Network Analysis Algorithm Research Articles

Identification of SCN7A as the key gene associated with tumor mutation burden in gastric cancer

ObjectivePrevious studies have shown that tumor mutation burden (TMB) in cancer is associated with prognosis. The purpose of this study is to identify TMB related genes in gastric cancer (GC) and to explore their prognostic value.MethodsIn our research, weighted gene coexpression network analysis (WGCNA) algorithm was used to cluster the most relevant TMB modules in the Cancer Genome Atlas (TCGA) database. Limma package was used to screen the differentially expressed genes, and the intersection was identified as hub genes. We used gene expression profiling interactive analysis (GEPIA) and survival algorithm to analyze the clinical characteristics and prognosis of hub genes in tumor and normal tissue samples of TCGA and Gene Expression Omnibus cohort respectively. We also used CIBERSORT algorithm to calculate the proportion of 22 tumor immune cells in the high and low expression subgroups of hub genes. In addition, we used gene set enrichment analysis (GSEA) to predict the biological function of hub genes. P < 0.05 was considered statistically significant.ResultsIn the TCGA cohort, TMB was significantly correlated with the clinical features of GC (P < 0.05). Through WGCNA and differential gene analysis, we identified SCN7A as the hub gene (P < 0.05, |log2fc|> 1, and mm > 0.8). We found that the expression of SCN7A in tumor tissues was lower than that in normal tissues, and its expression level was also related to overall survival rate and tumor stage. GSEA analysis showed that SCN7A low expression group was enriched with "DNA replication", "base extension repair" and "proteasome" gene sets in GC. In addition, we found that there were significant differences in the infiltration degree of 7 kinds of immune cells between the two groups.ConclusionTMB can indicate the prognosis of gastric cancer. SCN7A is a hub gene associated with TMB, and its low expression is associated with better prognosis.

The Downregulation of PTGS2 Mediated by ncRNAs is Tightly Correlated with Systemic Sclerosis-Interstitial Lung Disease.

Background: Interstitial lung disease in systemic sclerosis (SSc-ILD) is one of the most severe complications of systemic sclerosis (SSc) and is the main cause of mortality. In this study, we aimed to explore the key genes in SSc-ILD and analyze the relationship between key genes and immune cell infiltration as well as the key genes relevant to the hallmarks of cancer. Methods: Weighted gene co-expression network analysis (WGCNA) algorithm was implemented to explore hub genes in SSc-ILD samples from the Gene Expression Omnibus (GEO) database. Logistic regression analysis was performed to screen and verify the key gene related to SSc-ILD. CIBERSORT algorithms were utilized to analyze immune cell infiltration. Moreover, the correlation between the key genes and genes relevant to cancer was also evaluated. Furthermore, non-coding RNAs (ncRNAs) linking to PTGS2 were also explored. Results: In this study, we first performed WGCNA analysis for three GEO databases to find the potential hub genes in SSc-ILD. Subsequently, we determined PTGS2 was the key gene in SSC-ILD. Furthermore, in CIBERSORT analyses, PTGS2 were tightly correlated with immune cells such as regulatory T cells (Tregs) and was negatively correlated with CD20 expression. Moreover, PTGS2 was associated with tumor growth. Then, MALAT1, NEAT1, NORAD, XIST identified might be the most potential upstream lncRNAs, and LIMS1 and RANBP2 might be the two most potential upstream circRNAs. Conclusion: Collectively, our findings elucidated that ncRNAs-mediated downregulation of PTGS2, as a key gene in SSc-ILD, was positively related to the occurrence of SSc-ILD and abnormal immunocyte infiltration. It could be a promising factor for SSc-ILD progression to malignancy.

Identification of Hub Genes of Keloid Fibroblasts by Coexpression Network Analysis and Degree Algorithm.

Background Keloid is a benign dermal tumor characterized by abnormal proliferation and invasion of fibroblasts. The establishment of biomarkers is essential for the diagnosis and treatment of keloids. Methods We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Differential expressed genes (DEGs) in GSE145725 and genes in significant modules were integrated to identify overlapping key genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed, as well as protein-protein interaction (PPI) network construction for hub gene screening. Results Using the R package of WGCNA, 22 coexpression modules consisting of different genes were identified from the top 5,000 genes with maximum mean absolute deviation in 19 human fibroblast samples. Blue-green and yellow modules were identified as the most important modules, where genes overlapping with DEGs were identified as key genes. We identified the most critical functions and pathways as extracellular structure organization, vascular smooth muscle contraction, and the cGMP-PKG signaling pathway. Hub genes from key genes as BMP4, MSX1, HAND2, TBX2, SIX1, IRX1, EDN1, DLX5, MEF2C, and DLX2 were identified. Conclusion The blue-green and yellow modules may play an important role in the pathogenesis of keloid. 10 hub genes were identified as potential biomarkers and therapeutic targets for keloid.

Identification of Immune Infiltration and Effective Immune Biomarkers in Acute Lung Injury by Bioinformatics Analysis

Acute lung injury (ALI) is a serious complication in clinical settings. This study aimed to elucidate the immune molecular mechanisms underlying ALI by bioinformatics analysis. Human ALI and six ALI mouse model datasets were collected. Immune cell infiltration between the ALI samples and non-ALI controls was estimated using the ssGSEA algorithm. Least absolute shrinkage and selection operator (LASSO) regression analysis and Wilcoxon test were performed to obtain the significantly different immune cell infiltration types. Immune feature genes were screened by differential analysis and the weighted correlation network analysis (WGCNA) algorithm. Functional enrichment was then performed and candidate hub biomarkers were identified. Finally, the receiver operator characteristic curve (ROC) analysis was used to predict their diagnostic performances. Three significantly different immune cell types (B cells, CD4 T cells, and CD8 T cells) were identified between the ALI samples and controls. A total of 13 immune feature genes were obtained by WGCNA and differential analysis and found to be significantly associated with immune functions and lung diseases. Four hub genes, including CD180, CD4, CD74, and MCL1 were identified using cytoHubba and were shown to have good specificity and sensitivity for the diagnosis of ALI. Correlation analysis suggested that CD4 was positively associated with T cells, whereas MCL1 was negatively correlated with B and T cells. We found that CD180, CD4, CD74, and MCL1 can serve as specific immune biomarkers for ALI. MCL1–B cell, MCL1–T cell, and CD4–T cell axes may be involved in the progression of ALI.

Investigation and Validation of Molecular Characteristics of Endometrium in Recurrent Miscarriage and Unexplained Infertility from a Transcriptomic Perspective.

Recurrent miscarriage (RM) and unexplained infertility (UI) are gordian knots in reproductive medicine, which are troubling many patients, doctors, and researchers. Although these two diseases of early pregnancy have a significant impact on human reproductive health, little is known about the specific mechanisms, which caused treatment difficulties. This study focused on the molecular signatures underlying the pathological phenotypes of two diseases, with the hope of using statistical methods to identify the significant core genes. An unbiased Weighted Correlation Network Analysis (WGCNA) algorithm was used for endometrial transcriptome data analysis and the disease-related gene modules were screened out. Through enrichment analysis of the candidate genes, we found similarities between both diseases and shared enrichment of immune-related pathways. Therefore, we used immune algorithms to assess the infiltration of immune cells and found abnormal increases of CD8+T cells and neutrophils. In order to explore the molecular profile behind the immunophenotypic changes, we used the SVM algorithm and LASSO regression to identify the core genes with diagnostic capacity in both diseases and discussed their significance of immune disorders in the endometrium. In the end, the satisfactory diagnostic ability of these core genes was verified in the broader group. Our results demonstrated the presence of immune disorders in non-pregnancy tissues of RM and UI, and identified the core molecules of this phenotype, and discuss mechanisms. This provides exploratory evidence for the in-depth understanding of the mechanism of RM and UI and may provide potential targets for their future treatment.

Identifying the Therapeutic and Prognostic Role of the CD8+ T Cell-Related Gene ALDH2 in Head and Neck Squamous Cell Carcinoma.

Head and neck squamous cell carcinoma (HNSC) is a widely known malignancy which is usually diagnosed late and has a poor prognosis. This study focuses on finding a new gene linked with CD8+ T cell infiltration as a prognostic marker for patients with HNSC. Differential analysis of transcriptomic data was performed between HNSC and control tissues from TCGA and GEO database. The CD8+ T cell infiltration score was quantified using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) algorithms were used to identify key modules associated with CD8+ T cell infiltration. Kaplan-Meier (K-M) survival analysis was used to compare overall survival (OS) between the 2 groups. Univariate and multivariate Cox analyses were used to assess independent prognostic markers. The results showed CD8+ T cell infiltration score was an independent favorable prognostic marker in HNSC. Differential analysis and WGCNA identified 93 differential gene related to high CD8+ T infiltration. Amog the 93 genes, ALDH2 was an independent favorable prognostic marker in HNSC. ALDH2 expression was found to be much lower in HNSC, and patients with low ALDH2 expression had higher T stage and N stage. The correlation analysis showed that ALDH2 was linked with immune cell infiltration in the tumor microenvironment of HNSC. Patients having increased expression of ALDH2 tend to be sensitive to immune checkpoint inhibitors (ICIs). In addition, we showed the relationship between ALDH2 expression and chemotherapeutic drug sensitivity. In conclusion, this study identified ALDH2 as a prognostic marker, associated with CD8+ T cell infiltration in HNSC.

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma.

BackgroundUterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.MethodsCIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan–Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.ResultsRed module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.ConclusionWe developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

Shexiang Tongxin Dropping Pill Protects Against Chronic Heart Failure in Mice via Inhibiting the ERK/MAPK and TGF-β Signaling Pathways.

Background: Chronic heart failure (CHF) is a major public health problem with high mortality and morbidity worldwide. Shexiang Tongxin Dropping Pill (STDP) is a widely used traditional Chinese medicine preparation for coronary heart disease and growing evidence proves that STDP exerts beneficial effects on CHF in the clinic. However, the molecular mechanism of the therapeutic effects of STDP on CHF remains largely unknown. Objective: This study aimed to elucidate the mechanism of action of STDP against CHF by integrating network pharmacology analysis and whole-transcriptome sequencing. Methods: First, the mouse model of CHF was established by the transverse aortic constriction (TAC) surgery, and the efficacy of STDP against CHF was evaluated by assessing the alterations in cardiac function, myocardial fibrosis, and cardiomyocyte hypertrophy with echocardiography, Masson’s trichrome staining, and wheat germ agglutinin staining. Next, a CHF disease network was constructed by integrating cardiovascular disease-related genes and the transcriptome sequencing data, which was used to explore the underlying mechanism of action of STDP. Then, the key targets involved in the effects of STDP on CHF were determined by network analysis algorithms, and pathway enrichment analysis was performed to these key genes. Finally, important targets in critical pathway were verified in vivo. Results: STDP administration obviously improved cardiac function, relieved cardiomyocyte hypertrophy, and ameliorated myocardial fibrosis in CHF mice. Moreover, STDP significantly reversed the imbalanced genes that belong to the disease network of CHF in mice with TAC, and the number of genes with the reverse effect was 395. Pathway analysis of the crucial genes with recovery efficiency revealed that pathways related to fibrosis and energy metabolism were highly enriched, while TGF-β pathway and ERK/MAPK pathway were predicted to be significantly affected. Consistently, validation experiments confirmed that inhibiting ERK/MAPK and TGF-β signaling pathways via reduction of the phosphorylation level of Smad3 and ERK1/2 is the important mechanism of STDP against CHF. Conclusion: Our data demonstrated that STDP can recover the imbalanced CHF network disturbed by the modeling of TAC through the multi-target and multi-pathway manner in mice, and the mechanisms are mainly related to inhibition of ERK/MAPK and TGF-β signaling pathways.

Immune landscape of advanced gastric cancer tumor microenvironment identifies immunotherapeutic relevant gene signature

BackgroundAdvanced gastric cancer (AGC) is a disease with poor prognosis due to the current lack of effective therapeutic strategies. Immune checkpoint blockade treatments have shown effective responses in patient subgroups but biomarkers remain challenging. Traditional classification of gastric cancer (GC) is based on genomic profiling and molecular features. Therefore, it is critical to identify the immune-related subtypes and predictive markers by immuno-genomic profiling.MethodsSingle-sample gene-set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to identify the immue-related subtypes of AGC in two independent GEO datasets. Weighted gene co-expression network analysis (WGCNA) and Molecular Complex Detection (MCODE) algorithm were applied to identify hub-network of immune-related subtypes. Hub genes were confirmed by prognostic data of KMplotter and GEO datasets. The value of hub-gene in predicting immunotherapeutic response was analyzed by IMvigor210 datasets. MTT assay, Transwell migration assay and Western blotting were performed to confirm the cellular function of hub gene in vitro.ResultsThree immune-related subtypes (Immunity_H, Immunity_M and Immunity_L) of AGC were identified in two independent GEO datasets. Compared to Immunity_L, the Immuntiy_H subtype showed higher immune cell infiltration and immune activities with favorable prognosis. A weighted gene co-expression network was constructed based on GSE62254 dataset and identified one gene module which was significantly correlated with the Immunity_H subtype. A Hub-network which represented high immune activities was extracted based on topological features and Molecular Complex Detection (MCODE) algorithm. Furthermore, ADAM like decysin 1 (ADAMDEC1) was identified as a seed gene among hub-network genes which is highly associated with favorable prognosis in both GSE62254 and external validation datasets. In addition, high expression of ADAMDEC1 correlated with immunotherapeutic response in IMvigor210 datasets. In vitro, ADAMDEC1 was confirmed as a potential protein in regulating proliferation and migration of gastric cancer cell. Deficiency of ADAMDEC1 of gastric cancer cell also associated with high expression of PD-L1 and Jurkat T cell apoptosis.ConclusionsWe identified immune-related subtypes and key tumor microenvironment marker in AGC which might facilitate the development of novel immune therapeutic targets.

The Bioinformatics-Based Analysis Identifies 7 Immune-Related Genes as Prognostic Biomarkers for Colon Cancer.

Colon cancer poses a great threat to human health. Currently, there is no effective treatment for colon cancer due to its complex causative factors. Immunotherapy has now become a new method for tumor treatment. In this study, 487 DEGs were screened from The Cancer Genome Atlas (TCGA) database and ImmPort database, and GeneOntology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. Hierarchical clustering of all samples revealed a significant correlation between colon cancer and immunity. The weighted gene co-expression network analysis (WGCNA) algorithm was used to identify key gene modules associated with immunity in colon cancer, here, module grey60 showed the highest correlation. A protein-protein interaction (PPI) network was constructed using the STRING database to screen hub genes, and subsequently, 7 immune-related genes the most closely associated with colon cancer were identified by differential expression in cancer and paracancer. Finally, a risk prediction model was developed using least absolute shrinkage and selection operator (LASSO) COX analysis, and the accuracy of the model was validated by GSE14333. This study determined that IRF4 and TNFRSF17 were immune-related genes in colon cancer, providing immune-related prognostic biomarkers for colon cancer.

Identification of Immune-Related Key Genes in Ovarian Cancer Based on WGCNA.

Background: Ovarian cancer (OV) is a fatal gynecologic malignancy and has poor survival rate in women over the age of forty. In our study, we aimed to identify genes related to immune microenvironment regulations and explore genes associated with OV prognosis. Methods: The RNA-seq data of GDC TCGA Ovarian Cancer cohort of 376 patients was retrieved from website. Weighted gene co-expression network analysis (WGCNA) and ESTIMATE algorithm were applied to identify the key genes associated with the immune scores. The correlation between key genes and 22 immune cell types were estimated by using CIBERSORT algorithms. Results: WGCNA showed that the pink module was most correlated with the immune score. Seven of 14 key genes (FCRL3, IFNG, KCNA3, LY9, PLA2G2D, THEMIS, and TRAT1) were significantly associated with the OS of OV patients. Correlation analysis showed our key genes positively related to M1 macrophages, CD8 T cells, plasma cells, regulatory T (Treg) cells and activated memory CD4 T cells, and negatively related to naive CD4 T cells, M0 macrophages, activated dendritic cells (DCs) and memory B cells. Kaplan-Meier survival analysis showed that lower abundances of neutrophils and higher abundances of M1 macrophages, plasma cells, T cells gamma delta (γδT) cells and follicular helper T (Tfh) cells predicted better OV prognosis. Conclusion: Forteen key genes related to the immune infiltrating of OV were identified, and seven of them were significantly related to prognosis. These key genes have potential roles in tumor infiltrating immune cells differentiation and proliferation. This study provided potential prognostic markers and immunotherapy targets for OV.

Down-regulation of EPB41L4A-AS1 mediated the brain aging and neurodegenerative diseases via damaging synthesis of NAD+ and ATP

BackgroundAging and neurodegenerative diseases are typical metabolic-related processes. As a metabolism-related long non-coding RNA, EPB41L4A-AS has been reported to be potentially involved in the development of brain aging and neurodegenerative diseases. In this study, we sought to reveal the mechanisms of EPB41L4A-AS in aging and neurodegenerative diseases.MethodsHuman hippocampal gene expression profiles downloaded from the Genotype-Tissue Expression database were analyzed to obtain age-stratified differentially expressed genes; a weighted correlation network analysis algorithm was then used to construct a gene co-expression network of these differentially expressed genes to obtain gene clustering modules. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, protein–protein interaction network, and correlation analysis were used to reveal the role of EPB41L4A-AS1. The mechanism was verified using Gene Expression Omnibus dataset GSE5281 and biological experiments (construction of cell lines, Real-time quantitative PCR, Western blot, measurement of ATP and NAD+ levels, nicotinamide riboside treatment, Chromatin Immunoprecipitation) in neurons and glial-derived cells.ResultsEPB41L4A-AS1 was downregulated in aging and Alzheimer's disease. EPB41L4A-AS1 related genes were found to be enriched in the electron transport chain and NAD+ synthesis pathway. Furthermore, these genes were highly associated with neurodegenerative diseases and positively correlated with EPB41L4A-AS1. In addition, biological experiments proved that the downregulation of EPB41L4A-AS1 could reduce the expression of these genes via histone H3 lysine 27 acetylation, resulting in decreased NAD+ and ATP levels, while EPB41L4A-AS1 overexpression and nicotinamide riboside treatment could restore the NAD+ and ATP levels.ConclusionsDownregulation of EPB41L4A-AS1 not only disturbs NAD+ biosynthesis but also affects ATP synthesis. As a result, the high demand for NAD+ and ATP in the brain cannot be met, promoting the development of brain aging and neurodegenerative diseases. However, overexpression of EPB41L4A-AS1 and nicotinamide riboside, a substrate of NAD+ synthesis, can reduce EPB41L4A-AS1 downregulation-mediated decrease of NAD+ and ATP synthesis. Our results provide new perspectives on the mechanisms underlying brain aging and neurodegenerative diseases.

Identification and Validation of Immune- and Stemness-Related Prognostic Signature of Melanoma.

Purpose: Our aim was to construct a signature that accurately predicted the prognostic and immune response of melanoma.Methods: First, the weighted co-expression network analysis (WGCNA) algorithm was used to identify the hub genes related to clinical phenotypes of melanoma in the cancer genome atlas (TCGA) database. Nest, the least absolute shrinkage and selection operator (LASSO) analysis was used to dimensionality reduction of these hub genes and constructed a prognostic signature to predict the prognosis and immunosuppressive response of melanoma.Result: Through in-depth analysis, we constructed a 5-mRNA prognostic signature and verified its prognostic value in internal (TCGA-SKCM, n = 452) and external independent datasets (GSE53118, n = 79). Based on this signature, the tumor immune microenvironment (TME) of melanoma was characterized, and the result was found that patients in the high-risk group had lower CD8 T cell infiltration and immune checkpoint expression (PD-1, PD-L1, CTLA4), as well as higher M0/M2 macrophage infiltration. Our results also found the risk score based on a 5-mRNA signature was significantly associated with tumor mutational burden (TMB) and tumor stem cell markers (CD20, CD38, ABCB5, CD44, etc.). Lastly, we built a nomogram for clinician prediction for the prognosis of patients with melanoma.Conclusion: Our findings indicated that the 5-mRNA signature has an important predictive value for the overall survival of melanoma. By analyzing the tumor immune microenvironment and tumor stem cell marker between different groups, a new method is provided for the stratified diagnosis and treatment of melanoma.

Proteomic profile of mesothelial exosomes isolated from peritoneal dialysis effluent of children with focal segmental glomerulosclerosis

Peritoneal dialysis (PD) is the worldwide recognized preferred dialysis treatment for children affected by end-stage kidney disease (ESKD). However, due to the unphysiological composition of PD fluids, the peritoneal membrane (PM) of these patients may undergo structural and functional alterations, which may cause fibrosis. Several factors may accelerate this process and primary kidney disease may have a causative role. In particular, patients affected by steroid resistant primary focal segmental glomerulosclerosis, a rare glomerular disease leading to nephrotic syndrome and ESKD, seem more prone to develop peritoneal fibrosis. The mechanism causing this predisposition is still unrecognized. To better define this condition, we carried out, for the first time, a new comprehensive comparative proteomic mass spectrometry analysis of mesothelial exosomes from peritoneal dialysis effluent (PDE) of 6 pediatric patients with focal segmental glomerular sclerosis (FSGS) versus 6 patients affected by other primary renal diseases (No FSGS). Our omic study demonstrated that, despite the high overlap in the protein milieu between the two study groups, machine learning allowed to identify a core list of 40 proteins, with ANXA13 as most promising potential biomarker, to distinguish, in our patient population, peritoneal dialysis effluent exosomes of FSGS from No FSGS patients (with 100% accuracy). Additionally, the Weight Gene Co-expression Network Analysis algorithm identified 17 proteins, with PTP4A1 as the most statistically significant biomarker associated to PD vintage and decreased PM function. Altogether, our data suggest that mesothelial cells of FSGS patients are more prone to activate a pro-fibrotic machinery. The role of the proposed biomarkers in the PM pathology deserves further investigation. Our results need further investigations in a larger population to corroborate these findings and investigate a possible increased risk of PM loss of function or development of encapsulating peritoneal sclerosis in FSGS patients, thus to eventually carry out changes in PD treatment and management or implement new solutions.

Identification of joint gene players implicated in the pathogenesis of HTLV-1 and BLV through a comprehensive system biology analysis

BackgroundHuman T-cell lymphotropic virus type 1 (HTLV-1) and bovine leukemia virus (BLV) are oncogenic viruses that induce adult T cell leukemia/lymphoma (ATLL) and enzootic bovine leukosis (EBL), respectively. HTLV-1 principally infects CD4+ T cells comprising regulatory T cells (Tregs), T helper 1 (Th1), and T helper 2 (Th2), while BLV infects B lymphocytes. Both viruses may impel cell proliferation and malignancy. MethodsTo survey the transcriptomic variations due to HTLV-1 and BLV infection and further hematologic malignancies, differential expression genes (DEGs) were explored between leukemia and normal samples using the DESeq2 package. Gene set enrichment analyses (GSEA) were then performed to identify significant gene sets using the FGSEA package. Afterward, the protein-protein interaction (PPI) networks were reconstructed using the STRING online database. Eventually, the hub significant genes and modules were determined through network analysis and MCODE algorithm, respectively. ResultsOur results uncloaked that four common functional gene sets including mitotic-spindle, G2M-checkpoint, E2F-targets, and MYC-targets-V1 are involved in the human and ovine hosts. Furthermore, twelve up-regulated hub genes including BIRC5, CCNA2, CCNB2, BUB1, DLGAP5, TOP2A, PBK, ASPM, UBE2C, CEP55, KIF20A, and NUSAP1 were identified which were similarly activated in both human and ovine hosts. They mostly participate in pathways including cell cycle, cell division, DNA damage responses, growth factors production, and p53 signaling pathway. The dysregulated hub genes and pathways seem to be involved in the development and progression of the infected cells toward malignancy. ConclusionThere is common gene groups between HTLV-1 and BLV infections that promote viral malignancy through enhancing cell proliferation and overall survival of cancer cells. The dysregulated genes and pathways may be the efficient candidates for the therapy of the mentioned life-threatening diseases.

Identification of uterine leiomyosarcoma-associated hub genes and immune cell infiltration pattern using weighted co-expression network analysis and CIBERSORT algorithm

BackgroundWhile large-scale genomic analyses symbolize a precious attempt to decipher the molecular foundation of uterine leiomyosarcoma (ULMS), bioinformatics results associated with the occurrence of ULMS based totally on WGCNA and CIBERSORT have not yet been reported. This study aimed to screen the hub genes and the immune cell infiltration pattern in ULMS by bioinformatics methods.MethodsFirstly, the GSE67463 dataset, including 25 ULMS tissues and 29 normal myometrium (NL) tissues, was downloaded from the public database. The differentially expressed genes (DEGs) were screened by the ‘limma’ package and hub modules were identified by weighted gene co-expression network analysis (WGCNA). Subsequently, gene function annotations were performed to investigate the biological role of the genes from the intersection of two groups (hub module and DEGs). The above genes were calculated in the protein–protein interaction (PPI) network to select the hub genes further. The hub genes were validated using external data (GSE764 and GSE68295). In addition, the differential immune cell infiltration between UL and ULMS tissues was investigated using the CIBERSORT algorithm. Finally, we used western blot to preliminarily detect the hub genes in cell lines.ResultsWGCNA analysis revealed a green-yellow module possessed the highest correlation with ULMS, including 1063 genes. A total of 172 DEGs were selected by thresholds set in the ‘limma’ package. The above two groups of genes were intersected to obtain 72 genes for functional annotation analysis. Interestingly, it indicated that 72 genes were mainly involved in immune processes and the Neddylation pathway. We found a higher infiltration of five types of cells (memory B cells, M0-type macrophages, mast cells activated, M1-type macrophages, and T cells follicular helper) in ULMS tissues than NL tissues, while the infiltration of two types of cells (NK cells activated and mast cells resting) was lower than in NL tissues. In addition, a total of five genes (KDR, CCL21, SELP, DPT, and DCN) were identified as the hub genes. Internal and external validation demonstrated that the five genes were over-expressed in NL tissues compared with USML tissues. Finally, the correlation analysis results indicate that NK cells activated and mast cells activated positively correlated with the hub genes. However, M1-type macrophages had a negative correlation with the hub genes. Moreover, only the DCN may be associated with the Neddylation pathway.ConclusionA series of evidence confirm that the five hub genes and the infiltration of seven types of immune cells are related to USML occurrence. These hub genes may affect the occurrence of USML through immune-related and Neddylation pathways, providing molecular evidence for the treatment of USML in the future.

VarSAn: associating pathways with a set of genomic variants using network analysis.

There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn's pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.

The Heterogeneity of Infiltrating Macrophages in Metastatic Osteosarcoma and Its Correlation with Immunotherapy.

Background Metastatic osteosarcoma is a common and fatal bone tumor. Several studies have found that tumor-infiltrating immune cells play pivotal roles in the progression of metastatic osteosarcoma. However, the heterogeneity of infiltrating immune cells across metastatic and primary osteosarcoma remains unclear. Methods Immune infiltration analysis was carried out via the “ESTIMATE” and “xCell” algorithms in primary and metastatic osteosarcoma. Then, we evaluated the prognostic value of infiltrating immune cells in 85 osteosarcomas through the Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curve. Infiltrations of macrophage M1 and M2 were evaluated in metastatic osteosarcoma, as well as their correlation with immune checkpoints. Macrophage-related prognostic genes were identified through Weighted Gene Coexpression Network Analysis (WGCNA), Lasso analysis, and Random Forest algorithm. Finally, a macrophage-related risk model had been constructed and validated. Results Macrophages, especially the macrophage M1, sparingly infiltrated in metastatic compared with the primary osteosarcoma and predicted the worse overall survival (OS) and disease-free survival (DFS). Macrophage M1 was positively correlated with immune checkpoints PDCD1, CD274 (PD-L1), PDCD1LG2, CTLA4, and TIGIT. In addition, four macrophage-related prognostic genes (IL10, VAV1, CD14, and CCL2) had been identified, and the macrophage-related risk model had been validated to be reliable for evaluating prognosis in osteosarcoma. Simultaneously, the risk score showed a strong correlation with several immune checkpoints. Conclusion Macrophages potentially contribute to the regulation of osteosarcoma metastasis. It can be used as a candidate marker for metastatic osteosarcoma' prognosis and immune checkpoints blockades (ICBs) therapy. We constructed a macrophage-related risk model through machine-learning, which might help us evaluate patients' prognosis and response to ICBs therapy.

Comprehensive analysis of diabetic nephropathy expression profile based on weighted gene co-expression network analysis algorithm

BackgroundDiabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression.MethodsThe identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed.ResultsAfter normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes.ConclusionsThe identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease.

Identification of Mucus-Associated Molecular Subtypes of Chronic Obstructive Pulmonary Disease: A Latent Profile Analysis Based on MUC5B-Associated Genes.

BackgroundChronic obstructive pulmonary disease (COPD) is a disease with high heterogeneity, which is a major challenge in clinical individualized treatment. A mucus phenotype is one of the main characteristics of COPD.Material/MethodsGene expression profiles of lung tissue samples were from the Lung Genomics Research Consortium. MUC5B-associated gene signatures were obtained based on a nonlinear feature screening algorithm. These signatures were used to fit a latent profile analysis (LPA) model to identify COPD molecular subtypes and build a subtype classifier to verify the subtypes. Then, we explored the characteristics of cilium assembly and beating signatures, transcriptome features, immune infiltration among the 3 subtypes by xCell, single-sample gene set enrichment analysis, network perturbation amplitude, and weighted gene co-expression network analysis algorithms. An external dataset was used to verify the above COPD subtypes.ResultsThree subtypes associated with mucus were identified by LPA and verified in an external dataset. Subtype 1 displayed higher T helper type 1 (Th1) and basophil infiltration, higher Th17/regulatory T cells (Tregs) ratio, a higher level of cilium assembly and beating, and lower mast cell and Treg infiltration. The subtypes 2 and 3 demonstrated higher macrophage M2 infiltration in lung tissue, while subtype 3 had higher neutrophil and eosinophil infiltration than subtype 2.ConclusionsOverall, this work identified 3 mucus-associated molecular subtypes related to MUC5B expression, which deepens the understanding of airway mucus secretion in COPD and potentially provides valuable information for precision therapy.