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.

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Identification of Potential ceRNA Network and Patterns of Immune Cell Infiltration in Systemic Sclerosis-Associated Interstitial Lung Disease

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.622021 ◽

2021 ◽

Vol 9 ◽

Author(s):

Qiuhong Wu ◽

Yang Liu ◽

Yan Xie ◽

Shixiong Wei ◽

Yi Liu

Keyword(s):

Systemic Sclerosis ◽

Interstitial Lung Disease ◽

Lung Disease ◽

Immune Cells ◽

Regulatory Network ◽

Target Genes ◽

Immune Cell ◽

Differentially Expressed ◽

Immune Cell Infiltration ◽

Overlapping Genes

PurposeSystemic sclerosis-associated interstitial lung disease (SSc-ILD) is one of the most severe complications of systemic sclerosis (SSc) and is the leading cause of SSc-related deaths. However, the precise pathogenesis of pulmonary fibrosis in SSc-ILD remains unknown. This study aimed to evaluate the competing endogenous RNA (ceRNA) regulatory network and immune cell infiltration patterns in SSc-ILD.MethodsOne microRNA (miRNA) and three messenger RNA (mRNA) microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Then, the differentially expressed miRNAs (DEmiRs) and mRNAs (DEMs) between SSc-ILD patients and normal controls were identified, respectively, followed by the prediction of the target genes and target lncRNAs of DEmiRs. The overlapping genes between DEmiRs target genes and DEMs were identified as core mRNAs to construct the ceRNA network. In addition, the “Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT)” algorithm was used to analyze the composition of infiltrating immune cells in lung tissues of SSc-ILD patients and controls, and differentially expressed immune cells were recognized. The correlation between immune cells and core mRNAs was evaluated by Pearson correlation analysis.ResultsTotally, 42 SSc-ILD lung tissues and 18 normal lung tissues were included in this study. We identified 35 DEmiRs and 142 DEMs and predicted 1,265 target genes of DEmiRs. Then, 9 core mRNAs related to SSc-ILD were recognized, which were the overlapping genes between DEmiRs target genes and DEMs. Meanwhile, 9 DEmiRs related to core mRNAs were identified reversely, and their target lncRNAs were predicted. In total, 9 DEmiRs, 9 core mRNAs, and 51 predicted lncRNAs were integrated to construct the ceRNA regulatory network of SSc-ILD. In addition, 9 types of immune cells were differentially expressed in lung tissues between SSc-ILD patients and controls. Some core mRNAs, such as COL1A1, FOS, and EDN1, were positively or negatively correlated with the number of infiltrating immune cells.ConclusionThis is the first comprehensive study to construct the potential ceRNA regulatory network and analyze the composition of infiltrating immune cells in lung tissues of SSc-ILD patients, which improves our understanding of the pathogenesis of SSc-ILD.

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Identification of Molecular Subtypes and Key Genes of Atherosclerosis Through Gene Expression Profiles

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.628546 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yujia Yang ◽

Yue Cai ◽

Yuan Zhang ◽

Xu Yi ◽

Zhiqiang Xu

Keyword(s):

Gene Expression ◽

Immune Cell ◽

Cell Infiltration ◽

Validation Dataset ◽

Support Vector ◽

Molecular Characteristics ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Significant Difference ◽

Key Genes

Atherosclerotic cardiovascular disease (ASCVD) caused by atherosclerosis (AS) is one of the highest causes of mortality worldwide. Although there have been many studies on AS, its etiology remains unclear. In order to carry out molecular characterization of different types of AS, we retrieved two datasets composed of 151 AS samples and 32 normal samples from the Gene Expression Omnibus database. Using the non-negative matrix factorization (NMF) algorithm, we successfully divided the 151 AS samples into two subgroups. We then compared the molecular characteristics between the two groups using weighted gene co-expression analysis (WGCNA) and identified six key modules associated with the two subgroups. Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) enrichment analysis were used to identify the potential functions and pathways associated with the modules. In addition, we used the cytoscape software to construct and visualize protein–protein networks so as to identify key genes in the modules of interest. Three hub genes including PTGER3, GNAI1, and IGFBP5 were further screened using the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Since the modules were associated with immune pathways, we performed immune cell infiltration analysis. We discovered a significant difference in the level of immune cell infiltration by naïve B cells, CD8 T cells, T regulatory cells (Tregs), resting NK cells, Monocytes, Macrophages M0, Macrophages M1, and Macrophages M2 between the two subgroups. In addition, we observed the three hub genes were positively correlated with Tregs but negatively correlated with Macrophages M0. We also found that the three key genes are differentially expressed between normal and diseased tissue, as well as in the different subgroups. Receiver operating characteristic (ROC) results showed a good performance in the validation dataset. These results may provide novel insight into cellular and molecular characteristics of AS and potential markers for diagnosis and targeted therapy.

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Molecular profile reveals immune-associated markers of medulloblastoma for different subtypes

10.21203/rs.3.rs-816357/v2 ◽

2021 ◽

Author(s):

shenglan li ◽

Zhuang Kang ◽

jinyi Chen ◽

Can Wang ◽

Zehao Cai ◽

...

Keyword(s):

Immune Cell ◽

Intracranial Tumor ◽

Cell Infiltration ◽

Molecular Profile ◽

Immune Cell Infiltration ◽

Ppi Network ◽

Hub Genes ◽

Data Set ◽

Hub Gene ◽

External Data

Abstract Background Medulloblastoma is a common intracranial tumor among children. In recent years, research on cancer genome has established four distinct subtypes of medulloblastoma: WNT, SHH, Group3, and Group4. Each subtype has its own transcriptional profile, methylation changes, and different clinical outcomes. Treatment and prognosis also vary depending on the subtype. Methods Based on the methylation data of medulloblastoma samples, methylCIBERSORT was used to evaluate the level of immune cell infiltration in medulloblastoma samples and identified 10 kinds of immune cells with different subtypes. Combined with the immune database, 293 Imm-DEGs were screened. Imm-DEGs were used to construct the co-expression network, and the key modules related to the level of differential immune cell infiltration were identified. Three immune hub genes (GAB1, ABL1, CXCR4) were identified according to the gene connectivity and the correlation with phenotype in the key modules, as well as the PPI network involved in the genes in the modules. Results The subtype marker was recognized according to the immune hub, and the subtype marker was verified in the external data set, the methylation level of immune hub gene among different subtypes was compared and analyzed, at the same time, tissue microarray was used for immunohistochemical verification, and a multi-factor regulatory network of hub gene was constructed. Conclusions Identifying subtype marker is helpful to accurately identify the subtypes of medulloblastoma patients, and can accurately evaluate the treatment and prognosis, so as to improve the overall survival of patients.

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Peer Review #2 of "Significance of hub genes and immune cell infiltration identified by bioinformatics analysis in pelvic organ prolapse (v0.1)"

10.7287/peerj.9773v0.1/reviews/2 ◽

2020 ◽

Keyword(s):

Pelvic Organ Prolapse ◽

Peer Review ◽

Bioinformatics Analysis ◽

Immune Cell ◽

Pelvic Organ ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes

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Peer Review #1 of "Significance of hub genes and immune cell infiltration identified by bioinformatics analysis in pelvic organ prolapse (v0.2)"

10.7287/peerj.9773v0.2/reviews/1 ◽

2020 ◽

Author(s):

L Gul

Keyword(s):

Pelvic Organ Prolapse ◽

Peer Review ◽

Bioinformatics Analysis ◽

Immune Cell ◽

Pelvic Organ ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes

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Association Between Tumor Mutation Burden and Prognosis in Lower-Grade Glioma: An Exploratory Study

10.21203/rs.3.rs-137165/v1 ◽

2021 ◽

Author(s):

Di Cao ◽

Jun Wang ◽

Yan Lin ◽

Guangwei Li

Keyword(s):

Immune Cell ◽

Differential Expression Analysis ◽

Cell Infiltration ◽

Lower Grade ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Tumor Mutation Burden ◽

Mutation Burden ◽

Lower Grade Glioma ◽

Genome Atlas

Abstract Background: The therapeutic efficacy of immune checkpoint inhibitor therapy is highly influenced by tumor mutation burden (TMB). The relationship between TMB and prognosis in lower-grade glioma is still unclear. We aimed to explore the relationships and mechanisms between them in lower-grade glioma.Methods: We leveraged somatic mutation data from The Cancer Genome Atlas (TCGA). Clinical cases were divided into high- and low-TMB groups based on the median of TMB. Infiltrating immune cells were analyzed using CIBERSORT and differential expression analysis between the prognostic groups performed. The key genes were identified as intersecting between immune-related genes. Cox regression and survival analysis were performed on the intersecting genes. A total of 7 hub genes were identified. The effect of somatic copy number alterations (SCNA) of the hub genes on immune cell infiltration was analyzed using TIMER, which was used to determine the risk factors and immune infiltration status in LGG. Subsequently, based on hub genes, a TMB Prognosis Index (TMBPI) model was constructed to predict the risk in LGG patients. Besides, this model was validated using data from TCGA and Chinese Glioma Genome Atlas (CGGA).Results: High-TMB favored worse prognosis (P＜0.001) and macrophage infiltration was an independent risk factor (P＜0.001). In the high-TMB group (P=0.033, P=0.009), the proportion of macrophages M0 and M2 increased and monocytes decreased (P=0.006). Besides, the SCNA of the hub genes affected the level of immune cell infiltration by varying degrees among which IGF2BP3, NPNT, and PLA2G2A had a significant impact. The AUC of the ROC curve at 1-, 3- and 5-years were all above 0.74.Conclusions: This study implies that high-TMB correlated with unfavorable prognosis in lower-grade glioma. And high-TMB may have an impact on prognosis by changing tumor microenvironment, caused by the SCNAs of genes. The TMBPI model accurately predicted prognosis in LGG patients.

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BATF2 and PDK4 As Diagnostic Molecular Markers of Sarcoidosis and Their Relationship With Immune Infiltration

10.21203/rs.3.rs-557951/v1 ◽

2021 ◽

Author(s):

Xiaoyan Li ◽

Jing Zhou ◽

Jie He

Keyword(s):

Immune Cells ◽

Immune Cell ◽

External Validation ◽

Pulmonary Sarcoidosis ◽

Gene Expression Omnibus ◽

Diagnostic Markers ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Immune Disorder ◽

Qpcr Method

Abstract Background: Sarcoidosis (SA) is an immune disorder disease featured with granulomas formation. The work purposed to uncover potential markers for sarcoidosis (SA) diagnosis and explore how immune cell infiltration contributes to the pathogenesis of SA.Methods: Sarcoidosis GSE83456 samples and GSE42834 from Gene Expression Omnibus (GEO) were analyzed as the training and external validation sets, respectively. R statistical software was employed to uncover the differentially expressed genes (DEGs) of GSE83456. SVM algorithms and LASSO logistic regression were applied for screening and verification of the diagnostic markers for key module genes. The infiltration of immune cells in sarcoidosis patients’ blood samples was assessed by CIBERSORT. The expression of serum BATF2 and PDK4 was detected by RT-qPCR method, and the value of BATF2 and PDK4 mRNA expression in the diagnosis of pulmonary sarcoidosis was analyzed.Results: In total, 580 DEGs were identified from the key module. PDK4 (AUC=0.942) and BATF4 (AUC=0.980) were revealed as diagnostic markers of sarcoidosis. We found that monocytes, T cells regulatory (Tregs), mast cells, macrophages，NK cells, and dendritic cells may contribute to sarcoidosis development. In addition, PDK4 and BATF4 were closely associated with these immune cells. BATF2 and PDK4 were highly expressed in pulmonary sarcoidosis. BATF2 and PDK4 combined to predict the area under the ROC curve of pulmonary sarcoidosis was 0.922.Conclusions: PDK4 and BATF4 could be used as diagnostic markers of sarcoidosis, and immune cell infiltration severs an important role in sarcoidosis.

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Identification of Methylated Differentially Expressed Hub Genes and Immune Infiltration in Diabetic Retinopathy

10.21203/rs.3.rs-1136469/v1 ◽

2021 ◽

Author(s):

Qi Zhou ◽

Xin Xiong ◽

Min Tang ◽

Yingqing Lei ◽

Hongbin Lv

Keyword(s):

Gene Expression ◽

Extracellular Matrix ◽

Immune Cell ◽

Interaction Network ◽

Bioinformatic Analysis ◽

Differentially Expressed ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hub Genes ◽

Critical Pathways

Abstract BackgroundDiabetic retinopathy (DR), a severe complication of diabetes mellitus (DM), is a global social and economic burden. However, the pathological mechanisms mediating DR are not well-understood. This study aimed to identify differentially methylated and differentially expressed hub genes (DMGs and DEGs, respectively) and associated signaling pathways, and to evaluate immune cell infiltration involved in DR. MethodsTwo publicly available datasets were downloaded from the Gene Expression Omnibus database. Transcriptome and epigenome microarray data and multi-component weighted gene coexpression network analysis (WGCNA) were utilized to determine hub genes within DR. One dataset was utilized to screen DEGs and to further explore their potential biological functions using functional annotation analysis. A protein-protein interaction network was constructed. Gene set enrichment and variation analyses (GSVA and GSEA, respectively) were utilized to identify the potential mechanisms mediating the function of hub genes in DR. Infiltrating immune cells were evaluated in one dataset using CIBERSORT. The Connectivity Map (CMap) database was used to predict potential therapeutic agents. ResultsIn total, 673 DEGs (151 upregulated and 522 downregulated genes) were detected. Gene expression was significantly enriched in the extracellular matrix and sensory organ development, extracellular matrix organization, and glial cell differentiation pathways. Through WGCNA, one module was found to be significantly related with DR (r=0.34, P =0.002), and 979 hub genes were identified. By comparing DMGs, DEGs, and genes in WGCNA, we identified eight hub genes in DR ( AKAP13, BOC, ACSS1, ARNT2, TGFB2, LHFPL2, GFPT2, TNFRSF1A ), which were significantly enriched in critical pathways involving coagulation, angiogenesis, TGF-β, and TNF-α-NF-κB signaling via GSVA and GSEA. Immune cell infiltration analysis revealed that activated natural killer cells, M0 macrophages, resting mast cells, and CD8 + T cells may be involved in DR. ARNT2, TGFB2, LHFPL2 , and AKAP13 expression were correlated with immune cell processes, and ZG-10, JNK-9L, chromomycin-a3, and calyculin were identified as potential drugs against DR. Finally, TNFRSF1A , GFPT2 , and LHFPL2 expression levels were consistent with the bioinformatic analysis. ConclusionsOur results are informative with respect to correlations between differentially methylated and expressed hub genes and immune cell infiltration in DR, providing new insight towards DR drug development and treatment.

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Identification of new biomarkers and pathways associated with treatment and prognosis in melanoma patients

10.21203/rs.2.23305/v1 ◽

2020 ◽

Author(s):

Biao Huang ◽

Wei Han ◽

Zu-Feng Sheng ◽

Guo-Liang Shen

Keyword(s):

Gene Expression ◽

T Cells ◽

Tumor Microenvironment ◽

Prognostic Value ◽

Immune Cell ◽

Gene Interaction ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Strong Positive Correlation ◽

Hub Genes

Abstract Background Skin cutaneous melanoma (SKCM) is known as the most malignancy and treatment-resistant in human tumor, causing about 72% of deaths in skin carcinoma. However, the potential mechanism and new effective targets remain to be further elucidated. Available datasets such as Gene Expression Omnibus (GEO) can be utilized to search for novel therapeutic targets and prognostic biomarkers. Methods Three data sets were downloaded from GEO database . The differentially expressed genes (DEGs) were identified via Venn software. Protein‐protein interaction network of DEGs was developed and the module hub genes analysis was constructed by Cytoscape. Subsequently, multiple online tools and Kaplan-Meier survival curves were analyzed to detect underlying signaling pathways, gene expression, drug-gene interaction and prognostic value of hub genes. In addition, we explored the correlation between hub genes and immune cell infiltration. At last, the related miRNA, lncRNA networks were constructed by R software. Results A total of 308 DEGs and 12 hub genes were identified. Function and pathway enrichment results demonstrated a correlation between DEGs and the tumor microenvironment, immune response and melanoma tumorigenesis. Subsequently, we focused on assessing potential value of 12 hub genes. Seven hub genes ( CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 ) were identified with significant overall survival for prognosis. What’s more, five of these seven hub genes were found to be related to clinical stages (P values<0.05). In addition, the most important pathways of hub genes include interleukin-10 signaling, peptide ligand-binding receptors, which play important roles in tumor microenvironment for immune activation or immunosuppressive by regulating the infiltration of immune cells. Our results revealed a strong positive correlation between gene expression (CCL4, CCL5, CXCL9, CXCL10 and CXCL13) and immune cell infiltration (B-cell, CD8+ T cells, CD4+ T cells, macrophages, Neutrophils, Dendritic cells). Interestingly, 8 of 12 hub genes (CXCL10, CCL4, CCL5, IL6, CXCL2, PTGER3, GAL, NPY1R) were also found in the predicted drug-gene interaction. The related miRNA, lncRNA for diagnosis and prognosis were found in networks. Conclusion In conclusion, CCL4, CCL5, NMU, GAL, CXCL9, CXCL10, CXCL13 were of high prognostic value and may be potential targets for the diagnosis and therapy of patients with melanoma.

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