRC8A, SLC7A5). 3 downregulated miRNAs (hsa-miR-17-5p, hsa-miR-876-3p, hsa-miR-139-5p) that were targeted by 9 upregulated lncRNAs, potentially targeted 21 upregulated mRNAs. Black and blue modules significantly related to HCM were identified by WGCNA. Hub gene IGFBP5 regulated by hsa-miR-17-5p, AC007389.5, AC104667.1, and AC002511.2 was identified. GSEA indicated that IGFBP5 might involve in the synthesis of myosin complex, participate in kinesin binding, motor activity and function via the regulation of actin cytoskeleton. Conclusion: The results provide a potential molecular regulatory mechanism for the diagnosis and treatment of HCM. IGFBP5 might play an important role in the progression of HCM.