Atlas of clinically distinct cell states and ecosystems across human solid tumors.

Determining how cells vary with their local signaling environment and organize into distinct cellular communities is critical for understanding processes as diverse as development, aging, and cancer. Here we introduce EcoTyper, a machine learning framework for large-scale identification and validation of cell states and multicellular communities from bulk, single-cell, and spatially resolved gene expression data. When applied to 12 major cell lineages across 16 types of human carcinoma, EcoTyper identified 69 transcriptionally defined cell states. Most states were specific to neoplastic tissue, ubiquitous across tumor types, and significantly prognostic. By analyzing cell-state co-occurrence patterns, we discovered ten clinically distinct multicellular communities with unexpectedly strong conservation, including three with myeloid and stromal elements linked to adverse survival, one enriched in normal tissue, and two associated with early cancer development. This study elucidates fundamental units of cellular organization in human carcinoma and provides a framework for large-scale profiling of cellular ecosystems in any tissue.

Human endogenous retrovirus W in multiple sclerosis: transcriptional activity is associated with decline in oligodendrocyte proportions in the white matter of the brain.

Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease. One of the basic mechanisms in this disease is the autoimmune response against the myelin sheet leading to axonal damage. There is strong evidence showing that this response is regulated by both genetic and environmental factors. In addition, the role of viruses has been extensively studied, especially in the case of human endogenous retroviruses (HERVs). However, although several associations with MS susceptibility, especially in the case of HERV-W family have been observed, the pathogenic mechanisms have remained enigmatic. To clarify these HERV-mediated mechanisms as well as the responsible HERV-W loci, we utilized RNA sequencing data obtained from the white matter of the brain of individuals with and without MS. CIBERSORTx tool was applied to estimate the proportions of neuronal, glial, and endothelial cells in the brain. In addition, the transcriptional activity of 215 HERV-W loci were analyzed. The results indicated that 65 HERV-W loci had detectable expression, of which 14 were differentially expressed between MS and control samples. Of these, 12 HERV-W loci were upregulated in MS. Expression levels of the 8 upregulated HERV-W loci had significant negative correlation with estimated oligodendrocyte proportions, suggesting that they are associated with the dynamics of oligodendrocyte generation and/or maintenance. Furthermore, Gene Set Enrichment Analysis (GSEA) results indicated that expression levels of three upregulated HERV-W loci: 2p16.2, 2q13, and Xq13.3, are associated with suppression of oligodendrocyte development and myelination. Taken together, these data suggest new HERV-W loci candidates that might take part in MS pathogenesis.

Transcriptomic and genomic characteristics of intrahepatic metastases of primary liver cancer.

BACKGROUND: Patients with primary multifocal hepatocellular carcinoma (HCC) have a poor prognosis and often experience a high rate of treatment failure. Multifocal HCC is mainly caused by intrahepatic metastasis (IM), and though portal vein tumor thrombosis (PVTT) is considered a hallmark of IM, the molecular mechanism by which primary HCC cells invade the portal veins remains unclear. Therefore, it is necessary to recognize the early signs of metastasis of HCC to arrange better treatment for patients. RESULTS: To determine the differential molecular features between primary HCC with and without phenotype of metastasis, we used the CIBERSORTx software to deconvolute cell types from bulk RNA-Seq based on a single-cell transcriptomic dataset. According to the relative abundance of tumorigenic and metastatic hepatoma cells, VEGFA+ macrophages, effector memory T cells, and natural killer cells, HCC samples were divided into five groups: Pro-T, Mix, Pro-Meta, NKC, and MemT, and the transcriptomic and genomic features of the first three groups were analyzed. We found that the Pro-T group appeared to retain native hepatic metabolic activity, whereas the Pro-Meta group underwent dedifferentiation. Genes highly expressed in the group Pro-Meta often signify a worse outcome. CONCLUSIONS: The HCC cohort can be well-typed and prognosis predicted according to tumor microenvironment components. Primary hepatocellular carcinoma may have obtained corresponding molecular features before metastasis occurred.

An Integrative Analysis of Identified Schizophrenia-Associated Brain Cell Types and Gene Expression Changes.

Schizophrenia (SCZ) is a severe mental disorder that may result in hallucinations, delusions, and extremely disordered thinking. How each cell type in the brain contributes to SCZ occurrence is still unclear. Here, we leveraged the human dorsolateral prefrontal cortex bulk RNA-seq data, then used the RNA-seq deconvolution algorithm CIBERSORTx to generate SCZ brain single-cell RNA-seq data for a comprehensive analysis to understand SCZ-associated brain cell types and gene expression changes. Firstly, we observed that the proportions of brain cell types in SCZ differed from normal samples. Among these cell types, astrocyte, pericyte, and PAX6 cells were found to have a higher proportion in SCZ patients (astrocyte: SCZ = 0.163, control = 0.145, P.adj = 4.9 × 10-4, effect size = 0.478; pericyte: SCZ = 0.057, control = 0.066, P.adj = 1.1 × 10-4, effect size = 0.519; PAX6: SCZ = 0.014, control = 0.011, P.adj = 0.014, effect size = 0.377), while the L5/6_IT_CAR3 cells and LAMP5 cells are the exact opposite (L5/6_IT_Car3: SCZ = 0.102, control = 0.108, P.adj = 0.016, effect size = 0.369; LAMP5: SCZ = 0.057, control = 0.066, P.adj = 2.2 × 10-6, effect size = 0.617). Next, we investigated gene expression in cell types and functional pathways in SCZ. We observed chemical synaptic transmission dysregulation in two types of GABAergic neurons (PVALB and LAMP5), and immune reaction involvement in GABAergic neurons (SST) and non-neuronal cell types (endothelial and oligodendrocyte). Furthermore, we observed that some differential expression genes from bulk RNA-seq displayed cell-type-specific abnormalities in the expression of molecules in SCZ. Finally, the cell types with the SCZ-related transcriptomic changes could be considered to belong to the same module since we observed two major similar coordinated transcriptomic changes across these cell types. Together, our results offer novel insights into cellular heterogeneity and the molecular mechanisms underlying SCZ.

Identification of hub genes associated with neutrophils infiltration in colorectal cancer.

Colorectal cancer (CRC) is the leading cause of cancer-related mortality in the world. Accumulating evidence indicate that tumour infiltrating immune cells participated in cancer progression. Among them, tumour infiltrating neutrophils (TINs) are reported to play crucial role in various cancers. In this study, we used CIBERSORTx, a digital cytometry tool to evaluate the neutrophils infiltration in CRC based on gene expression data of CRC tissues from GSE39582 data set and The Cancer Genome Atlas data set (TCGA-COAD and TCGA-READ). Weighted gene co-expression network analysis (WGCNA) was conducted in GSE39582 data set to identify hub genes associated with neutrophil infiltration. The association of hub gene and neutrophils was then validated in TCGA cohorts and an independent RJ cohort. Functional analysis was performed to investigate the molecular mechanisms of the interested hub gene. We found that neutrophil infiltration is elevated in CRC tissues, and it is related to a poorer prognosis. A total of 18 gene modules are identified by WGCNA in GSE39582 data set, among which lightcyan module is significantly correlated with neutrophils infiltration. Furthermore, Superoxide Dismutase 2 (SOD2) in lightcyan module was proved to correlated with neutrophils infiltration in various cancer types. In addition, SOD2 expression is highly associated with several chemokines, including CXCL8, a neutrophils-related attractant, and functional analysis revealed that SOD2 is involved in neutrophils recruitment biological process. These results indicate that an 'SOD2-CXCL8-neutrophil recruitment' axis plays a potential role in colorectal cancer progression.

Deconvolution of RNA-Seq Analysis of Hyperbaric Oxygen-Treated Mice Lungs Reveals Mesenchymal Cell Subtype Changes.

Hyperbaric oxygen (HBO) is widely applied to treat several hypoxia-related diseases. Previous studies have focused on the immediate effect of HBO-exposure induced oxidative stress on the lungs, but knowledge regarding the chronic effects from repetitive HBO exposure is limited, especially at the gene expression level. We found that repetitive HBO exposure did not alter the morphology of murine lungs. However, by deconvolution of RNA-seq from those mice lungs using CIBERSORTx and the expression profile matrices of 8 mesenchymal cell subtypes obtained from bleomycin-treated mouse lungs, we identify several mesenchymal cell subtype changes. These include increases in Col13a1 matrix fibroblasts, mesenchymal progenitors and mesothelial cell populations and decreases in lipofibroblasts, endothelial and Pdgfrb high cell populations. Our data suggest that repetitive HBO exposure may affect biological processes in the lungs such as response to wounding, extracellular matrix, vasculature development and immune response.

Tumor-infiltrating immune cell score as an independent prognostic predictor for endometrial carcinoma: Insights from a comprehensive analysis of the immune landscape.

BACKGROUND: Immune cells are crucial components in the tumor microenvironment and have a significant impact on the outcomes of patients. AIMS: Here, we aimed to establish a prognostic score based on different types of tumor-infiltrating immune cells for Endometrial Carcinoma (EC). METHODS AND RESULTS: We enrolled and analyzed 516 EC patients from The Cancer Genome Atlas. The relative abundance of 22 immune cells were estimated by using the CIBERSORTx algorithm. Cox regression was performed to identify potential prognostic immune cells, which were used to develop a Tumor-infiltrating Immune Cell Score (TICS). The prognostic and incremental value of TICS for overall survival were compared with traditional prognostic factors using the C-index and decision curves. Clustering analysis using all immune cells identified three immune landscape subtypes, which had weak correlation with survival. A TICS was constructed using CD8T cells, resting memory CD4 T cells, activated NK and activated DCs, and classified patients as low-, moderate- and high-risk subgroups. The low-risk subgroup had higher tumor mutation burden and activation of IL2/STAT5, IL2/STAT3 and IFN-gamma response pathways. Conversely, the high-risk subgroup was associated with DNA copy number variation, hypoxia and EMT process. The TICS subgroups significantly predicted overall survival, which was independent of patient age, tumor stage, grade and molecular classification. Moreover, we developed a nomogram incorporating TICS and clinicopathologic factors, which significantly improved the predictive accuracy compared to the clinicopathologic model alone. CONCLUSION: The TICS is an effective and independent prognostic predictor for EC patients and may serve as a useful supplement to clinicopathological factors and molecular subtyping.

Deconvolution analysis identified altered hepatic cell landscape in primary sclerosing cholangitis and primary biliary cholangitis.

Introduction: Primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) are characterized by ductular reaction, hepatic inflammation, and liver fibrosis. Hepatic cells are heterogeneous, and functional roles of different hepatic cell phenotypes are still not defined in the pathophysiology of cholangiopathies. Cell deconvolution analysis estimates cell fractions of different cell phenotypes in bulk transcriptome data, and CIBERSORTx is a powerful deconvolution method to estimate cell composition in microarray data. CIBERSORTx performs estimation based on the reference file, which is referred to as signature matrix, and allows users to create custom signature matrix to identify specific phenotypes. In the current study, we created two custom signature matrices using two single cell RNA sequencing data of hepatic cells and performed deconvolution for bulk microarray data of liver tissues including PSC and PBC patients. Methods: Custom signature matrix files were created using single-cell RNA sequencing data downloaded from GSE185477 and GSE115469. Custom signature matrices were validated for their deconvolution performance using validation data sets. Cell composition of each hepatic cell phenotype in the liver, which was identified in custom signature matrices, was calculated by CIBERSORTx and bulk RNA sequencing data of GSE159676. Deconvolution results were validated by analyzing marker expression for the cell phenotype in GSE159676 data. Results: CIBERSORTx and custom signature matrices showed comprehensive performance in estimation of population of various hepatic cell phenotypes. We identified increased population of large cholangiocytes in PSC and PBC livers, which is in agreement with previous studies referred to as ductular reaction, supporting the effectiveness and reliability of deconvolution analysis in this study. Interestingly, we identified decreased population of small cholangiocytes, periportal hepatocytes, and interzonal hepatocytes in PSC and PBC liver tissues compared to healthy livers. Discussion: Although further studies are required to elucidate the roles of these hepatic cell phenotypes in cholestatic liver injury, our approach provides important implications that cell functions may differ depending on phenotypes, even in the same cell type during liver injury. Deconvolution analysis using CIBERSORTx could provide a novel approach for studies of specific hepatic cell phenotypes in liver diseases.

Defining trophoblast injury patterns in the transcriptomes of dysfunctional placentas.

Trophoblast injury is central to clinically relevant placenta dysfunction. We hypothesized that the mRNA of primary human trophoblasts, exposed to distinct injuries in vitro, capture transcriptome patterns of placental biopsies obtained from common obstetrical syndromes. We deployed a CIBERSORTx deconvolution method to correlate trophoblastic RNAseq-based expression matrices with the transcriptome of omics-defined placental dysfunction patterns in vivo. We found distinct trophoblast injury patterns in placental biopsies from women with fetal growth restriction and a hypertensive disorder, or in biopsies clustered by their omics analysis. Our RNAseq data are useful for defining the contribution of trophoblast injuries to placental dysfunction syndromes.

Human endogenous retroviruses of the HERV-K (HML-2) family are expressed in the brain of healthy individuals and modify the composition of the brain-infiltrating immune cells.

Human endogenous retroviruses (HERVs) are remnants of ancient retroviral infections in the human genome. RNA expression of individual HERVs has frequently been observed in various pathologic conditions, but some activity can also be seen in healthy individuals, e.g. in the blood. To quantitate the basal expression levels of HERVs in the brain, we now used high-throughput sequencing-based metagenomic analysis to characterize the expression profiles of the HERV-K (HML-2) family proviruses in different brain regions of healthy brain tissue. To this end, RNA-seq data from the Genotype-Tissue Expression (GTEx) project was used. The GTEx project is a public resource to study tissue-specific gene expression and regulation, consisting of a large selection of sequenced samples from different tissues. The GTEx data used in this study consisted of 378 samples taken from 13 brain regions from 55 individuals. The data demonstrated that out of 99 intact proviruses in the family 58 were expressed, but the expression profiles were highly divergent and there were no significant differences in the expression profiles between the various anatomic regions of the brain. It is known that the brain contains a variety of infiltrating immune cells, which are probably of great importance both in the normal defense mechanisms as well as in the various pathogenic processes. Digital cytometry (CIBERSORTx) was used to quantify the proportions of the infiltrating immune cells in the same brain samples. Six most abundant (>5 % of the total population) cell types were observed to be CD4 memory resting T cells, M0 macrophages, plasma cells, CD8 T cells, CD4 memory activated T cells, and monocytes. Analysis of the correlations between the individual HERVs and infiltrating cell types indicated that a cluster of 6 HERVs had a notable correlation signature between T cell type infiltrating cell proportions and HERV RNA expression intensity. The correlations between inflammatory type infiltrating cells were negative or weak. Taken together, these data indicate that the expression of HERVs is associated with a T cell type immunity.

Identification of hub genes associated with oxidative stress in heart failure and their correlation with immune infiltration using bioinformatics analysis.

Both oxidative stress and the immune response are associated with heart failure (HF). In this study, our aim was to identify the hub genes associated with oxidative stress andimmune infiltration of HF by bioinformatics analysis and experimental verification. The expression profile of GSE36074 was obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were screened by GEO2R. The genes related to oxidative stress were extracted from GeneCards websites. Then, the functional enrichment analysis of oxidative stress-related DEGs (OSRDEGs) was performed using DAVID. In addition, we constructed a protein-protein interaction (PPI) network using the STRING database and screened for hub genes with Cytoscape software. We also used CIBERSORTx to analyze immune infiltration in mice heart tissues between the TAC and Sham groups and explored the correlation between immune cells and hub genes. Finally, the hub genes were carried out using reverse transcription quantitative PCR (RT-qPCR), immunohistochemistry (IHC) and western blot. A total of 136 OSRDEGs were found in GSE36074. Enrichment analysis revealed that these OSRDEGs were enriched in the mitochondrion, HIF-1, FoxO, MAPK and TNF signaling pathway. The five hub genes (Mapk14, Hif1a, Myc, Hsp90ab1, and Hsp90aa1) were screened by the cytoHubba plugin. The correlation analysis between immune cells and hub genes showed that Mapk14 was positively correlated with Th2 Cells, while Hif1a and Hsp90ab1exhibited a negative correlation with Th2 Cells; Myc exhibited a negative correlation with Monocytes; whereas, Hsp90aa1 was negatively correlated with NK Resting. Finally, five hub genes were validated by RT-qPCR, IHC and western blot. Mapk14, Hif1a, Myc, Hsp90ab1, and Hsp90aa1 are hub genes of HF and may play a critical role in the oxidative stress of HF. This study may provide new targets for the treatment of HF, and the potential immunotherapies are worthy of further study.

Role of immune cell infiltration and small molecule drugs in adhesive capsulitis: Novel exploration based on bioinformatics analyses.

Background: Adhesive capsulitis (AC) is a type of arthritis that causes shoulder joint pain, stiffness, and limited mobility. The pathogenesis of AC is still controversial. This study aims to explore the role of immune related factors in the occurrence and development of AC. Methods: The AC dataset was downloaded from Gene Expression Omnibus (GEO) data repository. Differentially expressed immune-related genes (DEIRGs) were obtained based on R package "DESeq2" and Immport database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to explore the functional correlation of DEIRGs. MCC method and Least Absolute Shrinkage and Selection Operator (LASSO) regression were conducted to identify the hub genes. The immune cell infiltration in shoulder joint capsule between AC and control was evaluated by CIBERSORTx, and the relationship between hub genes and infiltrating immune cells was analyzed by Spearman's rank correlation. Finally, potential small molecule drugs for AC were screened by the Connectivity Map database (CMap) and further verified by molecular docking. Results: A total of 137 DEIRGs and eight significantly different types of infiltrating immune cells (M0 macrophages, M1 macrophages, regulatory T cells, Tfh cells, monocytes, activated NK cells, memory resting CD4+T cells and resting dendritic cells) were screened between AC and control tissues. MMP9, FOS, SOCS3, and EGF were identified as potential targets for AC. MMP9 was negatively correlated with memory resting CD4+T cells and activated NK cells, but positively correlated with M0 macrophages. SOCS3 was positively correlated with M1 macrophages. FOS was positively correlated with M1 macrophages. EGF was positively correlated with monocytes. Additionally, dactolisib (ranked first) was identified as a potential small-molecule drug for the targeted therapy of AC. Conclusions: This is the first study on immune cell infiltration analysis in AC, and these findings may provide a new idea for the diagnosis and treatment of AC.

Bioinformatics Tools for Bulk Gene Expression Deconvolution in Diabetic Retinopathy.

Retinal neovascularization is one of the leading causes of vision loss and a hallmark of proliferative diabetic retinopathy (PDR). The immune system is observed to be involved in the pathogenesis of diabetic retinopathy (DR). The specific immune cell type that contributes to retinal neovascularization can be identified via a bioinformatics analysis of RNA sequencing (RNA-seq) data, known as deconvolution analysis. Previous study has identified the infiltration of macrophages in the retina of rats with hypoxia-induced retinal neovascularization and patients with PDR through a deconvolution algorithm, known as CIBERSORTx. Here, we describe the protocols of using CIBERSORTx to perform the deconvolution analysis and downstream analysis of RNA-seq data.

Single-cell transcriptome analysis reveals heterogeneity and convergence of the tumor microenvironment in colorectal cancer.

Introduction: Colorectal cancer (CRC) ranks second for mortality and third for morbidity among the most commonly diagnosed cancers worldwide. We aimed to investigate the heterogeneity and convergence of tumor microenvironment (TME) in CRC. Methods: We analyzed the single-cell RNA sequencing data obtained from the Gene Expression Omnibus (GEO) database and identified 8 major cell types and 25 subgroups derived from tumor, para-tumor and peripheral blood. Results: In this study, we found that there were significant differences in metabolic patterns, immunophenotypes and transcription factor (TF) regulatory patterns among different subgroups of each major cell type. However, subgroups manifested similar lipid metabolic patterns, immunosuppressive functions and TFs module at the end of the differentiation trajectory in CD8+ T cells, myeloid cells and Fibroblasts. Meanwhile, TFs regulated lipid metabolism and immunosuppressive ligand-receptor pairs were detected by tracing the differentiation trajectory. Based on the cell subgroup fractions calculated by CIBERSORTx and bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA), we constructed an immune risk model and clinical risk model of CRC which presented excellent prognostic value. Conclusion: This study identified that the differentiation was accompanied by remodeling of lipid metabolism and suppression of immune function, which suggest that lipid remodeling may be an important trigger of immunosuppression. More importantly, our work provides a new perspective for understanding the heterogeneity and convergence of the TME and will aid the development of prognosis and immunotherapies of CRC patients.

Immune cell gene expression signatures in diffuse glioma are associated with IDH mutation status, patient outcome and malignant cell state, and highlight the importance of specific cell subsets in glioma biology.

The tumor micro-environment (TME) plays an important role in various cancers, including gliomas. We estimated immune cell type-specific gene expression profiles in 3 large clinically annotated glioma datasets using CIBERSORTx and LM22/LM10 blood-based immune signatures and found that the proportions and estimated gene expression patterns of specific immune cells significantly varied according to IDH mutation status. When IDH-WT and IDH-MUT tumors were considered separately, cluster-of-cluster analyses of immune cell gene expression identified groups with distinct survival outcomes. We confirmed and extended these findings by applying a signature matrix derived from single-cell RNA-sequencing data derived from 19 glioma tumor samples to the bulk profiling data, validating findings from the LM22/LM10 results. To link immune cell signatures with outcomes in checkpoint therapy, we then showed a significant association of monocytic lineage cell gene expression clusters with patient survival and with mesenchymal gene expression scores. Integrating immune cell-based gene expression with previously described malignant cell states in glioma demonstrated that macrophage M0 abundance significantly correlated with mesenchymal state in IDH-WT gliomas, with evidence of a previously implicated role of the Oncostatin-M receptor and macrophages in the mesenchymal state. Among IDH-WT tumors that were enriched for the mesenchymal cell state, the estimated M0 macrophage expression signature coordinately also trended to a mesenchymal signature. We also examined IDH-MUT tumors stratified by 1p/19q status, showing that a mesenchymal gene expression signature the M0 macrophage fraction was enriched in IDH-MUT, non-codeleted tumors. Overall, these results highlight the biological and clinical significance of the immune cell environment related to IDH mutation status, patient prognosis and the mesenchymal state in diffuse gliomas.

Identification of hub genes correlated with tumor-associated M1-like macrophage infiltration in soft tissue sarcomas.

Soft tissue sarcomas (STS) are a heterogeneous series of tumors that might result in severe disability and death. Tumor-associated M1-like macrophage infiltration plays a critical role in tumor development and progression. This study aimed at identifying the hub genes associated with M1-like macrophage infiltration in STS cells. First, the expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were imported to calculate the level of M1-like macrophage infiltration by CIBERSORTx. Afterward, the Kaplan-Meier survival analysis was performed to evaluate the correlation between macrophage infiltration and prognosis. Then, weighted gene co-expression network analysis (WGCNA) and protein-protein interaction analysis of GEO data were applied to identify the key gene related to M1-like macrophage infiltration, followed by the functional analysis using TCGA cohort to validate downstream signaling associated with the gene. Finally, pan-cancer analysis was conducted to investigate the gene function in other types of tumors. We found LCK expression positively related to the M1-like macrophage infiltration level, and it positively regulated the expression level of genes regulated to macrophage polarization, and chemotaxis, including interferon-γ (INF-γ), interleukin-12 (IL12), tumor necrosis factor (TNF), PI3K, NF-κB, and CXCL9, 10, and 11. In summary, an 'LCK-INF-γ/IL-12-TNF/PI3K-NF-κB' axis might exist in STS cells that regulate M1-like macrophage infiltration.

The human blood transcriptome exhibits time-of-day-dependent response to hypoxia: Lessons from the highest city in the world.

High altitude exposes humans to hypobaric hypoxia, which induces various physiological and molecular changes. Recent studies point toward interaction between circadian rhythms and the hypoxic response, yet their human relevance is lacking. Here, we examine the effect of different high altitudes in conjunction with time of day on human whole-blood transcriptome upon an expedition to the highest city in the world, La Rinconada, Peru, which is 5,100 m above sea level. We find that high altitude vastly affects the blood transcriptome and, unexpectedly, does not necessarily follow a monotonic response to altitude elevation. Importantly, we observe daily variance in gene expression, especially immune-related genes, which is largely altitude dependent. Moreover, using a digital cytometry approach, we estimate relative changes in abundance of different cell types and find that the response of several immune cell types is time- and altitude dependent. Taken together, our data provide evidence for interaction between the transcriptional response to hypoxia and the time of day in humans.

A Prognostic Model of Colon Cancer Based on the Microenvironment Component Score via Single Cell Sequencing.

BACKGROUND/AIM: The development of colon cancer is influenced by the tumour immune microenvironment, in which specific immune cell subsets may be useful predictors for patient's clinical outcome and devising treatment strategies. MATERIALS AND METHODS: The distribution of tumour-infiltrating immune cell subpopulations of three cohorts of The Cancer Genome Atlas (n=225), GSE39582 (n=493), and GSE17536 (n=137) datasets were analysed on the basis of single cell RNA sequencing data via the Cibersortx software. A prognostic model was constructed via a penalised Cox regression model with least absolute shrinkage and selection operator (LASSO) penalty according to the one standard error rule. RESULTS: Conventional type 2 dendritic cells were correlated with a good prognosis, whereas NLRP3-expressing macrophages, C1QC-expressing tumour-associated macrophages, and GALTB-expressing B cells were correlated with a poor prognosis. We constructed a prognostic model based on prognosis related cell subsets including nine specific immune cell subsets. By using the LASSO method, we found that the model had a superior prediction ability in all three cohorts of patients. CONCLUSION: Multiple immune cell subpopulations in the tumour microenvironment are associated with the prognosis of colon cancer. The established prognostic model has important clinical value in predicting the clinical outcome of patients with colon cancer and in treatment decision.

IDO1 as a new immune biomarker for diabetic nephropathy and its correlation with immune cell infiltration.

INTRODUCTION: Indoleamine 2,3-dioxygenase 1(IDO1) has complicated roles in immune-inflammatory response regulation, but its correlation with immune cell infiltration in diabetic nephropathy (DN) remains unknown. METHODS: Gene expression data were extracted from the GEO database. Differentially expressed genes (DEGs) were identified and functional correlation analysis was performed. The immune hub gene was screened using Maximal Clique Centrality, and verified in DN model mice via western blotting, immunohistochemistry, and immunofluorescence analysis. CIBERSORTx was used to assign values to immune cell infiltration in DN and determine a correlation with the hub gene. The prognostic significance of the hub gene was then validated. RESULTS: The 330 screened DEGs from the GEO dataset were most enriched in GO functions and KEGG pathways associated with immune inflammation. IDO1 was identified as a hub immune gene, with upregulated expression in DN model mice. IDO1 expression was positively correlated with M1 macrophages (R = 0.58, P < 0.001) and monocytes (R = 0.44, P = 0.049), and was negatively correlated with resting memory CD4 T cells (R = -0.51, P = 0.019). IDO1 expression was upregulated in peritoneal macrophages after high glucose stimulation, and inflammatory factor production was reversed by IDO1 inhibition. Higher IDO1 expression was associated with worse prognosis in DN patients via multivariate survival analysis (P < 0.001). CONCLUSIONS: IDO1 was identified as a diagnostic and prognostic biomarker for DN and shown to play a vital role in immune cell infiltration in DN, ascertained using microarray data and CIBERSORTx for the first time.

Diagnostic Value of Immune-Related Genes in Kawasaki Disease.

Kawasaki disease (KD) is a systemic vasculitis that predominantly damages medium- and small-sized vessels, and mainly causes coronary artery lesions (CALs). The diagnostic criterion of KD mainly depends on clinical features, so children could be easily misdiagnosed and could suffer from CALs. Through analysis, a total of 14 immune-related DEGs were obtained, of which IL1B, ADM, PDGFC, and TGFA were identified as diagnostic markers of KD. Compared with the non-KD group, KD patients contained a higher proportion of naive B cells, activated memory CD4 T cells, gamma delta T cells, and neutrophils, while the proportions of memory B cells, CD8 T cells, activated memory CD4 T cells, and activated NK cells were relatively lower. In conclusion, immune-related genes can be used as diagnostic markers of KD, and the difference in immune cells between KD and non-KD might provide new insight into understanding the pathogenesis of KD.