Emerging Biomarkers in Bladder Cancer Identified by Network Analysis of Transcriptomic Data

Incorporating machine learning and PPI networks to identify mitochondrial fission-related immune markers in abdominal aortic aneurysms

Purpose

The aim of this study is to investigate abdominal aortic aneurysm (AAA), a disease characterised by inflammation and progressive vasodilatation, for novel gene-targeted therapeutic loci.

Methods

To do this, we used weighted co-expression network analysis (WGCNA) and differential gene analysis on samples from the GEO database. Additionally, we carried out enrichment analysis and determined that the blue module was of interest. Additionally, we performed an investigation of immune infiltration and discovered genes linked to immune evasion and mitochondrial fission. In order to screen for feature genes, we used two PPI network gene selection methods and five machine learning methods. This allowed us to identify the most featrue genes (MFGs). The expression of the MFGs in various cell subgroups was then evaluated by analysis of single cell samples from AAA. Additionally, we looked at the expression levels of the MFGs as well as the levels of inflammatory immune-related markers in cellular and animal models of AAA. Finally, we predicted potential drugs that could be targeted for the treatment of AAA.

Results

Our research identified 1249 up-regulated differential genes and 3653 down-regulated differential genes. Through WGCNA, we also discovered 44 genes in the blue module. By taking the point where several strategies for gene selection overlap, the MFG (ITGAL and SELL) was produced. We discovered through single cell research that the MFG were specifically expressed in T regulatory cells, NK cells, B lineage, and lymphocytes. In both animal and cellular models of AAA, the MFGs' mRNA levels rose.

Conclusion

We searched for the AAA novel targeted gene (ITGAL and SELL), which most likely function through lymphocytes of the B lineage, NK cells, T regulatory cells, and B lineage. This analysis gave AAA a brand-new goal to treat or prevent the disease.

Tozasertib activates anti-tumor immunity through decreasing regulatory T cells in melanoma

Although immune checkpoint therapy has significantly improved the prognosis of patients with melanoma, urgent attention still needs to be paid to the low patient response rates and the challenges of precisely identifying patients before treatment. Therefore, it is crucial to investigate novel immunosuppressive mechanisms and targets in the tumor microenvironment in order to reverse tumor immune escape. In this study, we found that the cell cycle checkpoint Aurora kinase B (AURKB) suppressed the anti-tumor immune response, and its inhibitor, Tozasertib, effectively activated T lymphocyte cytokine release in vitro and anti-tumor immunity in vivo. Tozasertib significantly inhibited melanoma xenograft tumor growth by decreasing the number of inhibitory CD4+ Treg cells in the tumors, which, in turn, activated CD8+ T cells. Single-cell analysis revealed that AURKB suppressed anti-tumor immunity by increasing MIF-CD74/CXCR4 signaling between tumor cells and lymphocytes. Our study suggests that AURKB is a newly identified anti-tumor immunity suppressor, whose inhibitors may be developed as novel anti-tumor immunity drugs and may have synergistic anti-melanoma effects with immune checkpoint therapies.

Identification of Hub Genes Associated with Gastric Cancer via Bioinformatics Analysis and Validation Studies

Introduction

Hub genes related to the development of gastric cancer (GC) were identified based on bioinformatics methods. This study aimed to identify GC hub genes, explore the expression of genes in GC and their correlation with prognosis, so as to provide strategies for GC diagnosis and targeted therapy.

Methods

Two messenger RNA (mRNA) microarray datasets were downloaded from GEO database. These data were combined with TCGA database to obtain common DEGs between GC tissues and normal tissues. GO and KEGG pathway enrichment analysis was performed. Visualized PPI network analysis was performed by Cytoscape to further identify hub genes. GEPIA database was used to evaluate the prognostic value of hub genes. The online software Ualcan was applied to analyze the expression of the prognosis-related genes in cancer tissues and normal tissues from different perspectives of primary GC, TNM stage, nodal metastasis status and tumor grade. Immunohistochemical staining of GC tissues and normal tissues was performed to evaluate the expression of signature genes in GC.

Results

Eighty-four common differentially expressed genes (DEGs) in GC were identified. These genes were closely related to the P13K-Akt signal pathway and other signaling pathways. Ten hub genes were identified. Collagen type I alpha 1 (COL1A1) and collagen type IV alpha 1 (COL4A1) were significantly associated with poor prognosis of GC and were all positively correlated with T stage, distant metastasis, and TNM stage of GC. Immunohistochemistry revealed that the expression of these 2 genes was upregulated in GC tissues. These 2 genes expression was negatively related with 5-year survival rate of GC patients.

Conclusion

Ten highly expressed hub genes in GC tissue were mined by bioinformatics method. COL1A1 and COL4A1 were significantly associated with the prognosis of GC. This study provided a theoretical basis for the pathogenesis, clinical diagnosis and therapeutic targets of GC.

Identification and verification of novel immune-related ferroptosis signature with excellent prognostic predictive and clinical guidance value in hepatocellular carcinoma

Background: Immunity and ferroptosis often play a synergistic role in the progression and treatment of hepatocellular carcinoma (HCC). However, few studies have focused on identifying immune-related ferroptosis gene biomarkers. Methods: We performed weighted gene co-expression network analysis (WGCNA) and random forest to identify prognostic differentially expressed immune-related genes (PR-DE-IRGs) highly related to HCC and characteristic prognostic differentially expressed ferroptosis-related genes (PR-DE-FRGs) respectively to run co-expression analysis for prognostic differentially expressed immune-related ferroptosis characteristic genes (PR-DE-IRFeCGs). Lasso regression finally identified 3 PR-DE-IRFeCGs for us to construct a prognostic predictive model. Differential expression and prognostic analysis based on shared data from multiple sources and experimental means were performed to further verify the 3 modeled genes' biological value in HCC. We ran various performance testing methods to test the model's performance and compare it with other similar signatures. Finally, we integrated composite factors to construct a comprehensive quantitative nomogram for accurate prognostic prediction and evaluated its performance. Results: 17 PR-DE-IRFeCGs were identified based on co-expression analysis between the screened 17 PR-DE-FRGs and 34 PR-DE-IRGs. Multi-source sequencing data, QRT-PCR, immunohistochemical staining and testing methods fully confirmed the upregulation and significant prognostic influence of the three PR-DE-IRFeCGs in HCC. The model performed well in the performance tests of multiple methods based on the 5 cohorts. Furthermore, our model outperformed other related models in various performance tests. The immunotherapy and chemotherapy guiding value of our signature and the comprehensive nomogram's excellent performance have also stood the test. Conclusion: We identified a novel PR-DE-IRFeCGs signature with excellent prognostic prediction and clinical guidance value in HCC.

Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients

BACKGROUND AND PURPOSE

Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria.

MATERIALS AND METHODS

We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria.

RESULTS

The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract.

CONCLUSION

The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.

Development and validation of asthma risk prediction models using co-expression gene modules and machine learning methods

Asthma is a heterogeneous respiratory disease characterized by airway inflammation and obstruction. Despite recent advances, the genetic regulation of asthma pathogenesis is still largely unknown. Gene expression profiling techniques are well suited to study complex diseases including asthma. In this study, differentially expressed genes (DEGs) followed by weighted gene co-expression network analysis (WGCNA) and machine learning techniques using dataset generated from airway epithelial cells (AECs) and nasal epithelial cells (NECs) were used to identify candidate genes and pathways and to develop asthma classification and predictive models. The models were validated using bronchial epithelial cells (BECs), airway smooth muscle (ASM) and whole blood (WB) datasets. DEG and WGCNA followed by least absolute shrinkage and selection operator (LASSO) method identified 30 and 34 gene signatures and these gene signatures with support vector machine (SVM) discriminated asthmatic subjects from controls in AECs (Area under the curve: AUC = 1) and NECs (AUC = 1), respectively. We further validated AECs derived gene-signature in BECs (AUC = 0.72), ASM (AUC = 0.74) and WB (AUC = 0.66). Similarly, NECs derived gene-signature were validated in BECs (AUC = 0.75), ASM (AUC = 0.82) and WB (AUC = 0.69). Both AECs and NECs based gene-signatures showed a strong diagnostic performance with high sensitivity and specificity. Functional annotation of gene-signatures from AECs and NECs were enriched in pathways associated with IL-13, PI3K/AKT and apoptosis signaling. Several asthma related genes were prioritized including SERPINB2 and CTSC genes, which showed functional relevance in multiple tissue/cell types and related to asthma pathogenesis. Taken together, epithelium gene signature-based model could serve as robust surrogate model for hard-to-get tissues including BECs to improve the molecular etiology of asthma.

Identification of stemness index-related long noncoding RNA SNHG12 in human bladder cancer based on WGCNA

BACKGROUND

Cancer stem cells (CSCs) have an key role in the beginning, progression and treatment of bladder cancer. In the current study, our target was to identify CSCS-related genes in bladder cancer.

METHODS

Bladder cancer (BLCA) transcriptome data were acquired from The Cancer Genome Atlas (TCGA) database. WGCNA was used to screen genes connected with the mRNA expression-based stemness index (mRNAsi).Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to analyze the biological function of mRNAsi-related genes. Univariate Cox regression and LASSO Cox regression algorithms were applied to build a risk score model. Additionally, a ceRNA regulatory netwok based on key mRNAsi-related genes was established via TargetScan, miRDB, miRTarBas and miRcode database,and lncRNA SNHG12 was selected for further in vitro and invivo functional assays.

RESULTS

Between BLCA and normal samples were identified 1560 differentially expressed genes (DEGs).845 DEGs were most significantly associated with mRNAsi according to WGCNA analysis, which were mainly enriched in GO terms and KEGG pathways related to cell proliferation. Univariate Cox regression and LASSO Cox regression algorithms screened 25 mRNAsi-related genes to construct the risk score model with the significant ability to estimate prognosis of BLCA patients. A ceRNA network, including 8 lncRNA, 11 miRNA and 9 mRNAsi-related mRNA, was constructed.We found that lncRNAs ADAMTS9-AS1 and SNHG12 were observably related to the survival of BLCA patients. To verify this finding, we selected SNHG12 for further study. RT-PCR experiments revealed that SNHG12 was high expression in both bladder cancer tissues and cells.SNHG12 promoted proliferation, invasion, migration, apoptosis and stemness of bladder cancer cells in vitro and tumour proliferation in vivo.

CONCLUSION

Our study identified 25 biomarkers associated with stemness indices in BLCA and established a ceRNA network based on key mRNAsi-related genes.SNHG12 promoted BLCA proliferation, invasion, migration, apoptosis and stemness in vitro. It was also showed that SNHG12 promoted tumour growth.

Identification of Key Genes as Early Warning Signals of Acute Myocardial Infarction Based on Weighted Gene Correlation Network Analysis and Dynamic Network Biomarker Algorithm

Purpose

The specific mechanisms and biomarkersunderlying the progression of stable coronary artery disease (CAD) to acute myocardial infarction (AMI) remain unclear. The current study aims to explore novel gene biomarkers associated with CAD progression by analyzing the transcriptomic sequencing data of peripheral blood monocytes in different stages of CAD.

Material and Methods

A total of 24 age- and sex- matched patients at different CAD stages who received coronary angiography were enrolled, which included 8 patients with normal coronary angiography, 8 patients with angiographic intermediate lesion, and 8 patients with AMI. The RNA from peripheral blood monocytes was extracted and transcriptome sequenced to analyze the gene expression and the differentially expressed genes (DEG). A Gene Oncology (GO) enrichment analysis was performed to analyze the biological function of genes. Weighted gene correlation network analysis (WGCNA) was performed to classify genes into several gene modules with similar expression profiles, and correlation analysis was carried out to explore the association of each gene module with a clinical trait. The dynamic network biomarker (DNB) algorithm was used to calculate the key genes that promote disease progression. Finally, the overlapping genes between different analytic methods were explored.

Results

WGCNA analysis identified a total of nine gene modules, of which two modules have the highest positive association with CAD stages. GO enrichment analysis indicated that the biological function of genes in these two gene modules was closely related to inflammatory response, which included T-cell activation, cell response to inflammatory stimuli, lymphocyte activation, cytokine production, and the apoptotic signaling pathway. DNB analysis identified a total of 103 genes that may play key roles in the progression of atherosclerosis plaque. The overlapping genes between DEG/WGCAN and DNB analysis identified the following 13 genes that may play key roles in the progression of atherosclerosis disease: SGPP2, DAZAP2, INSIG1, CD82, OLR1, ARL6IP1, LIMS1, CCL5, CDK7, HBP1, PLAU, SELENOS, and DNAJB6.

Conclusions

The current study identified a total of 13 genes that may play key roles in the progression of atherosclerotic plaque and provides new insights for early warning biomarkers and underlying mechanisms underlying the progression of CAD.

P3H4 and PLOD1 expression associates with poor prognosis in bladder cancer

PURPOSE

The prolyl 3-hydroxylase family member 4 gene (P3H4) is involved in the development of human cancers. The association of P3H4 with bladder cancer (BC) prognosis is unclear. This study aimed to analyze the association of P3H4 with BC prognosis.

METHODS

RNA-Seq data were downloaded from The Cancer Genome Atlas project and BC microarray datasets (GSE13507, GSE31684, and GSE32548) were downloaded from the Gene Expression Omnibus database. We analyzed the differences in P3H4 expression levels between BC tumors and non-tumor tissues and between samples with different clinical information. The association of P3H4 and P3H4-related genes with BC prognosis and the possibility of using P3H4 expression as a prognostic biomarker in BC patients were also analyzed. RevMan was used to perform the meta-analysis.

RESULTS

P3H4 was upregulated in BC tissues compared with the adjacent non-tumor tissues (p = 4.06e-08). Univariate Cox regression analysis and meta-analysis showed that high P3H4 expression level contributed to a poor BC prognosis (Hazard ratio, HR = 1.348, 95% CI 1.140-1.594, p = 4.89e-04; meta-analysis: HR = 1.45, 95% CI 1.10-1.91; p = 9.00e-03). Among the genes related to P3H4, the PLOD1 gene was closely associated with P3H4 expression (r = 0.620, p = 2.49e-44). Also, a meta-analysis showed that PLOD1 expression was associated with a poor prognosis in BC patients (HR = 1.77, 95% CI 1.31-2.38; p = 2.00e-04).

CONCLUSIONS

The P3H4 and PLOD1 genes might be used as reliable prognostic biomarkers for BC.

CASP5 and CR1 as potential biomarkers for Kawasaki disease: an Integrated Bioinformatics-Experimental Study

BACKGROUND

Kawasaki disease (KD) is a pediatric inflammatory disorder causes coronary artery complications. The disease overlapping manifestations with a set of symptomatically like diseases such as bacterial and viral infections, juvenile idiopathic arthritis, Henoch-Schönlein purpura, infection of unknown etiology, group-A streptococcal and adenoviral infections, and incomplete KD could lead to misdiagnosis of the disease.

METHODS

In the present study, we applied weighted gene co-expression network analysis (WGCNA) to identify network modules of co-expressed genes in GSE73464 and also, limma package was used to identify the differentially expressed genes (DEGs) in KD expression arrays composed of GSE73464, GSE18606, GSE109351, and GSE68004. By merging the results of WGCNA and limma, we detected hub genes. Then, analyzed the peripheral blood mononuclear cells (PBMCs) of 16 patients and 8 control subjects using Real-Time Polymerase Chain Reaction (RT-PCR) to evaluate the previous results.

RESULTS

We assessed the diagnostic potency of the screened genes by plotting the area under curve (AUC). We finally identified 2 genes CASP5(Caspase 5) and CR1(Complement C3b/C4b Receptor 1) which were shown to potentially discriminate KD from other similar diseases and also from healthy people.

CONCLUSIONS

The results of RT-PCR and AUC confirmed the diagnostic potentials of two suggested biomarkers for KD.

Identification of Immune-Cell-Related Prognostic Biomarkers of Esophageal Squamous Cell Carcinoma Based on Tumor Microenvironment

Background

Esophageal squamous cell carcinoma (ESCC) is one of the most fatal cancers in the world. The 5-year survival rate of ESCC is <30%. However, few biomarkers can accurately predict the prognosis of patients with ESCC. We aimed to identify potential survival-associated biomarkers for ESCC to improve its poor prognosis.

Methods

ImmuneAI analysis was first used to access the immune cell abundance of ESCC. Then, ESTIMATE analysis was performed to explore the tumor microenvironment (TME), and differential analysis was used for the selection of immune-related differentially expressed genes (DEGs). Weighted gene coexpression network analysis (WGCNA) was used for selecting the candidate DEGs. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to build the immune-cell-associated prognostic model (ICPM). Kaplan–Meier curve of survival analysis was performed to evaluate the efficacy of the ICPM.

Results

Based on the ESTIMATE and ImmuneAI analysis, we obtained 24 immune cells’ abundance. Next, we identified six coexpression module that was associated with the abundance. Then, LASSO regression models were constructed by selecting the genes in the module that is most relevant to immune cells. Two test dataset was used to testify the model, and we finally, obtained a seven-genes survival model that performed an excellent prognostic efficacy.

Conclusion

In the current study, we filtered seven key genes that may be potential prognostic biomarkers of ESCC, and they may be used as new factors to improve the prognosis of cancer.

Identifying the hub genes in non-small cell lung cancer by integrated bioinformatics methods and analyzing the prognostic values

BACKGROUND

Lung cancer, a malignant tumor, has the highest mortality and second most common morbidity worldwide. Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. This study aimed to identify the gene signature associated with the NSCLC prognosis using bioinformatics analysis.

MATERIALS AND METHODS

The dataset GSE103512 was utilized to construct co-expression networks using weighted gene co-expression network analysis (WGCNA). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using Database for Annotation, Visualization, and Integrated Discovery. Gene set enrichment analysis was conducted to ascertain the function of the hub genes more accurately. The relationship between the hub genes and immune infiltration was investigated using a single sample gene set enrichment analysis. Hub genes were screened and validated by other datasets and online websites.

RESULTS

The results of WGCNA demonstrated that the blue module was most significantly related to tumor progression in NSCLC. Functional enrichment analysis showed that the blue module was associated with DNA replication, cell division, mitotic nuclear division, and cell cycle. A total of five hub genes (RFC5, UBE2S, CHAF1A, FANCI, and TMEM194A) were chosen to be identified and validated at transcriptional and translational levels. Receiver operating characteristic curve verified that the mRNA levels of these five genes can excellently discriminate between normal and tumor tissues. Survival analysis was also performed. Additionally, the protein levels of these five genes were also significantly different between tumor and normal tissues. Immune infiltration analysis showed that the expression levels of the hub genes had a negative correlation with the infiltration levels of many cells related to innate immune response, antigen-presenting process, humoral immune response, or T cell-mediated immune responses.

CONCLUSIONS

We identified five hub genes associated with the NSCLC tumorigenesis. NSCLC patients with higher expressions of each hub gene had a worse prognosis than those with lower expressions. Moreover, the hub genes might serve as biomarkers and therapeutic targets for precise diagnosis, target therapy, and immunotherapy of NSCLC in the future.

Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

OBJECTIVES

Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO).

MATERIALS AND METHODS

The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve.

RESULTS

Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF.

CONCLUSION

The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

A Co-Expression Network Reveals the Potential Regulatory Mechanism of lncRNAs in Relapsed Hepatocellular Carcinoma

Background

The mechanistic basis for relapsed hepatocellular carcinoma (HCC) remains poorly understood. Recent research has highlighted the important roles of long non-coding RNAs (lncRNAs) in HCC. However, there are only a few studies on the association between lncRNAs and HCC relapse.

Methods

Differentially expressed lncRNAs and mRNAs between a primary HCC group and relapsed HCC group were identified using the edge R package to analyze the GSE101432 dataset. The differentially expressed lncRNAs and mRNAs were used to construct a lncRNA–mRNA co-expression network. Weighted gene co-expression network analysis followed by Gene Ontology (GO) enrichment analyses were conducted on the database. Furthermore, correlation and survival analyses were performed using The Cancer Genome Atlas database, and expression in the clinical samples was verified by qRT-PCR. Thereafter, we inputted the genes from the two groups into the HCC TNM stage and tumor grade database from TCGA. Finally, we performed Kaplan–Meier survival analysis on the lncRNAs related to relapsed HCC.

Results

In this study, lncRNAs and mRNAs associated with HCC relapse were identified. Two gene modules were found to be closely linked to this. The GO terms in the yellow and black modules were related to cell proliferation, differentiation, and survival, as well as some transcription-related biological processes. Through qRT-PCR, we found that the expression levels of LINC00941 and LINC00668 in relapsed HCC were higher than those in primary HCC. Further, mRNA levels of LOX, OTX1, MICB, NDUFA4L2, BAIAP2L2, and KCTD17 were changed in relapsed HCC compared to levels in primary HCC. In addition, we verified that these genes could predict the overall survival and recurrence-free survival of HCC. Moreover, we found that LINC00668 and LINC00941 could affect tumor grade and TNM stages. In total, we identified and validated two lncRNAs (LINC00941 and LINC00668) and six mRNAs (LOX, MICB, OTX1, BAIAP2L2, KCTD17, NDUFA4L2) associated with HCC relapse.

Conclusion

In summary, we identified the key gene modules and central genes associated with relapsed HCC and constructed lncRNA–mRNA networks related to this. These genes are likely to have potential prognostic value for relapsed HCC and might shed new light on novel biomarkers or diagnostic targets for relapsed HCC.

Increased MMP12 mRNA expression in induced sputum was correlated with airway eosinophilic inflammation in asthma patients: evidence from bioinformatic analysis and experiment verification

BACKGROUND

Asthma is a common chronic airway inflammatory disease worldwide. Studies on gene expression profiles in induced sputum may provide noninvasive diagnostic biomarkers and therapeutic targets for asthma.

OBJECTIVE

To investigate mRNA expression of MMP12 in induced sputum and its relationship with asthma airway eosinophilic inflammation.

METHODS

GSE76262 dataset was analyzed using R software, weighted gene coexpression network analysis (WGCNA), and protein-protein interaction (PPI) network construction. The top ten hub genes were screened with Cytoscape software (version 3.8.4). We then verified the mRNA expression of MMP12 in two other datasets (GSE137268 and GSE74075) via ROC curve estimates and our induced sputum samples using real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). Finally, we explored the correlation between MMP12 with asthmatic eosinophilic-related indicators.

RESULTS

We obtained the top ten hub genes, namely, CCL17, CCL2, CSF1, CCL22, CCR3, CD69, FCGR2B, CD1C, CD1E, and MMP12 via expression profile screening and validation on the GSE76262 dataset. MMP12 was selected as the candidate gene through further validation on GSE137268 and GSE74075 datasets. Finally, we demonstrated that the mRNA expression of MMP12 is significantly upregulated in induced sputum of asthmatic patients (p<0.05) and significantly correlated with eosinophilic-related indicators (p<0.05). These findings indicated that MMP12 can act as a diagnostic biomarker for asthma.

CONCLUSION

Our study successfully identified and demonstrated that MMP12 is a potential diagnostic biomarker for asthma due to its high expression and association with eosinophilic-related indicators. The results of this study can provide novel insights into asthmatic diagnosis and therapy in the future.

Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis

The aim of this study was to identify hub genes closely related to the pathogenesis and prognosis of thyroid carcinoma (THCA) by integrated bioinformatics analysis. In this study, through differential gene expression analysis, 1916 and 665 differentially expressed genes (DEGs) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, and 7 and 11 co-expressed modules were identified from the TCGA-THCA and GSE153659 datasets, respectively, by weighted gene co-expression network analysis. We identified 162 overlapping genes between the DEGs and co-expression module genes as candidate hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the 162 overlapping DEGs identified significant functions and pathways of THCA, such as thyroid hormone generation and metabolic process. A protein-protein interaction (PPI) analysis detected the top 10 hub genes (QSOX1, WFS1, EVA1A, FSTL3, CHRDL1, FABP4, PRDM16, PPARGC1A, PPARG, COL23A1). Finally, survival analysis, clinical correlation analysis, and protein abundance validation confirmed that 3 of the 10 hub genes were associated with survival prognosis of patients with THCA, and 8 of them were associated with the clinical stages of THCA. In summary, we identified hub genes and key modules that were closely related to THCA, and validated these genes by survival analysis, clinical correlation analysis, and Human Protein Atlas image analysis. Our results provide important information that will help to elucidate the pathogenesis of THCA and identify novel candidate prognostic biomarkers and potential therapeutic targets.

KIF4A promotes the development of bladder cancer by transcriptionally activating the expression of CDCA3

Bladder cancer (BC) is among the most common urinary system tumors with a high morbidity and mortality worldwide. Despite advancements being made in the diagnosis and treatment of bladder cancer, targeted therapy remains the most promising treatment, and novel therapeutic targets are urgently required in to improve the outcomes of patients with BC. Kinesin family member 4A (KIF4A) is a plus-end directed motor protein involved in the regulation of multiple cellular processes, such as mitosis and axon growth. Notably, KIF4A plays important roles in tumor growth and progression, and its expression is associated with the prognosis of several types of cancer. However, the potential role and molecular mechanisms of KIF4A in bladder cancer development remain unclear. The present study demonstrated that KIF4A was highly expressed in human BC tissues, and its expression was associated with patient clinicopathological characteristics, such as tumor stage (P=0.012) and with the prognosis of patients with BC. It was further found that KIF4A promoted the cell proliferation of bladder cancer both in vitro and in vivo. On the whole, the data presented herein provide evidence that KIF4A promotes the development of BC through the transcriptional activation of the expression of CDCA3. The present study indicates the involvement of KIF4A in the progression of BC and suggests that KIF4A may be a promising therapeutic target for the treatment of BC.

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.

Identification of a Gene Signature for Renal Cell Carcinoma-Associated Fibroblasts Mediating Cancer Progression and Affecting Prognosis

Background: Cancer-associated fibroblasts (CAFs) are mainly involved in cancer progression and treatment failure. However, the specific signature of CAFs and their related clinicopathological parameters in renal cell carcinoma (RCC) remain unclear. Here, methods to recognize gene signatures were employed to roughly assess the infiltration of CAFs in RCC, based on the data from The Cancer Genome Atlas (TCGA). Weighted Gene Coexpression Network Analysis (WGCNA) was used to cluster transcriptomes and correlate with CAFs to identify the gene signature. Single-cell and cell line sequencing data were used to verify the expression specificity of the gene signature in CAFs. The gene signature was used to evaluate the infiltration of CAFs in each sample, and the clinical significance of each key gene in the gene signature and CAFs was analyzed. We observed that the CAF infiltration was higher in kidney cancer and advanced tumor stage and grade than in normal tissues. The seven key genes of the CAF gene signature identified using WGCNA showed high expression of CAF-related characteristics in the cell clustering landscape and fibroblast cell lines; these genes were found to be associated with extracellular matrix function, collagen synthesis, cell surface interaction, and adhesion. The high CAF infiltration and the key genes were verified from the TCGA and Gene Expression Omnibus data related to the advanced grade, advanced stage, and poor prognosis of RCC. In summary, our findings indicate that the clinically significant gene signature may serve as a potential biomarker of CAFs in RCC, and the infiltration of CAFs is associated with the pathological grade, stage, and prognosis of RCC.

HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BACKGROUND

Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH.

METHODS

The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve.

RESULTS

Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14.

CONCLUSIONS

Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

Selection of endogenous control genes for normalising gene expression data derived from formalin-fixed paraffin-embedded tumour tissue

Quantitative real time polymerase chain reaction (qPCR) data are normalised using endogenous control genes. We aimed to: (1) demonstrate a pathway to identify endogenous control genes for qPCR analysis of formalin-fixed paraffin-embedded (FFPE) tissue using bladder cancer as an exemplar; and (2) examine the influence of probe length and sample age on PCR amplification and co-expression of candidate genes on apparent expression stability. RNA was extracted from prospective and retrospective samples and subject to qPCR using TaqMan human endogenous control arrays or single tube assays. Gene stability ranking was assessed using coefficient of variation (CoV), GeNorm and NormFinder. Co-expressed genes were identified from The Cancer Genome Atlas (TCGA) using the on-line gene regression analysis tool GRACE. Cycle threshold (Ct) values were lower for prospective (19.49 ± 2.53) vs retrospective (23.8 ± 3.32) tissues (p < 0.001) and shorter vs longer probes. Co-expressed genes ranked as the most stable genes in the TCGA cohort by GeNorm when analysed together but ranked lower when analysed individually omitting co-expressed genes indicating bias. Stability values were < 1.5 for the 20 candidate genes in the prospective cohort. As they consistently ranked in the top ten by CoV, GeNorm and Normfinder, UBC, RPLP0, HMBS, GUSB, and TBP are the most suitable endogenous control genes for bladder cancer qPCR.

Weighted gene co-expression network analysis can sort cancer-associated fibroblast-specific markers promoting bladder cancer progression

The role of cancer-associated fibroblasts (CAFs) has been thoroughly investigated in tumour microenvironments but not in bladder urothelial carcinoma (BLCA). The cell fraction of CAFs gradually increased with BLCA progression. Weighted gene co-expression network analysis (WGCNA) revealed a specific gene expression module of CAFs that are relevant to cancer progression and survival status. Fifteen key genes of the module were consistent with a fibroblast signature in single-cell RNA sequencing, functionally related to the extracellular matrix, and significant in survival analysis and tumour staging. A comparison of the luminal-infiltrated versus luminal-papillary subtypes and fibroblast versus urothelial carcinoma cell lines and immunohistochemical data analysis demonstrated that the key genes were specifically expressed in CAFs. Moreover, these genes are highly correlated with previously reported CAF markers. In summary, CAFs play a major role in the progression of BLCA, and the 15 key genes act as BLCA-specific CAF markers and can predict CAF changes. WGCNA can, therefore, be used to sort CAF-specific gene set in cancer tissues.

Identification of novel biomarkers correlated with prostate cancer progression by an integrated bioinformatic analysis

Prostate cancer (PCa) is a highly aggressive malignant tumor and the biological mechanisms underlying its progression remain unclear.We performed weighted gene co-expression network analysis in PCa dataset from the Cancer Genome Atlas database to identify the key module and key genes related to the progression of PCa. Furthermore, another independent datasets were used to validate our findings.A total of 744 differentially expressed genes were screened out and 5 modules were identified for PCa samples from the Cancer Genome Atlas database. We found the brown module was the key module and related to tumor grade (R2 = 0.52) and tumor invasion depth (R2 = 0.39). Besides, 24 candidate hub genes were screened out and 2 genes (BIRC5 and DEPDC1B) were identified and validated as real hub genes that associated with the progression and prognosis of PCa. Moreover, the biological roles of BIRC5 were related to G-protein coupled receptor signal pathway, and the functions of DEPDC1B were related to the G-protein coupled receptor signal pathway and retinol metabolism in PCa.Taken together, we identified 1 module, 24 candidate hub genes and 2 real hub genes, which were prominently associated with PCa progression. With more experiments and clinical trials, these genes may provide a promising future for PCa treatment.

Mechanism of Astragalus membranaceus in the treatment of laryngeal cancer based on gene co-expression network and molecular docking

Astragalus membranaceus (HUANG QI, HQ) is a kind of traditional Chinese medicine. Researchers have widely concerned its antitumor effect. At present, there is still a lack of research on the treatment of laryngeal cancer with HQ. In this study, we integrated data from the weighted gene co-expression network of laryngeal cancer samples and the components and targets of HQ. A new method for dividing PPI network modules is proposed. Important targets of HQ treatment for laryngeal cancer were obtained through the screening of critical modules. These nodes performed differential expression analysis and survival analysis through external data sets. GSEA enrichment analysis reveals pathways for important targets participation. Finally, molecular docking screened active ingredients in HQ that could interact with important targets. Combined with the laryngeal cancer gene co expression network and HQ PPI network, we obtained the critical module related to laryngeal cancer. Among them, MMP1, MMP3, and MMP10 were chosen as important targets. External data sets demonstrate that their expression in tumor samples is significantly higher than in normal samples. The survival time of patients with high expression group was significantly shortened, which is a negative factor for prognosis. GSEA enrichment analysis found that they are mainly involved in tumor-related pathways such as ECM receptor interaction and Small cell lung cancer. The docking results show that the components that can well bind to important targets of HQ are quercetin, rutin, and Chlorogenic acid, which may be the primary mechanism of the anti-cancer effect of HQ. These findings provide a preliminary research basis for Chinese medicine treatment of laryngeal cancer and offer ideas to related drug design.

Role of miRNAs in prostate cancer: Do we really know everything?

Many different genetic alterations, as well as complex epigenetic interactions, are the basis of the genesis and progression of prostate cancer (CaP). This is the reason why until now the molecular pathways related to development of this cancer were only partly known, and even less those that determine aggressive or indolent tumour behaviour. MicroRNAs (miRNAs) represent a class of about 22 nucleotides long, small non-coding RNAs, which are involved in gene expression regulation at the post-transcriptional level. MiRNAs play a crucial role in regulating several biological functions and preserving homeostasis, as they carry out a wide modulatory activity on various molecular signalling pathways. MiRNA genes are placed in cancer-related genomic regions or in fragile sites, and they have been proven to be involved in the main steps of carcinogenesis as oncogenes or oncosuppressors in many types of cancer, including CaP. We performed a narrative review to describe the relationship between miRNAs and the crucial steps of development and progression of CaP. The aims of this study were to improve the knowledge regarding the mechanisms underlying miRNA expression and their target genes, and to contribute to understanding the relationship between miRNA expression profiles and CaP.

Suppressed microRNA-195-5p expression in mycosis fungoides promotes tumor cell proliferation

BACKGROUND

Several cancers, including mycosis fungoides (MF), have reported dysregulation of miR-195-5p. miR-195-5p plays a role in cell cycle regulation in several malignant diseases.

OBJECTIVES

This study aimed to investigate: (a) the expression level of miR-195-5p in lesional MF skin biopsies and (b) the potential regulatory roles of miR-195-5p in MF.

METHODS

Quantitative real-time polymerase chain reaction (RT-qPCR) was used to determine miR-195-5p expression in MF skin biopsies and cell lines. The effect of miR-195-5p and ADP-ribosylation factor-like protein 2 (ARL2) on cell cycle and apoptosis was measured by flow cytometry assays. Changes in ARL2 expression were determined by RT-qPCR and Western blotting (WB).

RESULTS

We found lower expression levels of miR-195-5p in lesional skin from MF patients compared with non-lesional MF skin and skin from healthy volunteers. Additionally, miR-195-5p showed lower expression levels in the skin from patients with disease progression compared with patients with stable disease. In vitro studies showed that overexpression of miR-195-5p induced a cell cycle arrest in G0G1. Using microarray analysis, we identified several genes that were regulated after miR-195-5p overexpression. The most downregulated gene after miR-195-5p mimic transfection was ARL2. RT-qPCR and WB analyses confirmed downregulation of ARL2 following transfection with miR-195-5p mimic. Lastly, transfection with siRNA against ARL2 also induced a G0G1 arrest.

CONCLUSION

Upregulation of miR-195-5p in MF inhibits cycle arrest by downregulation of ARL2. miR-195-5p may thus function as a tumor suppressor in MF and low miR-195-5p expression in lesional MF skin may promote disease progression.

Novel Potential Biomarkers Associated With Epithelial to Mesenchymal Transition and Bladder Cancer Prognosis Identified by Integrated Bioinformatic Analysis

Bladder cancer (BC) is one of the most common malignancies in terms of incidence and recurrence worldwide. The aim of this study was to identify novel prognostic biomarkers related to BC progression utilizing weighted gene co-expression network analysis (WGCNA) and further bioinformatic analysis. First, we constructed a co-expression network by using WGCNA among 274 TCGA-BLCA patients and preliminarily screened out four genes (CORO1C, TMPRSS4, PIK3C2B, and ZNF692) associated with advanced clinical traits. In support, GSE19915 and specimens from 124 patients were used to validate the genes selected by WGCNA; then, CORO1C and TMPRSS4 were confirmed as hub genes with strong prognostic values in BC. Moreover, the result of gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) indicated that CORO1C and TMPRSS4 might be involved in the process of epithelial to mesenchymal transition (EMT) reversely. In addition, high expression of CORO1C was found to be significantly correlated with tumor-infiltrating neutrophils (TINs), a negative regulatory component that facilitates tumor distant progression and induces poor clinical outcome. In conclusion, our study first identified CORO1C and TMPRSS4 as vital regulators in the process of tumor progression through influencing EMT and could be developed to effective prognostic and therapeutic targets in future BC treatment.

CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis

Background

Hepatocellular carcinoma (HCC) is characterized by increased mortality and poor prognosis. We aimed to identify potential prognostic markers by weighted gene coexpression network analysis (WGCNA), to assist clinical outcome prediction and improve treatment decisions for HCC patients.

Methods

Prognosis-related gene modules were first established by WGCNA. Venn diagrams obtained intersection genes of module genes and differentially expressed genes. The Kaplan-Meier overall survival curves and disease-free survival curves of intersection genes were further analyzed on the Gene Expression Profiling Interactive Analysis website. Chi-square tests were performed to explore the associations between prognostic gene expressions and clinicopathological features.

Results

CCNB2, TOP2A, and ASPM were identified as both prognosis-related genes and differentially expressed genes. TOP2A (HR: 1.7, P = 0.003) and ASPM (HR: 1.8, P < 0.001) exhibited a significant difference between the high- and low-expression groups in the overall survival analysis, while CCNB2 (HR: 1.4, P = 0.052) was not statistically significant. CCNB2 (HR: 1.5, P = 0.006), TOP2A (HR: 1.7, P < 0.001), and ASPM (HR: 1.6, P = 0.003) were all statistically significant in the disease-free survival analysis. All three genes were significantly associated with race and fetoprotein values (P < 0.05). CCNB2 expression was associated with tumor stage (P = 0.01), and ASPM expression was associated with new tumor events (P = 0.03).

Conclusion

Overexpression of CCNB2, TOP2A, and ASPM are associated with poor prognosis, and these genes could serve as potential prognostic markers and therapeutic targets for HCC.

Different Cardiotoxicity of Palbociclib and Ribociclib in Breast Cancer: Gene Expression and Pharmacological Data Analyses, Biological Basis, and Therapeutic Implications

Breast cancer is the most frequent tumor in women. The recent advent of cyclin-dependent kinase (CDK) 4/6 inhibitors palbociclib and ribociclib has represented a major step forward for patients with hormone receptor-positive breast cancer. These two agents have showed similar efficacy in terms of breast cancer outcome but different cardiotoxic effects. In particular, ribociclib, but not palbociclib, has been associated with QT interval prolongation, and the mechanisms underlying this event are still unclear. In order to clarify such difference, we matched the candidate genes associated with QT interval prolongation with genes whose expression is altered following palbociclib or ribociclib treatment. We also investigated whether pharmacokinetic and pharmacodynamic characteristics, such as IC₅₀ (hERG) [concentration of drug producing 50% inhibition (human ether-à-go-go related gene)] and maximum concentration (C_max), could justify the different effects on QT interval prolongation. Our results show that ribociclib, but not palbociclib, could act by down-regulating the expression of KCNH2 (encoding for potassium channel hERG) and up-regulating SCN5A and SNTA1 (encoding for sodium channels Nav1.5 and syntrophin-α1, respectively), three genes associated with long QT syndrome. Consistent with the cardiotoxicity induced by ribociclib, its IC₅₀ (hERG)/free concentration (C_{max free}) ratio is closer to the safety threshold than that of palbociclib. In summary, we hypothesize that the different cardiotoxicity associated with ribociclib and palbociclib could be due to the alteration of potassium and sodium channels induced by ribociclib. A better comprehension of the mechanisms of cardiac channelopathies and drug-induced QT interval prolongation will be fundamental to avoid serious and potentially lethal adverse events and, as a consequence, optimize the management of breast cancer patients.

Integrated Analysis to Evaluate the Prognostic Value of Signature mRNAs in Glioblastoma Multiforme

Background

Gliomas are the most common intracranial tumors and are classified as I-IV. Among them, glioblastoma multiforme (GBM) is the most common invasive glioma with a poor prognosis. New molecular biomarkers that can predict clinical outcomes in GBM patients must be identified, which will help comprehend their pathogenesis and supply personalized treatment. Our research revealed four powerful survival indicators in GBM by reanalyzing microarray data and genetic sequencing data in public databases. Moreover, it unraveled new potential therapeutic targets which could help improve the survival time and quality of life of GBM patients.

Materials and Methods

To identify prognostic signatures in GBMs, we analyzed the gene profiling data of GBM and standard brain samples from the Gene Expression Omnibus, including four datasets and RNA sequencing data from The Cancer Genome Atlas (TCGA) containing 152 glioblastoma tissues. We performed the differential analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, weighted gene co-expression network analysis (WGCNA) and Cox regression analysis.

Results

After differential analysis in GSE12657, GSE15824, GSE42656 and GSE50161, overlapping differentially expressed genes were identified. We identified 110 up-regulated DEGs and 75 down-regulated DEGs in the GBM samples. Significantly enriched subclasses of the GO classification of these genes included mitotic sister chromatid separation, mitotic nuclear division and so on. In KEGG pathway analysis, the most abundant terms were ECM-receptor interaction and protein digestion and absorption. WGCNA analysis was performed on these 185 DEGs in 152 glioblastoma samples obtained from TCGA, and gene co-expression networks were constructed. We then performed a multivariate Cox analysis and established a Cox proportional hazards regression model using the top 20 genes significantly correlated with survival time. We identified a four-protein prognostic signature that could divide patients into high-risk and low-risk groups. Increased expression of SLC12A5, CCL2, IGFBP2, and PDPN was associated with increased risk scores. Finally, the K-M curves confirmed that these genes could be used as independent predictors of survival in patients with glioblastoma.

Conclusion

Our analytical study identified a set of potential biomarkers that could predict survival and may contribute to successful treatment of GBM patients.

Key genes and co-expression modules involved in asthma pathogenesis

Machine learning and weighted gene co-expression network analysis (WGCNA) have been widely used due to its well-known accuracy in the biological field. However, due to the nature of a gene’s multiple functions, it is challenging to locate the exact genes involved in complex diseases such as asthma. In this study, we combined machine learning and WGCNA in order to analyze the gene expression data of asthma for better understanding of associated pathogenesis. Specifically, the role of machine learning is assigned to screen out the key genes in the asthma development, while the role of WGCNA is to set up gene co-expression network. Our results indicated that hormone secretion regulation, airway remodeling, and negative immune regulation, were all regulated by critical gene modules associated with pathogenesis of asthma progression. Overall, the method employed in this study helped identify key genes in asthma and their roles in the asthma pathogenesis.

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.

Selected by gene co-expression network and molecular docking analyses, ENMD-2076 is highly effective in glioblastoma-bearing rats

Background: Glioblastoma is the most common type of malignant brain tumor. Bioinformatics technology and structure biology were effectively and systematically used to identify specific targets in malignant tumors and screen potential drugs.

Results: GBM patients have higher AURKA and KDR mRNA expression compared with normal samples. Then, we identified a small molecular compound, ENMD-2076, could effectively inhibit Aurora kinase A and VEGFR-2 (encoded by KDR) activities. ENMD-2076 is predicted without toxic properties and also has absorption and gratifying brain/blood barrier penetration ability. Further results demonstrated that ENMD-2076 could significantly inhibit GBM cell lines proliferation and vitality, it also suppressed GBM cells migration and invasion. ENMD-2076 induced glioblastoma cell cycle arrest in G2-M phase and apoptosis by inhibiting PI3K/AKT/mTOR signaling pathways. Additionally, ENMD-2076 prolonged the median survival time of tumor-bearing rats and restrained growth rate of tumor volume in vivo.

Conclusions: Our findings reveal that ENMD-2076 is a promising drug in dealing with glioblastoma and have a perspective application.

Methods: We show that AURKA and KDR genes are hub driver genes in glioblastoma with bioinformatics technology including WGCNA analysis, PPI network, GO, KEGG analysis and GSEA analysis. After identifying a compound via virtual screening analysis, further experiments were carried out to examine the anti-glioblastoma activities of the compound in vivo and in vitro.

Gene regulatory network inference resources: A practical overview

Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of life, from homeostasis to development, from metabolism to behavior, from reaction to stimuli to disease progression. In recent years, the concept of Gene Regulatory Networks (GRNs) has grown popular as an effective applied biology approach for describing the complex and highly dynamic set of transcriptional interactions, due to its easy-to-interpret features. Since cataloguing, predicting and understanding every GRN connection in all species and cellular contexts remains a great challenge for biology, researchers have developed numerous tools and methods to infer regulatory processes. In this review, we catalogue these methods in six major areas, based on the dominant underlying information leveraged to infer GRNs: Coexpression, Sequence Motifs, Chromatin Immunoprecipitation (ChIP), Orthology, Literature and Protein-Protein Interaction (PPI) specifically focused on transcriptional complexes. The methods described here cover a wide range of user-friendliness: from web tools that require no prior computational expertise to command line programs and algorithms for large scale GRN inferences. Each method for GRN inference described herein effectively illustrates a type of transcriptional relationship, with many methods being complementary to others. While a truly holistic approach for inferring and displaying GRNs remains one of the greatest challenges in the field of systems biology, we believe that the integration of multiple methods described herein provides an effective means with which experimental and computational biologists alike may obtain the most complete pictures of transcriptional relationships. This article is part of a Special Issue entitled: Transcriptional Profiles and Regulatory Gene Networks edited by Dr. Federico Manuel Giorgi and Dr. Shaun Mahony.

The coexpression of fibroblast activation protein (FAP) and basal-type markers (CK 5/6 and CD44) predicts prognosis in high-grade invasive urothelial carcinoma of the bladder

High-grade urothelial carcinoma (UC) of the bladder is a heterogeneous disease with dismal prognosis. Bladder tumors with basal phenotype are intrinsically aggressive, and morphological parameters that define disease staging remain main prognosticators. We intend to evaluate the role of cancer-associated fibroblasts (CAFs) in the prognosis of bladder cancer and its association with basal and luminal phenotypes. Clinical and pathological parameters, including the immunohistochemical expression of fibroblast activation protein (FAP) and markers of basal (CK5/6, CD44) and luminal (CK20, GATA3) phenotypes, have been investigated in a series of 121 patients with UC of the bladder treated by radical cystectomy with lymph node dissection, and their implication in long-term cancer-specific survival has been evaluated. A cytoplasmic immunostaining of FAP in CAFs implies worse disease-specific survival (hazard ratio [HR] = 1.68; P = .048). FAP expression is associated with tumor staging (P < .0001), with best discrimination at T2a/T2b level, and with negative expression of markers of luminal phenotype, such as CK20 (P < .0001) and GATA3 (P = .005). In the multivariate analysis, simultaneous expression of FAP, CK5/6, and CD44 is a strong prognosticator of disease-specific survival (HR = 2.3; P = .001), together with nodal invasion (HR = 3.47; P < .0001) and bladder infiltration up to deep muscle or beyond (HR = 2.47; P = .02). There is no association between positive FAP expression in primary tumor and nodal disease (P = .22). FAP expression in CAFs favors tumor invasion in high-grade invasive UC of the bladder with basal phenotype. This new immunohistochemical marker could be added to the routine immunohistochemical protocol to predict clinical behavior in these patients.

Integrated analyses identify potential prognostic markers for uveal melanoma

Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults, which has a high rate of metastases and can induce vision loss and even death to the patients. To identify suitable prognostic markers of UM for the early detection or prognosis prediction would be an essential step toward successful management of the disease. Herein, we extracted the mRNA expression data along with the clinical information from The Cancer Genome Atlas (TCGA) database. A total of eight co-expression modules were constructed by 5,000 genes based on the weighted gene co-expression network analysis (WGCNA). We found the blue and yellow modules were significantly associated with clinical stage. The Cox regression analyses found the blue, yellow, green and brown modules were significantly associated with overall survival (OS), while the blue, yellow, brown, green and pink modules were significantly associated with recurrence-free survival (RFS). Furthermore, the hallmark pathway enrichment analyses found the genes encompassed in the blue, yellow, and brown modules were significantly enriched in critical pathways involved in tumorigenesis and progression process, such as EMT and KRAS pathways. The hub-genes in these three modules were visualized by Cytoscape software and further validated by an external Gene Expression Omnibus (GEO) dataset. Besides, the OS and RFS predicting signatures were constructed based on the validated hub-genes according to the LASSO Cox regression model. The UM patients were assigned to low-/high-risk population. The survival analyses indicated high-risk patients mostly had bad OS/RFS rate compared with the low-risk population. The receiver operating characteristic (ROC) curve proved the stability and superiority of the two signatures. To sum up, our findings provide a framework of co-expression network of UM and identify a series of biomarkers, which will benefit from improving the prognosis prediction of UM patients.

A Network of SLC and ABC Transporter and DME Genes Involved in Remote Sensing and Signaling in the Gut-Liver-Kidney Axis

Genes central to drug absorption, distribution, metabolism and elimination (ADME) also regulate numerous endogenous molecules. The Remote Sensing and Signaling Hypothesis argues that an ADME gene-centered network-including SLC and ABC "drug" transporters, "drug" metabolizing enzymes (DMEs), and regulatory genes-is essential for inter-organ communication via metabolites, signaling molecules, antioxidants, gut microbiome products, uremic solutes, and uremic toxins. By cross-tissue co-expression network analysis, the gut, liver, and kidney (GLK) formed highly connected tissue-specific clusters of SLC transporters, ABC transporters, and DMEs. SLC22, SLC25 and SLC35 families were network hubs, having more inter-organ and intra-organ connections than other families. Analysis of the GLK network revealed key physiological pathways (e.g., involving bile acids and uric acid). A search for additional genes interacting with the network identified HNF4α, HNF1α, and PXR. Knockout gene expression data confirmed ~60-70% of predictions of ADME gene regulation by these transcription factors. Using the GLK network and known ADME genes, we built a tentative gut-liver-kidney "remote sensing and signaling network" consisting of SLC and ABC transporters, as well as DMEs and regulatory proteins. Together with protein-protein interactions to prioritize likely functional connections, this network suggests how multi-specificity combines with oligo-specificity and mono-specificity to regulate homeostasis of numerous endogenous small molecules.

Identification of biomarkers associated with progression and prognosis in bladder cancer via co-expression analysis

BACKGROUND

Bladder cancer is one of the most common genitourinary malignancies, with a high rate of recurrence and progression. The prognosis for patients with bladder cancer, especially muscle-invasive bladder cancer, remains poor despite systemic therapy.

OBJECTIVE

To explore the underlying disease mechanisms and identify more effective biomarkers for bladder cancer.

METHODS

Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network analysis were applied to identify hub genes correlated with the bladder cancer progression. Survival analyses were then conducted to identify potential biomarkers correlated with the prognosis of bladder cancer. Finally, validation and analysis of these potential biomarkers were conducted by a series of bioinformatics analyses.

RESULTS

Based on the results of weighted gene co-expression network analysis and protein-protein interaction network analysis, ten hub genes closely correlated with bladder cancer progression were identified in the relevant module. Survival analyses of these genes indicated that elevated expressions of six potential biomarkers (COL3A1, FN1, COL5A1, FBN1, COL6A1 and THBS2) were significantly associated with a worse overall survival. Furthermore, these 6 potential biomarkers were validated in association with the progression of bladder cancer. Bladder cancer samples with higher expression of these genes were most significantly enriched in gene set associated with ECM-receptor interaction.

CONCLUSIONS

This study identified several biomarkers associated with bladder cancer progression and prognosis. As novel findings, these may have important clinical implications for diagnosis, treatment and prognosis prediction.

Identification of Biomarkers for Controlling Cancer Stem Cell Characteristics in Bladder Cancer by Network Analysis of Transcriptome Data Stemness Indices

Background: Stem cells characterized by self-renewal and therapeutic resistance play crucial roles in bladder cancer (BLCA). However, the genes modulating the maintenance and proliferation of BLCA stem cells are still unclear. In this study, we aimed to characterize the expression of stem cell-related genes in BLCA.

Methods: The mRNA expression-based stemness index (mRNAsi) of The Cancer Genome Atlas (TCGA) was evaluated and corrected by tumor purity. Corrected mRNAsi were further analyzed with regard to muscle-invasive bladder cancer molecular subtypes, survival analysis, pathological staging characteristics, and outcomes after primary treatment. Next, weighted gene co-expression network analysis was used to find modules of interest and key genes. Functional enrichment analysis was performed to functionally annotate the modules and key genes. The expression levels of key genes in all cancers were validated using Oncomine and Gene Expression Omnibus (GEO) database containing molecular subtypes in BLCA. Protein interaction networks were used to identify upstream genes, and the relationships between genes were analyzed at the protein and transcription levels.

Findings: mRNAsi was significantly upregulated in cancer tissues. Corrected mRNAsi in BLCA increased as tumor stage increased, with T3 having the highest stem cell characteristics. Lower corrected mRNAsi scores had better overall survival and treatment outcome. The modules of interest and key genes were determined based on topological overlap measurement clustering results and the inclusion criteria. For 13 key genes (AURKA, BUB1B, CDCA5, CDCA8, KIF11, KIF18B, KIF2C, KIFC1, KPNA2, NCAPG, NEK2, NUSAP1, and RACGAP1), enriched gene ontology terms related to cell proliferation (e.g., mitotic nuclear division, spindle, and microtubule binding) were determined. Their expression did not differ according to the pathological stages of BLCA, and these genes were clearly overexpressed in many types of cancers. In GEO database, the expression levels of 13 key genes were higher in basal subtype with the highest stem cell characteristics than in luminal and its subtypes. AURKB and PLK1 may be regulated upstream of other key genes, and the key genes were found to be strongly correlated with each other and with upstream genes.

Interpretation: The 13 key genes identified in this study were found to play important roles in the maintenance of BLCA stem cells. Controlling the upstream genes AURKB and PLK1 may have applications in the treatment of BLCA. These genes may act as therapeutic targets for inhibiting the stemness characteristics of BLCA.

Bladder cancer stage-associated hub genes revealed by WGCNA co-expression network analysis

Background

Bladder cancer was a malignant disease in patients, our research aimed at discovering the possible biomarkers for the diseases.

Results

The gene chip GSE31684, including 93samples, was downloaded from the GEO datasets and co-expression network was constructed by the data. Molecular complex detection(MCODE) was used to identify hub genes. The most significant cluster including 16 genes: CDH11, COL3A1, COL6A3, COL5A1, AEBP1, COL1A2, NTM, COL11A1, THBS2, COL8A1, COL1A1, BGN, MMP2, PXDN, THY1, and TGFB1I1 was identified. After annotated by BiNGO, they were suggested associated with collagen fibril organization and blood vessel development. In addition, the Kaplan Meier curves were obtained by UALCAN. The high expression of THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1, and TGFB1I1 indicated poor prognosis of the patients(P < 0.05). Finally, we examined genes’ expression between low and high tumor stage by the Wilcoxon test(P < 0.05), TGFB1I1 was excluded.

Conclusion

THY1, AEBP1, CDH11, COL1A1, COL1A2, COL11A1, MMP2, PXDN, BGN, COL5A1, COL8A1 associated with the tumor stage as well as tumor patients’ prognosis. COL5A1, COL8A1(P < 0.01) may serve as therapeutic targets for the disease.