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

Identification of GINS1 as a potential prognostic biomarker for sarcoma using bioinformatic analysis.

The GINS complex is related to cancer development, invasion, and poor prognosis in multiple tumors. In the study, we attempted to investigate the prognostic value of GINS1 in sarcoma patients. We analyzed GINS1 expression using Tumor IMmune Estimation Resource (TIMER) 2.0, Gene Expression Omnibus (GEO; GSE21122, GSE39262, and GSE21050), and The Cancer Genome Atlas (TCGA) databases. The prognostic value of GINS1 was explored using the survival and survminer packages of R. Genetic alteration analysis was investigated using cBioPortal. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) R script was used for the immunocyte infiltration analysis. MicroRNAs (miRNAs) targeting GINS1 were predicted using GEO (GSE69470) and MicroRNA Target Prediction Database (miRDB). We found that GINS1 was overexpressed in sarcoma, especially in metastatic samples, and was associated with a worse prognosis. High GINS1 expression was a poor prognostic indicator for sarcoma patients. Moreover, GINS1 alteration was associated with worse survival of sarcoma patients. Immune infiltration analysis indicated that GINS1 expression was correlated with the infiltration of M0 and M2 macrophages in sarcoma. Finally, the miRNA hsa-miR-376a-3p was identified to potentially regulate GINS1 in sarcoma. These results indicate that GINS1 may be a promising prognostic biomarker and therapeutic target for sarcoma.

Development and validation of a novel 5 cuproptosis-related long noncoding RNA signature to predict diagnosis, prognosis, and drug therapy in clear cell renal cell carcinoma.

Cuproptosis has been reported as a new form of cell death. However, its potential mechanism of action in clear cell renal cell carcinoma (ccRCC) remains unclear. Therefore, we systematically clarified the role of cuproptosis in ccRCC and aimed to develop a novel signature of cuproptosis-related long noncoding RNAs (lncRNA) (CRLs) to assess the clinical characteristics of ccRCC patients. Gene expression, copy number variation, gene mutation, and clinical data for ccRCC were obtained from The Cancer Genome Atlas (TCGA). CRL signature was constructed with least absolute shrinkage and selection operator (LASSO) regression analysis. The clinical diagnostic value of the signature was verified by clinical data. The prognostic value of the signature was detected by Kaplan-Meier analysis and receiver operating characteristic (ROC) curve. The prognostic value of the nomogram was evaluated by calibration curves, ROC curves, and decision curve analysis (DCA). Gene set enrichment analysis (GSEA), single sample GSEA (ssGSEA) and cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm were used to analyze the differences of immune function and immune cell infiltration among different risk groups. Prediction of clinical treatment differences in populations with different risks and susceptibilities was completed with R package (The R Foundation of Statistical Computing). Verification of key lncRNA expression was performed by quantitative real-time polymerase chain reaction (qRT-PCR). The cuproptosis-related genes were extensively dysregulated in ccRCC. A total of 153 differentially expressed prognostic CRLs were identified in ccRCC. Furthermore, a 5-lncRNA signature (AC015912.3, AC026401.3, AC103706.1, AC134312.5, and EMX2OS) were obtained that showed good performance in the diagnosis and prognosis of ccRCC. The nomogram could more accurately predict overall survival (OS). Immune functions such as T-cell and B-cell receptor signaling pathways showed differences between different risk groups. Clinical treatment value analysis showed that the signature may be able to effectively guide immunotherapy and target therapy. In addition, qRT-PCR results showed significant differences in the expression of key lncRNAs in ccRCC. Cuproptosis plays an important role in the progression of ccRCC. The 5-CRL signature can guide the prediction of clinical characteristics and tumor immune microenvironment of ccRCC patients.

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

Neurological prognostication after cardiac arrest (CA) is important to avoid pursuing futile treatments for poor outcome and inappropriate withdrawal of life-sustaining treatment for good outcome. To predict neurological outcome after CA through biomarkers in peripheral blood mononuclear cells, four datasets were downloaded from the Gene Expression Omnibus database. GSE29546 and GSE74198 were used as training datasets, while GSE92696 and GSE34643 were used as verification datasets. The intersection of differentially expressed genes and hub genes from multiscale embedded gene co-expression network analysis (MEGENA) was utilized in the machine learning screening. Key genes were identified using support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF). The results were validated using receiver operating characteristic curve analysis. An mRNA-miRNA network was constructed. The distribution of immune cells was evaluated using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). Five biomarkers were identified as predictors for neurological outcome after CA, with an area under the curve (AUC) greater than 0.7: CASP8 and FADD-like apoptosis regulator (CFLAR), human protein kinase X (PRKX), miR-483-5p, let-7a-5p, and let-7c-5p. Interestingly, the combination of CFLAR minus PRKX showed an even higher AUC of 0.814. The mRNA-miRNA network consisted of 30 nodes and 76 edges. Statistical differences were found in immune cell distribution, including neutrophils, NK cells active, NK cells resting, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, B cells memory, and mast cells resting between individuals with good and poor neurological outcome after CA. In conclusion, our study identified novel predictors for neurological outcome after CA. Further clinical and laboratory studies are needed to validate our findings.

Identification of Diagnostic Markers in Synovial Tissue of Osteoarthritis by Weighted Gene Coexpression Network.

Osteoarthritis (OA) is a serious threat to human health. However, the etiology and pathogenesis of the disease are not fully understood. Most researchers believe that the degeneration and imbalance of articular cartilage, extracellular matrix, and subchondral bone are the fundamental causes of osteoarthritis. However, recent studies have shown that synovial lesions may precede cartilage, which may be an important precipitating factor in the early stage of OA and the whole course of the disease. This study aimed to conduct an analysis based on sequence data from the Gene Expression Omnibus (GEO) database to investigate the presence of effective biomarkers in the synovial tissue of osteoarthritis for the diagnosis and control of OA progression. In this study, the differentially expressed OA-related genes (DE-OARGs) in osteoarthritis synovial tissues were extracted in the GSE55235 and GSE55457 datasets using the Weighted Gene Co-expression Network Analysis (WGCNA) and limma. Least-Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to select the diagnostic genes based on the DE-OARGs by glmnet package. 7 genes were selected as diagnostic genes including SAT1, RLF, MAFF, SIK1, RORA, ZNF529, and EBF2. Subsequently, the diagnostic model was constructed and the results of the Area Under the Curve (AUC) demonstrated that the diagnostic model had high diagnostic performance for OA. Additionally, among the 22 immune cells of the Cell type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) and the 24 immune cells of the single sample Gene Set Enrichment Analysis (ssGSEA), 3 immune cells and 5 immune cells were different between the OA and normal samples, respectively. The expression trends of the 7 diagnostic genes were consistent in the GEO datasets and the results of the real-time reverse transcriptionPCR (qRT-PCR). The results of this study demonstrate that these diagnostic markers have important significance in the diagnosis and treatment of OA, and will provide further evidence for the clinical and functional studies of OA.

LRP1B mutation is associated with tumor immune microenvironment and progression-free survival in lung adenocarcinoma treated with immune checkpoint inhibitors.

Only a fraction of lung adenocarcinoma (LUAD) patients are eligible for immunotherapy. The identification of biomarkers for immunotherapy is crucial to improve patient outcomes. This study aims to systemically analyze LRP1B mutation and its association with the tumor immune microenvironment (TIME) and immunotherapy. A cohort of immune checkpoint inhibitors (ICIs)-treated LUAD patients was analyzed to assess the association of LRP1B mutation with immunotherapy prognosis. Another cohort of LUAD patients with genetic and transcriptomic data was also obtained from The Cancer Genome Atlas (TCGA). By investigating the ICIs and the TCGA-LUAD cohorts, we compared the differences in mutation profiles, immunogenicity, TIME, and DNA damage repair (DDR) mutations between the LRP1B-mutated and LRP1B wild-type groups. Additionally, we performed multiplex immunohistochemistry (mIHC) to validate the differences in the tumor microenvironment. Our results revealed that LRP1B mutation is associated with multiple immune-related pathways. Analysis of TIME indicated that LUAD patients with LRP1B mutation expressed significant levels of genes involved in antigen presentation, cytotoxicity, chemokines, and pro-inflammatory mediators, whereas a few immune checkpoint genes were highly expressed in the LRP1B-mutated group as well. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) analysis indicated that LRP1B-mutated LUAD patients had higher infiltration of active immune cells. Multiplex IHC analysis showed that LRP1B-mutated LUAD patients had elevated programmed death ligand-1 (PD-L1) expression and immune cell infiltration. Patients with LRP1B mutation had higher tumor mutation burden, neoantigens, as well as more mutated genes in the DDR-related pathways. Finally, LRP1B-mutated LUAD patients showed a significant prolongation of progression-free survival (PFS) in the ICIs cohort and could be effectively predicted by our constructed nomogram. Our study suggests that LRP1B mutation is associated with higher immune cell infiltration and elevated immune gene expression in TIME and potentially serves as a prognostic biomarker for LUAD patients treated with ICIs.

Identification of a hypoxia-related gene prognostic signature in colorectal cancer based on bulk and single-cell RNA-seq

Colorectal cancer (CRC) is the most common and fatal tumor in the gastrointestinal system. Its incidence and mortality rate have increased in recent years. Hypoxia, a persistent physiological tumor feature, plays a vital role in CRC tumorigenesis, metastasis, and tumor microenvironment (TME). Therefore, we constructed a hypoxia-related gene (HRG) nomogram to predict overall survival (OS) and explored the role of HRGs in the CRC TME. The Cancer Genome Atlas (TCGA) dataset was used as the training set, and two Gene Expression Omnibus datasets (GSE39582 and GSE103479) were used as the testing sets. HRGs were identified using the Gene Set Enrichment Analysis (GSEA) database. An HRG prognostic model was constructed in the training set using the least absolute shrinkage and selection operator regression algorithm and validated in the testing sets. Then, we analyzed tumor-infiltrating cells (TICs) using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Furthermore, single-cell next-generation RNA sequencing (RNA-seq) was used to investigate HRG expression in different TICs in the GSE139555 dataset. Finally, reverse transcription polymerase chain reactions (RT-PCR) were used to validate HRG mRNA expression in ten pairs of CRC normal and cancer tissue samples. A six HRG prognostic signature was constructed, with a superior OS prediction ability in CRC patients (area under the receiver operating characteristic curve (AUC) at one year: 0.693, AUC at three years: 0.712, and AUC at five years: 0.780). GSEA enrichment analysis identified six pathways enriched in the high-risk group. The TIC analysis indicated that the high-risk group had lower T-cell expression and higher neutrophil expression than the low-risk group. Furthermore, immune-related genes had an inseparable relationship with the HRG prognostic signature. Based on single-cell RNA-seq data, we found elevated hexokinase 1 (HK1) and glucose-6-phosphate isomerase (GPI) gene expression in natural killer (NK) and CD8+ T cells. RT-PCR in ten CRC normal-tumor tissue pairs showed that expression of the signature’s six HRGs varied differently in cancerous and paracancerous tissues. The constructed HRG signature successfully predicted the OS of whole-stage CRC patients. In addition, we showed that the signature’s six HRGs were closely associated with the TME in CRC, where hypoxia inhibits the antitumor function of T cells.

Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis.

This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms. Microarray datasets of IPF were extracted from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs). The DEGs were subjected to enrichment analysis, and two machine learning algorithms were used to identify candidate genes associated with IPF. These genes were verified in a validation cohort from the GEO database. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the IPF-associated genes. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the proportion of immune cells in IPF and normal tissues. Additionally, the correlation between the expression of IPF-associated genes and the infiltration levels of immune cells was examined. A total of 302 upregulated and 192 downregulated genes were identified. Functional annotation, pathway enrichment, Disease Ontology and gene set enrichment analyses revealed that the DEGs were related to the extracellular matrix and immune responses. COL3A1, CDH3, CEBPD, and GPIHBP1 were identified as candidate biomarkers using machine learning algorithms, and their predictive value was verified in a validation cohort. Additionally, ROC analysis revealed that the four genes had high predictive accuracy. The infiltration levels of plasma cells, M0 macrophages and resting dendritic cells were higher and those of resting natural killer (NK) cells, M1 macrophages and eosinophils were lower in the lung tissues of patients with IPF than in those of healthy individuals. The expression of the abovementioned genes was correlated with the infiltration levels of plasma cells, M0 macrophages and eosinophils. COL3A1, CDH3, CEBPD, and GPIHBP1 are candidate biomarkers of IPF. Plasma cells, M0 macrophages and eosinophils may be involved in the development of IPF and may serve as immunotherapeutic targets in IPF.

An immune cell infiltration landscape classification to predict prognosis and immunotherapy effect in oral squamous cell carcinoma

Tumor immune cell infiltration (ICI) is associated with the prognosis of oral squamous cell carcinoma (OSCC) patients and the effect of immunotherapy. The combat algorithm was used to merge the data from three databases and the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm to quantify the amount of infiltrated immune cells. Unsupervised consistent cluster analysis was used to determine ICI subtypes, and differentially expressed genes (DEGs) were determined according to these subtypes. The DEGs were then clustered again to obtain the ICI gene subtypes. The principal component analysis (PCA) and the Boruta algorithm were used to construct the ICI scores. Three different ICI clusters and gene clusters with a prognosis of significant difference were found and the ICI score was constructed. Patients with higher ICI scores have a better prognosis following internal and external verification. Besides, the proportion of patients with effective immunotherapy was higher than those with low scores in two external datasets with immunotherapy. This study shows that the ICI score is an effective prognostic biomarker and a predictor of immunotherapy.

The heat shock protein DNAJB2 as a novel biomarker for essential thrombocythemia diagnosis associated with immune infiltration

Background and objectiveEssential thrombocythemia (ET) is a rare myeloproliferative malignancy which may lead to severe thrombohemorrhagic complications. The diagnosis of ET is primarily based on bone marrow morphology and exclusion of other possibilities of myeloproliferative neoplastic diseases; the lack of gene biomarkers fails to provide a prompt diagnosis of ET. Therefore, this study was designed to identify biomarkers for early ET diagnosis, especially that associated with immune cell infiltration, by using the Gene Expression Omnibus (GEO) database and machine-learning algorithms. MethodsTwo publicly available gene expression profiles (GSE9827 and GSE123732) from the GEO database were used to identify the differentially expressed genes (DEGs) between bone marrow samples of ET patients and healthy individuals, and functional enrichment analyses were conducted. The least absolute shrinkage and selection operator (LASSO) regression model and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) machine-learning algorithm were performed to select the candidate gene biomarker. The expression level and diagnostic effectiveness of the identified gene biomarker were further validated using GSE567 and GSE2006 datasets. The involvement of infiltrating immune cells and their correlations with the gene biomarker were examined using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. ResultsThere were 105 DEGs identified between ET and healthy control samples. Disease Ontology (DO) analysis showed that the diseases enriched by those DEGs were mainly human cancers, neurological diseases and inflammation while Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that pathways related to immune responses were primarily involved. The heat shock protein, DNAJB2, was identified as the potential biomarker for ET diagnosis with high effectiveness, with the area under the receiver operating characteristic (ROC) curve (AUC) equals to 0.905 in the validation cohort. The expression level of DNAJB2 in ET samples was indeed significantly higher than that in healthy control ones. The immune cell infiltration analysis showed that DNAJB2 was positively correlated with CD8+ T cells in ET with the proportion significantly higher than that in normal controls. ConclusionThe present study identified DNAJB2 as a novel diagnostic biomarker for ET with high effectiveness based on ET and normal samples from the GEO database, which provides new insights into predicting ET with accuracy and promptness in clinical practice.

Prognostic and immunological role of alpha-L-fucosidase 2 (FUCA2) in hepatocellular carcinoma.

This study investigated the prognostic and immunological significance of alpha-L-fucosidase 2 (FUCA2) in hepatocellular cancer (HCC). The expression of FUCA2 and its clinical and prognostic values were explored across several databases, namely the University of Alabama Cancer Database, The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. The prognostic relevance of FUCA2 was investigated using Kaplan-Meier curves, nomograms, and Cox analysis. The "limma" package in R was used to identify differentially expressed genes between high and low FUCA2 expression. A protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes (STRING), whereas hub genes and clustering modules were identified using Cytoscape. "clusterProfiler", an R package, was used to examine the potential function of FUCA2. Using gene set enrichment analysis, signaling pathways associated with FUCA2 expression were identified. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT), Tumor Immune Estimation Resource (TIMER) 2.0, and Tumor and Immune System Interaction Database (TISIDB) were used to examine immune infiltration and FUCA2 in HCC. Many datasets indicated that FUCA2 expression is higher in HCC, and that this is related to age and overall survival (OS). With the cutoff value of 50% as the dividing threshold, the patients were divided into a high-FUCA2 expression group (n=167) and a low-FUCA2 expression group (n=168). High levels of FUCA2 expression coincided with decreased OS. FUCA2 expression in HCC was associated with immune infiltrates. The functional mechanisms of FUCA2 depend on signal release, extracellular matrix collagen, and neuroactive ligands and receptors. In HCC, increased FUCA2 expression is associated with a poor prognosis and immune infiltration. FUCA2 may serve as an immunological and predictive biomarker for HCC.

Construction and validation of a macrophage polarization-related prognostic index to predict the overall survival in patients with early-stage triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease and the current prognostic system cannot meet the clinical need. Interactions between immune responsiveness and tumor cells plays a key role in the progression of TNBC and macrophages are vital component of immune cells. A prognostic model based on macrophages may have great accuracy and clinical utility. For model development, we screened early stage (without metastasis) TNBC patients from The Cancer Genome Atlas (TCGA) database. We extracted messenger RNA (mRNA) expression data and clinical data including age, race, tumor size, lymph node status and tumor stage. The follow up time and vital status were also retrieved for overall survival calculation. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used to calculate the immune cell composition of each sample. Weighted gene co-expression network analysis (WGCNA) was used to identify M1-like macrophage-related genes. Combining least absolute shrinkage and selection operator (LASSO) with multivariate Cox regression, the M1-like macrophage polarization-related prognostic index (MRPI) was established. We obtained TNBC patients in Gene Expression Omnibus (GEO) database through PAM50 method and retrieved the mRNA expression data and survival data. The Harrell's concordance index (CI), the area under the receiver operating characteristic (ROC) curves (AUCs) and the calibration curve were used to evaluate the developed model. We obtained 166 early TNBC cases and 113 normal tissue cases for model building, along with 76 samples from GSE58812 cohort for model validation. CIBERSORT analysis suggested obvious infiltration of macrophages, especially M1-like macrophages in early TNBC. Four genes were eventually identified for the construction of MPRI in the training set. The AUCs at 2 years, 3 years, and 5 years in the training cohort were 0.855, 0.881 and 0.893, respectively; and the AUCs at 2 years, 3 years, and 5 years in the validation cohort were 0.887, 0.792 and 0.722, respectively. Calibration curves indicated good predictive ability and high consistency of our model. MRPI is a promising biomarker for predicting the prognosis of early-stage TNBC, which may indicate personalized treatment and follow-up strategies and thus may improve the prognosis.

A novel cuproptosis-related lncRNA signature to predict prognosis and immune landscape of lung adenocarcinoma.

Cuproptosis, a recently discovered type of programmed cell death (PCD), paves a new avenue for cancer treatment. It has been revealed that PCD-related lncRNAs play a critical role in various biological processes of lung adenocarcinoma (LUAD). However, the role of cuproptosis-related lncRNA (CuRLs) remains unclear. This study aimed to identify and validate a CuRLs-based signature for the prognostic prediction of patients with LUAD. RNA sequencing data and clinical information of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Pearson correlation analysis was used to identify CuRLs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, and stepwise multivariate Cox analysis were applied to construct a novel prognostic CuRLs signature. A nomogram was developed for the prediction of patient survival outcomes. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to explore potential functions underlying the CuRLs signature. Patients were divided into low- and high-risk groups. Several algorithms, such as tumor immune estimation resource (TIMER), cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and QuanTIseq, were combined to comprehensively investigate the differences in immune landscape between different risk groups. Sensitivity to common anticancer drugs was analyzed using the pRRophetic algorithm. We constructed a novel prognostic signature based on 10 CuRLs, including CARD8-AS1, RUNDC3A-AS1, TMPO-AS1, MIR31HG, SEPSECS-AS1, DLGAP1-AS1, LINC01137, ZSCAN16-AS1, APTR, and ELOA-AS1. This 10-CuRLs risk signature showed great diagnostic accuracy combined with traditional clinical risk factors, and a nomogram was constructed for potential clinical translation. The tumor immune microenvironment was significantly different between different risk groups. Among drugs commonly used in the treatment of lung cancer, the sensitivity of cisplatin, docetaxel, gemcitabine, gefitinib, and paclitaxel was higher in low-risk patients, and patients in the low-risk group may benefit more from imatinib. These results revealed the outstanding contribution of the CuRLs signature to the evaluation of prognosis and treatment modalities for patients with LUAD. The differences in characteristics between different risk groups provide an opportunity for better patient stratification and to explore novel drugs in different risk groups.

Integrated analysis reveals the microenvironment of non-small cell lung cancer and a macrophage-related prognostic model.

In the treatment of non-small cell lung cancer (NSCLC), recent advances in immunotherapy have heralded a new era. Despite the success of immune therapy, a subset of patients persistently fails to respond. Therefore, to better improve the efficacy of immunotherapy and achieve the purpose of precision therapy, the research and exploration of tumor immunotherapy biomarkers have received much attention. Single-cell transcriptomic profiling was used to reveal tumor heterogeneity and the microenvironment in NSCLC. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was utilized to speculate the relative fractions of 22 infiltration immunocyte types in NSCLC. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used for the construction of risk prognostic models and predictive nomograms of NSCLC. Spearman's correlation analysis was employed to explore the relationship between risk score and tumor mutation burden (TMB) and immune checkpoint inhibitors (ICIs). Screening of chemotherapeutic agents in the high- and low-risk groups was performed with the "pRRophetic" package in R. Intercellular communication analysis was conducted using the "CellChat" package. We found that most tumor-infiltrating immune cells were T cells and monocytes. We also found that there was a significant difference in the tumor-infiltrating immune cells and ICIs across different molecular subtypes. Further analysis showed that M0 and M1 mononuclear macrophages were significantly different in different molecular subtypes. The risk prediction model was shown to have to ability to accurately predict the prognosis, immune cell infiltration, and chemotherapy efficacy of patients in the high and low-risk groups. Finally, we found that the carcinogenic effect of migration inhibitory factor (MIF) is mediated by binding to CD74, CXCR4, and CD44 receptors involved in MIF cell signaling. We have revealed the tumor microenvironment (TME) of NSCLC through single-cell data analysis and constructed a prognosis model of macrophage-related genes. These results could provide new therapeutic targets for NSCLC.

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.