Cell Type Identification By Estimating Relative Subsets Of RNA Transcripts Research Articles

Integrated analyses reveal the diagnostic and predictive values of COL5A2 and association with immune environment in Crohn's disease.

The pathogenesis of Crohn's disease (CD) involves abnormal immune cell infiltration and dysregulated immune response. Therefore, thorough research on immune cell abnormalities in CD is crucial for improved treatment of this disease. Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data of CD were obtained from the Gene Expression Omnibus (GEO) database. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) networks evaluated the proportion of immune infiltrating cells, constructed co-expression network and identified key genes, respectively. Based on the dataset (GSE134809), 15 cell clusters were defined and labeled as different cell types. Among the 11 modules, the yellow module had the closest relationship with plasma cells (cluster 5). Confirmed using RNA sequencing and IHC assay, the expression of COL5A2 in CD samples was higher than that in control samples. Furthermore, the COL5A2 protein expression remarkably decreased in the group of patients who responded to anti-tumor necrosis factor (TNF) treatments, compared to the non-response group. The comprehensive analyses described here provided novel insight into the landscape of CD-associated immune environment. In addition, COL5A2 were identified as potential diagnostic indicators for CD, as well as promising predictive markers for CD patients.

Identification of red blood cell distribution width as a prognostic factor in acute myeloid leukemia

Many prognostic factors have been identified in acute myeloid leukemia (AML). In this study, we investigated novel prognostic biomarkers using machine learning and Cox regression models in a prospective cohort of 591 patients with AML and tried to identify potential therapeutic targets based on transcriptomic data. We found that elevated red blood cell distribution width (RDW) at diagnosis was an adverse prognostic factor for AML, independent of the 2022 European LeukemiaNet (ELN2022) genetic risk. As a continuous variable, higher RDW was associated with shorter overall survival (OS) (hazard ratio [HR] 1.087, 95% confidence interval [CI] 1.036-1.139, p < 0.001) and event-free survival (EFS) (HR 1.078, 95% CI 1.033-1.124, p < 0.001). Elevated RDW returned to normal after consolidation therapy, which indicated that leukemia cells resulted in abnormal RDW. We further investigated the relationship between RDW and transcriptome in another cohort of 191 patients with AML and public datasets using gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). We found that patients in the high-RDW group were significantly enriched in the positive regulation of erythroid differentiation and inflammation-related pathways. Finally, we identified the inflammation-associated gene IL12RB2 and verified its prognostic relevance with patients with AML in public databases, suggesting it as a potential therapy target.

Unraveling the copper-death connection: Decoding COVID-19‘s immune landscape through advanced bioinformatics and machine learning approaches

ABSTRACT This study aims to analyze Coronavirus Disease 2019 (COVID-19)-associated copper-death genes using the Gene Expression Omnibus (GEO) dataset and machine learning, exploring their immune microenvironment correlation and underlying mechanisms. Utilizing GEO, we analyzed the GSE217948 dataset with control samples. Differential expression analysis identified 16 differentially expressed copper-death genes, and Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) quantified immune cell infiltration. Gene classification yielded two copper-death clusters, with Weighted Gene Co-expression Network Analysis (WGCNA) identifying key module genes. Machine learning models (random forest, Support Vector Machine (SVM), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost)) selected 6 feature genes validated by the GSE213313 dataset. Ferredoxin 1 (FDX1) emerged as the top gene, corroborated by Area Under the Curve (AUC) analysis. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) revealed enriched pathways in T cell receptor, natural killer cytotoxicity, and Peroxisome Proliferator-Activated Receptor (PPAR). We uncovered differentially expressed copper-death genes and immune infiltration differences, notably CD8 T cells and M0 macrophages. Clustering identified modules with potential implications for COVID-19. Machine learning models effectively predicted COVID-19 risk, with FDX1‘s pivotal role validated. FDX1‘s high expression was associated with immune pathways, suggesting its role in COVID-19 pathogenesis. This comprehensive approach elucidated COVID-19-related copper-death genes, their immune context, and risk prediction potential. FDX1‘s connection to immune pathways offers insights into COVID-19 mechanisms and therapy.

Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms

Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.

Immunogenic cell death related mRNAs associated signature to predict immunotherapeutic response in osteosarcoma

BackgroundImmunogenic cell death (ICD) is related to cancer prognosis, which has a synergic effect in combination with chemotherapy or immunotherapy. Yet, the relationship between ICD and osteosarcoma remained unclear. Materials and methodsThree osteosarcoma datasets including therapeutically applicable research to generate effective treatments (TARGET), GSE126209 and GSE21257 datasets were included. A protein-protein interaction network was constructed based on ICD-related genes. We performed unsupervised consensus clustering to classify molecular subtypes (clusters). Survival analysis, Estimation of stromal and immune cells in malignant tumour tissues using expression data (ESTIMATE), Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and differential analysis were employed to characterize the molecular differences between different clusters. Univariate Cox regression analysis was conducted to confirm prognostic genes. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to demonstrate the aberrant expression of ICD-correlated signature genes in osteosarcoma. A series of cellular experiments, including cell counting kit-8 (CCK-8), transwell, and flow cytometry, were used to demonstrate the regulatory role of key genes in the ICD model on the malignant phenotype of osteosarcoma. ResultsThree clusters (cluster1, 2, 3) were constructed and they showed distinct overall survival and immune infiltration. ICD-related genes were highly expressed in cluster1. Moreover, Cluster1 had the best prognosis, high immune score and high expression of human leukocyte antigen (HLA)-related genes. TLR4, LY96, IFNGR1, CD4, and CASP1 were identified as prognostic genes for establishing an ICD-related risk signature. According to the risk signature, two risk groups (high and low risks) showing differential prognosis and response to immunotherapy. The low risks group had a better prognosis but was not sensitive to immunotherapy. Molecular assays verified that prognostic genes were abnormally under-expressed in osteosarcoma. Cellular assays demonstrated that LY96, the most significantly down-regulated gene in osteosarcoma, inhibited the migration, invasion, and proliferation phenotypes of osteosarcoma cells and prolonged the cell cycle. Analysis of oxidative stress related pathway enrichment in tumor microenvironment was conducted by single-sample gene set enrichment analysis (ssGSEA). ConclusionsThis study demonstrated the prognostic significance of ICD-correlated genes in osteosarcoma patients. The five-gene risk signature facilitate prognostic evaluation and prediction of osteosarcoma patients’ response to immunotherapy. The risk signature also offered a possibility for the exploit of novel ICD-related treatment.

Comprehensive analysis of senescence-associated genes in sepsis based on bulk and single-cell sequencing data.

Background: Sepsis is a pathological state resulting from dysregulated immune response in host during severe infection, leading to persistent organ dysfunction and ultimately death. Senescence-associated genes (SAGs) have manifested their potential in controlling the proliferation and dissemination of a variety of diseases. Nevertheless, the correlation between sepsis and SAGs remains obscure and requires further investigation. Methods: Two RNA expression datasets (GSE28750 and GSE57065) specifically related to sepsis were employed to filter hub SAGs, based on which a diagnostic model predictive of the incidence of sepsis was developed. The association between the expression of the SAGs identified and immune-related modules was analyzed employing Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Microenvironment Cell Populations-counter (MCP-counter) analysis. The identified genes in each cohort were clustered by unsupervised agreement clustering analysis and weighted gene correlation network analysis (WGCNA). Results: A diagnostic model for sepsis established based on hub genes (IGFBP7, GMFG, IL10, IL18, ETS2, HGF, CD55, and MMP9) exhibited a strong clinical reliability (AUC = 0.989). Sepsis patients were randomly assigned and classified by WGCNA into two clusters with distinct immune statuses. Analysis on the single-cell RNA sequencing (scRNA-seq) data revealed high scores of SAGs in the natural killer (NK) cells of the sepsis cohort than the healthy cohort. Conclusion: These findings suggested a close association between SAGs and sepsis alterations. The identified hub genes had potential to serve as a viable diagnostic marker for sepsis.

Integration of single-cell RNA-seq and bulk RNA-seq to construct liver hepatocellular carcinoma stem cell signatures to explore their impact on patient prognosis and treatment.

Liver hepatocellular carcinoma (LIHC) is a prevalent form of primary liver cancer. Research has demonstrated the contribution of tumor stem cells in facilitating tumor recurrence, metastasis, and treatment resistance. Despite this, there remains a lack of established cancer stem cells (CSCs)-associated genes signatures for effectively predicting the prognosis and guiding the treatment strategies for patients diagnosed with LIHC. The single-cell RNA sequencing (scRNA-seq) and bulk RNA transcriptome data were obtained based on public datasets and computerized firstly using CytoTRACE package and One Class Linear Regression (OCLR) algorithm to evaluate stemness level, respectively. Then, we explored the association of stemness indicators (CytoTRACE score and stemness index, mRNAsi) with survival outcomes and clinical characteristics by combining clinical information and survival analyses. Subsequently, weighted co-expression network analysis (WGCNA) and Cox were applied to assess mRNAsi-related genes in bulk LIHC data and construct a prognostic model for LIHC patients. Single-sample gene-set enrichment analysis (ssGSEA), Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and Tumor Immune Estimation Resource (TIMER) analysis were employed for immune infiltration assessment. Finally, the potential immunotherapeutic response was predicted by the Tumor Immune Dysfunction and Exclusion (TIDE), and the tumor mutation burden (TMB). Additionally, pRRophetic package was applied to evaluate the sensitivity of high and low-risk groups to common chemotherapeutic drugs. A total of four genes (including STIP1, H2AFZ, BRIX1, and TUBB) associated with stemness score (CytoTRACE score and mRNAsi) were identified and constructed a risk model that could predict prognosis in LIHC patients. It was observed that high stemness cells occurred predominantly in the late stages of LIHC and that poor overall survival in LIHC patients was also associated with high mRNAsi scores. In addition, pathway analysis confirmed the biological uniqueness of the two risk groups. Personalized treatment predictions suggest that patients with a low risk benefited more from immunotherapy, while those with a high risk group may be conducive to chemotherapeutic drugs. The current study developed a novel prognostic risk signature with genes related to CSCs, which provides novel ideas for the diagnosis, prognosis and treatment of LIHC.

Evaluation of immune microenvironment in IgG4-related sialadenitis.

IgG4-related sialadenitis is a systemic autoimmune disease that can lead to fibro-inflammatory conditions. This study aims to investigate the immune microenvironment and potential signaling pathways associated with IgG4-related sialadenitis. Datasets related to IgG4-related sialadenitis were retrieved from the GEO database. Immune cell infiltration analysis was conducted using the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) method. Differentially immune-related expressed genes (DIEG) and immune-related functional enrichment were identified. Moreover, potential treatment targets for IgG4-related sialadenitis were predicted using The Connectivity Map. Only two datasets from GEO were included for further analysis. The CIBERSORT results indicated dominant immune cell populations in IgG4-related sialadenitis, including CD8+ T cells, resting NK cells, monocytes, and naïve B cells in peripheral blood mononuclear cells. Additionally, high abundance of plasma cells was observed in labial salivary gland tissues. Furthermore, a total of 42 DIEGs were identified, with tumor necrosis factor (TNF) signaling via the NF-Kappa B signaling pathway and the p53 signaling pathway being highly enriched. Autophagy inhibitors and DNA topoisomerase inhibitors were strongly associated with potential targets for the treatment of IgG4-related sialadenitis (P<0.05). This study reveals altered immune microenvironment in peripheral blood mononuclear cells and labial salivary gland tissues in IgG4-related sialadenitis. Autophagy inhibitors and DNA topoisomerase inhibitors show promise as potential targets for treating IgG4-related sialadenitis, providing a novel therapeutic strategy for this disease.

Prognosis and immunotherapy significances of a cancer-associated fibroblasts-related gene signature in lung adenocarcinoma.

Cells associated with cancer (CAFs) contribute significantly to the stroma of a tumor microenvironment (TME), which is related to the occurrence, treatment, and prognosis of lung adenocarcinoma (LUAD). Therefore, this study investigated the function of CAF-associated genes in the microenvironment of LUAD. The Cancer Genome Atlas (TCGA) database was used to download RNA-seq data from the TCGA Lung Adenocarcinoma cohort (TCGA-LUAD). The GSE68465 dataset, as the external validation set, was from the Gene Expression Omnibus (GEO) database. Besides, CAF-associated genes were sourced from the GeneCards and Molecular Signatures Database (MsigDB). For LUAD, differentially expressed CAF-related genes were selected from overlapping CAF and LUAD patient and control samples. Next, LASSO and Univariate Cox analyses were used to construct the risk model. Additionally, an analysis of Cox regression was used to construct a nomogram. Next, the immune infiltration in malignant tumour tissues was compared between high- and low-risk groups using Estimation of STromal and Immune cells in MAlignant Tumours (ESTIMATE) tissues and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT). The sensitivity differences of immunotherapy between the two risk groups were estimated by Tumor Immune Dysfunction and Exclusion (TIDE), and compared by rank-sum test. Finally, the model genes were detected by fluorescent real-time quantitative polymerase chain reaction (qRT-PCR). A total of 57 DE-CAFGs were acquired, and 9 of them (SHCBP1, CCNA2, AKAP12, CCNB1, GALNT3, SCGB1A1, CPS1, CDC6, and CXCL13) were selected as prognostic biomarkers. The Cox independent prognosis revealed the RiskScore and Stage were the two LUAD independent prognosis factors Moreover, 11 types of immune cells (memory B cells, resting natural killer cells (NK cells), Eosinophils, Macrophages M0, CD4 memory resting T cells, CD4 memory activated T cells, resting Mast cells, naive B cells, T cells regulatory (Tregs), neutrophils, and plasma cell), and 18 human leukocyte antigen (HLA) genes were different with the two risk groups. Lastly, the TIDE analysis showed differences between the two risk groups for TIDE, T cell dysfunction, and T cell exclusion, PD-L1 treatment scores. Lastly, Both LUAD and normal samples expressed the 9 model genes differently. A CAF-related prognostic model was constructed, which may have potential immunotherapy guiding significance for LUAD patients.

Identifying CD1c as a potential biomarker by the comprehensive exploration of tumor mutational burden and immune infiltration in diffuse large B cell lymphoma.

Tumor mutational burden (TMB) is a valuable prognostic biomarker. This study explored the predictive value of TMB and the potential association between TMB and immune infiltration in diffuse large B-cell lymphoma (DLBCL). We downloaded the gene expression profile, somatic mutation, and clinical data of DLBCL patients from The Cancer Genome Atlas (TCGA) database. We classified the samples into high-and low-TMB groups to identify differentially expressed genes (DEGs). Functional enrichment analyses were performed to determine the biological functions of the DEGs. We utilized the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm to estimate the abundance of 22 immune cells, and the significant difference was determined by the Wilcoxon rank-sum test between the high- and low-TMB group. Hub gene had been screened as the prognostic TMB-related immune biomarker by the combination of the Immunology Database and Analysis Portal (ImmPort) database and the univariate Cox analysis from the Gene Expression Omnibus (GEO) database including six DLBCL datasets. Various database applications such as Tumor Immune Estimation Resource (TIMER), CellMiner, konckTF, and Genotype-Tissue Expression (GTEx) verified the functions of the target gene. Wet assay confirmed the target gene expression at RNA and protein levels in DLBCL tissue and cell samples. Single nucleotide polymorphism (SNP) occurred more frequently than insertion and deletion, and C > T was the most common single nucleotide variant (SNV) in DLBCL. Survival analysis showed that the high-TMB group conferred poor survival outcomes. A total of 62 DEGs were obtained, and 13 TMB-related immune genes were identified. Univariate Cox analysis results illustrated that CD1c mutation was associated with lower TMB and manifested a satisfactory clinical prognosis by analysis of large samples from the GEO database. In addition, infiltration levels of immune cells in the high-TMB group were lower. Using the TIMER database, we systematically analyzed that the expression of CD1c was positively correlated with B cells, neutrophils, and dendritic cells and negatively correlated with CD8+ T cells, CD4+ T cells, and macrophages. Drug sensitivity showed a significant positive correlation between CD1c expression level and clinical drug sensitivity from the CellMiner database. CREB1, AHR, and TOX were used to comprehensively explore the regulation of CD1c-related transcription factors and signaling pathways by the KnockTF database. We searched the GETx database to compare the mRNA expression levels of CD1c between DLBCL and normal tissues, and the results suggested a significant difference between them. Moreover, wet experiments were conducted to verify the high expression of CD1c in DLBCL at the RNA and protein levels. Higher TMB correlated with poor survival outcomes and inhibited the immune infiltrates in DLBCL. Our results suggest that CD1c is a TMB-related prognostic biomarker.

Immunological role and clinical prognostic significance of P2RY6 in lung adenocarcinoma: a multi-omics studies and single-cell sequencing analysis

BackgroundThere is increasing evidence that recombinant human P2Y purinoceptor 6 (P2RY6) may be involved in inflammatory responses. However, the role of P2RY6 in lung adenocarcinoma (LUAD) remains unknown.MethodsWe used transcriptomic, genomic, single-cell transcriptomic, and methylation sequencing data from The Cancer Genome Atlas database to analyze the aberrant status and prognostic value of P2RY6 in a variety of tumors. The LUAD single-cell sequencing dataset was used to explore the effect of P2RY6 on the tumor microenvironment. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was used to quantify immune cells in the tumor microenvironment. We also analyzed the correlation of P2RY6 with immune checkpoints and immune regulation-related genes. The correlation of between tumor mutation burden (TMB), microsatellite instability (MSI), and P2RY6 expression was also analyzed simultaneously. Tissue microarray and immunohistochemistry were employed to assess the expression of P2RY6 in internal tumor samples.ResultsOur findings indicate that P2RY6 exhibits significantly higher expression levels in various cancer tissues, particularly in LUAD. High expression of P2RY6 was closely associated with a poor prognosis for patients, and it plays a role in regulating immune-related pathways, such as cytokine-cytokine receptor interaction. Notably, P2RY6 expression is closely linked to the abundance of CD8 + T cells. Furthermore, we have developed a P2RY6-related inflammation prediction model that demonstrates promising results in predicting the prognosis of LUAD patients, with an AUC (area under the curve) value of 0.83. This performance is significantly better than the traditional TNM staging system. Through single-cell transcriptome sequencing analysis, we observed that high P2RY6 expression is associated with increased intercellular communication. Additionally, pathway enrichment analysis revealed that P2RY6 influences antigen presentation and processing pathways within the LUAD microenvironment.ConclusionsThis study suggests that P2RY6 would be a new target for immunotherapy in LUAD.

Prognostic biomarker SYK and its correlation with immune infiltrates in glioma.

The tumor microenvironment (TME) provides excellent conditions for the development of glioma. The present study sought to identify the prognostic factors of glioma that could be used to improve the prognosis of patients with this disease. In the present study, Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data calculations were employed to estimate the ratio of tumor-infiltrating immune cells and the quantity of immune and stromal components in 698 glioma cases from the Cancer Genome Atlas database. In addition, certain differentially expressed genes were studied by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses and single genes associated with prognosis were identified by protein-protein interaction (PPI) network and Cox combined analysis. The immune and stromal scores of the TME were significantly associated with glioma patient survival. By using the PPI network and Cox regression analyses, spleen tyrosine kinase (SYK) was eventually identified as the best prognostic factor for patients with glioma. In addition, Gene Set Enrichment Analysis and CIBERSORT analyses were employed. The former indicated that the high-expression SYK group genes were mainly enriched in immune-related activities. The latter revealed that SYK expression was positively associated with T cell cluster of differentiation 4 memory resting and monocytes. The aforementioned experimental analyses provided the theoretical basis for the biological prediction of SYK. The data indicated that SYK contributed to immune predictors in patients with glioma by facilitating the shift of the TME from immune dominance to metabolic activity. Finally, reverse transcription-quantitative PCR and western blotting were used to verify the single gene expression in glioma cells. This may provide prognostic value for the treatment of glioma.

Diagnostic and treatment value of XPNPEP3 in acute myocardial infarction.

At present, acute myocardial infarction (AMI) is a serious cardiovascular disease with high morbidity and mortality. Discovering biomarkers of AMI is important for clinical diagnosis and needs. Therefore, this study aimed to elucidate the role of XPNPEP3 as a potential biomarker for AMI. Expression profiling data were downloaded for AMI patients and healthy patients in the GSE24548 and GSE24519 datasets, respectively. The limma package in R was conducted to determine differentially expressed microRNA (DEmiRNA)/messenger RNA (mRNA) [differentially expressed genes (DEGs)]. TargetScan and Cytoscape were used to build regulatory network of miRNA-mRNA. The Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) were applied to determine immune cell score. The gene set variation analysis (GSVA) package was used to calculate pathway score. Key drugs were determined by protein-protein interaction (PPI) and molecular docking. Totals of 36 DEmiRNAs and 63 DEGs were determined in the GSE24584 dataset and GSE24519 dataset, respectively, and then we constructed a miRNA-mRNA network including 31 DEmiRNAs and 47 DEGs. The correlation analysis between immune cells and 47 DEGs identified that XPNPEP3 was most associated with AMI. Furthermore, XPNPEP3 was negatively correlated with inflammatory response score. A diagnosis model based on XPNPEP3 expression showed an area under the curve (AUC) of 93.38%, and 159 genes were highly correlated with XPNPEP3. Molecular docking analysis showed that DB06909 had the lowest docking score with XPNPEP3, revealing it to be a potential XPNPEP3 inhibitor. This work discovered that XPNPEP3 is correlated with the development of AMI. These findings may provide theoretical basis for the diagnosis and treatment of AMI.

Imbalance of Innate and Adaptive Immunity in Esophageal Achalasia.

Previous studies reveal that immune-mediated neuroinflammation plays a key role in the etiology of esophageal achalasia. However, the understanding of leucocyte phenotype and proportion is limited. This study aim to evaluate the phenotypes of leukocytes and peripheral blood mononuclear cells transcriptomes in esophageal achalasia. We performed high-dimensional flow cytometry to identified subsets of peripheral leukocytes, and further validated in lower esophageal sphincter histologically. RNA sequencing was applied to investigate the transcriptional changes in peripheral blood mononuclear cells of patients with achalasia. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used for estimating the immune cell types. A differential gene expression analysis was performed and the differential expressed genes were subjected to gene ontology, Kyoto Encyclopedia of Genes and Genomes network, protein-protein interaction network construction. An imbalance between innate and adaptive immune cells occurred in achalasia. Specifically, neutrophils and CD8+ T cells increased both in peripheral blood and lower esophageal sphincter in achalasia. Eosinophils decreased in peripheral blood but massively infiltrated in lower esophageal sphincter. CIBERSORT analysis of peripheral blood mononuclear cells RNA sequencing displayed an increased prevalence of CD8+ T cells. 170 dysregulated genes were identified in achalasia, which were enriched in immune cells migration, immune response, etc. Proton pump inhibitor analysis revealed the intersections and gained 7 hub genes in achalasia, which were IL-6, Toll-like receptor 2, IL-1β, tumor necrosis factor, complement C3, and complement C1q A chain. Patients with achalasia exhibited an imbalance of systematic innate and adaptive immunity, which may play an important role in the development of achalasia.

Identification of cancer stemness and M2 macrophage-associated biomarkers in lung adenocarcinoma

ObjectiveCancer stemness and M2 macrophages are intimately linked to the prognosis of lung adenocarcinoma (LUAD). For this reason, this investigation sought to identify the key genes relevant to cancer stemness and M2 macrophages, explore the relationship between these genes and clinical characteristics, and determine the potential mechanism. MethodsLUAD transcriptomic data was analyzed from The Cancer Genome Atlas (TCGA) as well as the Gene Expression Omnibus databases. Differential expression analysis was performed to discern abnormally expressed genes between LUAD and control samples in TCGA cohort. The Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was applied to determine varyingly infiltrated immune cells in LUAD compared with the control samples in TCGA cohort. Weighted correlation network analysis (WGCNA) was performed to identify genes associated with mRNA expression-based stemness index (mRNAsi) and M2 macrophages. Least absolute shrinkage and selection operator (LASSO), RandomForest (RF) and support vector machine-recursive feature elimination (SVM-RFE) machine learning methods were conducted to detect gene signatures. Global survival evaluation (Kaplan-Meier curve) was applied to investigate the relationship between gene signatures and the survival time of LUAD patients. Receiver operating characteristic (ROC) curves were produced to define biomarkers relevant to diagnosis. Gene Set Enrichment Analysis (GSEA) was performed to probe the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to diagnostic biomarkers. The public single-cell dataset of LUAD (GSE123902) was used to investigate the expression differences of diagnostic biomarkers in various cell types in LUAD. Real-time quantitative PCR (qRT-PCR) was performed to confirm key genes in lung adenocarcinoma cells. ResultsA total of 5,410 differentialy expressed genes (DEGs) as well as 15 differentially infiltrated immune cells were identified between LUAD and control sepcimens in TCGA cohort. Thirty-seven DEGs were associated with both M2 macrophages and mRNAsi according to the WGCNA analysis. Sixteen common gene signatures were obtained using three diverse algorithms. CBFA2T3, DENND3 and FCAMR were correlated to overall and disease-free survival of LUAD patients. ROC curves revealed that CBFA2T3 and DENND3 expression accurately classified LUAD and control samples. The results of single cell related analysis showed that two diagnostic biomarkers expressions were differed between the different tissue sources in M2-like macrophages. QRT-PCR demonstrated the mRNA expressions of CBFA2T3 and DENND3 were upregulated in lung adenocarcinoma cells A549 and H2122. ConclusionOur study identified CBFA2T3 and DENND3 as key genes associated with mRNAsi and M2 macrophages in LUAD and investigated the potential molecular mechanisms underlying this relationship.

Identification of HPV16 positive cervical cancer subsets characterized by divergent immune and oncogenic phenotypes with potential implications for immunotherapy.

Cervical cancers (CaCx), like many other cancer types, portray high molecular heterogeneity that affects response to therapy, including immunotherapy. In India and other developing countries, CaCx mortality rates are very high because women report to the clinics with advanced cancers in absence of organized screening programs. This calls for implementation of newer therapeutic regimens for CaCx, like immunotherapy, which is again not used commonly in such countries. Therefore, we focused on dissecting tumour immune heterogeneity, if any, identify immune gene-based biomarkers of heterogeneity and subsets of such cancers with the potential for immunotherapy. We also attempted to characterize the cancer-associated phenotypes of such subsets, including viral load, to decipher the relationship of tumour immunogenicity with oncogenicity. Employing RNA-seq analysis of 44 HPV16 positive CaCx patients, immune subtypes were identified by unsupervised hierarchical clustering of global immune-gene expression profiles. Proportions of tumor infiltrating immune cells in the tumor milieu were estimated, employing Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), using gene expression data from RNA-seq. The oncogenic phenotypes of the immune subtypes of CaCx were deciphered through differential gene expression (DEGs) and pathway enrichment analysis. Viral load was estimated through TaqMan-based qRT-PCR analysis. Analysis revealed the presence of two immune subtypes of CaCx, A (26/44; 59.09%) and B (18/44; 40.90%). Compared to Subtype-A, Subtype-B portrayed overexpression of immune genes and high infiltration of immune cells, specifically CD8+ T cells (p < 0.0001). Besides, a significant correlation between PD-1 and PD-L1 co-expression among Subtype-B, as opposed to Subtype-A, confirmed the interactive roles of these immune checkpoint molecules in Subtype B. Stepwise discriminant analysis pin-pointed ten immune-genes that could classify 100% of the patients significantly (p < 0.0001) into the two immune subtypes and serve as potential biomarkers of CaCx immunity. Differential gene expression analysis between the subtypes unveiled that Subtype-B was more biologically aggressive than Subtype-A, reflecting loss of structural integrity and promotion of cancer progression. The viral load was significantly lower in Subtype-B (average viral load = 10.74/100 ng of genomic DNA) compared to Subtype-A (average viral load = 14.29/100 ng of genomic DNA). Thus viral load and the ten-gene panel underscore their association with immunogenicity and oncogenicity. Our study provides strong evidence that only a subset, about 41% of HPV16 positive CaCx patients in India, portray immune enrichment of the tumor milieu coupled with aggressive phenotypes. Such subtypes are therefore likely to benefit through checkpoint molecule-based or tumor infiltrating lymphocyte-based immunotherapy, which could be a leap forward in tackling aggressive forms of such CaCx in India and other developing countries.

The prognostic significance of KLRB1 and its further association with immune cells in breast cancer.

Killer cell lectin-like receptor B1 (KLRB1) is an important member of the natural killer cell gene family. This study explored the potential value of KLRB1 as a breast cancer (BC) biomarker and its close association with the tumor immune microenvironment during the development of BC. We examined the differential expression of KLRB1 in pan-cancer. Clinical and RNA-Seq data from BC samples were evaluated in The Cancer Genome Atlas (TCGA) and validated in Gene Expression Omnibus (GEO) datasets and by immunohistochemistry (IHC) staining. The relationship between KLRB1 and clinical parameters was explored through Chi-square tests. The diagnostic value of KLRB1 was evaluated using a receiver operating characteristic (ROC) curve. Survival analysis was tested by Kaplan-Meier curves to demonstrate the relationship between KLRB1 and survival. Univariable and multivariate cox regression analyses were carried out as well. The analysis of immune infiltration level and gene set enrichment analysis (GSEA) were conducted to examine KLRB1's mechanism during the progression of BC. We used the Tumor Immune Estimation Resource (TIMER), the Cancer Single-cell Expression Map (CancerSCEM) database, the Tumor Immune Single-cell Hub (TISCH) database, and the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) method to explore KLRB1's association with immune infiltration level and different quantitative distribution of immune cells. The relevant signaling pathways in BC associated with KLRB1 were identified using GSEA. The expression of KLRB1 was downregulated across the majority of cancers including BC. The lower KLRB1 expression group exhibited shorter relapse free survival (RFS) and overall survival (OS). IHC staining showed that KLRB1 staining was weaker in breast tumor tissues than in paratumors. Additionally, GSEA identified several pathway items distinctly enriched in BC. KLRB1 expression level was also positively related to the infiltrating number of immune cells in BC. Moreover, the CancerSCEM and TISCH databases as well as the CIBERSORT method demonstrated the close relationship between KLRB1 and immune cells, particularly macrophages. Low KLRB1 expression was considered an independent prognostic biomarker and played an important role in the tumor immune microenvironment of BC patients.

Comprehensive Immune Profiling Unveils a Subset of Leiomyosarcoma with "Hot" Tumor Immune Microenvironment.

Purpose: To investigate the immune biomarker in Leiomyosarcoma (LMS), which is rare and recognized as an immune cold cancer showing a poor response rate (<10%) to immune checkpoint inhibitors (ICIs). However, durable response and clinical benefit to ICIs has been observed in a few cases of LMS, including, but not only, LMS with tertiary lymphoid structure (TLS) structures. Patients and methods: We used comprehensive transcriptomic profiling and a deconvolution method extracted from RNA-sequencing gene expression data in two independent LMS cohorts, the International Cancer Genome Consortium (ICGC, N = 146) and The Cancer Genome Atlas (TCGA, N = 75), to explore tumor immune microenvironment (TIME) in LMS. Results: Unsupervised clustering analysis using the previously validated two methods, 90-gene signature and Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), identified immune hot (I-H) and immune high (I-Hi) LMS, respectively, in the ICGC cohort. Similarly, immune active groups (T-H, T-Hi) were identified in the TCGA cohort using these two methods. These immune active ("hot") clusters were significantly associated, but not completely overlapping, with several validated immune signatures such as sarcoma immune class (SIC) classification and TLS score, T cell inflamed signature (TIS) score, immune infiltration score (IIS), and macrophage score (M1/M2), with more patients identified by our clustering as potentially immune hot. Conclusions: Comprehensive immune profiling revealed a subset of LMS with a distinct active ("hot") TIME, consistently associated with several validated immune signatures in other cancers. This suggests that the methodologies that we used in this study warrant further validation and development, which can potentially help refine our current immune biomarkers to select the right LMS patients for ICIs in clinical trials.

Analysis of the role of glucose metabolism-related genes in dilated cardiomyopathy based on bioinformatics.

Dilated cardiomyopathy (DCM) is a prevalent condition with diverse etiologies, including viral infection, autoimmune response, and genetic factors. Despite the crucial role of energy metabolism in cardiac function, therapeutic targets for key genes in DCM's energy metabolism remain scarce. Our study employed the GSE79962 and GSE42955 datasets from the Gene Expression Omnibus (GEO) database for myocardial tissue sample collection and target gene identification via differential gene expression screening. Using various R packages, GSEA software, and the STRING database, we conducted data analysis, gene set enrichment, and protein-protein interaction predictions. The least absolute shrinkage and selection operator (LASSO) and Support Vector Machine (SVM) algorithms aided in feature gene selection, while the predictive model's efficiency was evaluated via the receiver operating characteristic (ROC) curve analysis. We used the non-negative matrix factorization (NMF) method for molecular typing and the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm for predicting immune cell infiltration. The DLAT and LDHA genes may regulate the immune microenvironment of DCM by influencing activated dendritic cells, activated mast cells, and M0 macrophages, respectively. The BPGM, DLAT, PGM2, ADH1A, ADH1C, LDHA, and PFKM genes may regulate m6A methylation in DCM by affecting the ZC3H13, ALKBH5, RBMX, HNRNPC, METTL3, and YTHDC1 genes. Further regulatory mechanism analysis suggested that PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C could be involved in the development of cardiomyopathy by regulating the Toll-like receptor signaling pathway. PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C may serve as potential targets for guiding the diagnosis, treatment, and follow-up of DCM.

A bioinformatics analysis and an experimental validation of the hypoxia-related prognostic model.

Hypoxia plays an important role in the development of pancreatic cancer (PCA). However, there is few research on the application of hypoxia molecules in predicting the prognosis of PCA. We aimed to establish a prognostic model based on hypoxia-related genes (HRGs) for PCA to discover new biomarkers, and to reveal the potential of this prognostic model for evaluating the tumor microenvironment (TME). Univariate Cox regression analysis was used to identify HRGs associated with overall survival (OS) of PCA samples. A hypoxia-related prognostic model was established based on least absolute shrinkage and selection operator (LASSO) regression analysis in The Cancer Genome Atlas (TCGA) cohort. The model was validated in the Gene Expression Omnibus (GEO) datasets. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to estimate the infiltration of immune cells. A wound healing assay and transwell invasion assay were used to explore the biological functions of target genes in PCA. A total of 18 HRGs were differentially expressed between the tumor and normal pancreatic tissue, 4 (BHLHE40, ENO1, SDC4, and TGM2) of which were selected to construct a prognostic model. According to this model, patients in the high-risk group had a less favorable prognosis. Furthermore, the proportion of M0 macrophages was significantly higher in high-risk tissue-type patients, whereas naïve B cells, plasma cells, CD8+ T cells, and activated CD4+ memory T cells were significantly lower. The expression of BHLHE40 in PCA cells was significantly up-regulated under hypoxic conditions. Moreover, BHLHE40 was shown to regulate the transcription and expression of the downstream target gene TLR3. The wound healing assay and transwell invasion assay indicated that BHLHE40 mediated PCA cell migration and invasion by targeting the downstream gene TLR3. The hypoxia-related prognostic model established by the expression pattern of 4 HRGs can be used to predict the prognosis and assess the TME of PCA patients. Mechanically, activation of the BHLHE40/TLR3 axis is responsible for the promoted invasion and migration of PCA cells in a hypoxic environment.