Identification of Key Pathways and Genes in the Dynamic Progression of HCC Based on WGCNA

Integrative iTRAQ-based proteomic and transcriptomic analysis reveals the accumulation patterns of key metabolites associated with oil quality during seed ripening of Camellia oleifera

Camellia oleifera (C. oleifera) is one of the four major woody oil-bearing crops in the world and has relatively high ecological, economic, and medicinal value. Its seeds undergo a series of complex physiological and biochemical changes during ripening, which is mainly manifested as the accumulation and transformation of certain metabolites closely related to oil quality, especially flavonoids and fatty acids. To obtain new insights into the underlying molecular mechanisms, a parallel analysis of the transcriptome and proteome profiles of C. oleifera seeds at different maturity levels was conducted using RNA sequencing (RNA-seq) and isobaric tags for relative and absolute quantification (iTRAQ) complemented with gas chromatography-mass spectrometry (GC-MS) data. A total of 16,530 transcripts and 1228 proteins were recognized with significant differential abundances in pairwise comparisons of samples at various developmental stages. Among these, 317 were coexpressed with a poor correlation, and most were involved in metabolic processes, including fatty acid metabolism, α-linolenic acid metabolism, and glutathione metabolism. In addition, the content of total flavonoids decreased gradually with seed maturity, and the levels of fatty acids generally peaked at the fat accumulation stage; these results basically agreed with the regulation patterns of genes or proteins in the corresponding pathways. The expression levels of proteins annotated as upstream candidates of phenylalanine ammonia-lyase (PAL) and chalcone synthase (CHS) as well as their cognate transcripts were positively correlated with the variation in the flavonoid content, while shikimate O-hydroxycinnamoyltransferase (HCT)-encoding genes had the opposite pattern. The increase in the abundance of proteins and mRNAs corresponding to alcohol dehydrogenase (ADH) was associated with a reduction in linoleic acid synthesis. Using weighted gene coexpression network analysis (WGCNA), we further identified six unique modules related to flavonoid, oil, and fatty acid anabolism that contained hub genes or proteins similar to transcription factors (TFs), such as MADS intervening keratin-like and C-terminal (MIKC_MADS), type-B authentic response regulator (ARR-B), and basic helix-loop-helix (bHLH). Finally, based on the known metabolic pathways and WGCNA combined with the correlation analysis, five coexpressed transcripts and proteins composed of cinnamyl-alcohol dehydrogenases (CADs), caffeic acid 3-O-methyltransferase (COMT), flavonol synthase (FLS), and 4-coumarate: CoA ligase (4CL) were screened out. With this exploratory multiomics dataset, our results presented a dynamic picture regarding the maturation process of C. oleifera seeds on Hainan Island, not only revealing the temporal specific expression of key candidate genes and proteins but also providing a scientific basis for the genetic improvement of this tree species.

HTR2B and SLC5A3 Are Specific Markers in Age-Related Osteoarthritis and Involved in Apoptosis and Inflammation of Osteoarthritis Synovial Cells

Objective: Osteoarthritis (OA) is a heterogeneous age-related disease, which is badly difficult to cure due to its complex regulatory networks of pathogenesis. This study explored OA-specific genes in synovial tissues and validated their roles on apoptosis and inflammation of OA synovial cells.

Methods: Weighted correlation network analysis (WGCNA) was employed to explore OA-related co-expression modules in the GSE55235 and GSE55457 datasets. Then, this study screened OA-specific genes. After validation of these genes in the GSE12021 and GSE32317 datasets, HTR2B and SLC5A3 were obtained. Their expression was detected in human OA and healthy synovial tissues by RT-qPCR and western blot. OA rat models were constructed by anterior cruciate ligament transection (ACLT) operation. In OA synovial cells, HTR2B and SLC5A3 proteins were examined via western blot. After transfection with sh-HTR2B or sh-SLC5A3, apoptosis and inflammation of OA synovial cells were investigated by flow cytometry and western blot.

Results: A total of 17 OA-specific DEGs were identified, which were significantly enriched in inflammation pathways. Among them, HTR2B and SLC5A3 were highly expressed in end-than early-stage OA. Their up-regulation was validated in human OA synovial tissues and ACLT-induced OA synovial cells. Knockdown of HTR2B and SLC5A3 restrained apoptosis and increased TGF-β and IL-4 expression as well as reduced TNF-α and IL-1β expression in OA synovial cells.

Conclusion: Collectively, this study identified two OA-specific markers HTR2B and SLC5A3 and their knockdown ameliorated apoptosis and inflammation of OA synovial cells.

Potential prognostic biomarkers related to immunity in clear cell renal cell carcinoma using bioinformatic strategy

The clear cell renal cell carcinoma (ccRCC) is the main pathological subtype of renal cell carcinoma. Immune system evasion, one hallmark of cancer, contributes to cancer cells in escaping from the attack of immune cells. In order to identify potential prognostic biomarkers in ccRCC patients and immune cells fraction, we collected and downloaded profiles from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. We obtained 2 modules significantly associated with tumor stage and immune cells; functional enrichment analysis showed that genes in the module ‘yellow’ were significantly enriched in proteins targeting to membrane and ribosome, as well as the oxidative phosphorylation pathway, while genes in the module ‘green’ mainly participate in molecular functions associated with immunity like activation of T cells. Four LncRNAs (LINC00472, AL590094.1, AL365203.3, and AC147651.3) and RPL27A and RPL22L1 in the module ‘yellow’ and two lncRNAs (LINC00426 and AC129507.2) and five protein-coding genes (CSF1, NOD2, ITGAE, CD7, and PDCD1) in the module ‘green’ represented independent prognostic values in patients with ccRCC. Expression of LINC0042, NOD2, CD7, and PDCD1 were significantly correlated with ratio of immune cells (like T cells CD8 and resting mast cells). LINC00426, with significant correlation with immune cell fraction, shows potential prognostic value in ccRCC patients. Our findings provide a strategy in exploring biomarkers with prognostic significance and significant association with the fraction of immune cells.

Identification of Hub Genes and Their Correlation With Immune Infiltration Cells in Hepatocellular Carcinoma Based on GEO and TCGA Databases

Hepatocellular carcinoma (HCC) is a primary liver cancer with extremely high mortality in worldwide. HCC is hard to diagnose and has a poor prognosis due to the less understanding of the molecular pathological mechanisms and the regulation mechanism on immune cell infiltration during hepatocarcinogenesis. Herein, by performing multiple bioinformatics analysis methods, including the RobustRankAggreg (RRA) rank analysis, weighted gene co-expression network analysis (WGCNA), and a devolution algorithm (CIBERSORT), we first identified 14 hub genes (NDC80, DLGAP5, BUB1B, KIF20A, KIF2C, KIF11, NCAPG, NUSAP1, PBK, ASPM, FOXM1, TPX2, UBE2C, and PRC1) in HCC, whose expression levels were significantly up-regulated and negatively correlated with overall survival time. Moreover, we found that the expression of these hub genes was significantly positively correlated with immune infiltration cells, including regulatory T cells (Treg), T follicular helper (TFH) cells, macrophages M0, but negatively correlated with immune infiltration cells including monocytes. Among these hub genes, KIF2C and UBE2C showed the most significant correlation and were associated with immune cell infiltration in HCC, which was speculated as the potential prognostic biomarker for guiding immunotherapy.

Identification of Hub Genes and MicroRNAs Associated With Idiopathic Pulmonary Arterial Hypertension by Integrated Bioinformatics Analyses

Objective

The aim of this study is the identification of hub genes associated with idiopathic pulmonary arterial hypertension (IPAH).

Materials and Methods

GSE15197 gene expression data was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by screening IPAH patients and controls. The 5,000 genes with the greatest variances were analyzed using a weighted gene co-expression network analysis (WGCNA). Modules with the strongest correlation with IPAH were chosen, followed by a functional enrichment analysis. Protein–protein interaction (PPI) networks were constructed to identify hub gene candidates using calculated degrees. Real hub genes were found from the overlap of DEGs and candidate hub genes. microRNAs (miRNAs) targeting real hub genes were found by screening miRNet 2.0. The most important IPAH miRNAs were identified.

Results

There were 4,395 DEGs identified. WGCNA indicated that green and brown modules associated most strongly with IPAH. Functional enrichment analysis showed that green and brown module genes were mainly involved in protein digestion and absorption and proteoglycans in cancer, respectively. The top ten candidate hub genes in green and brown modules were identified, respectively. After overlapping with DEGs, 11 real hub genes were identified: EP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1. These genes were expressed with significant differences in IPAH versus controls, indicating a high diagnostic ability. The miRNA–gene network showed that hsa-mir-1-3p could associate with IPAH.

Conclusion

EP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1 may play essential roles in IPAH. Predicted miRNA hsa-mir-1-3p could regulate gene expression in IPAH. Such hub genes may contribute to the pathology and progression in IPAH, providing potential diagnostic and therapeutic opportunities for IPAH patients.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Consensus analysis via weighted gene co-expression network analysis (WGCNA) reveals genes participating in early phase of acute respiratory distress syndrome (ARDS) induced by sepsis

The understanding of mechanism during conversion from sepsis to sepsis-related ARDS remains limited. In this study, we collected gene expression matrix from the Gene Expression Omnibus (GEO) database and constructed networks using weighted gene co-expression network analysis (WGCNA) to identify the consensus and opposite modules between sepsis and sepsis-induced ARDS and obtained 27 consensus modules associated with sepsis and sepsis-related ARDS, including one model (160 genes) with opposite correlation and 1 sepsis-ARDS specific model with 34 genes. Differentially expressed genes analysis, functional enrichment and protein-protein interactions analyses of candidate genes were performed; 15 of these genes showed different expressions in sepsis-induced ARDS patients, compared with sepsis patients; genes were enriched in processes associated with ribosome, tissue mechanics and extracellular matrix. Feature selection analysis revealed that three genes, TLCD4, PRSS30P, and ZNF493, showed moderate performance in identifying sepsis-induced ARDS from sepsis. Ribosome-related genes indicate crucial roles in the development of sepsis-induced ARDS.

Comprehensive analysis of immune-related prognostic genes in the tumour microenvironment of hepatocellular carcinoma

BACKGROUND

The mortality rate of hepatocellular carcinoma (HCC) remains high worldwide despite surgery and chemotherapy. Immunotherapy is a promising treatment for the rapidly expanding HCC spectrum. Therefore, it is necessary to further explore the immune-related characteristics of the tumour microenvironment (TME), which plays a vital role in tumour initiation and progression.

METHODS

In this research, 866 immune-related differentially expressed genes (DEGs) were identified by integrating the DEGs of samples from The Cancer Genome Atlas (TCGA)-HCC dataset and the immune-related genes from databases (InnateDB; ImmPort). Afterwards, 144 candidate prognostic genes were defined through weighted gene co-expression network analysis (WGCNA).

RESULTS

Seven immune-related prognostic DEGs were identified using the L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model, and the ImmuneRiskScore model was constructed on this basis. The prognostic index of the ImmuneRiskScore model was then validated in the relevant dataset. Patients were divided into high- and low-risk groups according to the ImmuneRiskScore. Differences in the immune cell infiltration of patients with different ImmuneRiskScore values were clarified, and the correlation of immune cell infiltration with immunotherapy biomarkers was further explored.

CONCLUSION

The ImmuneRiskScore of HCC could be a prognostic marker and can reflect the immune characteristics of the TME. Furthermore, it provides a potential biomarker for predicting the response to immunotherapy in HCC patients.

Weighted gene co-expression network analysis (WGCNA) to explore genes responsive to Streptococcus oralis biofilm and immune infiltration analysis in human gingival fibroblasts cells

The correlation between oral bacteria and dental implants failure has been reported. However, the effect and mechanism of bacteria during dental implants is unclear. In this study, we explored key genes and candidate gene clusters in human gingival fibroblasts (HGF) cells in response to Streptococcus oralis biofilm through weighted gene co-expression network analysis (WGCNA) and differential genes analysis using gene expression matrix, GSE134481, downloaded from the Gene Expression Omnibus (GEO) database. We obtained 325 genes in the module significantly associated with S. oralis infection and 113 differentially expressed genes (DEGs) in the S. oralis biofilm; 62 DEGs indicated significant correlation with S. oralis injury. Multiple immune pathways, such as the tumor necrosis factor (TNF) signaling pathway, were considerably enriched. We obtained a candidate genes cluster containing 12 genes – IL6, JUN, FOS, CSF2, HBEGF, EDN1, CCL2, MYC, NGF, SOCS3, CXCL1, and CXCL2; we observed 5 candidate hub genes associated with S. oralis infection – JUN, IL6, FOS, MYC, and CCL2. The fraction of macrophage M0 cells was significantly increased in biofilm treatment compared with control; expression of FOS and MYC was significantly positively correlated with macrophage M0 cells. Our findings present a fierce inflammation changes in the transcript level of HGF in response to S. oralis.

Identification of metastasis and prognosis-associated genes for serous ovarian cancer

Serous ovarian cancer is one of the most fatal gynecological tumors with an extremely low 5-year survival rate. Most patients are diagnosed at an advanced stage with wide metastasis. The dysregulation of genes serves an important role in the metastasis progression of ovarian cancer. Differentially expressed genes (DEGs) between primary tumors and metastases of serous ovarian cancer were screened out in the gene expression profile of GSE73168 from Gene Expression Omnibus (GEO). Cytoscape plugin cytoHubba and weighted gene co-expression network analysis (WGCNA) were utilized to select hub genes. Univariate and multivariate Cox regression analyses were used to screen out prognosis-associated genes. Furthermore, the Oncomine validation, prognostic analysis, methylation mechanism, gene set enrichment analysis (GSEA), TIMER database analysis and administration of candidate molecular drugs were conducted for hub genes. Nine hundred and fifty-seven DEGs were identified in the gene expression profile of GSE73168. After using Cytoscape plugin cytoHubba, 83 genes were verified. In co-expression network, the blue module was most closely related to tumor metastasis. Furthermore, the genes in Cytoscape were analyzed, showing that the blue module and screened 17 genes were closely associated with tumor metastasis. Univariate and multivariate Cox regression revealed that the age, stage and STMN2 were independent prognostic factors. The Cancer Genome Atlas (TCGA) suggested that the up-regulated expression of STMN2 was related to poor prognosis of ovarian cancer. Thus, STMN2 was considered as a new key gene after expression validation, survival analysis and TIMER database validation. GSEA confirmed that STMN2 was probably involved in ECM receptor interaction, focal adhesion, TGF beta signaling pathway and MAPK signaling pathway. Furthermore, three candidate small molecule drugs for tumor metastasis (diprophylline, valinomycin and anisomycin) were screened out. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blot showed that STMN2 was highly expressed in ovarian cancer tissue and ovarian cancer cell lines. Further studies are needed to investigate these prognosis-associated genes for new therapy target.

Weighted Gene Coexpression Network Analysis Uncovers Critical Genes and Pathways for Multiple Brain Regions in Parkinson's Disease

Objective

In this study, we aimed to identify critical genes and pathways for multiple brain regions in Parkinson's disease (PD) by weighted gene coexpression network analysis (WGCNA).

Methods

From the GEO database, differentially expressed genes (DEGs) were separately identified between the substantia nigra, putamen, prefrontal cortex area, and cingulate gyrus of PD and normal samples with the screening criteria of p value < 0.05 and ∣log₂fold change (FC) | >0.585. Then, a coexpression network was presented by the WGCNA package. Gene modules related to PD were constructed. Then, PD-related DEGs were used for construction of PPI networks. Hub genes were determined by the cytoHubba plug-in. Functional enrichment analysis was then performed.

Results

DEGs were identified for the substantia nigra (17 upregulated and 52 downregulated genes), putamen (317 upregulated and 317 downregulated genes), prefrontal cortex area (39 upregulated and 72 downregulated genes), and cingulate gyrus (116 upregulated and 292 downregulated genes) of PD compared to normal samples. Gene modules were separately built for the four brain regions of PD. PPI networks revealed hub genes for the substantia nigra (SLC6A3, SLC18A2, and TH), putamen (BMP4 and SNAP25), prefrontal cortex area (SNAP25), and cingulate gyrus (CTGF, CDH1, and COL5A1) of PD. These DEGs in multiple brain regions were involved in distinct biological functions and pathways. GSEA showed that these DEGs were all significantly enriched in electron transport chain, proteasome degradation, and synaptic vesicle pathway.

Conclusion

Our findings revealed critical genes and pathways for multiple brain regions in PD, which deepened the understanding of PD-related molecular mechanisms.

Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis

Background

Spinal cord injury (SCI) has an immediate and devastating impact on the control over various movements and sensations. However, no effective therapies for SCI currently exist.

Methods

To identify and analyze SCI subtypes, we obtained the expression profile data of the 1,057 genes (889 intersection genes) in GSE45550 using weighted gene co-expression network analysis (WGCNA), and 14 co-expression gene modules were identified. Next, we filtered out the network degree top 10 (degree >80) genes, considered the final key SCI genes. A multifactor regulatory network (105 interaction pairs), consisting of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and transcription factors (TFs) was constructed. This network was involved in the co-expression of key genes. We selected the top 10 regulatory factors (degree >4) as core regulators in the multifactor regulatory network.

Results

The results of functional enrichment analysis of the target gene expressing the core regulatory factor [1,059] showed that these target genes were enriched in pathways for human cytomegalovirus infection, chronic myeloid leukemia, and pancreatic cancer. Further, we used the key genes in the co-expression network to categorize the SCI samples in GSE45550. The expression levels of the top 6 genes (CCNB2, CCNB1, CKS2, COL5A1, KIF20A, and RACGAP1) may act as potential marker genes for different SCI subtypes. On the basis of these different subtypes, 8 SCI core gene CDK1-associated drugs were also found to provide potential therapeutic options for SCI.

Conclusions

These results may provide a novel therapeutic strategy for the treatment of SCI.

Identification of a disease-specific gene expression profile of children with acute asthma by weighted gene co-expression network analysis

Asthma is one of the most common diseases, with a high prevalence among children. To date, systemic co-expression analysis for this disease has not been undertaken to explain its pathogenesis. Here we identified differentially expressed genes (DEGs) in 87 samples, and then constructed co-expression modules via weighted gene co-expression network analysis (WGCNA) and investigated the functional enrichment of co-expressed genes in terms of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). Meanwhile, protein-protein interaction (PPI) network and miRNA-transcription factor-target (miRNA-TF-target) regulatory network analyses were performed to screen hub genes. As a result, 3,469 DEGs were identified in this study, of which 1,860 genes were up-regulated and 1,609 genes were down-regulated. Using WGCNA, we identified two key modules, named MEbrown and MEblue, that may play important roles in asthma. Functional enrichment analysis revealed that MEbrown was enriched in 37 KEGG pathways and 472 biological processes (BPs), while MEblue was enriched in 16 KEGG pathways and 449 BPs. From PPI and miRNA-TF-target regulatory network analysis, a total of 31 TFs, seven miRNAs and 28 nodes were identified. Our findings should provide a framework of therapeutic targets for treating children with acute asthma.

Effects of super-chilling storage on shelf-life and quality indicators of Coregonus peled based on proteomics analysis

The advantages of super-chilling storage at -2 °C for maintaining the quality of Coregonus peled muscle were investigated using the rigor-mortis index (RM), ATP-related compounds, K-value, muscle hardness, impedance measurement, and total viable count. The results indicated that the softening of fish muscle and increase in K-value were substantially suppressed following storage at -2 °C compared to that at 0 °C. In particular, the hardness of fish muscle stored for 6 days at -2 °C was much higher than that of the samples stored for 2 days at 0 °C. The K-value increased to 81% after 6 days at 0 °C, while increased to 57% at -2 °C. The impedance changed in a biphasic manner throughout the storage period. The initial increase accompanied by the progression of RM was followed by a gradual decrease. However, this decrease was much slower at -2 °C than 0 °C. Furthermore, proteomics analysis demonstrated that the mechanism of fish freshness changes between the two storage temperatures. Differentially abundant proteins between the samples stored at two temperatures were mainly involved in the cellular component and molecular function (GO pathway) as well as collagen digestion (KEGG pathway), which might be related to muscle textural properties. Therefore, super-chilling storage is a possible method for maintaining the freshness of Coregonus peled.

Transcriptional Profiling Uncovers Biologically Significant RNAs and Regulatory Networks in Nucleus Pulposus from Intervertebral Disc Degeneration Patients

Objective. This study aimed to uncover biologically significant RNAs in nucleus pulposus tissues of human intervertebral disc degeneration (IVDD) by integrated transcriptional profiling. Methods. From the Gene Expression Omnibus (GEO) database, three IVDD-related microarray profiling datasets were retrieved and assessed by intragroup data repeatability test. Then, differentially expressed circRNAs, lncRNAs, mRNAs, and miRNAs were screened in nucleus pulposus tissues between IVDD and control samples via the limma package. Coexpression networks were separately conducted via weighted gene correlation network analysis (WGCNA). Based on the feature RNAs in the IVDD-related modules, IVDD-related circRNA-miRNA-mRNA and lncRNA-miRNA-mRNA networks were conducted. The differentially expressed mRNAs in the two networks were analyzed by protein-protein interaction (PPI) and functional enrichment analyses. Results. By the intragroup data repeatability test, outlier samples were removed. Abnormally expressed RNAs were separately identified in nucleus pulposus between IVDD and controls. Via WGCNA, IVDD-related coexpression modules were constructed and the feature circRNAs, lncRNAs, mRNAs, and miRNAs were identified. Then, the circRNA- and lncRNA-miRNA-mRNA networks were built for IVDD. These mRNAs in the network exhibited complex interactions. Moreover, they were involved in distinct IVDD-related biological processes and pathways such as transcription, cell proliferation, TNF, TGF-β, and HIF signaling pathways. Conclusion. This study revealed biologically significant noncoding RNAs and their complex regulatory networks for IVDD.

Integrated analysis identifies oxidative stress genes associated with progression and prognosis in gastric cancer

Oxidative stress (OS) reactions are reported to be associated with oncogenesis and tumor progression. However, little is known about the potential diagnostic value of OS in gastric cancer (GC). This study identified hub OS genes associated with the prognosis and progression of GC and illustrated the underlying mechanisms. The transcriptome data and corresponding GC clinical information were collected from The Cancer Genome Atlas (TCGA) database. Aberrantly expressed OS genes between tumors and adjacent normal tissues were screened, and 11 prognosis-associated genes were identified with a series of bioinformatic analyses and used to construct a prognostic model. These genes were validated in the Gene Expression Omnibus (GEO) database. Furthermore, weighted gene co-expression network analysis (WGCNA) was subsequently conducted to identify the most significant hub genes for the prediction of GC progression. Analysis revealed that a good prognostic model was constructed with a better diagnostic accuracy than other clinicopathological characteristics in both TCGA and GEO cohorts. The model was also significantly associated with the overall survival of patients with GC. Meanwhile, a nomogram based on the risk score was established, which displayed a favorable discriminating ability for GC. In the WGCNA analysis, 13 progression-associated hub OS genes were identified that were also significantly associated with the progression of GC. Furthermore, functional and gene ontology (GO) analyses were performed to reveal potential pathways enriched with these genes. These results provide novel insights into the potential applications of OS-associated genes in patients with GC.

Identification of novel prognostic genes of triple-negative breast cancer using meta-analysis and weighted gene co-expressed network analysis

Background

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with high rates of metastasis and recurrence. Conventional clinical treatments are ineffective for it as it lacks therapeutic biomarkers. Figuring out the biomarkers related to TNBC will be beneficial for its clinical treatment and prognosis.

Methods

Five independent datasets downloaded from the Gene Expression Omnibus database were merged to identify differentially expressed genes between TNBC and non-TNBC samples by using the MetaDE.ES method followed by mapping the differentially expressed genes into a protein-protein interaction network. Meanwhile, the weighted gene co-expressed network analysis (WGCNA) of The Cancer Genome Atlas data was performed to screen the hub genes. The gene functional analyses were conducted by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The correlation between gene expression level and patient overall survival was evaluated by survival analysis.

Results

A total of 11 differentially expressed genes (CDH1, SP1, MYC, FAF2, IFI16, MDM2, AR, DBN1, HSPB1, FLNA, YWHAB) were obtained from the protein-protein interaction network with degree >10. WGCNA revealed 5 hub genes (TPX2, CTPS1, KIF2C, MELK, CDCA8) that were significantly associated with TNBC. Cell cycle, oocyte meiosis, spliceosome were the pathways significantly enriched in these genes according to GO functionally annotated terms and KEGG pathways analysis. The Kaplan-Meier curves showed that the expression levels of HSPB1, IFI16, TPX2 were significantly associated with the survival time of TNBC patients (P<0.05).

Conclusions

A total of 16 genes significantly associated with TNBC were identified by bioinformatic analyses. Among these 16 genes, HSPB1, IFI16, TPX2 might be able to be used as biomarkers of TNBC.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

Catechin gallate acts as a key metabolite induced by trypsin in Hylocereus undatus during storage indicated by omics

Trypsin is a novel superoxide scavenger. The storage quality of H. undatus was significantly improved by trypsin. To investigate the mechanism of flavonoid metabolism regulated by trypsin, combined analysis of widely targeted metabolomic and transcriptome were performed. GO and KEGG enrichment analyses of the transcriptome profiles of H. undatus revealed that some of the flavonoid related biosynthesis pathways were regulated by up or down patterns with the treatment of trypsin. Correlation analysis of flavonoid related genes expression in H. undatus provided a rationale for the functional significance of them. Furthermore, it has been revealed that the most significantly regulated flavonoid was catechin gallate in metabolomic profiles of H. undatus. The major route of flavonoid biosynthesis regulated by trypsin was also illustrated by both transcriptomic and metabolomic data. Finally, the results of PPI network revealed that C4H, HCT, and CYP75B1 acted as hub proteins involved in flavonoid metabolism regulated by trypsin.

Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer

BACKGROUND

Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV.

METHODS

Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored.

RESULTS

A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P < 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8).

CONCLUSION

The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

Comparative transcriptome analysis reveals evolutionary divergence and shared network of cold and salt stress response in diploid D-genome cotton

BACKGROUND

Wild species of cotton are excellent resistance to abiotic stress. Diploid D-genome cotton showed abundant phenotypic diversity and was the putative donor species of allotetraploid cotton which produce the largest textile natural fiber.

RESULTS

A total of 41,053 genes were expressed in all samples by mapping RNA-seq Illumina reads of G. thurberi (D1), G. klotzschianum (D3-k), G. raimondii (D5) and G. trilobum (D8) to reference genome. The numbers of differently expressed genes (DEGs) were significantly higher under cold stress than salt stress. However, 34.1% DEGs under salt stress were overlapped with cold stress in four species. Notably, a potential shared network (cold and salt response, including 16 genes) was mined out by gene co-expression analysis. A total of 47,180-55,548 unique genes were identified in four diploid species by De novo assembly. Furthermore, 163, 344, 330, and 161 positively selected genes (PSGs) were detected in thurberi, G. klotzschianum, G. raimondii and G. trilobum by evolutionary analysis, respectively, and 9.5-17% PSGs of four species were DEGs in corresponding species under cold or salt stress. What's more, most of PSGs were enriched GO term related to response to stimulation. G. klotzschianum showed the best tolerance under both cold and salt stress. Interestingly, we found that a RALF-like protein coding gene not only is PSGs of G. klotzschianum, but also belongs to the potential shared network.

CONCLUSION

Our study provided new evidence that gene expression variations of evolution by natural selection were essential drivers of the morphological variations related to environmental adaptation during evolution. Additionally, there exist shared regulated networks under cold and salt stress, such as Ca2+ signal transduction and oxidation-reduction mechanisms. Our work establishes a transcriptomic selection mechanism for altering gene expression of the four diploid D-genome cotton and provides available gene resource underlying multi-abiotic resistant cotton breeding strategy.

Hub genes and sub-networks of stoma-related genes in Hylocereus undatus through trypsin treatment during storage revealed by transcriptomic analysis

To further investigate the preservation mechanisms of trypsin, the synergistic mechanisms of trypsin and stoma-related genes were evaluated in Hylocereus undatus. Trypsin significantly induced the stoma closure and improved the storage quality of H. undatus. Transcriptomic analyses of H. undatus revealed that important antioxidant signal pathway, such as SREBP signaling pathway, cellular response to H₂ O₂ or cellular response to molecule of bacterial origin, were induced; while responses to water deprivation were impeded by trypsin. These results indicated that trypsin relieved pitaya of pressure of water deprivation and exhibited the protection on pitaya during storage. Furthermore, the analyses of networks of protein-protein interaction suggested that OST1, HK5, AT4G27585, and HIR1 act as hubs of stoma-related proteins induced by trypsin during storage of H. undatus. PRACTICAL APPLICATIONS: Preservation of fruit is becoming increasingly important to the world. Keep the balance of production and scavenging of reactive oxygen species is efficient to improve the storage quality of fruit. Trypsin had a novel superoxide anion scavenging activity and protect fruit cells from cellular injury induced by excess ROS. This article investigates the hub genes and interaction mechanisms of stoma closure induced by trypsin during the storage of H. undatus. The application of trypsin provides a new strategy for the quality control of fruit storage. Trypsin will have a broad market and development potential in the area of food additives.

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Background

Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority.

Methods

We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model.

Results

Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset.

Conclusion

Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

Weighted gene co-expression network analysis identified underlying hub genes and mechanisms in the occurrence and development of viral myocarditis

Background

Myocarditis is an inflammatory myocardial disease, which may lead to heart failure and sudden death. Despite extensive research into the pathogenesis of myocarditis, effective treatments for this condition remain elusive. This study aimed to explore the potential pathogenesis and hub genes for viral myocarditis.

Methods

A weighted gene co-expression network analysis (WGCNA) was performed based on the gene expression profiles derived from mouse models at different stages of viral myocarditis (GSE35182). Functional annotation was executed within the key modules. Potential hub genes were predicted based on the intramodular connectivity (IC). Finally, potential microRNAs that regulate gene expression were predicted by miRNet analysis.

Results

Three gene co-expression modules showed the strongest correlation with the acute or chronic disease stage. A significant positive correlation was detected between the acute disease stage and the turquoise module, the genes of which were mainly enriched in antiviral response and immune-inflammatory activation. Furthermore, a significant positive correlation and a negative correlation were identified between the chronic disease stage and the brown and yellow modules, respectively. These modules were mainly associated with the cytoskeleton, phosphorylation, cellular catabolic process, and autophagy. Subsequently, we predicted the underlying hub genes and microRNAs in the three modules.

Conclusions

This study revealed the main biological processes in different stages of viral myocarditis and predicted hub genes in both the acute and chronic disease stages. Our results may be helpful for developing new therapeutic targets for viral myocarditis in future research.

Transcriptomic Data Analyses Reveal a Reprogramed Lipid Metabolism in HCV-Derived Hepatocellular Cancer

Reprograming lipid metabolism, one of the major metabolic alterations in cancer, is believed to play an essential role in cancer development, but the exact molecular mechanism remains elusive. Here, we present a computational study of transcriptomic data of HCC with HCV etiology to investigate how lipid metabolism alters during HCC progression. Our analyses reveal that: (1) cancer tissue cells tend to synthesize fatty acids de novo and its phospholipid derivatives; (2) lipid catabolism and fatty acid oxidation are remarkably down-regulated in HCC; (3) the lipid metabolism in HCC is largely independent of lipids in blood circulation; (4) stage-specific co-expression networks for lipid metabolic genes were identified during HCC progression; and (5) the expression levels of several lipid metabolic genes that are differentially expressed or co-expressed specifically at the HCC stage have a strong correlation with cancer survival. Overall, the results provide detailed information about the reprogramed lipid metabolism in HCV-derived HCC.

Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data

Background

With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC).

Methods

Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis.

Results

A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes (P < 0.01). Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC.

Conclusion

The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.

Epigenomic and Transcriptomic Dynamics During Human Heart Organogenesis

RATIONALE

There is growing evidence that common variants and rare sequence alterations in regulatory sequences can result in birth defects or predisposition to disease. Congenital heart defects are the most common birth defect and have a clear genetic component, yet only a third of cases can be attributed to structural variation in the genome or a mutation in a gene. The remaining unknown cases could be caused by alterations in regulatory sequences.

OBJECTIVE

Identify regulatory sequences and gene expression networks that are active during organogenesis of the human heart. Determine whether these sites and networks are enriched for disease-relevant genes and associated genetic variation.

METHODS AND RESULTS

We characterized ChromHMM (chromatin state) and gene expression dynamics during human heart organogenesis. We profiled 7 histone modifications in embryonic hearts from each of 9 distinct Carnegie stages (13-14, 16-21, and 23), annotated chromatin states, and compared these maps to over 100 human tissues and cell types. We also generated RNA-sequencing data, performed differential expression, and constructed weighted gene coexpression networks. We identified 177 412 heart enhancers; 12 395 had not been previously annotated as strong enhancers. We identified 92% of all functionally validated heart-positive enhancers (n=281; 7.5× enrichment; P<2.2×10-16). Integration of these data demonstrated novel heart enhancers are enriched near genes expressed more strongly in cardiac tissue and are enriched for variants associated with ECG measures and atrial fibrillation. Our gene expression network analysis identified gene modules strongly enriched for heart-related functions, regulatory control by heart-specific enhancers, and putative disease genes.

CONCLUSIONS

Well-connected hub genes with heart-specific expression targeted by embryonic heart-specific enhancers are likely disease candidates. Our functional annotations will allow for better interpretation of whole genome sequencing data in the large number of patients affected by congenital heart defects.

Identification of key gene modules and genes in colorectal cancer by co-expression analysis weighted gene co-expression network analysis

Colorectal cancer (CRC) has been one of the most common malignancies worldwide, which tends to get worse for the growth and aging of the population and westernized lifestyle. However, there is no effective treatment due to the complexity of its etiology. Hence, the pathogenic mechanisms remain to be clearly defined. In the present study, we adopted an advanced analytical method—Weighted Gene Co-expression Network Analysis (WGCNA) to identify the key gene modules and hub genes associated with CRC. In total, five gene co-expression modules were highly associated with CRC, of which, one gene module correlated with CRC significantly positive (R = 0.88). Functional enrichment analysis of genes in primary gene module found metabolic pathways, which might be a potentially important pathway involved in CRC. Further, we identified and verified some hub genes positively correlated with CRC by using Cytoscape software and UALCAN databases, including PAICS, ATR, AASDHPPT, DDX18, NUP107 and TOMM6. The present study discovered key gene modules and hub genes associated with CRC, which provide references to understand the pathogenesis of CRC and may be novel candidate target genes of CRC.

Application of weighted gene co-expression network analysis to reveal key modules and hub genes in generalized aggressive periodontitis

OBJECTIVE

The aim of this study was to construct a gene co-expression network to identify key modules and genes in people with generalized aggressive periodontitis.

METHODS

We used database GSE79705 to construct a co-expression network by weighted gene co-expression network analysis (WGCNA). In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted.

RESULTS

A total of 51 co-expression modules were conducted, darkseagreen1 and blue1 modules were the most significantly related to generalized aggressive periodontitis. Genes in the darkseagreen1 module enriched in affecting cellular response to tumor necrosis factor and vascular endothelial growth factor production, and the blue1 module enriched in the regulation of ion transport, proteinaceous extracellular matrix and neuropeptide binding. Besides, we found that 4 hub genes (SNRPG, MRPL22, MRPS18C and CEP290) played an important role in the occurrence of generalized aggressive periodontitis.

CONCLUSION

Through this study, we identified two modules and four hub genes associated with generalized aggressive periodontitis. Besides, 4 hub genes (SNRPG, MRPL22, MRPS18C and CEP290) can be expected to trigger new therapeutic drug development for generalized aggressive periodontitis.

Differences in Alternative Splicing between Yellow and Black-Seeded Rapeseed

Yellow seed coat color is a desirable characteristic in rapeseed (Brassica napus), as it is associated with higher oil content and higher quality of meal. Alternative splicing (AS) is a vital post-transcriptional regulatory process contributing to plant cell differentiation and organ development. To identify novel transcripts and differences at the isoform level that are associated with seed color in B. napus, we compared 31 RNA-seq libraries of yellow- and black-seeded B. napus at five different developmental stages. AS events in the different samples were highly similar, and intron retention accounted for a large proportion of the observed AS pattern. AS mainly occurred in the early and middle stage of seed development. Weighted gene co-expression network analysis (WGCNA) identified 23 co-expression modules composed of differentially spliced genes, and we picked out two of the modules whose functions were highly associated with seed color. In the two modules, we found candidate DAS (differentially alternative splicing) genes related to the flavonoid pathway, such as TT8 (BnaC09g24870D), TT5 (BnaA09g34840D and BnaC08g26020D), TT12 (BnaC06g17050D and BnaA07g18120D), AHA10 (BnaA08g23220D and BnaC08g17280D), CHI (BnaC09g50050D), BAN (BnaA03g60670D) and DFR (BnaC09g17150D). Gene BnaC03g23650D, encoding RNA-binding family protein, was also identified. The splicing of the candidate genes identified in this study might be used to develop stable, yellow-seeded B. napus. This study provides insight into the formation of seed coat color in B. napus.

Anlotinib suppresses tumor progression via blocking the VEGFR2/PI3K/AKT cascade in intrahepatic cholangiocarcinoma

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor derived from bile duct epithelium. Its characteristics include an insidious onset and frequent recurrence or metastasis after surgery. Current chemotherapies and molecular target therapies provide only modest survival benefits to patients with ICC. Anlotinib is a novel multi-target tyrosine kinase inhibitor that has good antitumor effects in a variety of solid tumors. However, there are few studies of anlotinib-associated mechanisms and use as a treatment in ICC. In this study using in vitro experiments, we found that anlotinib had significant effects on proliferation inhibition, migration and invasion restraint, and cell-cycle arrestment. Anlotinib treatment affected induction of apoptosis and the mesenchymal–epithelial transition. Patient-derived xenograft models generated directly from patients with ICC revealed that anlotinib treatment dramatically hindered in vivo tumor growth. We also examined anlotinib’s mechanism of action using transcriptional profiling. We found that anlotinib treatment might mainly inhibit tumor cell proliferation and invasion and promote apoptosis via cell-cycle arrestment by inactivating the VEGF/PI3K/AKT signaling pathway, as evidenced by significantly decreased phosphorylation levels of these kinases. The activation of vascular endothelial growth factor receptor 2 (VEGFR2) can subsequently activate PI3K/AKT signaling. We identified VEGRF2 as the main target of anlotinib. High VEGFR2 expression might serve as a promising indicator when used to predict a favorable therapeutic response. Taken together, these results indicated that anlotinib had excellent antitumor activity in ICC, mainly via inhibiting the phosphorylation level of VEGFR2 and subsequent inactivation of PIK3/AKT signaling. This work provides evidence and a rationale for using anlotinib to treat patients with ICC in the future.

Rh type C-glycoprotein functions as a novel tumor suppressor gene by inhibiting tumorigenicity and metastasis in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), a major histologic subtype of head and neck cancer, presents great mortality and morbidity worldwide. The aim of this study is to discover new potential biomarkers closely correlated with HNSCC progression. In this study, weighted gene co-expression network analysis was applied to construct a co-expression network, and the brown module was identified as the most correlated with HNSCC progression. Hub gene identification combined with survival analyses determined RHCG as a candidate biomarker for cancer progression and prognosis prediction. Further experimental results proved that RHCG was aberrantly downregulated in HNSCC tissues and cell lines. Moreover, decreased RHCG expression was shown to be associated with advanced stage and dismal prognosis in HNSCC patients. Functional assays revealed that RHCG could inhibit cell viability, clonogenicity, cell migration in vitro and suppress tumor formation in vivo. Further bioinformatics study demonstrated that DNA promoter hypermethylation of RHCG could lead to its downregulation and serve as potential prognostic maker in HNSCC. Our study reveals that RHCG acts as a tumor suppressor gene that plays a crucial role in inhibiting tumorigenicity and metastasis in HNSCC, which will shed light on the potential diagnostic and therapeutic strategies for HNSCC.

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma

In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN, USHBP1 and ZNF532 as the top five mRNAsi-related genes. We constructed our survival model with these five genes and tested its performance using 243 HCC and 202 normal liver samples from the ICGC database. Kaplan-Meier survival curve and receive operating characteristic curve analyses showed that the survival model accurately predicted the prognosis and survival of high- and low-risk HCC patients with high sensitivity and specificity. The expression of these five genes was significantly higher in the HCC tissues from the TCGA, ICGC, and GEO datasets (GSE25097 and GSE14520) than in normal liver tissues. These findings demonstrate that a new survival model derived from five strongly correlating mRNAsi-related genes provides highly accurate prognoses for HCC patients.

Transcriptomic analysis reveals hub genes and subnetworks related to ROS metabolism in Hylocereus undatus through novel superoxide scavenger trypsin treatment during storage

BACKGROUND

It was demonstrated in our previous research that trypsin scavenges superoxide anions. In this study, the mechanisms of storage quality improvement by trypsin were evaluated in H. undatus.

RESULTS

Trypsin significantly delayed the weight loss and decreased the levels of ROS and membrane lipid peroxidation. Transcriptome profiles of H. undatus treated with trypsin revealed the pathways and regulatory mechanisms of ROS genes that were up- or downregulated following trypsin treatment by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses. The current results showed that through the regulation of the expression of hub redox enzymes, especially thioredoxin-related proteins, trypsin can maintain low levels of endogenous active oxygen species, reduce malondialdehyde content and delay fruit aging. In addition, the results of protein-protein interaction networks suggested that the downregulated NAD(P) H and lignin pathways might be the key regulatory mechanisms governed by trypsin.

CONCLUSIONS

Trypsin significantly prolonged the storage life of H. undatus through regulatory on the endogenous ROS metabolism. As a new biopreservative, trypsin is highly efficient, safe and economical. Therefore, trypsin possesses technical feasibility for the quality control of fruit storage.

Clinical Features and Prognostic Impact of Coexpression Modules Constructed by WGCNA for Diffuse Large B-Cell Lymphoma

Objective

Diffuse large B-cell lymphoma (DLBCL) is a highly aggressive malignant tumor, accounting for 30-40% of non-Hodgkin's lymphoma. Our aim was to construct novel prognostic models of candidate genes based on clinical features.

Methods

RNA-seq and clinical data of DLBCL were retrieved from TCGA database. Coexpression modules were constructed by WGCNA. Then, we investigated the interactions between modules and clinical features. By overall survival analysis, prognostic candidate genes from modules of interest were identified. A coexpression network of prognostic candidate genes was then constructed through WGCNA. GEPIA was used to analyze the expression of a candidate gene between DLBCL and normal samples.

Results

19 coexpression modules were constructed by 12813 genes from 52 DLBCL samples. The number of genes in modules ranged from 34 to 5457. We found that the purple module was significantly related with histological type (p value = 1e-04). Overall survival analysis revealed that MAFA-AS1, hsa-mir-338, and hsa-mir-891a were related with prognosis of DLBCL (p value = 0.027, 0.039, and 0.022, respectively). A coexpression network was constructed for the three prognostic genes. MAFA-AS1 was interacted with 36 genes, hsa-mir-891a was interacted with 11 genes, while no gene showed interaction with hsa-mir-338. Using GEPIA, we found that MAFA-AS1 showed low expression in DLBCL samples (p < 0.01).

Conclusion

We constructed a coexpression module related with histological type and identified three candidate genes (MAFA-AS1, hsa-mir-338, and hsa-mir-891a) that possessed potential value as prognostic biomarkers and therapeutic targets of DLBCL.

Identification of 40S ribosomal protein S8 as a novel biomarker for alcohol‑associated hepatocellular carcinoma using weighted gene co‑expression network analysis

Alcohol‑associated hepatocellular carcinoma (HCC) is a subtype of HCC with poor prognosis. The present study aimed to identify key biomarkers for alcohol‑associated HCC. The gene data profiles and corresponding clinical traits of patients with alcohol‑associated HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Firstly, good genes and good samples were identified, which were subsequently used to conduct weighted gene co‑expression network analysis (WGCNA). Hub genes in the significant modules were selected following Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and from constructing a protein‑protein interaction (PPI) network. Real hub genes among hub genes were determined following progression, survival analysis and gene set enrichment analysis (GSEA), as well as reverse transcription‑quantitative PCR and immunohistochemical staining of non‑alcohol‑ and alcohol‑associated HCC samples. In total, 64 good samples of alcohol‑associated HCC with height score <160 were selected, from which 15,195 good genes were identified and used to conduct WGCNA; 8 gene co‑expressed modules were identified using WGCNA, while 3 modules (including pink, magenta and turquoise) were significantly associated with Child‑Pugh score, T‑stage and body weight. Following GO and KEGG analysis and construction of the PPI network, a total of 30 hub genes were identified in the aforementioned 3 gene co‑expressed modules, while 16 hub genes (including AURKB, BUB1, BUB1B, CCNB1, CCNB2, CDC20, CDCA8, CDK1, PLK1, RPS5, RPS7, RPS8, RPS14, RPS27, RPSA and TOP2A) were associated with the development of alcohol‑associated HCC, and had a significant prognosis value. Among these genes, only RPS8 was highly expressed in alcohol‑associated HCC, but not in non‑alcohol‑associated HCC, while RPS5 was not significantly associated in either alcohol‑ or non‑alcohol‑associated HCC. GSEA demonstrated that 10 pathways, including RNA polymerase and ribosome pathways were enriched in alcohol‑associated HCC samples where RPS8 was highly expressed. Taken together, the results of the present study demonstrate that RPS8 may be a novel biomarker for the diagnosis of patients with alcohol‑associated HCC.

Gene Coexpression Network and Module Analysis across 52 Human Tissues

Gene coexpression analysis is widely used to infer gene modules associated with diseases and other clinical traits. However, a systematic view and comparison of gene coexpression networks and modules across a cohort of tissues are more or less ignored. In this study, we first construct gene coexpression networks and modules of 52 GTEx tissues and cell lines. The network modules are enriched in many tissue-common functions like organelle membrane and tissue-specific functions. We then study the correlation of tissues from the network point of view. As a result, the network modules of most tissues are significantly correlated, indicating a general similar network pattern across tissues. However, the level of similarity among the tissues is different. The tissues closing in a physical location seem to be more similar in their coexpression networks. For example, the two adjacent tissues fallopian tube and bladder have the highest Fisher's exact test p value 8.54E-291 among all tissue pairs. It is known that immune-associated modules are frequently identified in coexperssion modules. In this study, we found immune modules in many tissues like liver, kidney cortex, lung, uterus, adipose subcutaneous, and adipose visceral omentum. However, not all tissues have immune-associated modules, for example, brain cerebellum. Finally, by the clique analysis, we identify the largest clique of modules, in which the genes in each module are significantly overlapped with those in other modules. As a result, we are able to find a clique of size 40 (out of 52 tissues), indicating a strong correlation of modules across tissues. It is not surprising that the 40 modules are most commonly enriched in immune-related functions.

Identification of specific modules and hub genes associated with the progression of gastric cancer

Gastric cancer (GC) has high morbidity and mortality rates worldwide. Abundant literature has reported several individual genes and their related pathways intimately involved in tumor progression. However, little is known about GC progression at the gene network level. Therefore, understanding the underlying mechanisms of pathological transition from early stage to late stage is urgently needed. This study aims to identify potential vital genes and modules involved in the progression of GC. To understand the gene regulatory network of GC progression, we analyzed micro RNAs and messenger RNA s expression profiles by using a couple of bioinformatics tools. miR-205 was identified by differentially expressed analysis and was further confirmed through using multiple kernel learning-based Kronecker regularized least squares. Using weighted gene co-expression network analysis, the gastric cancer progression-related module, which has the highest correlation value with cancer progression, was obtained. Kyoto Encyclopedia of Genes and Genomes pathways and biological processes of the GCPR module genes were related to cell adhesion. Meanwhile, large-scale genes of GCPR module were found to be targeted by miR-205, including two hub genes SORBS1 and LPAR1. In brief, through multiple analytical methods, we found that miR-205 and the GCPR module play critical roles in GC progression. In addition, miR-205 might maintain cell adhesion by regulating SORBS1 and LPAR1. To screen the potential drug candidates, the gene expression profile of the GCPR module was mapped connectivity map (Cmap), and the mTOR inhibitor (Sirolimus) was found to be the most promising candidate. We further confirmed that Sirolimus can suppress cell proliferation of GC cell in vitro.

Identification of key miRNAs in the progression of hepatocellular carcinoma using an integrated bioinformatics approach

Backgroud

It has been shown that aberrant expression of microRNAs (miRNAs) and transcriptional factors (TFs) is tightly associated with the development of HCC. Therefore, in order to further understand the pathogenesis of HCC, it is necessary to systematically study the relationship between the expression of miRNAs, TF and genes. In this study, we aim to identify the potential transcriptomic markers of HCC through analyzing common microarray datasets, and further establish the differential co-expression network of miRNAs-TF-mRNA to screen for key miRNAs as candidate diagnostic markers for HCC.

Method

We first downloaded the mRNA and miRNA expression profiles of liver cancer from the GEO database. After pretreatment, we used a linear model to screen for differentially expressed genes (DEGs) and miRNAs. Further, we used weighed gene co-expression network analysis (WGCNA) to construct the differential gene co-expression network for these DEGs. Next, we identified mRNA modules significantly related to tumorigenesis in this network, and evaluated the relationship between mRNAs and TFs by TFBtools. Finally, the key miRNA was screened out in the mRNA-TF-miRNA ternary network constructed based on the target TF of differentially expressed miRNAs, and was further verified with external data set.

Results

A total of 465 DEGs and 215 differentially expressed miRNAs were identified through differential genes expression analysis, and WGCNA was used to establish a co-expression network of DEGs. One module that closely related to tumorigenesis was obtained, including 33 genes. Next, a ternary network was constructed by selecting 256 pairs of mRNA-TF pairs and 100 pairs of miRNA-TF pairs. Network mining revealed that there were significant interactions between 18 mRNAs and 25 miRNAs. Finally, we used another independent data set to verify that miRNA hsa-mir-106b and hsa-mir-195 are good classifiers of HCC and might play key roles in the progression of HCC.

Conclusion

Our data indicated that two miRNAs-hsa-mir-106b and hsa-mir-195-are identified as good classifiers of HCC.

Identification of microRNA-mRNA regulatory networks and pathways related to retinoblastoma across human and mouse

AIM

To explore the mRNA and pathways related to retinoblastoma (RB) genesis and development.

METHODS

Microarray datasets GSE29683 (human) and GSE29685 (mouse) were downloaded from NCBI GEO database. Homologous genes between the two species were identified using WGCNA, followed by protein-protein interaction (PPI) network construction and gene enrichment analysis. Disease-related miRNAs and pathways were retrieved from miR2Disease database and Comparative Toxicogenomics Database (CTD), respectively.

RESULTS

A total of 352 homologous genes were identified. Two pathways including "cell cycle" and "pathway in cancer" in CTD and enrichment analysis were identified and seven miRNAs (including hsa-miR-373, hsa-miR-34a, hsa-miR-129, hsa-miR-494, hsa-miR-503, hsa-let-7 and hsa-miR-518c) were associated with RB. miRNAs modulate "cell cycle" and "pathway in cancer" pathways via regulating 13 genes (including CCND1, CDC25C, E2F2, CDKN2D and TGFB2).

CONCLUSION

These results suggest that these miRNAs play crucial roles in RB genesis through "cell cycle" and "pathway in cancer" pathways by regulating their targets including CCND1, CDC25C, E2F2 and CDKN2D.

Identification of pancreatic cancer type related factors by Weighted Gene Co-Expression Network Analysis

This study aims to identify the core modules associated with pancreatic cancer (PC) types and the ncRNAs and transcription factors (TFs) that regulate core module genes by weighted gene co-expression network analysis (WGCNA). WGCNA was used to analyze the union of genes related to PC in NCBI and OMIM databases and the differentially expressed genes screened by TCGA-PAAD database. Samples were clustered according to gene expression in gene modules and Fisher exact method was performed. GO and KEGG were used for enrichment analysis to visually display module genes and screen driver genes. Hypergeometric test method was used to calculate pivot nodes among ncRNAs, TFs and mRNA based on RAID 2.0 and TRRUST v2 databases. The blue and yellow modules were identified as the core modules associated with PC types. MST1R, TMPRSS, MIR198, SULF1, COL1A1 and FAP were the core genes in the modules. Hypergeometric test results showed that ANCR, miR-3134, MT1DP, LOC154449, LOC28329 and other ncRNAs were key factors driving blue module genes, while LINC-ROR, UCA1, SNORD114-4, HEIH, SNORD114-6 and other ncRNAs were key factors driving yellow module genes. TFs with significant regulatory effect on blue module included LCOR, PIAS4, ZEB1, SNAI2, SMARCA4, etc. and on yellow module included HOXC6, PER2, HOXD3, TWIST2, VHL, etc. The core modules associated with PC types were proved as yellow and blue modules, and important ncRNAs and TFs regulating yellow and blue modules were found. This study provides relevant evidence for further identification of PC types.

Identification of aberrantly methylated differentially expressed genes and associated pathways in endometrial cancer using integrated bioinformatic analysis

Endometrial cancer (EC) is a fatal female reproductive tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. In this study, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17025 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40032 were obtained from Gene Expression Omnibus (GEO). “limma” package and Venn diagram tool were used to identify hub genes. FunRich was used for functional analysis. Retrieval of Interacting Genes Database (STRING) was used to analyze protein‐protein interaction (PPI) complex. Cancer Genome Atlas (TCGA), GEPIA, immunohistochemistry staining, and ROC curve analysis were carried out for validation. Univariate and multivariate regression analyses were performed to predict the risk score. Compound muscle action potential (CMap) was used to find potential drugs. GSEA was also done. We retrieved seven oncogenes which were upregulated and hypomethylated and 12 tumor suppressor genes (TSGs) which were downregulated and hypermethylated. The upregulated and hypomethylated genes were strikingly enriched in term “immune response” while the downregulated and hypermethylated genes were mainly focused on term “aromatic compound catabolic process.” TCGA and GEPIA were used to screen out EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1. Among them, ESPL1 and ROR2 were identified by Cox regression analysis and were used to construct prognostic risk model. The result showed that ESPL1 was a negative independent prognostic factor. Cmap identified aminoglutethimide, luteolin, sulfadimethoxine, and maprotiline had correlation with EC. GSEA results showed that “hedgehog signaling pathway” was enriched. This research inferred potential aberrantly methylated DEGs and dysregulated pathways may participate in EC development and firstly reported eight hub genes, including EDNRB, CDO1, NDN, PLCD1, ROR2, ESPL1, PRAME, and PTTG1 that could be used to predict EC prognosis. Aminoglutethimide and luteolin may be used to fight against EC.

Transcriptomic Analysis Reveals Cu/Zn SODs Acting as Hub Genes of SODs in Hylocereus undatus Induced by Trypsin during Storage

It has been revealed by us that superoxide scavenging is a new activity of trypsin. In this study, the synergistic mechanisms of trypsin and superoxide dismutases (SODs) were evaluated in Hylocereus undatus (pitaya). Trypsin significantly improved the storage quality of H. undatus, including weight loss impediment and decrease of cellular injury. The regulatory mechanisms of 16 SOD genes by trypsin were revealed using transcriptomic analysis on H. undatus. Results revealed that important physiological metabolisms, such as antioxidant activities or metal ion transport were induced, and defense responses were inhibited by trypsin. Furthermore, the results of protein–protein interaction (PPI) networks showed that besides the entire ROS network, the tiny SODs sub-network was also a scale-free network. Cu/Zn SODs acted as the hub that SODs synergized with trypsin during the storage of H. undatus.

Genetic regulatory networks for salt-alkali stress in Gossypium hirsutum with differing morphological characteristics

Background

Cotton grows in altering environments that are often unfavorable or stressful for its growth and development. Consequently, the plant must cope with abiotic stresses such as soil salinity, drought, and excessive temperatures. Alkali-salt stress response remains a cumbersome biological process and is regulated via a multifaceted transcriptional regulatory network in cotton.

Results

To discover the molecular mechanisms of alkali-salt stress response in cotton, a comprehensive transcriptome analysis was carried out after alkali-salt stress treatment in three accessions of Gossypium hirsutum with contrasting phenotype. Expression level analysis proved that alkali-salt stress response presented significant stage-specific and tissue-specific. GO enrichment analysis typically suggested that signal transduction process involved in salt-alkali stress response at SS3 and SS12 stages in leaf; carbohydrate metabolic process and oxidation-reduction process involved in SS48 stages in leaf; the oxidation-reduction process involved at all three phases in the root. The Co-expression analysis suggested a potential GhSOS3/GhCBL10-SOS2 network was involved in salt-alkali stress response. Furthermore, Salt-alkali sensitivity was increased in GhSOS3 and GhCBL10 Virus-induced Gene Silencing (VIGS) plants.

Conclusion

The findings may facilitate to elucidate the underlying mechanisms of alkali-salt stress response and provide an available resource to scrutinize the role of candidate genes and signaling pathway governing alkali-salt stress response.

Molecular Signature of Subtypes of Non-Small-Cell Lung Cancer by Large-Scale Transcriptional Profiling: Identification of Key Modules and Genes by Weighted Gene Co-Expression Network Analysis (WGCNA)

Non-small-cell lung cancer (NSCLC) represents a heterogeneous group of malignancies consisting essentially of adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Although the diagnosis and treatment of ADC and SCC have been greatly improved in recent decades, there is still an urgent need to identify accurate transcriptome profile associated with the histological subtypes of NSCLC. The present study aims to identify the key dysregulated pathways and genes involved in the development of lung ADC and SCC and to relate them with the clinical traits. The transcriptional changes between tumour and normal lung tissues were investigated by RNA-seq. Gene ontology (GO), canonical pathways analysis with the prediction of upstream regulators, and weighted gene co-expression network analysis (WGCNA) to identify co-expressed modules and hub genes were used to explore the biological functions of the identified dysregulated genes. It was indicated that specific gene signatures differed significantly between ADC and SCC related to the distinct pathways. Of identified modules, four and two modules were the most related to clinical features in ADC and SCC, respectively. CTLA4, MZB1, NIP7, and BUB1B in ADC, as well as GNG11 and CCNB2 in SCC, are novel top hub genes in modules associated with tumour size, SUVmax, and recurrence-free survival. Our research provides a more effective understanding of the importance of biological pathways and the relationships between major genes in NSCLC in the perspective of searching for new molecular targets.

Weighted gene co‑expression network analysis to identify key modules and hub genes associated with atrial fibrillation

Atrial fibrillation (AF) is the most common form of cardiac arrhythmia and significantly increases the risks of morbidity, mortality and health care expenditure; however, treatment for AF remains unsatisfactory due to the complicated and incompletely understood underlying mechanisms. In the present study, weighted gene co‑expression network analysis (WGCNA) was conducted to identify key modules and hub genes to determine their potential associations with AF. WGCNA was performed in an AF dataset GSE79768 obtained from the Gene Expression Omnibus, which contained data from paired left and right atria in cardiac patients with persistent AF or sinus rhythm. Differentially expressed gene (DEG) analysis was used to supplement and validate the results of WGCNA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were also performed. Green and magenta modules were identified as the most critical modules associated with AF, from which 6 hub genes, acetyl‑CoA Acetyltransferase 1, death domain‑containing protein CRADD, gypsy retrotransposon integrase 1, FTX transcript, XIST regulator, transcription elongation factor A like 2 and minichromosome maintenance complex component 3 associated protein, were hypothesized to serve key roles in the pathophysiology of AF due to their increased intramodular connectivity. Functional enrichment analysis results demonstrated that the green module was associated with energy metabolism, and the magenta module may be associated with the Hippo pathway and contain multiple interactive pathways associated with apoptosis and inflammation. In addition, the blue module was identified to be an important regulatory module in AF with a higher specificity for the left atria, the genes of which were primarily correlated with complement, coagulation and extracellular matrix formation. These results suggest that may improve understanding of the underlying mechanisms of AF, and assist in identifying biomarkers and potential therapeutic targets for treating patients with AF.

In silico identification of EP400 and TIA1 as critical transcription factors involved in human hepatocellular carcinoma relapse

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-associated mortality worldwide. Transcription factors (TFs) are crucial proteins that regulate gene expression during cancer progression; however, the roles of TFs in HCC relapse remain unclear. To identify the TFs that drive HCC relapse, the present study constructed co-expression network and identified the Tan module the most relevant to HCC relapse. Numerous hub TFs (highly connected) were subsequently obtained from the Tan module according to the intra-module connectivity and the protein-protein interaction network connectivity. Next, E1A-binding protein p400 (EP400) and TIA1 cytotoxic granule associated RNA binding protein (TIA1) were identified as hub TFs differentially connected between the relapsed and non-relapsed subnetworks. In addition, zinc finger protein 143 (ZNF143) and Yin Yang 1 (YY1) were also identified by using the plugin iRegulon in Cytoscape as master upstream regulatory elements, which could potentially regulate expression of the genes and TFs of the Tan module, respectively. The Kaplan-Meier (KM) curves obtained from KMplot and Gene Expression Profiling Interactive Analysis tools confirmed that the high expression of EP400 and TIA1 were significantly associated with shorter relapse-free survival and disease-free survival of patients with HCC. Furthermore, the KM curves from the UALCAN database demonstrated that high EP400 expression significantly reduced the overall survival of patients with HCC. EP400 and TIA1 may therefore serve as potential prognostic and therapeutic biomarkers.

Co-expression analysis and ceRNA network reveal eight novel potential lncRNA biomarkers in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world, with a high degree of malignancy and recurrence. The influence of the ceRNA network in tumor on the biological function of liver cancer is very important, It has been reported that many lncRNA play a key role in liver cancer development. In our study, integrated data analysis revealed potential eight novel lncRNA biomarkers in hepatocellular carcinoma.

METHODS

Transcriptome data and clinical data were downloaded from the The Cancer Genome Atlas (TCGA) data portal. Weighted gene co-expression network analysis was performed to identify the expression pattern of genes in liver cancer. Then, the ceRNA network was constructed using transcriptome data.

RESULTS

The integrated analysis of miRNA and RNAseq in the database show eight novel lncRNAs that may be involved in important biological pathways, including TNM and disease development in liver cancer. We performed function enrichment analysis of mRNAs affected by these lncRNAs.

CONCLUSIONS

By identifying the ceRNA network and the lncRNAs that affect liver cancer, we showed that eight novel lncRNAs play an important role in the development and progress of liver cancer.

Investigation of the underlying hub genes and mechanisms of reperfusion injury in patients undergoing coronary artery bypass graft surgery by integrated bioinformatic analyses

Background

Although coronary artery bypass graft (CABG) surgery is the main method to revascularize the occluded coronary vessels in coronary artery diseases, the full benefits of the operation are mitigated by ischemia-reperfusion (IR) injury. Although many studies have been devoted to reducing IR injury in animal models, the translation of this research into the clinical field has been disappointing. Our study aimed to explore the underlying hub genes and mechanisms of IR injury.

Methods

A weighted gene co-expression network analysis (WGCNA) was executed based on the expression profiles in patients undergoing CABG surgery (GSE29396). Functional annotation and protein-protein interaction (PPI) network construction were executed within the modules of interest. Potential hub genes were predicted, combining both intramodular connectivity (IC) and degrees. Meanwhile, potential transcription factors (TFs) and microRNAs (miRNAs) were predicted by corresponding bioinformatics tools.

Results

A total of 336 differentially expressed genes (DEGs) were identified. DEGs were mainly enriched in neutrophil activity and immune response. Within the modules of interest, 5 upregulated hub genes (IL-6, CXCL8, IL-1β, MYC, PTGS-2) and 6 downregulated hub genes (C3, TIMP1, VSIG4, SERPING1, CD163, and HP) were predicted. Predicted miRNAs (hsa-miR-333-5p, hsa-miR-26b-5p, hsa-miR-124-3p, hsa-miR-16-5p, hsa-miR-98-5p, hsa-miR-17-5p, hsa-miR-93-5p) and TF (STAT1) might have regulated gene expression in the most positively related module, while hsa-miR-333-5p and HSF-1 were predicted to regulate the genes within the most negatively related module.

Conclusions

Our study illustrates an overview of gene expression changes in human atrial samples from patients undergoing CABG surgery and might help translate future research into clinical work.