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

Immune infiltration analysis reveals immune cell signatures in salivary gland tissue of primary Sjögren's syndrome.

Mouse models are the basis for primary Sjögren's syndrome (pSS) research. However, the depth of comparisons between mice and humans in salivary gland (SG) immune cells remains limited. The gene expression profiles of SGs from normal subjects and pSS patients were downloaded from the Gene Expression Comprehensive Database. The proportion of infiltrating immune cell subsets was then assessed by cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT). An experimental Sjögren's syndrome (ESS) mouse model was successfully constructed using SG protein. Based on mouse SG tissue RNA-Seq data, the seq-ImmuCC model was used to quantitatively analyze the compositional ratios of 10 immune cells in pSS patients and mouse model SG tissues. Computed and obtained 31 human data samples using the CIBERSORT deconvolution method. The immune cell infiltration results showed that, compared to normal human SG tissue, the content of gamma delta T cells was significantly different from naive CD4+ T cells and significantly increased, while the plasma cell content decreased. Principal component analysis indicated differences in immune cell infiltration between pSS patients and normal subjects. Meanwhile, for ESS model mouse data analysis, we found that the proportion of macrophages increased, while the proportion of CD4+ T cells, B cells, and monocytes decreased. Furthermore, we found that the proportion of monocytes was decreased, while the proportion of macrophages was increased in the SG tissues of pSS patients and model mice. The infiltration of CD4+ T, CD8+ T, and B cells also showed some differences. We comprehensively analyzed SG immune infiltration in pSS patients and model mice. We demonstrated conserved and nonconserved aspects of the immune system in mice and humans at the level of immune cells to help explain the primary regulation of immune mechanisms during the development of Sjögren's syndrome.

Distinct gene signatures of monocytes and B cells in patients with giant cell arteritis: a longitudinal transcriptome analysis

BackgroundGiant cell arteritis (GCA) is a primary large-vessel vasculitis (LVV) of unknown origin. Its management is a challenge due to the late onset of disease symptoms and frequent relapse; therefore, clarifying the pathophysiology of GCA is essential to improving treatment. This study aimed to identify the transition of molecular signatures in immune cells relevant to GCA pathogenesis by analyzing longitudinal transcriptome data in patients.MethodsWe analyzed the whole blood transcriptome of treatment-naive patients with GCA, patients with Takayasu arteritis (TAK), age-matched, old healthy controls (HCs), and young HCs. Characteristic genes for GCA were identified, and the longitudinal transition of those genes was analyzed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT).ResultsRepeated measures analysis of variance revealed 739 differentially expressed genes among all patients and HCs. Of the 739 genes, 15 were characteristically upregulated and 36 were downregulated in patients with GCA compared to those with TAK and HCs. Pathway enrichment analysis showed that downregulated genes in GCA were associated with B cell activation. CIBERSORT analysis revealed that upregulation of “M0-macrophages” and downregulation of B cells were characteristic of GCA. Upregulation of “M0-macrophages” reflects the activation of monocytes in GCA toward M0-like phenotypes, which persisted under 6 weeks of treatment. Combined treatment with prednisolone and an interleukin-6 receptor antagonist normalized molecular profiles more efficiently than prednisolone monotherapy.ConclusionsGene signatures of monocyte activation and B cell inactivation were characteristic of GCA and associated with treatment response.

Long Noncoding RNA LINC01614 is a Diagnostic and Prognostic Marker for Breast Cancer.

The long intergenic non-coding RNA 01614 (LINC01614) is aberrantly expressed in various malignancies, suggesting its role in oncogenesis. However, it has not been well studied in breast cancer. The cancer genome atlas databases (TCGA) and public database of breast cancer gene-expression miner (bc-GenExMiner) were utilized to analyze the prognostic role of LINC01614 in breast cancer. Kaplan-Meier, and Cox regression analyses were conducted for survival analysis. Nomograms were built to predict survival. We used deconvolution-based methods, such as TIMER (Tumor Immune Estimation Resource) and CIBERSORT (cell-type identification by estimating relative subsets of RNA transcripts), to explore the relationship between LINC01614 and immune cell characteristics. The very abnormal expression of LINC01614 was found in 14 types of malignancy, including breast cancer. The LINC01614 was significantly overexpressed in human epidermal growth factor receptor 2 (HER2)+, estrogen receptor (ER)+, progesterone receptor (PR)+, and non-triple negative breast cancer (non-TNBC). According to survival analysis, the higher expression of LINC01614 was related with poor survival. The co-expressed genes analysis exhibited that LINC01614 was closely associated with the collagen-associated process and phosphoinositide 3-kinases-protein kinase B (PI3K-Akt) signaling pathway. Moreover, this study has explored the association among LINC01614 expression, tumor-infiltrating immune cells, and the efficacy of chemotherapeutics. Our data reveal the expression pattern of LINC01614 in breast carcinoma with different molecular subtypes. The results also indicated that the LINC01614 could be a novel diagnostic and prognostic marker for breast carcinoma.

Comprehensive analysis of immune-related genes associated with the microenvironment of patients with unexplained infertility.

Disturbances in immunological responses and modulation lead to implantation and pregnancy failure and might be involved in the pathogenesis of infertility. This project aimed to screen and identify immune-related genes as potential biomarkers for treatment. Gene expression profiles were obtained from Gene Expression Omnibus (GEO) databases. Differentially expressed genes (DEGs) were screened using GEO 2R to explore potential biomarkers. Protein-protein interaction (PPI) network analysis and functional enrichment analysis were applied to explore possible mechanisms. The deconvolution algorithm [referred to as Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)] was employed to assess tissue-infiltrating immune cells. Western blot analysis and immunohistochemistry (IHC) were conducted for determination of protein levels. In this research, we identified 24 candidate immune-related DEGs via combined DEGs and functional analysis. We also found that the ratio of M0 macrophages and resting mast cells was higher in infertile group (P<0.05), whereas the amounts of activated natural killer (NK) cells was significantly lower compared with the control group (P<0.05). Furthermore, we evaluated the relationship between immune cells and candidate genes and found that 17 genes were related to M0 macrophages, resting mast cells, or activated NK cells. The genes CD40, PRF1, and EDN3 were chosen based on validation from independent datasets. Finally, our clinical samples confirmed the expression of the 3 genes. The study recognized 3 genes that are signatures and could be potential biomarkers for unexplained infertility. These genes might guide the immunotherapy of these patients and become new treatment targets.

Identifying M1-like macrophage related genes for prognosis prediction in lung adenocarcinoma based on a gene co-expression network

Macrophages are one of the most important players in the tumor microenvironment. But the contribution of macrophages to lung adenocarcinoma (LUAD) is still controversial. The current study aimed to display an immune landscape to clarify the function of macrophages and detect prognostic hub genes in LUAD. The transcriptome data were adopted to screen differently expressed genes (DEGs) in The Cancer Genome Atlas database (TCGA). The cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to reveal the immune landscape. Weighted gene co-expression network analysis (WGCNA) analysis was performed to identify the hub module associated with macrophages. Function Enrichment analysis was conducted on hub module genes. Moreover, univariate and multivariate Cox regression analyses were performed to identify prognostic hub genes. Kaplan-Meier (KM) and Time-dependent receiver operating characteristic (ROC) curves were plotted to assess the prognostic capacity of the four prognostic hub genes. The GES1196959 dataset from the Gene Expression Omnibus (GEO) database was downloaded to verify the differential expression of the 4 prognostic hub genes.

Identification of key genes in late-onset major depressive disorder through a co-expression network module.

Late-onset major depressive disorder (LOD) increases the risk of disability and suicide in elderly patients. However, the complex pathological mechanism of LOD still remains unclear. We selected 10 LOD patients and 12 healthy control samples from the GSE76826 dataset for statistical analysis. Under the screening criteria, 811 differentially expressed genes (DEGs) were screened. We obtained a total of two most clinically significant modules through the weighted gene co-expression network analysis (WGCNA). Functional analysis of the genes in the most clinically significant modules was performed to explore the potential mechanism of LOD, followed by protein-protein interaction (PPI) analysis and hub gene identification in the core area of the PPI network. Furthermore, we identified immune infiltrating cells using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm between healthy subjects and LOD patients with the GSE98793 dataset. Next, six hub genes (CD27, IL7R, CXCL1, CCR7, IGLL5, and CD79A) were obtained by intersecting hub genes with DEGs, followed by verifying the diagnostic accuracy with the receiver operating characteristic curve (ROC). In addition, we constructed the least absolute shrinkage and selection operator (LASSO) regression model for hub gene cross-validation. Finally, we found that CD27 and IGLL5 were good diagnostic indicators of LOD, and CD27 may be the key gene of immune function change in LOD. In conclusion, our research shows that the changes in the immune function may be an important mechanism in the development of LOD, which can provide some guidance for the related research of LOD in the future.

Analysis of C-X-C motif chemokine receptors in breast cancer: potential value in immunotherapy and prognostic prediction.

The concept of individualized therapy has advanced the development of prognostic biomarkers to manage patients with breast cancer (BRCA). Immunotherapy has shown great potential in treating BRCA, and the C-X-C motif chemokine receptor (CXCR) has generated interest in regulating tumor progression through the immune microenvironment. Although CXCRs were utilized for prognosis prediction in glioma with favourable capability, the prognostic and therapeutic role of CXCR in BRCA is unclear. We used The Cancer Genome Atlas (TCGA) database to analyze 1,095 BRCA patients' transcription, mutation, survival time and survival status. Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE), Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), quanTIseq, and Estimating the Proportion of Immune and Cancer cells (EPIC) algorithms were performed to infer the association of CXCR genes with immune cells. We randomly divided the TCGA dataset into a training set and a validation set according to 1:1, constructed a LASSO Cox regression model based on CXCR family genes using the glmnet R package in the training set, assembled clinical variables to draw a visual Nomogram using the R package rms, and validated the model by receiver operating characteristic (ROC) curves, calibration curves with clinical decision curves in the validation set efficacy. Compared to normal samples, CXCR3/4/5 messenger RNA (mRNA) expression levels were upregulated in BRCA samples, whereas CXCR1/2 mRNA expression levels were downregulated. High CXCR3/5/6 expression was associated with a good prognosis. Subsequently, we divided the CXCRs into 2 molecular subgroups based on their expression patterns and explored prognosis, immune infiltration, functional enrichment, hallmarks, and immune response differentiation between the two subgroups. After LASSO Cox regression modeling, a CXCR score predicting overall survival (OS) was constructed, and the predictive accuracy was assessed. By pooling clinical variables, a nomogram individual risk assessment method was established to measure the identification of genuinely high-risk patients who should receive interventions. In summary, CXCR genes were associated with immune infiltration and survival in BRCA patients, and our CXCR-based prognostic model could better predict the prognosis of BRCA patients and provide potential immunotherapy targets for clinical purposes.

Integrating RNA-seq and scRNA-seq to explore the biological significance of NAD + metabolism-related genes in the initial diagnosis and relapse of childhood B-cell acute lymphoblastic leukemia.

Nicotinamide Adenine Dinucleotide (NAD) depletion is reported to be a potential treatment for B-cell Acute Lymphoblastic Leukemia (B-ALL), but the mechanism of NAD metabolism-related genes (NMRGs) in B-ALL relapse remains unclear. Transcriptome data (GSE3912), and single-cell sequencing data (GSE130116) of B-ALL patients were downloaded from Gene Expression Omnibus (GEO) database. NMRGs were sourced from Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Further, the differentially expressed NMRGs (DE-NMRGs) were selected from the analysis between initial diagnosis and relapse B-ALL samples, which further performed functional enrichment analyses. The biomarkers were obtained through random forest (RF) algorithm and repeated cross validation. Additionally, cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the immune cell differences between the initial diagnosis and relapse samples, and the correlations between biomarkers and gene markers of differential immune cells were analyzed. Furthermore, single cell RNA sequencing was conducted in the GSE130116 dataset to find key cell clusters. In addition, according to biomarkers expressions, cell clusters were categorized into high and low biomarker expression groups, and Gene Set Enrichment Analysis (GSEA) analysis was performed on them. Finally, the cell clusters with the highest expression of biomarkers were selected to explore the roles of biomarkers in different cell clusters and identify transcription factors (TFs) influencing biological markers. 23 DE-NMRGs were screened out, which were mainly enriched in nucleoside phosphate metabolic process, nucleotide metabolic process, and Nicotinate and nicotinamide metabolism. Moreover, 3 biomarkers (NADSYN1, SIRT3, and PARP6) were identified from the machine learning. CIBERSORT results demonstrated that four types of immune cells (B Cells naive, Monocyte, Neutrophils, and T cells CD4 memory Activated) were significantly different between the initial diagnosis and the relapse B-ALL samples, and there were strong correlations between biomarkers and differential immune cells such as positive correlation between NADSYN1 and B Cells naive. The single cell analyses showed that the biomarkers were highly expressed in common myeloid progenitors (CMP), granulocyte-macrophage progenitor (GMP), and megakaryocyte-erythroid progenitor (MEP) cell clusters. Gene set enrichment analysis (GSEA) results indicated that 55 GO terms and 3 KEGG pathways were enriched by the genes in high and low biomarker expression groups. It was found that TF CREB3L2(+) was significantly reduced in the high expression group, which may be the TF affecting biomarkers in the high expression group. This study identified NADSYN1, SIRT3, and PARP6 as the biomarkers of B-ALL, explored biological significance of NMRGs in the initial diagnosis and relapse of B-ALL, and revealed mechanism of biomarkers at the level of the single cell.

Investigation of hub genes and immune infiltration in androgenetic alopecia using bioinformatics analysis

Androgenetic alopecia (AGA) is a type of non-scarring hair loss. Current drugs for AGA are accompanied by adverse reactions and a high recurrence rate. Thus, the discovery of diagnostic biomarkers and therapeutic targets for AGA remains imperatively warranted. The GSE90594 dataset, which contained scalp skin biopsies from 14 male AGA cases and healthy volunteers, was used to identify the differentially expressed genes (DEGs). Functional enrichment analysis was subsequently performed. Next, the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database combined with the cytoHubba plugin of Cytoscape were used to obtain the key genes of AGA. Thereafter, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was performed to evaluate the relative abundance of immune cells between male AGA patients and healthy controls. The correlation between key genes and infiltrating immune cells was analyzed to obtain the significant immune-cell related genes (IRGs), then intersected with the DEGs between immortalized balding and non-balding human dermal papilla cells (DPCs) of the GSE93766 dataset as well as the DEGs obtained by the GSE90594 dataset, thus obtaining the hub genes of AGA. Finally, the hub genes were validated using GSE36169, which contained expression profiling of tissues biopsied from haired and bald scalps of five individuals with AGA. A total of 234 DEGs were obtained from the GSE90594 dataset, which were mainly enriched in the extracellular matrix (ECM)-related pathways and immune-related activities. The STRING database and ten algorithms in the cytoHubba plugin of Cytoscape disclosed 21 key DEGs. The results of the CIBERSORT algorithm revealed the relative abundances of 20 kinds of immune cells between diseased and healthy individuals, and yielded 15 IRGs involved in the pathogenesis of AGA. Next, the intersection analysis identified four hub genes of AGA, comprising COL1A2, PCOLCE, ITGAX, and LOX. The GSE36169 dataset validated the expression pattern of hub genes in the haired scalp of AGA patients. We discovered that the hub genes identified are closely linked with the causative factors of AGA, which could be used as the viable diagnostic and therapeutic target in the clinical applications.

Capsular inflammation after immediate breast reconstruction – Gene expression patterns and inflammatory cell infiltration in irradiated and non-irradiated breasts

Capsular contracture following post-mastectomy radiotherapy (PMRT) is commonly seen in patients undergoing implant-based immediate breast reconstruction (IBR). Further understanding of the underlying biology is needed for the development of preventive or therapeutic strategies. Therefore, we conducted a comparative study of gene expression patterns in capsular tissue from breast cancer patients who had received versus those who had not received PMRT after implant-based IBR. Biopsies from irradiated and healthy non-irradiated capsular tissue were harvested during implant exchange following IBR. Biopsies from irradiated (n=13) and non-irradiated (n=12) capsules were compared using Affymetrix microarrays to identify the most differentially regulated genes. Further analysis using immunohistochemistry was performed in a subset of materials to compare the presence of T cells, B cells, and macrophages. Enrichment testing using Gene Ontology (GO) analysis revealed that the 227 most differentially expressed genes were mainly involved in an inflammatory response. Twenty-one GO biological processes were identified [p < 0.05, false discovery rate (FDR) < 5%], several with B-cell-associated inflammation. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis identified macrophages as the most common inflammatory cell type in both groups, further supported by immunostaining of CD68. Radiation remarkably increased B-cell infiltration in the capsular region of biopsies, as quantified by immunostaining of CD20 (p=0.016). Transcript analysis and immunohistochemistry revealed inflammatory responses in capsular biopsies regardless of radiotherapy. However, the radiation response specifically involved B-cell-associated inflammatory responses.

Identification of senescence-associated long non-coding RNAs to predict prognosis and immune microenvironment in patients with hepatocellular carcinoma.

Background: Cellular senescence plays a complicated and vital role in cancer development because of its divergent effects on tumorigenicity. However, the long non-coding RNAs (lncRNAs) associated with tumor senescence and their prognostic value in hepatocellular carcinoma (HCC) remain unexplored. Methods: The trans-cancer oncogene-induced senescence (OIS) signature was determined by gene set variation analysis (GSVA) in the cancer genome atlas (TCGA) dataset. The OIS-related lncRNAs were identified by correlation analyses. Cox regression analyses were used to screen lncRNAs associated with prognosis, and an optimal predictive model was created by regression analysis of the least absolute shrinkage and selection operator (LASSO). The performance of the model was evaluated by Kaplan-Meier survival analyses, nomograms, stratified survival analyses, and receiver operating characteristic curve (ROC) analyses. Gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) were carried out to explore the functional relevance and immune cell infiltration, respectively. Results: Firstly, we examined the pan-cancer OIS signature, and found several types of cancer with OIS strongly associated with the survival of patients, including HCC. Subsequently, based on the OIS signature, we identified 76 OIS-related lncRNAs with prognostic values in HCC. We then established an optimal prognostic model based on 11 (including NRAV, AC015908.3, MIR100HG, AL365203.2, AC009005.1, SNHG3, LINC01138, AC090192.2, AC008622.2, AL139423.1, and AC026356.1) of these lncRNAs by LASSO-Cox regression analysis. It was then confirmed that the risk score was an independent and potential risk indicator for overall survival (OS) (HR [95% CI] = 4.90 [2.74-8.70], p < 0.001), which outperforms those traditional clinicopathological factors. Furthermore, patients with higher risk scores also showed more advanced levels of a proinflammatory senescence-associated secretory phenotype (SASP), higher infiltration of regulatory T (Treg) cells and lower infiltration of naïve B cells, suggesting the regulatory effects of OIS on immune microenvironment. Additionally, we identified NRAV as a representative OIS-related lncRNA, which is over-expressed in HCC tumors mainly driven by DNA hypomethylation. Conclusion: Based on 11 OIS-related lncRNAs, we established a promising prognostic predictor for HCC patients, and highlighted the potential immune microenvironment-modulatory roles of OIS in HCC, providing a broad molecular perspective of tumor senescence.

Prognostic analysis and validation of diagnostic marker genes in patients with osteoporosis.

BackgroundsAs a systemic skeletal dysfunction, osteoporosis (OP) is characterized by low bone mass and bone microarchitectural damage. The global incidences of OP are high.MethodsData were retrieved from databases like Gene Expression Omnibus (GEO), GeneCards, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), Gene Expression Profiling Interactive Analysis (GEPIA2), and other databases. R software (version 4.1.1) was used to identify differentially expressed genes (DEGs) and perform functional analysis. The Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression and random forest algorithm were combined and used for screening diagnostic markers for OP. The diagnostic value was assessed by the receiver operating characteristic (ROC) curve. Molecular signature subtypes were identified using a consensus clustering approach, and prognostic analysis was performed. The level of immune cell infiltration was assessed by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The hub gene was identified using the CytoHubba algorithm. Real-time fluorescence quantitative PCR (RT-qPCR) was performed on the plasma of osteoporosis patients and control samples. The interaction network was constructed between the hub genes and miRNAs, transcription factors, RNA binding proteins, and drugs.ResultsA total of 40 DEGs, eight OP-related differential genes, six OP diagnostic marker genes, four OP key diagnostic marker genes, and ten hub genes (TNF, RARRES2, FLNA, STXBP2, EGR2, MAP4K2, NFKBIA, JUNB, SPI1, CTSD) were identified. RT-qPCR results revealed a total of eight genes had significant differential expression between osteoporosis patients and control samples. Enrichment analysis showed these genes were mainly related to MAPK signaling pathways, TNF signaling pathway, apoptosis, and Salmonella infection. RT-qPCR also revealed that the MAPK signaling pathway (p38, TRAF6) and NF-kappa B signaling pathway (c-FLIP, MIP1β) were significantly different between osteoporosis patients and control samples. The analysis of immune cell infiltration revealed that monocytes, activated CD4 memory T cells, and memory and naïve B cells may be related to the occurrence and development of OP.ConclusionsWe identified six novel OP diagnostic marker genes and ten OP-hub genes. These genes can be used to improve the prognostic of OP and to identify potential relationships between the immune microenvironment and OP. Our research will provide insights into the potential therapeutic targets and pathogenesis of osteoporosis.

Fatty acid metabolism-related genes in bronchoalveolar lavage fluid unveil prognostic and immune infiltration in idiopathic pulmonary fibrosis.

BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic and progressive condition with an unfavorable prognosis. A recent study has demonstrated that IPF patients exhibit characteristic alterations in the fatty acid metabolism in their lungs, suggesting an association with IPF pathogenesis. Therefore, in this study, we have explored whether the gene signature associated with fatty acid metabolism could be used as a reliable biological marker for predicting the survival of IPF patients.MethodsData on the fatty acid metabolism-related genes (FAMRGs) were extracted from databases like Kyoto Encyclopedia of Genes and Genomes (KEGG), Hallmark, and Reactome pathway. The GSE70866 dataset with information on IPF patients was retrieved from the Gene Expression Omnibus (GEO). Next, the consensus clustering method was used to identify novel molecular subgroups. Gene Set Enrichment Analysis (GSEA) was performed to understand the mechanisms involved. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the level of immune cell infiltration in the identified subgroups based on gene expression signatures of immune cells. Finally, the Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression analysis were performed to develop a prognostic risk model.ResultsThe gene expression signature associated with fatty acid metabolism was used to create two subgroups with significantly different prognoses. GSEA reveals that immune-related pathways were significantly altered between the two subgroups, and the two subgroups had different metabolic characteristics. High infiltration of immune cells, mainly activated NK cells, monocytes, and activated mast cells, was observed in the subgroup with a poor prognosis. A risk model based on FAMRGs had an excellent ability to predict the prognosis of IPF. The nomogram constructed using the clinical features and the risk model could accurately predict the prognosis of IPF patients.ConclusionThe fatty acid metabolism-related gene expression signature could be used as a potential biological marker for predicting clinical outcomes and the level of infiltration of immune cells. This could eventually enhance the accuracy of the treatment of IPF patients.

Identification of the key immune-related genes in aneurysmal subarachnoid hemorrhage.

Subarachnoid hemorrhage (SAH) is a major cause of death and morbidity worldwide, often due to rupture of intracranial aneurysms (IAs). Immune infiltration and inflammatory activation play key roles in the process of aneurysmal SAH (aSAH). This study aimed to elaborate the immune infiltration and identify related biomarkers both in blood and tissue samples from patients with aSAH. Expression data of aSAH and healthy control samples were obtained from gene expression omnibus (GEO) database. Overall, a blood sample dataset GSE36791 and a tissue sample dataset GSE122897 were included. Differentially expressed genes (DEGs) between aSAH and healthy samples were explored. We applied GO biological and Gene Set Enrichment Analyses (GSEA) processes to access the functional enrichment. Then feature elimination algorithms based on random forest were used to screen and verify the biomarkers of aSAH. We performed three computational algorithms including Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), Microenvironment Cell Populations-counter (MCPcounter), and xcell to evaluate the immune cell infiltration landscape to identify the unique infiltration characteristics associated with rupturing. We found 2,220 DEGs (856 upregulated and 1,364 downregulated) in the original dataset. Functional analysis revealed most of these genes are enriched in immunological process, especially related with neutrophil response. Similar signaling pathway enrichment patterns were observed in tissue sample dataset and ClueGo. Analysis of immune microenvironment infiltration suggested neutrophils were abnormally upregulated in aSAH compared with those in the control group. Key gene SRPK1 was then filtered based on feature elimination algorithms, and transcription factor (TF) ZNF281 is assumed to participate in immunomodulation by regulating expression of SRPK1. Several immunomodulators such as CXCR1 and CXCR2 also appear to be involved in the progression of aSAH. In the present study, we performed a comprehensive stratification and quantification of the immune infiltration status of aSAH. By exploring the potential mechanism for aSAH based on several computational algorithms, key genes including SRPK1 and ZNF281 were filtered. This study may be of benefit to patients who are at high risk of suffering aSAH which allows for early diagnosis and potential therapy.

Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas

BackgroundAlthough intrinsic immune-evasion is important in cancer proliferation, metastasis and response to treatment, it is unclear whether intrinsic immune-evasion patterns of gliomas can aid in predicting clinical prognosis and determining treatment.MethodsA total of 182 immune-evasion genes intrinsic to cancer were subjected to consensus clustering to identify immune-evasion patterns in 1421 patients with lower-grade glioma (LGG). The levels of each cancer hallmark were determined by the Gene Set Variant Analysis (GSVA) method, and immune cell infiltrations were quantified using two algorithms, the single-sample Gene Set Enrichment Analysis (ssGSEA) and the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) methods. IEVscore was determined by a method that combined univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression and principal component analysis (PCA).ResultsTranscriptional and genomic analysis showed that most immune evasion genes (IEVGs) were upregulated in LGGs, with aberrant expression driven by alterations in copy number variants (CNV). Based on the mRNA expression profiles of cancer-intrinsic IEVGs could be divided into three LGG subgroups with distinct prognosis, clinicopathological features and immune infiltrations. A combined scoring scheme designed to assess the immune-evasion levels of LGGs divided these 1421 patients into two subgroups that differed in IEVscores. LGG patients with low-IEVscore had a better prognosis, would be more likely to benefit from immune check-point inhibitors and would be more susceptible to temozolomide (TMZ) chemotherapy.ConclusionIntrinsic immune evasion in the tumor microenvironment (TME) has a crucial effect on glioma formation. Quantitatively assessing the IEV scores of individual LGG patients could enhance knowledge about the intra-glioma microenvironment and lead to the development of individualized therapeutic strategies for patients with LGG.

Landscape and Construction of a Novel N6-methyladenosine-related LncRNAs in Cervical Cancer.

Cervical cancer is a crucial clinical problem with high mortality. Despite much research in therapy, the prognosis of patients with cervical cancer is still not ideal. The data on cervical cancer were downloaded from The Cancer Genome Atlas (TCGA) portal. R language was used to screen out the N6-methyladenosine (m6A)-related lncRNAs (long non-coding RNA). A consensus clustering algorithm was performed to identify m6A-related lncRNAs in cervical cancer; 10 m6A-related lncRNAs showing a significant association with survival were filtrated through a gradually univariate Cox regression model, least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression preliminarily. Furthermore, we conducted Kaplan-Meier curves, receiver operating curve (ROC) analyses, and proportional hazards model to quantify the underlying character of the m6A-related model in the prevision of cervical cancer patients. Gene set enrichment analysis (GSEA) was used to explore several pathways significantly. Finally, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) was applied to estimate the immune cell infiltration in the profiling. In the present study, 10 m6A-related lncRNAs make up our prediction model. This prediction model can do duty for an independent predictive biomolecular element. Subsequently, we then found that the model was still valid in further validation of the training group and the test group. Our signature was correlated with immune cell infiltration and partial signaling pathway. These lncRNAs played a no negligible biomolecular role in contributing to the prognosis of cervical cancer.

Modification of m6A mediates tissue immune microenvironment in calcific aortic valve disease.

BackgroundSeveral human diseases are associated with aberrant expression of regulators involved in N6-methyladenosine (m6A) RNA modification. However, their role in aortic valve calcification (AVC) is largely unknown. The aim of this study was to determine the general expression pattern and potential function of m6A regulators in AVC by bioinformatics methods.MethodsWe obtained AVC datasets from the Gene Expression Omnibus (GEO). The identification of m6A-related differentially expressed genes (DEGs) and the Consensus Clustering method was performed to type AVC individuals based DEGs. Then, we quantified the effect of typing by principal component analysis (PCA). Next, we performed the weighted gene co-expression network analysis (WGCNA) and identified the main modules as well as functional analysis. Additionally, the key genes were screened by protein-protein interaction network (PPIN) analysis and identifying important genes of important modules. We again typed AVC individuals by the same method using key genes. Finally, we evaluated the link between key genes and immune infiltration.ResultsWe discovered that METTL14, ZC3H13, FTO, FMR1, HNRNPA2B1, HNRNPC, LRPPRC, YTHDC1, YTHDC2, and YTHDF1 expression levels decreased considerably in AVC tissues. Based on 10 genes, we typed 240 AVC samples as clusters A and B. We assessed the immune cell content in 240 samples using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and found that B cell memory, CD8 T cells, T follicular helper cells, monocytes, M0 macrophages, resting dendritic cells (DCs), and interleukin-10 (IL-10) were concentrated in the cluster A group. Additionally, based on the important WGCNA modules, we identified 7 key genes. Next, 240 samples were retyped based on 7 key genes; we found that T cells CD8, T cells CD4 memory activated, T cells follicular helper, and macrophages M1 were significantly increased in gene cluster-1. Finally, we performed functional enrichment of gene cluster-typed samples, showing potential functional differences between different types.ConclusionsOur study provides a review of the m6A regulators’ expression pattern and functional importance in human AVC. The data from this study might serve as a significant resource for future mechanistic and therapeutic investigations into the role of critical m6A regulators in AVC.

Identification and validation of a novel prognostic model of inflammation-related gene signature of lung adenocarcinoma

Previous literatures have suggested the importance of inflammatory response during lung adenocarcinoma (LUAD) development. This study aimed at exploring the inflammation-related genes and developing a prognostic signature for predicting the prognosis of LUAD. Survival‑associated inflammation-related genes were identified by univariate Cox regression analysis in the dataset of The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) penalized Cox regression model was used to derive a risk signature which is significantly negatively correlated with OS and divide samples into high-, medium- and low-risk group. Univariate and multivariate Cox analyses suggested that the level of risk group was an independent prognostic factor of the overall survival (OS). Time-dependent receiver operating characteristic (ROC) curve indicated the AUC of 1-, 3- and 5-years of the risk signature was 0.715, 0.719, 0.699 respectively. A prognostic nomogram was constructed by integrating risk group and clinical features. The independent dataset GSE30219 of Gene Expression Omnibus (GEO) was used for verification. We further explored the differences among risk groups in Gene set enrichment analysis (GSEA), tumor mutation and tumor microenvironment. Furthermore, Single Sample Gene Set Enrichment Analysis (ssGSEA) and the results of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) suggested the status of immune cell infiltration was highly associated with risk groups. We demonstrated the prediction effect of CTLA-4 and PD-1/PD-L1 inhibitors in the low-risk group was better than that in the high-risk group using two methods of immune score include immunophenoscore from The Cancer Immunome Atlas (TCIA) and TIDE score from Tumor Immune Dysfunction and Exclusion (TIDE). In addition, partial targeted drugs and chemotherapy drugs for lung cancer had higher drug sensitivity in the high-risk group. Our findings provide a foundation for future research targeting inflammation-related genes to predictive prognosis and some reference significance for the selection of immunotherapy and drug regimen for lung adenocarcinoma.

A genomic instability-related lncRNA model for predicting prognosis and immune checkpoint inhibitor efficacy in breast cancer.

Breast cancer has overtaken lung cancer as the most frequently diagnosed cancer type and is the leading cause of death for women worldwide. It has been demonstrated in published studies that long non-coding RNAs (lncRNAs) involved in genomic stability are closely associated with the progression of breast cancer, and remarkably, genomic stability has been shown to predict the response to immune checkpoint inhibitors (ICIs) in cancer therapy, especially colorectal cancer. Therefore, it is of interest to explore somatic mutator-derived lncRNAs in predicting the prognosis and ICI efficacy in breast cancer patients. In this study, the lncRNA expression data and somatic mutation data of breast cancer patients from The Cancer Genome Atlas (TCGA) were downloaded and analyzed thoroughly. Univariate and multivariate Cox proportional hazards analyses were used to generate the genomic instability-related lncRNAs in a training set, which was subsequently used to analyze a testing set and combination of the two sets. The qRT-PCR was conducted in both normal mammary and breast cancer cell lines. Furthermore, the Kaplan–Meier and receiver operating characteristic (ROC) curves were applied to validate the predictive effect in the three sets. Finally, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to evaluate the association between genomic instability-related lncRNAs and immune checkpoints. As a result, a six-genomic instability-related lncRNA signature (U62317.4, MAPT-AS1, AC115837.2, EGOT, SEMA3B-AS1, and HOTAIR) was identified as the independent prognostic risk model for breast cancer patients. Compared with the normal mammary cells, the qRT-PCR showed that HOTAIR was upregulated while MAPT-AS1, EGOT, and SEMA3B-AS1 were downregulated in breast cancer cells. The areas under the ROC curves at 3 and 5 years were 0.711 and 0.723, respectively. Moreover, the patients classified in the high-risk group by the prognostic model had abundant negative immune checkpoint molecules. In summary, this study suggested that the prognostic model comprising six genomic instability-related lncRNAs may provide survival prediction. It is necessary to identify patients who are suitable for ICIs to avoid severe immune-related adverse effects, especially autoimmune diseases. This model may predict the ICI efficacy, facilitating the identification of patients who may benefit from ICIs.

Construction and analysis of competing endogenous RNA network and patterns of immune infiltration in abdominal aortic aneurysm.

BackgroundVarious studies have highlighted the role of circular RNAs (circRNAs) as critical molecular regulators in cardiovascular diseases, but its role in abdominal aortic aneurysm (AAA) is unclear. This study explores the potential molecular mechanisms of AAA based on the circRNA-microRNA (miRNA)-mRNA competing endogenous RNA (ceRNA) network and immune cell infiltration patterns.MethodsThe expression profiles of circRNAs (GSE144431) and mRNAs (GSE57691 and GSE47472) were obtained from the Gene Expression Omnibus (GEO). Then, the differentially expressed circRNAs (DEcircRNAs) and mRNAs (DEmRNAs) between AAA patients and healthy control samples, and the target miRNAs of these DEmRNAs and DEcircRNAs were identified. Based on the miRNA-DEmRNAs and miRNA-DEcircRNAs pairs, the ceRNA network was constructed. Furthermore, the proportion of the 22 immune cell types in AAA patients was assessed using cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. The expressions of key genes and immune cell infiltration were validated using clinical specimens.ResultsA total of 214 DEmRNAs were identified in the GSE57691 and GSE47472 datasets, and 517 DEcircRNAs were identified in the GSE144431 dataset. The ceRNA network included 19 circRNAs, 36 mRNAs, and 68 miRNAs. Two key genes, PPARG and FOXO1, were identified among the hub genes of the established protein-protein interaction between mRNAs in the ceRNA network. Moreover, seven types of immune cells were differentially expressed between AAA patients and healthy control samples. Hub genes in ceRNA, such as FOXO1, HSPA8, and RAB5C, positively correlated with resting CD4 memory T cells or M1 macrophages, or both.ConclusionIn conclusion, a ceRNA interaction axis was constructed. The composition of infiltrating immune cells was analyzed in the abdominal aorta of AAA patients and healthy control samples. This may help identify potential therapeutic targets for AAA.