Gene Set Variation Analysis Scores Research Articles

Comprehensive analysis of macrophage-associated inflammatory genes in AMI based on bulk combined with single-cell sequencing data.

Previous studies have highlighted the crucial role of macrophages in the post-acute myocardial infarction (AMI) inflammatory response. This study specifically focused on investigating macrophage-related targets involved in the inflammatory response after AMI. Bioinformatics methods were applied for identifying differentially expressed genes (DEGs) in datasets GSE163465, GSE236374, and GSE183272 obtained from the Gene Expression Omnibus (GEO) database. Communication analysis was conducted to analyze macrophages in AMI. Subsequent analyses encompassed functional enrichment analysis of Co-DEGs using Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG). Gene set variation analysis (GSVA) and immune infiltration analysis were carried out for screening key genes. Validation of the bioinformatics analysis results involved original and GSE114695 datasets, supported by quantitative real time polymerase chain reaction (qRT-PCR). Animal experiments confirmed the upregulation of Saa3, Acp5, and Fcgr4 genes in AMI mouse myocardial tissues. A total of 80 and 1,907 DEGs were respectively identified by analyzing scRNA-seq and bulk RNA-seq data. The overlapping Co-DEGs were found to be closely associated with inflammation-associated pathways, specifically the PI3K-Akt-mTOR pathway. Screening based on GSVA scores and macrophage-associated scores highlighted four key genes (Saa3, Ms4a4c, Acp5, and Fcgr4). Immunoinfiltration analysis revealed their close association with macrophages. Dataset validation corroborated these findings. Experimental validation focused on Saa3, Ms4a4c, Acp5, and Fcgr4, demonstrating the upregulation of their expression in cardiac macrophages in the AMI group, consistent with previous reports. This study provides new perspectives on AMI treatment. In addition, Saa3, Acp5, and Fcgr4 exhibit potential as biomarkers for improving cardiac repair and slowing down the development of heart failure after AMI.

Nasal epithelial gene expression identifies relevant asthma endotypes in the ATLANTIS study

IntroductionAsthma is an inflammatory airways disease encompassing multiple phenotypes and endotypes. Several studies suggested gene expression in nasal epithelium to serve as a proxy for bronchial epithelium, being a non-invasive...

The signature genes of cuproptosis associates with tumor immune microenvironment and predicts prognosis in kidney renal clear cell carcinoma.

Cuproptosis is a new form of cell death, which has great potential to be developed in tumors treatment. Our study aimed to explore the predictive value of cuproptosis-related genes (CRGs) in various cancers, with a focus on kidney renal clear cell carcinoma (KIRC). A total of 9502 pan-cancer patients from TCGA cohort were enrolled. The relationships between CRGs and overall survival (OS) or disease-free survival (DFS) were analyzed. Gene Set Variation Analysis (GSVA) enrichment analysis was performed to explore the expression differences of CRGs. Multivariate Cox regression analysis was used to evaluate the association between GSVA scores and patient survival. KEGG and GO analyses were employed to identify the biological functions and pathways. The expression and prognostic characteristics of FDX1 were examined to evaluate the correlation between FDX1 and KIRC. Cell experiments were conducted to verify whether FDX1 was involved in cuproptosis of Caki-1 cells induced by Elesclomol. Positive cuproptosis signature genes(pos.cu.sig) exhibited the correlation with prognosis in KIRC, and all of these genes showed differential expression between KIRC and normal tissues. The GSVA score of pos.cu.sig was associated with excellent survival (HR=0.61, P<0.05), which can also serve as an independent prognostic factor for KIRC. There was a close correlation between pos.cu.sig and the tumor immune microenvironment in KIRC by KEGG and GO analysis. FDX1 expression was correlated with KIRC grade and positively associated with prognosis in KIRC patients. Compared with the control group, cell proliferation and migration were significantly inhibited, FDX1 expression was up-regulated, and Fe-S cluster protein content was decreased of Caki-1 cells after Elesclomol treatment. This study provides compelling evidence that cuproptosis is closely linked to the prognosis of KIRC. FDX1 holds promise as a viable biomarker and therapeutic target for assessing the effectiveness of tumor immunotherapy in KIRC.

Features of HPV+ OPSCC predictive of recurrence identified by transcriptional profiling of a case-control cohort.

6055 Background: De-escalating therapy for HPV+ oropharyngeal cancers (OPSCCs) is hampered by poor ability to predict recurrence, and limited insight into the biologic traits predisposing to recurrence impedes personalizing therapy. We aimed to (1) define biologic features underlying therapy resistance by transcriptional profiling and (2) evaluate the features for prognostic utility. Methods: A single institution cohort of 851 HPV+ OPSCC patients undergoing transoral robotic surgery during 2007-2020 was used to identify 50 cases that recurred locoregionally in the adjuvant RT field (n=14) and/or at distant sites (n=43). As controls, we used 49 recurrence-free patients with long-term follow up and similar stage, smoking history, and adjuvant therapy. RNAseq of pretreatment tumors was used to compare individual gene expression and Hallmark/Kegg pathway activity between cases and controls using unpaired t-test (p&lt;.05) and Gene Set Enrichment Analysis (p-adj&lt;.05), respectively. Activity of the significant pathways was quantified in individual tumors using Gene Set Variation Analysis (GSVA), and a regression-based composite of GSVA scores was developed to distinguish cases from controls by ROC analysis. The composite score was used to test for stratification of recurrence free survival (RFS) in external cohorts using Youden Index and logrank test. Results: The21 Hallmark/Kegg pathways downregulated in cases indicated suppression of anti-tumor immunity, and the 20 upregulated ones revealed increased biosynthetic function and tumor cell proliferation. The 1472 upregulated mRNAs contained several components of the ATR-chk1 DNA repair pathway and trans-lesion synthesis polymerases, suggesting that mitigation of DNA replication stress enhanced tumor growth. The 958 downregulated mRNAs suggested reductions in the cytoplasmic dsDNA sensing and downstream NF-kB signals that lead to replication stress-driven anti-tumor immunity. Two GSVA scores were created to quantify the key tumor-intrinsic and immune suppressive features separately in each tumor. The scores correlated with each other (R=0.6, p&lt;.001), and a regression-based combination of them distinguished cases from controls (AUC=.76, p&lt;.001). This composite score also stratified RFS in three external cohorts: TCGA (n=52, p&lt;0.001) and two single institution cohorts containing both surgical and nonsurgical cases (n=46, p=.002; n=81, p&lt;0.001). Conclusions: HPV+ OPSCCs failing primary surgical therapy show evidence of reduced replication stress mediating tumor progression and low anti-tumor immunity. These features appear generalizable to heterogeneously treated external cohorts, where they predicted recurrence. Our results provide a new basis for creating transcriptomic predictors of treatment response and suggest targetable molecular mechanisms to overcome therapy resistance.

Multi-Omics Pan-Cancer Analysis of Procollagen N-Propeptidase Gene Family of ADAMTS as Novel Biomarkers to Associate with Prognosis, Tumor Immune Microenvironment, Signaling Pathways, and Drug Sensitivities.

The extracellular matrix (ECM) modeling induced by the metalloproteinases is a vital characteristic for tumor progression. Previous studies mainly focus on the functions of two subgroups of metalloproteinases: matrix metalloproteinases (MMPs) and a disintegrin and metalloproteases (ADAMs) in tumors. The roles of another important group: the ADAMs with thrombospondin motifs (ADAMTS) remain unclear. This study aimed to perform a pan-cancer analysis of procollagen N-propeptidase subgroup of ADAMTS (PNPSA). We systematically analyzed expression landscape, genomic variations, prognostic value, and cell expression clusters of PNPSA in pan-cancer based on the multiple integrated open databases. Besides, we also analyzed the impacts of expressions and genomic variations of PNPSA members on tumor immune microenvironment (TIME) and immune-related molecules in pan-cancer based on the immune-related open databases. The Gene Set Variation Analysis (GSVA) was performed to evaluate the associations of the whole PNPSA with prognosis, tumor indicators, TIME, and drug sensitivities. Meanwhile, the Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed to reveal related signaling pathways. Finally, immunohistochemical staining was used to validate the differential analysis results. We found a dual prognostic role of PNPSA members in pan-cancer and they were significantly correlated with TIME and immune-related molecules. Interestingly, the copy number variations (CNVs) of all PNPSA members were revealed to be negatively correlated with NK cell infiltration in most cancers. Single-cell sequencing analysis reveals expressions of PNPSA gene family members on some specific tumor and immune cells in addition to the fibroblasts. The GSVA score was found to have some predictive value for survival status in Brain Lower Grade Glioma (LGG), Mesothelioma (MESO), and Uveal Melanoma (UVM) and to be significantly correlated with tumorigenesis-related pathways such as PI3K-Akt, AGE-RAGE, etc. The GSVA score also shows some predictive value for chemotherapy and immunotherapy efficacy in some tumors. PNPSA was correlated with tumor development and might be potential tumor biomarker and therapeutic target.

Pan-cancer and single-cell analysis of actin cytoskeleton genes related to disulfidptosis.

Disulfidptosis was recently reported to be caused by abnormal disulfide accumulation in cells with high SLC7A11 levels subjected to glucose starvation, suggesting that targeting disulfidptosis was a potential strategy for cancer treatment. We analyzed the relationships between gene expression and mutations and prognoses of patients. In addition, the correlation between gene expression and immune cell infiltration was explored. The potential regulatory mechanisms of these genes were assessed by investigating their related signaling pathways involved in cancer, their expression patterns, and their cellular localization. Most cancer types showed a negative correlation between the gene-set variation analysis (GSVA) scores and infiltration of B cells and neutrophils, and a positive correlation between GSVA scores and infiltration of natural killer T and induced regulatory T cells. Single-cell analysis revealed that ACTB, DSTN, and MYL6 were highly expressed in different bladder urothelial carcinoma subtypes, but MYH10 showed a low expression. Immunofluorescence staining showed that actin cytoskeleton proteins were mainly localized in the actin filaments and plasma membrane. Notably, IQGAP1 was localized in the cell junctions. In conclusion, this study provided an overview of disulfidptosis-related actin cytoskeleton genes in pan-cancer. These genes were associated with the survival of patients and might be involved in cancer-related pathways.

Machine learning-driven immunophenotypic stratification of mixed connective tissue disease corroborating the clinical heterogeneity.

To stratify patients with mixed connective tissue disease (MCTD) based on their immunophenotype. We analyzed the immunophenotype and transcriptome of 24 immune cell subsets from patients with MCTD, systemic lupus erythematosus (SLE), idiopathic inflammatory myopathy (IIM), and systemic sclerosis (SSc) from our functional genome database, ImmuNexUT (https://www.immunexut.org/). MCTD patients were stratified by employing machine learning models including Random Forest, trained by immunophenotyping data from SLE, IIM, and SSc patients. Transcriptomes were analyzed with gene set variation analysis (GSVA) and clinical features of MCTD subgroups were compared. This study included 215 patients, including 22 patients with MCTD. Machine learning models, constructed to classify SLE, IIM, and SSc patients based on immunophenotyping, were applied to MCTD patients, resulting in 16 classified as SLE-immunophenotype and 6 as non-SLE-immunophenotype. Among MCTD patients, patients with the SLE-immunophenotype had higher proportions of Th1 cells [2.85% (interquartile range (IQR) 1.54-3.91) vs 1.33% (IQR 0.99-1.74) p= 0.027] and plasmablasts [6.35% (IQR 4.17-17.49) vs 2.00% (IQR 1.20-2.80) p= 0.010]. Notably, the number of SLE-related symptoms was higher in patients with the SLE-immunophenotype [2.0 (IQR 1.0-2.0) vs 1.0 (IQR1.0-1.0) p= 0.038]. Moreover, GSVA scores of interferon-α and -γ responses were significantly higher in patients with the SLE-immunophenotype in central memory CD8+ T cells, while hedgehog signalling was higher in non-SLE-immunophenotype patients in 5 cell subsets. This study describes the stratification of MCTD patients based on immunophenotyping, suggesting the presence of distinct immunological processes behind the clinical subtypes of MCTD.

Exploring the Mechanisms of Sanguinarine in the Treatment of Osteoporosis by Integrating Network Pharmacology Analysis and Deep Learning Technology.

Sanguinarine (SAN) has been reported to have antioxidant, antiinflammatory, and antimicrobial activities with potential for the treatment of osteoporosis (OP). This work purposed to unravel the molecular mechanisms of SAN in the treatment of OP. OP-related genes and SAN-related targets were predicted from public databases. Differential expression analysis and VennDiagram were adopted to detect SAN-related targets against OP. Protein-protein interaction (PPI) network was served for core target identification. Molecular docking and DeepPurpose algorithm were further adopted to investigate the binding ability between core targets and SAN. Gene pathway scoring of these targets was calculated utilizing gene set variation analysis (GSVA). Finally, we explored the effect of SAN on the expressions of core targets in preosteoblastic MC3T3-E1 cells. A total of 21 candidate targets of SAN against OP were acquired. Furthermore, six core targets were identified, among which CASP3, CTNNB1, and ERBB2 were remarkably differentially expressed in OP and healthy individuals. The binding energies of SAN with CASP3, CTNNB1, and ERBB2 were -6, -6.731, and -7.162 kcal/mol, respectively. Moreover, the GSVA scores of the Wnt/calcium signaling pathway were significantly lower in OP cases than in healthy individuals. In addition, the expression of CASP3 was positively associated with Wnt/calcium signaling pathway. CASP3 and ERBB2 were significantly lower expressed in SAN group than in DMSO group, whereas the expression of CTNNB1 was in contrast. CASP3, CTNNB1, and ERBB2 emerge as potential targets of SAN in OP prevention and treatment.

Transcriptomic signatures of chronic active antibody-mediated rejection deciphered by RNA sequencing of human kidney allografts

Natural killer (NK) cells mediate spontaneous cell-mediated cytotoxicity and antibody-dependent cell-mediated cytotoxicity. This dual functionality could enable their participation in chronic active antibody-mediated rejection (CA-ABMR). Earlier microarray profiling studies have not subcategorized antibody-mediated rejection into CA-ABMR and active-ABMR, and the gene expression pattern of CA-ABMR has not been compared with that of T cell-mediated rejection (TCMR). To fill these gaps, we RNA sequenced human kidney allograft biopsies categorized as CA-ABMR, active-ABMR, TCMR, or No Rejection (NR). Among the 15,910 genes identified in the biopsies, 60, 114, and 231 genes were uniquely overexpressed in CA-ABMR, TCMR, and active-ABMR, respectively, compared to NR, 50 genes were shared between CA-ABMR and active-ABMR, and 164 genes between CA-ABMR and TCMR. The overexpressed genes were annotated to NK cells and T cells in CA-ABMR and TCMR, and to neutrophils and monocytes in active-ABMR. The NK cell cytotoxicity and allograft rejection pathways were enriched in CA-ABMR. Genes encoding perforin, granzymes, and death receptor, were overexpressed in CA-ABMR versus ABMR but not compared to TCMR. NK cell cytotoxicity pathway gene set variation analysis score was higher in CA-ABMR compared to active-ABMR but not in TCMR. Principal component analysis of the deconvolved immune cellular transcriptomes separated CA-ABMR and TCMR from active-ABMR and NR. Immunohistochemistry of kidney allograft biopsies validated a higher proportion of CD56+ NK cells in CA-ABMR than in active-ABMR. Thus, CA-ABMR was exemplified by the overexpression of the NK cell cytotoxicity pathway gene set and, surprisingly, molecularly more like TCMR than active-ABMR.

Adrenergic and mesenchymal signatures are identifiable in cell-free DNA and correlate with metastatic disease burden in children with neuroblastoma.

Cell-free DNA (cfDNA) profiles of 5-hydroxymethylcytosine (5-hmC), an epigenetic marker of open chromatin and active gene expression, are correlated with metastatic disease burden in patients with neuroblastoma. Neuroblastoma tumors are comprised of adrenergic (ADRN) and mesenchymal (MES) cells, and the relative abundance of each in tumor biopsies has prognostic implications. We hypothesized that ADRN and MES-specific signatures could be quantified in cfDNA 5-hmC profiles and would augment the detection of metastatic burden in patients with neuroblastoma. We previously performed an integrative analysis to identify ADRN and MES-specific genes (n=373 and n=159, respectively). Purified DNA from cell lines was serial diluted with healthy donor cfDNA. Using Gene Set Variation Analysis (GSVA), ADRN and MES signatures were optimized. We then quantified signature scores, and our prior neuroblastoma signature, in cfDNA from 84 samples from 46 high-risk patients including 21 patients with serial samples. Samples from patients with higher metastatic burden had increased GSVA scores for both ADRN and MES gene signatures (p<.001). While ADRN and MES signature scores tracked together in serially collected samples, we identified instances of patients with increases in either MES or ADRN score at relapse. While it is feasible to identify ADRN and MES signatures using 5-hmC profiles of cfDNA from neuroblastoma patients and correlate these signatures to metastatic burden, additional data are needed to determine the optimal strategies for clinical implementation. Prospective evaluation in larger cohorts is ongoing.

Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma.

This study aims to establish a risk prediction model based on prognosis-related genes (PRGs) and clinicopathological factors, and investigate the biological activities of PRGs in lung adenocarcinoma (LUAD). Risk score signatures were developed by employing multiple algorithms and their amalgamations. A predictive model for overall survival was established through the integration of risk score signatures and several clinicopathological parameters. A comprehensive single-cell atlas, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to investigate the biological activities of prognosis-related genes in LUAD. A risk prediction model was established based on 16 PRGs, exhibiting robust performance in predicting overall survival. The single-cell analysis revealed that epithelial cells were primarily associated with worse survival of LUAD, and PRGs were predominantly enriched in malignant epithelial cells and influenced epithelial cell growth and progression. Furthermore, GSEA and GSVA analysis showed that PRGs were involved in tumor pathways such as epithelial-mesenchymal transition, hypoxia and KRAS_UP, and high GSVA scores are correlated with worse outcome in LUAD patients. The constructed risk prediction model in this study offers clinicians a valuable tool for tailoring treatment strategies of LUAD and provides a comprehensive interpretation on the biological activities of PRGs in LUAD.

Classification of subtypes and identification of dysregulated genes in sepsis.

Sepsis is a clinical syndrome with high mortality. Subtype identification in sepsis is meaningful for improving the diagnosis and treatment of patients. The purpose of this research was to identify subtypes of sepsis using RNA-seq datasets and further explore key genes that were deregulated during the development of sepsis. The datasets GSE95233 and GSE13904 were obtained from the Gene Expression Omnibus database. Differential analysis of the gene expression matrix was performed between sepsis patients and healthy controls. Intersection analysis of differentially expressed genes was applied to identify common differentially expressed genes for enrichment analysis and gene set variation analysis. Obvious differential pathways between sepsis patients and healthy controls were identified, as were developmental stages during sepsis. Then, key dysregulated genes were revealed by short time-series analysis and the least absolute shrinkage and selection operator model. In addition, the MCPcounter package was used to assess infiltrating immunocytes. Finally, the dysregulated genes identified were verified using 69 clinical samples. A total of 898 common differentially expressed genes were obtained, which were chiefly related to increased metabolic responses and decreased immune responses. The two differential pathways (angiogenesis and myc targets v2) were screened on the basis of gene set variation analysis scores. Four subgroups were identified according to median expression of angiogenesis and myc target v2 genes: normal, myc target v2, mixed-quiescent, and angiogenesis. The genes CHPT1, CPEB4, DNAJC3, MAFG, NARF, SNX3, S100A9, S100A12, and METTL9 were recognized as being progressively dysregulated in sepsis. Furthermore, most types of immune cells showed low infiltration in sepsis patients and had a significant correlation with the key genes. Importantly, all nine key genes were highly expressed in sepsis patients. This study revealed novel insight into sepsis subtypes and identified nine dysregulated genes associated with immune status in the development of sepsis. This study provides potential molecular targets for the diagnosis and treatment of sepsis.

Integration of Vitreous Lipidomics and Metabolomics for Comprehensive Understanding of the Pathogenesis of Proliferative Diabetic Retinopathy.

As a vision-threatening complication of diabetes mellitus (DM), proliferative diabetic retinopathy (PDR) is associated with sustained metabolic disorders. Herein, we collected the vitreous cavity fluid of 49 patients with PDR and 23 control subjects without DM for metabolomics and lipidomics analyses. Multivariate statistical methods were performed to explore relationships between samples. For each group of metabolites, gene set variation analysis scores were generated, and we constructed a lipid network by using weighted gene co-expression network analysis. The association between lipid co-expression modules and metabolite set scores was investigated using the two-way orthogonal partial least squares (O2PLS) model. A total of 390 lipids and 314 metabolites were identified. Multivariate statistical analysis revealed significant vitreous metabolic and lipid differences between PDR and controls. Pathway analysis showed that 8 metabolic processes might be associated with the development of PDR, and 14 lipid species were found to be altered in PDR patients. Combining metabolomics and lipidomics, we identified fatty acid desaturase 2 (FADS2) as an important potential contributor to the pathogenesis of PDR. Collectively, this study integrates vitreous metabolomics and lipidomics to comprehensively unravel metabolic dysregulation and identifies genetic variants associated with altered lipid species in the mechanistic pathways for PDR.

Identifying osteosarcoma signatures in cell free DNA using 5-hydroxymethylcytosine profiling.

10022 Background: Osteosarcoma is the most common primary bone tumor in children and adolescents. Prognostic biomarkers are limited, and post-treatment surveillance relies heavily on radiologic imaging. Capture and analysis of cell-free DNA (cfDNA) from the bloodstream is an emerging technique that could be used to provide additional prognostic information and track residual disease following treatment. 5-hydroxymethylcytosine (5-hmC) is an epigenetic marker of open chromatin and active gene expression, and tumor specific patterns of global 5-hmC deposition are identifiable in several cancers and vary according to disease burden. We profiled 5-hmC using plasma-derived cfDNA from osteosarcoma patients and healthy children to assess its utility as a biomarker of disease status. Methods: cfDNA was isolated from patients diagnosed with osteosarcoma and well child controls. Nano-hmC-Seal, a technique developed to isolate 5-hmC-modified DNA fragments, was used to generate sequencing libraries of cfDNA. Genomic DNA from osteosarcoma cell lines was serially diluted with healthy donor-derived cfDNA. DESeq2 was used to identify genes with high 5-hmC deposition in osteosarcoma cell line DNA relative to serial dilutions. In patient cfDNA samples, the relative abundance of 5-hmC deposition on these osteosarcoma genes was determined using gene set variation analysis (GSVA). GSVA returns a score ranging from -1 to 1, with positive values indicating a relative abundance of 5-hmC deposition on osteosarcoma genes. Results: The cohort consisted of 33 healthy children and 17 patients with osteosarcoma including 53 serially collected samples from these patients. 139 osteosarcoma genes were identified and used for GSVA analysis. Patients with osteosarcoma had significantly higher GSVA scores than well children (0.26 vs -0.38; p &lt; 0.001). Hierarchal clustering identified two clusters of samples from children with osteosarcoma. Cluster 1 contained samples with scores correlating with osteosarcoma-derived DNA compared to Cluster 2 (0.14 vs -0.18; p &lt; 0.001). Furthermore, Cluster 1 contained 17 of 23 samples from patients with active disease on clinical imaging (sensitivity = 74%, specificity = 57%). High 5-hmC deposition on MYC was readily identified in one sample obtained at diagnosis from the only patient with a MYC amplified tumor. Additional samples from this patient during treatment showed decreasing 5-hmC deposition on MYC, consistent with a treated tumor shedding less DNA into the blood. Additionally, decreased disease burden following surgical resection was identifiable in 5-hmC profiles (presurgical GSVA score 0.22 vs postsurgical -0.08; p = 0.004). Conclusions: Osteosarcoma 5-hmC signatures can identify patients with active disease. Monitoring cfDNA in osteosarcoma using 5-hmC profiling is a promising technique that warrants further investigation with larger patient cohorts.

Use of immune checkpoint gene (ICG) signatures to identify selective enrichment of FGL1 and CEACAM1 in histologic subtypes of lung cancer.

e20527 Background: Immune checkpoint blockade has changed the management paradigm for lung cancer. Identification of vulnerable subsets using reliable predictive biomarkers is an area of unmet need. We systematically compared the pattern of expression of validated mediators of adaptive and innate immune response in clinical and histologic subsets of lung cancer using publicly available datasets. Methods: Gene expression data for lung adenocarcinoma (LUAD, N = 501) and and lung squamous cell carcinoma (LUSC, N = 491) was obtained from The Cancer Genome Atlas. We compared the expression of ICGs between LUSC and LUAD using DESeq2 and estimated the proportion of different immune cells in the tumor microenvironment by CIBERSORT. An expanded panel of ICG signatures from MSigDB was compared between LUSC and LUAD using gene set variation analysis (GSVA). Weighted gene co-expression network analysis (WGCNA) identified modules of immune checkpoint inhibitor genes with significant correlation with histology, which were further investigated for biologic pathway involvement and survival analysis using Ingenuity Pathway Analysis and Gene Expression Profiling Interactive Analysis (GEPIA2), respectively. Results: Majority of the ICGs had a higher expression in LUAD than LUSC with the strongest differential expression recorded for FGL1 (LogFC = 6.2, FDR = 1.2E-239) and CEACAM1 (LogFC = 1.4, FDR = 2E-71). Treg cells and monocytes were higher in LUAD while NK cells and macrophages were higher in LUSC. There was no difference in CD8(+) T cell population by histology. Genesets related to PD1 signaling from MSigDB had significantly higher GSVA scores in LUAD than LUSC. WGCNA identified 2 modules containing immune checkpoint genes – one module containing 15 of the 23 ICGs involved in allograft rejection, interferon gamma response, interferon alpha response, IL6-signaling, IL2-signaling and TNF-alpha signaling showed moderate correlation with LUAD (module-trait correlation = 0.3, P = 8e-26). A second module with 4 ICGs ( FGL1, LGALS3, CEACAM1, PVRL2) enriched in pathways related to carbohydrate and lipid metabolism (galactose degradation, gamma-linolenate biosynthesis) showed very strong correlation with LUAD (module-trait correlation = 0.85, P = 5e-278). Additional comparison by gender and smoking status along with external validation using independent dataset is ongoing. Conclusions: ICG expression profile differs between LUAD and LUSC with a stronger expression profile in LUAD than LUSC.

Single-cell RNA-seq uncovers distinct pathways and genes in endothelial cells during atherosclerosis progression.

Background: Atherosclerosis (AS) is a chronic inflammatory disease involving various cell types, cytokines, and adhesion molecules. Herein, we aimed to uncover its key molecular mechanisms by single-cell RNA-seq (scRNA-seq) analysis. Methods: ScRNA-seq data of cells from atherosclerotic human coronary arteries were analyzed using the Seurat package. Cell types were clustered, and differentially expressed genes (DEGs) were screened. GSVA (Gene Set Variation Analysis) scores of hub pathways were compared among different cell clusters. DEGs in endothelial cells between apolipoprotein-E (ApoE)-/- mice and specific TGFbR1/2 KO ApoE-/- mice fed with high-fat diet were overlapped with those from human AS coronary arteries. In fluid shear stress and AS, hub genes were determined based on the protein-protein interaction (PPI) network, which were verified in ApoE-/- mice. Finally, hub genes were validated in three pairs of AS coronary arteries and normal tissues by histopathological examination. Results: ScRNA-seq identified nine cell clusters in human coronary arteries, namely, fibroblasts, endothelial cells, macrophages, B cells, adipocytes, HSCs, NK cells, CD8+ T cells, and monocytes. Among them, endothelial cells had the lowest fluid shear stress and AS and TGF-beta signaling pathway scores. Compared to ApoE-/- mice fed with normal diet, fluid shear stress and AS and TGF-beta scores were both significantly lower in endothelial cells from TGFbR1/2 KO ApoE-/- mice fed with normal or high-fat diet. Furthermore, the two hub pathways had a positive correlation. Three hub genes (ICAM1, KLF2, and VCAM1) were identified, and their expression was distinctly downregulated in endothelial cells from TGFbR1/2 KO ApoE-/- mice fed with normal or high-fat diet than in those from ApoE-/- mice fed with a normal diet, which were confirmed in human AS coronary artery. Conclusion: Our findings clarified the pivotal impacts of pathways (fluid shear stress and AS and TGF-beta) and genes (ICAM1, KLF2, and VCAM1) in endothelial cells on AS progression.

T-cell exhaustion: A potential target biomarker of the tumour microenvironment affecting oesophageal adenocarcinoma.

Oesophageal adenocarcinoma (EAC) is one of the most common malignant tumours, and the number of patients is increasing year by year. T-cell exhaustion (TEX) is an important risk factor for tumour immunosuppression and invasion, but its underlying mechanism in the pathogenesis of EAC is not clear. Unsupervised clustering was performed to screen relevant genes based on Gene Set Variation Analysis scores of the three pathways of the HALLMARK gene set IL2/IFNG/TNFA. Multiple enrichment analyses and data combinations were used to depict the relationship between TEX-related risk models and CIBERSORTx immune infiltrating cells. In addition, to explore the impact of TEX on EAC therapeutic resistance, we assessed the impact of TEX risk models on the therapeutic sensitivity of various novel drugs using single-cell sequencing and searched for their potential therapeutic targets and cellular communication. Four risk clusters of EAC patients were identified by unsupervised clustering and searched for potential TEX-related genes. Based on this, LASSO regression and decision trees were used to construct risk prognostic models containing a total of three TEX-associated genes in EAC. The results showed that TEX risk scores were significantly associated with the survival prognosis of EAC patients in both the Cancer Genome Atlas dataset and the independent validation set of Gene Expression Omnibus. Immune infiltration and cell communication analyses identified mast cell resting as a protective factor in TEX, and pathway enrichment analyses showed that the TEX risk model was highly associated with multiple chemokines as well as inflammation-associated pathways. In addition, higher TEX risk scores were associated with a weak responsiveness to immunotherapy. We describe the immune infiltration, prognostic significance and potential possible mechanisms of TEX in the EAC patient population. This is a novel attempt to promote the development of novel therapeutic modalities and immunological target construction for oesophageal adenocarcinoma. It is expected to make a potential contribution to advancing the exploration of immunological mechanisms and the opening of target drugs in EAC.

Role of glycosphingolipid biosynthesis coregulators in malignant progression of thymoma.

As the most common malignancy from mediastinum, the metabolic reprogramming of thymoma is important in its development. Nevertheless, the connection between the metabolic map and thymoma development is yet to be discovered. Thymoma was categorized into three subcategories by unsupervised clustering of molecular markers for metabolic pathway presentation in the TCGA dataset. Different genes and functions enriched were demonstrated through the utilization of metabolic Gene Ontology (GO) analysis. To identify the main contributors in the development of thymic malignancy, we utilized Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The prognosis of thymoma was evaluated by screening the essential pathways and genes using GSVA scores and machine learning classifiers. Furthermore, we integrated the transcriptomics findings with spectrum metabolomics investigation, detected through LC-MS/MS, in order to establish the essential controller network of metabolic reprogramming during thymoma progression. The thymoma prognosis is related to glycosphingolipid biosynthesis-lacto and neolacto series pathway, of what high B3GNT5 indicate poor survival. The investigation revealed that glycosphingolipid charts have a significant impact on metabolic dysfunction and could potentially serve as crucial targets in the clinical advancement of metabolic therapy.

Multi-omics profiling identifies C1QA/B+ macrophages with multiple immune checkpoints associated with esophageal squamous cell carcinoma (ESCC) liver metastasis

Esophageal squamous cell carcinoma (ESCC) is a highly lethal malignant tumor lacking effective treatments; 20% of ESCC patients develop liver metastasis with an extremely short survival time of ≈5 months. The tumor microenvironment (TME) plays a crucial role in tumor homeostasis, but the relationship between the ESCC TME and liver metastasis is still unknown. To identify potential cell populations contributing to ESCC liver metastasis, single-cell RNA (scRNA) sequencing data were analyzed to identify the major cell populations within the TME. Each of the major cell populations was re-clustered to define detailed cell subsets. Thereafter, the gene set variation analysis (GSVA) score was calculated for the bulk RNA-seq data based on the gene signatures of each cell subset. The relationship between the GSVA score of each cellular subset and clinical outcome was further analyzed to identify the cellular subset associated with ESCC liver metastasis, which was validated by multiplex immunohistochemistry. C1QA/B+ tumor-associated macrophages (TAMs) acted as the central regulator of the ESCC TME, closely associated with several key cell subsets. Several immune checkpoints, including CD40, CD47 and LGALS9, were all positively expressed in C1QA/B+ macrophages, which may exert central regulatory control of immune evasion by ESCC via these immune checkpoints expressions. Our results comprehensively revealed the landscape of tumor-infiltrating immune cells associated with ESCC prognosis and metastasis, and suggest a novel strategy for developing immunotherapies for ESCC liver metastasis by targeting C1QA/B+ TAMs.

A Four-Gene Signature Associated with Radioresistance in Head and Neck Squamous Cell Carcinoma Identified by Text Mining and Data Analysis.

Purpose Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer globally, and radiotherapy plays a crucial part in its treatment. This study was designed to identify potential genes related to radiation resistance in HNSCC. Method We first used text mining to obtain common genes related to radiotherapy resistance and HNSCC in published articles. Functional enrichment analyses were conducted to identify the significantly enriched pathways and genes. Protein and protein interactions were performed, and the most significant gene modules were determined; then, genes in the gene modules were validated at transcriptional levels and overall survival. Gene set variation analysis (GSVA) score was calculated, and the association between GSVA score and survival/pathway was estimated. Immune cell infiltration, methylation, and genetic alteration analysis of these genes was conducted in HNSCC patients. Finally, potential sensitive anticancer drugs related to target genes were obtained. Result We identified 583 common genes through text mining. After further validation, a four-gene signature (EPHB2, SPP1, SERPINE1, and VEGFC) was constructed. The patients with higher GSVA scores have a worse prognosis than those with lower GSVA scores. Differences in methylation of these four genes in HNSCC tumor tissue and normal tissue were compared, with higher methylation levels of EBPH2 and SPP1 in normal tissue and higher methylation levels of SERPINE1 in the tumor. Immune cell infiltration revealed that the increased expression of these genes was closely related to the infiltration level of CD4+ T cell, neutrophil, macrophage, and dendritic cell. Thirty drugs, including 22 positively and eight negatively correlated drugs that most correlated with related genes, were available for treating HNSCC. Conclusion In this study, we identified four potential genes as well as corresponding drugs that might be related to radioresistance in HNSCC patients. These candidate genes may provide a promising avenue to further elevate radiotherapy efficacy.