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Abstract 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.

Although advancements in genomic and epigenetic research have deepened our understanding of acute myeloid leukemia (AML), only one-third of patients can achieve durable remission. Growing evidence suggests that the immune microenvironment in bone marrow influences prognosis and survival in AML. There is a specific association between CD8+ T cells and the prognosis of AML patients. To develop a CD8+ T cell-related immune risk score for AML, we first evaluated the accuracy of CIBERSORTx in predicting the abundance of CD8+ T cells in bulk RNA-seq and found it significantly correlated with observed single-cell RNA sequencing data and the proportions of CD8+ T cells derived from flow cytometry. Next, we constructed the CTCG15, a 15-gene prognostic signature, using univariate and LASSO regression on the differentially expressed genes between CD8+ THigh and CD8+ TLow groups. The CTCG15 was further validated across six datasets in different platforms. The CTCG15 has been shown to be independent of established prognostic markers, and can distill transcriptomic consequences of several genetic abnormalities closely related to prognosis in AML patients. Finally, integrating this model into the 2022 European LeukemiaNet contributed to a higher predictive power for prognosis prediction. Collectively, our study demonstrates that CD8+ T cell-related signature could improve the comprehensive risk stratification and prognosis prediction in AML.

IntroductionPrimary 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.MethodsCustom 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.ResultsCIBERSORTx 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.DiscussionAlthough 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.

Abstract 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.

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.

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.

Abstract 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.

BackgroundAdhesive 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.MethodsThe 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.ResultsA 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.ConclusionsThis 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.

IntroductionColorectal 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.MethodsWe 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.ResultsIn 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.ConclusionThis 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.

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.

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.

Hepatocellular carcinoma (HCC) is a commonly diagnosed cancer with high mortality rates. The immune response plays an important role in the progression of HCC. Immunotherapies are becoming an increasingly promising tool for treating cancers. Advancements in scRNA-seq (single-cell RNA sequencing) have allowed us to identify new subsets in the immune microenvironment of HCC. Yet, distribution of these new cell types and their potential prognostic value in bulk samples from large cohorts remained unclear. This study aimed to investigate the tumor-infiltration and prognostic value of new cell subsets identified by a previous scRNA-seq study in a TCGA HCC cohort using CIBERSORTx, a machine learning method to estimate cell proportion and infer cell-type-specific gene expression profiles. We observed different distributions of tumor-infiltrating lymphocytes between tumor and normal cells. Among these, the CD4-GZMA cell subset showed association with prognosis (log-rank test, p < 0.05). We further analyzed CD4-GZMA cell specific gene expression with CIBERSORTx, and found 19 prognostic genes (univariable cox regression, p < 0.05). Finally, we applied Least absolute shrinkage and selection operator (LASSO) Cox regression to construct an immune risk score model and performed a prognostic assessment of our model in TCGA and ICGC cohorts. Taken together, the immune landscape in HCC bulk samples may be more complex than assumed, with heterogeneity and different tumor-infiltration relative to scRNA-seq results. Additionally, CD4-GZMA cells and their characteristics may yield therapeutic benefits in the immune treatment of HCC.

Crohn’s disease (CD) has an increasing incidence and prevalence worldwide. The etiology of CD remains unclear and there is no gold standard for diagnosis. The dysregulated immune response and different infiltration status of immune cells are critical for CD pathogenesis; therefore, it is important to provide an overview of immune-cell alterations in CD and explore a novel method for auxiliary diagnosis. Here we analyzed microarray datasets from Gene Expression Omnibus (GEO), and an extended version of Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORTx) was utilized to estimate the fraction of 22 types of immune cells. Differentially expressed genes (DEGs) and a protein-protein interaction (PPI) network were identified, and we performed gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) to identify differentially regulated pathways in CD. Least absolute shrinkage and selection operator (LASSO) regression was conducted to filter features, and a diagnostic nomogram based on logistic regression was built and validated in an independent validation cohort. In the derivation cohort, we found a proportion of 17 immune-cell types to be significantly altered between CD and healthy controls and a total of 150 DEGs were identified, which were mostly related to the immune response. Among the 15 hub genes based on the PPI network, C-X-C chemokine ligand 8 (CXCL8) and interleukin-1B (IL-1B) showed the highest degree of interaction. Additionally, GSEA and GSVA identified five significantly enriched pathways, among which the nucleotide-binding oligomerization domain (NOD)-like receptor signaling pathway was critical in the CD development. Furthermore, six variables comprising of CXCL8, IL-1B, M1 macrophages, regulatory T cells, CD8+ T cells, and plasma cells were identified by LASSO regression and incorporated into a logistic regression model. The nomogram displayed a good prediction, with a 0.915 area under the receiver operating curve (AUC) and a C-index of 0.915 [95% confidence interval (CI): 0.875–0.955]. Similar results were found in the validation cohort, with an AUC of 0.884 and a 0.884 C-index (95% CI: 0.843–0.924). These results provide novel in silico insight into cellular and molecular characteristics of CD and potential biomarkers for diagnosis and targeted therapy.