Results Of Gene Set Enrichment Analysis Research Articles

A novel tumor microenvironment-related gene signature with immune features for prognosis of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is an aggressive subset of non-small-cell lung cancer (NSCLC). The tumor microenvironment (TME) plays an important role in the development of LUSC. We aim to identify potential therapeutic targets and a TME-related prognostic signature and for LUSC. TME-related genes were obtained from TCGA-LUSC dataset. LUSC samples were clustered by the non-negative matrix clustering algorithm (NMF). The prognostic signature was constructed through univariate Cox regression, multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) analyses. Gene set enrichment analysis (GSEA) was carried out to explore the enrichment pathways. This study constructed a prognostic signature which contained 12 genes: HHIPL2, PLK4, SLC6A4, LSM1, TSLP, P4HA1, AMH, CLDN5, NRTN, CDH2, PTGIS, and STX1A. Patients were classified into high-risk and low-risk groups according to the median risk score of this signature. Compared with low-risk group patients, patients in high-risk group patients had poorer overall survival, which demonstrated this signature was an independent prognostic factor. Besides, correlation analysis and GSEA results revealed that genes of this signature were correlated with immune cells and drug response. Our novel signature based on 12 TME-related genes might be applied as an independent prognostic indicator. Importantly, the signature could be a promising biomarker and accurately predict the prognosis of LUSC patients.

Hub genes, diagnostic model, and predicted drugs in systemic sclerosis by integrated bioinformatics analysis.

Background: Systemic sclerosis (scleroderma; SSc), a rare and heterogeneous connective tissue disease, remains unclear in terms of its underlying causative genes and effective therapeutic approaches. The purpose of the present study was to identify hub genes, diagnostic markers and explore potential small-molecule drugs of SSc. Methods: The cohorts of data used in this study were downloaded from the Gene Expression Complex (GEO) database. Integrated bioinformatic tools were utilized for exploration, including Weighted Gene Co-Expression Network Analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, gene set enrichment analysis (GSEA), Connectivity Map (CMap) analysis, molecular docking, and pharmacokinetic/toxicity properties exploration. Results: Seven hub genes (THY1, SULF1, PRSS23, COL5A2, NNMT, SLCO2B1, and TIMP1) were obtained in the merged gene expression profiles of GSE45485 and GSE76885. GSEA results have shown that they are associated with autoimmune diseases, microorganism infections, inflammatory related pathways, immune responses, and fibrosis process. Among them, THY1 and SULF1 were identified as diagnostic markers and validated in skin samples from GSE32413, GSE95065, GSE58095 and GSE125362. Finally, ten small-molecule drugs with potential therapeutic effects were identified, mainly including phosphodiesterase (PDE) inhibitors (BRL-50481, dipyridamole), TGF-β receptor inhibitor (SB-525334), and so on. Conclusion: This study provides new sights into a deeper understanding the molecular mechanisms in the pathogenesis of SSc. More importantly, the results may offer promising clues for further experimental studies and novel treatment strategies.

Novel cell subtypes of SPP1 + S100P+, MS4A1-SPP1 + S100P+ were key subpopulations in intrahepatic cholangiocarcinoma

BackgroundIn this study, we integrated single-cell RNA sequencing (scRNA-seq) data to investigate cell heterogeneity and utilized MSigDB and CIBERSORTx to explore the pathways of major cell types and the relationships between different cell subtypes. Subsequently, we explored the correlation of cell subtypes with survival and used Gene Set Enrichment Analysis (GSEA) analyses to assess the pathways associated with the infiltration of specific cell subtypes. Finally, multiplex immunohistochemistry in tissue microarray cohort were performed to validate differences in protein level and their correlation with survival. ResultsiCCA presented a unique immune ecosystem, with increased proportions of Epi (epithelial)-SPP1–2, Epi-S100P-1, Epi-DN (double negative for SPP1 and S100P expression)-1, Epi-DN-2, Epi-DP (double positive for SPP1 and S100P expression)-1, Plasma B-3, Plasma B-2, B-HSPA1A-1, B-HSPA1A-2 cells, and decreased proportions of B-MS4A1. High level of Epi-DN-2, Epi-SPP1–1, Epi-SPP1–2, B-MS4A1, and low level of Epi-DB-1, Epi-S100P-1, and Epi-S100P-2 was significantly associated with longer overall survival (OS), and high level of B-MS4A1_Low_Epi-DN-2_Low was associated with the shortest OS. Moreover, the results of MsigDB and GSEA suggest that bile acid metabolism is a crucial process in iCCA. Finally, we found that S100P+, SPP1+, SPP1 + S100P+, and MS4A1-SPP1 + S100P+ were highly expressed, whereas MS4A1 was lowly expressed in iCCA, and patients with high level of S100P+, SPP1 + S100P+, and MS4A1-SPP1 + S100P+ exhibited shorter survival. ConclusionsWe identified the cell heterogeneity of iCCA, found that iCCA is a unique immune ecosystem with many cell subtypes, and showed that the novel cell subtypes of SPP1 + S100P+ and MS4A1-SPP1 + S100P+ were key subpopulations in iCCA.

Identification and validation of a novel prognostic model for gastric cancer based on m7G-related genes.

The role of N7-methyladenosine (m7G)-related genes in the progression and prognosis of gastric cancer (GC) remains unclear. This study aimed to explore prognostic biomarkers for GC based on m7G methylation regulators and to construct a prognostic risk model. RNA sequencing profiles with corresponding clinicopathological information associated with GC of which the histological type was stomach adenocarcinoma (STAD) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A total of 29 m7G regulators were extracted from previous studies. According to the expression similarity of m7G regulators, the GC samples obtained from TCGA were further classified into 2 clusters demonstrating different overall survival (OS) rates and genetic heterogeneity, and the differentially expressed genes (DEGs) between these 2 clusters were defined as m7G-related genes. Univariate regression analysis and regression analysis were then used to obtain the prognostic m7G-related genes. The samples in TCGA and Genotype-Tissue Expression (GTEx) were used to verify the differential expression and prognostic value of these m7G-related genes contained in the prognostic model. Subsequently, the risk score was combined with other prognostic factors to develop a nomogram. The predictive ability of the nomogram was evaluated by the standard receiver operating characteristic (ROC) curve. Gene set enrichment analysis (GSEA) was used to identify activation pathways in both groups. Finally, the association between the prognostic model and the immune characteristics of GC were appraised. A prognostic model consisting of 11 m7G-related genes was constructed. GC patients in the high-risk group were shown to have a poor prognosis and this result was further demonstrated in each group. The risk model can be applied for patients with different clinical features. The results of GSEA showed that cell adhesion, cell junction, and focal adhesion were highly enriched in the high-risk group. In addition, we found that the expression of programmed cell death ligand 1 (PD-L1) was significantly elevated in the low-risk group, whereas programmed cell death ligand 2 (PD-L2) and tumor necrosis factor receptor superfamily member 4 (TNFRSF4) were overexpressed in the high-risk group. We successfully built and verified a m7G relevant prognostic model for predicting prognosis and providing a new train of thought for improving the treatment of GC.

Innovative breakthroughs facilitated by single-cell multi-omics: manipulating natural killer cell functionality correlates with a novel subcategory of melanoma cells.

Melanoma is typically regarded as the most dangerous form of skin cancer. Although surgical removal of in situ lesions can be used to effectively treat metastatic disease, this condition is still difficult to cure. Melanoma cells are removed in great part due to the action of natural killer (NK) and T cells on the immune system. Still, not much is known about how the activity of NK cell-related pathways changes in melanoma tissue. Thus, we performed a single-cell multi-omics analysis on human melanoma cells in this study to explore the modulation of NK cell activity. Cells in which mitochondrial genes comprised > 20% of the total number of expressed genes were removed. Gene ontology (GO), gene set enrichment analysis (GSEA), gene set variation analysis (GSVA), and AUCcell analysis of differentially expressed genes (DEGs) in melanoma subtypes were performed. The CellChat package was used to predict cell-cell contact between NK cell and melanoma cell subtypes. Monocle program analyzed the pseudotime trajectories of melanoma cells. In addition, CytoTRACE was used to determine the recommended time order of melanoma cells. InferCNV was utilized to calculate the CNV level of melanoma cell subtypes. Python package pySCENIC was used to assess the enrichment of transcription factors and the activity of regulons in melanoma cell subtypes. Furthermore, the cell function experiment was used to confirm the function of TBX21 in both A375 and WM-115 melanoma cell lines. Following batch effect correction, 26,161 cells were separated into 28 clusters and designated as melanoma cells, neural cells, fibroblasts, endothelial cells, NK cells, CD4+ T cells, CD8+ T cells, B cells, plasma cells, monocytes and macrophages, and dendritic cells. A total of 10137 melanoma cells were further grouped into seven subtypes, i.e., C0 Melanoma BIRC7, C1 Melanoma CDH19, C2 Melanoma EDNRB, C3 Melanoma BIRC5, C4 Melanoma CORO1A, C5 Melanoma MAGEA4, and C6 Melanoma GJB2. The results of AUCell, GSEA, and GSVA suggested that C4 Melanoma CORO1A may be more sensitive to NK and T cells through positive regulation of NK and T cell-mediated immunity, while other subtypes of melanoma may be more resistant to NK cells. This suggests that the intratumor heterogeneity (ITH) of melanoma-induced activity and the difference in NK cell-mediated cytotoxicity may have caused NK cell defects. Transcription factor enrichment analysis indicated that TBX21 was the most important TF in C4 Melanoma CORO1A and was also associated with M1 modules. In vitro experiments further showed that TBX21 knockdown dramatically decreases melanoma cells' proliferation, invasion, and migration. The differences in NK and T cell-mediated immunity and cytotoxicity between C4 Melanoma CORO1A and other melanoma cell subtypes may offer a new perspective on the ITH of melanoma-induced metastatic activity. In addition, the protective factors of skin melanoma, STAT1, IRF1, and FLI1, may modulate melanoma cell responses to NK or T cells.

Analysis of immunogenic cell death in ascending thoracic aortic aneurysms based on single-cell sequencing data.

At present, research on immunogenic cell death (ICD) is mainly associated with cancer therapy. Little is known about the role of ICD in cardiovascular disease, especially in ascending thoracic aortic aneurysms (ATAA). ATAA single-cell RNA (scRNA) sequencing data were analyzed to identify the involved cell types and determine their transcriptomic characteristics. The chi-square test, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, Gene Set Enrichment Analysis (GSEA), and CellChat for cell-to-cell communication analysis from the Gene Expression Omnibus (GEO) database were used. A total of 10 cell types were identified, namely, monocytes, macrophages, CD4 T/NK (CD4+ T cells and natural killer T cells), mast cells, B/Plasma B cells, fibroblasts, endothelial cells, cytotoxic T cells (CD8+ T cells, CTLs), vascular smooth muscle cells (vSMCs), and mature dendritic cells (mDCs). A large number of inflammation-related pathways were present in the GSEA results. A large number of ICD-related pathways were found in the KEGG enrichment analysis of differentially expressed genes in endothelial cells. The number of mDCs and CTLs in the ATAA group was significantly different from that in the control group. A total of 44 pathway networks were obtained, of which 9 were associated with ICD in endothelial cells (CCL, CXCL, ANNEXIN, CD40, IL1, IL6, TNF, IFN-II, GALECTIN). The most important ligand-receptor pair by which endothelial cells act on CD4 T/NK cells, CTLs and mDCs is CXCL12-CXCR4. The most important ligand-receptor pair by which endothelial cells act on monocytes and macrophages is ANXA1-FPR1. The most important ligand-receptor pair by which CD4 T/NK cells and CTLs act on endothelial cells is CCL5-ACKR1. The most important ligand-receptor pair that myeloid cells (macrophages, monocytes and mDCs) act on endothelial cells is CXCL8-ACKR1. Moreover, vSMCs and fibroblasts mainly promote inflammatory responses through the MIF signaling pathway. ICD is present in ATAA and plays an important role in the development of ATAA. The target cells of ICD may be mainly endothelial cells, in which the aortic endothelial cell ACKR1 receptor can not only promote T-cell infiltration through the CCL5 ligand but also promote myeloid cell infiltration through the CXCL8 ligand. ACKR1 and CXCL12 may become target genes for ATAA drug therapy in the future.

C6orf15 acts as a potential novel marker of adverse pathological features and prognosis for colon cancer.

To investigate the expression of chromosome 6 open reading frame 15 (C6orf15) in colon cancer and its effects on clinicopathological features and prognosis. Using the transcriptome and clinical data of colon cancer and normal tissues in The Cancer Genome Atlas (TCGA) database, the expression of C6orf15 mRNA in colon cancer samples and its relationship with clinicopathological characteristics and prognosis were explored. The expression level of C6orf15 protein in 23 colon cancer tissues was detected by immunohistochemistry (IHC). The possible mechanism of C6orf15 involved in the occurrence and development of colon cancer was explored by gene set enrichment analysis (GSEA). Compared with normal tissues, C6orf15 was highly expressed in colon cancer (1.207±0.694 vs 0.276±0.166, t=8.281, P<0.01). The expression level of C6orf15 was associated with tumor invasion depth (χ2=8.30, P=0.04), lymph node metastasis (χ2=36.97, P<0.001), distant metastasis (χ2=8.69, P=0.003) and pathological stage (χ2=34.17, P<0.001). High expression of C6orf15 was associated with poor prognosis (χ2=6.43, P<0.05). The results of GSEA showed that C6orf15 promotes the occurrence and development of colon cancer by promoting the ECM receptor interaction pathway, Hedgehog signaling pathway and Wnt signaling pathway. Immunohistochemical results showed that the expression of C6orf15 protein in colon cancer tissues was correlated with the depth of invasion (P=0.023) and lymph node metastasis (P=0.048). C6orf15 is highly expressed in colon cancer tissue and is related to adverse pathological features and poor prognosis of colon cancer. It is involved in multiple oncogenic signaling pathways and may serve as a prognostic marker of colon cancer.

Potential prognostic and immunotherapeutic value of calponin 1: A pan-cancer analysis.

Background: Emerging evidence has suggested a pro-oncogenic role of calponin 1 (CNN1) in the initiation of a variety of cancers. Despite this, CNN1 remains unknown in terms of its effects and mechanisms on angiogenesis, prognosis, and immunology in cancer. Materials and Methods: The expression of CNN1 was extracted and analyzed using the TIMER, UALCAN, and GEPIA databases. Meanwhile, we analyzed the diagnostic value of CNN1 by using PrognoScan and Kaplan-Meier plots. To elucidate the value of CNN1 in immunotherapy, we used the TIMER 2.0 database, TISIDB database, and Sangerbox database. Gene set enrichment analysis (GSEA) was used to analyze the expression pattern and bio-progression of CNN1 and the vascular endothelium growth factor (VEGF) in cancer. The expressions of CNN1 and VEGF in gastric cancer were confirmed using immunohistochemistry. We used Cox regression analysis to investigate the association between pathological characteristics, clinical prognosis, and CNN1 and VEGF expressions in patients with gastric cancer. Results: CNN1 expression was higher in normal tissues than it was in tumor tissues of most types of cancers. However, the expression level rebounds during the development of tumors. High levels of CNN1 indicate a poor prognosis for 11 tumors, which include stomach adenocarcinoma (STAD). There is a relationship between CNN1 and tumor-infiltrating lymphocytes (TILs), and the marker genes NRP1 and TNFRSF14 of TILs are significantly related to CNN1 expression in gastric cancers. The GSEA results confirmed the lower expression of CNN1 in tumors when compared to normal tissues. However, CNN1 again showed an increasing trend during tumor development. In addition, the results also suggest that CNN1 is involved in angiogenesis. The immunohistochemistry results validated the GSEA result (take gastric cancer as an example). Cox analysis suggested that high CNN1 expression and high VEGF expression are closely associated with poor clinical prognosis. Conclusion: Our study has shown that CNN1 expression is aberrantly elevated in various cancers and positively correlates with angiogenesis and the immune checkpoint, contributing to cancer progression and poor prognosis. These results suggest that CNN1 could serve as a promising candidate for pan-cancer immunotherapy.

Integrative analysis of TP53 mutations in lung adenocarcinoma for immunotherapies and prognosis

BackgroundThe TP53 tumor suppressor gene is one of the most mutated genes in lung adenocarcinoma (LUAD) and plays a vital role in regulating the occurrence and progression of cancer. We aimed to elucidate the association between TP53 mutations, response to immunotherapies and the prognosis of LUAD.MethodsGenomic, transcriptomic, and clinical data of LUAD were downloaded from The Cancer Genome Atlas (TCGA) dataset. Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, gene set enrichment analysis (GSEA). Gene set variation analysis (GSVA) were performed to determine the differences in biological pathways. A merged protein–protein interaction (PPI) network was constructed and analyzed. MSIpred was used to analyze the correlation between the expression of the TP53 gene, tumor mutation burden (TMB) and tumor microsatellite instability (MSI). CIBERSORT was used to calculate the abundance of immune cells. Univariate and multivariate Cox regression analyses were used to determine the prognostic value of TP53 mutations in LUAD.ResultsTP53 was the most frequently mutated in LUAD, with a mutational frequency of 48%. GO and KEGG enrichment analysis, GSEA, and GSVA results showed a significant upregulation of several signaling pathways, including PI3K-AKT mTOR (P < 0.05), Notch (P < 0.05), E2F target (NES = 1.8, P < 0.05), and G2M checkpoint (NES = 1.7, P < 0.05). Moreover, we found a significant correlation between T cells, plasma cells, and TP53 mutations (R2 < 0.01, P = 0.040). Univariate and multivariate Cox regression analyses revealed that the survival prognosis of LUAD patients was related to TP53 mutations (Hazard Ratio (HR) = 0.72 [95% CI, 0.53 to 0.98], P < 0.05), cancer status (P < 0.05), and treatment outcomes (P < 0.05). Lastly, the Cox regression models showed that TP53 exhibited good power in predicting three- and five-year survival rates.ConclusionsTP53 may be an independent predictor of response to immunotherapy in LUAD, and patients with TP53 mutations have higher immunogenicity and immune cell infiltration.

Identification of immune-related hub genes and analysis of infiltrated immune cells of idiopathic pulmonary artery hypertension.

Idiopathic pulmonary artery hypertension (IPAH) is a rare but life-threaten disease. However, the mechanism underlying IPAH is unclear. In this study, underlying mechanism, infiltration of immune cells, and immune-related hub genes of IPAH were analyzed via bioinformatics. GSE15197, GSE48149, GSE113439, and GSE117261 were merged as lung dataset. Weighted gene correlation network analysis (WGCNA) was used to construct the co-expression gene networks of IPAH. Gene Ontology and pathway enrichment analysis were performed using DAVID, gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA). Infiltration of immune cells in lung samples was analyzed using CIBERSORT. GSE22356 and GSE33463 were merged as peripheral blood mononuclear cells (PBMCs) dataset. Immune-related differentially expressed genes (IRDEGs) of lung and PBMCs dataset were analyzed. Based on the intersection between two sets of IRDEGs, hub genes were screened using machine learning algorithms and validated by RT-qPCR. Finally, competing endogenous RNA (ceRNA) networks of hub genes were constructed. The gray module was the most relevant module and genes in the module enriched in terms like inflammatory and immune responses. The results of GSEA and GSVA indicated that increasement in cytosolic calcium ion, and metabolism dysregulation play important roles in IPAH. The proportions of T cells CD4 memory resting and macrophage M1 were significantly greater in IPAH group, while the proportions of monocytes and neutrophils were significantly lower in IPAH group. IRDEGs of two datasets were analyzed and the intersection between two set of IRDEGs were identified as candidate hub genes. Predictive models for IPAH were constructed using data from PBMCs dataset with candidate hub genes as potential features via LASSO regression and XGBoost algorithm, respectively. CXCL10 and VIPR1 were identified as hub genes and ceRNA networks of CXCL10 was constructed. Inflammatory response, increasement in cytosolic calcium ion, and metabolism dysregulation play important roles in IPAH. T cells CD4 memory resting and macrophage M1 were significantly infiltrated in lung samples from patients with IPAH. IRDEGs of lung dataset and PBMCs dataset were analyzed, and CXCL10 and VIPR1 were identified as hub genes.

The expression of SERPINE1 in colon cancer and its regulatory network and prognostic value

BackgroundSerpin Peptidase Inhibitor 1 (SERPINE1) promotes cancer progression by making it easier for cancer cells to spread to surrounding normal tissue. We expect to understand the prognostic value and regulatory network of SERPINE1 in colon cancer using bioinformatics methods.MethodsThe expression of target gene SERPINE1 in varying cancers was analyzed by the Tumor Immune Estimation Resource (TIMER) database. SERPINE1 expression in Colon Adenocarcinoma and normal tissue samples was assessed by starBase and UALCAN databases. SERPINE1 expression in clinical tissues was assayed using quantitative reverse transcription Polymerase Chain Reaction (qRT-PCR). SERPINE1 expression was detected in colon cancer patients with various clinical features (age, gender, nodal metastasis status, race, stages, and subtype) using analysis of variance. Survival curve was used to analyze the effect of high and low expression of SERPINE1 on the survival time of patients with different clinical phenotypes. Gene Set Enrichment Analysis (GSEA) was conducted on the results of LinkFinder calculation using LinkInterpreter module, which was combined with Pearson correlation analysis to obtain the kinase targets and miRNA targets, transcription factor targets, and corresponding signaling pathways associated with SERPINE1. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed on GSEA result. Finally, Gene Multiple Association Network Integration Algorithm (GeneMANIA) was utilized to establish a network of genes related to the kinases MAPK1, miR-18a, and SRF_Q, and biological functions were analyzed.ResultsBased on TIMER, starBase, and UALCAN databases, SERPINE1 was found to be remarkably highly expressed in colon cancer patients, which was further verified by clinical tissue. It was also associated with different clinical features (nodal metastasis status, stages, subtypes). Additionally, survival analysis showed that patients with low expression of SERPINE1 had a longer survival time, suggesting that SERPINE1 was a prognostic risk factor for colon cancer. Pearson correlation analysis revealed that the expression of Integrin Alpha 5 (ITGA5), Matrix Metallopeptidase 19 (MMP19), and ADAM Metallopeptidase with Thrombospondin Type 1 Motif, 4 (ADAMTS4) had the highest correlation with that of SERPINE1. The GSEA results indicated that these genes were mainly enriched in the pathways of RNA expression and kinases. Finally, GeneMANIA analysis was introduced to construct the molecular network of SERPINE1.ConclusionOverall, our bioinformatics analyses comprehensively described the networks involved SERPINE1 in colon cancer and the potentially associated molecular mechanisms.

Methylation factors as biomarkers of fibromyalgia.

Fibromyalgia (FM) is a common and intractable chronic musculoskeletal pain syndrome, but its exact underlying mechanisms are unknown. This study sought to identify biomarkers of FM and the underlying molecular mechanisms of the disease. FM-related gene expression profiles (GSE67311) and methylation profiles (GSE85506) were obtained from the Gene Expression Omnibus database, and a differential expression analysis was performed to identify the methylation factors. Subsequently, an enrichment analysis and gene set enrichment analysis (GSEA) were conducted to examine the methylation factors. In addition, the transcriptional regulators of the methylation factors were predicted, and key methylation factors were identified by a receiver operating characteristic curve analysis and nomogram models. Finally, the relationship between FM and cell death (pyroptosis, necroptosis, and cuproptosis) was assessed by a GSEA and gene set variation analysis. A total of 455 methylation factors were identified. The enrichment analysis and GSEA results showed that methylation factors were clearly involved in the biological functions and signaling pathways related to neural, immune inflammation, and pain responses. The transcriptional regulator specificity protein 1 (SP1) may have a broad regulatory role. Finally, seven key methylation factors were identified, of which amino beta (A4) precursor protein binding family B member 2 (APBB2), A-kinase anchor protein 12 (AKAP12), and cluster of differentiation 38 (CD38) had strong clinical diagnostic power. In addition, AKAP12 and CD38 were significantly and negatively associated with sepsis, necrotizing sepsis, and cupular sepsis. Our study suggests that FM is associated with deoxyribonucleic acid methylation. The methylation factors APBB2, AKAP12, and CD38 may be potential biomarkers and should be further examined to provide a new biological framework of the possible disease mechanisms underlying FM.

Proteomic and single-cell landscape reveals novel pathogenic mechanisms of HBV-infected intrahepatic cholangiocarcinoma

Despite the epidemiological association between intrahepatic cholangiocarcinoma (ICC) and hepatitis B virus (HBV) infection, little is known about the relevant oncogenic effects. A cohort of 32 HBV-infected ICC and 89 non-HBV-ICC patients were characterized using whole-exome sequencing, proteomic analysis, and single-cell RNA sequencing. Proteomic analysis revealed decreased cell-cell junction levels in HBV-ICC patients. The cell-cell junction level had an inverse relationship with the epithelial-mesenchymal transition (EMT) program in ICC patients. Analysis of the immune landscape found that more CD8 Tcells and Th2 cells were present in HBV-ICC patients. Single-cell analysis indicated that transforming growth factor beta signaling-related EMT program changes increased in tumor cells of HBV-ICC patients. Moreover, ICAM1+ tumor-associated macrophages are correlated with a poor prognosis and contributed to the EMT in HBV-ICC patients. Our findings provide new insights into the behavior of HBV-infected ICC driven by various pathogenic mechanisms involving decreased cell junction levels and increased progression of the EMT program.

Bioinformatics and Connectivity Map Analysis Suggest Viral Infection as a Critical Causative Factor of Hashimoto's Thyroiditis.

Hashimoto's thyroiditis (HT) is a common autoimmune disease, and its prevalence is rapidly increasing. Both genetic and environmental risk factors contribute to the development of HT. Recently, viral infection has been suggested to act as a trigger of HT by eliciting the host immune response and subsequent autoreactivity. We analyzed the features of HT through bioinformatics analysis so as to identify the markers of HT development. We accessed public microarray data of HT patients from the Gene Expression Omnibus (GEO) and obtained differentially expressed genes (DEGs) under HT. Gene Ontology (GO) and KEGG-pathway-enrichment analyses were performed for functional clustering of our protein-protein interaction (PPI) network. Utilizing ranked gene lists, we performed a Gene Set Enrichment Analysis (GSEA) by using the clusterprofiler R package. By comparing the expression signatures of the huge perturbation database with the queried rank-ordered gene list, a connectivity map (CMap) analysis was performed to screen potential therapeutic targets and agents. The gene expression profile of the HT group was in line with the general characteristics of HT. Biological processes related to the immune response and viral infection pathways were obtained for the upregulated DEGs. The GSEA results revealed activation of autoimmune-disease-related pathways and several viral-infection pathways. Autoimmune-disease and viral-infection pathways were highly interconnected by common genes, while the HLA genes, which are shared by both, were significantly upregulated. The CMap analysis suggested that perturbagens, including SRRM1, NLK, and CCDC92, have the potential to reverse the HT expression profile. Several lines of evidence suggested that viral infection and the host immune response are activated during HT. Viral infection is suspected to act as a key trigger of HT by causing autoimmunity. SRRM1, an alternative splicing factor which responds to viral activity, might serve as potential marker of HT.

Identification and Analysis of Hub Genes and Immune Cells Associated with the Formation of Acute Aortic Dissection.

Acute type A aortic dissection (AAD) is a catastrophic disease with high mortality, but the pathogenesis has not been fully elucidated. This study is aimed at identifying hub genes and immune cells associated with the pathogenesis of AAD. The datasets were downloaded from Gene Expression Omnibus (GEO). Gene Set Enrichment Analysis (GSEA), gene set variation analysis (GSVA), and differential analysis were performed. The differentially expressed genes (DEGs) were intersected with specific genes collected from MSigDB. The gene function and pathway enrichment analysis were also performed on intersecting genes. The key modules were selected by weighted gene coexpression network analysis (WGCNA). Hub genes were identified by least absolute shrinkage and selection operator (LASSO) analysis and were verified in the metadataset. The immune cell infiltration was analyzed by CIBERSORT, and the relationship between hub genes and immune cells was performed by Pearson's correlation analysis. The single-cell RNA sequencing (scRNA-seq) dataset was used to verify the differences in DNA damage and repair signaling pathways and hub genes in different cell types. The results of GSEA and GSVA indicated that DNA damage and repair processes were activated in the occurrence of AAD. The gene function and pathway enrichment analysis on differentially expressed DNA damage- and repair-related genes showed that these genes were mainly involved in the regulation of the cell cycle process, cellular response to DNA damage stimulus, response to wounding, p53 signaling pathway, and cellular senescence. Three key modules were identified by WGCNA. Five genes were screened as hub genes, including CDK2, EIF4A1, GLRX, NNMT, and SLCO2A1. Naive B cells and Gamma delta T cells (γδ T cells) were decreased in AAD, but monocytes and M0 macrophages were increased. scRNA-seq analysis included that DNA damage and repair processes were activated in smooth muscle cells (SMCs), tissue stem cells, and monocytes in the aortic wall of patients with AAD. Our results suggested that DNA damage- and repair-related genes may be involved in the occurrence of AAD by regulating many biological processes. The hub genes and immune cells reported in this study also increase the understanding of AAD.

NUDT1 Could Be a Prognostic Biomarker and Correlated with Immune Infiltration in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is a malignant tumor with high morbidity and mortality. As a member of the Nudix hydrolase superfamily, Nudix (nucleoside diphosphate-linked moiety X)-type motif 1 (NUDT1) is closely related to the occurrence and development of cancer. Our study aims to explore the role of NUDT1 in ccRCC and its relationship with immune infiltration. The NUDT1 expression matrix and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The expression difference of NUDT1 in ccRCC and its relationship with the clinical characteristics were investigated using R software. Kaplan-Meier (K-M) analysis, univariate Cox regression, multivariate Cox regression, receiver operating characteristic (ROC) curve, and nomogram were utilized to evaluate the survival and prognosis of patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to explore the function of differential genes in low- or high-expression group of NUDT1. TCGA dataset and Tumor IMmune Estimation Resource (TIMER) database were utilized to explore the relationship between NUDT1 and immune infiltration. Finally, TCGA dataset was utilized for gene set enrichment analysis (GSEA). NUDT1 was not only overexpressed in ccRCC but also significantly correlated with clinicopathological features (P < 0.05). K-M survival analysis showed that upregulated NUDT1 was closely related to the decrease of overall survival (OS) and progression-free survival (PFS) in ccRCC patients. Multivariate Cox regression revealed that NUDT1 was a independent prognostic indicator (HR = 1.437, 95% CI: 1.065-1.939, P=0.018). The ROC curve showed that NUDT1 had a certain accuracy in predicting the outcome of ccRCC patiens. Furthermore, a total of 150 coexpressed genes and 1,886 differentially expressed genes (DEGs) were identified. GO/KEGG and GSEA results suggested that NUDT1 and its DEGs were involved in the immune-related pathways. NUDT1 expression was positively correlated with infiltrating levels of regulatory T cells (Tregs), CD8+ T cells, follicular helper T cells, and M0 macrophages. In addition, NUDT1 was positively related to immune checkpoints, such as PD-1, LAG3, CTLA4, and CD70, in ccRCC. NUDT1 plays a key role in the prognosis and immune cell infiltration of ccRCC patients, indicating its potential use as a prognostic biomarker and therapeutic target.

Analysis of infiltrated immune cells in left atriums from patients with atrial fibrillation and identification of circRNA biomarkers for postoperative atrial fibrillation.

Background: Atrial fibrillation (AF) increases the risk of stroke and heart failure. Postoperative AF (POAF) increases the risk of mortality after cardiac surgery. This study aims to explore mechanisms underlying AF, analyze infiltration of immune cells in left atrium (LA) from patients with AF, and identify potential circular RNA (circRNA) biomarkers for POAF. Methods: Raw data of GSE797689, GSE115574, and GSE97455 were downloaded and processed. AF-related gene co-expression network was constructed using weighted gene correlation network analysis and enrichment analysis of genes in relevant module was conducted. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were applied to investigate pathways significantly enriched in AF group. Infiltration of immune cells was analyzed using single-sample GSEA. Differentially expressed genes (DEGs) between patients with or without AF were identified and competing endogenous RNA (ceRNA) networks of DEGs were constructed. To screen biomarkers for POAF, differentially expressed circRNAs (DEcircRNAs) between patients with or without POAF were identified. Intersection between DEcircRNAs and circRNAs in ceRNA networks of DEGs were extracted and circRNAs in the intersection were further screened using support vector machine, random forest, and neural network to identify biomarkers for POAF. Results: Three modules were found to be relevant with AF and enrichment analysis indicated that genes in these modules were enriched in synthesis of extracellular matrix and inflammatory response. The results of GSEA and GSVA suggested that inflammatory response-related pathways were significantly enriched in AF group. Immune cells like macrophages, mast cells, and neutrophils were significantly infiltrated in LA tissues from patients with AF. The expression levels of immune genes such as CHGB, HLA-DRA, LYZ, IGKV1-17 and TYROBP were significantly upregulated in patients with AF, which were correlated with infiltration of immune cells. ceRNA networks of DEGs were constructed and has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF. Conclusion: Synthesis of extracellular matrix and inflammatory response were main processes involved in development and progression of AF. Infiltration of immune cells was significantly different between patients with or without AF. Has_circ_0006314 and hsa_circ_0055387 were found to have potential predictive values for POAF.

Identification of Diagnostic Markers in Infantile Hemangiomas.

Infantile Hemangiomas (IHs) are common benign vascular tumors of infancy that may have serious consequences. The research on diagnostic markers for IHs is scarce. The "limma" R package was applied to identify differentially expressed genes (DEGs) in developing IHs. Plugin ClueGO in Cytoscape software performed functional enrichment of DEGs. The Search Tool for Retrieving Interacting Genes (STRING) database was utilized to construct the PPI network. The least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis were used to identify diagnostic genes for IHs. The receiver operating characteristic (ROC) curve evaluated diagnostic genes' discriminatory ability. Single-gene based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) was conducted by Gene Set Enrichment Analysis (GSEA). The chemicals related to the diagnostic genes were excavated by the Comparative Toxicogenomics Database (CTD). Finally, the online website Network Analyst was used to predict the transcription factors targeting the diagnostic genes. A total of 205 DEGs were singled out from IHs samples of 6-, 12-, and 24-month-old infants. These genes principally participated in vasculogenesis and development-related, endothelial cell-related biological processes. Then we mined 127 interacting proteins and created a network with 127 nodes and 251 edges. Furthermore, LASSO and SVM-RRF algorithms identified five diagnostic genes, namely, TMEM2, GUCY1A2, ISL1, WARS, and STEAP4. ROC curve analysis results indicated that the diagnostic genes had a powerful ability to distinguish IHs samples from normal samples. Next, the results of GSEA for a single gene illustrated that all five diagnostic genes inhibited the "valine, leucine, and isoleucine degradation" pathway in the development of IHs. WARS, TMEM2, and STEAP4 activated the "blood vessel development" and "vasculature development" in IHs. Subsequently, inhibitors targeting TMEM2, GUCY1A2, ISL1, and STEAP4 were mined. Finally, 14 transcription factors regulating GUCY1A2, 14 transcription factors regulating STEAP4, and 26 transcription factors regulating ISL1 were predicted. This study identified five diagnostic markers for IHs and further explored the mechanisms and targeting drugs, providing a basis for diagnosing and treating IHs.

8661 Identification of Distinct Pathways in Ovarian Endometrioma and Deep Endometriosis

<h3>Study Objective</h3> To identify the difference in molecular mechanisms between ovarian endometrioma (OMA) and deep endometriosis (DE). <h3>Design</h3> N/A. <h3>Setting</h3> N/A. <h3>Patients or Participants</h3> N/A. <h3>Interventions</h3> Gene expression matrix of GSE141549 was downloaded from Gene Expression Omnibus (GEO) database and conducted to compare deep endometriosis with ovarian endometrioma. Differentially expressed genes (DEGs) were identified by limma package. Specific pathways related to OMA and DE were identified by gene set enrichment analysis (GSEA) and Weighted correlation network analysis (WGCNA). xCELL and CIBERSORT were used to analyze the difference in immune and stromal cell enrichment between OMA and DE. <h3>Measurements and Main Results</h3> 91 DE samples and 28 OMA samples were analyzed, from which we identified 406 DEGs between DE and OMA. WGCNA identified 14 co-expression modules, which were constructed by the top 5000 genes according to median absolute deviations. Module turquoise which was enriched in fibrosis and adhesion is most positively correlated with DE. Module blue, which was enriched in embryonic organ development and response to chemicals, is most positively correlated with OMA. The result of GSEA showed that cell adhesion-, fibrosis - and inflammation- related pathways were significantly enriched in DE samples, while pathways enriched in OMA related to steroid biosynthesis, metabolism of carbohydrates and amino acids. xCELL and CIBERSORT indicated that the differences between two groups were mainly in stromal cells rather than immune cells. <h3>Conclusion</h3> Through bioinformatic analysis, we explored the difference in gene expression profile between two common subtypes of endometriosis-OMA and DE. Fibrosis and inflammation seemed to be more associated with DE while OMA tended to be more related with steroid synthesis and metabolism. Further exploration of key genes and underling molecular mechanisms is needed to help developing novel therapeutic targets in treating endometriosis.

Role of a lipid metabolism-related lncRNA signature in risk stratification and immune microenvironment for colon cancer

BackgroundEnergy metabolism disorder, especially lipid metabolism disorder, is an important biological characteristic of colon cancer. This research sought to examine the association between lipid metabolism-related long non-coding RNAs (lncRNAs) and prognoses among colon cancer patients.MethodsThe transcriptome profile and clinical data of patients with colon cancer were retrieved from The Cancer Genome Atlas database. Using consensus clustering, cases were divided into two clusters and Kaplan–Meier analysis was executed to analyze differences in their prognoses. The gene set enrichment analysis (GSEA) was used to discover biological processes and signaling pathways. A lipid metabolism-related lncRNA prognostic model (lipid metabolism-LncRM) was created utilizing the least absolute shrinkage and selection operator (LASSO) regression. The tumor microenvironment was evaluated on the basis of the composition of immune and stromal cells.ResultsThe patients in Cluster 2 were found to have a better prognosis and higher expression of programmed cell death 1 (PD-1) and programmed cell death ligand 1 (PD-L1) relative to Cluster 1. The results of GSEA showed the enrichment of energy metabolism pathways in Cluster 2. LASSO regression was used to identify the five LncRNAs that were shown to be most substantially linked to patient prognosis. These were NSMCE1-DT, LINC02084, MYOSLID, LINC02428, and MRPS9-AS1. Receiver operating characteristic (ROC) curves and survival analysis illustrated that the lipid metabolism-LncRM had a significant prognostic value. Further analysis showed that high- and low-risk groups were significantly different in terms of clinical characteristics and immune cells infiltration.ConclusionsLipid metabolism-related lncRNAs could predict the prognoses and tumor microenvironment of colon cancer and might be important biomarkers relevant to immunotherapy.