Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target

Cross-Talking Pathways of Rapidly Accelerated Fibrosarcoma-1 (RAF-1) in Alzheimer's Disease

Alzheimer's disease (AD) becomes one of the main global burden diseases with the aging population. This study was to investigate the potential molecular mechanisms of rapidly accelerated fibrosarcoma-1 (RAF-1) in AD through bioinformatics analysis. Differential gene expression analysis was performed in GSE132903 dataset. We used weight gene correlation network analysis (WGCNA) to evaluate the relations among co-expression modules and construct global regulatory network. Cross-talking pathways of RAF-1 in AD were identified by functional enrichment analysis. Totally, 2700 differentially expressed genes (DEGs) were selected between AD versus non-dementia control and RAF-1-high versus low group. Among them, DEGs in turquoise module strongly associated with AD and high expression of RAF-1 were enriched in vascular endothelial growth factor (VEGF), neurotrophin, mitogen-activated protein kinase (MAPK) signaling pathway, oxidative phosphorylation, GABAergic synapse, and axon guidance. Moreover, cross-talking pathways of RAF-1, including MAPK, VEGF, neurotrophin signaling pathways, and axon guidance, were identified by global regulatory network. The performance evaluation of AUC was 84.2%. The gene set enrichment analysis (GSEA) indicated that oxidative phosphorylation and synapse-related biological processes were enriched in RAF-1-high and AD group. Our findings strengthened the potential roles of high RAF-1 level in AD pathogenesis, which were mediated by MAPK, VEGF, neurotrophin signaling pathways, and axon guidance.

Genome-wide association studies reveal differences in genetic susceptibility between single events vs. recurrent events of atrial fibrillation and myocardial infarction: the HUNT study

Genetic research into atrial fibrillation (AF) and myocardial infarction (MI) has predominantly focused on comparing afflicted individuals with their healthy counterparts. However, this approach lacks granularity, thus overlooking subtleties within patient populations. In this study, we explore the distinction between AF and MI patients who experience only a single disease event and those experiencing recurrent events. Integrating hospital records, questionnaire data, clinical measurements, and genetic data from more than 500,000 HUNT and United Kingdom Biobank participants, we compare both clinical and genetic characteristics between the two groups using genome-wide association studies (GWAS) meta-analyses, phenome-wide association studies (PheWAS) analyses, and gene co-expression networks. We found that the two groups of patients differ in both clinical characteristics and genetic risks. More specifically, recurrent AF patients are significantly younger and have better baseline health, in terms of reduced cholesterol and blood pressure, than single AF patients. Also, the results of the GWAS meta-analysis indicate that recurrent AF patients seem to be at greater genetic risk for recurrent events. The PheWAS and gene co-expression network analyses highlight differences in the functions associated with the sets of single nucleotide polymorphisms (SNPs) and genes for the two groups. However, for MI patients, we found that those experiencing single events are significantly younger and have better baseline health than those with recurrent MI, yet they exhibit higher genetic risk. The GWAS meta-analysis mostly identifies genetic regions uniquely associated with single MI, and the PheWAS analysis and gene co-expression networks support the genetic differences between the single MI and recurrent MI groups. In conclusion, this work has identified novel genetic regions uniquely associated with single MI and related PheWAS analyses, as well as gene co-expression networks that support the genetic differences between the patient subgroups of single and recurrent occurrence for both MI and AF.

Screening of mRNA markers in early bovine tuberculosis blood samples

Bovine tuberculosis (bTB) is a chronic zoonotic disease caused by Mycobacterium bovis. A large number of cattle are infected with bTB every year, resulting in huge economic losses. How to control bTB is an important issue in the current global livestock economy. In this study, the original transcriptome sequences related to this study were obtained from the dataset GSE192537 by searching the Gene Expression Omnibus (GEO) database. Our differential gene analysis showed that there were obvious biological activities related to immune activation and immune regulation in the early stage of bTB. Immune-related biological processes were more active in the early stage of bTB than in the late. There were obvious immune activation and immune cell recruitment in the early stage of bTB. Regulations in immune receptors are associated with pathophysiological processes of the early stage of bTB. A gene module consisting of 236 genes significantly related to the early stage of bTB was obtained by weighted gene co-expression network analysis, and 18 hub genes were further identified as potential biomarkers or therapeutic targets. Finally, by random forest algorithm and logistic regression modeling, FCRL1 was identified as a representative mRNA marker in early bTB blood. FCRL1 has the potential to be a diagnostic biomarker in early bTB.

Dissecting gene expression networks in the developing hippocampus through the lens of NEIL3 depletion

Gene regulation in the hippocampus is fundamental for its development, synaptic plasticity, memory formation, and adaptability. Comparisons of gene expression among different developmental stages, distinct cell types, and specific experimental conditions have identified differentially expressed genes contributing to the organization and functionality of hippocampal circuits. The NEIL3 DNA glycosylase, one of the DNA repair enzymes, plays an important role in hippocampal maturation and neuron functionality by shaping transcription. While differential gene expression (DGE) analysis has identified key genes involved, broader gene expression patterns crucial for high-order hippocampal functions remain uncharted. By utilizing the weighted gene co-expression network analysis (WGCNA), we mapped gene expression networks in immature (p8-neonatal) and mature (3 m-adult) hippocampal circuits in wild-type and NEIL3-deficient mice. Our study unveiled intricate gene network structures underlying hippocampal maturation, delineated modules of co-expressed genes, and pinpointed highly interconnected hub genes specific to the maturity of hippocampal subregions. We investigated variations within distinct gene network modules following NEIL3 depletion, uncovering NEIL3-targeted hub genes that influence module connectivity and specificity. By integrating WGCNA with DGE, we delve deeper into the NEIL3-dependent molecular intricacies of hippocampal maturation. This study provides a comprehensive systems-level analysis for assessing the potential correlation between gene connectivity and functional connectivity within the hippocampal network, thus shaping hippocampal function throughout development.

ASPM stabilizes the NOTCH intracellular domain 1 and promotes oncogenesis by blocking FBXW7 binding in hepatocellular carcinoma cells

Notch signaling is aberrantly activated in approximately 30% of hepatocellular carcinoma (HCC), significantly contributing to tumorigenesis and disease progression. Expression of the major Notch receptor, NOTCH1, is upregulated in HCC cells and correlates with advanced disease stages, although the molecular mechanisms underlying its overexpression remain unclear. Here, we report that expression of the intracellular domain of NOTCH1 (NICD1) is upregulated in HCC cells due to antagonism between the E3-ubiquitin ligase F-box/WD repeat-containing protein 7 (FBXW7) and the large scaffold protein abnormal spindle-like microcephaly-associated protein (ASPM) isoform 1 (ASPM-i1). Mechanistically, FBXW7-mediated polyubiquitination and the subsequent proteasomal degradation of NICD1 are hampered by the interaction of NICD1 with ASPM-i1, thereby stabilizing NICD1 and rendering HCC cells responsive to stimulation by Notch ligands. Consistently, downregulating ASPM-i1 expression reduced the protein abundance of NICD1 but not its FBXW7-binding-deficient mutant. Reinforcing the oncogenic function of this regulatory module, the forced expression of NICD1 significantly restored the tumorigenic potential of ASPM-i1-deficient HCC cells. Echoing these findings, NICD1 was found to be strongly co-expressed with ASPM-i1 in cancer cells in human HCC tissues (P < 0.001). In conclusion, our study identifies a novel Notch signaling regulatory mechanism mediated by protein-protein interaction between NICD1, FBXW7, and ASPM-i1 in HCC cells, representing a targetable vulnerability in human HCC.

The levels of immunosuppressive checkpoint protein PD-L1 and tumor-infiltrating lymphocytes were integrated to reveal the glioma tumor microenvironment

In spite of significant strides in the realm of cancer biology and therapeutic interventions, the clinical prognosis for patients afflicted with glioblastoma (GBM) remains distressingly dismal. The tumor immune microenvironment (TIME), a crucial player in the progression, treatment response, and prognostic trajectory of glioma, warrants thorough exploration. Within this intricate microcosm, the immunosuppressive checkpoint protein PD-L1 and tumor-infiltrating lymphocytes (TILs) emerge as pivotal constituents, underscoring their potential role in deciphering glioma biology and informing treatment strategies. However, prognostic models based on the association between PD-L1 expression and TIL infiltration in the tumor immune microenvironment have not been established. The aim of this study was to explore TIME genes associated with PD-L1 expression and TIL invasion and to construct a risk score for predicting the overall survival (OS) of GBM patients based on these genes. The samples were separately classified according to the PD-L1 expression level and TIL score and TIME-related genes were identified using differential expression and weighted gene co-expression network analysis. The DEGs were subjected to least absolute contraction and selection operator (LASSO) -Cox regression to construct TIME associated risk score (TIMErisk). A TIMErisk was developed based on STEAP3 and CXCL13 genes. The STLEAP3 was demonstrated to be involved in glioma progression. The results showed that the patients in the high TIMErisk group had poor OS compared with subjects in the low TIMErisk group. The biological phenotypes associated with TIMErisk were analyzed in terms of functional enrichment, tumor immune profile, and tumor mutation profile. The results on tumor immune dysfunction and exclusion dysfunction (TIDE) score and immune surface score (IPS) showed that GBM patients with different TIME risks had different responses to immunotherapy. Tumor purity analysis indicated that PD-L1 and TIL scores were positively correlated with TIMErisk score and negatively correlated with tumor purity. These results show that the TIMErisk-based prognostic model had high predictive value for the prognosis and immune characteristics of GBM patients. Immunohistochemical staining images of patients in the high and low TIMErisk groups were analyzed, showing that the degree of immune cell infiltration was higher in the high TIMErisk group relative to the low TIMErisk group. The present study provides a basis for understanding glioma tumor microenvironment and a foundation for conducting comprehensive immunogenomic analysis.

NetVA: an R package for network vulnerability and influence analysis

In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Similarly, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players (individual proteins and protein pairs/triplets) through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein-protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network.Communicated by Ramaswamy H. Sarma.

Machine learning-based identification of CYBB and FCAR as potential neutrophil extracellular trap-related treatment targets in sepsis

Objective

Sepsis related injury has gradually become the main cause of death in non-cardiac patients in intensive care units, but the underlying pathological and physiological mechanisms remain unclear. The Third International Consensus Definitions for Sepsis and Septic Shock (SEPSIS-3) definition emphasized organ dysfunction caused by infection. Neutrophil extracellular traps (NETs) can cause inflammation and have key roles in sepsis organ failure; however, the role of NETs-related genes in sepsis is unknown. Here, we sought to identify key NETs-related genes associate with sepsis.

Methods

Datasets GSE65682 and GSE145227, including data from 770 patients with sepsis and 54 healthy controls, were downloaded from the GEO database and split into training and validation sets. Differentially expressed genes (DEGs) were identified and weighted gene co-expression network analysis (WGCNA) performed. A machine learning approach was applied to identify key genes, which were used to construct functional networks. Key genes associated with diagnosis and survival of sepsis were screened out. Finally, mouse and human blood samples were collected for RT-qPCR verification and flow cytometry analysis. Multiple organs injury, apoptosis and NETs expression were measured to evaluated effects of sulforaphane (SFN).

Results

Analysis of the obtained DEGs and WGCNA screened a total of 3396 genes in 3 modules, and intersection of the results of both analyses with 69 NETs-related genes, screened out seven genes (S100A12, SLC22A4, FCAR, CYBB, PADI4, DNASE1, MMP9) using machine learning algorithms. Of these, CYBB and FCAR were independent predictors of poor survival in patients with sepsis. Administration of SFN significantly alleviated murine lung NETs expression and injury, accompanied by whole blood CYBB mRNA level.

Conclusion

CYBB and FCAR may be reliable biomarkers of survival in patients with sepsis, as well as potential targets for sepsis treatment. SFN significantly alleviated NETs-related organs injury, suggesting the therapeutic potential by targeting CYBB in the future.

Female Gene Networks Are Expressed in Myofibroblast-Like Smooth Muscle Cells in Vulnerable Atherosclerotic Plaques

BACKGROUND

Women presenting with coronary artery disease more often present with fibrous atherosclerotic plaques, which are currently understudied. Phenotypically modulated smooth muscle cells (SMCs) contribute to atherosclerosis in women. How these phenotypically modulated SMCs shape female versus male plaques is unknown.

METHODS

Gene regulatory networks were created using RNAseq gene expression data from human carotid atherosclerotic plaques. The networks were prioritized based on sex bias, relevance for smooth muscle biology, and coronary artery disease genetic enrichment. Network expression was linked to histologically determined plaque phenotypes. In addition, their expression in plaque cell types was studied at single-cell resolution using single-cell RNAseq. Finally, their relevance for disease progression was studied in female and male Apoe-/- mice fed a Western diet for 18 and 30 weeks.

RESULTS

Here, we identify multiple sex-stratified gene regulatory networks from human carotid atherosclerotic plaques. Prioritization of the female networks identified 2 main SMC gene regulatory networks in late-stage atherosclerosis. Single-cell RNA sequencing mapped these female networks to 2 SMC phenotypes: a phenotypically modulated myofibroblast-like SMC network and a contractile SMC network. The myofibroblast-like network was mostly expressed in plaques that were vulnerable in women. Finally, the mice ortholog of key driver gene MFGE8 (milk fat globule EGF and factor V/VIII domain containing) showed retained expression in advanced plaques from female mice but was downregulated in male mice during atherosclerosis progression.

CONCLUSIONS

Female atherosclerosis is characterized by gene regulatory networks that are active in fibrous vulnerable plaques rich in myofibroblast-like SMCs.

Oncogenic ASPM Is a Regulatory Hub of Developmental and Stemness Signaling in Cancers

Despite recent advances in molecularly targeted therapies and immunotherapies, the effective treatment of advanced-stage cancers remains a largely unmet clinical need. Identifying driver mechanisms of cancer aggressiveness can lay the groundwork for the development of breakthrough therapeutic strategies. Assembly factor for spindle microtubules (ASPM) was initially identified as a centrosomal protein that regulates neurogenesis and brain size. Mounting evidence has demonstrated the pleiotropic roles of ASPM in mitosis, cell-cycle progression, and DNA double-strand breaks (DSB) repair. Recently, the exon 18-preserved isoform 1 of ASPM has emerged as a critical regulator of cancer stemness and aggressiveness in various malignant tumor types. Here, we describe the domain compositions of ASPM and its transcript variants and overview their expression patterns and prognostic significance in cancers. A summary is provided of recent progress in the molecular elucidation of ASPM as a regulatory hub of development- and stemness-associated signaling pathways, such as the Wnt, Hedgehog, and Notch pathways, and of DNA DSB repair in cancer cells. The review emphasizes the potential utility of ASPM as a cancer-agnostic and pathway-informed prognostic biomarker and therapeutic target.

JGI Plant Gene Atlas: an updateable transcriptome resource to improve functional gene descriptions across the plant kingdom

Gene functional descriptions offer a crucial line of evidence for candidate genes underlying trait variation. Conversely, plant responses to environmental cues represent important resources to decipher gene function and subsequently provide molecular targets for plant improvement through gene editing. However, biological roles of large proportions of genes across the plant phylogeny are poorly annotated. Here we describe the Joint Genome Institute (JGI) Plant Gene Atlas, an updateable data resource consisting of transcript abundance assays spanning 18 diverse species. To integrate across these diverse genotypes, we analyzed expression profiles, built gene clusters that exhibited tissue/condition specific expression, and tested for transcriptional response to environmental queues. We discovered extensive phylogenetically constrained and condition-specific expression profiles for genes without any previously documented functional annotation. Such conserved expression patterns and tightly co-expressed gene clusters let us assign expression derived additional biological information to 64 495 genes with otherwise unknown functions. The ever-expanding Gene Atlas resource is available at JGI Plant Gene Atlas (https://plantgeneatlas.jgi.doe.gov) and Phytozome (https://phytozome.jgi.doe.gov/), providing bulk access to data and user-specified queries of gene sets. Combined, these web interfaces let users access differentially expressed genes, track orthologs across the Gene Atlas plants, graphically represent co-expressed genes, and visualize gene ontology and pathway enrichments.

A co-formulation of interferons alpha2b and gamma distinctively targets cell cycle in the glioblastoma-derived cell line U-87MG

BACKGROUND

HeberFERON is a co-formulation of α2b and γ interferons, based on their synergism, which has shown its clinical superiority over individual interferons in basal cell carcinomas. In glioblastoma (GBM), HeberFERON has displayed promising preclinical and clinical results. This led us to design a microarray experiment aimed at identifying the molecular mechanisms involved in the distinctive effect of HeberFERON compared to the individual interferons in U-87MG model.

METHODS

Transcriptional expression profiling including a control (untreated) and three groups receiving α2b-interferon, γ-interferon and HeberFERON was performed using an Illumina HT-12 microarray platform. Unsupervised methods for gene and sample grouping, identification of differentially expressed genes, functional enrichment and network analysis computational biology methods were applied to identify distinctive transcription patterns of HeberFERON. Validation of most representative genes was performed by qPCR. For the cell cycle analysis of cells treated with HeberFERON for 24 h, 48 and 72 h we used flow cytometry.

RESULTS

The three treatments show different behavior based on the gene expression profiles. The enrichment analysis identified several mitotic cell cycle related events, in particular from prometaphase to anaphase, which are exclusively targeted by HeberFERON. The FOXM1 transcription factor network that is involved in several cell cycle phases and is highly expressed in GBMs, is significantly down regulated. Flow cytometry experiments corroborated the action of HeberFERON on the cell cycle in a dose and time dependent manner with a clear cellular arrest as of 24 h post-treatment. Despite the fact that p53 was not down-regulated, several genes involved in its regulatory activity were functionally enriched. Network analysis also revealed a strong relationship of p53 with genes targeted by HeberFERON. We propose a mechanistic model to explain this distinctive action, based on the simultaneous activation of PKR and ATF3, p53 phosphorylation changes, as well as its reduced MDM2 mediated ubiquitination and export from the nucleus to the cytoplasm. PLK1, AURKB, BIRC5 and CCNB1 genes, all regulated by FOXM1, also play central roles in this model. These and other interactions could explain a G2/M arrest and the effect of HeberFERON on the proliferation of U-87MG.

CONCLUSIONS

We proposed molecular mechanisms underlying the distinctive behavior of HeberFERON compared to the treatments with the individual interferons in U-87MG model, where cell cycle related events were highly relevant.

The neurological and non-neurological roles of the primary microcephaly-associated protein ASPM

Primary microcephaly (MCPH), is a neurological disorder characterized by small brain size that results in numerous developmental problems, including intellectual disability, motor and speech delays, and seizures. Hitherto, over 30 MCPH causing genes (MCPHs) have been identified. Among these MCPHs, MCPH5, which encodes abnormal spindle-like microcephaly-associated protein (ASPM), is the most frequently mutated gene. ASPM regulates mitotic events, cell proliferation, replication stress response, DNA repair, and tumorigenesis. Moreover, using a data mining approach, we have confirmed that high levels of expression of ASPM correlate with poor prognosis in several types of tumors. Here, we summarize the neurological and non-neurological functions of ASPM and provide insight into its implications for the diagnosis and treatment of MCPH and cancer.

Co-Expression Network Analysis: A Future Approach for Pest Control Target Discovery

The striped stem borer (SSB, Chilo suppressalis Walker) is a major pest of rice worldwide. Double-stranded RNAs (dsRNAs) targeting essential genes can trigger a lethal RNA interference (RNAi) response in insect pests. In this study, we applied a Weighted Gene Co-expression Network Analysis (WGCNA) to diet-based RNA-Seq data as a method to facilitate the discovery of novel target genes for pest control. Nieman-Pick type c 1 homolog b (NPC1b) was identified as the gene with the highest correlation values to hemolymph cholesterol levels and larval size. Functional characterization of the gene supported CsNPC1b expression with dietary cholesterol uptake and insect growth. This study revealed the critical role of NPC1b for intestinal cholesterol absorption in lepidopteran insects and highlights the utility of the WGCNA approach for identifying new pest management targets.

Maternal embryonic leucine zipper kinase in tumor cell and tumor microenvironment: Emerging player and promising therapeutic opportunities

Maternal embryonic leucine zipper kinase (MELK) is a member of the AMPK (AMP-activated protein kinase) protein family, which is widely and highly expressed in multiple cancer types. Through direct and indirect interactions with other proteins, it mediates various cascades of signal transduction processes and plays an important role in regulating tumor cell survival, growth, invasion and migration and other biological functions. Interestingly, MELK also plays an important role in the regulation of the tumor microenvironment, which can not only predict the responsiveness of immunotherapy, but also affect the function of immune cells to regulate tumor progression. In addition, more and more small molecule inhibitors have been developed for the target of MELK, which exert important anti-tumor effects and have achieved excellent results in a number of clinical trials. In this review, we outline the structural features, molecular biological functions, potential regulatory mechanisms and important roles of MELK in tumors and tumor microenvironment, as well as substances targeting MELK. Although many molecular mechanisms of MELK in the process of tumor regulation are still unknown, it is worth affirming that MELK is a potential tumor molecular therapeutic target, and its unique superiority and important role provide clues and confidence for subsequent basic research and scientific transformation.

ASPM promotes migration and invasion of anaplastic thyroid carcinoma by stabilizing KIF11

Abnormal spindle-like microcephaly-associated (ASPM) protein is crucial to the mitotic spindle function during cell replication and tumor progression in multiple tumor types. However, the effect of ASPM in anaplastic thyroid carcinoma (ATC) has not yet been understood. The present study is to elucidate the function of ASPM in the migration and invasion of ATC. ASPM expression is incrementally upregulated in ATC tissues and cell lines. Knockout (KO) of ASPM pronouncedly attenuates the migration and invasion of ATC cells. ASPM KO significantly reduces the transcript levels of Vimentin, N-cadherin, and Snail and increases E-cadherin and Occludin, thereby inhibiting epithelial-to-mesenchymal transition (EMT). Mechanistically, ASPM regulates the movement of ATC cells by inhibiting the ubiquitin degradation of KIF11 and thus stabilizing it via direct binding to it. Moreover, xenograft tumors in nude mice proved that KO of ASPM could ameliorate tumorigenesis and tumor growth accompanied by a decreased protein expression of KIF11 and an inhibition of EMT. In conclusion, ASPM is a potentially useful therapeutic target for ATC. Our results also reveal a novel mechanism by which ASPM inhibits the ubiquitin process in KIF11.

Predicting the Clinical Outcome of Triple-Negative Breast Cancer Based on the Gene Expression Characteristics of Necroptosis and Different Molecular Subtypes

Necroptosis, a kind of programmed necrotic cell apoptosis, is the gatekeeper for the host to defend against the invasion of pathogens. It helps to regulate different biological processes regarding human cancer. Nevertheless, studies that determine the impact of death on triple-negative breast cancer (TNBC) are scarce. Therefore, this paper has comprehensively examined the expression as well as clinical significance of necroptosis in TNBC. ConsensusClusterPlus was used to establish a stable molecular classification that used the expression regarding the necroptosis-linked genes. The clinical and immune characteristics of different subclasses were evaluated. Then, the weighted gene coexpression network analysis (WGCNA) assisted in determining key modules, and we selected the genes exhibiting obvious association with necroptosis prognosis through the relationship with prognosis. The univariate Cox regression analysis together with least absolute shrinkage and selection operator (LASSO) techniques served for the construction of the necroptosis-related prognostic risk score (NPRS) model, and the pathway characteristics of NPRS model grouping were further studied. Finally, the NPRS, taking into account the clinicopathological features, used the decision tree model for enhancing the prognostic model as well as the survival prediction. First, two stable molecular subtypes with different prognosis and immune characteristics were identified using necroptosis marker genes. Then, the key modules were identified, and 10 genes significantly related to the prognosis of necroptosis were selected. Then, the clinical prognostic model of NPRS was developed considering the prognosis-linked necroptosis genes. Finally, the NPRS model, taking into account the clinicopathological features, adopted the decision tree model for enhancing the prognostic model as well as the survival prediction. Herein, two new molecular subgroups considering necroptosis-linked genes are proposed, and an NPRS model composed of 10 genes is developed, which maybe assist in the personalized treatment and clinical treatment guidance of TNBC patients.

OGT and FLAD1 Genes Had Significant Prognostic Roles in Progressive Pathogenesis in Prostate Cancer

PURPOSE

This study aimed to identify metabolic genes associated with non-metastatic prostate cancer progression using The Cancer Genome Atlas (TCGA) datasets and validate their prognostic role by assessing patients' immunohistochemical prostatectomy specimens.

MATERIALS AND METHODS

Several metabolic candidate genes analyzed were highly correlated with cancer progression to biochemical recurrence (BCR) and deaths in 335 patients' genetic information from TCGA datasets. Those candidate genes and their expressions in tissue specimens were validated retrospectively by immunohistochemical analysis of radical prostatectomy specimens collected from 514 consecutive patients with non-metastatic prostate cancer between 2000 and 2015. The Cox proportional-hazards model was used to predict the prognostic role of each candidate gene expression in BCR and survival prognoses with a statistical significance of p-value <0.05. Twenty metabolic genes were identified by own developed software (Targa; https://github.com/cgab-ncc/TarGA), whose median expression levels consistently increased with cancer progression to the BCR and deaths.

RESULTS

Five metabolic genes (MAT2A, FLAD1, UGDH, OGT, and RRM2) were found to be significantly involved in the overall survival in the TCGA dataset. The immunohistochemical validation and clinicopathological data showed that OGT (hazard ratio [HR], 1.002; 95% confidence interval [CI], 1.001-1.003) and FLAD1 (HR, 1.010; 95% CI, 1.003-1.017) remained significant factors for BCR and cancer-specific survival, respectively, in the multivariate analysis even after adjusting for confounding clinicopathological parameters (p<0.05).

CONCLUSIONS

OGT and FLAD1 showed significant prognostic factors of disease progression, even after adjustment for confounding clinicopathological parameters in non-metastatic prostate cancer.

Time-restricted eating alters the 24-hour profile of adipose tissue transcriptome in men with obesity

OBJECTIVE

Time-restricted eating (TRE) restores circadian rhythms in mice, but the evidence to support this in humans is limited. The objective of this study was to investigate the effects of TRE on 24-hour profiles of plasma metabolites, glucoregulatory hormones, and the subcutaneous adipose tissue (SAT) transcriptome in humans.

METHODS

Men (n = 15, age = 63 [4] years, BMI 30.5 [2.4] kg/m2 ) were recruited. A 35-hour metabolic ward stay was conducted at baseline and after 8 weeks of 10-hour TRE. Assessment included 24-hour profiles of plasma glucose, nonesterified fatty acid (NEFA), triglyceride, glucoregulatory hormones, and the SAT transcriptome. Dim light melatonin onset and cortisol area under the curve were calculated.

RESULTS

TRE did not alter dim light melatonin onset but reduced morning cortisol area under the curve. TRE altered 24-hour profiles of insulin, NEFA, triglyceride, and glucose-dependent insulinotropic peptide and increased transcripts of circadian locomotor output cycles protein kaput (CLOCK) and nuclear receptor subfamily 1 group D member 2 (NR1D2) and decreased period circadian regulator 1 (PER1) and nuclear receptor subfamily 1 group D member 1 (NR1D1) at 12:00 am. The rhythmicity of 450 genes was altered by TRE, which enriched in transcripts for transcription corepressor activity, DNA-binding transcription factor binding, regulation of chromatin organization, and small GTPase binding pathways. Weighted gene coexpression network analysis revealed eigengenes that were correlated with BMI, insulin, and NEFA.

CONCLUSIONS

TRE restored 24-hour profiles in hormones, metabolites, and genes controlling transcriptional regulation in SAT, which could underpin its metabolic health benefit.

A class of anti-inflammatory lipids decrease with aging in the central nervous system

Lipids contribute to the structure, development, and function of healthy brains. Dysregulated lipid metabolism is linked to aging and diseased brains. However, our understanding of lipid metabolism in aging brains remains limited. Here we examined the brain lipidome of mice across their lifespan using untargeted lipidomics. Co-expression network analysis highlighted a progressive decrease in 3-sulfogalactosyl diacylglycerols (SGDGs) and SGDG pathway members, including the potential degradation products lyso-SGDGs. SGDGs show an age-related decline specifically in the central nervous system and are associated with myelination. We also found that an SGDG dramatically suppresses LPS-induced gene expression and release of pro-inflammatory cytokines from macrophages and microglia by acting on the NF-κB pathway. The detection of SGDGs in human and macaque brains establishes their evolutionary conservation. This work enhances interest in SGDGs regarding their roles in aging and inflammatory diseases and highlights the complexity of the brain lipidome and potential biological functions in aging.

Identifying key multifunctional components shared by critical cancer and normal liver pathways via SparseGMM

Despite the abundance of multimodal data, suitable statistical models that can improve our understanding of diseases with genetic underpinnings are challenging to develop. Here, we present SparseGMM, a statistical approach for gene regulatory network discovery. SparseGMM uses latent variable modeling with sparsity constraints to learn Gaussian mixtures from multiomic data. By combining coexpression patterns with a Bayesian framework, SparseGMM quantitatively measures confidence in regulators and uncertainty in target gene assignment by computing gene entropy. We apply SparseGMM to liver cancer and normal liver tissue data and evaluate discovered gene modules in an independent single-cell RNA sequencing (scRNA-seq) dataset. SparseGMM identifies PROCR as a regulator of angiogenesis and PDCD1LG2 and HNF4A as regulators of immune response and blood coagulation in cancer. Furthermore, we show that more genes have significantly higher entropy in cancer compared with normal liver. Among high-entropy genes are key multifunctional components shared by critical pathways, including p53 and estrogen signaling.

Susceptibility identification for seasonal influenza A/H3N2 based on baseline blood transcriptome

Introduction

Influenza susceptibility difference is a widely existing trait that has great practical significance for the accurate prevention and control of influenza.

Methods

Here, we focused on the human susceptibility to the seasonal influenza A/H3N2 of healthy adults at baseline level. Whole blood expression data for influenza A/H3N2 susceptibility from GEO were collected firstly (30 symptomatic and 19 asymptomatic). Then to explore the differences at baseline, a suite of systems biology approaches - the differential expression analysis, co-expression network analysis, and immune cell frequencies analysis were utilized.

Results

We found the baseline condition, especially immune condition between symptomatic and asymptomatic, was different. Co-expression module that is positively related to asymptomatic is also related to immune cell type of naïve B cell. Function enrichment analysis showed significantly correlation with "B cell receptor signaling pathway", "immune response-activating cell surface receptor signaling pathway" and so on. Also, modules that are positively related to symptomatic are also correlated to immune cell type of neutrophils, with function enrichment analysis showing significantly correlations with "response to bacterium", "inflammatory response", "cAMP-dependent protein kinase complex" and so on. Responses of symptomatic and asymptomatic hosts after virus exposure show differences on resisting the virus, with more effective frontline defense for asymptomatic hosts. A prediction model was also built based on only baseline transcription information to differentiate symptomatic and asymptomatic population with accuracy of 0.79.

Discussion

The results not only improve our understanding of the immune system and influenza susceptibility, but also provide a new direction for precise and targeted prevention and therapy of influenza.

Identification and Validation of a Necroptosis-Related Prognostic Signature for Kidney Renal Clear Cell Carcinoma

Background

Necroptosis is progressively becoming an important focus of research because of its role in the pathogenesis of cancer and other inflammatory diseases. Our study is designed to anticipate the survival time of kidney renal clear cell carcinoma (KIRC) by constructing a prognostic signature of necroptosis-related genes.

Materials

Clinical information and RNA-seq data were acquired from Renal Cell Cancer-European Union (RECA-EU) and The Cancer Genome Atlas- (TCGA-) KIRC, respectively. ConsensusClusterPlus was used to identify molecular subtypes, and the distribution of immune cell infiltration, anticancer drug sensitivity, and somatic gene mutations was studied in these subtypes. Subsequently, LASSO-Cox regression and univariate Cox regression were also carried out to construct a necroptosis-related signature. Cox regression, survival analysis, clinicopathological characteristic correlation analysis, nomogram, cancer stem cell analysis, and receiver operating characteristic (ROC) curve were some tools employed to study the prognostic power of the signature.

Results

Based on the expression patterns of 66 survival-related necroptosis genes, we classified the KIRC into three subtypes (C1, C2, and C3) that are associated with necroptosis, which had significantly different tumor stem cell components. Among these, C2 patients had a longer survival time and enhanced immune status and were more sensitive to conventional chemotherapeutic drugs. Moreover, in order to predict the prognosis of KIRC patients, five genes (BMP8A, TLCD1, CLGN, GDF7, and RARB) were used to develop a necroptosis-related prognostic signature, which had an acceptable predictive potency. The results from Cox regression and stratified survival analysis revealed that the signature was an independent prognostic factor, whereas the nomogram and calibration curve demonstrated satisfactory survival time prediction based on the risk score.

Conclusions

Three molecular subtypes and five necroptosis-related genes were discovered in KIRC using data from TCGA-KIRC and RECA-EU. Thus, a new biomarker and a potentially effective therapeutic approach for KIRC patients were provided in the current study.

Identification of Oxidative Stress-Associated Molecular Subtypes and Signature for Predicting Survival Outcome of Cervical Squamous Cell Carcinoma

Background

Cervical squamous cell carcinoma (CESC) is the gynecologic malignancy with high incidence rate and high mortality rate. Oxidative stress participates in gene regulation and malignant tumor progression, including CESC.

Methods

RNA-seq, clinical information, and genomic mutation were from The Cancer Genome Atlas- (TCGA-) CESC and GSE44001 datasets. Oxidative stress-related genes were obtained from the gene set enrichment analysis (GSEA) website. ConsensusClusterPlus was used for clustering, which was assessed by the Kaplan-Meier (KM) survival curve analysis, mutation analysis, immunocharacteristic analysis, and therapy. Prognostic signatures were built by combining weighted correlation network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) algorithm, and stepAIC. The prognostic power of this model was evaluated using the KM survival curve analysis, receiver operating characteristic (ROC) curve analysis, nomogram, and decision curve analysis (DCA).

Results

218 of the 291 CESC cases (74.91%) presented oxidative stress-related gene mutation, especially FBXW7. Three clusters were determined based on oxidative stress-related genes, among which cluster 3 (C3) presented low-frequency mutation and hyperimmune state and was sensitive to immunotherapy. This research developed a 5-gene oxidative stress-related prognostic signature and a RiskScore model. As shown by ROC analysis, in the TCGA and GSE44001 datasets, the RiskScore model showed a high prediction accuracy for 1-, 3-, and 5-year CESC overall survival. High RiskScore was associated with enhanced immune status. The nomogram model was greatly predictive of the overall survival of CESC patients.

Conclusion

Our prognostic model was based on oxidative stress-related genes in CESC, potentially aids in CESC prognosis, and provides potential targets against CESC.

Pathway-based approach reveals differential sensitivity to E2F1 inhibition in glioblastoma

Analysis of tumor gene expression is an important approach for the classification and identification of therapeutic vulnerabilities. However, targeting glioblastoma (GBM) based on molecular subtyping has not yet translated into successful therapies. Here, we present an integrative approach based on molecular pathways to expose new potentially actionable targets. We used gene set enrichment analysis (GSEA) to conduct an unsupervised clustering analysis to condense the gene expression data from bulk patient samples and patient-derived gliomasphere lines into new gene signatures. We identified key targets that are predicted to be differentially activated between tumors and were functionally validated in a library of gliomasphere cultures. Resultant cluster-specific gene signatures associated not only with hallmarks of cell cycle and stemness gene expression, but also with cell-type specific markers and different cellular states of GBM. Several upstream regulators, such as PIK3R1 and EBF1 were differentially enriched in cells bearing stem cell like signatures and bear further investigation. We identified the transcription factor E2F1 as a key regulator of tumor cell proliferation and self-renewal in only a subset of gliomasphere cultures predicted to be E2F1 signaling dependent. Our in vivo work also validated the functional significance of E2F1 in tumor formation capacity in the predicted samples. E2F1 inhibition also differentially sensitized E2F1-dependent gliomasphere cultures to radiation treatment. Our findings indicate that this novel approach exploring cancer pathways highlights key therapeutic vulnerabilities for targeting GBM.

Integrative analysis of drug response and clinical outcome in acute myeloid leukemia

Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML.

Gene expression data analysis using Hellinger correlation in weighted gene co-expression networks (WGCNA)

Weighted gene co-expression network analysis (WGCNA) is used to detect clusters with highly correlated genes. Measurements of correlation most typically rely on linear relationships. However, a linear relationship does not always model pairwise functional-related dependence between genes. In this paper, we first compared 6 different correlation methods in their ability to capture complex dependence between genes in three different tissues. Next, we compared their gene-pairwise coefficient results and corresponding WGCNA results. Finally, we applied a recently proposed correlation method, Hellinger correlation, as a more sensitive correlation measurement in WGCNA. To test this method, we constructed gene networks containing co-expression gene modules from RNA-seq data of human frontal cortex from Alzheimer's disease patients. To test the generality, we also used a microarray data set from human frontal cortex, single cell RNA-seq data from human prefrontal cortex, RNA-seq data from human temporal cortex, and GTEx data from heart. The Hellinger correlation method captures essentially similar results as other linear correlations in WGCNA, but provides additional new functional relationships as exemplified by uncovering a link between inflammation and mitochondria function. We validated the network constructed with the microarray and single cell sequencing data sets and a RNA-seq dataset of temporal cortex. We observed that this new correlation method enables the detection of non-linear biologically meaningful relationships among genes robustly and provides a complementary new approach to WGCNA. Thus, the application of Hellinger correlation to WGCNA provides a more flexible correlation approach to modelling networks in gene expression analysis that uncovers novel network relationships.

Inference of a Boolean Network From Causal Logic Implications

Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)—induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.

The DNA Double-Strand Break Repair in Glioma: Molecular Players and Therapeutic Strategies

Gliomas are the most frequent type of tumor in the central nervous system, which exhibit properties that make their treatment difficult, such as cellular infiltration, heterogeneity, and the presence of stem-like cells responsible for tumor recurrence. The response of this type of tumor to chemoradiotherapy is poor, possibly due to a higher repair activity of the genetic material, among other causes. The DNA double-strand breaks are an important type of lesion to the genetic material, which have the potential to trigger processes of cell death or cause gene aberrations that could promote tumorigenesis. This review describes how the different cellular elements regulate the formation of DNA double-strand breaks and their repair in gliomas, discussing the therapeutic potential of the induction of this type of lesion and the suppression of its repair as a control mechanism of brain tumorigenesis.

ASPM Is a Prognostic Biomarker and Correlates With Immune Infiltration in Kidney Renal Clear Cell Carcinoma and Liver Hepatocellular Carcinoma

Background

Abnormal spindle microtubule assembly (ASPM) is a centrosomal protein and that is related to a poor clinical prognosis and recurrence. However, the relationship between ASPM expression, tumor immunity, and the prognosis of different cancers remains unclear.

Methods

ASPM expression and its influence on tumor prognosis were analyzed using the Tumor Immune Estimation Resource (TIMER), UALCAN, OncoLnc, and Gene Expression Profiling Interactive Analysis (GEPIA) databases. The relationship between ASPM expression and tumor immunity was analyzed using the TIMER and GEPIA databases, and the results were further verified using qPCR, western blot, and multiplex quantitative immuno fluorescence.

Results

The results showed that ASPM expression was significantly higher in most cancer tissues than in corresponding normal tissues, including kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), and breast invasive carcinoma (BRCA). ASPM expression was significantly higher in late-stage cancers than in early-stages cancers (e.g., KIRC, KIRP, LIHC, LUAD, and BRCA; p < 0.05), demonstrating a possible role of ASPM in cancer progression and invasion. Moreover, our data showed that high ASPM expression was associated with poor overall survival, and disease-specific survival in KIRC and LIHC (p < 0.05). Besides, Cox hazard regression analysis results showed that ASPM may be an independent prognostic factor for KIRC and LIHC. ASPM expression showed a strong correlation with tumor-infiltrating B cells, CD8⁺ T cells, and M2 macrophages in KIRC and LIHC.

Conclusions

These findings demonstrate that the high expression of ASPM indicates poor prognosis as well as increased levels of immune cell infiltration in KIRC and LIHC. ASPM expression may serve as a novel prognostic biomarker for both the clinical outcome and immune cell infiltration in KIRC and LIHC.

Coding and non-coding co-expression network analysis identifies key modules and driver genes associated with precursor lesions of gastric cancer

BACKGROUND

Helicobacter pylori infection is the most important risk factor for gastric cancer (GC). Human gastric adenocarcinoma develops after long-term H. pylori infection via the Correa cascade. This carcinogenic pathway describes the progression from gastritis to atrophy, intestinal metaplasia (IM), dysplasia and GC. Patients with atrophy and intestinal metaplasia are considered to have precancerous lesions of GC (PLGC). H. pylori eradication and endoscopy surveillance are currently the only interventions for preventing GC. Better knowledge of the biology of human PLGC may help find stratification markers and contribute to better understanding of biological mechanisms. One way to achieve this is by using co-expression network analysis. Weighted gene co-expression network analysis (WGCNA) is often used to identify modules from co-expression networks and relate them to clinical traits. It also allows identification of driver genes that may be critical for PLGC.

AIM

The purpose of this study was to identify co-expression modules and differential gene expression in dyspeptic patients at different stages of the Correa pathway.

METHODS

We studied 96 gastric biopsies from 78 patients that were clinically classified as: non-active (n = 10) and chronic-active gastritis (n = 20), atrophy (n = 12), and IM (n = 36). Gene expression of coding RNAs was determined by microarrays and non-coding RNAs by RNA-seq. The WGCNA package was used for network construction, module detection, module preservation and hub and driver gene selection.

RESULTS

WGCNA identified 20 modules for coding RNAs and 4 for each miRNA and small RNA class. Modules were associated with antrum and corpus gastric locations, chronic gastritis and IM. Notably, coding RNA modules correlated with the Correa cascade. One was associated with the presence of H. pylori. In three modules, the module eigengene (ME) gradually increased in the stages toward IM, while in three others the inverse relationship was found. One miRNA module was negatively correlated to IM and was used for a mRNA-miRNA integration analysis. WGCNA also uncovered driver genes. Driver genes show both high connectivity within a module and are significantly associated with clinical traits. Some of those genes have been previously involved in H. pylori carcinogenesis, but others are new. Lastly, using similar external transcriptomic data, we confirmed that the discovered mRNA modules were highly preserved.

CONCLUSION

Our analysis captured co-expression modules that provide valuable information to understand the pathogenesis of the progression of PLGC.

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application

Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through  GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.

Identification of Potential Candidate Genes From Co-Expression Module Analysis During Preadipocyte Differentiation in Landrace Pig

Preadipocyte differentiation plays an important role in lipid deposition and affects fattening efficiency in pigs. In the present study, preadipocytes isolated from the subcutaneous adipose tissue of three Landrace piglets were induced into mature adipocytes in vitro. Gene clusters associated with fat deposition were investigated using RNA sequencing data at four time points during preadipocyte differentiation. Twenty-seven co-expression modules were subsequently constructed using weighted gene co-expression network analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses revealed three modules (blue, magenta, and brown) as being the most critical during preadipocyte differentiation. Based on these data and our previous differentially expressed gene analysis, angiopoietin-like 4 (ANGPTL4) was identified as a key regulator of preadipocyte differentiation and lipid metabolism. After inhibition of ANGPTL4, the expression of adipogenesis-related genes was reduced, except for that of lipoprotein lipase (LPL), which was negatively regulated by ANGPTL4 during preadipocyte differentiation. Our findings provide a new perspective to understand the mechanism of fat deposition.

Gene Co-expression Analysis of the Human Substantia Nigra Identifies ZNHIT1 as an SNCA Co-expressed Gene that Protects Against α-Synuclein-Induced Impairments in Neurite Growth and Mitochondrial Dysfunction in SH-SY5Y Cells

Parkinson’s disease (PD) is neurodegenerative disorder with the pathological hallmarks of progressive degeneration of midbrain dopaminergic neurons from the substantia nigra (SN), and accumulation and spread of inclusions of aggregated α-synuclein (α-Syn). Since current PD therapies do not prevent neurodegeneration, there is a need to identify therapeutic targets that can prevent α-Syn-induced reductions in neuronal survival and neurite growth. We hypothesised that genes that are normally co-expressed with the α-Syn gene (SNCA), and whose co-expression pattern is lost in PD, may be important for protecting against α-Syn-induced dopaminergic degeneration, since broken correlations can be used as an index of functional misregulation. Gene co-expression analysis of the human SN showed that nuclear zinc finger HIT-type containing 1 (ZNHIT1) is co-expressed with SNCA and that this co-expression pattern is lost in PD. Overexpression of ZNHIT1 was found to increase deposition of the H2A.Z histone variant in SH-SY5Y cells, to promote neurite growth and to prevent α-Syn-induced reductions in neurite growth and cell viability. Analysis of ZNHIT1 co-expressed genes showed significant enrichment in genes associated with mitochondrial function. In agreement, bioenergetic state analysis of mitochondrial function revealed that ZNHIT1 increased cellular ATP synthesis. Furthermore, α-Syn-induced impairments in basal respiration, maximal respiration and spare respiratory capacity were not seen in ZNHIT1-overexpressing cells. These data show that ZNHIT1 can protect against α-Syn-induced degeneration and mitochondrial dysfunction, which rationalises further investigation of ZNHIT1 as a therapeutic target for PD.

Overexpression of abnormal spindle-like microcephaly-associated (ASPM) increases tumor aggressiveness and predicts poor outcome in patients with lung adenocarcinoma

Background

Cumulative evidence points to abnormal spindle-like microcephaly-associated (ASPM) protein being overexpressed in various cancers, and the aberrant expression of ASPM has been shown to promote cancer tumorigenicity and progression. However, its role and clinical significance in lung adenocarcinoma (LUAD) remains unclear. This study aimed to determine the expression patterns of ASPM and its clinical significance in LUAD.

Methods

In total, 4 original worldwide LUAD microarray mRNA expression datasets (N=1,116) with clinical and follow-up annotations were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The expression of ASPM protein in LUAD patients was detected by immunohistochemistry. Survival analysis and Cox regression analysis were used to examine the prognostic value of ASPM expression. Gene set enrichment analysis (GSEA) was performed to investigate the relationship between ASPM and LUAD.

Results

Dataset analyses and immunohistochemistry revealed that ASPM expression was significantly higher in the LUAD tissues compared with normal lung tissues, especially in the advanced tumor stage. Additionally, overexpression of ASPM was significantly correlated with shorter overall survival (OS) and relapse-free survival (RFS) in LUAD. Univariate and multivariate Cox regression analyses revealed that the overexpression of ASPM was a potential independent predictor of poor OS and RFS. However, ASPM overexpression was not significantly associated with predicting OS in lung squamous cell carcinoma. GSEA analysis demonstrated that ASPM was significantly enriched in the cell cycle, DNA replication, homologous recombination, RNA degradation, mismatch repair, and p53 signaling pathways.

Conclusions

These findings demonstrate the important role of ASPM in the tumorigenesis and progression of LUAD.

Downregulation of PIK3CB Involved in Alzheimer's Disease via Apoptosis, Axon Guidance, and FoxO Signaling Pathway

OBJECTIVE

To investigate the molecular function of phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit beta (PIK3CB) underlying Alzheimer's disease (AD).

METHODS

RNA sequencing data were used to filtrate differentially expressed genes (DEGs) in AD/nondementia control and PIK3CB-low/high groups. An unbiased coexpression network was established to evaluate module-trait relationships by using weight gene correlation network analysis (WGCNA). Global regulatory network was constructed to predict the protein-protein interaction. Further cross-talking pathways of PIK3CB were identified by functional enrichment analysis.

RESULTS

The mean expression of PIK3CB in AD patients was significantly lower than those in nondementia controls. We identified 2,385 DEGs from 16,790 background genes in AD/control and PIK3CB-low/high groups. Five coexpression modules were established using WGCNA, which participated in apoptosis, axon guidance, long-term potentiation (LTP), regulation of actin cytoskeleton, synaptic vesicle cycle, FoxO, mitogen-activated protein kinase (MAPK), and vascular endothelial growth factor (VEGF) signaling pathways. DEGs with strong relation to AD and low PIK3CB expression were extracted to construct a global regulatory network, in which cross-talking pathways of PIK3CB were identified, such as apoptosis, axon guidance, and FoxO signaling pathway. The occurrence of AD could be accurately predicted by low PIK3CB based on the area under the curve of 71.7%.

CONCLUSIONS

These findings highlight downregulated PIK3CB as a potential causative factor of AD, possibly mediated via apoptosis, axon guidance, and FoxO signaling pathway.

Identified Three Interferon Induced Proteins as Novel Biomarkers of Human Ischemic Cardiomyopathy

Ischemic cardiomyopathy is the most frequent type of heart disease, and it is a major cause of myocardial infarction (MI) and heart failure (HF), both of which require expensive medical treatment. Precise biomarkers and therapy targets must be developed to enhance improve diagnosis and treatment. In this study, the transcriptional profiles of 313 patients' left ventricle biopsies were obtained from the PubMed database, and functional genes that were significantly related to ischemic cardiomyopathy were screened using the Weighted Gene Co-Expression Network Analysis and protein-protein interaction (PPI) networks enrichment analysis. The rat myocardial infarction model was developed to validate these findings. Finally, the putative signature genes were blasted through the common Cardiovascular Disease Knowledge Portal to explore if they were associated with cardiovascular disorder. Three interferon stimulated genes (IFIT2, IFIT3 and IFI44L), as well as key pathways, have been identified as potential biomarkers and therapeutic targets for ischemic cardiomyopathy, and their alternations or mutations have been proven to be strongly linked to cardiac disorders. These novel signature genes could be utilized as bio-markers or potential therapeutic objectives in precise clinical diagnosis and treatment of ischemic cardiomyopathy.

A novel approach to co-expression network analysis identifies modules and genes relevant for moulting and development in the Atlantic salmon louse (Lepeophtheirus salmonis)

Background

The salmon louse (Lepeophtheirus salmonis) is an obligate ectoparasitic copepod living on Atlantic salmon and other salmonids in the marine environment. Salmon lice cause a number of environmental problems and lead to large economical losses in aquaculture every year. In order to develop novel parasite control strategies, a better understanding of the mechanisms of moulting and development of the salmon louse at the transcriptional level is required.

Methods

Three weighted gene co-expression networks were constructed based on the pairwise correlations of salmon louse gene expression profiles at different life stages. Network-based approaches and gene annotation information were applied to identify genes that might be important for the moulting and development of the salmon louse. RNA interference was performed for validation. Regulatory impact factors were calculated for all the transcription factor genes by examining the changes in co-expression patterns between transcription factor genes and deferentially expressed genes in middle stages and moulting stages.

Results

Eight gene modules were predicted as important, and 10 genes from six of the eight modules have been found to show observable phenotypes in RNA interference experiments. We knocked down five hub genes from three modules and observed phenotypic consequences in all experiments. In the infection trial, no copepodids with a RAB1A-like gene knocked down were found on fish, while control samples developed to chalimus-1 larvae. Also, a FOXO-like transcription factor obtained highest scores in the regulatory impact factor calculation.

Conclusions

We propose a gene co-expression network-based approach to identify genes playing an important role in the moulting and development of salmon louse. The RNA interference experiments confirm the effectiveness of our approach and demonstrated the indispensable role of a RAB1A-like gene in the development of the salmon louse. We propose that our approach could be generalized to identify important genes associated with a phenotype of interest in other organisms.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-021-08054-7).

Gene correlation network analysis to identify regulatory factors in sciatic nerve injury

BACKGROUND

Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.

METHODS

Sciatic nerve injury dataset GSE18803 with 24 samples was divided into adult group and neonate group. Weighted gene co-expression network analysis (WGCNA) was carried out to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network and protein-protein interaction (PPI) network were overlapped to identify hub genes, while functional enrichment and Reactome analysis were used for a comprehensive analysis of potential pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Gene set enrichment analysis (GSEA) was performed separately using difference between samples and gene expression level as phenotype label to further prove SNI-related signaling pathways. In addition, immune infiltration analysis was accomplished by CIBERSORT. Finally, Drug-Gene Interaction database (DGIdb) was employed for predicting the possible therapeutic agents.

RESULTS

14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of hub genes, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance were mostly related to the cytokine-cytokine receptor interaction. Analysis of hub genes possible related signaling pathways using gene expression level as phenotype label revealed an enrichment involved in Lysosome, Chemokine signaling pathway, and Neurotrophin signaling pathway. Immune infiltration analysis showed that Macrophages M2 and Regulatory T cells may participate in the development of SNI. At last, 25 drugs were screened from DGIdb to improve SNI treatment.

CONCLUSIONS

The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more light on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

Inhibiting microcephaly genes as alternative to microtubule targeting agents to treat brain tumors

Medulloblastoma (MB) and gliomas are the most frequent high-grade brain tumors (HGBT) in children and adulthood, respectively. The general treatment for these tumors consists in surgery, followed by radiotherapy and chemotherapy. Despite the improvement in patient survival, these therapies are only partially effective, and many patients still die. In the last decades, microtubules have emerged as interesting molecular targets for HGBT, as various microtubule targeting agents (MTAs) have been developed and tested pre-clinically and clinically with encouraging results. Nevertheless, these treatments produce relevant side effects since they target microtubules in normal as well as in cancerous cells. A possible strategy to overcome this toxicity could be to target proteins that control microtubule dynamics but are required by HGBT cells much more than in normal cell types. The genes mutated in primary hereditary microcephaly (MCPH) are ubiquitously expressed in proliferating cells, but under normal conditions are selectively required during brain development, in neural progenitors. There is evidence that MB and glioma cells share molecular profiles with progenitors of cerebellar granules and of cortical radial glia cells, in which MCPH gene functions are fundamental. Moreover, several studies indicate that MCPH genes are required for HGBT expansion. Among the 25 known MCPH genes, we focus this review on KNL1, ASPM, CENPE, CITK and KIF14, which have been found to control microtubule stability during cell division. We summarize the current knowledge about the molecular basis of their interaction with microtubules. Moreover, we will discuss data that suggest these genes are promising candidates as HGBT-specific targets.

Network-based approach to identify prognosis-related genes in tamoxifen-treated patients with estrogen receptor-positive breast cancer

Tamoxifen is an estrogen receptor (ER) antagonist that is most commonly used for the treatment of ER-positive breast cancer. However, tamoxifen resistance remains a major cause of cancer recurrence and progression. Here, we aimed to identify hub genes implicated in the progression and prognosis of ER-positive breast cancer following tamoxifen treatment. Microarray data (GSE9893) for 155 tamoxifen-treated primary ER-positive breast cancer samples were obtained from the Gene Expression Omnibus database. In total, 1706 differentially expressed genes (DEGs), including 859 up-regulated and 847 down-regulated genes, were identified between relapse and relapse-free samples. Weighted correlation network analysis clustered genes into 13 modules, among which the tan and blue modules were the most significantly related to prognosis. From these two modules, we further identified and validated two prognosis-related hub genes (G-rich RNA sequence binding factor 1 (GRSF1) and microtubule-associated protein τ (MAPT)) via survival analysis based on several publicly available datasets. High expression of GRSF1 predicted poor prognosis, whereas MAPT indicated favorable outcomes in ER-positive breast cancer. Using breast cancer cell lines and tissue samples, we confirmed that GRSF1 was significantly up-regulated and MAPT was down-regulated in the tamoxifen-resistant group compared with the tamoxifen-sensitive group. The prognostic value of GRSF1 and MAPT was also verified in 48 tamoxifen-treated ER-positive breast cancer patients in our hospital. Gene set enrichment analysis (GSEA) suggested that GRSF1 was potentially involved in RNA degradation and cell cycle pathways, while MAPT was strongly linked to immune-related signaling pathways. Taken together, our findings established novel prognostic biomarkers to predict tamoxifen sensitivity, which may facilitate individualized management of breast cancer.

Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

AIM

This study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer.

METHODS

Based on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve.

RESULTS

We screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable.

CONCLUSIONS

This study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

N6-methyladenosine (m6A) modification and its clinical relevance in cognitive dysfunctions

BACKGROUND

N6 adenosine methylation (m6A) is the most abundant internal RNA modification in eukaryotic cells. Dysregulation of m6A has been associated with the perturbations of cell proliferation and cell death in different diseases. However, the roles of m6A in the neurodegenerative process and cognitive dysfunction are unclear.

METHODS

We systematically investigated the molecular alterations of m6A regulators and their clinical relevance with cognitive dysfunctions using published datasets of Alzheimer's Disease (AD), vascular dementia, and mild cognitive impairment (MCI).

FINDINGS

The expressions of m6A regulators vary in different tissues and closely correlate with neurodegenerative pathways. We identified co-expressive m6A regulators SNRPG and SNRPD2 as potential biomarkers to predict transformation from MCI to AD. Moreover, we explored correlations between Apolipoprotein E4 and m6A methylations.

INTERPRETATION

Collectively, these findings suggest that m6A methylations as potential biomarkers and therapeutic targets for cognitive dysfunction.

FUNDING

This work was supported by the National Natural Science Foundation of China (81871040) and the Shanghai Health System Talent Training Program (2018BR29).

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.

ASPM promotes hepatocellular carcinoma progression by activating Wnt/β-catenin signaling through antagonizing autophagy-mediated Dvl2 degradation

Hepatocellular carcinoma (HCC) is one of the most fatal cancers worldwide. In this article, we show that expression of abnormal spindle‐like microcephaly‐associated protein (ASPM) is up‐regulated in liver cancer samples, and this up‐regulation is significantly associated with tumor aggressiveness and reduced survival times of patients. Down‐regulation of ASPM expression inhibits the proliferation, invasion, migration and epithelial‐to‐mesenchymal transition of HCC cells in vitro and inhibits tumor formation in nude mice. ASPM interacts with disheveled‐2 (Dvl2) and antagonizes autophagy‐mediated Dvl2 degradation by weakening the functional interaction between Dvl2 and the lipidated form of microtubule‐associated proteins 1A/1B light chain 3A (LC3II), thereby increasing Dvl2 protein abundance and leading to Wnt/β‐catenin signaling activation in HCC cells. Thus, our results define ASPM as a novel oncoprotein in HCC and indicate that disruption of the Wnt–ASPM–Dvl2–β‐catenin signaling axis might have potential clinical value.

Chondrocyte protein co-synthesis network analysis links ECM mechanosensing to metabolic adaptation in osteoarthritis

BACKGROUND

Knee osteoarthritis (OA) is one of the most common structural OA disorders globally. Incomplete understanding of the fundamental biological aspects of osteoarthritis underlies the current lack of effective treatment or disease modifying drugs.

RESEARCH DESIGN AND METHODS

We implemented a systems approach by making use of the statistical network concepts in Weighted Gene Co-expression Analysis to reconstruct the organization of the core proteome network in chondrocytes obtained from OA patients and healthy individuals. Protein modules reflect groups of tightly co-ordinated changes in protein abundance across healthy and OA chondrocytes.

RESULTS

The unbiased systems analysis identified extracellular matrix (ECM) mechanosensing and glycolysis as two modules that are most highly correlated with ΟΑ. The ECM module was enriched in the OA genetic risk factors tenascin-C (TNC) and collagen 11A1 (COL11A1), as well as in cartilage oligomeric matrix protein (COMP), a biomarker associated with cartilage integrity. Mapping proteins that are unique to OA or healthy chondrocytes onto the core interactome, which connects microenvironment sensing and regulation of glycolysis, identified differences in metabolic and anti-inflammatory adaptation.

CONCLUSION

The interconnection between cartilage ECM remodeling and metabolism is indicative of the dynamic chondrocyte states and their significance in osteoarthritis.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Pairwise interactions in gene expression determine a hierarchical transcriptional profile in the human brain

The orchestrated expression of thousands of genes gives rise to the complexity of the human brain. However, the structures governing these myriad gene-gene interactions remain unclear. By analyzing transcription data from more than 2000 sites in six human brains, we found that pairwise interactions between genes, without considering any higher-order interactions, are sufficient to predict the transcriptional pattern of the genome for individual brain regions and the transcriptional profile of the entire brain consisting of more than 200 areas. These findings suggest a quadratic complexity of transcriptional patterns in the human brain, which is much simpler than expected. In addition, using a pairwise interaction model, we revealed that the strength of gene-gene interactions in the human brain gives rise to the nearly maximal number of transcriptional clusters, which may account for the functional and structural richness of the brain.