Comparative proteomic analysis across the developmental stages of the Eimeria tenella

Eimeria tenella is the main pathogen responsible for coccidiosis in chickens. The life cycle of E. tenella is, arguably, the least complex of all Coccidia, with only one host. However, it presents different developmental stages, either in the environment or in the host and either intracellular or extracellular. Its signaling and metabolic pathways change with its different developmental stages. Until now, little is known about the developmental regulation and transformation mechanisms of its life cycle. In this study, protein profiles from the five developmental stages, including unsporulated oocysts (USO), partially sporulated (7 h) oocysts (SO7h), sporulated oocysts (SO), sporozoites (S) and second-generation merozoites (M2), were harvested using the label-free quantitative proteomics approach. Then the differentially expressed proteins (DEPs) for these stages were identified. A total of 314, 432, 689, and 665 DEPs were identified from the comparison of SO7h vs USO, SO vs SO7h, S vs SO, and M2 vs S, respectively. By conducting weighted gene coexpression network analysis (WGCNA), six modules were dissected. Proteins in blue and brown modules were calculated to be significantly positively correlated with the E. tenella developmental stages of sporozoites (S) and second-generation merozoites (M2), respectively. In addition, hub proteins with high intra-module degree were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway enrichment analyses revealed that hub proteins in blue modules were involved in electron transport chain and oxidative phosphorylation. Hub proteins in the brown module were involved in RNA splicing. These findings provide new clues and ideas to enhance our fundamental understanding of the molecular mechanisms underlying parasite development.

Identification of immune cell-related prognostic genes characterized by a distinct microenvironment in hepatocellular carcinoma

BACKGROUND

The development and progression of hepatocellular carcinoma (HCC) have been reported to be associated with immune-related genes and the tumor microenvironment. Nevertheless, there are not enough prognostic biomarkers and models available for clinical use. Based on seven prognostic genes, this study calculated overall survival in patients with HCC using a prognostic survival model and revealed the immune status of the tumor microenvironment (TME).

AIM

To develop a novel immune cell-related prognostic model of HCC and depict the basic profile of the immune response in HCC.

METHODS

We obtained clinical information and gene expression data of HCC from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. TCGA and ICGC datasets were used for screening prognostic genes along with developing and validating a seven-gene prognostic survival model by weighted gene coexpression network analysis and least absolute shrinkage and selection operator regression with Cox regression. The relative analysis of tumor mutation burden (TMB), TME cell infiltration, immune checkpoints, immune therapy, and functional pathways was also performed based on prognostic genes.

RESULTS

Seven prognostic genes were identified for signature construction. Survival receiver operating characteristic curve analysis showed the good performance of survival prediction. TMB could be regarded as an independent factor in HCC survival prediction. There was a significant difference in stromal score, immune score, and estimate score between the high-risk and low-risk groups stratified based on the risk score derived from the seven-gene prognostic model. Several immune checkpoints, including VTCN1 and TNFSF9, were found to be associated with the seven prognostic genes and risk score. Different combinations of checkpoint blockade targeting inhibitory CTLA4 and PD1 receptors and potential chemotherapy drugs hold great promise for specific HCC therapies. Potential pathways, such as cell cycle regulation and metabolism of some amino acids, were also identified and analyzed.

CONCLUSION

The novel seven-gene (CYTH3, ENG, HTRA3, PDZD4, SAMD14, PGF, and PLN) prognostic model showed high predictive efficiency. The TMB analysis based on the seven genes could depict the basic profile of the immune response in HCC, which might be worthy of clinical application.

Mapping the immunological battlefield in gastric cancer: prognostic implications of an immune gene expression signature

BACKGROUND

Gastric cancer (GC) is a heterogeneous malignancy with variable clinical outcomes. The immune system has been implicated in GC development and progression, highlighting the importance of immune-related gene expression patterns and their prognostic significance.

OBJECTIVE

This study aimed to identify differentially expressed immune-related genes (DEIRGs) and establish a prognostic index for GC patients using comprehensive bioinformatic analyses.

METHODS

We integrated RNA sequencing data from multiple databases and identified DEIRGs by overlapping differentially expressed genes with immune-related genes. Functional enrichment analysis was performed to uncover the biological processes and signaling pathways associated with DEIRGs. We conducted a Weighted Gene Co-expression Network Analysis (WGCNA) to identify key gene modules related to with GC. Cox regression analysis was conducted to determine independent prognostic DEIRGs for overall survival prediction. Based on these findings, we developed an immune-related gene prognostic index (IRGPI) based on these findings. The prognostic value of the IRGPI was validated using survival analysis and an independent validation cohort. Functional enrichment analysis, gene mutation analysis, and immune cell profiling were performed to gain insights into the biological functions and immune characteristics associated with the IRGPI-based subgroups.

RESULTS

We identified 493 DEIRGs significantly enriched in immune-related biological processes and signaling pathways associated with GC. WGCNA analysis revealed a significant module (turquoise module) associated with GC, revealing potential therapeutic targets. Cox regression analysis identified RNASE2, CGB5, CTLA4, and DUSP1 as independent prognostic DEIRGs. The IRGPI, incorporating the expression levels of these genes, demonstrated significant prognostic value in predicting overall survival. The IRGPI-based subgroups exhibited distinct biological functions, genetic alterations, and immune cell compositions.

CONCLUSION

Our study identified DEIRGs and established a prognostic index (IRGPI) for GC patients. The IRGPI exhibited promising prognostic potential and provided insights into GC tumor biology and immune characteristics. These findings have implications for guiding therapeutic strategies.

Establishment of a prognostic model related to tregs and natural killer cells infiltration in bladder cancer

BACKGROUND

Regulatory T cells (Tregs) and natural killer (NK) cells play an essential role in the development of bladder urothelial carcinoma (BUC).

AIM

To construct a prognosis-related model to judge the prognosis of patients with bladder cancer, meanwhile, predict the sensitivity of patients to chemotherapy and immunotherapy.

METHODS

Bladder cancer information data was obtained from The Cancer Genome Atlas and GSE32894. The CIBERSORT was used to calculate the immune score of each sample. Weighted gene co-expression network analysis was used to find genes that will have the same or similar expression patterns. Subsequently, multivariate cox regression and lasso regression was used to further screen prognosis-related genes. The prrophetic package was used to predict phenotype from gene expression data, drug sensitivity of external cell line and predict clinical data.

RESULTS

The stage and risk scores are independent prognostic factors in patients with BUC. Mutations in FGFR3 lead to an increase in Tregs percolation and affect the prognosis of the tumor, and additionally, EMP1, TCHH and CNTNAP3B in the model are mainly positively correlated with the expression of immune checkpoints, while CMTM8, SORT1 and IQSEC1 are negatively correlated with immune checkpoints and the high-risk group had higher sensitivity to chemotherapy drugs.

CONCLUSION

Prognosis-related models of bladder tumor patients, based on Treg and NK cell percolation in tumor tissue. In addition to judging the prognosis of patients with bladder cancer, it can also predict the sensitivity of patients to chemotherapy and immunotherapy. At the same time, patients were divided into high and low risk groups based on this model, and differences in genetic mutations were found between the high and low risk groups.

Lessons Learned From Parsing Genetic Risk for Schizophrenia Into Biological Pathways

The clinically heterogeneous presentation of schizophrenia is compounded by the heterogeneity of risk factors and neurobiological correlates of the disorder. Genome-wide association studies in schizophrenia have uncovered a remarkably high number of genetic variants, but the biological pathways they impact upon remain largely unidentified. Among the diverse methodological approaches employed to provide a more granular understanding of genetic risk for schizophrenia, the use of biological labels, such as gene ontologies, regulome approaches, and gene coexpression have all provided novel perspectives into how genetic risk translates into the neurobiology of schizophrenia. Here, we review the salient aspects of parsing polygenic risk for schizophrenia into biological pathways. We argue that parsed scores, compared to standard polygenic risk scores, may afford a more biologically plausible and accurate physiological modeling of the different dimensions involved in translating genetic risk into brain mechanisms, including multiple brain regions, cell types, and maturation stages. We discuss caveats, opportunities, and pitfalls inherent in the parsed risk approach.

Identification of the co-differentially expressed hub genes involved in the endogenous protective mechanism against ventilator-induced diaphragm dysfunction

BACKGROUND

In intensive care units (ICU), mechanical ventilation (MV) is commonly applied to save patients' lives. However, ventilator-induced diaphragm dysfunction (VIDD) can complicate treatment by hindering weaning in critically ill patients and worsening outcomes. The goal of this study was to identify potential genes involved in the endogenous protective mechanism against VIDD.

METHODS

Twelve adult male rabbits were assigned to either an MV group or a control group under the same anesthetic conditions. Immunostaining and quantitative morphometry were used to assess diaphragm atrophy, while RNA-seq was used to investigate molecular differences between the groups. Additionally, core module and hub genes were analyzed using WGCNA, and co-differentially expressed hub genes were subsequently discovered by overlapping the differentially expressed genes (DEGs) with the hub genes from WGCNA. The identified genes were validated by western blotting (WB) and quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

After a VIDD model was successfully built, 1276 DEGs were found between the MV and control groups. The turquoise and yellow modules were identified as the core modules, and Trim63, Fbxo32, Uchl1, Tmprss13, and Cst3 were identified as the five co-differentially expressed hub genes. After the two atrophy-related genes (Trim63 and Fbxo32) were excluded, the levels of the remaining three genes/proteins (Uchl1/UCHL1, Tmprss13/TMPRSS13, and Cst3/CST3) were found to be significantly elevated in the MV group (P < 0.05), suggesting the existence of a potential antiproteasomal, antiapoptotic, and antiautophagic mechanism against diaphragm dysfunction.

CONCLUSION

The current research helps to reveal a potentially important endogenous protective mechanism that could serve as a novel therapeutic target against VIDD.

Identification of immune-related key genes in the peripheral blood of ischaemic stroke patients using a weighted gene coexpression network analysis and machine learning

Background

The immune system plays a vital role in the pathological process of ischaemic stroke. However, the exact immune-related mechanism remains unclear. The current research aimed to identify immune-related key genes associated with ischaemic stroke.

Methods

CIBERSORT was utilized to reveal the immune cell infiltration pattern in ischaemic stroke patients. Meanwhile, a weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules significantly correlated with ischaemic stroke. The characteristic genes correlated with ischaemic stroke were identified by the following two machine learning methods: the support vector machine-recursive feature elimination (SVM-RFE) algorithm and least absolute shrinkage and selection operator (LASSO) logistic regression.

Results

The CIBERSORT results suggested that there was a decreased infiltration of naive CD4 T cells, CD8 T cells, resting mast cells and eosinophils and an increased infiltration of neutrophils, M0 macrophages and activated memory CD4 T cells in ischaemic stroke patients. Then, three significant modules (pink, brown and cyan) were identified to be significantly associated with ischaemic stroke. The gene enrichment analysis indicated that 519 genes in the above three modules were mainly involved in several inflammatory or immune-related signalling pathways and biological processes. Eight hub genes (ADM, ANXA3, CARD6, CPQ, SLC22A4, UBE2S, VIM and ZFP36) were revealed to be significantly correlated with ischaemic stroke by the LASSO logistic regression and SVM-RFE algorithm. The external validation combined with a RT‒qPCR analysis revealed that the expression levels of ADM, ANXA3, SLC22A4 and VIM were significantly increased in ischaemic stroke patients and that these key genes were positively associated with neutrophils and M0 macrophages and negatively correlated with CD8 T cells. The mean AUC value of ADM, ANXA3, SLC22A4 and VIM was 0.80, 0.87, 0.91 and 0.88 in the training set, 0.85, 0.77, 0.86 and 0.72 in the testing set and 0.87, 0.83, 0.88 and 0.91 in the validation samples, respectively.

Conclusions

These results suggest that the ADM, ANXA3, SLC22A4 and VIM genes are reliable serum markers for the diagnosis of ischaemic stroke and that immune cell infiltration plays a crucial role in the occurrence and development of ischaemic stroke.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03562-w.

Exploring biomarkers for ischemic stroke through integrated microarray data analysis

Stroke is the third leading cause of disability-adjusted life years worldwide, and drugs available for its treatment are limited. This study aimed to explore high-confidence candidate genes associated with ischemic stroke (IS) through bioinformatics analysis and identify potential diagnostic biomarkers and gene-drug interactions. Weighted gene coexpression network analysis (WGCNA) and differentially expressed genes (DEGs) were integrated to identify overlapping genes. Then, high-confidence candidate genes were screened by least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) curves were used to evaluate the diagnostic value of high-confidence candidate genes as biomarkers for IS. The NetworkAnalyst database was used to construct the TF-gene network and miRNA-TF regulatory network of the high-confidence candidate genes. The DGIdb database was used to identified gene-drug interactions. Through the comprehensive analysis of GSE58294 and GSE16561, 10 high-confidence candidate genes were identified by LASSO regression: ARG1, LY96, ABCA1, SLC22A4, CD163, TPM2, SLC25A42, ID3, FAM102A and CD79B. FAM102A had the highest diagnostic value, and the area under curve (AUC), sensitivity and specificity values were 0.974, 0.919 and 0.936, respectively. The HPA database demonstrated that 10 high-confidence candidate genes were expressed in the brain and blood in normal humans. Finally, DGIdb database analysis identified 8 gene-drug interactions. We identified IS-related diagnostic biomarkers and gene-drug interactions that potentially provide new insights into the diagnosis and treatment of IS.

Transcriptome analysis of pod mutant reveals plant hormones are important regulators in controlling pod size in peanut (Arachis hypogaea L.)

Pod size is an important yield-influencing trait in peanuts. It is affected by plant hormones and identifying the genes related to these hormones may contribute to pod-related trait improvements in peanut breeding programs. However, there is limited information on the molecular mechanisms of plant hormones that regulate pod size in peanuts. We identified a mutant with an extremely small pod (spm) from Yuanza 9102 (WT) by ⁶⁰Co γ-radiation mutagenesis. The length and width of the natural mature pod in spm were only 71.34% and 73.36% of those in WT, respectively. We performed comparative analyses for morphological characteristics, anatomy, physiology, and global transcriptome between spm and WT pods. Samples were collected at 10, 20, and 30 days after peg elongation into the soil, representing stages S1, S2, and S3, respectively. The differences in pod size between WT and spm were seen at stage S1 and became even more striking at stages S2 and S3. The cell sizes of the pods were significantly smaller in spm than in WT at stages S1, S2, and S3. These results suggested that reduced cell size may be one of the important contributors for the small pod in spm. The contents of indole-3-acetic acid (IAA), gibberellin (GA), and brassinosteroid (BR) were also significantly lower in spm pods than those in WT pods at all three stages. RNA-Seq analyses showed that 1,373, 8,053, and 3,358 differently expressed genes (DEGs) were identified at stages S1, S2, and S3, respectively. Functional analyses revealed that a set of DEGs was related to plant hormone biosynthesis, plant hormone signal transduction pathway, and cell wall biosynthesis and metabolism. Furthermore, several hub genes associated with plant hormone biosynthesis and signal transduction pathways were identified through weighted gene co-expression network analysis. Our results revealed that IAA, GA, and BR may be important regulators in controlling pod size by regulating cell size in peanuts. This study provides helpful information for the understanding of the complex mechanisms of plant hormones in controlling pod size by regulating the cell size in peanuts and will facilitate the improvement of peanut breeding.

Identification of crucial genes involved in pathogenesis of regional weakening of the aortic wall

Background

The diameter of the abdominal aortic aneurysm (AAA) is the most commonly used parameter for the prediction of occurrence of AAA rupture. However, the most vulnerable region of the aortic wall may be different from the most dilated region of AAA under pressure. The present study is the first to use weighted gene coexpression network analysis (WGCNA) to detect the coexpressed genes that result in regional weakening of the aortic wall.

Methods

The GSE165470 raw microarray dataset was used in the present study. Differentially expressed genes (DEGs) were filtered using the “limma” R package. DEGs were assessed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. WGCNA was used to construct the coexpression networks in the samples with regional weakening of the AAA wall and in the control group to detect the gene modules. The hub genes were defined in the significant functional modules, and a hub differentially expressed gene (hDEG) coexpression network was constructed with the highest confidence based on protein–protein interactions (PPIs). Molecular compound detection (MCODE) was used to identify crucial genes in the hDEG coexpression network. Crucial genes in the hDEG coexpression network were validated using the GSE7084 and GSE57691 microarray gene expression datasets.

Result

A total of 350 DEGs were identified, including 62 upregulated and 288 downregulated DEGs. The pathways were involved in immune responses, vascular smooth muscle contraction and cell–matrix adhesion of DEGs in the samples with regional weakening in AAA. Antiquewhite3 was the most significant module and was used to identify downregulated hDEGs based on the result of the most significant modules negatively related to the trait of weakened aneurysm walls. Seven crucial genes were identified and validated: ACTG2, CALD1, LMOD1, MYH11, MYL9, MYLK, and TPM2. These crucial genes were associated with the mechanisms of AAA progression.

Conclusion

We identified crucial genes that may play a significant role in weakening of the AAA wall and may be potential targets for medical therapies and diagnostic biomarkers. Further studies are required to more comprehensively elucidate the functions of crucial genes in the pathogenesis of regional weakening in AAA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-021-00200-1.

Screening of characteristic genes in ulcerative colitis by integrating gene expression profiles

Background

This study aimed to screen the feature modules and characteristic genes related to ulcerative colitis (UC) and construct a support vector machine (SVM) classifier to distinguish UC patients.

Methods

Four datasets that contained UC and control samples were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) with consistency were screened via the MetaDE method. The weighted gene coexpression network (WGCNA) was used to distinguish significant modules based on the four datasets. The protein–protein interaction network was established based on intersection genes. Enrichment analysis of Gene Ontology (GO) biological processes (BPs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were established based on DAVID. An SVM combined with recursive feature elimination was also applied to construct a disease classifier for the disease diagnosis of UC patients. The efficacy of the SVM classifier was evaluated through receiver operating characteristic curves.

Results

Twelve highly preserved modules were obtained using the WGCNA, and 2009 DEGs with significant consistency were selected using the MetaDE method. Sixteen significantly related GO BPs and 12 KEGG pathways were obtained, such as cytokine-cytokine receptor interaction, cell adhesion molecules, and leukocyte transendothelial migration. Subsequently, 41 genes were used to construct an SVM classifier, such as CXCL1, CCR2, IL1B, and IL1A. The area under the curve (AUC) was 0.999 in the training dataset, whereas the AUC was 0.886, 0.790, and 0.819 in the validation set (GSE65114, GSE37283, and GSE36807, respectively).

Conclusions

An SVM classifier based on feature genes might correctly identify healthy people or UC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-021-01940-0.

Weighted gene coexpression network analysis reveals ESR1, FLNA and Furin as hub genes for DEHP-induced prepubertal testicular injury

Di-(2-ethylhexyl) phthalate (DEHP) is an environmental endocrine disruptor that accumulates in organisms in various ways and induces male reproductive system disorders. In this study, we established a testicular injury model by gavage with different concentrations of DEHP. The testes were then collected for RNA sequencing (RNA-seq), and the results were analyzed by bioinformatics and verified by experiments. Our research results show that different concentrations of DEHP interfere with testicular development differently. Weighted gene coexpression network analysis (WGCNA) generated sixteen modules and identified the turquoise module as key. Then, estrogen receptor 1 (ESR1), filamin A (Flna) and Furin were identified as hub genes. qPCR and immunohistochemistry results revealed that all three hub genes were upregulated. We detected the locations of these genes by immunohistochemistry. ESR1 was mainly located in Leydig cells; Flna immunostaining is observed in the Leydig and some germ cells and Furin staining was seen in almost all types of testicular cells. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed enrichment mainly in MAPK signaling pathways, p53 signaling pathways, HIF-1 signaling pathways, protein processing in the endoplasmic reticulum, apoptosis, the cell cycle, RNA degradation, etc. This is the first study using WGCNA to investigate the mechanism of DEHP-induced injury in the prepubertal testis, providing new research angles to further understand the mechanism of DEHP-induced injury in the prepubertal testis.

Identification of Potential Diagnostic miRNAs Biomarkers for Alzheimer Disease Based on Weighted Gene Coexpression Network Analysis

BACKGROUND

Alzheimer disease (AD) is an age-related neurodegenerative disease that accounts for nearly three fourths of dementia cases. Searching for potential biomarkers will help clinicians in the early diagnosis and treatment of AD.

METHODS

Firstly, we downloaded detailed AD data from the Gene Expression Omnibus (GEO) database for identification of differentially expressed microribonucleic acids (DEmiRNAs) and differentially expressed messenger ribonucleic acids (DEmRNAs). Secondly, functional enrichment analysis was used to identify the biological functions of DEmRNAs. Thirdly, weighted gene coexpression network analysis was used to identify important modules and hub miRNAs. In addition, the miRNA-mRNA regulatory network was constructed. Fourthly, the GSE120584 dataset was used for electronic expression verification and diagnostic analysis. Finally, real-time polymerase chain reaction in vitro verification was performed.

RESULTS

We obtained 1005 DEmiRNAs and 97 DEmRNAs, respectively. Functional enrichment found that DEmRNAs was enriched in the N-glycan biosynthesis pathway, which was associated with AD. In the weighted gene coexpression network analysis, we found that the brown module was the optimal module. Moreover, 11 hub miRNAs were identified. A total of 216 negatively regulated miRNA-mRNA regulation effects are involved. Hub miRNAs were found to have potential diagnostic value in the receiver operating characteristic analysis.

CONCLUSION

Eleven hub miRNAs were identified, and DEmRNAs was found to be significantly enriched in the N-glycan biosynthesis pathway, which contributes to the early diagnosis and treatment of AD.

Low expression of CHRDL1 and SPARCL1 predicts poor prognosis of lung adenocarcinoma based on comprehensive analysis and immunohistochemical validation

Purpose

Exploring the molecular mechanisms of lung adenocarcinoma (LUAD) is beneficial for developing new therapeutic strategies and predicting prognosis. This study was performed to select core genes related to LUAD and to analyze their prognostic value.

Methods

Microarray datasets from the GEO (GSE75037) and TCGA-LUAD datasets were analyzed to identify differentially coexpressed genes in LUAD using weighted gene coexpression network analysis (WGCNA) and differential gene expression analysis. Functional enrichment analysis was conducted, and a protein–protein interaction (PPI) network was established. Subsequently, hub genes were identified using the CytoHubba plug-in. Overall survival (OS) analyses of hub genes were performed. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (THPA) databases were used to validate our findings. Gene set enrichment analysis (GSEA) of survival-related hub genes were conducted. Immunohistochemistry (IHC) was carried out to validate our findings.

Results

We identified 486 differentially coexpressed genes. Functional enrichment analysis suggested these genes were primarily enriched in the regulation of epithelial cell proliferation, collagen-containing extracellular matrix, transforming growth factor beta binding, and signaling pathways regulating the pluripotency of stem cells. Ten hub genes were detected using the maximal clique centrality (MCC) algorithm, and four genes were closely associated with OS. The CPTAC and THPA databases revealed that CHRDL1 and SPARCL1 were downregulated at the mRNA and protein expression levels in LUAD, whereas SPP1 was upregulated. GSEA demonstrated that DNA-dependent DNA replication and catalytic activity acting on RNA were correlated with CHRDL1 and SPARCL1 expression, respectively. The IHC results suggested that CHRDL1 and SPARCL1 were significantly downregulated in LUAD.

Conclusions

Our study revealed that survival-related hub genes closely correlated with the initiation and progression of LUAD. Furthermore, CHRDL1 and SPARCL1 are potential therapeutic and prognostic indicators of LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01933-9.

ESR1 as a recurrence-related gene in intrahepatic cholangiocarcinoma: a weighted gene coexpression network analysis

Background

Intrahepatic cholangiocarcinoma (iCCA) is the second most common malignant hepatic tumor and has a high postoperative recurrence rate and a poor prognosis. The key roles of most tumor recurrence-associated molecules in iCCA remain unclear. This study aimed to explore hub genes related to the postsurgical recurrence of iCCA.

Method

Differentially expressed genes (DEGs) between iCCA samples and normal liver samples were screened from The Cancer Genome Atlas (TCGA) database and used to construct a weighted gene coexpression network. Module-trait correlations were calculated to identify the key module related to recurrence in iCCA patients. Genes in the key module were subjected to functional enrichment analysis, and candidate hub genes were filtered through coexpression and protein–protein interaction (PPI) network analysis. Validation studies were conducted to detect the “real” hub gene. Furthermore, the biological functions and the underlying mechanism of the real hub gene in iCCA tumorigenesis and progression were determined via in vitro experiments.

Results

A total of 1019 DEGs were filtered and used to construct four coexpression modules. The red module, which showed the highest correlations with the recurrence status, family history, and day to death of patients, was identified as the key module. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses demonstrated that genes in the red module were enriched in genes and pathways related to tumorigenesis and tumor progression. We performed validation studies and identified estrogen receptor 1 (ESR1), which significantly impacted the prognosis of iCCA patients, as the real hub gene related to the recurrence of iCCA. The in vitro experiments demonstrated that ESR1 overexpression significantly suppressed cell proliferation, migration, and invasion, whereas ESR1 knockdown elicited opposite effects. Further investigation into the mechanism demonstrated that ESR1 acts as a tumor suppressor by inhibiting the JAK/STAT3 signaling pathway.

Conclusions

ESR1 was identified as the real hub gene related to the recurrence of iCCA that plays a critical tumor suppressor role in iCCA progression. ESR1 significantly impacts the prognosis of iCCA patients and markedly suppresses cholangiocarcinoma cell proliferation, migration and invasion by inhibiting JAK/STAT3 signaling pathway.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01929-5.

Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis

PURPOSE

Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values.

METHODS

Microarray datasets from the Gene Expression Omnibus (GEO) (GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein-protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated.

RESULTS

Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell-cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC.

CONCLUSIONS

Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.

The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

BACKGROUND

Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks.

METHODS

We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm.

RESULTS

We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells.

CONCLUSION

Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

Systematic analysis of molecular mechanisms of heart failure through the pathway and network-based approach

AIMS

The molecular networks and pathways involved in heart failure (HF) are still largely unknown. The present study aimed to systematically investigate the genes associated with HF, comprehensively explore their interactions and functions, and identify possible regulatory networks involved in HF.

MAIN METHODS

The weighted gene coexpression network analysis (WGCNA), crosstalk analysis, and Pivot analysis were used to identify gene connections, interaction networks, and molecular regulatory mechanisms. Functional analysis and protein-protein interaction (PPI) were performed using DAVID and STRING databases. Gene set variation analysis (GSVA) and receiver operating characteristic (ROC) curve analysis were also performed to evaluate the relationship of the hub genes with HF.

KEY FINDINGS

A total of 5968 HF-related genes were obtained to construct the co-expression networks, and 18 relatively independent and closely linked modules were identified. Pivot analysis suggested that four transcription factors and five noncoding RNAs were involved in regulating the process of HF. The genes in the module with the highest positive correlation to HF was mainly enriched in cardiac remodeling and response to stress. Five upregulated hub genes (ASPN, FMOD, NT5E, LUM, and OGN) were identified and validated. Furthermore, the GSVA scores of the five hub genes for HF had a relatively high areas under the curve (AUC).

SIGNIFICANCE

The results of this study revealed specific molecular networks and their potential regulatory mechanisms involved in HF. These may provide new insight into understanding the mechanisms underlying HF and help to identify more effective therapeutic targets for HF.

Identification of key genes controlling cancer stem cell characteristics in gastric cancer

BACKGROUND

Self-renewal of gastric cancer stem cells (GCSCs) is considered to be the underlying cause of the metastasis, drug resistance, and recurrence of gastric cancer (GC).

AIM

To characterize the expression of stem cell-related genes in GC.

METHODS

RNA sequencing results and clinical data for gastric adenoma and adenocarcinoma samples were obtained from The Cancer Genome Atlas database, and the results of the GC mRNA expression-based stemness index (mRNAsi) were analyzed. Weighted gene coexpression network analysis was then used to find modules of interest and their key genes. Survival analysis of key genes was performed using the online tool Kaplan-Meier Plotter, and the online database Oncomine was used to assess the expression of key genes in GC.

RESULTS

mRNAsi was significantly upregulated in GC tissues compared to normal gastric tissues (P < 0.0001). A total of 16 modules were obtained from the gene coexpression network; the brown module was most positively correlated with mRNAsi. Sixteen key genes (BUB1, BUB1B, NCAPH, KIF14, RACGAP1, RAD54L, TPX2, KIF15, KIF18B, CENPF, TTK, KIF4A, SGOL2, PLK4, XRCC2, and C1orf112) were identified in the brown module. The functional and pathway enrichment analyses showed that the key genes were significantly enriched in the spindle cellular component, the sister chromatid segregation biological process, the motor activity molecular function, and the cell cycle and homologous recombination pathways. Survival analysis and Oncomine analysis revealed that the prognosis of patients with GC and the expression of three genes (RAD54L, TPX2, and XRCC2) were consistently related.

CONCLUSION

Sixteen key genes are primarily associated with stem cell self-renewal and cell proliferation characteristics. RAD54L, TPX2, and XRCC2 are the most likely therapeutic targets for inhibiting the stemness characteristics of GC cells.

Construction of gene-classifier and co-expression network analysis of genes in association with major depressive disorder

Because the pathogenesis of major depressive disorder (MDD) is still unclear and the accurate diagnosis remains unavailable, we aimed to analyze its molecular mechanisms and develop a gene classifier to improve diagnostic accuracy. We extracted differentially expressed genes from two datasets, GSE45642 (from brain tissue samples) and GSE98793 (from blood samples), and found three key modules to have a significant correlation with MDD traits by weighted gene coexpression network analysis. Hub genes were identified from the key modules according to the connectivity degree in the network and subjected to least absolute shrinkage and selection operator regression analysis. A total of eighty-five hub genes were selected to construct the gene classifier, which had considerable ability to recognize MDD patients in the training set and test set. In addition, the relationship between the key MDD modules and brain tissues indicated that the anterior cingulate should be a notable region in the study of MDD pathogenesis. The results of Gene Ontology (GO) and pathway enrichment analyses reiterate the relationship between depression and immunity. Therefore we identified MDD hub genes in the InnateDB database, and found 14 genes involved in both MDD and the inflammatory response.

Major Depressive Disorder Is Associated With Differential Expression of Innate Immune and Neutrophil-Related Gene Networks in Peripheral Blood: A Quantitative Review of Whole-Genome Transcriptional Data From Case-Control Studies

BACKGROUND

Whole-genome transcription has been measured in peripheral blood samples as a candidate biomarker of inflammation associated with major depressive disorder.

METHODS

We searched for all case-control studies on major depressive disorder that reported microarray or RNA sequencing measurements on whole blood or peripheral blood mononuclear cells. Primary datasets were reanalyzed, when openly accessible, to estimate case-control differences and to evaluate the functional roles of differentially expressed gene lists by technically harmonized methods.

RESULTS

We found 10 eligible studies (N = 1754 depressed cases and N = 1145 healthy controls). Fifty-two genes were called significant by 2 of the primary studies (published overlap list). After harmonization of analysis across 8 accessible datasets (n = 1706 cases, n = 1098 controls), 272 genes were coincidentally listed in the top 3% most differentially expressed genes in 2 or more studies of whole blood or peripheral blood mononuclear cells with concordant direction of effect (harmonized overlap list). By meta-analysis of standardized mean difference across 4 studies of whole-blood samples (n = 1567 cases, n = 954 controls), 343 genes were found with false discovery rate <5% (standardized mean difference meta-analysis list). These 3 lists intersected significantly. Genes abnormally expressed in major depressive disorder were enriched for innate immune-related functions, coded for nonrandom protein-protein interaction networks, and coexpressed in the normative transcriptome module specialized for innate immune and neutrophil functions.

CONCLUSIONS

Quantitative review of existing case-control data provided robust evidence for abnormal expression of gene networks important for the regulation and implementation of innate immune response. Further development of white blood cell transcriptional biomarkers for inflamed depression seems warranted.

A Selective P2Y Purinergic Receptor Agonist 2-MesADP Enhances Locomotor Recovery after Acute Spinal Cord Injury

INTRODUCTION

Spinal cord injury (SCI) causes most severe motor and sensory dysfunctions. In Chinese traditional medicine, the agonist of a purinergic receptor is believed to have a positive effect on SCIs, and 2-Methylthio-adenosine-5'-diphosphate (2-MesADP) is a selective agonist of the P2Y purinergic receptor.

METHODS

To investigate its therapeutic function and molecular mechanism in SCI, transcriptome analysis associated with weighted gene co-expression network analysis (WGCNA) was carried out at various time points after T9 crush injury.

RESULTS

2-MesADP demonstrated recovery of limb motor function at the 6 weeks after injury, accompanied by neuronal regeneration and axon remyelination at 2 and 6 weeks. Furthermore, gene profiling revealed alternated gene expression with the treatment of 2-MesADP. These genes were assigned to a total of 38 modules, followed by gene ontology analysis; of these, 18 represented neuronal apoptosis and regeneration, immune response, synaptic transmission, cell cycle, and angiogenesis. In the neuronal apoptosis and regeneration module, Nefh, NeuroD6, and Dcx in the 2-MesADP group were noticed due to their interesting expression pattern. The gene expression patterns of Mag, Mog, and Cnp, which played key roles in myelination, were significantly changed with the treatment of 2-MesADP. Wnt signal pathway was the most important pathway in 2-MesADP treatment for acute SCI.

CONCLUSION

2-MesADP enhanced locomotor recovery in mouse SCI by altering the expression of neuronal apoptosis and remyelination-related genes and Wnt signaling pathways.

Long noncoding RNA NALT1-induced gastric cancer invasion and metastasis via NOTCH signaling pathway

BACKGROUND

Long noncoding RNAs (lncRNAs) are aberrant and play critical roles in gastric cancer (GC) progression and metastasis. Searching for coexpressed lncRNA clusters or representative biomarkers related to malignant phenotypes of GC may help to elucidate the mechanism of tumor development and predict the prognosis of GC.

AIM

To investigate the prognostic value of NOTCH1 associated with lncRNA in T cell acute lymphoblastic leukemia 1 (NALT1) in GC and the mechanism of its involvement in GC invasion and metastasis.

METHODS

RNA sequencing and corresponding clinical data were downloaded from The Cancer Genome Atlas database. The significance module was studied by weighted gene coexpression network analysis. A total of 336 clinical samples were included in the study. Gene silencing, reverse transcription polymerase chain reaction, western blotting, scrape motility assay, and Transwell migration assay were used to assess the function of hub-lncRNAs.

RESULTS

At the transcriptome level, 3339 differentially expressed lncRNAs were obtained. weighted gene coexpression network analysis was used to obtain 15 lncRNA clusters and observe their coexpression. Pearson’s correlation showed that blue module was correlated with tumor grade and survival. NALT1 was the hub-lncRNA of blue module and was an independent risk factor for GC prognosis. NALT1 was overexpressed in GC and its expression was closely related to invasion and metastasis. The mechanism may involve NALT1 regulation of NOTCH1, which is associated with lncRNA in T cell acute lymphoblastic leukemia, through cis regulation, thereby affecting the expression of the NOTCH signaling pathway.

CONCLUSION

NALT1 is overexpressed and promotes invasion and metastasis of GC. The mechanism may be related to regulation of NOTCH1 by NALT1 and its effect on NOTCH signaling pathway expression.

Loss of Trem2 in microglia leads to widespread disruption of cell coexpression networks in mouse brain

Rare heterozygous coding variants in the triggering receptor expressed in myeloid cells 2 (TREM2) gene, conferring increased risk of developing late-onset Alzheimer's disease, have been identified. We examined the transcriptional consequences of the loss of Trem2 in mouse brain to better understand its role in disease using differential expression and coexpression network analysis of Trem2 knockout and wild-type mice. We generated RNA-Seq data from cortex and hippocampus sampled at 4 and 8 months. Using brain cell-type markers and ontology enrichment, we found subnetworks with cell type and/or functional identity. We primarily discovered changes in an endothelial gene-enriched subnetwork at 4 months, including a shift toward a more central role for the amyloid precursor protein gene, coupled with widespread disruption of other cell-type subnetworks, including a subnetwork with neuronal identity. We reveal an unexpected potential role of Trem2 in the homeostasis of endothelial cells that goes beyond its known functions as a microglial receptor and signaling hub, suggesting an underlying link between immune response and vascular disease in dementia.

Using Baseline Transcriptional Connectomes in Rat to Identify Genetic Pathways Associated with Predisposition to Complex Traits

Although rat is a critical model organism in preclinical medications development, its use in systems genetics studies remains sparse. The PhenoGen database and website contain detailed information on the qualitative and quantitative aspects of the rat brain, liver, heart, and brown adipose transcriptome. This database has been generated using the HXB/BXH recombinant inbred panel and is being expanded to a hybrid rat diversity panel that includes many common inbred strains as well. By using such a panel, the PhenoGen project has created a renewable and cumulative resource for the rat genomics community. The database has been used to reconstruct the brain transcriptome identifying both annotated and unannotated transcribed elements that range in size from 20 nucleotides to over 30,000 nucleotides and elements that have a wide variety of roles in the cell including generation of proteins and regulation of the transcription and translation processes. In all 4 tissues, baseline transcriptional connectomes have been generated to model the relationships among transcripts. These connectomes can be used to identify genetic pathways associated with complex traits and to gain insight into biological function of individual transcripts. The PhenoGen website contains tools that allow the user to explore qualitative features of individual genes and to see how the gene relates to other genes within a tissue. The PhenoGen database and website continue to grow and to make use of the latest statistical methods for systems genetics creating a national resource for the rat genomics community.

Differential DNA methylation marks and gene comethylation of COPD in African-Americans with COPD exacerbations

Background

Chronic obstructive pulmonary disease (COPD) is the third-leading cause of death worldwide. Identifying COPD-associated DNA methylation marks in African-Americans may contribute to our understanding of racial disparities in COPD susceptibility. We determined differentially methylated genes and co-methylation network modules associated with COPD in African-Americans recruited during exacerbations of COPD and smoking controls from the Pennsylvania Study of Chronic Obstructive Pulmonary Exacerbations (PA-SCOPE) cohort.

Methods

We assessed DNA methylation from whole blood samples in 362 African-American smokers in the PA-SCOPE cohort using the Illumina Infinium HumanMethylation27 BeadChip Array. Final analysis included 19302 CpG probes annotated to the nearest gene transcript after quality control. We tested methylation associations with COPD case-control status using mixed linear models. Weighted gene comethylation networks were constructed using weighted gene coexpression network analysis (WGCNA) and network modules were analyzed for association with COPD.

Results

There were five differentially methylated CpG probes significantly associated with COPD among African-Americans at an FDR less than 5 %, and seven additional probes that approached significance at an FDR less than 10 %. The top ranked gene association was MAML1, which has been shown to affect NOTCH-dependent angiogenesis in murine lung. Network modeling yielded the “yellow” and “blue” comethylation modules which were significantly associated with COPD (p-value 4 × 10^-10 and 4 × 10^-9, respectively). The yellow module was enriched for gene sets related to inflammatory pathways known to be relevant to COPD. The blue module contained the top ranked genes in the concurrent differential methylation analysis (FXYD1/LGI4, gene significance p-value 1.2 × 10^-26; MAML1, p-value 2.0 × 10^-26; CD72, p-value 2.1 × 10^-25; and LPO, p-value 7.2 × 10^-25), and was significantly associated with lung development processes in Gene Ontology gene-set enrichment analysis.

Conclusion

We identified 12 differentially methylated CpG sites associated with COPD that mapped to biologically plausible genes. Network module comethylation patterns have identified candidate genes that may be contributing to racial differences in COPD susceptibility and severity. COPD-associated comethylation modules contained genes previously associated with lung disease and inflammation and recapitulated known COPD-associated genes. The genes implicated by differential methylation and WGCNA analysis may provide mechanistic targets contributing to COPD susceptibility, exacerbations, and outcomes among African-Americans.

Trial registration

Trial Registration: NCT00774176, Registry: ClinicalTrials.gov, URL: www.clinicaltrials.gov, Date of Enrollment of First Participant: June 2004, Date Registered: 04 January 2008 (retrospectively registered).

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-016-0459-8) contains supplementary material, which is available to authorized users.

RNA-Seq reveals novel transcriptional reorganization in human alcoholic brain

DNA microarrays have been used for over a decade to profile gene expression on a genomic scale. While this technology has advanced our understanding of complex cellular function, the reliance of microarrays on hybridization kinetics results in several technical limitations. For example, knowledge of the sequences being probed is required, distinguishing similar sequences is difficult because of cross-hybridization, and the relatively narrow dynamic range of the signal limits sensitivity. Recently, new technologies have been introduced that are based on novel sequencing methodologies. These next-generation sequencing methods do not have the limitations inherent to microarrays. Next-generation sequencing is unique since it allows the detection of all known and novel RNAs present in biological samples without bias toward known transcripts. In addition, the expression of coding and noncoding RNAs, alternative splicing events, and expressed single nucleotide polymorphisms (SNPs) can be identified in a single experiment. Furthermore, this technology allows for remarkably higher throughput while lowering sequencing costs. This significant shift in throughput and pricing makes low-cost access to whole genomes possible and more importantly expands sequencing applications far beyond traditional uses (Morozova & Marra, 2008) to include sequencing the transcriptome (RNA-Seq), providing detail on gene structure, alternative splicing events, expressed SNPs, and transcript size (Mane et al., 2009; Tang et al., 2009; Walter et al., 2009), in a single experiment, while also quantifying the absolute abundance of genes, all with greater sensitivity and dynamic range than the competing cDNA microarray technology (Mortazavi, Williams, McCue, Schaeffer, & Wold, 2008).