Identification of Prognostic Candidate Genes in Breast Cancer by Integrated Bioinformatic Analysis

Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.

A comprehensive analysis of WGCNA and serum metabolomics manifests the lung cancer-associated disordered glucose metabolism

Lung cancer is a worldwide disease and highly heterogeneous at a molecular level. In this study, we both performed the pathway enrichment analysis and the transcriptome-based weighted gene coexpression network analysis (WGCNA) so as to find the critical pathways involved in lung cancer. Our analysis results indicated that genes in viability modules (0 < Z-summary < 2) selected by WGCNA were more reliable for identifying crucial pathways, while gene enrichment analysis provided a wide range of pathways with a little emphasis on target pathways for lung cancer. On the basis of genes, which were classified into various modules by WGCNA, we found a significant aberration of glucose metabolism in lung cancer cells, demonstrating that the glucose metabolism has been perturbed, especially the glycolysis pathway. Our study revealed that disordered glucose metabolism might be closely associated with the carcinogenesis of lung cancer based on the integrated analysis of WGCNA and metabolomics, which could be a potential therapeutic target for lung cancer.

Prognostic genes of hepatocellular carcinoma based on gene coexpression network analysis

Hepatocellular carcinoma (HCC) is the most common subtype in liver cancer whose prognosis is affected by malignant progression associated with complex gene interactions. However, there is currently no available biomarkers associated with HCC progression in clinical application. In our study, RNA sequencing expression data of 50 normal samples and 374 tumor samples was analyzed and 9225 differentially expressed genes were screened. Weighted gene coexpression network analysis was then conducted and the blue module we were interested was identified by calculating the correlations between 17 gene modules and clinical features. In the blue module, the calculation of topological overlap was applied to select the top 30 genes and these 30 genes were divided into the green group (11 genes) and the yellow group (19 genes) through searching whether these genes were validated by in vitro or in vivo experiments. The genes in the green group which had never been validated by any experiments were recognized as hub genes. These hub genes were subsequently validated by a new data set GSE76427 and KM Plotter Online Tool, and the results indicated that 10 genes (FBXO43, ARHGEF39, MXD3, VIPR1, DNASE1L3, PHLDA1, CSRNP1, ADR2B, C1RL, and CDC37L1) could act as prognosis and progression biomarkers of HCC. In summary, 10 genes who have never been mentioned in HCC were identified to be associated with malignant progression and prognosis of patients. These findings may contribute to the improvement of the therapeutic decision, risk stratification, and prognosis prediction for HCC patients.

Transcriptional landscape of cholangiocarcinoma revealed by weighted gene coexpression network analysis

Cholangiocarcinoma (CCA) is a type of cancer with limited treatment options and a poor prognosis. Although some important genes and pathways associated with CCA have been identified, the relationship between coexpression and phenotype in CCA at the systems level remains unclear. In this study, the relationships underlying the molecular and clinical characteristics of CCA were investigated by employing weighted gene coexpression network analysis (WGCNA). The gene expression profiles and clinical features of 36 patients with CCA were analyzed to identify differentially expressed genes (DEGs). Subsequently, the coexpression of DEGs was determined by using the WGCNA method to investigate the correlations between pairs of genes. Network modules that were significantly correlated with clinical traits were identified. In total, 1478 mRNAs were found to be aberrantly expressed in CCA. Seven coexpression modules that significantly correlated with clinical characteristics were identified and assigned representative colors. Among the 7 modules, the green and blue modules were significantly related to tumor differentiation. Seventy-eight hub genes that were correlated with tumor differentiation were found in the green and blue modules. Survival analysis showed that 17 hub genes were prognostic biomarkers for CCA patients. In addition, we found five new targets (ISM1, SULT1B1, KIFC1, AURKB and CCNB1) that have not been studied in the context of CCA and verified their differential expression in CCA through experiments. Our results not only promote our understanding of the relationship between the transcriptome and clinical data in CCA but will also guide the development of targeted molecular therapy for CCA.

Sequencing-based gene network analysis provides a core set of gene resource for understanding thermal adaptation in Zhikong scallop Chlamys farreri

Marine organisms are commonly exposed to variable environmental conditions, and many of them are under threat from increased sea temperatures caused by global climate change. Generating transcriptomic resources under different stress conditions are crucial for understanding molecular mechanisms underlying thermal adaptation. In this study, we conducted transcriptome-wide gene expression profiling of the scallop Chlamys farreri challenged by acute and chronic heat stress. Of the 13 953 unique tags, more than 850 were significantly differentially expressed at each time point after acute heat stress, which was more than the number of tags differentially expressed (320-350) under chronic heat stress. To obtain a systemic view of gene expression alterations during thermal stress, a weighted gene coexpression network was constructed. Six modules were identified as acute heat stress-responsive modules. Among them, four modules involved in apoptosis regulation, mRNA binding, mitochondrial envelope formation and oxidation reduction were downregulated. The remaining two modules were upregulated. One was enriched with chaperone and the other with microsatellite sequences, whose coexpression may originate from a transcription factor binding site. These results indicated that C. farreri triggered several cellular processes to acclimate to elevated temperature. No modules responded to chronic heat stress, suggesting that the scallops might have acclimated to elevated temperature within 3 days. This study represents the first sequencing-based gene network analysis in a nonmodel aquatic species and provides valuable gene resources for the study of thermal adaptation, which should assist in the development of heat-tolerant scallop lines for aquaculture.

Ubiquitin-Like Modifier Activating Enzyme 1 as a Novel Diagnostic and Prognostic Indicator That Correlates With Ferroptosis and the Malignant Phenotypes of Liver Cancer Cells

PURPOSE

Ferroptosis is a type of cell death that is iron dependent, a characteristic that distinguishes it from necrosis, apoptosis, and autophagy. However, the ferroptotic mechanisms for hepatitis B virus-associated hepatocellular carcinoma (HCC) remain incompletely described.

METHODS

Two hepatitis B virus-associated HCC public datasets, GSE22058 (n=192) and GSE54238 (n=23), were obtained from the NCBI Gene Expression Omnibus (GEO) database. Bioinformatics methods, including weighted gene coexpression network analysis (WGCNA), Cox regression, and LASSO analysis, were used to identify signature markers for diagnosis and prognosis. CCK8, wound healing, Transwell migration/invasion, and ferroptosis assays were employed to explore the biological function of novel candidate markers weight gene coexpression network analysis.

RESULTS

In total, 926 differentially expressed genes (DEGs) were common between the GSE22058 and GSE54238 datasets. Following WGCNA, 515 DEGs derived from the MEturquoise gene module were employed to establish diagnosis and prognosis models in The Cancer Genome Atlas (TCGA) HCC RNA-Seq cohort (n=423). The score of the diagnostic model was strikingly upregulated in the TCGA HCC group (p<2.2e-16). The prognostic model exhibited high specificity and sensitivity in both training and validation (AUC=0.835 and 0.626, respectively), and the high-risk group showed dismal prognostic outcomes compared with the low-risk group (training: p=1.416e-10; validation: p=4.495e-02). Ubiquitin-like modifier activating enzyme 1 (UBA1) was identified among both diagnosis and prognosis signature genes, and its overexpression was associated with poor survival. We validated the expression level of UBA1 in eight pairs of HCC patient tissues and liver cancer cell lines. UBA1 silencing decreased proliferation, migration, and invasion in Huh7 cells while elevating the Fe2+ and malondialdehyde (MDA) levels. Additionally, these biological effects were recovered by oltipraz (an Nrf2 activator). Furthermore, blocking UBA1 strikingly repressed the protein expression levels of Nrf2, HO-1, NQO1, and FTH1 in the Nrf2 signal transduction pathway.

CONCLUSION

Our findings demonstrated that UBA1 participates in the development of HCC by modulating Huh7 phenotypes and ferroptosis via the Nrf2 signal transduction pathway and might be a promising diagnostic and prognostic indicator for HCC.

Identification of Distinct Immune Subtypes in Colorectal Cancer Based on the Stromal Compartment

The tumor environment is of vital importance for the incidence and development of colorectal cancer. Increasing evidence in recent years has elaborated the vital role of the tumor environment in cancer subtype classification and patient prognosis, but a comprehensive understanding of the colorectal tumor environment that is purely dependent on the stromal compartment is lacking. To decipher the tumor environment in colorectal cancer and explore the role of its immune context in cancer classification, we performed a gene expression microarray on the stromal compartment of colorectal cancer and adjacent normal tissues. Through the integrated analysis of our data with public gene expression microarray data of stromal and epithelial colorectal cancer tissues processed through laser capture microdissection, we identified four highly connected gene modules representing the biological features of four tissue compartments by applying a weighted gene coexpression network analysis algorithm and classified colorectal cancers into three immune subtypes by adopting a nearest template prediction algorithm. A systematic analysis of the four identified modules mainly reflected the close interplay between the biological changes of intrinsic and extrinsic characteristics at the initiation of colorectal cancer. Colorectal cancers were stratified into three immune subtypes based on gene templates identified from representative gene modules of the stromal compartment: active immune, active stroma, and mixed type. These immune subtypes differed by the immune cell infiltration pattern, expression of immune checkpoint inhibitors, mutation landscape, extent of mutation burden, extent of copy number burden, prognosis and chemotherapeutic sensitivity. Further analysis indicated that activation of the NF-kB signaling pathway was the major mechanism causing the no immune infiltration milieu in the active stroma subtype and that inhibitors of the NF-kB signaling pathway could be candidate drugs for treating patients with an active stroma. Overall, these results suggest that characterizing colorectal cancer by the tumor environment is of vital importance in predicting patients' clinical outcomes and helping guide precision and personalized treatment.

KIAA0101 is a novel transcriptional target of FoxM1 and is involved in the regulation of hepatocellular carcinoma microvascular invasion by regulating epithelial-mesenchymal transition

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths due to tumor invasiveness, frequent intrahepatic dissemination and extrahepatic metastasis. However, the genes and signaling pathways that are involved remain incompletely understood. In this study, weighted gene coexpression network analysis (WGCNA) was performed to jointly analyze clinical information and gene expression data to identify key genes associated with clinical features. Through the bioinformatic analysis, the yellow module and microvascular invasion (MVI) were found to be highly associated (r=0.41) by Pearson's correlation analysis, and 20 hub genes were identified with both high gene significance (GS) and high module membership (MM) in the yellow module. Among these genes, FoxM1 and KIAA0101 were upregulated in HCC with MVI and were significantly positively correlated in HCC samples, indicating a novel regulatory network in HCC microvascular invasion. Moreover, in vitro experiments demonstrated that KIAA0101 is a direct target of FoxM1 and that KIAA0101 is required for the FoxM1-induced promotion of HCC cell invasion and migration. In addition, the FoxM1-KIAA0101 axis promotes HCC metastasis by inducing epithelial-mesenchymal transition (EMT). In summary, KIAA0101 is a novel target of FoxM1 and contributes to HCC metastasis by activating EMT. The FoxM1-KIAA0101 axis might be applied as a potential prognostic biomarker and therapeutic target for HCC.

Identification of KIAA0513 and Other Hub Genes Associated With Alzheimer Disease Using Weighted Gene Coexpression Network Analysis

Alzheimer disease (AD) is the most common cause of dementia and creates a significant burden on society. As a result, the investigation of hub genes for the discovery of potential therapeutic targets and candidate biomarkers is warranted. In this study, we used the ComBat method to merge three gene expression datasets of AD from the Gene Expression Omnibus (GEO). During combined analysis, we identified 850 differentially expressed genes (DEGs) from the temporal cortex of AD and cognitively normal (CN) samples. We performed weighted gene coexpression network analysis to build gene coexpression networks incorporating these DEGs to identify key modules and hub genes. We found one module most strongly correlated with AD onset as the key module and 19 hub genes in the key module that were down-regulated in AD brains. According to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, DEGs were mostly enriched in synapse function, and genes in the key module were mostly related to learning and memory. We selected five little-studied genes, AP3B2, GABRD, GPR158, KIAA0513, and MAL2, to validate their expression in AD mouse model by performing quantitative real-time polymerase chain reaction. We found that all of them were down-regulated in cortices of 8-month 5xFAD mice compared to those of wild-type mice. We then further investigated their correlations with β-secretase activity and Aβ42 levels in AD samples of different Braak stages. We found that all five hub genes had significant negative associations with β-secretase activity and that AP3B2 and KIAA0513 had significant negative associations with Aβ42 levels. We tested the differential expressions of the five hub genes in two AD GEO datasets from the blood and found that KIAA0513 was significantly up-regulated in patients with both mild cognitive impairment (MCI) and AD and was able to differentiate MCI and AD from CN in the two datasets. In conclusion, these five novel vulnerable genes were involved in AD progression, and KIAA0513 was a promising candidate biomarker for early diagnosis of AD.

Identification of core gene networks and hub genes associated with progression of non-alcoholic fatty liver disease by RNA sequencing

AIM

Non-alcoholic fatty liver disease (NAFLD) progresses because of the interaction between numerous genes. Thus, we carried out a weighted gene coexpression network analysis to identify core gene networks and key genes associated with NAFLD progression.

METHODS

We enrolled 39 patients with mild NAFLD (fibrosis stages 0-2) and 21 with advanced NAFLD (fibrosis stages 3-4). Total RNA was extracted from frozen liver biopsies, and sequenced to capture a large dynamic range of expression levels.

RESULTS

A total of 1777 genes differentially expressed between mild and advanced NAFLD (q-value <0.05) clustered into four modules. One module was enriched for genes that encode cell surface or extracellular matrix proteins, and are involved in cell adhesion, proliferation, and signaling. This module formed a scale-free network containing four hub genes (PAPLN, LBH, DPYSL3, and JAG1) overexpressed in advanced NAFLD. PAPLN is a component of the extracellular matrix, LBH and DPYSL3 are reported to be tumor suppressors, and JAG1 is tumorigenic. Another module formed a random network, and was enriched for genes that accumulate in the mitochondria. These genes were downregulated in advanced NAFLD, reflecting impaired mitochondrial function. However, the other two modules did not form unambiguous networks. KEGG analysis indicated that 71 differentially expressed genes were involved in "pathways in cancer". Strikingly, expression of half of all differentially expressed genes was inversely correlated with methylation of CpG sites (q-value <0.05). Among clinical parameters, serum type IV collagen 7 s was most strongly associated with the epigenetic status in NAFLD.

CONCLUSIONS

Newly identified core gene networks suggest that the NAFLD liver undergoes mitochondrial dysfunction and fibrosis, and acquires tumorigenic potential epigenetically. Our data provide novel insights into the pathology and etiology of NAFLD progression, and identify potential targets for diagnosis and treatment.

Key genes and functional coexpression modules involved in the pathogenesis of systemic lupus erythematosus

We performed a systematic review of genome-wide gene expression datasets to identify key genes and functional modules involved in the pathogenesis of systemic lupus erythematosus (SLE) at a systems level. Genome-wide gene expression datasets involving SLE patients were searched in Gene Expression Omnibus and ArrayExpress databases. Robust rank aggregation (RRA) analysis was used to integrate those public datasets and identify key genes associated with SLE. The weighted gene coexpression network analysis (WGCNA) was adapted to identify functional modules involved in SLE pathogenesis, and the gene ontology enrichment analysis was utilized to explore their functions. The aberrant expressions of several randomly selected key genes were further validated in SLE patients through quantitative real-time polymerase chain reaction. Fifteen genome-wide gene expression datasets were finally included, which involved a total of 1,778 SLE patients and 408 healthy controls. A large number of significantly upregulated or downregulated genes were identified through RRA analysis, and some of those genes were novel SLE gene signatures and their molecular roles in etiology of SLE remained vague. WGCNA further successfully identified six main functional modules involved in the pathogenesis of SLE. The most important functional module involved in SLE included 182 genes and mainly enriched in biological processes, including defense response to virus, interferon signaling pathway, and cytokine-mediated signaling pathway. This study identifies a number of key genes and functional coexpression modules involved in SLE, which provides deepening insights into the molecular mechanism of SLE at a systems level and also provides some promising therapeutic targets.

Development and validation of a novel prognostic signature in gastric adenocarcinoma

Competing endogenous RNA networks have attracted increasing attention in gastric adenocarcinoma (GA). The current study aimed to explore ceRNA-based prognostic biomarkers for GA. RNA expression profiles were downloaded from TCGA and GEO databases. A ceRNA network was constructed based on the most relevant modules in the weighted gene coexpression network analysis. Kaplan-Meier (KM) survival analysis revealed prognosis-related RNAs, which were subjected to the multivariate Cox regression analysis. The predictive accuracy and discriminative ability of the signature were determined by KM analyses, receiver operating characteristic curves and area under the curve values. Ultimately, we constructed a ceRNA network consisting of 55 lncRNAs, 17 miRNAs and 73 mRNAs. Survival analyses revealed 3 lncRNAs (LINC01106, FOXD2-AS1, and AC103702.2) and 3 mRNAs (CCDC34, ORC6, and SOX4) as crucial prognostic factors; these factors were then used to construct a survival specific ceRNA network. Patients with high risk scores exhibited significantly worse overall survival than patients with low risk scores, and the AUC for 5-year survival was 0.801. A total of 112 GA specimens and the GSE84437 dataset were used to successfully validate the robustness of our signature by qRT-PCR. In summary, we developed a prognostic signature for GA, that shows better accuracy than the traditional TNM pathological staging system.

Identify signature regulatory network for glioblastoma prognosis by integrative mRNA and miRNA co-expression analysis

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults. Patients with this disease have a poor prognosis. The objective of this study is to identify survival-related individual genes (or miRNAs) and miRNA -mRNA pairs in GBM using a multi-step approach. First, the weighted gene co-expression network analysis and survival analysis are applied to identify survival-related modules from mRNA and miRNA expression profiles, respectively. Subsequently, the role of individual genes (or miRNAs) within these modules in GBM prognosis are highlighted using survival analysis. Finally, the integration analysis of miRNA and mRNA expression as well as miRNA target prediction is used to identify survival-related miRNA -mRNA regulatory network. In this study, five genes and two miRNA modules that significantly correlated to patient's survival. In addition, many individual genes (or miRNAs) assigned to these modules were found to be closely linked with survival. For instance, increased expression of neuropilin-1 gene (a member of module turquoise) indicated poor prognosis for patients and a group of miRNA -mRNA regulatory networks that comprised 38 survival-related miRNA -mRNA pairs. These findings provide a new insight into the underlying molecular regulatory mechanisms of GBM.

Identification of GSN and LAMC2 as Key Prognostic Genes of Bladder Cancer by Integrated Bioinformatics Analysis

Bladder cancer is a common malignancy with mechanisms of pathogenesis and progression. This study aimed to identify the prognostic hub genes, which are the central modulators to regulate the progression and proliferation in the specific subtype of bladder cancer. The identification of the candidate hub gene was performed by weighted gene co-expression network analysis to construct a free-scale gene co-expression network. The gene expression profile of GSE97768 from the Gene Expression Omnibus database was used. The association between prognosis and hub gene was evaluated by The Cancer Genome Atlas database. Four gene-expression modules were significantly related to bladder cancer disease: the red module (human adenocarcinoma lymph node metastasis), the darkturquioise module (grade 2 carcinoma), the lightgreen module (grade 3 carcinoma), and the royalblue module (transitional cell carcinoma lymphatic metastasis). Based on betweenness centrality and survival analysis, we identified laminin subunit gamma-2 (LAMC2) in the grade 2 carcinoma, gelsolin (GSN) in the grade 3 carcinoma, and homeodomain-interacting protein kinase 2 (HIPK2) in the transitional cell carcinoma lymphatic metastasis. Subsequently, the protein levels of LAMC2 and GSN were respectively down-regulated and up-regulated in tumor tissue with the Human Protein Atlas (HPA) database. Our results suggested that LAMC2 and GSN are the central modulators to transfer information in the specific subtype of the disease.

Coexpression network analysis linked H2AFJ to chemoradiation resistance in colorectal cancer

Neoadjuvant chemoradiotherapy (CRT) resistance is a complex phenomenon and it remains a major problem for patients with a priori resistant tumor. Therefore, there is a strong need to investigate molecular biomarkers which may guide for treatment decision-making. In our study, weighted gene coexpression network analysis was applied to identify CRT-resistance hub modules in 12 colorectal cancer (CRC) cell lines with different CRT sensitivities from GSE20298 data set. The green module and purple module had the highest correlations with CRT resistance. Gene ontology enrichment analysis indicated that the function of these two modules focused on interferon-mediated signaling pathway, immune response, chromatin modulation, Rho GTPases activities, and regulation of apoptotic process. Then, 15 hub genes in both the coexpression and protein-protein interaction networks were selected. Among these hub genes, higher H2A histone family member J (H2AFJ) expression was independently validated in patient cohorts from two testing data sets of GSE46862 and GSE68204 to be related to CRT resistance. The receiver operating characteristic curve showed that H2AFJ could efficiently distinguish CRT-resistance cases from CRT-sensitive cases in another two testing data sets. Furthermore, meta-analysis of 12 Gene Expression Omnibus-sourced data sets showed that H2AFJ messenger RNA levels were significantly higher in CRC tissues than in normal colon tissues. High H2AFJ expression was correlated with a significant worse event- and relapse-free survival by analyzing the data from the R2: Genomics Analysis and Visualization Platform. Gene set enrichment analysis determined that the mechanism of H2AFJ-mediated CRT resistance might involve the ERK5 (MAPK7), human immunodeficiency virus Nef (HIV Nef), and inflammatory pathways. This study is the first, to the best of our knowledge, to implicate and verify H2AFJ as an effective new marker for CRT response prediction.

Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis

As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure

The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis.

Transcriptome analyses and weighted gene coexpression network analysis reveal key pathways and genes involved in the rapid cold resistance of the Chinese white wax scale insect

The Chinese white wax scale insect, Ericerus pela, is an important resource insect in China. The rapid response of E. pela to decreasing temperatures plays key roles in the population distribution. In this study, we analyzed the gene expression of E. pela treated with low temperature using transcriptome analyses and weighted gene coexpression network analysis (WGCNA). The results showed that the cold resistance of E. pela involved changes in the expression of many genes. The genes were mainly involved in alcohol formation activity, lipid metabolism, membrane and structure, and oxidoreductase activity. According to the WGCNA results, some pathways related to cold resistance were found in the genes in the modules, such as cytoskeleton proteins, cytoskeleton protein pathway, biosynthesis of unsaturated fatty acids, glycerophospholipid metabolism, ether lipid metabolism, and thermogenesis. Some of the hub genes were nonspecific lipid-transfer proteins, DnaJ homolog subfamily C member 13, paramyosin, tropomodulin, and tubulin beta chain. In particular, the hub genes of the tan module included the heat shock protein (hsp) 10, hsp 60, hsp 70, and hsp 90 genes. Thirty-five antifreeze protein (afp) genes were identified according to the annotation results. Three afp genes were further identified among the hub genes. Six of these genes were selected for heterogeneous protein expression. One of them was expressed successfully. The thermal hysteresis activity (THA) analyses showed that the THA was 1.73°C. These results showed that the cytoskeleton, lipid metabolism, thermogenesis, HSPs and AFPs may play important roles in the cold resistance of E. pela.

Alternative Splicing Enhances the Transcriptome Complexity of Liriodendron chinense

Alternative splicing (AS) plays pivotal roles in regulating plant growth and development, flowering, biological rhythms, signal transduction, and stress responses. However, no studies on AS have been performed in Liriodendron chinense, a deciduous tree species that has high economic and ecological value. In this study, we used multiple tools and algorithms to analyze transcriptome data derived from seven tissues via hybrid sequencing. Although only 17.56% (8,503/48,408) of genes in L. chinense were alternatively spliced, these AS genes occurred in 37,844 AS events. Among these events, intron retention was the most frequent AS event, producing 1,656 PTC-containing and 3,310 non-PTC-containing transcripts. Moreover, 183 long noncoding RNAs (lncRNAs) also underwent AS events. Furthermore, weighted gene coexpression network analysis (WGCNA) revealed that there were great differences in the activities of transcription and post-transcriptional regulation between pistils and leaves, and AS had an impact on many physiological and biochemical processes in L. chinense, such as photosynthesis, sphingolipid metabolism, fatty acid biosynthesis and metabolism. Moreover, our analysis showed that the features of genes may affect AS, as AS genes and non-AS genes had differences in the exon/intron length, transcript length, and number of exons/introns. In addition, the structure of AS genes may impact the frequencies and types of AS because AS genes with more exons or introns tended to exhibit more AS events, and shorter introns tended to be retained, whereas shorter exons tended to be skipped. Furthermore, eight AS genes were verified, and the results were consistent with our analysis. Overall, this study reveals that AS and gene interaction are mutual-on one hand, AS can affect gene expression and translation, while on the other hand, the structural characteristics of the gene can also affect AS. This work is the first to comprehensively report on AS in L. chinense, and it can provide a reference for further research on AS in L. chinense.

Weighted gene coexpression network analysis identifies specific transcriptional modules and hub genes related to intramuscular fat traits in chicken breast muscle

Intramuscular fat (IMF) traits are important factors that influence meat quality. However, the molecular regulatory mechanisms that underlie this trait in chickens are still poorly understood at the gene coexpression level. Here, we performed a weighted gene coexpression network analysis between IMF traits and transcriptome profile in breast muscle in the Chinese domestic Gushi chicken breed at 6, 14, 22, and 30 weeks. A total of 26 coexpressed gene modules were identified. Six modules, which included the dark gray, purple, cyan, pink, light cyan, and blue modules, showed a significant positive correlation (P < 0.05) with IMF traits. The strongest correlation was observed between the dark gray module and IMF content (r = 0.85; P = 4e-04) and between the blue module and different fatty acid content (r = 0.87~0.91; P = 5e-05~2e-04). Enrichment analysis showed that the enrichment of biological processes, such as fatty acid metabolic process, fat cell differentiation, acylglycerol metabolic process, and glycerolipid metabolism were significantly different in the six modules. In addition, the 32, 24, 4, 7, 6, and 25 hub genes were identified from the blue, pink, light cyan, cyan, dark gray, and purple modules, respectively. These hub genes are involved in multiple links to fatty acid metabolism, phospholipid metabolism, cholesterol metabolism, diverse cellular behaviors, and cell events. These results provide novel insights into the molecular regulatory mechanisms for IMF-related traits in chicken and may also help to uncover the formation mechanism for excellent meat quality traits in local breeds of Chinese chicken.

LMTK3 promotes tumorigenesis in bladder cancer via the ERK/MAPK pathway

Lemur tyrosine kinase 3 (LMTK3) is a key member of the serine–threonine tyrosine kinase family. It plays an important role in breast cancer tumorigenesis and progression. However, its biological role in bladder cancer remains elusive. In this study, we demonstrated that LMTK3 was overexpressed in bladder cancer and was positively correlated with bladder cancer malignancy. High LMTK3 expression predicted poor overall survival. Knockdown of LMTK3 in bladder cancer cells triggered cell‐cycle arrest at G2/M phase, suppressed cell growth, and induced cell apoptosis in bladder cancer cells. Furthermore, Transwell assays revealed that reduction of LMTK3 decreased cell migration by regulating the epithelial‐to‐mesenchymal transition pathway. Conversely, LKTM3 overexpression was shown to promote proliferation and migration of bladder cancer cells. We assessed phosphorylation of MEK and ERK1/2 in bladder cancer cells depleted of LMTK3 and demonstrated a reduced phosphorylation status compared with the control group. Using an MAPK signaling‐specific inhibitor, U0126, we could rescue the promotion of proliferation and viability in LMTK3‐overexpressing cells. In conclusion, we extend the status of LMTK3 as an oncogene in bladder cancer and provide evidence for its function via the activation of the ERK/MAPK pathway. Thus, targeting LMTK3 may hold potential as a diagnostic and prognostic biomarker and as a possible future treatment for bladder cancer.

Identification of key genes and pathways involved in vitiligo development based on integrated analysis

Vitiligo is a chronic skin condition lack of melanocytes. However, researches on the aetiology and pathogenesis of vitiligo are still under debate. This study aimed to explore the key genes and pathways associated with occurrence and development of vitiligo.Weighted gene coexpression network analysis (WGCNA) was applied to reanalyze the gene expression dataset GSE65127 systematically. Functional enrichments of these modules were carried out at gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA). Then, a map of regulatory network was delineated according to pivot analysis and drug prediction. In addition, hub genes and crucial pathways were validated by an independent dataset GSE75819. The expressions of hub genes in modules were also tested by quantitative real-time polymerase chain reaction (qRT-PCR).Eight coexpressed modules were identified by WGCNA based on 5794 differentially expressed genes of vitiligo. Three modules were found to be significantly correlated with Lesional, Peri-Lesional, and Non-Lesional, respectively. The persistent maladjusted genes included 269 upregulated genes and 82 downregulated genes. The enrichments showed module genes were implicated in immune response, p53 signaling pathway, etc. According to GSEA and GSVA, dysregulated pathways were activated incessantly from Non-Lesional to Peri-Lesional and then to Lesional, 4 of which were verified by an independent dataset GSE75819. Finally, 42 transcription factors and 228 drugs were spotted. Focusing on the persistent maladjusted genes, a map of regulatory network was delineated. Hub genes (CACTIN, DCTN1, GPR143, HADH, MRPL47, NKTR, NUF2) and transcription factors (ITGAV, SYK, PDPK1) were validated by an independent dataset GSE75819. In addition, hub genes (CACTIN, DCTN1, GPR143, MRPL47, NKTR) were also confirmed by qRT-PCR.The present study, at least, might provide an integrated and in-depth insight for exploring the underlying mechanism of vitiligo and predicting potential diagnostic biomarkers and therapeutic targets.

Potentially Critical Roles of NDUFB5, TIMMDC1, and VDAC3 in the Progression of Septic Cardiomyopathy Through Integrated Bioinformatics Analysis

Septic cardiomyopathy (SC) is a rare and harmful cardiovascular disease with decreased left ventricular (LV) output and multiple organ failure, which poses a serious threat to human life. Despite the advances in SC, its diagnostic basis and treatment methods are limited, and the specific diagnostic biomarkers and its candidate regulatory targets have not yet been fully established. In this study, the GSE79962 gene expression profile was retrieved, with 20 patients with SC and 11 healthy donors as control. Weighted gene coexpression network analysis (WGCNA) was employed to investigate gene modules that were strongly correlated with clinical phenotypes. Blue module was found to be most significantly related to SC. Moreover, Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the coexpression genes in blue module and showed that it was associated with metabolic pathways, oxidative phosphorylation, and cardiac muscle contraction. Furthermore, a total of 10 hub genes NDUFB5, TIMMDC1, VDAC3, COQ10A, MRPL16 (mitochondrial ribosomal protein L16), C3orf43, TMEM182, DLAT, NDUFA8, and PDHB (pyruvate dehydrogenase E1 beta subunit) in the blue module were identified at transcriptional level and further validated at translational level in myocardium of an lipopolysaccharide-induced septic cardiac dysfunction mouse model. Overall, the results of quantitative real-time polymerase chain reaction were consistent with most of the microarray analysis results. Intriguingly, we observed that the highest change was NDUFB5, TIMMDC1, and VDAC3. These identified and validated genes provided references that would advance the understanding of molecular mechanisms of SC. Taken together, using WGCNA, the hub genes NDUFB5, TIMMDC1, and VDAC3 might serve as potential biomarkers for diagnosis and/or therapeutic targets for precise treatment of SC in the future.

Apolipoprotein D as a Potential Biomarker and Construction of a Transcriptional Regulatory-Immune Network Associated with Osteoarthritis by Weighted Gene Coexpression Network Analysis

OBJECTIVE

Synovial inflammation influences the progression of osteoarthritis (OA). Herein, we aimed to identify potential biomarkers and analyze transcriptional regulatory-immune mechanism of synovitis in OA using weighted gene coexpression network analysis (WGCNA).

DESIGN

A data set of OA synovium samples (GSE55235) was analyzed based on WGCNA. The most significant module with OA was identified and function annotation of the module was performed, following which the hub genes of the module were identified using Pearson correlation and a protein-protein interaction network was constructed. A transcriptional regulatory network of hub genes was constructed using the TRRUST database. The immune cell infiltration of OA samples was evaluated using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The hub genes coexpressed in multiple tissues were then screened out using data sets of synovium, cartilage, chondrocyte, subchondral bone, and synovial fluid samples. Finally, transcriptional factors and coexpressed hub genes were validated via experiments.

RESULTS

The turquoise module of GSE55235 was identified via WGCNA. Functional annotation analysis showed that "mineral absorption" and "FoxO signaling pathway" were mostly enriched in the module. JUN, EGR1, FOSB, and KLF4 acted as central nodes in protein-protein interaction network and transcription factors to connect several target genes. "Activated B cell," "activated CD4T cell," "eosinophil," "neutrophil," and "type 17 T helper cell" showed high immune infiltration, while FOSB, KLF6, and MYBL2 showed significant negative correlation with type 17 T helper cell.

CONCLUSIONS

Our results suggest that the expression level of apolipoprotein D (APOD) was correlated with OA. Furthermore, transcriptional regulatory-immune network was constructed, which may contribute to OA therapy.

Weighted gene coexpression network analysis identifies hub genes related to KRAS mutant lung adenocarcinoma

The aim of current study was to use Weighted Gene Coexpression Network Analysis (WGCNA) to identify hub genes related to the incidence and prognosis of KRAS mutant (MT) lung adenocarcinoma (LUAD).We involved 184 stage IIB to IV LUAD samples and 59 normal lung tissue samples from The Cancer Genome Atlas (TCGA) database. The R package "limma" was used to identify differentially expressed genes (DEGs). WGCNA and survival analyses were performed by R packages "WGCNA" and "survival," respectively. The functional analyses were performed by R package "clusterProfiler" and GSEA software. Network construction and MCODE analysis were performed by Cytoscape_v3.6.1.Totally 2590 KRAS MT specific DEGs were found between LUAD and normal lung tissues, and 10 WGCNA modules were identified. Functional analysis of the key module showed the ribosome biogenesis related terms were enriched. We observed the expression of 8 genes were positively correlated to the worse survival of KRAS MT LUAD patients, the 7 of them were validated by Kaplan-Meier plotter database (kmplot.com/) (thymosin Beta 10 [TMSB10], ribosomal Protein S16 [RPS16], mitochondrial ribosomal protein L27 [MRPL27], cytochrome c oxidase subunit 6A1 [COX6A1], HCLS1-associated protein X-1 [HAX1], ribosomal protein L38 [RPL38], and ATP Synthase Membrane Subunit DAPIT [ATP5MD]). The GSEA analysis found mTOR and STK33 pathways were upregulated in KRAS MT LUAD (P < .05, false discovery rate [FDR] < 0.25).In summary, our study firstly used WGCNA to identify hub genes in the development of KRAS MT LUAD. The identified prognostic factors would be potential biomarkers in clinical use. Further molecular studies are required to confirm the mechanism of those genes in KRAS MT LUAD.