Identification of breast cancer mechanism based on weighted gene coexpression network analysis

Transcription factor ZEB1 coordinating with NuRD complex to promote oncogenesis through glycolysis in colorectal cancer

Background

Colorectal cancer (CRC) is an aggressive primary intestinal malignancy with the third-highest incidence and second-highest mortality among all cancer types worldwide. Transcription factors (TFs) regulate cell development and differentiation owing to their ability to recognize specific DNA sequences upstream of genes. Numerous studies have demonstrated a strong correlation between TFs, the etiology of tumors, and therapeutic approaches. Here, we aimed to explore prognosis-related TFs and comprehend their carcinogenic mechanisms, thereby offering novel insights into the diagnosis and management of CRC.

Materials and Methods

Differentially expressed TFs between CRC and normal tissues were identified leveraging The Cancer Genome Atlas database, Weighted correlation network analysis and Cox regression analysis were performed to identify prognosis-related TFs. The cellular functions of hub TF zinc finger E-box binding homeobox 1 (ZEB1) were determined using by 5-ethynyl-2'-deoxyuridine and cell invasion assays in CRC cells. RNA-sequencing, Kyoto Encyclopedia of Genes and Genomes enrichment, and gene set enrichment analyses were used to identify the cellular processes in which ZEB1 participates. Immunoaffinity purification, silver staining mass spectrometry, and a chromatin immunoprecipitation assay were conducted to search for proteins that might interact with ZEB1 and the target genes they jointly regulate.

Results

Thirteen central TFs related to prognosis were identified through bioinformatics analysis techniques. Among these TFs, ZEB1 emerged as the TF most closely associated with CRC, as determined through a combination of regulatory network diagrams, survival curves, and phenotype analyses. ZEB1 promotes CRC cell growth by recruiting the NuRD(MTA1) complex, and the ZEB1/NuRD(MTA1) complex transcriptionally represses glycolysis-associated tumor suppressor genes.

Conclusion

Our study not only identified a hub biomarker related to CRC prognosis but also revealed the specific molecular mechanisms through which ZEB1 affects cancer progression. These insights provide crucial evidence for the diagnosis of CRC and potential treatment opportunities.

Biomarkers associated with pulmonary exacerbations in a randomized trial of nintedanib for radiation pneumonitis

BACKGROUND AND PURPOSE

Radiation pneumonitis (RP) is a common side effect of thoracic radiotherapy and often has a long course characterized by acute exacerbations and progression to permanent lung fibrosis. There are no validated biomarkers of prognosis in patients diagnosed with RP.

MATERIALS AND METHODS

We analyzed a time course of serum chemokines, cytokines, and other proteins from patients with grade 2+ RP in a randomized clinical trial of a steroid taper plus nintedanib, a multiple tyrosine kinase inhibitor, versus placebo plus a steroid taper for the treatment of RP. Weighted gene correlation network analysis (WGCNA) and univariable zero inflated Poisson models were used to identify groups of correlated analytes and their associations with clinical outcomes.

RESULTS

Thirty enrolled patients had biomarker data available, and 17 patients had enough analytes tested for network analysis. WGNCA identified ten analytes, including transforming growth factor beta-1 (TGF-β1), monocyte chemoattractant protein-1 (MCP-1), and platelet-derived growth factor (PDGF), that in aggregate were correlated with the occurrence of pulmonary exacerbations (p = 0.008), the total number of acute pulmonary exacerbations (p = 0.002), and treatment arm (p = 0.036). By univariable analysis, an increase in rate of change of two components of the RP module were associated with an increased incidence rate of pulmonary exacerbations: interleukin 5 (IL-5, incidence rate ratio (IRR) 1.02, 95% CI 1.01-1.04, p = 0.002), and tumor necrosis factor superfamily 12 (TNFSF12, IRR 1.06, CI 1-1.11, p = 0.036). An increased slope of epidermal growth factor (EGF) was associated with a decreased incidence rate of exacerbations (IRR 0.94, CI 0.89-1, p = 0.036).

CONCLUSION

We identified a panel of serum biomarkers that showed association with nintedanib treatment and acute pulmonary exacerbations in patients with RP. A confirmatory study will be needed to validate this panel for use as a prognostic tool in patients with RP.

POLE -related gene signature predicts prognosis, immune feature, and drug therapy in human endometrioid carcinoma

The POLE subtype of Endometrial carcinoma (EC) is linked to a favourable prognosis in the molecular classification. We proposed to ascertain the potential connection between the POLE subtype and improved prognosis. In order to forecast the prognosis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis and weighted gene co-expression network analysis (WGCNA) were employed, and a POLE-related risk signature (PRS) model was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) with the "GSVA" package was employed to analyse immunity characteristics. Drug susceptibility studies were conducted to compare the half-maximal inhibitory concentration (IC50) of medicines between high- and low-risk groups. The PRS model was generated employing the LASSO Cox regression coefficients of the ELF1, MMADHC, andAL021707.6 genes. Our study demonstrated that the risk score was linked to tumour stage, grade, and survival. Furthermore, the low-risk group possessed elevated levels of gene expression connected with immunological checkpoints and HLA. Our outcomes emerged that the PRS model might have value in identifying patients with a good prognosis and in facilitating personalised treatment in the clinic.

Introducing novel key genes and transcription factors associated with rectal cancer response to chemoradiation through co-expression network analysis

Preoperative radiochemotherapy is a promising therapeutic method for locally advanced rectal cancer patients. However, the response of colorectal cancer (CRC) patients to preoperative radiotherapy varies widely. In this study, we aimed to identify novel biomarkers that could predict the response of colorectal tumors to treatment using a systems biology approach. We applied the Weighted Gene Co-Expression Network Analysis to construct co-expression networks and evaluated the correlation of these networks with radiation using the module-trait relationship. We then identified hub genes and related transcription factors in the selected co-expression module. Our analysis of seven constructed modules revealed that one module, which contained 113 nodes and 6066 edges, had the strongest correlation with radiation effects on CRC (correlation = 0.85; p-value = 6e-7). By analyzing the selected module with the CytoHubba plugin, we identified four hub genes, including ZEB2, JAM2, NDN, and PPAP2A. We also identified seven important transcription factors, including KLF4, SUZ12, TCF4, NANOG, POU5F1, SOX2, and SMARCA4, which may play essential roles in regulating the four hub genes. In summary, our findings suggest that ZEB2, JAM2, NDN, and PPAP2A, along with the seven transcription factors related to these hub genes, may be associated with the response of colorectal tumors to chemoradiotherapy.

Identification of crucial genes for predicting the risk of atherosclerosis with system lupus erythematosus based on comprehensive bioinformatics analysis and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) has become a major public health problem over the years, and atherosclerosis (AS) is one of the main complications of SLE associated with serious cardiovascular consequences in this patient population. The present study aimed to identify potential biomarkers for SLE patients with AS.

METHODS

Five microarray datasets (GSE50772, GSE81622, GSE100927, GSE28829, GSE37356) were downloaded from the NCBI Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS. Weighted gene coexpression network analysis (WGCNA) was used to identify significant module genes associated with SLE. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (Lasso, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and random forest) were applied to identify hub genes. Subsequently, we generated a nomogram and receiver operating characteristic curve (ROC) for predicting the risk of AS in SLE patients. Finally, immune cell infiltrations were analyzed, and Consensus Cluster Analysis was conducted based on Single Sample Gene Set Enrichment Analysis (ssGSEA) scores.

RESULTS

Five hub genes (SPI1, MMP9, C1QA, CX3CR1, and MNDA) were identified and used to establish a nomogram that yielded a high predictive performance (area under the curve 0.900-0.981). Dysregulated immune cell infiltrations were found in AS, with positive correlations with the five hub genes. Consensus clustering showed that the optimal number of subtypes was 3. Compared to subtypes A and B, subtype C presented higher expression of the five hub genes, immune cell infiltration levels and immune checkpoint expression.

CONCLUSION

Our study systematically identified five candidate hub genes (SPI1, MMP9, C1QA, CX3CR1, MNDA) and established a nomogram that could predict the risk of AS with SLE using various bioinformatic analyses and machine learning algorithms. Our findings provide the foothold for future studies on potential crucial genes for AS in SLE patients. Additionally, the dysregulated immune cell proportions and immune checkpoint expressions in AS with SLE were identified.

A phenotype driven integrative framework uncovers molecular mechanisms of a rare hereditary thrombophilia

Antithrombin resistance is a rare subtype of hereditary thrombophilia caused by prothrombin gene variants, leading to thrombotic disorders. Recently, the Prothrombin Belgrade variant has been reported as a specific variant that leads to antithrombin resistance in two Serbian families with thrombosis. However, due to clinical data scarcity and the inapplicability of traditional genome-wide association studies (GWAS), a broader perspective on molecular and phenotypic mechanisms associated with the Prothrombin Belgrade variant is yet to be uncovered. Here, we propose an integrative framework to address the lack of genomic samples and support the genomic signal from the full genome sequences of five heterozygous subjects by integrating it with subjects' phenotypes and the genes' molecular interactions. Our goal is to identify candidate thrombophilia-related genes for which our subjects possess germline variants by focusing on the resulting gene clusters of our integrative framework. We applied a Non-negative Matrix Tri-Factorization-based method to simultaneously integrate different data sources, taking into account the observed phenotypes. In other words, our data-integration framework reveals gene clusters involved with this rare disease by fusing different datasets. Our results are in concordance with the current literature about antithrombin resistance. We also found candidate disease-related genes that need to be further investigated. CD320, RTEL1, UCP2, APOA5 and PROZ participate in healthy-specific or disease-specific subnetworks involving thrombophilia-annotated genes and are related to general thrombophilia mechanisms according to the literature. Moreover, the ADRA2A and TBXA2R subnetworks analysis suggested that their variants may have a protective effect due to their connection with decreased platelet activation. The results show that our method can give insights into antithrombin resistance even if a small amount of genetic data is available. Our framework is also customizable, meaning that it applies to any other rare disease.

Weighted gene co-expression network analysis reveals key module and hub genes associated with the anthocyanin biosynthesis in maize pericarp

Anthocyanins are the visual pigments that present most of the colors in plants. Its biosynthesis requires the coordinated expression of structural genes and regulatory genes. Pericarps are the rich sources of anthocyanins in maize seeds. In the experiment, the transcriptomes of transparent and anthocyanins-enriched pericarps at 15, 20, and 25 DAP were obtained. The results output 110.007 million raw reads and 51407 genes' expression matrix. Using data filtration in R language, 2057 genes were eventually identified for weighted gene co-expression network analysis. The results showed that 2057 genes were classified into ten modules. The cyan module containing 183 genes was confirmed to be the key module with the highest correlation value of 0.98 to the anthocyanins trait. Among 183 genes, seven structural genes were mapped the flavonoid biosynthesis pathway, and a transcription factor Lc gene was annotated as an anthocyanin regulatory gene. Cluster heatmap and gene network analysis further demonstrated that Naringenin, 2-oxoglutarate 3-dioxygenase (Zm00001d001960), Dihydroflavonol 4-reductase (Zm00001d044122), Leucoanthocyanidin dioxygenase (Zm00001d014914), anthocyanin regulatory Lc gene (Zm00001d026147), and Chalcone synthase C2 (Zm00001d052673) participated in the anthocyanins biosynthesis. And the transcription factor anthocyanin regulatory Lc gene Zm00001d026147 may act on the genes Chalcone synthase C2 (Zm00001d052673) and Dihydroflavonol 4-reductase (Zm00001d044122). The yeast one-hybrid assays confirmed that the Lc protein could combine with the promoter region of C2 and directly regulate the anthocyanin biosynthesis in the pericarp. These results may provide a new sight to uncover the module and hub genes related to anthocyanins biosynthesis in plants.

Integrated Bioinformatics Analysis for the Screening of Hub Genes and Therapeutic Drugs in Androgen Receptor-Positive TNBC

As the most invasive and lethal subtype of breast cancer (BC), triple-negative breast carcinoma (TNBC) is of increasing interest. However, the androgen receptor (AR) still has an unclear role in TNBC. The current study is aimed at testing the diagnostic and therapeutic performance of novel biomarkers for AR-positive TNBC. The GSE76124 dataset was analyzed by combining WGCNA and other bioinformatics methods. Subsequently, function enrichment analysis was applied to identify the relationships between these differential expression genes (DEGs). Subsequently, the protein-protein interaction network was established, and the hub genes were identified by Cytoscape software. Eventually, the miRNA-hub gene modulate network was developed and the Drug-Gene Interaction Database (DGIdb) was applied to verify the potential drugs for AR-positive TNBC. In the current research, 88 DEGs in total were selected from the intersection of the purple module genes identified by WGCNA and limma package. TFF1, FOXA1, ESR1, AGR2, TFF3, AGR3, GATA3, XBP1, SPDEF, and TOX3 were selected as hub genes by the MCC method, which were all upregulated. The survival analysis suggested that TFF1 was the only one related to significant lower survival rate in TNBC. Ultimately, hsa-miR-520g-3p and hsa-miR-520h were found taking part in the regulation of TFF1, and 2 small molecules were identified as the potential targets for AR-positive TNBC treatment. As a result, our study suggested that hsa-miR-520g-3p, hsa-miR-520h, and TFF1 might have significant potential values for AR-positive TNBC diagnosis and prognosis prediction. TFF1, hsa-miR-520g-3, and hsa-miR-520h may serve as the novel therapeutic targets, and our findings offer further insights into the therapy of AR-positive TNBC.

Using Weighted Gene Co-Expression Network Analysis to Identify Increased MND1 Expression as a Predictor of Poor Breast Cancer Survival

Objective

We used bioinformatics analysis to identify potential biomarker genes and their relationship with breast cancer (BC).

Materials and Methods

We used a weighted gene co-expression network analysis (WGCNA) to create a co-expression network based on the top 25% genes in the GSE24124, GSE33926, and GSE86166 datasets obtained from the Gene Expression Omnibus. We used the DAVID online platform to perform GO and KEGG pathway enrichment analyses and the Cytoscape CytoHubba plug-in to screen the potential genes. Then, we related the genes to prognostic values in BC using the Oncomine, GEPIA, and Kaplan–Meier Plotter databases. Findings were validated by immunohistochemical (IHC) staining in the Human Protein Atlas and the TCGA-BRCA cohort. LinkedOmics identified the interactive expressions of hub genes. We used UALCAN to evaluate the methylation levels of these hub genes. MethSurv and SurvivalMeth were used to assess the multilevel prognostic value. Finally, we assessed hub gene association with immune cell infiltration using TIMER.

Results

The mRNA levels of MKI67, UBE2C, GTSE1, CCNA2, and MND1 were significantly upregulated in BC, whereas ESR1, THSD4, TFF1, AGR2, and FOXA1 were significantly downregulated. The DNA methylation signature analysis showed a better prognosis in the low-risk group. Further subgroup analyses revealed that MND1 might serve as an independent risk factor for unfavorable BC prognosis. Additionally, MND1 expression levels positively correlate with the immune infiltration statuses of CD4+ T cells, CD8+ T cells, B cells, neutrophils, dendritic cells, and macrophages.

Conclusion

Our results indicate that the ten hub genes may be involved in BC’s carcinogenesis, development, or metastasis, and MND1 may be a potential biomarker and therapeutic target for BC.

Identification of Potential Biomarkers of Type 2 Diabetes Mellitus-Related Immune Infiltration Using Weighted Gene Coexpression Network Analysis

Background

Type 2 diabetes mellitus (T2DM) is characterized by chronic low-grade inflammation, showing an increasing trend. The infiltration of immune cells into adipose tissue has been shown to be an important pathogenic cause of T2DM. The purpose of this study is to use the relevant database to identify some abnormally expressed or dysfunctional genes related to diabetes from the perspective of immune infiltration.

Methods

Weighted gene coexpression network analysis (WGCNA) was employed to systematically identify the coexpressed gene modules and hub genes associated with T2DM development based on a microarray dataset (GSE23561) from the Gene Expression Omnibus (GEO) database. The key genes in modules highly related to clinical features were calculated and screened by using R software, and their participation in T2DM was determined by gene enrichment analysis. The mRNA levels of CSF1R, H2AFV, LCK, and TLR9 in pre-T2DM mice and normal wild-type mice were detected by WGCNA screening and real-time quantitative reverse transcriptase polymerase chain reaction (qRT-PCR).

Results

We constructed 14 coexpressed gene modules, and the brown module was shown to be significantly related to T2DM. Through verification of the protein-protein interaction (PPI) network, four upregulated hub genes, CSF1R, H2AFV, LCK, and TLR9, were screened from the brown module and successfully distinguishedT2DM patients from healthy people. These hub genes may be used as biomarkers and important indicators for patient diagnosis. Enrichment analysis showed that these hub genes were highly associated with IL-6-related inflammatory metabolism, immune regulation, and immune cell infiltration. Finally, we verified the hub genes CSF1R, LCK, and TLR9 in a T2DM animal model and found that their mRNA levels were significantly higher in animals with T2DM than in control group mice (NC).

Conclusions

In summary, our results suggest that these hub genes (CSF1R, LCK, and TLR9) can serve as biomarkers and immunotherapeutic targets for T2DM.

A Novel IGLC2 Gene Linked With Prognosis of Triple-Negative Breast Cancer

BACKGROUND

Immunoglobulin-related genes are associated with the favorable prognosis of triple-negative breast cancer (TNBC) patients. We aimed to analyze the function and prognostic value of immunoglobulin lambda constant 2 (IGLC2) in TNBC patients.

METHODS

We knocked down the gene expression of IGLC2 (IGLC2-KD) in MDA-MB-231 cells to evaluate the proliferation, migration, and invasion of tumors via 3-(4,5-Dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay, wound healing, and transwell cell migration assay respectively. Relapse-free survival (RFS) and distant metastasis-free survival (DMFS) analyses were conducted using the KM plotter online tool. The GSE76275 data set was used to analyze the association of IGLC2 and clinical characteristics. A pathway enrichment analysis was conducted using the next-generation sequencing data of wild-type and IGLC2-KD MDA-MB-231 cells.

RESULTS

The low gene expression of IGLC2 was related to unfavorable RFS, DMFS. The high expression of IGLC2 was exhibited in the basal-like immune-activated (BLIA) TNBC molecular subtype, which was immune-activated and showed excellent response to immune therapy. IGLC2 was positively correlated with programmed death-ligand 1 (PD-L1) as shown by Spearman correlation (r = 0.25, p < 0.0001). IGLC2 had a strong prognostic effect on lymph node-negative TNBC (RFS range: 0.31, q value= 8.2e-05; DMFS = 0.16, q value = 8.2e-05) but had no significance on lymph node-positive ones. The shRNA-mediated silencing of IGLC2 increased the proliferation, migration, and invasion of MDA-MB-231 cells. The results of pathway enrichment analysis showed that IGLC2 is related to the PI3K-Akt signaling pathway, MAPK signaling pathway, and extracellular matrix-receptor interaction. We confirmed that MDA-MB-231 tumor cells expressed IGLC2, subverting the traditional finding of generation by immune cells.

CONCLUSIONS

IGLC2 linked with the proliferation, migration, and invasion of MDA-MB-231 cells. A high expression of IGLC2 was related to favorable prognosis for TNBC patients. IGLC2 may serve as a biomarker for the identification of TNBC patients who can benefit the most from immune checkpoint blockade treatment.

Identification of Five Hub Genes as Key Prognostic Biomarkers in Liver Cancer via Integrated Bioinformatics Analysis

Liver cancer is one of the most common cancers and the top leading cause of cancer death globally. However, the molecular mechanisms of liver tumorigenesis and progression remain unclear. In the current study, we investigated the hub genes and the potential molecular pathways through which these genes contribute to liver cancer onset and development. The weighted gene co-expression network analysis (WCGNA) was performed on the main data attained from the GEO (Gene Expression Omnibus) database. The Cancer Genome Atlas (TCGA) dataset was used to evaluate the association between prognosis and these hub genes. The expression of genes from the black module was found to be significantly related to liver cancer. Based on the results of protein-protein interaction, gene co-expression network, and survival analyses, DNA topoisomerase II alpha (TOP2A), ribonucleotide reductase regulatory subunit M2 (RRM2), never in mitosis-related kinase 2 (NEK2), cyclin-dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) were identified as the hub genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that the differentially expressed genes (DEGs) were enriched in the immune-associated pathways. These hub genes were further screened and validated using statistical and functional analyses. Additionally, the TOP2A, RRM2, NEK2, CDK1, and CCNB1 proteins were overexpressed in tumor liver tissues as compared to normal liver tissues according to the Human Protein Atlas database and previous studies. Our results suggest the potential use of TOP2A, RRM2,&nbsp;NEK2, CDK1, and CCNB1 as prognostic biomarkers in liver cancer.

Integrated Analysis of Weighted Gene Coexpression Network Analysis Identifying Six Genes as Novel Biomarkers for Alzheimer's Disease

BACKGROUND

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease; however, there are no comprehensive therapeutic interventions. Therefore, this study is aimed at identifying novel molecular targets that may improve the diagnosis and treatment of patients with AD.

METHODS

In our study, GSE5281 microarray dataset from the GEO database was collected and screened for differential expression analysis. Genes with a P value of <0.05 and ∣log2FoldChange | >0.5 were considered differentially expressed genes (DEGs). We further profiled and identified AD-related coexpression genes using weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis was performed to determine the characteristics and pathways of the key modules. We constructed an AD-related model based on hub genes by logistic regression and least absolute shrinkage and selection operator (LASSO) analyses, which was also verified by the receiver operating characteristic (ROC) curve.

RESULTS

In total, 4674 DEGs were identified. Nine distinct coexpression modules were identified via WGCNA; among these modules, the blue module showed the highest positive correlation with AD (r = 0.64, P = 3e - 20), and it was visualized by establishing a protein-protein interaction network. Moreover, this module was particularly enriched in "pathways of neurodegeneration-multiple diseases," "Alzheimer disease," "oxidative phosphorylation," and "proteasome." Sixteen genes were identified as hub genes and further submitted to a LASSO regression model, and six genes (EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF) were identified based on the model index. Additionally, we assessed the accuracy of the LASSO model by plotting an ROC curve (AUC = 0.940).

CONCLUSIONS

Using the WGCNA and LASSO models, our findings provide a better understanding of the role of biomarkers EIF3H, RAD51C, FAM162A, BLVRA, ATP6V1H, and BRAF and provide a basis for further studies on AD progression.

Prediction of Disease Genes Based on Stage-Specific Gene Regulatory Networks in Breast Cancer

Breast cancer is one of the most common malignant tumors in women, which seriously endangers women’s health. Great advances have been made over the last decades, however, most studies predict driver genes of breast cancer using biological experiments and/or computational methods, regardless of stage information. In this study, we propose a computational framework to predict the disease genes of breast cancer based on stage-specific gene regulatory networks. Firstly, we screen out differentially expressed genes and hypomethylated/hypermethylated genes by comparing tumor samples with corresponding normal samples. Secondly, we construct three stage-specific gene regulatory networks by integrating RNA-seq profiles and TF-target pairs, and apply WGCNA to detect modules from these networks. Subsequently, we perform network topological analysis and gene set enrichment analysis. Finally, the key genes of specific modules for each stage are screened as candidate disease genes. We obtain seven stage-specific modules, and identify 20, 12, and 22 key genes for three stages, respectively. Furthermore, 55%, 83%, and 64% of the genes are associated with breast cancer, for example E2F2, E2F8, TPX2, BUB1, and CKAP2L. So it may be of great importance for further verification by cancer experts.

Transcriptional changes through menstrual cycle reveal a global transcriptional derepression underlying the molecular mechanism involved in the window of implantation

The human endometrium is a dynamic tissue that only is receptive to host the embryo during a brief time in the middle secretory phase, called the window of implantation (WOI). Despite its importance, regulation of the menstrual cycle remains incompletely understood. The aim of this study was to characterize the gene cooperation and regulation of menstrual cycle progression, to dissect the molecular complexity underlying acquisition of endometrial receptivity for a successful pregnancy, and to provide the scientific community with detailed gene co-expression information throughout the menstrual cycle on a user-friendly web-tool database. A retrospective gene co-expression analysis was performed based on the endometrial receptivity array (ERarray) gene signature from 523 human endometrial samples collected across the menstrual cycle, including during the WOI. Gene co-expression analysis revealed the WOI as having the significantly smallest proportion of negative correlations for transcriptional profiles associated with successful pregnancies compared to other cycle stages, pointing to a global transcriptional derepression being involved in acquisition of endometrial receptivity. Regulation was greatest during the transition between proliferative and secretory endometrial phases. Further, we prioritized nuclear hormone receptors as major regulators of this derepression and proved that some genes and transcription factors involved in this process were dysregulated in patients with recurrent implantation failure. We also compiled the wealth of gene co-expression data to stimulate hypothesis-driven single-molecule endometrial studies in a user-friendly database: Menstrual Cycle Gene Co-expression Network (www.menstrualcyclegcn.com). This study revealed a global transcriptional repression across the menstrual cycle, which relaxes when the WOI opens for transcriptional profiles associated with successful pregnancies. These findings suggest that a global transcriptional derepression is needed for embryo implantation and early development.

TSPAN1, TMPRSS4, SDR16C5, and CTSE as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation

Pancreatic cancer is a lethal malignancy with a poor prognosis. This study aims to identify pancreatic cancer-related genes and develop a robust diagnostic model to detect this disease. Weighted gene co-expression network analysis (WGCNA) was used to determine potential hub genes for pancreatic cancer. Their mRNA and protein expression levels were validated through reverse transcription PCR (RT-PCR) and immunohistochemical (IHC). Diagnostic models were developed by eight machine learning algorithms and ten-fold cross-validation. Four hub genes (TSPAN1, TMPRSS4, SDR16C5, and CTSE) were identified based on bioinformatics. RT-PCR showed that the four hub genes were expressed at medium to high levels, IHC revealed that their protein expression levels were higher in pancreatic cancer tissues. For the panel of these four genes, eight models performed with 0.87-0.92 area under the curve value (AUC), 0.91-0.94 sensitivity, and 0.84-0.86 specificity in the validation cohort. In the external validation set, these models also showed good performance (0.86-0.98 AUC, 0.84-1.00 sensitivity, and 0.86-1.00 specificity). In conclusion, this study has identified four hub genes that might be closely related to pancreatic cancer: TSPAN1, TMPRSS4, SDR16C5, and CTSE. Four-gene panels might provide a theoretical basis for the diagnosis of pancreatic cancer.

Co-expression network-based identification of biomarkers correlated with the lymph node metastasis of patients with head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is ranked as one of the most frequent malignancies worldwide with a high risk of lymph node metastasis, which serves as a main reason for cancer deaths. Identification of the potential biomarkers for lymph node metastasis in HNSCC patients may contribute to personalized treatment and better therapeutic effect. In the present study, GSE30788 microarray data and corresponding clinical parameters were downloaded from Gene Expression Omnibus (GEO) and Weighted Gene Co-expression Network Analysis (WGCNA) was performed to investigate significant modules associated with clinical traits. As a result, the genes in the blue module were determined as candidate genes related with HNSCC lymph node metastasis and ten hub genes were selected from the PPI network. Further analysis validated the close associations of hub gene expression with lymph node metastasis of HNSCC patients. Furthermore, survival analysis suggested the level of Loricrin (LOR) was statistically significantly associated with the disease-free survival of HNSCC patients, indicating the potential of utilizing it as prognosis predictor. Overall, our study conducted a co-expression network-based analysis to investigate significant genes underlying HNSCC metastasis, providing promising biomarkers and therapeutic targets.

Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

Background

To accurately predict the prognosis of glioma patients.

Methods

A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model.

Results

By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients.

Conclusion

We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07972-9.

Protein co-expression networks identified from HOT lesions of ER+HER2-Ki-67high luminal breast carcinomas

Patients with estrogen receptor-positive/human epidermal growth factor receptor 2-negative/Ki-67-high (ER+HER2-Ki-67high) luminal breast cancer have a worse prognosis and do not respond to hormonal treatment and chemotherapy. This study sought to identify disease-related protein networks significantly associated with this subtype, by assessing in-depth proteomes of 10 lesions of high and low Ki-67 values (HOT, five; COLD, five) microdissected from the five tumors. Weighted correlation network analysis screened by over-representative analysis identified the five modules significantly associated with the HOT lesions. Pathway enrichment analysis, together with causal network analysis, revealed pathways of ribosome-associated quality controls, heat shock response by oxidative stress and hypoxia, angiogenesis, and oxidative phosphorylation. A semi-quantitative correlation of key-protein expressions, protein co-regulation analysis, and multivariate correlation analysis suggested co-regulations via network-network interaction among the four HOT-characteristic modules. Predicted highly activated master and upstream regulators were most characteristic to ER-positive breast cancer and associated with oncogenic transformation, as well as resistance to chemotherapy and endocrine therapy. Interestingly, inhibited intervention causal networks of numerous chemical inhibitors were predicted within the top 10 lists for the WM2 and WM5 modules, suggesting involvement of potential therapeutic targets in those data-driven networks. Our findings may help develop therapeutic strategies to benefit patients.

Optimized combination methods for exploring and verifying disease-resistant transcription factors in melon

A large amount of omics data and number of bioinformatics tools has been produced. However, the methods for further exploring omics data are simple, in particular, to mine key regulatory genes, which are a priority concern in biological systems, and most of the specific functions are still unknown. First, raw data of two genotypes of melon (susceptible and resistant) were obtained by transcriptome analysis. Second, 391 transcription factors (TFs) were identified from the plant transcription factor database and cucurbit genomics database. Then, functional enrichment analysis indicated that these genes were mainly annotated in the process of transcription regulation. Third, 243 and 230 module-specific TFs were screened by weighted gene coexpression network analysis and short time series expression miner, respectively. Several TF genes, such as WRKYs and bHLHs, were regarded as key regulatory genes according to the values of significantly different modules. The coexpression network showed that these TF genes were significant correlated with resistance (R) genes, such as DRP2, RGA3, DRP1 and NB-ARC. Fourth, cis-acting element analysis illustrated that these R genes may bind to WRKY and bHLH. Finally, the expression of WRKY genes was verified by quantitative reverse transcription PCR (RT-qPCR). Phylogenetic analysis was carried out to further confirm that these TFs may play a critical role in Curcurbitaceae disease resistance. This study provides a new optimized combination strategy to explore the functions of TFs in a wide spectrum of biological processes. This strategy may also effectively predict potential relationships in the interactions of essential genes.

Identification of modules and novel prognostic biomarkers in liver cancer through integrated bioinformatics analysis

Liver cancer is a common malignant tumor with poor prognosis. Due to the lack of specific clinical manifestations at early stages, most patients are already at advanced stages of the disease by the time of diagnosis. Identification of novel biomarkers for liver cancer may thus enable earlier detection, improving outcome. MicroRNAs (miRNAs) are small endogenous noncoding RNAs of 18–22 nucleotides in length, which have a regulatory role in the expression of target proteins. Increased evidence suggests that miRNAs are abnormally expressed in a variety of cancer malignancies. Here, we combined RNA sequencing data and clinical information from The Cancer Genome Atlas Liver Hepatocellular Carcinoma database for weighted gene coexpression network analysis to identify potential miRNA prognostic biomarkers. We constructed nine coexpression modules, allowing us to identify that miR‐105‐5p, miR‐767‐5p, miR‐1266‐5p, miR‐4746‐5p, miR‐500a‐3p, miR‐1180‐3p and miR‐139‐5p are significantly associated with liver cancer prognosis. We found that these miRNAs exhibit significant association with prognosis of patients with liver cancer and confirmed the expression of these miRNAs in liver cancer tissues. Multivariate Cox regression analysis showed that miR‐105‐5p and miR‐139‐5p may be considered as independent factors. In summary, here we report that seven miRNAs have potential value as prognostic biomarkers of liver cancer.

Transcriptome-Based Network Analysis Unveils Eight Immune-Related Genes as Molecular Signatures in the Immunomodulatory Subtype of Triple-Negative Breast Cancer

Objective: Triple-negative breast cancer (TNBC) is a high heterogeneity cancer. The identification of genomic aberrations that drive each of the TNBC subtypes may predict the prognosis of patients with TNBC and provide novel therapeutic strategies in clinical practice. This study focuses on the transcriptome-based gene expression of TNBC and aims to generate comprehensive gene co-expression networks correlated with the immune-related subtype of TNBC.

Methods: The transcriptome profiles of 107 female patients with TNBC were analyzed. Weighted gene co-expression network analysis (WGCNA) was applied to construct related networks and to sort hub-genes associated with the survival of TNBC patients. The data of the transcriptional expression, genomic alteration, survival status, and tumor immune microenvironment, which associated with hub-genes, were extracted, retrieved, and analyzed from Oncomine, UALCAN, TCGA, starBase, Kaplan–Meier Plotter, cBioPortal, and TIMER databases.

Results: Immune-related hub-genes, including BIRC3, BTN3A1, CSF2RB, GIMAP7, GZMB, HCLS1, LCP2, and SELL, were found to be associated with clinical features of TNBC evaluated by WGCNA. These hub-genes belonged to the immunomodulatory subtype of TNBC and were upregulated in the TNBC cells. The protein expression of eight immune-related hub-genes was further confirmed to be upregulated in TNBC/CD8+ tissues detected by immunohistochemical staining. Survival analysis revealed that overexpression of eight immune-related hub-genes was in favor of the survival of patients with TNBC. Moreover, a positive correlation between eight immune-related hub-genes and immune cell infiltration was observed in TNBC patients. Furthermore, checkpoint inhibitor genes such as PD-L1, PD-1, and CTLA4 were more enrichment in the immunomodulatory subtype of TNBC and the expression of PD-L1, PD-1, and CTLA4 was positively correlated with eight immune-related hub-genes in the breast cancer dataset of TCGA.

Conclusions: Eight immune-related hub-genes were identified to be molecular signatures in the immunomodulatory subtype of TNBC, which may provide therapeutic targets for the treatment of patients with breast cancer.

LncRNA DLX6-AS1 Promotes the Progression of Neuroblastoma by Activating STAT2 via Targeting miR-506-3p

Background

Neuroblastoma (NB) is a common malignant tumor of the sympathetic nervous system, mainly disturbing children. Long non-coding RNAs (lncRNAs) serving as promising cancer biomarkers have been well recognized. Our study intends to explore the functions of lncRNA X–inactive specific transcript (DLX6-AS1) in NB and provide a potential action mechanism.

Methods

The expression of DLX6-AS1, miR-506-3p and signal transducer and activator of transcription 2 (STAT2) was measured by quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation was assessed using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and colony formation assay. Cell cycle distribution was determined by flow cytometry assay. The protein level of cell cycle-related markers and STAT2 was detected by Western blot. Glycolysis progress was evaluated according to glucose consumption, lactate production and ATP level. The target genes were predicted by the online database Starbase3.0 and verified by dual-luciferase reporter assay.

Results

DLX6-AS1 expression was highly elevated in NB tissues and cells. DLX6-AS1 deficiency inhibited NB cell proliferation, cell cycle and glycolysis in vitro. MiR-506-3p was a target of DLX6-AS1, and miR-506-3p absence partly reversed the effects of DLX6-AS1 deficiency. Besides, STAT2 was targeted by miR-506-3p, and its expression was regulated by DLX6-AS1 through miR-506-3p. MiR-506-3p restoration also inhibited NB cell malignant behaviors, and STAT2 overexpression partially abolished the role of miR-506-3p restoration. Moreover, DLX6-AS1 deficiency weakened tumor growth in vivo.

Conclusion

DLX6-AS1 regulated cell proliferation, cell cycle and glycolysis in vitro and tumor growth in vivo to promote the development of NB by upregulating STAT2 via targeting miR-506-3p.

Disease-related cellular protein networks differentially affected under different EGFR mutations in lung adenocarcinoma

It is unclear how epidermal growth factor receptor EGFR major driver mutations (L858R or Ex19del) affect downstream molecular networks and pathways. This study aimed to provide information on the influences of these mutations. The study assessed 36 protein expression profiles of lung adenocarcinoma (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Weighted gene co-expression network analysis together with analysis of variance-based screening identified 13 co-expressed modules and their eigen proteins. Pathway enrichment analysis for the Ex19del mutation demonstrated involvement of SUMOylation, epithelial and mesenchymal transition, ERK/mitogen-activated protein kinase signalling via phosphorylation and Hippo signalling. Additionally, analysis for the L858R mutation identified various pathways related to cancer cell survival and death. With regard to the Ex19del mutation, ROCK, RPS6KA1, ARF1, IL2RA and several ErbB pathways were upregulated, whereas AURK and GSKIP were downregulated. With regard to the L858R mutation, RB1, TSC22D3 and DOCK1 were downregulated, whereas various networks, including VEGFA, were moderately upregulated. In all mutation types, CD80/CD86 (B7), MHC, CIITA and IFGN were activated, whereas CD37 and SAFB were inhibited. Costimulatory immune-checkpoint pathways by B7/CD28 were mainly activated, whereas those by PD-1/PD-L1 were inhibited. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

Identification of a five-gene signature for predicting the progression and prognosis of stage I endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is often diagnosed at an early clinical stage based on abnormal vaginal bleeding. However, the prognosis of UCEC is poor. The present study was conducted to identify novel tumor grade-related genes with the potential to predict the prognosis and progression of UCEC. A total of three gene expression microarray datasets were downloaded from the Gene Expression Omnibus database, and one RNA-sequencing dataset with corresponding clinical information of patients with UCEC was obtained from The Cancer Genome Atlas database. In summary, 1,447 differentially expressed genes (DEGs) were identified between endometrial cancerous tissues and normal endometrial tissues. Weighted gene co-expression network analysis was performed to assess the associations between DEGs and clinical traits. In total, five genes were found to be highly associated with the tumorigenesis and prognosis of UCEC. Among them, BUB1 mitotic checkpoint serine/threonine kinase B, cyclin B1, cell-division cycle protein 20 and non-SMC condensing I complex subunit G were involved in cell cycle regulation pathways, and DLG-associated protein 5 was involved in the Notch receptor 3 signaling pathway based on functional enrichment analyses. Of the five genes, four were highly expressed in endometrial cancerous tissues compared with normal endometrial tissues at the protein level. In addition, the higher expression of these genes predicted a higher tumor grade and worse overall survival. In conclusion, the present study revealed a 5-gene signature that can be used to predict the progression of UCEC.

Identification of potential key pathways, genes and circulating markers in the development of intracranial aneurysm based on weighted gene co-expression network analysis

Background: Intracranial aneurysm (IA) is a disease resulted from weak brain control, characterized by local expansion or dilation of brain artery. This study aimed to construct a gene co-expression network by Weighted Gene Correlation Network Analysis (WGCNA) to explore the potential key pathways and genes for the development of IA.Method: Six IA-related gene expression data sets were downloaded from the Gene Expression Omnibus (GEO) database for identifying differentially expressed genes (DEGs). WGCNA was used to identify modules associated with IA. Functional enrichment analysis was used to explore the potential biological functions. ROC analysis was used to find markers for predicting IA.Results: Purple, greenyellow and yellow modules were significantly associated with unruptured intracranial aneurysms, while blue and turquoise modules were significantly associated with ruptured intracranial aneurysms. Functional modules significantly related to IA were enriched in Ribosome, Glutathione metabolism, cAMP signalling pathway, Lysosome, Glycosaminoglycan degradation and other pathways. CD163, FCEREG, FPR1, ITGAM, NLRC4, PDG, and TYROBP were up-regulated ruptured intracranial aneurysms and serum, these genes were potential circulating markers for predicting IA rupture.Conclusions: Potential IA-related key pathways, genes and circulating markers were identified for predicting IA rupture by WGCNA analysis.

Identification of Key Biological Processes, Pathways, Networks, and Genes with Potential Prognostic Values in Hepatocellular Carcinoma Using a Bioinformatics Approach

Aim: Hepatocellular carcinoma (HCC), as one primary liver cancer type, accounts for 75%-85% of liver cancer cases. HCC is the second leading cause of cancer death in East Asia and sub-Saharan Africa and the sixth most common in western countries. Identification of key genes would facilitate the development of therapies and improve the prognosis outcomes of HCC patients. This study was to identify the key biological processes, pathways, and key genes in HCC. Methods: Data were downloaded from Broad GDAC. Differentially expressed genes (DEGs) and weighted gene coexpression network (WGCNA) were analyzed by DESeq2 and WGCNA, respectively. Gene ontology (GO) and KEGG enrichment analyses were performed on all DEGs and the coexpressed genes in two significant modules. Kaplan-Meier plotter online database was used to identify the potential prognostic genes in HCC. Finally, GEO database was used to validate the analysis of gene expression of Broad GDAC data. Results: The authors identified the dark gray and red modules as the significant modules in HCC based on WGCNA. GO and KEGG enrichment of the two significant modules identified the mitochondrion-mediated metabolic processes and pathways, and the cell cycle as the key biological processes and pathways in HCC. Subsequently, 28 hub genes were screened out by constructing protein-protein interaction network using Metascape. Finally, three genes (NDUFAF6, CKAP5, and DSN1 genes) were identified to be potential prognostic and key genes in HCC based on Kaplan-Meier survival analysis, GEO dataset validation, and literature review. Conclusions: The authors found that mitochondrion-mediated metabolic processes and the cell cycle were the key biological processes and pathways in HCC. NDUFAF6, CKAP5, and DSN1 genes were valuable genes with the potential to be prognosis biomarkers and targeted therapies in HCC.

Identification of Candidate Genes Associated with Breast Cancer Prognosis

Breast cancer (BC) is the most malignant tumor in women. The molecular mechanisms underlying tumorigenesis still need to be further elucidated. It is necessary to investigate novel candidate genes involved in breast cancer progression and prognosis. In this study, we commit to explore candidate genes that associate with prognosis and therapy in BC by a comprehensive bioinformatic analysis. Four GEO datasets (GSE5764, GSE7904, GSE20711, and GSE29431) and the BC-related transcriptome data in TCGA database were downloaded and used to identify the differently expressed genes (DEGs). The function of DEGs was analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis. The protein-protein interaction (PPI) network of DEGs was constructed to identify hub genes. Prognostic candidate genes were identified through survival analysis. In addition, potential therapeutic targets were identified by constructed gene-drug interaction network through Comparative Toxicogenomics Database. A total of 547 DEGs (302 up and 245 down) were identified. Three core-subnetwork and 25 hub genes were identified in PPI network. Seven genes (namely COL12A1, QPRT, MRPL13, KRT14, KRT15, LAMB3, and MYBPC1) were identified as crucial prognostic candidate genes, which significantly associated with breast cancer overall survival. Furthermore, two representative candidate genes (COL12A1 and LAMB3) were optionally chosen for verification by reverse transcription and quantitative real-time polymerase chain reaction (RT-PCR). What's more, the gene-drugs interaction analysis indicates several antitumor drugs that could affect the expression of these prognostic markers, such as doxorubicin, cisplatin, and tamoxifen. These results identified seven crucial candidate genes that may serve as prognosis biomarkers and novel therapeutic targets of breast cancer, which may facilitate further understanding the molecular pathogenesis and providing potential therapeutic strategies for BC.

Association of miR-760 with cancer: An overview

MicroRNAs (miRNAs) are small non-coding RNA molecules of around 22 nucleotides in length. They are crucially involved in the post transcriptional regulation and thus play a significant role in the modulation of different diseases. Several studies have suggested that miRNA expression is dysregulated in various cancers through different mechanisms and the dysregulated miRNA in return affects different cancer hallmarks including cell proliferation, cell death suppression, metastasis and angiogenesis. Compilation of the available miRNA data can be a stimulator for proper understanding of the correlation between the miRNA expression and cancer progression. In this review, we have focussed on the role of miR-760 in the progression of different cancer. MicroRNA-760 (miR-760) has been found to be down regulated in various cancers, thus it can be utilized as a possible prognostic marker for cancer detection. Here, we have tried to fill a gap regarding the role of miR-760 in relation to cervical cancer also. Moreover, unravelling the role of miR-760 in different cancers will enlighten the researchers with proper understanding of biology of miR-760 in regulation of different cancers.

Identification of Hub Genes in Hepatocellular Carcinoma Related to Progression and Prognosis by Weighted Gene Co-Expression Network Analysis

BACKGROUND Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in the world. Bioinformatics studies have been widely used for screening genes involved in the initiation and progression of HCC. MATERIAL AND METHODS We obtained liver cancer microarray raw data from the GEO database (GSE54238). Next, weighted gene co-expression network analysis (WGCNA) was used to assess the critical modules. Then, we assessed the gene significance by calculating survival, expression level, and receiver operating characteristic (ROC) in the TCGA database. We also validated the expression of selected genes in the Oncomine database and calculated the relationship between 4 hub genes and immune infiltration. Finally, GSEA enrichment analysis was used to explore the potential mechanism. RESULTS We identified the red and blue modules as the critical modules, and found 176 candidate genes by assessing gene significance. GO and KEEG results suggested that the candidate genes are involved in the cell cycle. Four hub genes - SOX4, STK39, TARBP1, and TDRKH - were eventually screened after validating their expression and power in diagnosing HCC in the TCGA database. Immune infiltration analysis and GSEA enrichment analysis showed that these 4 hub genes were correlated with the immune cell populations infiltration and that multiple mechanisms were involved, such as angiogenesis and epithelial-mesenchymal transition. CONCLUSIONS Our findings revealed that these 4 genes can be regarded as potential prognosticators and therapeutic targets for HCC.

Identification of key genes and expression profiles in osteoarthritis by co-expressed network analysis

BACKGROUND

The underlying molecular characteristics of osteoarthritis (OA), a common age-related joint disease, remains elusive. Here, we aimed to identify potential early diagnostic biomarkers and elucidate underlying mechanisms of OA using weighted gene co-expression network analysis (WGCNA).

MATERIAL AND METHODS

We obtained the gene expression profile dataset GSE55235, GSE55457, and GSE55584, from the Gene Expression Omnibus. WGCNA was used to investigate the changes in co-expressed genes between normal and OA synovial membrane samples. Modules that were highly correlated to OA were subjected to functional enrichment analysis using the R clusterProfiler package. Differentially expressed genes (DEGs) between the two samples were screened using the "limma" package in R. A Venn diagram was constructed to intersect the genes in significant modules and DEGs. RT -PCR was used to further verify the hub gene expression levels between normal and OA samples.

RESULTS

The preserved significant module was found to be highly associated with OA development and progression (P < 1e-200, correlation = 0.92). Functional enrichment analysis suggested that the antiquewhite4 module was highly correlated to FoxO signaling pathway, and the metabolism of fatty acids and 2-oxocarboxylic acid. A total of 13 hub genes were identified based on significant module network topology and DEG analysis, and RT-PCR confirmed that these genes were significantly increased in OA samples compared with that in normal samples.

CONCLUSIONS

We identified 13 hub genes correlated to the development and progression of OA, which may provide new biomarkers and drug targets for OA.

Identification of differentially expressed genes between the colon and ileum of patients with inflammatory bowel disease by gene co-expression analysis

OBJECTIVE

We aimed to identify differentially expressed genes (DEG) in patients with inflammatory bowel disease (IBD).

METHODS

RNA-seq data were obtained from the Array Express database. DEG were identified using the edgeR package. A co-expression network was constructed and key modules with the highest correlation with IBD inflammatory sites were identified for analysis. The Cytoscape MCODE plugin was used to identify key sub-modules of the protein-protein interaction (PPI) network. The genes in the sub-modules were considered hub genes, and functional enrichment analysis was performed. Furthermore, we constructed a drug-gene interaction network. Finally, we visualized the hub gene expression pattern between the colon and ileum of IBD using the ggpubr package and analyzed it using the Wilcoxon test.

RESULTS

DEG were identified between the colon and ileum of IBD patients. Based on the co-expression network, the green module had the highest correlation with IBD inflammatory sites. In total, 379 DEG in the green module were identified for the PPI network. Nineteen hub genes were differentially expressed between the colon and ileum. The drug-gene network identified these hub genes as potential drug targets.

CONCLUSION

Nineteen DEG were identified between the colon and ileum of IBD patients.

Current status of clinical proteogenomics in lung cancer

Introduction: Lung cancer is the leading cause of cancer death worldwide. Proteogenomics, a way to integrate genomics, transcriptomics, and proteomics, have emerged as a way to understand molecular causes in cancer tumorigenesis. This understanding will help identify therapeutic targets that are urgently needed to improve individual patient outcomes. Areas covered: To explore underlying molecular mechanisms of lung cancer subtypes, several efforts have used proteogenomic approaches that integrate next generation sequencing (NGS) and mass spectrometry (MS)-based technologies. Expert opinion: A large-scale, MS-based, proteomic analysis, together with both NGS-based genomic data and clinicopathological information, will facilitate establishing extensive databases for lung cancer subtypes that can be used for further proteogenomic analyzes. Proteogenomic strategies will further be understanding of how major driver mutations affect downstream molecular networks, resulting in lung cancer progression and malignancy, and how therapy-resistant cancers resistant are molecularly structured. These strategies require advanced bioinformatics based on a dynamic theory of network systems, rather than statistics, to accurately identify mutant proteins and their affected key networks.

MicroRNA-124-3p suppresses cell migration and invasion by targeting ITGA3 signaling in bladder cancer

BACKGROUND

A growing body of studies have demonstrated the aberrant expression of microRNAs (miRNAs) contributes to human tumor metastasis. MicroRNA-124-3p (miR-124-3p), which is down-regulated in various cancers, has been found to be involved in several signaling pathways relevant to tumor cell migration and invasion. However, the roles of miR-124-3p in human bladder cancer remain unclear. This study aims to investigate the functional significance of miR-124-3p and to understand how it targets the integrin receptor, and thus affects the progression of human bladder cancer.

METHODS

Clinical specimens from 36 patients and three human bladder cancer cell lines were analyzed for miR-124-3p and integrin α3 (ITGA3) . To investigate the effects of miR-124-3p and ITGA3 on proliferation of bladder cancer cells, the MTT assay, colon-formation assay and flow cytometry were performed. In addition, wound healing assay and transwell assay were carried out to examine the migration and invasion of the bladder cancer cells transfected with miR-124-3p mimics or si-ITGA3. The luciferase reporter assay, quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were applied to validate the miR-124-3p directly binding with ITGA3. Finally, western blot was used to examine the expression level of the proteins involved in FAK/PI3K/AKT and FAK/Src signal pathway as well as epithelial-mesenchymal transition (EMT) process.

RESULTS

The down-regulation of miR-124-3p and up-regulation of ITGA3 were observed in clinical specimens and bladder cancer cell lines. Overexpression of miR-124-3p or silencing ITGA3 inhibited tumor cell migration and invasion. Luciferase assay confirmed miR-124-3p directly targets ITGA3, and western blot suggested that miR-124-3p plays a crucial role in the EMT and metastasis of human bladder cancer through FAK/PI3K/AKT and FAK/Src signaling mechanism. Also, by targeting ITGA3, miR-124-3p can modulate the expression of N- and E-cadherin, and thus inhibit the EMT.

CONCLUSIONS

By targeting ITGA3 and downstream FAK/PI3K/AKT and FAK/Src signaling pathways, miR-124-3p suppresses cell migration and invasion in bladder cancer. Our study reasonably speculates that miR-124-3p can be potentially developed as a therapeutic target and prognostic biomarker for bladder cancer.

Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

BACKGROUND

Breast cancer is one of the most common endocrine cancers among females worldwide. Distant metastasis of breast cancer is causing an increasing number of breast cancer-related deaths. However, the potential mechanisms of metastasis and candidate biomarkers remain to be further explored.

RESULTS

The gene expression profiles of GSE102484 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to screen for the most potent gene modules associated with the metastatic risk of breast cancer, and a total of 12 modules were identified based on the analysis. In the most significant module (R2 = 0.68), 21 network hub genes (MM > 0.90) were retained for further analyses. Next, protein-protein interaction (PPI) networks were used to further explore the biomarkers with the most interactions in gene modules. According to the PPI networks, five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) were identified as key genes associated with breast cancer progression. Furthermore, the prognostic value and differential expression of these genes were validated based on data from The Cancer Genome Atlas (TCGA) and Kaplan-Meier (KM) Plotter. Receiver operating characteristic (ROC) curve analysis revealed that the mRNA expression levels of these five hub genes showed excellent diagnostic value for breast cancer and adjacent tissues. Moreover, these five hub genes were significantly associated with worse distant metastasis-free survival (DMFS) in the patient cohort based on KM Plotter.

CONCLUSION

Five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) associated with the risk of distant metastasis were extracted for further research, which might be used as biomarkers to predict distant metastasis of breast cancer.

Identification of key modules and hub genes for small-cell lung carcinoma and large-cell neuroendocrine lung carcinoma by weighted gene co-expression network analysis of clinical tissue-proteomes

Small-cell lung carcinoma (SCLC) and large-cell neuroendocrine lung carcinoma (LCNEC) are high-grade lung neuroendocrine tumors (NET). However, comparative protein expression within SCLC and LCNEC remains unclear. Here, protein expression profiles were obtained via mass spectrometry-based proteomic analysis. Weighted gene co-expression network analysis (WGCNA) identified co-expressed modules and hub genes. Of 34 identified modules, six were significant and selected for protein-protein interaction (PPI) network analysis and pathway enrichment. Within the six modules, the activation of cellular processes and complexes, such as alternative mRNA splicing, translation initiation, nucleosome remodeling and deacetylase (NuRD) complex, SWItch/Sucrose Non-Fermentable (SWI/SNF) superfamily-type complex, chromatin remodeling pathway, and mRNA metabolic processes, were significant to SCLC. Modules enriched in processes, including signal recognition particle (SRP)-dependent co-translational protein targeting to membrane, nuclear-transcribed mRNA catabolic process of nonsense-mediated decay (NMD), and cellular macromolecule catabolic process, were characteristically activated in LCNEC. Novel high-degree hub genes were identified for each module. Master and upstream regulators were predicted via causal network analysis. This study provides an understanding of the molecular differences in tumorigenesis and malignancy between SCLC and LCNEC and may help identify potential therapeutic targets.

The role of microRNAs in chronic pseudomonas lung infection in Cystic fibrosis

BACKGROUND

Cystic Fibrosis (CF) is the most common life limiting genetic disorder, characterized by chronic respiratory failure secondary to inflammation and chronic bacterial lung infection. Pseudomonas aeruginosa lung infection is associated with more severe lung disease and rapid progression of respiratory failure when compared to Staphylococcus aureus infection. We hypothesized that a specific signature of epigenetic factors targeting specific gene transcripts contributes to the increased morbidity seen in CF patients with chronic Pseudomonas infection.

METHODS

We collected exhaled breath condensate (EBC) from 27 subjects and evaluated miRNA signatures in these samples using commercial PCR array. We identified predicted mRNA targets and associated signaling pathways using Ingenuity Pathway Analysis.

RESULTS

We found 11 differentially expressed miRNAs in EBC of patients infected with Pseudomonas aeruginosa compared to EBC from CF patients who were not chronically infected with Pseudomonas aeruginosa (p < 0.05). Six of these miRNAs (hsa-miRNA-1247, hsa-miRNA-1276, hsa-miRNA-449c, hsa-miRNA-3170, hsa-miRNA-432-5p and hsa-miR-548) were significantly different in the CF Pseudomonas positive group when compared to both the CF Pseudomonas negative group and healthy control group. Ingenuity pathway analysis (IPA) revealed organismal injury and abnormalities, reproductive system disease and cancer as the top diseases and bio functions associated with these miRNAs. IPA also detected RELA, JUN, TNF, IL-10, CTNNB1, IL-13, SERPINB8, CALM1, STARD3NL, SFI1, CD55, RPS6KA4, TTC36 and HIST1H3D as the top target genes for these miRNAs.

CONCLUSION

Our study identified 6 miRNAs as epigenetic factors specifically associated with chronic Pseudomonas infection in patients with CF.

Identification of lncRNA TRPM2-AS/miR-140-3p/PYCR1 axis's proliferates and anti-apoptotic effect on breast cancer using co-expression network analysis

Breast cancer (BC) is one of the most common malignancies occurring in women worldwide. Weighted gene co-expression network analysis (WGCNA) has not been widely utilized in uncovering the biomarkers which played pivotal roles in BC treatment. This study aimed to verify the proliferative and anti-apoptotic effect of lncRNA TRPM2-AS/miR-140-3p/PYCR1 axis on BC based on WGCNA. WGCNA was applied for determining hub genes using gene expression data gained from breast cancer and adjacent tissues which were downloaded from the Cancer Genome Atlas (TCGA) database. The correlative curves showed the correlation between OS/DFS of BC patients and TRPM2-AS expression or PYCR1 expression based on the data of survival rate of BC patients obtained from the TCGA database. QRT-PCR was employed in detecting the expression levels of TRPM2-AS, miR-140-3p and PYCR1, and western blot analysis was adopted for determination of protein expression level of PYCR1. Dual luciferase assay was applied to verify the targeting relationship between TRPM2-AS and miR-140-3p, as well as miR-140-3p and PYCR1. The roles of TRPM2-AS, miR-140-3p, and PYCR1 in proliferation, migration, and apoptosis of BC cell were identified by CCK-8 assay, cell migration assay and flow cytometry. Hub genes were also gained from WGCNA test. The prognostic study showed a significant negative correlation between the high expression of PYCR1 and TRPM2-AS and the BC survival. QRT-PCR demonstrated that PYCR1 and TRPM2-AS were both overexpressed, while miR-140-3p was greatly down-regulated in BC cell. In addition, it was validated by dual luciferase assay that miR-140-3p directly targeted both TRPM2-AS and PYCR1. Furthermore, down-regulation of TRPM2-AS and PYCR1 inhibited proliferation yet promoted apoptosis of BC cell, and up-regulation of miR-140-3p in BC cell showed the same tendency. Taken together, TRPM2-AS could promote proliferation and inhibit apoptosis of BC cell through TRPM2-AS/miR-140-3p/PYCR1 axis.

Gene co-expression network approach for predicting prognostic microRNA biomarkers in different subtypes of breast cancer

New diagnostic miRNA biomarkers for different types of cancer have been studied extensively, particularly for breast cancer (BC), which is a leading cause of death among women and has many different subtypes. In the present study, a systems biology approach was used to find remarkable and novel miRNA biomarkers for five molecular subtypes of BC: luminal A, luminal B, ERBB2, basal-like and normal-like. The mRNA expression data from the five BC subtypes was used to reconstruct co-expression networks. The important mRNA-miRNA interactions were considered when reconstructing the bipartite networks from which the five bipartite sub-networks were reconstructed for further analysis. The novel biomarkers detected for each subtype are as follows: miRNAs 26b-5p and 124-3p for basal-like, 26b-5p, 124-3p and 5011-5p for ERBB2, 26b-5p and 5011-5p for LumA, 124-3p, 26b-5p and 7-5p for LumB and 26b-5p, 124-3p and 193b-3p for normal-like. The roles of the identified miRNAs in the occurrence or development of each subtype of BC remain unclear and should be investigated in future studies. In addition, the target genes of these miRNAs may be critical to the mechanisms underlying each subtype and should be analyzed as therapeutic targets in future studies.

Mutual inhibitions between epidermal growth factor receptor signaling and miR-124a control pancreatic progenitor proliferation

Pancreatic stem/progenitor cells convert from a proliferative to a differentiated fate passing through proliferation cease to a resting state. However, the molecular mechanisms of cell cycle arrest are poorly understood. In this study, we demonstrated that the microRNA-124a (miR-124a) inhibited the proliferation of pancreatic progenitor cells both in vitro and ex vivo and promoted a quiescent state. The miR-124a directly targeted SOS Ras/Rac guanine nucleotide exchange factor 1 (SOS1), IQ motif-containing GTPase-activating protein 1 (IQGAP1), signal transducer and activator of transcription 3 (STAT3), and cyclin D2 (CCND2), thereby inactivating epidermal growth factor receptor (EGFR) downstream signaling pathways including mitogen-activated protein kinase/extracellular signal-regulated kinase (MEK/ERK), phosphatidylinositol 3-kinase-protein kinase B (PI3K/AKT) and Janus kinase (JAK)/STAT3. miR-124a blocked cell proliferation mainly through targeting STAT3 to inhibit PI3K/AKT and JAK/STAT3 signaling. Moreover, miR-124a expression was negatively regulated by EGFR downstream PI3K/AKT signaling. These results indicated that miR-124a and EGFR signaling mutually interact to form a regulating circuit that determines the proliferation of pancreatic progenitor cells.

MicroRNA‑506‑3p reverses gefitinib resistance in non‑small cell lung cancer by targeting Yes‑associated protein 1

Epidermal growth factor receptor‑tyrosine kinase inhibitors, such as gefitinib, have been found to be clinically effective in the treatment of patients with non‑small cell lung cancer (NSCLC). However, the therapeutic effect of gefitinib is often limited by the development of gefitinib resistance. MicroRNAs (miRNAs), a group of small non‑coding RNAs, have been demonstrated to be frequently dysregulated in human malignancies. For instance, the downregulation of miR‑506‑3p has been reported in NSCLC patients. The aim of the present study was to determine the role and underlying molecular mechanism of miR‑506‑3p in the regulation of gefitinib sensitivity in NSCLC. A gefitinib‑resistant PC‑9 (PC‑9GR) cell line was established, and reduced miR‑506‑3p expression was observed in PC‑9GR cells as compared with that in parental cells. The results of cell cytotoxicity and cell apoptosis assays indicated that PC‑9GR cells were more sensitive to gefitinib following the transfection with an miR‑506‑3p mimic, while transfection with an miR‑506‑3p antagonist reduced the sensitivity of PC‑9GR cells to gefitinib. It was further revealed that Yes‑associated protein 1 (YAP1) was directly suppressed by miR‑506‑3p in PC‑9GR cells. The elevated sensitivity of PC‑9GR cells to gefitinib following transfection with the miR‑506‑3p mimic was counteracted by the overexpression of YAP1. Furthermore, an inverse correlation between the miR‑506‑3p and YAP1 mRNA levels was detected in lung adenocarcinoma specimens. Collectively, the results of the present study suggested that the downregulation of miR‑506‑3p contributes to gefitinib resistance, and thus, the restoration of miR‑506‑3p may be a potential therapeutic approach for overcoming NSCLC gefitinib resistance.

The synthetic histone-binding regulator protein PcTF activates interferon genes in breast cancer cells

Background

Mounting evidence from genome-wide studies of cancer shows that chromatin-mediated epigenetic silencing at large cohorts of genes is strongly linked to a poor prognosis. This mechanism is thought to prevent cell differentiation and enable evasion of the immune system. Drugging the cancer epigenome with small molecule inhibitors to release silenced genes from the repressed state has emerged as a powerful approach for cancer research and drug development. Targets of these inhibitors include chromatin-modifying enzymes that can acquire drug-resistant mutations. In order to directly target a generally conserved feature, elevated trimethyl-lysine 27 on histone H3 (H3K27me3), we developed the Polycomb-based Transcription Factor (PcTF), a fusion activator that targets methyl-histone marks via its N-terminal H3K27me3-binding motif, and co-regulates sets of silenced genes.

Results

Here, we report transcriptome profiling analyses of PcTF-treated breast cancer model cell lines. We identified a set of 19 PcTF-upregulated genes, or PUGs, that were consistent across three distinct breast cancer cell lines. These genes are associated with the interferon response pathway.

Conclusions

Our results demonstrate for the first time a chromatin-mediated interferon-related transcriptional response driven by an engineered fusion protein that physically links repressive histone marks with active transcription.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0608-4) contains supplementary material, which is available to authorized users.

Transcriptome Signatures of Canine Mammary Gland Tumors and Its Comparison to Human Breast Cancers

Breast cancer (BC)/mammary gland carcinoma (MGC) is the most frequently diagnosed and leading cause of cancer-related mortality in both women and canines. To better understand both canine MGC and human BC-specific genes, we sequenced RNAs obtained from eight pairs of carcinomas and adjacent normal tissues in dogs. By comprehensive transcriptome analysis, 351 differentially expressed genes (DEGs) were identified in overall canine MGCs. Based on the DEGs, comparative analysis revealed correlation existing among the three histological subtypes of canine MGC (ductal, simple, and complex) and four molecular subtypes of human BC (HER2+, ER+, ER&HER2+, and TNBC). Eight DEGs shared by all three subtypes of canine MGCs had been previously reported as cancer-associated genes in human studies. Gene ontology and pathway analyses using the identified DEGs revealed that the biological processes of cell proliferation, adhesion, and inflammatory responses are enriched in up-regulated MGC DEGs. In contrast, fatty acid homeostasis and transcription regulation involved in cell fate commitment were down-regulated in MGC DEGs. Moreover, correlations are demonstrated between upstream promoter transcripts and DEGs. Canine MGC- and subtype-enriched gene expression allows us to better understand both human BC and canine MGC, yielding new insight into the development of biomarkers and targets for both diseases.

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

Hepatocellular carcinoma (HCC) is a devastating disease worldwide. Though many efforts have been made to elucidate the process of HCC, its molecular mechanisms of development remain elusive due to its complexity. To explore the stepwise carcinogenic process from pre-neoplastic lesions to the end stage of HCC, we employed weighted gene co-expression network analysis (WGCNA) which has been proved to be an effective method in many diseases to detect co-expressed modules and hub genes using eight pathological stages including normal, cirrhosis without HCC, cirrhosis, low-grade dysplastic, high-grade dysplastic, very early and early, advanced HCC and very advanced HCC. Among the eight consecutive pathological stages, five representative modules are selected to perform canonical pathway enrichment and upstream regulator analysis by using ingenuity pathway analysis (IPA) software. We found that cell cycle related biological processes were activated at four neoplastic stages, and the degree of activation of the cell cycle corresponded to the deterioration degree of HCC. The orange and yellow modules enriched in energy metabolism, especially oxidative metabolism, and the expression value of the genes decreased only at four neoplastic stages. The brown module, enriched in protein ubiquitination and ephrin receptor signaling pathways, correlated mainly with the very early stage of HCC. The darkred module, enriched in hepatic fibrosis/hepatic stellate cell activation, correlated with the cirrhotic stage only. The high degree hub genes were identified based on the protein-protein interaction (PPI) network and were verified by Kaplan-Meier survival analysis. The novel five high degree hub genes signature that was identified in our study may shed light on future prognostic and therapeutic approaches. Our study brings a new perspective to the understanding of the key pathways and genes in the dynamic changes of HCC progression. These findings shed light on further investigations.

Identification of key candidate genes and pathways in hepatitis B virus-associated acute liver failure by bioinformatical analysis

Hepatitis B virus-associated acute liver failure (HBV-ALF) is a rare but life-threatening syndrome that carried a high morbidity and mortality. Our study aimed to explore the possible molecular mechanisms of HBV-ALF by means of bioinformatics analysis. In this study, genes expression microarray datasets of HBV-ALF from Gene Expression Omnibus were collected, and then we identified differentially expressed genes (DEGs) by the limma package in R. After functional enrichment analysis, we constructed the protein-protein interaction (PPI) network by the Search Tool for the Retrieval of Interacting Genes online database and weighted genes coexpression network by the WGCNA package in R. Subsequently, we picked out the hub genes among the DEGs. A total of 423 DEGs with 198 upregulated genes and 225 downregulated genes were identified between HBV-ALF and normal samples. The upregulated genes were mainly enriched in immune response, and the downregulated genes were mainly enriched in complement and coagulation cascades. Orosomucoid 1 (ORM1), orosomucoid 2 (ORM2), plasminogen (PLG), and aldehyde oxidase 1 (AOX1) were picked out as the hub genes that with a high degree in both PPI network and weighted genes coexpression network. The weighted genes coexpression network analysis found out 3 of the 5 modules that upregulated genes enriched in were closely related to immune system. The downregulated genes enriched in only one module, and the genes in this module majorly enriched in the complement and coagulation cascades pathway. In conclusion, 4 genes (ORM1, ORM2, PLG, and AOX1) with immune response and the complement and coagulation cascades pathway may take part in the pathogenesis of HBV-ALF, and these candidate genes and pathways could be therapeutic targets for HBV-ALF.