Immune Infiltration Characteristics Research Articles

Prognostic Implications and Immune Infiltration Characteristics of Chromosomal Instability-Related Dysregulated CeRNA in Lung Adenocarcinoma.

An accumulating body of research indicates that long-noncoding RNAs (lncRNAs) regulate the target genes and act as competitive endogenous RNAs (ceRNAs) playing an indispensable role in lung adenocarcinoma (LUAD). LUAD is frequently accompanied by the feature of chromosomal instability (CIN); however, CIN-related ceRNAs have not been investigated yet. We systematically analyzed and integrated CIN-related dysregulated ceRNAs characteristics in LUAD samples for the first time. In TCGA LUAD cohort, CIN in tumor samples was significantly higher than that in those of adjacent, and patients with high CIN risk tended to have worse clinical outcomes. We constructed a double-weighted CIN-related dysregulated ceRNA network, in which edge weight and node weight represented the disorder extent of ceRNA and the correlation of RNA expression level and prognosis, respectively. After module mining and analysis, a potential prognostic biomarker composed of 12 RNAs (8 mRNAs and 4 lncRNAs) named CIN-related dysregulated ceRNAs (CRDC) was obtained. The CRDC risk score had a positive relation with clinical stage and CIN, and patients with high CRDC risk scores exhibited poor prognosis. Moreover, CRDC tended to be an independent risk factor with high robustness to overcome the effect of multicollinearity among other explanatory variables for disease-specific survival (DSS) in TCGA and two GEO cohorts. The result of functional analysis indicated that CRDC was involved in multiple cancer progresses, especially immune-related pathways. The patients with lower CRDC risk had higher B cell, T cell CD4+, T cell CD8+, neutrophil, macrophage, and myeloid dendritic cell infiltration than the patients with higher CRDC risk. Meanwhile, patients with lower CRDC risk could get more benefits from immunological therapy. The results suggested that the CRDC could be a potential prognostic biomarker and an immunotherapy predictor for lung adenocarcinoma.

N6-Methyladenosine Modification Participates in the Progression of Hepatitis B Virus-Related Liver Fibrosis by Regulating Immune Cell Infiltration.

AimN6-methyladenosine (m6A) modification has been demonstrated to play an important part in hepatitis B virus (HBV) infection and immune response. This study aims to further investigate whether m6A modification plays an important role in the progression of HBV-related liver fibrosis through the regulation of immune cell infiltration.MethodsIn this study, 124 chronically HBV infected cases were obtained from the Gene Expression Omnibus database. In total, 489 m6A-and-stage related genes were selected to be associated with the m6A modification and the stage of liver fibrosis. Based on these genes, we identified two distinct gene clusters, gene clusterA and gene clusterB. The immune characteristics of the two clusters were comprehensively compared. The m6A-S score was constructed as quantification of individual m6A status. The correlations between m6A regulators and infiltrating immune cells were examined and compared in different pairs of groups with various m6A traits.ResultsBiological functions, immune cell infiltration, and cytokines expression were compared between the two gene clusters proving that the gene clusterB was more immune active and had a more advanced liver fibrosis stage. The m6A-S score was associated with immune infiltration and the progression of liver fibrosis. Five different grouping conditions with different m6A traits were set up. According to the intersection of significant genes and cells, ALKBH5 interacting with macrophage and WTAP interacting with nature killer T cells may be key points in the progress of liver fibrosis.ConclusionsN6-methyladenosine modification is closely related to the immune cell infiltration and the fibrosis stage of chronic HBV-infected liver tissue. It provides us a better understanding of the progression of liver cirrhosis via evaluating the m6A modification pattern and immune infiltration characteristics.

Identification of Potential Ferroptosis-Related Biomarkers and Immune Infiltration in Human Coronary Artery Atherosclerosis.

ObjectiveFerroptosis is a specific subtype of programmed cell death, which plays an essential role in the immune-associated disease, atherosclerosis (AS). The purpose of this study was to identify potential ferroptosis-related gene biomarkers and its association with immune infiltration characteristics in atherosclerosis with bioinformatics methods.MethodsDifferentially expressed genes (DEGs) between AS and control groups were screened from GSE40231, analyzed for functional enrichment and then intersected with ferroptosis-related genes. Then, a random forest model was constructed based on these differentially expressed ferroptosis-related genes (DE-FRGs) and validated with dataset GSE132651. The performance of the models was evaluated with the area under receiver operating characteristic curves (AUC). Finally, we analyzed the correlation between DE-FRGs above and the characteristics of immune infiltration via CIBERSORT method.ResultsSix DE-FRGs (IL6, ANGPTL7, CDKN1A, AKR1C3, NOX4 and VLDLR) were detected based on dataset of GSE40231. Furthermore, a random forest model was constructed based on them with a compelling diagnostic performance of AUC = 0.8974 in the validation dataset GSE132651. In addition, the proportion of follicular helper T (Tfh) cells was significantly higher in AS group (P < 0.001). And we found significant correlation relationship between Tfh and expression level of ANGPTL7 (R = 0.35, P < 0.01), CDKN1A (R = 0.4, P < 0.0001), AKR1C3 (R = 0.64, P < 0.0001), NOX4 (R = 0.32, P < 0.01) and VLDLR (R = −0.43, P < 0.0001).ConclusionThis study identified 6 DE-FRGs and validated a predicted model for the early prediction of AS, which also proved the close relationship between ferroptosis and immunity in the pathogenesis of AS.

Integrative bioinformatics analysis of potential therapeutic targets and immune infiltration characteristics in dilated cardiomyopathy.

BackgroundDilated cardiomyopathy (DCM) is currently the major cause of systolic heart failure. This study explored potential therapeutic targets and investigated the role of immune cell infiltration in DCM.MethodsThree DCM datasets (GSE3585, GSE9800, and GSE84796) from the Gene Expression Omnibus (GEO) database were merged into an integrated dataset, and batch effects were removed. Differentially expressed genes (DEGs) were screened and the associations between gene co-expression modules and clinical traits were assessed by weighted gene co-expression network analysis (WGCNA) in R software. Any DEGs from the integrated dataset overlapped with the significant module genes were defined as common genes (CGs). Enrichment analysis of the CGs was performed. The protein-protein interaction (PPI) network of the CGs was visualized and the hub gene was identified by using Cytoscape 3.8.2 software. The miRNA-transcription factor-mRNA (miRNA-TF-mRNA) network was constructed using Cytoscape to unveil the regulatory relationships in DCM. Finally, the CIBERSORT method (https://cibersort.stanford.edu/) was used to investigate immune cell infiltration in DCM.ResultsA total of 53 DEGs were identified, and 5 gene co-expression modules were detected by WGCNA of the DCM and control group samples of cardiac tissue. Genes such as FRZB, ASPN, and PHLDA1 were significantly upregulated, whereas IDH2 and ENDOG were significantly downregulated. Functional enrichment analysis showed that CGs were mainly enriched in the extracellular matrix (ECM) signaling pathway. ASPN was the hub gene in the PPI network. The miRNA-TF-mRNA network revealed that FRZB and ASPN were targeted by paired related homeobox 2 (Prrx2). We also found that miR-129-5p could regulate ASPN, PHLDA1, and IDH2 simultaneously. The immune infiltration analysis revealed higher levels of M1 macrophages in DCM samples than in the control samples.ConclusionsIn conclusion, we speculate that miR-129-5p might target ASPN in regulating DCM via the ECM signaling pathway. Macrophage infiltration may be involved in ECM remodeling and eventually lead to DCM.

RNA modification "writer"-mediated RNA modification patterns and tumor microenvironment characteristics of cervical cancer.

As an epigenetic regulation mechanism after transcription, RNA modification is installed by endogenous "writer" enzymes and is widely involved in a variety of physiological processes, including cancer progression. This study explored the RNA modification patterns of cervical cancer to clarify overall effect of RNA modification on tumor microenvironment (TME) characteristics and immune/targeted therapy. 26 RNA modification "writers" were clustered, and the RNA modification patterns and TME characteristics of cervical cancer patients in TCGA were systematically evaluated. Based on differentially expressed genes (DEGs) between different RNA modification patterns, an RNA modification "writer" score (WM score) system was developed to assess the RNA modification of a single sample. Two different RNA modification patterns of cervical cancer were identified, and these patterns were significantly related to the prognosis and TME infiltration characteristics of patients. WM score was an independent risk factor for the prognosis of cervical cancer. High WM score was characterized by poor prognosis, low immune infiltration and low tumor mutation burden (TMB), while low-WM score was related to relatively long overall survival (OS), more immune components in TME and increased TMB. In addition, the low-WM score group was expected to be more sensitive to programmed cell death protein 1 (PD-1) therapy and showed lower predicted IC50 of chemotherapy drugs paclitaxel and cisplatin treatment. This study identified and characterized RNA modification patterns, and clarified potential relationship between RNA modification patterns and immune infiltration characteristics and immunotherapy of cervical cancer, offering a new evaluation scheme for treatment of cervical cancer patients.

Identification of immune-infiltrated hub genes as potential biomarkers of Moyamoya disease by bioinformatics analysis

BackgroundMoyamoya disease (MMD) is a rare chronic progressive cerebrovascular disease. Recent studies have shown that autoimmune inflammation may also be an important pathology in MMD but the molecular mechanisms of inflammation in this disease are still large unknown. This study was designed to identify key biomarkers and the immune infiltration in vessel tissue of MMD using bioinformatics analysis.MethodsRaw gene expression profiles (GSE157628, GSE141024) were downloaded from the Gene Expression Omnibus (GEO) database, identified differentially expressed genes (DEGs) and performed functional enrichment analysis. The CIBERSORT deconvolution algorithm was used to analyze the proportion of immune cells between MMD and an MMD-negative control group. We screened for neutrophil-associated DEGs, constructed a protein–protein interaction network (PPI) using STRING, and clarified the gene cluster using the Cytoscape plugin MCODE analysis. The receiver operating characteristic (ROC) curve was applied to test and filter the best gene signature.ResultsA total of 570 DEGs were detected, including 212 downregulated and 358 up-regulated genes. Reactome and KEGG enrichment revealed that DEGs were involved in the cell cycle, molecular transport, and metabolic pathways. The immune infiltration profile demonstrated that MMD cerebrovascular tissues contained a higher proportion of neutrophils, monocytes, and natural killer cells in MMD than in controls. The PPI network and MCODE cluster identified nine DEGs (UNC13D, AZU1, PYCARD, ELANE, SDCBP, CCL11, CCL15, CCL20, and CXCL5) associated with neutrophil infiltration. ROC results showed that UNC13D has good specificity and sensitivity (AUC = 0.7846).ConclusionsThe characteristics of immune infiltration in the cerebrovascular tissues of MMD patients and abnormal expression of hub genes provide new insights for understanding MMD progression. UNC13D is shows promise as a candidate molecule to determine neutrophil infiltration characteristics in MMD.

Identification and Validation of a Hypoxia-Immune-Based Prognostic mRNA Signature for Oral Squamous Cell Carcinoma.

Background Oral squamous cell carcinoma (OSCC) is a commonly encountered head and neck malignancy. Increasing evidence shows that there are abnormal immune response and chronic cell hypoxia in the development of OSCC. However, there is a lack of a reliable hypoxia-immune-based gene signature that may serve to accurately prognosticate OSCC. Methods The mRNA expression data of OSCC patients were extracted from the TCGA and GEO databases. Hypoxia status was identified using the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm. Both ESTIMATE and single-sample gene-set enrichment analysis (ssGSEA) were used for further evaluation of immune status. The DEGs in different hypoxia and immune status were determined, and univariate Cox regression was used to identify significantly prognostic genes. A machine learning method, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, allowed us to construct prognostic gene signature to predict the overall survival (OS) of OSCC patients. Results A total of 773 DEGs were identified between hypoxia high and low groups. According to immune cell infiltration, patients were divided into immune high, medium, and low groups and immune-associated DEGs were identified. A total of 193 overlapped DEGs in both immune and hypoxia status were identified. With the univariate and LASSO Cox regression model, eight signature mRNAs (FAM122C, RNF157, RANBP17, SOWAHA, KIAA1211, RIPPLY2, INSL3, and DNAH1) were selected for further calculation of their respective risk scores. The risk score showed a significant association with age and perineural and lymphovascular invasion. In the GEO validation cohort, a better OS was observed in patients from the low-risk group in comparison with those in the high-risk group. High-risk patients also demonstrated different immune infiltration characteristics from the low-risk group and the low-risk group showed potentially better immunotherapy efficacy in contrast to high-risk ones. Conclusion The hypoxia-immune-based gene signature has prognostic potential in OSCC.

High frequency of PDGFRA and MUC family gene mutations in diffuse hemispheric glioma, H3 G34-mutant: a glimmer of hope?

BackgroundDiffuse hemispheric glioma H3 G34-mutant (G34-DHG) is a new type of pediatric-type diffuse high-grade glioma in the fifth edition of the WHO Classification of Tumors of the Central Nervous System. The current treatment for G34-DHG involves a combination of surgery and conventional radiotherapy or chemotherapy; however, the therapeutic efficacy of this approach is not satisfactory. In recent years, molecular targeted therapy and immunotherapy have achieved significant benefits in a variety of tumors. In-depth understanding of molecular changes and immune infiltration in G34-DHGs will help to establish personalized tumor treatment strategies. Here, we report the clinicopathological, molecular and immune infiltration characteristics of G34-DHG cases from our center along with cases from the HERBY Trial and the Chinese Glioma Genome Atlas database (CGGA).MethodsHematoxylin–eosin (HE) and immunohistochemistry (IHC) staining were used to present the clinicopathological characteristics of 10 Chinese G34-DHG patients treated at our institution. To address the molecular characteristics of G34-DHG, we performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) analyses of 5 patients from our center and 3 Chinese patients from the Chinese Glioma Genome Atlas (CGGA) database. Additionally, 7 European G34-DHG patients from the HERBY Trail were also subjected to analyses, with 7 cases of WES data and 2 cases of RNA-seq data. Six G34-DHG patients from another organization were used as external validation.ResultsWES showed a high frequency of PDGFRA mutation in G34-DHGs (12/15). We further identified frequent mutations in MUC family genes in G34-DHGs, including MUC16 (8/15) and MUC17 (8/15). Although no statistical difference was found, PDGFRA mutation tended to be an indicator for worse prognosis whereas MUC16/MUC17 mutation indicated a favorable prognosis in G34-DHGs. RNA sequencing results revealed that most G34-DHG are considered to be immune cold tumors. However, one patient in our cohort with MUC16 mutation showed significant immune infiltration, and the total overall survival of this patient reached 75 months.ConclusionsOur results demonstrate that G34-DHG is a new high-grade glioma with high frequency of PDGFRA and MUC gene family mutations. PDGFRA may serve as an indicator of poor prognosis and an effective therapeutic target. Moreover, MUC16 tends to be a favorable prognostic factor and indicates high immune infiltration in certain patients, and these findings may provide a new direction for targeted therapy and immunotherapy of patients with G34-DHGs.

Prognostic Significance and Therapeutic Target of CXC Chemokines in the Microenvironment of Lung Adenocarcinoma.

BackgroundLung adenocarcinoma (LUAD) is one of the most important subtypes of lung cancer and has a high morbidity and mortality. Inflammatory CXC chemokines in tumor microenvironment can stimulate tumor growth, invasion, and metastasis, affecting the prognosis of patients. However, the differential expression profiles, prognostic values, and specific mechanisms of the CXC chemokine family in LUAD have not been clarified.MethodsTranscriptome expression profile data were extracted from TIMER and TCGA. GEPIA was used to compare the relationship between CXC chemokines and clinicopathologic parameters. The prognostic analysis was performed using a Kaplan–Meier curve in GEPIA. LinkedOmics and TRRUST were applied to conduct the enrichment analysis of the regulatory networks containing the kinase targets, miRNA targets, and transcriptional factor targets. The characteristics of immune infiltration and immune-related clinical outcomes were evaluated with TIMER algorithms. Single-cell RNA sequencing localization analysis of genes as prognostic biomarkers were performed by PanglaoDB.ResultsNine differentially expressed genes were identified in LUAD compared to normal tissues. Aberrant expression of CXCL2 (P =0.0017), CXCL13 (P= 0.0271), CXCL16 (P= 0.016), and CXCL17 (P= 2.14e-5) was significantly correlated with clinical cancer stage. Furthermore, patients with low gene transcription of CXCL 7 (P = 0.017) and high expression of CXCL 17 (P = 0.00045) had a better prognosis in LUAD. We also found that immune cell infiltration was significantly correlated with LUAD microenvironment mediated by CXC chemokines. Cox proportional hazard model test was conducted and indicated that B cell infiltration could prolong the survival of the LUAD patients. CXCL17 exerted anti-tumors effect through pulmonary alveolar type II cells according to single-cell analysis.ConclusionOur research identified the aberrant expression profiles and prognostic biomarkers of CXC chemokines in LUAD. This detailed analysis of the regulatory factor networks for CXC chemokine gene expression may provide novel insights for selecting potential immunotherapeutic targets.

Prognostic tumor microenvironment gene and the relationship with immune infiltration characteristics in metastatic breast cancer

The aim of this study was to reveal genes associated with breast cancer metastasis, to investigate their intrinsic relationship with immune cell infiltration in the tumor microenvironment, and to screen for prognostic biomarkers. Gene expression data of breast cancer patients and their metastases were downloaded from the GEO, TCGA database. R language package was used to screen for differentially expressed genes, enrichment analysis of genes, PPI network construction, and also to elucidate key genes for diagnostic and prognostic survival. Spearman’s r correlation was used to analyze the correlation between key genes and infiltrating immune cells. We screened 25 hub genes, FN1, CLEC5A, ATP8B4, TLR7, LY86, PTGER3 and other genes were differentially expressed in cancer and paraneoplastic tissues. However, patients with higher expression of CD1C, IL-18 breast cancer had a better prognosis in the 10 years survival period, while patients with high expression of FN1, EIF4EBP1 tumors had a worse prognosis. In addition, TP53 and HIF1 genes are closely related to the signaling pathway of breast cancer metastasis. In this study, gene expression of ATP8B4 and CD1C were correlated with cancer tissue infiltration of CD8+ T lymphocytes, while GSE43816, GSE62327 and TCGA databases showed that CD8+ T lymphocytes were closely associated with breast cancer progression. Functional enrichment analysis of genes based on expression differences yielded key genes of prognostic value in the breast cancer microenvironment.

Predicting potential biomarkers and immune infiltration characteristics in heart failure.

Studies have demonstrated that immune cell activation and their infiltration in the myocardium can have adverse effects on the heart, contributing to the pathogenesis of heart failure (HF). The purpose of this study is used by bioinformatics analysis to determine the potential diagnostic markers of heart failure and establish an applicable model to predict the association between heart failure and immune cell infiltration. Firstly, gene expression profiles of dilated heart disease GSE3585 and GSE120895 were obtained in Gene Expression Omnibus (GEO) database. This study then selected differentially expressed genes (DEGs) in 54 patients with HF and 13 healthy controls. In this study, biomarkers were identified using Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). Additionally, we evaluated the prognostic discrimination performance by the receiver operating characteristic (ROC) curve. Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used for analyzing immune cell infiltration in HF tissues. Lastly, immune biomarkers were correlated with each other. After 24 DEGs were analyzed using a combinatorial model of LASSO regression and SVM-RFE analysis, four key genes were obtained, namely NSG1, NPPB, PHLDA1, and SERPINE2.The area under the curve (AUC) of these four genes were greater than 0.8. Subsequently, using CIBERPORT, we also found that compared with normal people, the proportion of M1 macrophages and activated mast cells in heart failure tissues decreased. In addition, correlation analysis showed that NPPB, PHLDA1 and SERPINE2 were associated with immune cell infiltration. NSG1, NPPB, PHLDA1 and SERPINE2 were identified as potential biomarkers of heart failure. It reveals the comprehensive role of relevant central genes in immune infiltration, which provides a new research idea for the treatment and early detection in heart failure.

Identification of potential biomarkers and immune infiltration characteristics in severe asthma.

Objectives: We hope to identify key molecules that can be used as markers of asthma severity and investigate their correlation with immune cell infiltration in severe asthma. Methods: An asthma dataset was downloaded from the Gene Expression Omnibus database and then processed by R software to obtain differentially expressed genes (DEGs). First, multiple enrichment platforms were applied to analyze crucial biological processes and pathways and protein–protein interaction networks related to the DEGs. We next combined least absolute shrinkage and selection operator logistic regression and the support vector machine-recursive feature elimination algorithms to screen diagnostic markers of severe asthma. Then, a local cohort consisting of 40 asthmatic subjects (24 with moderate asthma and 16 with severe asthma) was used for biomarker validation. Finally, infiltration of immune cells in asthma bronchoalveolar lavage fluid and their correlation with the screened markers was evaluated by CIBERSORT. Results: A total of 97 DEGs were identified in this study. Most of these genes are enriched in T cell activation and immune response in the asthma biological process. CC-chemokine receptor 7 (CCR7) and natural killer cell protein 7(NKG7) were identified as markers of severe asthma. The highest area under the ROC curve (AUC) was from a new indicator combining CCR7 and NKG7 (AUC = 0.851, adj. p < 0.05). Resting and activated memory CD4 T cells, activated NK cells, and CD8 T cells were found to be significantly higher in the severe asthma group (adj. p < 0.01). CCR7 and NKG7 were significantly correlated with these infiltrated cells that showed differences between the two groups. In addition, CCR7 was found to be significantly positively correlated with eosinophils (r = 0.38, adj. p < 0.05) infiltrated in bronchoalveolar lavage fluid. Conclusion: CCR7 and NKG7 might be used as potential markers for asthma severity, and their expression may be associated with differences in immune cell infiltration in the moderate and severe asthma groups.

Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis.

Aims This study is aimed at investigating the pathogenesis of rheumatoid arthritis (RA) by identifying key biomarkers, associated immune infiltration, and small-molecule compounds using bioinformatic analysis. Methods Six datasets were obtained from the Gene Expression Omnibus database, and the batch effect was adjusted. Functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyse differentially expressed genes (DEGs). Furthermore, candidate small-molecule drugs associated with RA were selected from the Connectivity Map (CMap) database. The least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and multivariate logistic regression analyses were performed on DEGs to screen for RA diagnostic markers. The receiver operating characteristic curve, concordance index, and GiViTi calibration band were the metrics used to assess the diagnostic markers of RA identified in this analysis. The single-sample gene set enrichment analysis was performed to calculate the scores of infiltrating immune cells and evaluate the activities of immune-related pathways. Finally, the correlation between screening markers and RA diagnosis was determined. Results A total of 227 DEGs were identified. Functional enrichment analysis and KEGG revealed that DEGs were enriched by the immune response. CMap analysis identified 11 small-molecule compounds with therapeutic potential for RA. In gene expression, the activities of 13 immune cells and 12 immune-related pathways significantly differed between patients with RA and healthy controls. DPYSL3 and SPP1 had the potential to diagnose RA. SPP1 expression was positively correlated with DPYSL3 in 11 immune cells and 10 immune-related pathways. Conclusion This study comprehensively analysed DEGs and immune infiltration and screened for potential diagnostic markers and small-molecule compounds of RA.

M6A Regulator Expression Segregates Meningiomas Into Biologically Distinct Subtypes.

BackgroundMeningiomas are the most common primary intracranial tumors in adults. According to the 2021 World Health Organization (WHO) classification of central nervous system tumors, approximately 80% of meningiomas are WHO grade 1, that is, histopathologically benign, whereas about 20% are WHO grade 2 or grade 3, showing signs of atypia or malignancy. The dysregulation of N6-methylation (m6A) regulators is associated with disorders of diverse critical biological processes in human cancer. This study aimed to explore whether m6A regulator expression was associated with meningioma molecular subtypes and immune infiltration.MethodsWe evaluated the m6A modification patterns of 160 meningioma samples based on 19 m6A regulators and correlated them with immune infiltration characteristics. Novel molecular subtypes were defined based on prognostic hub gene expression.ResultsTwo meningioma clusters were identified based on the expression of 19 m6A regulators. In cluster 1, 607 differentially expressed genes (DEGs) were upregulated and 519 were downregulated. A total of 1,126 DEGs comprised three gene expression modules characterized by turquoise, blue, and gray. Functional annotation suggested that the turquoise module was involved in Wnt-related and other important cancer-related pathways. We identified 32 hub genes in this module by constructing a protein–protein interaction network. The meningioma samples were divided into two molecular subtypes. EPN1, EXOSC4, H2AX, and MZT2B not only showed significant differences between meningioma molecular subtypes but also had the potential to be the marker genes of specific meningioma subtypes.Conclusionm6A regulator gene expression may be a novel prognostic marker in meningioma.

Identification and Validation of ATF3 Serving as a Potential Biomarker and Correlating With Pharmacotherapy Response and Immune Infiltration Characteristics in Rheumatoid Arthritis.

Background: Although disease-modifying antirheumatic drugs (DMARDs) have significantly improved the prognosis of patients with rheumatoid arthritis (RA), approximately 40% of RA patients have limited response. Therefore, it was essential to explore new biomarkers to improve the therapeutic effects on RA. This study aimed to develop a new biomarker and validate it by an in vitro study. Methods: The RNA-seq and the clinicopathologic data of RA patients were downloaded from Gene Expression Omnibus (GEO) databases. Differentially expressed genes were screened in the GPL96 and GPL570 databases. Then, weighted gene co-expression network analysis (WGCNA) was used to explore the most correlated gene modules to normal and RA synovium in the GPL96 and GPL570 databases. After that, the differentially expressed genes were intersected with the correlated gene modules to find the potential biomarkers. The CIBERSORT tool was applied to investigate the relationship between activated transcription factor 3 (ATF3) expression and the immune cell infiltration, and Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in the high and low ATF3 groups. Furthermore, the relationships between ATF3 expression and clinical parameters were also explored in the GEO database. Finally, the role of ATF3 was verified by in vitro cell experiments. Results: We intersected the differentially expressed genes and the most correlated gene modules in the GPL570 and GPL96 databases and identified that ATF3 is a significant potential biomarker and correlates with some clinical–pharmacological variables. Immune infiltration analysis showed that activated mast cells had a significant infiltration in the high ATF3 group in the two databases. GSEA showed that metabolism-associated pathways belonged to the high ATF3 groups and that inflammation and immunoregulation pathways were enriched in the low ATF3 group. Finally, we validated that ATF3 could promote the proliferation, migration, and invasion of RA fibroblast-like synoviocyte (FLS) and MH7A. Flow cytometry showed that ATF3 expression could decrease the proportion of apoptotic cells and increase the proportion of S and G2/M phase cells. Conclusion: We successfully identified and validated that ATF3 could serve as a novel biomarker in RA, which correlated with pharmacotherapy response and immune cell infiltration.

VEGF Family Gene Expression as Prognostic Biomarkers for Alzheimer's Disease and Primary Liver Cancer.

Background To analyze the expression of vascular endothelial growth factor (VEGF) in hepatocellular carcinoma (HCC) and cognitive impairment, explore the relationship between the expression of VEGF family genes and prognosis of patients with HCC, and evaluate the predictive ability of VEGF in cognitive impairment using computerized methods. Methods VEGF expression in liver cancer tissues and normal tissues was analyzed using bioinformatics methods. The Kaplan-Meier survival analysis method was also used to analyze the relationship between VEGF expression and the prognosis of patients with HCC. Furthermore, immune infiltration assessment and gene set enrichment analysis were performed. Meanwhile, the differential expression of VEGF family genes between patients with Alzheimer's disease (AD) and healthy controls was also checked. Results Based on The Cancer Genome Atlas (TCGA) database, the VEGF family genes (VEFGA, VEGFB, VEGFC, and VEGFD) were highly expressed in cancer tissues and were significantly associated with poor prognosis in HCC. In HCC, the VEGF family genes showed significant heterogeneity in their functional and immune infiltration characteristics. Finally, VEGF family genes were identified as prognostic biomarkers in AD and risk prediction markers in HCC. Conclusions VEGF is highly expressed in patients with HCC and lowly expressed in patients with AD. VEGF has opposite opposing roles in the treatment of tumors and cognitive impairment.

DNA Damage Response and Repair Gene Alterations Increase Tumor Mutational Burden and Promote Poor Prognosis of Advanced Lung Cancer.

DNA damage response and repair (DDR) gene alterations increase tumor-infiltrating lymphocytes, genomic instability, and tumor mutational burden (TMB). Whether DDR-related alterations relate to therapeutic response and prognosis in lung cancer lacking oncogenic drivers remains unknown. Pretherapeutic cancer samples of 122 patients [86 non-small cell lung cancer and 36 small cell lung cancer (SCLC)] harboring no EGFR/ALK alterations were collected. Through whole-exome sequencing, we outlined DDR mutational landscape and determined relationships between DDR gene alterations and TMB or intratumoral heterogeneity. Then, we evaluated the impacts of DDR gene alterations on therapeutic response and prognosis and established a DDR-based model for prognosis prediction. In addition, we investigated somatic interactions of DDR genes and immunomodulatory genes, immune expression patterns, immune microenvironment, and immune infiltration characteristics between DDR-deficient and DDR-proficient samples. Samples from cBioportal datasets were utilized for verification. We found that deleterious DDR gene alterations were closely associated with higher TMB than proficient-types (p < 0.001). DDR mechanisms attach great importance to the determination of patients’ prognosis after chemotherapy, and alterations of base excision repair pathway in adenocarcinoma, nucleotide excision repair in squamous carcinoma, and homologous recombination pathway in SCLC tend to associate with worse progression-free survival to first-line chemotherapy (all p < 0.05). A predictive nomogram model was constructed incorporating DDR-related alterations, clinical stage, and smoking status, with the area under curve values of 0.692–0.789 for 1- and 2-year receiver operating characteristic curves in training and testing cohorts. Furthermore, DDR-altered tumors contained enhanced frequencies of alterations in various genes of human leukocyte antigen (HLA) class I pathway including TAP1 and TAP2 than DDR-proficient samples. DDR-deficient types had lower expressions of STING1 (p = 0.01), CD28 (p = 0.020), HLA-DRB6 (p = 0.014) in adenocarcinoma, lower TNFRSF4 (p = 0.017), and TGFB1 expressions (p = 0.033) in squamous carcinoma, and higher CD40 (p = 0.012) and TNFRSF14 expressions (p = 0.022) in SCLC. DDR alteration enhanced activated mast cells in adenocarcinoma (p = 0.044) and M2 macrophage in squamous carcinoma (p = 0.004) than DDR-proficient types. Collectively, DDR gene alterations in lung cancer without oncogenic drivers are positively associated with high TMB. Specific DDR gene alterations tend to associate with worse progression-free survival to initial chemotherapy.

Predictive Value of the TP53/PIK3CA/ATM Mutation Classifier for Patients With Bladder Cancer Responding to Immune Checkpoint Inhibitor Therapy.

BackgroundOnly a proportion of patients with bladder cancer may benefit from durable response to immune checkpoint inhibitor (ICI) therapy. More precise indicators of response to immunotherapy are warranted. Our study aimed to construct a more precise classifier for predicting the benefit of immune checkpoint inhibitor therapy.MethodsThis multi-cohort study examined the top 20 frequently mutated genes in five cohorts of patients with bladder cancer and developed the TP53/PIK3CA/ATM mutation classifier based on the MSKCC ICI cohort. The classifier was then validated in a validation set consisting of IMvigor210 cohort and Broad/Dana-Farber cohort. The molecular profile and immune infiltration characteristics in each subgroup as defined by this classifier were explored.ResultsAmong all 881 patients with bladder cancer, the mutation frequency of TP53, PIK3CA, and ATM ranked in the top 20 mutated genes. The TP53/PIK3CA/ATM mutation classifier was constructed based on the Memorial Sloan Kettering Cancer Center (MSKCC) ICI cohort and only showed predictive value for patients with bladder cancer who received ICI therapy (median overall survival: low-risk group, not reached; moderate-risk group, 13.0 months; high-risk group, 8.0 months; P<0.0001). Similar results were found in subgroups of MSKCC ICI cohort defined by tumor mutation burden. Multivariate Cox analysis revealed that the risk group defined by the classifier served as an independent prognostic factor for overall survival in patients with bladder cancer. Efficacy of the classifier was verified in a validation set consisting of IMvigor210 cohort and Broad/Dana-Farber cohort. Lower expression of PD-1/PD-L1 and less tumor immune infiltration were observed in the high-risk group than the other two groups of the TCGA cohort and the IMvigor210 cohort.ConclusionOur study constructed a TP53/PIK3CA/ATM mutation classifier to predict the benefit of immune checkpoint inhibitor therapy for patients with bladder cancer. This classifier can potentially complement the tumor mutation burden and guide clinical ICI treatment decisions according to distinct risk levels.

M6A regulator-mediated methylation modification patterns and characteristics of immunity and stemness in low-grade glioma.

m6A RNA methylation is an emerging epigenetic modification, and its potential role in immunity and stemness remains unknown. Based on 17 widely recognized m6A regulators, the m6A modification patterns and corresponding characteristics of immune infiltration and stemness of 1152 low-grade glioma samples were comprehensively analyzed. Machine-learning strategies for constructing m6AScores were trained to quantify the m6A modification patterns of individual samples. Here, we reveal a significant correlation between the multi-omics data of regulators and clinicopathological parameters. We identified two distinct m6A modification patterns (an immune-activated differentiation pattern and an immune-desert dedifferentiation pattern) and four regulatory patterns of m6A methylation on immunity and stemness. We show that the m6AScores can predict the molecular subtype of low-grade glioma, the abundance of immune infiltration, the enrichment of signaling pathways, gene variation and prognosis. The concentration of high immunogenicity and clinical benefits in the low-m6AScore group confirmed the sensitive response to radio-chemotherapy and immunotherapy in patients with high-m6AScore. The results of the pan-cancer analyses illustrate the significant correlation between m6AScore and clinical outcome, the burden of neoepitope, immune infiltration and stemness. The assessment of individual tumor m6A modification patterns will guide us in improving treatment strategies and developing objective diagnostic tools.

Identification of Potential Biomarkers and Immune Infiltration Characteristics in Idiopathic Pulmonary Arterial Hypertension Using Bioinformatics Analysis.

Objectives: Idiopathic pulmonary arterial hypertension (IPAH) is a rare but severe lung disorder, which may lead to heart failure and early mortality. However, little is known about the etiology of IPAH. Thus, the present study aimed to establish the differentially expressed genes (DEGs) between IPAH and normal tissues, which may serve as potential prognostic markers in IPAH. Furthermore, we utilized a versatile computational method, CIBERSORT to identify immune cell infiltration characteristics in IPAH.Materials and Methods: The GSE117261 and GSE48149 datasets were obtained from the Gene Expression Omnibus database. The GSE117261 dataset was adopted to screen DEGs between IPAH and the control groups with the criterion of |log2 fold change| ≥ 1, adjusted P < 0.05, and to further explore their potential biological functions via Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes Pathway analysis, and Gene Set Enrichment Analysis. Moreover, the support vector machine (SVM)-recursive feature elimination and the least absolute shrinkage and selection operator regression model were performed jointly to identify the best potential biomarkers. Then we built a regression model based on these selected variables. The GSE48149 dataset was used as a validation cohort to appraise the diagnostic efficacy of the SVM classifier by receiver operating characteristic (ROC) analysis. Finally, immune infiltration was explored by CIBERSORT in IPAH. We further analyzed the correlation between potential biomarkers and immune cells.Results: In total, 75 DEGs were identified; 40 were downregulated, and 35 genes were upregulated. Functional enrichment analysis found a significantly enrichment in heme binding, inflammation, chemokines, cytokine activity, and abnormal glycometabolism. HBB, RNASE2, S100A9, and IL1R2 were identified as the best potential biomarkers with an area under the ROC curve (AUC) of 1 (95%CI = 0.937–1.000, specificity = 100%, sensitivity = 100%) in the discovery cohort and 1(95%CI = 0.805–1.000, specificity = 100%, sensitivity = 100%) in the validation cohort. Moreover, immune infiltration analysis by CIBERSORT showed a higher level of CD8+ T cells, resting memory CD4+ T cells, gamma delta T cells, M1 macrophages, resting mast cells, as well as a lower level of naïve CD4+ T cells, monocytes, M0 macrophages, activated mast cells, and neutrophils in IPAH compared with the control group. In addition, HBB, RNASE2, S100A9, and IL1R2 were correlated with immune cells.Conclusion: HBB, RNASE2, S100A9, and IL1R2 were identified as potential biomarkers to discriminate IPAH from the control. There was an obvious difference in immune infiltration between patient with IPAH and normal groups.