Construction and Analysis of the Tumor-Specific mRNA-miRNA-lncRNA Network in Gastric Cancer

Weighted correlation network analysis (WGCNA) is a statistical method that has been widely used in recent years to explore gene co-expression modules. Competing endogenous RNA (ceRNA) is commonly involved in the cancer gene expression regulation mechanism. Some ceRNA networks are recognized in gastric cancer; however, the prognosis-associated ceRNA network has not been fully identified using WGCNA. We performed WGCNA using datasets from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) to identify cancer-associated modules. The criteria of differentially expressed RNAs between normal stomach samples and gastric cancer samples were set at the false discovery rate (FDR) < 0.01 and |fold change (FC)| > 1.3. The ceRNA relationships obtained from the RNAinter database were examined by both the Pearson correlation test and hypergeometric test to confirm the mRNA-lncRNA regulation. Overlapped genes were recognized at the intersections of genes predicted by ceRNA relationships, differentially expressed genes, and genes in cancer-specific modules. These were then used for univariate and multivariate Cox analyses to construct a risk score model. The ceRNA network was constructed based on the genes in this model. WGCNA-uncovered genes in the green and turquoise modules are those most associated with gastric cancer. Eighty differentially expressed genes were observed to have potential prognostic value, which led to the identification of 12 prognosis-related mRNAs (KIF15, FEN1, ZFP69B, SP6, SPARC, TTF2, MSI2, KYNU, ACLY, KIF21B, SLC12A7, and ZNF823) to construct a risk score model. The risk genes were validated using the GSE62254 and GSE84433 datasets, with 0.82 as the universal cutoff value. 12 genes, 12 lncRNAs, and 35 miRNAs were used to build a ceRNA network with 86 dysregulated lncRNA-mRNA ceRNA pairs. Finally, we developed a 12-gene signature from both prognosis-related and tumor-specific genes, and then constructed a ceRNA network in gastric cancer. Our findings may provide novel insights into the treatment of gastric cancer.

Development of an immune-related lncRNA-miRNA-mRNA network based on competing endogenous RNA in periodontitis

AIM

To explore the relationship between long non-coding RNAs (lncRNAs) and immune response and to construct an immune-related competing endogenous RNA (ceRNA) network in periodontitis.

MATERIALS AND METHODS

Gene expression profiles in gingival tissues were acquired from the Gene Expression Omnibus database. Bioinformatic analysis was performed to establish an immune-related ceRNA network. Subsequently, functional enrichment analysis was performed to detect the biological processes in which the ceRNA network might be involved.

RESULTS

A combined classification model involving seven lncRNAs was constructed. Receiver operating characteristic curve analysis showed satisfactory classification ability of the established model. Further analysis revealed that the screened lncRNAs were significantly correlated with patient immunity. Finally, an immune-related ceRNA network was constructed based on the lncRNA MIAT, miR-1246, miR-1260b, miR-3652, miR-4286, and 27 mRNAs. Accordingly, functional enrichment analysis demonstrated that this network is closely related to the proliferation, differentiation, and activation of B cells.

CONCLUSIONS

The lncRNA MIAT and the MIAT-based ceRNA network may be instrumental in regulating the immune response, especially of B cells, during the progression of periodontitis.

Multimodal analysis of gene expression from postmortem brains and blood identifies synaptic vesicle trafficking genes to be associated with Parkinson's disease

OBJECTIVE

We aimed to identify key susceptibility gene targets in multiple datasets generated from postmortem brains and blood of Parkinson's disease (PD) patients and healthy controls (HC).

METHODS

We performed a multitiered analysis to integrate the gene expression data using multiple-gene chips from 244 human postmortem tissues. We identified hub node genes in the highly PD-related consensus module by constructing protein-protein interaction (PPI) networks. Next, we validated the top four interacting genes in 238 subjects (90 sporadic PD, 125 HC and 23 Parkinson's Plus Syndrome (PPS)). Utilizing multinomial logistic regression analysis (MLRA) and receiver operating characteristic (ROC), we analyzed the risk factors and diagnostic power for discriminating PD from HC and PPS.

RESULTS

We identified 1333 genes that were significantly different between PD and HCs based on seven microarray datasets. The identified MEturquoise module is related to synaptic vesicle trafficking (SVT) dysfunction in PD (P < 0.05), and PPI analysis revealed that SVT genes PPP2CA, SYNJ1, NSF and PPP3CB were the top four hub node genes in MEturquoise (P < 0.001). The levels of these four genes in PD postmortem brains were lower than those in HC brains. We found lower blood levels of PPP2CA, SYNJ1 and NSF in PD compared with HC, and lower SYNJ1 in PD compared with PPS (P < 0.05). SYNJ1, negatively correlated to PD severity, displayed an excellent power to discriminating PD from HC and PPS.

CONCLUSIONS

This study highlights that SVT genes, especially SYNJ1, may be promising markers in discriminating PD from HCs and PPS.

DYSF promotes monocyte activation in atherosclerotic cardiovascular disease as a DNA methylation-driven gene

Dysferlin (DYSF) has drawn much attention due to its involvement in dysferlinopathy and was reported to affect monocyte functions in recent studies. However, the role of DYSF in the pathogenesis of atherosclerotic cardiovascular diseases (ASCVD) and the regulation mechanism of DYSF expression have not been fully studied. In this study, Gene Expression Omnibus (GEO) database and epigenome-wide association study (EWAS) literatures were searched to find the DNA methylation-driven genes (including DYSF) of ASCVD. The hub genes related to DYSF were also identified through weighted correlation network analysis (WGCNA). Regulation of DYSF expression through its promoter methylation status was verified using peripheral blood leucocytes (PBLs) from ASCVD patients and normal controls, and experiments on THP1 cells and Apoe^-/- mice. Similarly, the expressions of DYSF related hub genes, mainly contained SELL, STAT3 and TMX1, were also validated. DYSF functions were then evaluated by phagocytosis, transwell and adhesion assays in DYSF knock-down and overexpressed THP1 cells. The results showed that DYSF promoter hypermethylation up-regulated its expression in clinical samples, THP1 cells and Apoe^-/- mice, confirming DYSF as a DNA methylation-driven gene. The combination of DYSF expression and methylation status in PBLs had a considerable prediction value for ASCVD. Besides, DYSF could enhance the phagocytosis, migration and adhesion ability of THP1 cells. Among DYSF related hub genes, SELL was proven to be the downstream target of DYSF by wet experiments. In conclusion, DYSF promoter hypermethylation upregulated its expression and promoted monocytes activation, which further participated in the pathogenesis of ASCVD.

Habitual aerobic exercise and circulating proteomic patterns in healthy adults: relation to indicators of healthspan

Habitual aerobic exercise enhances physiological function and reduces risk of morbidity and mortality throughout life, but the underlying molecular mechanisms are largely unknown. The circulating proteome reflects the intricate network of physiological processes maintaining homeostasis and may provide insight into the molecular transducers of the health benefits of physical activity. In this exploratory study, we assessed the plasma proteome (SOMAscan proteomic assay; 1,129 proteins) of healthy sedentary or aerobic exercise-trained young women and young and older men ( n = 47). Using weighted correlation network analysis to identify clusters of highly co-expressed proteins, we characterized 10 distinct plasma proteomic modules (patterns). In healthy young (24 ± 1 yr) men and women, 4 modules were associated with aerobic exercise status and 1 with participant sex. In healthy young and older (64 ± 2 yr) men, 5 modules differed with age, but 2 of these were partially preserved at young adult levels in older men who exercised; among all men, 4 modules were associated with exercise status, including 3 of the 4 identified in young adults. Exercise-linked proteomic patterns were related to pathways involved in wound healing, regulation of apoptosis, glucose-insulin and cellular stress signaling, and inflammation/immune responses. Importantly, several of the exercise-related modules were associated with physiological and clinical indicators of healthspan, including diastolic blood pressure, insulin resistance, maximal aerobic capacity, and vascular endothelial function. Overall, these findings provide initial insight into circulating proteomic patterns modulated by habitual aerobic exercise in healthy young and older adults, the biological processes involved, and their relation to indicators of healthspan. NEW & NOTEWORTHY This is the first study to assess the relation between plasma proteomic patterns and aerobic exercise status in healthy adults. Weighted correlation network analysis identified 10 distinct proteomic modules, including 5 patterns specific for exercise status. Additionally, 5 modules differed with aging in men, two of which were preserved in older exercising men. Exercise-associated modules included proteins related to inflammation, stress pathways, and immune function and correlated with clinical and physiological indicators of healthspan.

Comprehensive Construction of a Circular RNA-Associated Competing Endogenous RNA Network Identified Novel Circular RNAs in Hypertrophic Cardiomyopathy by Integrated Analysis

Hypertrophic cardiomyopathy (HCM), the most common heritable cardiomyopathy, is associated with a high risk of sudden cardiac death. The complexity and behavior of the circular RNA (circRNA)-associated competing endogenous RNA (ceRNA) network in HCM have not been thoroughly elucidated. Plasma circRNA and messenger RNA (mRNA) expression profiles were acquired by using a microarray. Weighted correlation network analysis (WGCNA) and linear models for microarray data (Limma) were used to analyze microarray data. Gene modules, consisting of genes with high correlations, were detected and represented by a designated color. The ceRNA network, including circRNA, microRNA (miRNA), and mRNA, was constructed based on the “ceRNA hypothesis” using an integrated systems biology method. By WGCNA, two modules, namely magenta and red modules, were identified as being positively correlated with HCM. In the combined analysis of WGCNA and Limma, 36 hub circRNAs in the magenta module and 83 hub circRNAs in the red module were significantly upregulated compared with the controls. By coexpression analysis, 270 circRNA–mRNA pairs were identified with a coefficient ≥0.9 and p < 0.05. With Starbase and miRWalk tools, circRNA–miRNA pairs and miRNA–mRNA pairs were predicted. Once these pairs were combined, the ceRNA network with 6 circRNAs, 29 miRNAs, and 6 mRNAs was constructed. Functional analysis demonstrated that these circRNAs in the ceRNA network were associated with calcium-release channel activity and muscle filament sliding. Our study provided a global perspective and systematic analysis of the circRNA-associated ceRNA network in HCM. The identified circRNAs hsa_circ_0043762, hsa_circ_0036248, and hsa_circ_0071269 may be key regulators involved in HCM pathogenesis.

Screening of potential biomarkers in peripheral blood of patients with depression based on weighted gene co-expression network analysis and machine learning algorithms

BACKGROUND

The prevalence of depression has been increasing worldwide in recent years, posing a heavy burden on patients and society. However, the diagnostic and therapeutic tools available for this disease are inadequate. Therefore, this research focused on the identification of potential biomarkers in the peripheral blood of patients with depression.

METHODS

The expression dataset GSE98793 of depression was provided by the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/gds). Initially, differentially expressed genes (DEGs) were detected in GSE98793. Subsequently, the most relevant modules for depression were screened according to weighted gene co-expression network analysis (WGCNA). Finally, the identified DEGs were mapped to the WGCNA module genes to obtain the intersection genes. In addition, Gene Ontology (GO), Disease Ontology (DO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted on these genes. Moreover, biomarker screening was carried out by protein-protein interaction (PPI) network construction of intersection genes on the basis of various machine learning algorithms. Furthermore, the gene set enrichment analysis (GSEA), immune function analysis, transcription factor (TF) analysis, and the prediction of the regulatory mechanism were collectively performed on the identified biomarkers. In addition, we also estimated the clinical diagnostic ability of the obtained biomarkers, and performed Mfuzz expression pattern clustering and functional enrichment of the most potential biomarkers to explore their regulatory mechanisms. Finally, we also perform biomarker-related drug prediction.

RESULTS

Differential analysis was used for obtaining a total of 550 DEGs and WGCNA for obtaining 1,194 significant genes. Intersection analysis of the two yielded 140 intersection genes. Biological functional analysis indicated that these genes had a major role in inflammation-related bacterial infection pathways and cardiovascular diseases such as atherosclerosis. Subsequently, the genes S100A12, SERPINB2, TIGIT, GRB10, and LHFPL2 in peripheral serum were identified as depression biomarkers by using machine learning algorithms. Among them, S100A12 is the most valuable biomarker for clinical diagnosis. Finally, antidepressants, including disodium selenite and eplerenone, were predicted.

CONCLUSION

The genes S100A12, TIGIT, SERPINB2, GRB10, and LHFPL2 in peripheral serum are viable diagnostic biomarkers for depression. and contribute to the diagnosis and prevention of depression in clinical practice.

Pleiotropy facilitates parallel adaptation in sticklebacks

Highly pleiotropic genes are predicted to be used less often during adaptation, as mutations in these loci are more likely to have negative fitness consequences. Following this logic, we tested whether pleiotropy impacts the probability that a locus will be used repeatedly in adaptation. We used two proxies to estimate pleiotropy: number of phenotypic traits affected by a given genomic region and gene connectivity. We first surveyed 16 independent stream‐lake and three independent benthic‐limnetic ecotype pairs of threespine stickleback to estimate genome‐wide patterns in parallel genomic differentiation. Our analysis revealed parallel divergence across the genome; 30%–37% of outlier regions were shared between at least two independent pairs in either the stream‐lake or benthic‐limnetic comparisons. We then tested whether parallel genomic regions are less pleiotropic than nonparallel regions. Counter to our a priori prediction, parallel genomic regions contained genes with significantly more pleiotropy; that is, influencing a greater number of traits and more highly connected. The increased pleiotropy of parallel regions could not be explained by other genomic factors, as there was no significant difference in mean gene count, mutation or recombination rates between parallel and nonparallel regions. Interestingly, although nonparallel regions contained genes that were less connected and influenced fewer mapped traits on average than parallel regions, they also tended to contain the genes that were predicted to be the most pleiotropic. Taken together, our findings are consistent with the idea that pleiotropy only becomes constraining at high levels and that low or intermediate levels of pleiotropy may be beneficial for adaptation.

Identification of crucial genes mediating abdominal aortic aneurysm pathogenesis based on gene expression profiling of perivascular adipose tissue by WGCNA

Background

With a mortality rate of 65–85%, a ruptured abdominal aortic aneurysm (AAA) can have catastrophic consequences for patients. However, few effective pharmaceutical treatments are available to treat this condition. Therefore, elucidating the pathogenesis of AAA and finding the potential molecular targets for medical therapies are vital lines of research.

Methods

An mRNA microarray dataset of perivascular adipose tissue (PVAT) in AAA patients was downloaded and differentially expressed gene (DEG) screening was performed. Weighted gene co-expression networks for dilated and non-dilated PVAT samples were constructed via weighted correlation network analysis (WGCNA) and used to detect gene modules. Functional annotation analysis was performed for the DEGs and gene modules. We identified the hub genes of the modules and created a DEG co-expression network. We then mined crucial genes based on this network using Molecular Complex Detection (MCODE) in Cytoscape. Crucial genes with top-6 degree in the crucial gene cluster were visualized, and their potential clinical significance was determined.

Results

Of the 173 DEGs screened, 99 were upregulated and 74 were downregulated. Co-expression networks were built and we detected 6 and 5 modules for dilated and non-dilated PVAT samples, respectively. The turquoise and black modules for dilated PVAT samples were related to inflammation and immune response. MAP4K1 and PROK2 were the hub genes of these 2 modules, respectively. Then a DEG co-expression network with 112 nodes and 953 edges was created. PLAU was the crucial gene with the highest connectivity and showed potential clinical significance.

Conclusions

Using WGCNA, gene modules were detected and hub genes and crucial genes were identified. These crucial genes might be potential targets for pharmaceutic therapies and have potential clinical significance. Future in vitro and in vivo experiments are required to more comprehensively explore the biological mechanisms by which these genes affect AAA pathogenesis

Identification of circulating hub long noncoding RNAs associated with hypertrophic cardiomyopathy using weighted correlation network analysis

Hypertrophic cardiomyopathy (HCM) is one of the most commonly inherited heart diseases and the leading cause of sudden cardiac death among adolescents and young adults. Circulating long noncoding RNAs (lncRNAs) have demonstrated potential as diagnostic and therapeutic targets in several cardiovascular diseases. However, the circulating extracellular lncRNA expression profile of patients with HCM remains unclear. Plasma lncRNA expression was evaluated in patients with HCM and healthy controls using a human lncRNA microarray. A weighted correlation network analysis (WGCNA) and linear models for microarray data (Limma) were used. GSE68316 data from cardiac tissue in the Gene Expression Omnibus database were analysed for further validation. Using WGCNA, two modules (referred to as the magenta and the light‑yellow module) were identified that were positively associated with HCM. Gene Ontology analysis revealed that lncRNAs in the magenta module targeted 'heart growth'. Using Limma, a total of 290 lncRNAs were differentially expressed (210 upregulated and 80 downregulated) in the plasma of HCM patients, compared with controls. Moreover, combined WGCNA and Limma analysis demonstrated that 27 hub lncRNAs in the magenta module and 13 hub lncRNAs in the light‑yellow module were significantly upregulated, compared with the controls. Moreover, of the 40 differentially expressed hub lncRNAs identified in the two modules, three circulating lncRNAs (lnc‑P2RY6‑1:1, ENST00000488040 and ENST00000588047) were also significantly upregulated in the HCM cardiac tissue validation dataset. These lncRNAs may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of HCM.

Bioinformatics analysis of microRNA profiles and identification of microRNA-mRNA network and biological markers in intracranial aneurysm

Intracranial aneurysm (IA) is a kind of cerebrovascular disorder, which may result in the subarachnoid hemorrhage with high lethality and disability. The purpose of this study was to reveal the pathogenesis and identify novel biomarkers in IA.We processed the raw microRNA (miRNA) expression profile data of IA obtained from Gene Expression Omnibus. Then weighted correlation network analysis was performed to identify the hub miRNAs in IA. Target genes of hub miRNAs were predicted using multiR package. In addition, a protein-protein network as well as miRNA-mRNA network was constructed and functional and pathway enrichment analyses were done. Finally, the prediction value of hub miRNAs in IA was tested in validation set.Two modules that had relation with IA were identified and 10 hub miRNAs in each module with higher gene-module association were selected. The protein-protein network and miRNA-mRNA network contained 243 nodes and 1496 edges. Functional and pathway enrichment analyses showed that they were mainly enriched in cell cycle, cell proliferation, and PI3K/Akt signaling pathways. Besides, hsa-miR-191-3p, hsa-miR-423-5p, hsa-miR-424-5p, hsa-miR-425-3p were proven to be valuable in prediction IA occurrence.In a word, this study reveals hub miRNAs, target genes and pathways potentially participating in formation and development of IA and screens out some candidate biomarkers. Our findings provide some new perspectives for research and treatment of IA.

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

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

Prediction of a Competing Endogenous RNA Co-expression Network by Comprehensive Methods in Systemic Sclerosis-Related Interstitial Lung Disease

Systemic sclerosis (SSc) is an immune-mediated connective tissue disease characterized by fibrosis of multi-organs, and SSc-related interstitial lung disease (SSc-ILD) is a leading cause of morbidity and mortality. To explore molecular biological mechanisms of SSc-ILD, we constructed a competing endogenous RNA (ceRNA) network for prediction. Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database, and differential expressed mRNAs and miRNAs analysis was further conducted between normal lung tissue and SSc lung tissue. Also, the interactions of miRNA–lncRNA, miRNA–mRNA, and lncRNA–mRNA were predicted by online databases including starBase, LncBase, miRTarBase, and LncACTdb. The ceRNA network containing 11 lncRNAs, 7 miRNAs, and 20 mRNAs were constructed. Based on hub genes and miRNAs identified by weighted correlation network analysis (WGCNA) method, three core sub-networks—SNHG16, LIN01128, RP11-834C11.4(LINC02381)/hsa-let-7f-5p/IL6, LINC01128/has-miR-21-5p/PTX3, and LINC00665/hsa-miR-155-5p/PLS1—were obtained. Combined with previous studies and enrichment analyses, the lncRNA-mediated network affected LPS-induced inflammatory and immune processes, fibrosis development, and tumor microenvironment variations. The ceRNA network, especially three core sub-networks, may be served as early biomarkers and potential targets for SSc, which also provides further insights into the occurrence, progression, and accurate treatment of SSc at the molecular level.

Susceptibility Modules and Genes in Hypertrophic Cardiomyopathy by WGCNA and ceRNA Network Analysis

Background: We attempted to identify a regulatory competing endogenous RNA (ceRNA) network and a hub gene of Hypertrophic Cardiomyopathy (HCM). Methods: Microarray datasets of HCM tissue were obtained from NCBI Gene Expression Omnibus (GEO) database. The R package "limma" was used to identify differentially expressed genes. Online search databases were utilized to match the relation among differentially expressed long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs. Weighted correlation network analysis (WGCNA) was used to identify the correlations between key modules and HCM. STRING database was applied to construct PPI networks. Gene Set Enrichment Analysis (GSEA) was used to perform functional annotations and verified the hub genes. Results: A total of 269 DE-lncRNAs, 63 DE-miRNAs and 879 DE-mRNAs were identified in myocardial tissues from microarray datasets GSE130036, GSE36946 and GSE36961, respectively. According to online databases, we found 1 upregulated miRNA hsa-miR-184 that was targeted by 2 downregulated lncRNAs (SNHG9, AC010980.2), potentially targeted 2 downregulated mRNAs (LRRC8A, SLC7A5). 3 downregulated miRNAs (hsa-miR-17-5p, hsa-miR-876-3p, hsa-miR-139-5p) that were targeted by 9 upregulated lncRNAs, potentially targeted 21 upregulated mRNAs. Black and blue modules significantly related to HCM were identified by WGCNA. Hub gene IGFBP5 regulated by hsa-miR-17-5p, AC007389.5, AC104667.1, and AC002511.2 was identified. GSEA indicated that IGFBP5 might involve in the synthesis of myosin complex, participate in kinesin binding, motor activity and function via the regulation of actin cytoskeleton. Conclusion: The results provide a potential molecular regulatory mechanism for the diagnosis and treatment of HCM. IGFBP5 might play an important role in the progression of HCM.

POPDC3 is a potential biomarker for prognosis and radioresistance in patients with head and neck squamous cell carcinoma

Radiotherapy is the primary means of treatment for patients with head and neck squamous cell carcinoma (HNSCC); however, radioresistance-induced recurrence is the primary cause of HNSCC treatment failure. Therefore, identifying specific predictive biomarkers of the response to radiotherapy may improve prognosis. In the present study, to identify the potential candidate genes associated with radioresistance in patients with HNSCC, the microarray datasets GSE9716, GSE61772 and GSE20549 were downloaded from the Gene Expression Omnibus database. The original CEL files were preprocessed using the Affymetrix package and quantile normalization and background correction were conducted using the Core package in Bioconductor. The GSE9716 dataset, consisting of 18 irradiated and 16 non-irradiated samples, was divided into two groups according to their exposure to irradiation: i) Non-irradiation group, which included 8 radioresistant samples and 8 radiosensitive samples; and ii) post-irradiation group, which included 9 radioresistant samples and 9 radiosensitive samples. The two groups were treated as separate datasets and screened. A total of 86 differentially expressed genes (DEGs) were identified in the non-irradiation group and 405 DEGs in the post-irradiation group. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis detected several significant functions associated with the DEGs. In the co-expression analysis, 76 hub genes in the light green module and 917 hub genes with a high connectivity were selected for further analysis. Finally, overlapping the DEGs and hub genes from the two groups yielded a map of 13 shared differentially expressed genes. The 13 genes showed significantly different expression in radioresistant samples compared with the radiosensitive samples before and after irradiation. Out of these genes, popeye domain-containing protein 3 (POPDC3) was highly expressed in the post-irradiation group compared with the non-irradiation group. In survival analysis, high POPDC3 expression correlated with poor a prognosis for patients with HNSCC. The independent prognostic factors were identified using univariate and multivariate Cox analyses based on The Cancer Genome Atlas database. These were incorporated into a nomogram to predict 3- and 5-year overall survival. Receiver operating characteristic curves were used to estimate the accuracy of the nomogram. Together these studies suggest that POPDC3 may serve as a potential predictive biomarker for prognosis and radioresistance of patients with HNSCC as well as clinical diagnosis and treatment of patients.

Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is a common malignant tumor with the highest morbidity and mortality worldwide. The degree of tumor immune infiltration and clinical prognosis depend on immune-related genes, but their interaction with the tumor immune microenvironment, the specific mechanism driving immune infiltration and their prognostic value are still not very clear. Therefore, the aim of this work was focused on the elucidation of these unclear aspects.

Methods

TCGA LUAD samples were divided into three immune infiltration subtypes according to the single sample gene set enrichment analysis (ssGSEA), in which the associated gene modules and hub genes were screened by weighted correlation network analysis (WGCNA). Four key genes related to immune infiltration were found and screened by differential expression analysis, univariate prognostic analysis, and Lasso-COX regression, and their PPI network was constructed. Finally, a Nomogram model based on the four genes and tumor stages was constructed and confirmed in two GEO data sets.

Results

Among the three subtypes—high, medium, and low immune infiltration subtype—the survival rate of the patients in the high one was higher than the rate in the other two subtypes. The four key genes related to LUAD immune infiltration subtypes were CD69, KLRB1, PLCB2, and P2RY13. The PPI network revealed that the downstream genes of the G-protein coupled receptors (GPCRs) pathway were activated by these four genes through the S1PR1. The risk score signature based on these four genes could distinguish high and low-risk LUAD patients with different prognosis. The Nomogram constructed by risk score and clinical tumor stage showed a good ability to predict the survival rate of LUAD patients. The universality and robustness of the Nomogram was confirmed by two GEO datasets.

Conclusions

The prognosis of LUAD patients could be predicted by the constructed risk score signature based on the four genes, making this score a potential independent biomarker. The screening, identification, and analysis of these four genes could contribute to the understanding of GPCRs and LUAD immune infiltration, thus guiding the formulation of more effective immunotherapeutic strategies.

Quantitative Proteomics and Weighted Correlation Network Analysis of Tear Samples in Type 2 Diabetes Patients Complicated with Dry Eye

PURPOSE

Diabetic patients are more likely to experience dry eye (DE). TMT-based proteomics and WGCNA are used to identify the differentially expressed proteins in tear proteome of type 2 diabetes with DE. The aim is to provide a molecular basis for exploring possible mechanisms underlying the pathogenesis of diabetic DE.

EXPERIMENTAL DESIGN

Subjects are divided into four groups (ten in each): type 2 diabetes with DE; type 2 diabetes without DE; non-diabetes with DE and normal controls. All subjects undergo DE tests. Total proteins are extracted and quantitatively labeled with TMT, then analyzed using liquid chromatography-mass spectrometry. WGCNA is used to identify the hub genes. Finally, differentially expressed proteins are validated by ELISA.

RESULTS

A total of 1922 proteins are identified, of which 1814 contain quantitative information. Ultimately, 650 of these proteins yield quantitative values. WGCNA performed on these 650 proteins reveal four distinct hub genes of diabetic DE.

CONCLUSIONS AND CLINICAL RELEVANCE

DE is associated with the differential expression of tear proteins in type 2 diabetes. Inflammation, immune factors, and lipid metabolism may play a role in the development of diabetic DE. LTF, LYZ, ZAG, and DNAJC3 have the potential to be the biomarkers of DE in diabetes.

Identification and verification of pivotal genes promoting the progression of atherosclerosis based on WGCNA

As the main pathological basis for the development of cardiovascular and cerebrovascular diseases, atherosclerosis (AS) seriously affects human health. The key targets of biological information analysis of AS can help exploit therapeutic targets. The expression data of early and progressive atherosclerotic tissues were downloaded from the Gene Expression Omnibus (GEO) database. Based on GSE28829 and GSE120521, 74 key genes were obtained through differential expression analysis and weighted correlation network analysis (WGCNA) analysis, which were mainly enriched in the regulating of inflammatory response, chemokine signalling pathway, apoptosis, lipid and AS, Toll-like receptor signalling pathway and so on according to the results of the enrichment analysis. Cytoscape software was applied to screen four pivotal genes (TYROBP, ITGB2, ITGAM and TLR2) based on PPI. The results of the correlation analysis showed that the expression level of pivotal genes was positively related to macrophages M0, and was negatively related to T cells follicular helper. In addition, the expression of ITGB2 was positively related to Tregs. In this study, bioinformatics was applied to screen pivotal genes affecting the progress of AS, which were significantly related to immune-related biological functions and signal pathways of atherosclerotic tissues and the infiltration level of immune cells. Therefore, pivotal genes were expected to become therapeutic targets for AS.

Identification of a novel 10 immune-related genes signature as a prognostic biomarker panel for gastric cancer

Background

Emerging evidence indicates that immune infiltrating cells in tumor microenvironment (TME) correlates with the development and progression of gastric cancer (GC). This study aimed to systematically investigate the immune‐related genes (IRGs) to develop a prognostic signature to predict the overall survival (OS) in GC.

Method

The gene expression profiles of training dataset (GSE62254), validation dataset I (GSE15459), and validation dataset II (GSE84437) were retrieved from GEO and TCGA databases. In the present study, we developed a 10 IRGs prognostic signature with the combination of weighted gene co‐expression network analysis (WGCNA) and least absolute shrinkage and selection operator method (LASSO) COX model.

Results

In the training dataset, the accuracy of the signature was 0.681, 0.741, and 0.72 in predicting 1, 3, and 5‐year OS separately. The signature also had good performance in validation dataset Ⅰ with the accuracy of 0.57, 0.619, and 0.694, and in validation dataset Ⅱ with the accuracy of 0.559, 0.624, and 0.585. Then, we constructed a nomogram using the signature and clinical information which had strong discrimination ability with the c‐index of 0.756. In the immune infiltration analysis, the signature was correlated with multiple immune infiltrating cells such as CD8 T cells, CD4 memory T cells, NK cells, and macrophages. Furthermore, several significant pathways were enriched in gene set enrichment analysis (GSEA) analysis, including TGF‐beta signaling pathway and Wnt signaling pathway.

Conclusion

The signature of 10 IRGs we identified can effectively predict the prognosis of GC and provides new insight into discovering candidate prognostic biomarkers of GC.

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

Purpose

The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC).

Methods

A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer–Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan–Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis.

Results

A total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation.

Conclusions

The nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.

Pan-organ transcriptome variation across 21 cancer types

It is widely accepted that some messenger RNAs are evolutionarily conserved across species, both in sequence and tissue-expression specificity. To date, however, little effort has been made to exploit the transcriptome divergence between cancer and adjacent normal tissue at the pan-organ level. In this work, a transcriptome sequencing dataset from 675 normal-tumor pairs, representing 21 solid organs in The Cancer Genome Atlas, is used to evaluate expression evolution. The results show that in most cancer types, gene expression divergence and organ-specificity are reduced in cancer tissue compared to adjacent normal tissue. Furthermore, we observe that all cancers share cell cycle dysregulation through interrogating differentially expressed protein coding genes. Meanwhile, weighted correlation network analysis is used to detect of the gene module structure variation between cancer and adjacent normal tissue. And modules consisting of tightly co-regulated genes in cancer change substantially compared with those in adjacent normal tissue. We thus assume that the destruction of a coordinated regulatory network might result in tumorigenesis and tumor progression. Our results provide new insights into the complex cancer biology and shed light on the mysterious regulation mode for cancer.

Regulatory Master Genes Identification and Drug Repositioning by Integrative mRNA-miRNA Network Analysis for Acute Type A Aortic Dissection

Acute type A aortic dissection (ATAAD) is a life-threatening disease. The understanding of its pathogenesis and treatment approaches remains unclear. In the present work, differentially expressed genes (DEGs) from two ATAAD datasets GSE52093 and GSE98770 were filtered. Transcription factor TEAD4 was predicted as a key modulator in protein-protein interaction (PPI) network. Weighted correlation network analysis (WGCNA) identified five modules in GSE52093 and four modules in GSE98770 were highly correlated with ATAAD. 71 consensus DEGs of highly correlated modules were defined and functionally annotated. L1000CDS² was executed to predict drug for drug repositioning in ATAAD treatment. Eight compounds were filtered as potential drugs. Integrative analysis revealed the interaction network of five differentially expressed miRNA and 16 targeted DEGs. Finally, master DEGs were validated in human ATAAD samples and AD cell model in vitro. TIMP3 and SORBS1 were downregulated in ATAAD samples and AD cell model, while PRUNE2 only decreased in vitro. Calcium channel blocker and glucocorticoid receptor agonist might be potential drugs for ATAAD. The present study offers potential targets and underlying molecular mechanisms ATAAD pathogenesis, prevention and drug discovery.

Study of the co-expression gene modules of non-small cell lung cancer metastases

BACKGROUND

Metastasis regularly is a marker of the disease development of cancers. Some metastatic sites significantly showed more serious clinical outcomes in non-small cell lung cancer (NSCLC). Whether they are caused by tissue-specific (TS) or non-tissue-specific (NTS) mechanisms is still unclear.

OBJECTIVE

Explore co-expression gene modules of non-small cell lung cancer metastases.

METHODS

Weighted Correlation Network Analysis (WGCNA) was used to identify the gene modules among the metastases of NSCLC. The clinical significance of those gene modules was evaluated with the Cox hazard proportional model with another independent dataset. Functions of each gene module were analyzed with gene ontology. Typical genes were further studied.

RESULTS

There were two TS gene modules and two NTS gene modules identified. One TS gene module (green module) and one NTS gene module (purple module) significantly correlated with survival. This NTS gene module (purple module) was significantly enriched in the epithelial-to-mesenchymal transition (EMT) process. Higher expression of the typical genes (CA14, SOX10, TWIST1, and ALX1) from EMT process was significantly associated with a worse survival.

CONCLUSION

The lethality of NSCLC metastases was caused by TS gene modules and NTS gene modules, among which the EMT-related gene module was critical for a worse clinical outcome.

Bioinformatics analysis to screen key genes implicated in the differentiation of induced pluripotent stem cells to hepatocytes

Due to the lack of potential organs, hepatocellular transplantation has been considered for treating end-stage liver disease. Induced pluripotent stem cells (iPSCs) are reverted from somatic cells and are able to differentiate into hepatocytes. The present study aimed to investigate the mechanisms underlying iPSC differentiation to hepatocytes. GSE66076 was downloaded from the Gene Expression Omnibus; this database includes data from 3 undifferentiated (T0), 3 definitive endoderm (T5), and 3 early hepatocyte (T24) samples across hepatic‑directed differentiation of iPSCs. Differentially expressed genes (DEGs) between T0 and T5 or T24 samples were identified using the linear models for microarray data package in Bioconductor, and enrichment analyses were performed. Using the weighted correlation network analysis package in R, clusters were identified for the merged DEGs. Cytoscape was used to construct protein‑protein interaction (PPI) networks for DEGs identified to belong to significant clusters. Using the ReactomeFI plugin in Cytoscape, functional interaction (FI) networks were constructed for the common genes. A total of 433 and 1,342 DEGs were identified in the T5 and T24 samples respectively, compared with the T0 samples. Blue and turquoise clusters were identified as significant gene clusters. In the PPI network for DEGs in the blue cluster, the key node fibroblast growth factor 2 (FGF2) could interact with bone morphogenetic protein 2 (BMP2). Cyclin‑dependent kinase 1 (CDK1) was demonstrated to have the highest degree (degree=71) in the PPI network for DEGs in the turquoise cluster. Enrichment analysis for the common genes, including hepatocyte nuclear factor 4α (HNF4A) and epidermal growth factor (EGF), in the FI network indicated that EGF and FGF2 were enriched in the Ras and Rap1 signaling pathways. The present results suggest that FGF2, BMP2, CDK1, HNF4A and EGF may participate in the differentiation of iPSCs into hepatocytes.

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

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

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

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

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