Identify the signature genes for diagnose of uveal melanoma by weight gene co-expression network analysis

Exploring the molecular mechanism of comorbidity of autism spectrum disorder and inflammatory bowel disease by combining multiple data sets

BACKGROUND

Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is difficult to diagnose. Inflammatory bowel disease (IBD) is a common chronic digestive disease. Previous studies have shown a potential correlation between ASD and IBD, but the pathophysiological mechanism remains unclear. The purpose of this research was to examine the biological mechanisms underlying the differentially expressed genes (DEGs) of ASD and IBD using bioinformatics tools.

METHODS

Limma software was used to evaluate the DEGs between ASD and IBD. The GSE3365, GSE18123, and GSE150115 microarray data sets were acquired from the Gene Expression Omnibus (GEO) database. We then performed 6 analyses, namely, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional annotation; weighted gene coexpression network analysis; correlation analysis of hub genes with autophagy, ferroptosis and immunity; transcriptional regulation analysis of hub genes; single-cell sequencing analysis; and potential therapeutic drug prediction.

RESULTS

A total of 505 DEGs associated with ASD and 616 DEGs associated with IBD were identified, and 7 genes overlapped between these sets. GO and KEGG analyses revealed several pathways enriched in both diseases. A total of 98 common genes related to ASD and IBD were identified by weighted gene coexpression network analysis (WGCNA), and 4 hub genes were obtained by intersection with the 7 intersecting DEGs, which were PDGFC, CA2, GUCY1B3 and SDPR. We also found that 4 hub genes in the two diseases were related to autophagy, ferroptosis or immune factors. In addition, motif-TF annotation analysis showed that cisbp__M0080 was the most relevant motif. We also used the Connectivity Map (CMap) database to identify 4 potential therapeutic agents.

CONCLUSION

This research reveals the shared pathogenesis of ASD and IBD. In the future, these common hub genes may provide new targets for further mechanistic research as well as new therapies for patients with ASD and IBD.

Identification and validation of immunogenic cell death-related score in uveal melanoma to improve prediction of prognosis and response to immunotherapy

BACKGROUND

Immunogenic cell death (ICD) could activate innate and adaptive immune response. In this work, we aimed to develop an ICD-related signature in uveal melanoma (UVM) patients and facilitate assessment of their prognosis and immunotherapy.

METHODS

A set of machine learning methods, including non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithms were used to evaluate the infiltration of immune cells. The Genomics of Drug Sensitivity in Cancer (GDSC), cellMiner and tumor immune dysfunction and exclusion (TIDE) databases were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures was also compared.

RESULTS

The ICDscore could predict the prognosis of UVM patients in both the training and four validating cohorts. The ICDscore outperformed 19 previously published signatures. Patients with high ICDscore exhibited a substantial increase in immune cell infiltration and expression of immune checkpoint inhibitor-related genes, leading to a higher response rate to immunotherapy. Furthermore, the downregulation of poly (ADP-ribose) polymerase family member 8 (PARP8), a critical gene involved in the development of the ICDscore, resulted in decreased cell proliferation and slower migration of UVM cells.

CONCLUSION

In conclusion, we developed a robust and powerful ICD-related signature for evaluating the prognosis and benefits of immunotherapy that could serve as a promising tool to guide decision-making and surveillance for UVM patients.

The Identification and Clinical Value Evaluation of CYCS Related to Asthma through Bioinformatics Analysis and Functional Experiments

Background

Asthma is one of the most common respiratory diseases and one of the largest burdens of health care resources across the world. This study is aimed at using bioinformatics methods to find effective clinical indicators for asthma and conducting experimental validation.

Methods

We downloaded GSE64913 data and performed differentially expressed gene (DEG) screening. Weighted gene coexpression network analysis (WGCNA) on DEGs was applied to identify key module most associated with asthma for protein-protein interaction (PPI) analysis. According to the degree value, ten genes were obtained and subjected to expression analysis and receiver operating characteristic (ROC) analysis. Next, key genes were screened for expression analysis and immunological analysis. Finally, cell counting kit-8 (CCK-8) and qRT-PCR were also conducted to observe the influence of hub gene on cell proliferation and inflammatory cytokines.

Results

From the GSE64913 dataset, 711 upregulated and 684 downregulated DEGs were found. In WGCNA, the top 10 genes in the key module were examined by expression analysis in asthma, and CYCS was determined as an asthma-related oncogene with a good predictive ability for the prognosis of asthmatic patients. CYCS is significantly associated with immune cells, such as HHLA2, IDO1, TGFBR1, and CCL18 and promoted the proliferation of asthmatic cells in vitro.

Conclusion

CYCS plays an oncogenic role in the pathophysiology of asthma, indicating that this gene may become a novel diagnostic biomarker and promising target of asthma treatment.

Identification of hub genes in the subacute spinal cord injury in rats

Background

Spinal cord injury (SCI) is a common trauma in clinical practices. Subacute SCI is mainly characterized by neuronal apoptosis, axonal demyelination, Wallerian degeneration, axonal remodeling, and glial scar formation. It has been discovered in recent years that inflammatory responses are particularly important in subacute SCI. However, the mechanisms mediating inflammation are not completely clear.

Methods

The gene expression profiles of GSE20907, GSE45006, and GSE45550 were downloaded from the GEO database. The models of the three gene expression profiles were all for SCI to the thoracic segment of the rat. The differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA) were performed using R software, and functional enrichment analysis and protein–protein interaction (PPI) network were performed using Metascape. Module analysis was performed using Cytoscape. Finally, the relative mRNA expression level of central genes was verified by RT-PCR.

Results

A total of 206 candidate genes were identified, including 164 up-regulated genes and 42 down-regulated genes. The PPI network was evaluated, and the candidate genes enrichment results were mainly related to the production of tumor necrosis factors and innate immune regulatory response. Twelve core genes were identified, including 10 up-regulated genes and 2 down-regulated genes. Finally, seven hub genes with statistical significance in both the RT-PCR results and expression matrix were identified, namely Itgb1, Ptprc, Cd63, Lgals3, Vav1, Shc1, and Casp4. They are all related to the activation process of microglia.

Conclusion

In this study, we identified the hub genes and signaling pathways involved in subacute SCI using bioinformatics methods, which may provide a molecular basis for the future treatment of SCI.

Identification of co-expression hub genes for ferroptosis in kidney renal clear cell carcinoma based on weighted gene co-expression network analysis and The Cancer Genome Atlas clinical data

Renal clear cell carcinoma (KIRC) is one of the most common tumors worldwide and has a high mortality rate. Ferroptosis is a major mechanism of tumor occurrence and development, as well as important for prognosis and treatment of KIRC. Here, we conducted bioinformatics analysis to identify KIRC hub genes that target ferroptosis. By Weighted gene co-expression network analysis (WGCNA), 11 co-expression-related genes were screened out. According to Kaplan Meier's survival analysis of the data from the gene expression profile interactive analysis database, it was identified that the expression levels of two genes, PROM2 and PLIN2, are respectively related to prognosis. In conclusion, our findings indicate that PROM2 and PLIN2 may be effective new targets for the treatment and prognosis of KIRC.

Identification of Potential Cytokinin Responsive Key Genes in Rice Treated With Trans-Zeatin Through Systems Biology Approach

Rice is an important staple food grain consumed by most of the population around the world. With climate and environmental changes, rice has undergone a tremendous stress state which has impacted crop production and productivity. Plant growth hormones are essential component that controls the overall outcome of the growth and development of the plant. Cytokinin is a hormone that plays an important role in plant immunity and defense systems. Trans-zeatin is an active form of cytokinin that can affect plant growth which is mediated by a multi-step two-component phosphorelay system that has different roles in various developmental stages. Systems biology is an approach for pathway analysis to trans-zeatin treated rice that could provide a deep understanding of different molecules associated with them. In this study, we have used a weighted gene co-expression network analysis method to identify the functional modules and hub genes involved in the cytokinin pathway. We have identified nine functional modules comprising of different hub genes which contribute to the cytokinin signaling route. The biological significance of these identified hub genes has been tested by applying well-proven statistical techniques to establish the association with the experimentally validated QTLs and annotated by the DAVID server. The establishment of key genes in different pathways has been confirmed. These results will be useful to design new stress-resistant cultivars which can provide sustainable yield in stress-specific conditions.

Potential Novel Modules and Hub Genes as Prognostic Candidates of Thyroid Cancer by Weighted Gene Co-Expression Network Analysis

Background

Although thyroid cancer (THCA) is one of the most common type of endocrine malignancy, its highly complex molecular mechanisms of carcinogenesis are not completely known.

Materials and Methods

In this study, weighted gene co-expression network analysis (WGCNA) was utilized to construct gene co-expression networks and evaluate the relations between modules and clinical traits to identify potential prognostic biomarkers for THCA patients. RNA-seq data and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Other independent datasets from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas database were performed to validate findings.

Results

Finally, 11 co-expression modules were constructed and four hub genes, CCDC146, SLC4A4, TDRD9 and MUM1L1, were identified and validated statistically, which were considerably interrelated to worse survival of THCA patients.

Conclusion

This research study revealed four hub genes may be considered candidate prognostic biomarkers and potential therapeutic targets for THCA patients in the future.

Hub Gene and Its Key Effects on Diffuse Large B-Cell Lymphoma by Weighted Gene Coexpression Network Analysis

Diffuse large B-cell lymphoma (DLBC) is a kind of tumor with rapid progress and poor prognosis. Therefore, it is necessary to explore new biomarkers or therapeutic targets to assist in diagnosis or treatment. This study is aimed at screening hub genes by weighted gene coexpression network analysis (WGCNA) and exploring the significance of overall survival (OS) in DLBC patients. Statistical data using WGCNA to analyze mRNA expression in DLBC patients came from The Cancer Genome Atlas (TCGA) dataset. After analyzing with clinical information, the biological functions of hub genes were detected. Survival analysis, Cox regression detection, and correlation analysis of the hub genes were carried out. The potential function of the hub gene related to prognosis was predicted by gene set enrichment analysis (GSEA). The results showed that APOE, CTSD, LGALS2, and TMEM176B expression in normal tissues was significantly higher than that in cancer tissues (P < 0.01). Survival analysis showed that patients with high APOE and CTSD were associated with better OS (P < 0.01). APOE and CTSD genes were mainly enriched in the regulation of ROS and oxidative stress. The two hub genes related to the prognosis of DLBC were identified and verified based on WGCNA. Survival analysis showed that the overexpression of APOE and CTSD in DLBC might be beneficial to the prognosis. These findings identified vital pathways and genes that may become new therapeutic targets and contribute to prognostic indicators.

Transcriptome research identifies four hub genes related to primary myelofibrosis: a holistic research by weighted gene co-expression network analysis

Objectives: This study aimed to identify specific diagnostic as well as predictive targets of primary myelofibrosis (PMF).

Methods: The gene expression profiles of GSE26049 were obtained from Gene Expression Omnibus (GEO) dataset, WGCNA was constructed to identify the most related module of PMF. Subsequently, Gene Ontology (GO), Kyoto Encyclopedia Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) and Protein-Protein interaction (PPI) network were conducted to fully understand the detailed information of the interested green module. Machine learning, Principal component analysis (PCA), and expression pattern analysis including immunohistochemistry and immunofluorescence of genes and proteins were performed to validate the reliability of these hub genes.

Results: Green module was strongly correlated with PMF disease after WGCNA analysis. 20 genes in green module were identified as hub genes responsible for the progression of PMF. GO, KEGG revealed that these hub genes were primarily enriched in erythrocyte differentiation, transcription factor binding, hemoglobin complex, transcription factor complex and cell cycle, etc. Among them, EPB42, CALR, SLC4A1 and MPL had the most correlations with PMF. Machine learning, Principal component analysis (PCA), and expression pattern analysis proved the results in this study.

Conclusions: EPB42, CALR, SLC4A1 and MPL were significantly highly expressed in PMF samples. These four genes may be considered as candidate prognostic biomarkers and potential therapeutic targets for early stage of PMF. The effects are worth expected whether in the diagnosis at early stage or as therapeutic target.

Weighted gene co-expression network analysis reveals that CXCL10, IRF7, MX1, RSAD2, and STAT1 are related to the chronic stage of spinal cord injury

Background

The process of spinal cord injury involves acute, subacute, and chronic stages; however, the specific pathological mechanism remains unclear. In this study, weighted gene co-expression network analysis (WGCNA) was used to clarify specific modules and hub genes that associated with SCI.

Methods

The gene expression profiles GEO Series (GSE)45006 and GEO Series (GSE)2599 were downloaded, and the co-expression network modules were identified by the WGCNA package. The protein-protein interaction (PPI) network and Venn diagram were constructed to identify hub genes. Quantitative real-time polymerase chain reaction (QRT-PCR) was used to quantify the degree of the top five candidate genes. Correlation analysis was also carried out between hub genes and immune infiltration.

Results

In total, 14,402 genes and seven modules were identified. The brown module was considered to be the most critical module for the chronic stage of SCI, which contained 775 genes that were primarily associated with various biological processes, including extracellular structure organization, lysosome, isoprenoid biosynthesis, response to nutrients, response to wounding, sulfur compound metabolic process, cofactor metabolic process, and ossification. Furthermore, C-X-C motif chemokine ligand 10 (CXCL10), myxovirus (influenza virus) resistance 1 (MX1), signal transducer and activator of transcription 1 (STAT1), interferon regulatory factor 7 (IRF7) and radical S-adenosyl methionine domain containing 2 (RSAD2) were identified as the hub genes in the PPI and Venn diagram network, and verified by qRT-PCR. Immune infiltration analysis revealed that CD8+ T cells, macrophages, neutrophils, plasmacytoid dendritic cells, helper T cells, Th2 cells, and tumor-infiltrating lymphocytes may be involved in the SCI process.

Conclusions

There were significant differences among the five hub genes (CXCL10, IRF7, MX1, RSAD2, and STAT1) of the brown module, which may be potential diagnostic and prognostic markers of SCI, and immune cell infiltration may play an important role in the chronic stage of SCI.

Identification of a Five-Gene Prognostic Signature Related to B Cells Infiltration in Pancreatic Adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is an extremely malignant cancer. Immunotherapy is a promising avenue to increase the survival time of patients with PAAD.

Methods

RNA sequencing and clinical data for PAAD were downloaded from the TCGA database. The ssGSEA method and weighted gene co-expression network analysis were used to calculate the relative abundance of tumor-infiltrating immune cells and identify the related modules. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to construct a prognostic model. MCPcounter and EPIC were also used to assess immune cell components using gene expression profiles.

Results

The B cells closely related module was identified, and five genes, including ARID5A, CLEC2B, MICAL1, MZB1, and RAPGEF1, were ultimately selected to establish a prognostic signature to calculate the risk scores of PAAD patients. Kaplan–Meier curves showed worse survival in the high-risk patients (p < 0.05), and the area under the receiver operating characteristic (ROC) curves of risk score for 1-year and 3-year survival were 0.78 and 0.80, respectively, based on the training set. Similar results were verified using the validated and combined sets. Interestingly, the low-risk group presented significantly elevated immune and stromal scores, proportion of B cells, and associations between these five genes and B cells were identified using multiple methods including ssGSEA, MCPcounter, and EPIC.

Conclusion

This is the first attempt to study a B cells-related prognostic signature, which is instrumental in the exploration of novel prognostic biomarkers in PAAD.

Construction of co-expression modules related to survival by WGCNA and identification of potential prognostic biomarkers in glioblastoma

Glioblastoma (GBM) is a malignant brain tumour with poor prognosis. The potential pathogenesis and therapeutic target are still need to be explored. Herein, TCGA expression profile data and clinical information were downloaded, and the WGCNA was conducted. Hub genes which closely related to poor prognosis of GBM were obtained. Further, the relationship between the genes of interest and prognosis of GBM, and immune microenvironment were analysed. Patients from TCGA were divided into high‐ and low‐risk group. WGCNA was applied to the high‐ and low‐risk group and the black module with the lowest preservation was identified which could distinguish the prognosis level of these two groups. The top 10 hub genes which were closely related to poor prognosis of patients were obtained. GO analysis showed the biological process of these genes mainly enriched in: Cell cycle, Progesterone‐mediated oocyte maturation and Oocyte meiosis. CDCA5 and CDCA8 were screened out as the genes of interest. We found that their expression levels were closely related to overall survival. The difference analysis resulted from the TCGA database proved both CDCA5 and CDCA8 were highly expressed in GBM. After transfection of U87‐MG cells with small interfering RNA, it revealed that knockdown of the CDCA5 and CDCA8 could influence the biological behaviours of proliferation, clonogenicity and apoptosis of GBM cells. Then, single‐gene analysis was performed. CDCA5 and CDCA8 both had good correlations with genes that regulate cell cycle in the p53 signalling pathway. Moreover, it revealed that high amplification of CDCA5 was correlated with CD8⁺ T cells while CDCA8 with CD4⁺ T cells in GBM. These results might provide new molecular targets and intervention strategy for GBM.

Development and Validation of an Immune-Related Prognostic Signature for Ovarian Cancer Based on Weighted Gene Coexpression Network Analysis

Background

Ovarian cancer is one of the most lethal diseases of women. The prognosis of ovarian cancer patients was closely correlated with immune cell expression and immune responses. Therefore, it is important to identify a robust prognostic signature, which correlates not only with prognoses but also with immune responses in ovarian cancer, thus, providing immune-related patient therapies.

Methods

The weighted gene coexpression network analysis (WGCNA) was used to identify candidate genes correlated with ovarian cancer prognoses. Univariate and multivariate Cox regression analyses were used to construct the prognostic signature. The Kaplan-Meier method was used to predict survival, and the immune-related bioinformatics analysis was performed using the R software. The relationship between the signature and clinical parameters was analyzed with the GraphPad Prism 7 and SPSS software.

Results

Gene expression from The Cancer Genome Atlas dataset was used to perform the WGCNA analysis, and candidate prognostic-related genes in patients with ovarian cancer were identified. According to the Cox regression analysis, the prognostic signature was constructed, which divided patients into two groups. The high-risk group showed the least favorable prognosis. Three independent cohorts from the Gene Expression Omnibus (GEO) database were used for the validation studies. According to the immune analyses, the GEO database signatures were significantly correlated with the immune statuses of ovarian cancer patients. By analyzing the combination of the prognostic signature and total mutational burden (TMB), ovarian cancer patients were divided into four groups. In these groups, memory B cell, resting memory CD4 T cell, M2 macrophage, resting mast cell, and neutrophil were found with significant distinctions among these groups.

Conclusions

This novel signature predicted the prognosis of ovarian cancer patients precisely and independently and showed significant correlations with immune responses. Therefore, this prognostic signature could indicate targeted immunotherapies for ovarian cancer patients.

Identification of a Novel Cancer Stemness-Associated ceRNA Axis in Lung Adenocarcinoma via Stemness Indices Analysis

The aim of this study was to identify a novel cancer stemness-related ceRNA regulatory axis in lung adenocarcinoma (LUAD) via weighted gene coexpression network analysis of a stemness index. The RNA sequencing expression profiles of 513 cancer samples and 60 normal samples were obtained from the TCGA database. Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) were identified with R software. Functional enrichment analysis was conducted using DAVID 6.8. The ceRNA network was constructed via multiple bioinformatics analyses, and the correlations between possible ceRNAs and prognosis were analyzed using Kaplan–Meier plots. WGCNA was then applied to distinguish key genes related to the mRNA expression-based stemness index (mRNAsi) in LUAD. After combining the weighted gene coexpression and ceRNA networks, a novel ceRNA regulatory axis was identified, and its biological functions were explored in vitro and vivo. In total, 1,825 DElncRNAs, 291 DEmiRNAs, and 3,742 DEmRNAs were identified. Functional enrichment analysis revealed that the DEmRNAs might be associated with LUAD onset and progression. The ceRNA network was constructed with 14 lncRNAs, 10 miRNAs, and 52 mRNAs. Kaplan–Meier analysis identified 2 DEmiRNAs, 5 DElncRNAs, and 41 DEmRNAs with remarkable prognostic power. One gene (MFAP4) in the ceRNA network was found to be closely related to mRNAsi by using WGCNA. Functional investigation further confirmed that the C8orf34-as1/miR-671-5p/MFAP4 regulatory axis has important functions in LUAD cell migration and stemness. This study provides a deeper understanding of the lncRNA–miRNA–mRNA ceRNA network and, more importantly, reveals a novel ceRNA regulatory axis, which may provide new insights into novel molecular therapeutic targets for inhibiting LUAD stem characteristics.

Weighted gene correlation network analysis reveals novel regulatory modules associated with recurrent early pregnancy loss

At present, the etiology and pathogenesis of recurrent early pregnancy loss (REPL) are not completely clear. Therefore, identifying the underlying diagnostic and prognostic biomarkers of REPL can provide new ideas for the diagnosis and treatment of REPL. The chip data of REPL (GSE63901) were downloaded from the NCBI Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to construct a co-expression module for studying the relationship between gene modules and clinical features. In addition, functional analysis of hub genes in modules of interest was performed. A total of 23 co-expression modules were identified, two of which were most significantly associated with three clinical features. The MEbrown module was positively correlated with cyclin E level and the out-of-phase trait while the MEred module was positively correlated with the effect of progesterone. We identified 17 hub genes in the MEred module. The functional enrichment analysis indicated that such hub genes were mainly involved in pathways related to cellular defense response and natural killer (NK) cell-mediated cytotoxicity. In the MEbrown module, we identified 19 hub genes, which were mainly enriched in cell adhesion molecule production, regulation of cellular response to growth factor stimulus, epithelial cell proliferation, and transforming growth factor-β (TGF-β) signaling pathway. In addition, the hub genes were validated by using other datasets and three true hub genes were finally obtained, namely DOCK2 for the MEred module, and TRMT44 and ERVMER34-1 for the MEbrown module. In conclusion, our results screened potential biomarkers that might contribute to the diagnosis and treatment of REPL.

Identification of co‑expression modules and hub genes of retinoblastoma via co‑expression analysis and protein‑protein interaction networks

Retinoblastoma is a common intraocular malignant tumor in children. However, the molecular and genetic mechanisms of retinoblastoma remain unclear. The gene expression dataset GSE110811 was retrieved from Gene Expression Omnibus. After preprocessing, coexpression modules were constructed by weighted gene coexpression network analysis (WGCNA), and modules associated with clinical traits were identified. In addition, functional enrichment analysis was performed for genes in the indicated modules, and protein-protein interaction (PPI) networks and subnetworks were constructed based on these genes. Eight coexpression modules were constructed through WGCNA. Of these, the yellow module had the highest association with severity and age (r=0.82 and P=3e-07; r=0.72 and P=3e-05). The turquoise module had the highest association with months (r=−0.63 and P=5e-04). The genes in the two modules participate in multiple pathways of retinoblastoma, and by combining the PPI network and subnetworks; 10 hub genes were identified in the two modules. The present study identified coexpression modules and hub genes associated with clinical traits of retinoblastoma, providing novel insight into retinoblastoma progression.

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 an immune-related signature for the prognosis of uveal melanoma

AIM

To construct an immune-related prognostic signature (IPS) that can distinguish and predict prognosis in uveal melanoma (UM).

METHODS

The transcriptomic data and clinicopathological information of 80 UM patients were extracted from the TCGA database. These patients were randomly assigned to a training and a testing set.

RESULTS

Lasso Cox analysis was performed for the prognostic immune-related genes to develop an IPS for UM in the training set. The signature was validated in both the testing set and entire cohort. We confirmed the prognostic value of our IPS in distinct subgroups and found its association with the T stage and basal diameter of the tumor. Tumor Immune Estimation Resource database analysis revealed that the IPS was negatively correlated with the infiltration of neutrophils and dendritic cells, but positively correlated with the infiltration level of CD8+ T cells. In addition, we demonstrated that immune checkpoints containing PD-1, CTLA-4, IDO, and TIGIT were moderately associated with the IPS.

CONCLUSION

This is the first study to develop and validate an immune-related signature with the ability of predicting prognosis for UM patients. Further studies are needed to validate its prediction accuracy.

Identification of potential novel biomarkers to differentiate malignant thyroid nodules with cytological indeterminate

Background

The fine-needle aspiration (FNA) biopsy was broadly applied to clinical diagnostics evaluation for thyroid carcinomas nodule, while companioning with higher uncertainty rate (15~30%) to identify malignancy for cytological indeterminate cases. It is requirement to discover novel molecular biomarkers to differentiate malignant thyroid nodule more precise.

Methods

We employed weighted gene co-expression network analysis (WGCNA) to discover genes significantly associated with malignant histopathology for cytological indeterminate nodules. In addition, identified significantly genes were validated through another independently investigations of thyroid carcinomas patient’s samples via cBioportal and Geipa. The key function pathways of significant genes involving were blast through GenClip.

Results

Twenty-four signature genes were identified significantly related to thyroid nodules malignancy. Furthermore, five novel genes with missense mutation, FN1 (R534P), PROS1((K200I), (Q571K)), SCEL (T320S), SLC34A2(T688M) and TENM1 (S1131F), were highlighted as potential biomarkers to rule out nodules malignancy. It was identified that the key functional pathways involving in thyroid carcinomas.

Conclusion

These results will be helpful to better understand the mechanism of thyroid nodules malignant transformation and characterize the potentially biomarkers for thyroid carcinomas early diagnostics.

Identification and validation of key modules and hub genes associated with the pathological stage of oral squamous cell carcinoma by weighted gene co-expression network analysis

Background

Oral squamous cell carcinoma (OSCC) is a major lethal malignant cancer of the head and neck region, yet its molecular mechanisms of tumourigenesis are still unclear.

Patients and methods

We performed weighted gene co-expression network analysis (WGCNA) on RNA-sequencing data with clinical information obtained from The Cancer Genome Atlas (TCGA) database. The relationship between co-expression modules and clinical traits was investigated by Pearson correlation analysis. Furthermore, the prognostic value and expression level of the hub genes of these modules were validated based on data from the TCGA database and other independent datasets from the Gene Expression Omnibus (GEO) database and the Human Protein Atlas database. The significant modules and hub genes were also assessed by functional analysis and gene set enrichment analysis (GSEA).

Results

We found that the turquoise module was strongly correlated with pathologic T stage and significantly enriched in critical functions and pathways related to tumourigenesis. PPP1R12B, CFD, CRYAB, FAM189A2 and ANGPTL1 were identified and statistically validated as hub genes in the turquoise module and were closely implicated in the prognosis of OSCC. GSEA indicated that five hub genes were significantly involved in many well-known cancer-related biological functions and signaling pathways.

Conclusion

In brief, we systematically discovered a co-expressed turquoise module and five hub genes associated with the pathologic T stage for the first time, which provided further insight that WGCNA may reveal the molecular regulatory mechanism involved in the carcinogenesis and progression of OSCC. In addition, the five hub genes may be considered candidate prognostic biomarkers and potential therapeutic targets for the precise early diagnosis, clinical treatment and prognosis of OSCC in the future.

Identification and functional analysis of long non-coding RNAs in the synovial membrane of osteoarthritis patients

Osteoarthritis (OA), the most common chronic joint disease in the elderly, has become a significant economic burden for families and societies worldwide. Although treatments are continually improving, current drugs only target joint pain, with no effective therapies modifying OA progression. Long noncoding RNAs (lncRNAs), which have received increasing attention in recent years, are abnormally expressed in OA cartilage. In the present study, weighted coexpression network analysis (WGCNA) was applied to identify modules related to certain OA clinical traits. In total, 4404 coding genes and 161 lncRNAs were differentially expressed based on two OA expression profile data sets and normal control samples. Subsequently, 11 independent modules were acquired, and the green module, with a total of 49 hub genes, was identified as the most relevant to OA. These hub genes were validated using the GSE12021 data set. There was only one lncRNA among the hub genes, namely, NONHSAG034351. Thus, we further explored the function of NONHSAG034351‐related genes in the network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that NONHSAG034351‐associated genes are involved in the response to lipopolysaccharide, angiogenesis, tumour necrosis factor (TNF) signalling, and mitogen‐activated protein kinase (MAPK) signalling pathways. In conclusion, we identified modules through WGCNA related to OA clinical traits. NONHSAG034351, the only hub‐lncRNA, was downregulated in OA synovial tissue and might play a significant role in the pathological progression of this disease. Our findings have important clinical implications and could provide novel biomarkers that indicate the molecular mechanisms of OA and act as potential therapeutic targets.

Significance of this study

Long noncoding RNAs (lncRNAs) have been reported to be abnormally expressed in osteoarthritis (OA), which is the most common chronic joint disease among the elderly. In the present study, we report the expression profiles of lncRNAs in OA and the identification of modules through WGCNA related to OA clinical traits. NONHSAG034351, the only hub‐lncRNA identified to be downregulated in the synovial tissue of OA patients, might play a significant role in the pathological progression of OA. Furthermore, our findings provide novel biomarkers associated with the molecular mechanisms underlying OA pathogenesis, thus implying potential therapeutic targets with important clinical implications.

Gene co-expression network for analysis of plasma exosomal miRNAs in the elderly as markers of aging and cognitive decline

BACKGROUND

Evidence has shown that microRNA (miRNAs) are involved in molecular pathways responsible for aging and age-related cognitive decline. However, there is a lack of research linked plasma exosome-derived miRNAs changes with cognitive function in older people and aging, which might prove a new insight on the transformation of miRNAs on clinical applications for cognitive decline for older people.

METHODS

We applied weighted gene co-expression network analysis to investigated miRNAs within plasma exosomes of older people for a better understanding of the relationship of exosome-derived miRNAs with cognitive decline in elderly adults. We identified network modules of co-expressed miRNAs in the elderly exosomal miRNAs dataset. In each module, we selected vital miRNAs and carried out functional enrichment analyses of their experimentally known target genes and their function.

RESULTS

We found that plasma exosomal miRNAs hsa-mir-376a-3p, miR-10a-5p, miR-125-5p, miR-15a-5p have critical regulatory roles in the development of aging and cognitive dysfunction in the elderly and may serve as biomarkers and putative novel therapeutic targets for aging and cognitive decline.

Weighted Gene Co-Expression Network Analysis Identified Cancer Cell Proliferation as a Common Phenomenon During Perineural Invasion

Purpose

Perineural invasion (PNI) is the neoplastic invasion of nerves by cancer cells, a process that may prove to be another metastatic route besides direct invasion, lymphatic spread, and vascular dissemination. Given the increasing incidence and association with poor prognosis, revealing the pathogenesis of perineural invasion is of great importance.

Materials and methods

Four datasets related to PNI were downloaded from the Gene Expression Omnibus database and used to construct weighted gene co-expression network analysis (WGCNA). The intersection of potential pathways obtained from further correlation and enrichment analyses of different datasets was validated by the coculture model of Schwann cells (SCs), flow cytometry and immunohistochemistry (IHC).

Results

GSE7055 and GSE86544 datasets were brought into the analysis for there were some significant modules related to PNI, while GSE103479 and GSE102238 datasets were excluded for insignificant differences. In total, 13,841 genes from GSE86544 and 10,809 genes from GSE7055 were used for WGCNA. As a consequence, 19 and 26 modules were generated, respectively. The purple module of GSE86544 and the dark gray module of GSE7055 were positively correlated with perineural invasion. Further correlation and enrichment analyses of genes from the two modules suggested that these genes were mainly enriched in cell cycle processes; especially, the terms S/G2/M phase were enriched. Three kinds of cells grew vigorously after coculture with SCs ex vivo. The Ki67 staining of the cervical cancer samples revealed that the Ki67 index of cancer cells surrounding nerves was higher than of those distant ones.

Conclusion

Our work has identified cancer cell proliferation as a common response to neural cancerous microenvironments, proving a foundation for cancer cell colonization and metastasis.

Identification of differentially expressed genes in the endothelial precursor cells of patients with type 2 diabetes mellitus by bioinformatics analysis

Type 2 diabetes mellitus (DM) is a metabolic disease with worldwide prevalence that is associated with a decrease in the number and function of endothelial progenitor cells (EPCs). The aim of the present study was to explore the potential hub genes of EPCs in patients with type 2 DM. Differentially expressed genes (DEGs) were screened from a public microarray dataset (accession no. GSE43950). Pathway and functional enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction (PPI) network was visualized. The most significantly clustered modules and hub genes were identified using Cytoscape. Furthermore, hub genes were validated by quantitative PCR analysis of EPCs isolated from diabetic and normal subjects. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the modules incorporating the genes exhibiting the most significant variance. A total of 970 DEGs were obtained and they were mainly accumulated in inflammation-associated pathways. A total of 9 hub genes were extracted from the PPI network and the highest differential expression was determined for the interleukin 8 (IL8) and CXC chemokine ligand 1 (CXCL1) genes. In the WGCNA performed to determine the modules associated with type 2 DM, one module incorporated IL8 and CXCL1. Finally, pathway enrichment of 10% genes in the pink module ordered by intramodular connectivity (IC) was associated with the IL17 and the chemokine signaling pathways. The present results revealed that the expression of IL8 and CXCL1 may serve important roles in the pathophysiology of EPCs during type 2 DM and inflammatory response may be critical for the reduced number and hypofunction of EPCs isolated from patients with diabetes.

Identification of biomarkers associated with histological grade and prognosis of gastric cancer by co-expression network analysis

The biological characteristics and clinical outcomes of gastric cancer (GC) are largely dependent on the histopathological type and degree of differentiation. The identification of the molecular mechanisms underlying the histological grade of GC may provide information about tumorigenesis and tumor progression, and may subsequently be used to develop novel therapeutic agents. The present study obtained the RNA sequencing data and clinical characteristics of patients with GC from The Cancer Genome Atlas. A total of 1,400 differentially expressed genes (DEGs) were screened between two histological grades. Weighted gene co-expression network analysis (WGCNA) was subsequently used to identify nine co-expressed gene modules, and the black module was found to be the most significant for prognosis prediction of tumor. Additionally, the black module was associated with overall survival time, death event, N stage and tumor-node-metastasis (TNM) stage. Functional enrichment analysis revealed that the biological processes of the genes in the black module included ‘Wnt signaling pathway’ and ‘structural molecule activity’. Additionally, 10 network hub genes that were significantly associated with the progression of GC were identified from the black module, and the significance of each hub gene was determined across different TNM stages. Kaplan-Meier survival curves revealed that keratin 40 and glycine decarboxylase were significantly associated with patient prognosis (P<0.05), suggesting that these genes may serve as potential progression and prognosis biomarkers in GC. The present study identified molecular markers that correlated with histological grade in GC. Therefore, the results obtained in the present study may have important clinical implications on treatment selection, risk stratification and prognosis prediction in patients with GC.

Potential four‑miRNA signature associated with T stage and prognosis of patients with pancreatic ductal adenocarcinoma identified by co‑expression analysis

With a 5‑year survival rate of only 8%, pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer‑associated mortality worldwide. Unfortunately, even following radical surgery, patient outcomes remain poor. Emerging as a new class of biomarkers in human cancer, microRNAs (miRNAs/miRs) have been reported to have various tumor suppressor and oncogenic functions. In the present study, miRNA expression profiles of patients with PDAC and corresponding clinical data with survival profiles were obtained from The Cancer Genome Atlas database. A co‑expression network was constructed to detect the modules significantly associated with clinical features by weighted gene co‑expression network analysis. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on the hub miRNAs in the module of interest for functional annotation. A prognosis model consisting of hub miRNAs was generated using the R package 'rbsurv' and validated in survival analysis. The expression data of 523 miRNAs in 124 patients with PDAC were analyzed in a co‑expression network. The turquoise module containing 131 miRNAs was identified to be associated with pathological T stage (cor=‑0.21; P=0.02). The 39 hub miRNAs of the turquoise module were then detected using the 'networkScreening' function in R. These miRNAs were predominantly involved in biological processes including 'regulation of transcription', 'apoptotic process', 'TGF‑β receptor signaling pathway', 'Ras protein signal transduction' and significantly enriched in 'cell cycle', 'adherens junction', 'FoxO', 'Hippo' and 'PI3K‑Akt signaling' pathways. A prognostic signature consisting of four hub miRNAs (miR‑1197, miR‑218‑2, miR‑889 and miR‑487a) associated with pathological T stage was identified to stratify the patients with early‑stage PDAC into high and low risk groups. The signature may serve as a potential prognostic biomarker for patients with early‑stage PDAC who undergo radical resection.

The temporal expression patterns of brain transcriptome during chicken development and ageing

Background

The transcriptional profiles of mammals during brain development and ageing have been characterized. However the global expression patterns of transcriptome in the chicken brain have not been explored. Here, we systematically investigated the temporal expression profiles of lncRNAs and mRNAs across 8 stages (including 3 embryonic stages, 2 growth stages and 3 adult stages) in the female chicken cerebrum.

Results

We identified 39,907 putative lncRNAs and 14,558 mRNAs, investigated the temporal expression patterns by tracking a set of age-dependent genes and predicted potential biological functions of lncRNAs based on co-expression network. The results showed that genes with functions in development, synapses and axons exhibited a progressive decay; genes related to immune response were up-regulated with age.

Conclusions

These results may reflect changes in the regulation of transcriptional networks and provide non-coding RNA gene candidates for further studies and would contribute to a comprehensive understanding of the molecular mechanisms of chicken development and may provide insights or deeper understanding regarding the regulatory mechanisms of age-dependent protein coding and non-protein coding genes in chicken. In addition, as the chicken is an important model organism bridging the evolutionary gap between mammals and other vertebrates, these high resolution data may provide a novel evidence to improve our comprehensive understanding of the brain transcriptome during vertebrate evolution.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5301-x) contains supplementary material, which is available to authorized users.

Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis

Although papillary renal cell carcinoma (PRCC) accounts for 10%–15% of renal cell carcinoma (RCC), no predictive molecular biomarker is currently applicable to guiding disease stage of PRCC patients. The mRNASeq data of PRCC and adjacent normal tissue in The Cancer Genome Atlas was analyzed to identify 1148 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 11 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = 0.45) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on nuclear division, cell cycle phase, and spindle (all P < 1e-10). All 40 hub genes in blue module can distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) PRCC (P < 0.01). A good molecular biomarker for pathological stage of RCC must be a prognostic gene in clinical practice. Survival analysis was performed to reversely validate if hub genes were associated with pathological stage. Survival analysis unveiled that all hub genes were associated with patient prognosis (P < 0.01). The validation cohort GSE2748 verified that 30 hub genes can differentiate localized from non-localized PRCC (P < 0.01), and 18 hub genes are prognosis-associated (P < 0.01).

ROC curve indicated that the 17 hub genes exhibited excellent diagnostic efficiency for localized and non-localized PRCC (AUC > 0.7). These hub genes may serve as a biomarker and help to distinguish different pathological stages for PRCC patients.

Transcriptome sequencing identified hub genes for hepatocellular carcinoma by weighted-gene co-expression analysis

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, and it remains a challenge to understand the genetic mechanisms underlying hepatocarcinogenesis. A global gene network of differential expression profiles in HCC has yet to be fully characterized. In the present study, we performed transcriptome sequencing (mRNA and lncRNA) in liver cancer and cirrhotic tissues of nine HCC patients. We identified differentially expressed genes (DEGs) and constructed a weighted gene co-expression network for the DEGs. In total, 755 DEGs (747 mRNA and eight lncRNA) were identified, and several co-expression modules were significantly associated with HCC clinical traits, including tumor location, tumor grade, and the α-fetoprotein (AFP) level. Of note, we identified 15 hub genes in the module associated with AFP level, and three (SPX, AFP and ADGRE1) of four hub genes were validated in an independent HCC cohort (n=78). Identification of hub genes for HCC clinical traits has implications for further understanding of the molecular genetic basis of HCC.

Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

Human clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumors. We constructed a weighted gene co-expression network to identify gene modules associated with clinical features of ccRCC (n = 97). Six hub genes (CCNB2, CDC20, CEP55, KIF20A, TOP2A and UBE2C) were identified in both co-expression and protein-protein interaction (PPI) networks, which were highly correlated with pathologic stage. The significance of expression of the hub genes in ccRCC was ranked top 4 among all cancers and correlated with poor prognosis. Functional analysis revealed that the hub genes were significantly enriched in cell cycle regulation and cell division. Gene set enrichment analysis suggested that the samples with highly expressed hub gene were correlated with cell cycle and p53 signaling pathway. Taken together, six hub genes were identified to be associated with progression and prognosis of ccRCC, and they might lead to poor prognosis by regulating p53 signaling pathway.

Systematic analysis of microarray datasets to identify Parkinson's disease‑associated pathways and genes

In order to investigate commonly disturbed genes and pathways in various brain regions of patients with Parkinson's disease (PD), microarray datasets from previous studies were collected and systematically analyzed. Different normalization methods were applied to microarray datasets from different platforms. A strategy combining gene co-expression networks and clinical information was adopted, using weighted gene co-expression network analysis (WGCNA) to screen for commonly disturbed genes in different brain regions of patients with PD. Functional enrichment analysis of commonly disturbed genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Co-pathway relationships were identified with Pearson's correlation coefficient tests and a hypergeometric distribution-based test. Common genes in pathway pairs were selected out and regarded as risk genes. A total of 17 microarray datasets from 7 platforms were retained for further analysis. Five gene coexpression modules were identified, containing 9,745, 736, 233, 101 and 93 genes, respectively. One module was significantly correlated with PD samples and thus the 736 genes it contained were considered to be candidate PD-associated genes. Functional enrichment analysis demonstrated that these genes were implicated in oxidative phosphorylation and PD. A total of 44 pathway pairs and 52 risk genes were revealed, and a risk gene pathway relationship network was constructed. Eight modules were identified and were revealed to be associated with PD, cancers and metabolism. A number of disturbed pathways and risk genes were unveiled in PD, and these findings may help advance understanding of PD pathogenesis.

Weighted gene co-expression network analysis in identification of metastasis-related genes of lung squamous cell carcinoma based on the Cancer Genome Atlas database

BACKGROUND

Lung squamous cell carcinoma (lung SCC) is a common type of malignancy. Its pathogenesis mechanism of tumor development is unclear. The aim of this study was to identify key genes for diagnosis biomarkers in lung SCC metastasis.

METHODS

We searched and downloaded mRNA expression data and clinical data from The Cancer Genome Atlas (TCGA) database to identify differences in mRNA expression of primary tumor tissues from lung SCC with and without metastasis. Gene co-expression network analysis, protein-protein interaction (PPI) network, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and quantitative real-time polymerase chain reactions (qRT-PCR) were used to explore the biological functions of the identified dysregulated genes.

RESULTS

Four hundred and eighty-two differentially expressed genes (DEGs) were identified between lung SCC with and without metastasis. Nineteen modules were identified in lung SCC through weighted gene co-expression network analysis (WGCNA). Twenty-three DEGs and 26 DEGs were significantly enriched in the respective pink and black module. KEGG pathway analysis displayed that 26 DEGs in the black module were significantly enriched in bile secretion pathway. Forty-nine DEGs in the two gene co-expression module were used to construct PPI network. CFTR in the black module was the hub protein, had the connectivity with 182 genes. The results of qRT-PCR displayed that FIGF, SFTPD, DYNLRB2 were significantly down-regulated in the tumor samples of lung SCC with metastasis and CFTR, SCGB3A2, SSTR1, SCTR, ROPN1L had the down-regulation tendency in lung SCC with metastasis compared to lung SCC without metastasis.

CONCLUSIONS

The dysregulated genes including CFTR, SCTR and FIGF might be involved in the pathology of lung SCC metastasis and could be used as potential diagnosis biomarkers or therapeutic targets for lung SCC.