Co-Expression Network and Machine Learning Analysis of Transcriptomics Data Identifies Distinct Gene Signatures and Pathways in Lesional and Non-Lesional Atopic Dermatitis.

BACKGROUND: Atopic dermatitis (AD) is a common inflammatory skin condition with complex origins. Current treatments often yield suboptimal results due to an incomplete understanding of its underlying mechanisms. This study aimed to identify pathway and gene signatures that distinguish between lesional AD, non-lesional AD, and healthy skin. METHOD: We conducted differential gene expression and co-expression network analyses to identify differentially co-expressed genes (DCEGs) in lesional AD vs. healthy skin, lesional vs. non-lesional AD, and non-lesional AD vs. healthy skin. Modules associated with lesional and non-lesional AD were identified based on the correlation coefficients between module eigengenes and clinical phenotypes (|R| ≥ 0.5, p-value < 0.05). Subsequently, we employed Ingenuity Pathway Analysis (IPA) on the identified DCEGs, followed by machine learning (ML) analysis within the pathway expression framework. The ML analysis of pathway expressions, selected by IPA and derived from gene expression data, identified relevant pathway signatures, which were validated using an independent dataset and correlated with AD severity measures (EASI and SCORAD). RESULTS: We identified 975, 441, and 40 DCEGs in lesional vs. healthy skin, lesional vs. non-lesional, and non-lesional vs. healthy skin, respectively. IPA and ML analyses revealed 25 relevant pathway signatures, including wound healing, glucocorticoid receptor signaling, and S100 gene family signaling pathways. Validation confirmed the significance of 10 pathway signatures, which were correlated with the AD severity measures. DCEGs such as MMP12 and S100A8 demonstrated high diagnostic efficacy (AUC > 0.70) in both the discovery and validation datasets. CONCLUSIONS: Differential gene expression, co-expression networks and ML analyses of pathway expression have unveiled relevant pathways and gene signatures that distinguish between lesional, non-lesional, and healthy skin, providing valuable insights into AD pathogenesis.

Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study.

Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

Single-cell sequencing analysis and bulk-seq identify IGFBP6 and TNFAIP6 as novel differential diagnosis markers for postburn pathological scarring.

BACKGROUND: If not accurately diagnosed and treated, postburn pathological scars, such as keloids and hypertrophic scars, can lead to negative clinical outcomes. However, differential diagnosis at the molecular level for postburn pathological scars remains limited. Using single-cell sequencing analysis, we investigated the genetic nuances of pathological scars at the cellular level. This study aimed to identify molecular diagnostic biomarkers to distinguish between postburn keloids and hypertrophic scars. METHODS: Single-cell sequencing, differential expression, and weighted co-expression network analyses were performed to identify potential key genes for discriminating between keloids and hypertrophic scars. Postburn clinical samples were collected from our centre to validate the expression levels of the identified key genes. RESULTS: Single-cell sequencing analysis unveiled 29 and 30 cell clusters in keloids and hypertrophic scars, respectively, predominantly composed of fibroblasts. Bulk differential gene analysis showed 96 highly expressed genes and 69 lowly expressed genes in keloids compared to hypertrophic scars. By incorporating previous research, Gene Set Enrichment Analysis was conducted to select fibroblasts as the focus of research. According to the single-cell data, 301 genes were stably expressed in fibroblasts from both types of pathological scars. Consistently, Weighted Gene Co-expression Network Analysis revealed that the blue module genes were mostly hub genes associated with fibroblasts. After intersecting fibroblast-related genes in single-cell data, Weighted Gene Co-expression Network Analysis-hub module genes, and bulk differential expression genes, insulin-like growth factor binding protein 6 and tumour necrosis factor alpha-induced protein 6 were identified as key genes to distinguish keloids from hypertrophic scars, resulting in diagnostic accuracies of 1.0 and 0.75, respectively. Immunohistochemical Staining and Quantitative Reverse Transcription PCR revealed that the expression levels of tumour necrosis factor alpha induced protein 6 and insulin-like growth factor binding protein 6 were significantly lower in postburn keloids than in hypertrophic scars- CONCLUSIONS: Tumour necrosis factor alpha induced protein 6 and insulin-like growth factor binding protein 6, exhibiting high diagnostic accuracy, provide valuable guidance for the differential diagnosis and treatment of postburn pathological scars.

Single-cell sequencing analysis of peripheral blood in patients with moyamoya disease.

BACKGROUND: At present, the etiology of moyamoya disease is not clear, and it is necessary to explore the mechanism of its occurrence and development. Although some bulk sequencing data have previously revealed transcriptomic changes in Moyamoya disease, single-cell sequencing data has been lacking. METHODS: Two DSA(Digital Subtraction Angiography)-diagnosed patients with moyamoya disease were recruited between January 2021 and December 2021. Their peripheral blood samples were single-cell sequenced. CellRanger(10 x Genomics, version 3.0.1) was used to process the raw data, demultiplex cellular barcodes, map reads to the transcriptome, and dowm-sample reads(as required to generate normalized aggregate data across samples). There were 4 normal control samples, including two normal samples GSM5160432 and GSM5160434 of GSE168732, and two normal samples of GSE155698, namely GSM4710726 and GSM4710727. Weighted co-expression network analysis was used to explore the gene sets associated with moyamoya disease. GO analysis and KEGG analysis were used to explore gene enrichment pathways. Pseudo-time series analysis and cell interaction analysis were used to explore cell differentiation and cell interaction. RESULTS: For the first time, we present a peripheral blood single cell sequencing landscape of Moyamoya disease, revealing cellular heterogeneity and gene expression heterogeneity. In addition, by combining with WGCNA analysis in public database and taking intersection, the key genes in moyamoya disease were obtained. namely PTP4A1, SPINT2, CSTB, PLA2G16, GPX1, HN1, LGALS3BP, IFI6, NDRG1, GOLGA2, LGALS3. Moreover, pseudo-time series analysis and cell interaction analysis revealed the differentiation of immune cells and the relationship between immune cells in Moyamoya disease. CONCLUSIONS: Our study can provide information for the diagnosis and treatment of moyamoya disease.

Identification of lncRNAs involved in response to ionizing radiation in fibroblasts of long-term survivors of childhood cancer and cancer-free controls.

Introduction: Long non-coding ribonucleic acids (lncRNAs) are involved in the cellular damage response following exposure to ionizing radiation as applied in radiotherapy. However, the role of lncRNAs in radiation response concerning intrinsic susceptibility to late effects of radiation exposure has not been examined in general or in long-term survivors of childhood cancer with and without potentially radiotherapy-related second primary cancers, in particular. Methods: Primary skin fibroblasts (n=52 each) of long-term childhood cancer survivors with a first primary cancer only (N1), at least one second primary neoplasm (N2+), as well as tumor-free controls (N0) from the KiKme case-control study were matched by sex, age, and additionally by year of diagnosis and entity of the first primary cancer. Fibroblasts were exposed to 0.05 and 2 Gray (Gy) X-rays. Differentially expressed lncRNAs were identified with and without interaction terms for donor group and dose. Weighted co-expression networks of lncRNA and mRNA were constructed using WGCNA. Resulting gene sets (modules) were correlated to the radiation doses and analyzed for biological function. Results: After irradiation with 0.05Gy, few lncRNAs were differentially expressed (N0: AC004801.4; N1: PCCA-DT, AF129075.3, LINC00691, AL158206.1; N2+: LINC02315). In reaction to 2 Gy, the number of differentially expressed lncRNAs was higher (N0: 152, N1: 169, N2+: 146). After 2 Gy, AL109976.1 and AL158206.1 were prominently upregulated in all donor groups. The co-expression analysis identified two modules containing lncRNAs that were associated with 2 Gy (module1: 102 mRNAs and 4 lncRNAs: AL158206.1, AL109976.1, AC092171.5, TYMSOS, associated with p53-mediated reaction to DNA damage; module2: 390 mRNAs, 7 lncRNAs: AC004943.2, AC012073.1, AC026401.3, AC092718.4, MIR31HG, STXBP5-AS1, TMPO-AS1, associated with cell cycle regulation). Discussion: For the first time, we identified the lncRNAs AL158206.1 and AL109976.1 as involved in the radiation response in primary fibroblasts by differential expression analysis. The co-expression analysis revealed a role of these lncRNAs in the DNA damage response and cell cycle regulation post-IR. These transcripts may be targets in cancer therapy against radiosensitivity, as well as provide grounds for the identification of at-risk patients for immediate adverse reactions in healthy tissues. With this work we deliver a broad basis and new leads for the examination of lncRNAs in the radiation response.

Metataxonomic analysis and host proteome response in dairy cows with high and low somatic cell count: a quarter level investigation.

Host response to invasive microbes in the bovine udder has an important role on the animal health and is essential to the dairy industry to ensure production of high-quality milk and reduce the mastitis incidence. To better understand the biology behind these host-microbiome interactions, we investigated the somatic cell proteomes at quarter level for four cows (collected before and after milking) using a shotgun proteomics approach. Simultaneously, we identified the quarter microbiota by amplicon sequencing to detect presence of mastitis pathogens or other commensal taxa. In total, 32 quarter milk samples were analyzed divided in two groups depending on the somatic cell count (SCC). The high SCC group (>100,000 cell/mL) included 10 samples and significant different proteome profiles were detected. Differential abundance analysis uncovers a specific expression pattern in high SCC samples revealing pathways involved in immune responses such as inflammation, activation of the complement system, migration of immune cells, and tight junctions. Interestingly, different proteome profiles were also identified in quarter samples containing one of the two mastitis pathogens, Staphylococcus aureus and Streptococcus uberis, indicating a different response of the host depending on the pathogen. Weighted correlation network analysis identified three modules of co-expressed proteins which were correlated with the SCC in the quarters. These modules contained proteins assigned to different aspects of the immune response, but also amino sugar and nucleotide sugar metabolism, and biosynthesis of amino acids. The results of this study provide deeper insights on how the proteome expression changes at quarter level in naturally infected cows and pinpoint potential interactions and important biological functions during host-microbe interaction.

Gene Regulatory Network of the Noncoding RNA Qrr5 Involved in the Cytotoxicity of Vibrio parahaemolyticus during Infection.

Small non-coding RNAs (sRNAs) in bacteria are important regulatory molecules for controlling virulence. In Vibrio spp., Qrr sRNAs are critical for quorum-sensing pathways and regulating the release of some virulence factors. However, the detailed role of Qrr sRNAs in the virulence of Vibrio parahaemolyticus remains poorly understood. In this study, we identified a Vibrio sRNA Qrr5 that positively regulates cytotoxicity and adherence in Caco-2 cells by primarily regulating the T3SS1 gene cluster. A number of 185, 586, 355, and 74 differentially expressed genes (DEGs) detected at 0, 2, 4, and 6 h post-infection, respectively, were mainly associated with ABC transporters and two-component system pathways. The DEGs exhibited a dynamic change in expression at various time points post-infection owing to the deletion of Qrr5. Accordingly, 17 related genes were identified in the co-expression network, and their interaction with Qrr5 was determined based on weighted co-expression network analysis during infection. Taken together, our results provide a comprehensive transcriptome profile of V. parahaemolyticus during infection in Caco-2 cells.

Single-Cell Sequencing Analysis and Multiple Machine Learning Methods Identified G0S2 and HPSE as Novel Biomarkers for Abdominal Aortic Aneurysm.

Identifying biomarkers for abdominal aortic aneurysms (AAA) is key to understanding their pathogenesis, developing novel targeted therapeutics, and possibly improving patients outcomes and risk of rupture. Here, we identified AAA biomarkers from public databases using single-cell RNA-sequencing, weighted co-expression network (WGCNA), and differential expression analyses. Additionally, we used the multiple machine learning methods to identify biomarkers that differentiated large AAA from small AAA. Biomarkers were validated using GEO datasets. CIBERSORT was used to assess immune cell infiltration into AAA tissues and investigate the relationship between biomarkers and infiltrating immune cells. Therefore, 288 differentially expressed genes (DEGs) were screened for AAA and normal samples. The identified DEGs were mostly related to inflammatory responses, lipids, and atherosclerosis. For the large and small AAA samples, 17 DEGs, mostly related to necroptosis, were screened. As biomarkers for AAA, G0/G1 switch 2 (G0S2) (Area under the curve [AUC] = 0.861, 0.875, and 0.911, in GSE57691, GSE47472, and GSE7284, respectively) and for large AAA, heparinase (HPSE) (AUC = 0.669 and 0.754, in GSE57691 and GSE98278, respectively) were identified and further verified by qRT-PCR. Immune cell infiltration analysis revealed that the AAA process may be mediated by T follicular helper (Tfh) cells and the large AAA process may also be mediated by Tfh cells, M1, and M2 macrophages. Additionally, G0S2 expression was associated with neutrophils, activated and resting mast cells, M0 and M1 macrophages, regulatory T cells (Tregs), resting dendritic cells, and resting CD4 memory T cells. Moreover, HPSE expression was associated with M0 and M1 macrophages, activated and resting mast cells, Tregs, and resting CD4 memory T cells. Additional, G0S2 may be an effective diagnostic biomarker for AAA, whereas HPSE may be used to confer risk of rupture in large AAAs. Immune cells play a role in the onset and progression of AAA, which may improve its diagnosis and treatment.

A rice protein interaction network reveals high centrality nodes and candidate pathogen effector targets.

Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein-protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks representing susceptible and resistant responses to strains of the bacterial pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola (Xoc) and generated a RIce-Xanthomonas INteractome (RIXIN). This revealed that previously identified candidate targets of pathogen transcription activator-like (TAL) effectors are enriched in nodes with enhanced connectivity, bottlenecks, and information spreaders that are located in the inner layers of the network, and these nodes are involved in several important biological processes. Overall, our integrative multi-omics network-based platform provides a potentially useful approach to prioritizing candidate pathogen effector targets for functional validation, suggesting that this computational framework can be broadly translatable to other complex pathosystems.

Identification of a prognostic chemoresistance-related gene signature associated with immune microenvironment in breast cancer.

Breast cancer is the most common form of cancer among women globally, and chemoresistance is a major challenge to disease treatment that is associated with a poor prognosis. This study was formulated to identify a reliable prognostic biosignature capable of predicting the survival of patients with chemoresistant breast cancer (CRBC) and evaluating the associated tumor immune microenvironment. Through a series of protein-protein interaction and weighted correlation network analyses, genes that were significantly associated with breast cancer chemoresistance were identified. Moreover, univariate Cox regression and lasso-penalized Cox regression analyses were employed to generate a prognostic model, and the prognostic utility of this model was then assessed using time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curves. Finally, The CIBERSORT and ESTIMATE algorithms were additionally leveraged to assess relationships between the tumor immune microenvironment and patient prognostic signatures. Overall, a multigenic prognostic biosignature capable of predicting CRBC patient risk was successfully developed based on bioinformatics analysis and in vitro experiments. This biosignature was able to stratify CRBC patients into high- and low-risk subgroups. ROC curves also revealed that this biosignature achieved high diagnostic efficiency, and multivariate regression analyses indicated that this risk signature was an independent risk factor linked to CRBC patient outcomes. In addition, this signature was associated with the infiltration of the tumor microenvironment by multiple immune cell types. In conclusion, the chemoresistance-associated prognostic gene signature developed herein was able to effectively evaluate the prognosis of CRBC patients and to reflect the overall composition of the tumor immune microenvironment.

The landscape of gene co-expression modules correlating with prognostic genetic abnormalities in AML.

BACKGROUND: The heterogenous cytogenetic and molecular variations were harbored by AML patients, some of which are related with AML pathogenesis and clinical outcomes. We aimed to uncover the intrinsic expression profiles correlating with prognostic genetic abnormalities by WGCNA. METHODS: We downloaded the clinical and expression dataset from BeatAML, TCGA and GEO database. Using R (version 4.0.2) and 'WGCNA' package, the co-expression modules correlating with the ELN2017 prognostic markers were identified (R2 ≥ 0.4, p < 0.01). ORA detected the enriched pathways for the key co-expression modules. The patients in TCGA cohort were randomly assigned into the training set (50%) and testing set (50%). The LASSO penalized regression analysis was employed to build the prediction model, fitting OS to the expression level of hub genes by 'glmnet' package. Then the testing and 2 independent validation sets (GSE12417 and GSE37642) were used to validate the diagnostic utility and accuracy of the model. RESULTS: A total of 37 gene co-expression modules and 973 hub genes were identified for the BeatAML cohort. We found that 3 modules were significantly correlated with genetic markers (the 'lightyellow' module for NPM1 mutation, the 'saddlebrown' module for RUNX1 mutation, the 'lightgreen' module for TP53 mutation). ORA revealed that the 'lightyellow' module was mainly enriched in DNA-binding transcription factor activity and activation of HOX genes. The 'saddlebrown' module was enriched in immune response process. And the 'lightgreen' module was predominantly enriched in mitosis cell cycle process. The LASSO- regression analysis identified 6 genes (NFKB2, NEK9, HOXA7, APRC5L, FAM30A and LOC105371592) with non-zero coefficients. The risk score generated from the 6-gene model, was associated with ELN2017 risk stratification, relapsed disease, and prior MDS history. The 5-year AUC for the model was 0.822 and 0.824 in the training and testing sets, respectively. Moreover, the diagnostic utility of the model was robust when it was employed in 2 validation sets (5-year AUC 0.743-0.79). CONCLUSIONS: We established the co-expression network signature correlated with the ELN2017 recommended prognostic genetic abnormalities in AML. The 6-gene prediction model for AML survival was developed and validated by multiple datasets.

Identification of novel candidate biomarkers for pancreatic adenocarcinoma based on TCGA cohort.

Pancreatic adenocarcinoma (PAAD) is the most serious solid tumor type throughout the world. The present study aimed to identify novel biomarkers and potential efficacious small drugs in PAAD using integrated bioinformatics analyses. A total of 4777 differentially expressed genes (DEGs) were filtered, 2536 upregulated DEGs and 2241 downregulated DEGs. Weighted gene co-expression network analysis was then used and identified 12 modules, of which, blue module with the most significant enrichment result was selected. KEGG and GO enrichment analyses showed that all DEGs of blue module were enriched in EMT and PI3K/Akt pathway. Three hub genes (ITGB1, ITGB5, and OSMR) were determined as key genes with higher expression levels, significant prognostic value and excellent diagnostic efficiency for PAAD. Additionally, some small molecule drugs that possess the potential to treat PAAD were screened out, including thapsigargin (TG). Functional in vitro experiments revealed that TG repressed cell viability via inactivating the PI3K/Akt pathway in PAAD cells. Totally, our findings identified three key genes implicated in PAAD and screened out several potential small drugs to treat PAAD.

Single-Cell Sequencing Analysis and Weighted Co-Expression Network Analysis Based on Public Databases Identified That TNC Is a Novel Biomarker for Keloid.

Background: The pathophysiology of keloid formation is not yet understood, so the identification of biomarkers for kelod can be one step towards designing new targeting therapies which will improve outcomes for patients with keloids or at risk of developing keloids. Methods: In this study, we performed single-cell RNA sequencing analysis, weighted co-expression network analysis, and differential expression analysis of keloids based on public databases. And 3 RNA sequencing data from keloid patients in our center were used for validation. Besides, we performed QRT-PCR on keloid tissue and adjacent normal tissues from 16 patients for further verification. Results: We identified the sensitive biomarker of keloid: Tenascin-C (TNC). Then, Pseudotime analysis found that the expression level of TNC decreased first, then stabilized and finally increased with fibroblast differentiation, suggesting that TNC may play an potential role in fibroblast differentiation. In addition, there were differences in the infiltration level of macrophages M0 between the TNC-high group and the TNC-low group. Macrophages M0 had a higher infiltration level in low TNC- group (P<0.05). Conclusion: Our results can provide a new idea for the diagnosis and treatment of keloid.

Fractional-order modeling of lithium-ion batteries using additive noise assisted modeling and correlative information criterion.

In this paper, the fractional-order modeling of multiple groups of lithium-ion batteries with different states is discussed referring to electrochemical impedance spectroscopy (EIS) analysis and iterative learning identification method. The structure and parameters of the presented fractional-order equivalent circuit model (FO-ECM) are determined by EIS from electrochemical test. Based on the working condition test, a P-type iterative learning algorithm is applied to optimize certain selected model parameters in FO-ECM affected by polarization effect. What's more, considering the reliability of structure and adaptiveness of parameters in FO-ECM, a pre-tested nondestructive 1 / f noise is superimposed to the input current, and the correlative information criterion (CIC) is proposed by means of multiple correlations of each parameter and confidence eigen-voltages from weighted co-expression network analysis method. The tested batteries with different state of health (SOH) can be successfully simulated by FO-ECM with rarely need of calibration when excluding polarization effect. Particularly, the small value of CIC α indicates that the fractional-order α is constant over time for the purpose of SOH estimation. Meanwhile, the time-varying ohmic resistance R 0 in FO-ECM can be regarded as a wind vane of SOH due to the large value of CIC R 0 . The above analytically found parameter-state relations are highly consistent with the existing literature and empirical conclusions, which indicates the broad application prospects of this paper.

Lipidomic architecture shared by subclinical markers of osteoporosis and atherosclerosis: The Cardiovascular Risk in Young Finns Study.

BACKGROUND: Studies have shown that osteoporosis and atherosclerosis are comorbid conditions sharing common risk factors and pathophysiological mechanisms. Understanding these is crucial in order to develop shared methods for risk stratification, prevention, diagnosis and treatment. The aim of this study was to apply a system-level bioinformatics approach to lipidome-wide data in order to pinpoint the lipidomic architecture jointly associated with surrogate markers of these complex comorbid diseases. SUBJECTS AND METHODS: The study was based on the Cardiovascular Risk in Young Finns Study cohort from the 2007 follow-up (n = 1494, aged 30-45 years, women: 57%). Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to analyse the serum lipidome, involving 437 molecular lipid species. The subclinical osteoporotic markers included indices of bone mineral density and content, measured using peripheral quantitative computer tomography from the distal and shaft sites of both the tibia and the radius. The subclinical atherosclerotic markers included carotid and bulbus intima media thickness measured with high-resolution ultrasound. Weighted co-expression network analysis was performed to identify networks of densely interconnected lipid species (i.e. lipid modules) associated with subclinical markers of both osteoporosis and atherosclerosis. The levels of lipid species (lipid profiles) of each of the lipid modules were summarized by the first principal component termed as module eigenlipid. Then, Pearson's correlation (r) was calculated between the module eigenlipids and the markers. Lipid modules that were significantly and jointly correlated with subclinical markers of both osteoporosis and atherosclerosis were considered to be related to the comorbidities. The hypothesis that the eigenlipids and profiles of the constituent lipid species in the modules have joint effects on the markers was tested with multivariate analysis of variance (MANOVA). RESULTS: Among twelve studied molecular lipid modules, we identified one module with 105 lipid species significantly and jointly associated with both subclinical markers of both osteoporosis (r = 0.24, p-value = 2 × 10-20) and atherosclerosis (r = 0.16, p-value = 2 × 10-10). The majority of the lipid species in this module belonged to the glycerolipid (n = 60), glycerophospholipid (n = 13) and sphingolipid (n = 29) classes. The module was also enriched with ceramides (n = 20), confirming their significance in cardiovascular outcomes and suggesting their joint role in the comorbidities. The top three of the 37 statistically significant (adjusted p-value < 0.05) lipid species jointly associated with subclinical markers of both osteoporosis and atherosclerosis within the module were all triacylglycerols (TAGs) - TAG(18:0/18:0/18:1) with an adjusted p-value of 8.6 × 10-8, TAG(18:0/18:1/18:1) with an adjusted p-value of 3.7 × 10-6, and TAG(16:0/18:0/18:1) with an adjusted p-value of 8.5 × 10-6. CONCLUSION: This study identified a novel lipid module associated with both surrogate markers of both subclinical osteoporosis and subclinical atherosclerosis. Alterations in the metabolism of the identified lipid module and, more specifically, the TAG related molecular lipids within the module may provide potential new biomarkers for testing the comorbidities, opening avenues for the emergence of dual-purpose prevention measures.

Novel Biomarkers Associated With Progression and Prognosis of Bladder Cancer Identified by Co-expression Analysis.

Our study's goal was to screen novel biomarkers that could accurately predict the progression and prognosis of bladder cancer (BC). Firstly, we used the Gene Expression Omnibus (GEO) dataset GSE37815 to screen differentially expressed genes (DEGs). Secondly, we used the DEGs to construct a co-expression network by weighted gene co-expression network analysis (WGCNA) in GSE71576. We then screened the brown module, which was significantly correlated with the histologic grade (r = 0.85, p = 1e-12) of BC. We conducted functional annotation on all genes of the brown module and found that the genes of the brown module were mainly significantly enriched in "cell cycle" correlation pathways. Next, we screened out two real hub genes (ANLN, HMMR) by combining WGCNA, protein-protein interaction (PPI) network and survival analysis. Finally, we combined the GEO datasets (GSE13507, GSE37815, GSE31684, GSE71576). Oncomine, Human Protein Atlas (HPA), and The Cancer Genome Atlas (TCGA) dataset to confirm the predict value of the real hub genes for BC progression and prognosis. A gene-set enrichment analysis (GSEA) revealed that the real hub genes were mainly enriched in "bladder cancer" and "cell cycle" pathways. A survival analysis showed that they were of great significance in predicting the prognosis of BC. In summary, our study screened and confirmed that two biomarkers could accurately predict the progression and prognosis of BC, which is of great significance for both stratification therapy and the mechanism study of BC.

Identification of possible molecular markers and functional gene modules associated with lung adenocarcinoma based on weighted co-expression network analysis method / 中草药

Objective: The potential biological targets for anti-lung adenocarcinoma of Solanum nigrum were scored using the weighted co-expression network analysis (WGCNA) method. Methods: A database of chemical components of S. nigrum was established through oral bioavailability (OB), drug-likeness (DL) based on Traditional Chinese Medicine Systems Pharmacology (TCMSP) and literature retrieval. The targets of active ingredients of S. nigrum were predicted based on reverse docking with DRAR-CPI server, and combined with WGCNA to mine GSE10072 dataset in Gene Expression Omnibus (GEO) database to obtain coexpression gene module. Furthermore, the potential anti-lung adenocarcinoma targets of S. nigrum were confirmed under intersected with predicted targets and coexpression genes. The GO terms of biological processes and KEGG pathway enrichment analysis of predicted targets and anti-lung adenocarcinoma targets were performed by Metascape database, respectively. Using the targets-pathways networks to study the mechanisms of S. nigrum in the fight against cancer. The transcriptional level expression of key String database combined with Cytoscape software to draw the proteins-proteins interactions (PPI), and active ingredients-targets-pathways networks to study the mechanisms of S. nigrum in the fight against cancer. The transcriptional level expression of key genes in lung adenocarcinoma cancer tissues and normal lung tissues was assessed based on UALCAN dataset. And the correlation between key genes and prognosis of lung cancer patients was calculated by KM plotter analysis. Results: This study collected nine active components of S. nigrum, including medioresinol, sitosterol, diosgenin, solanocapsine, quercetin, α-chaconine, solasonin, solamargine, and solasodine. Totally 271 targets were predicted, and 41 potential anticancer targets were confirmed. The potential regulatory pathways included pathway in cancer, PI3K-Akt signaling pathway, chemical carcinogenesis, central carbon metabolism in cancer and so on. From the PPI network, we found that hub genes EGFR, CASP8, HPGDS, FYN, and high expression of EGFR and CASP8 were related to the poor overall survival in patient with lung adenocarcinoma. Oncontrary, lower expression of HPGDS and FYN were also associated with poor overall survival. Conclusion: This study reflects the multi-component, multi-target and multi-pathway features of S. nigrum, and provides a scientific basis for anticancer substance and elucidating the mechanisms of action of S. nigrum, as well as a reference for the study of mechanisms.

Development and Validation of a 9-Gene Prognostic Signature in Patients With Multiple Myeloma.

Background: Multiple myeloma (MM) is one of the most common types of hematological malignance, and the prognosis of MM patients remains poor. Objective: To identify and validate a genetic prognostic signature in patients with MM. Methods: Co-expression network was constructed to identify hub genes related with International Staging System (ISS) stage of MM. Functional analysis of hub genes was conducted. Univariate Cox proportional hazard regression analysis was conducted to identify genes correlated with the overall survival (OS) of MM patients. Least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards regression model was used to minimize overfitting and construct a prognostic signature. The prognostic value of the signature was validated in the test set and an independent validation cohort. Results: A total of 758 hub genes correlated with ISS stage of MM patients were identified, and these hub genes were mainly enriched in several GO terms and KEGG pathways involved in cell proliferation and immune response. Nine hub genes (HLA-DPB1, TOP2A, FABP5, CYP1B1, IGHM, FANCI, LYZ, HMGN5, and BEND6) with non-zero coefficients in the LASSO Cox regression model were used to build a 9-gene prognostic signature. Relapsed MM and ISS stage III MM was associated with high risk score calculated based on the signature. Patients in the 9-gene signature low risk group was significantly associated with better clinical outcome than those in the 9-gene signature high risk group in the training set, test, and validation set. Conclusions: We developed a 9-gene prognostic signature that might be an independent prognostic factor in patients with MM.

Condition-dependent co-regulation of genomic clusters of virulence factors in the grapevine trunk pathogen Neofusicoccum parvum.

The ascomycete Neofusicoccum parvum, one of the causal agents of Botryosphaeria dieback, is a destructive wood-infecting fungus and a serious threat to grape production worldwide. The capability to colonize woody tissue, combined with the secretion of phytotoxic compounds, is thought to underlie its pathogenicity and virulence. Here, we describe the repertoire of virulence factors and their transcriptional dynamics as the fungus feeds on different substrates and colonizes the woody stem. We assembled and annotated a highly contiguous genome using single-molecule real-time DNA sequencing. Transcriptome profiling by RNA sequencing determined the genome-wide patterns of expression of virulence factors both in vitro (potato dextrose agar or medium amended with grape wood as substrate) and in planta. Pairwise statistical testing of differential expression, followed by co-expression network analysis, revealed that physically clustered genes coding for putative virulence functions were induced depending on the substrate or stage of plant infection. Co-expressed gene clusters were significantly enriched not only in genes associated with secondary metabolism, but also in those associated with cell wall degradation, suggesting that dynamic co-regulation of transcriptional networks contributes to multiple aspects of N. parvum virulence. In most of the co-expressed clusters, all genes shared at least a common motif in their promoter region, indicative of co-regulation by the same transcription factor. Co-expression analysis also identified chromatin regulators with correlated expression with inducible clusters of virulence factors, suggesting a complex, multi-layered regulation of the virulence repertoire of N. parvum.