Differences of the immune cell landscape between normal and tumor tissue in human prostate

Identification of the Key Genes of Immune Infiltration in Dilated Cardiomyopathy

Dilated cardiomyopathy (DCM) is a common cause of heart failure. In this study, we screened the immune infiltration-related genes associated with DCM to explore the potential molecular mechanisms and provide a basis for the early diagnosis and development of new immunotherapeutic targets. A dataset related to DCM was downloaded from the Gene Expression Omnibus (GEO) database. R software was applied to the genetic differential analysis of patients with DCM and healthy individuals, and the obtained differential expressed genes (DEGs) were screened for differentially expressed immune-related genes (DEIRGs) after comparison with the immune microsatellite database. Gene functional analysis established a protein interaction network (PPI). The immune infiltration in patients with DCM versus normal controls was assessed using the CIBERSORT algorithm, the hub genes were screened using the MOCDE app, and the hubs were validated in multiple datasets. A total of 246 DEGs were screened (adj. P < 0.05 and |logFC| > 0.3), and a total of 170 DEIRGs were compared. Gene Ontology analysis showed significant (adj. P < 0.05) Biological Process entries of 591, Cellular Component of 10, and Molecular Function of 39; Kyoto Encyclopedia of Genes and Genomes showed 20 significant entries, mainly focused on cytokines involved in immune-related response, etc. A protein interaction network comprising 28 hub DEGs was constructed in combination with the PPI network interactions. DEIRG was mainly distributed in the T-cell receptor pathway by immune infiltration detection analysis, and significant changes in central memory T-cells were found by analyzing T-cell-related subpathways, where INSR, HLA-B, IFITM1, and HBEGF were significantly differentially expressed. We selected 632 hospitalized patients for validation and found that INSR and HLA-B expression were associated with DCM development by Nomogram. The expression of HLA-B in peripheral blood T-cells was higher in DCM patients than in the normal group, as verified by qRT-PCR. However, the detailed mechanism needs to be further explored.

Identification and validation of immune-related hub genes based on machine learning in prostate cancer and AOX1 is an oxidative stress-related biomarker

To investigate potential diagnostic and prognostic biomarkers associated with prostate cancer (PCa), we obtained gene expression data from six datasets in the Gene Expression Omnibus (GEO) database. The datasets included 127 PCa cases and 52 normal controls. We filtered for differentially expressed genes (DEGs) and identified candidate PCa biomarkers using a least absolute shrinkage and selector operation (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. A difference analysis was conducted on these genes in the test group. The discriminating ability of the train group was determined using the area under the receiver operating characteristic curve (AUC) value, with hub genes defined as those having an AUC greater than 85%. The expression levels and diagnostic utility of the biomarkers in PCa were further confirmed in the GSE69223 and GSE71016 datasets. Finally, the invasion of cells per sample was assessed using the CIBERSORT algorithm and the ESTIMATE technique. The possible prostate cancer (PCa) diagnostic biomarkers AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were identified and validated using the GSE69223 and GSE71016 datasets. Among these biomarkers, AOX1 was found to be associated with oxidative stress and could potentially serve as a prognostic biomarker. Experimental validations showed that AOX1 expression was low in PCa cell lines. Overexpression of AOX1 significantly reduced the proliferation and migration of PCa cells, suggesting that the anti-tumor effect of AOX1 may be attributed to its impact on oxidative stress. Our study employed a comprehensive approach to identify PCa biomarkers and investigate the role of cell infiltration in PCa.

M7G-related LncRNAs: A comprehensive analysis of the prognosis and immunity in glioma

Today, numerous international researchers have demonstrated that N7-methylguanosine (m7G) related long non-coding RNAs (m7G-related lncRNAs) are closely linked to the happenings and developments of various human beings’ cancers. However, the connection between m7G-related lncRNAs and glioma prognosis has not been investigated. We did this study to look for new potential biomarkers and construct an m7G-related lncRNA prognostic signature for glioma. We identified those lncRNAs associated with DEGs from glioma tissue sequences as m7G-related lncRNAs. First, we used Pearson’s correlation analysis to identify 28 DEGs by glioma and normal brain tissue gene sequences and predicated 657 m7G-related lncRNAs. Then, eight lncRNAs associated with prognosis were obtained and used to construct the m7G risk score model by lasso and Cox regression analysis methods. Furthermore, we used Kaplan-Meier analysis, time-dependent ROC, principal component analysis, clinical variables, independent prognostic analysis, nomograms, calibration curves, and expression levels of lncRNAs to determine the model’s accuracy. Importantly, we validated the model with external and internal validation methods and found it has strong predictive power. Finally, we performed functional enrichment analysis (GSEA, aaGSEA enrichment analyses) and analyzed immune checkpoints, associated pathways, and drug sensitivity based on predictors. In conclusion, we successfully constructed the formula of m7G-related lncRNAs with powerful predictive functions. Our study provides instructional value for analyzing glioma pathogenesis and offers potential research targets for glioma treatment and scientific research.

Integrated Bioinformatic Analysis of DNA Methylation and Immune Infiltration in Endometrial Cancer

Background

Endometrial cancer greatly threatens the health of female. Emerging evidences have demonstrated that DNA methylation and immune infiltration are involved in the occurrence and development of endometrial cancer. However, the mechanism and prognostic biomarkers of endometrial cancer are still unclear. In this study, we assess DNA methylation and immune infiltration via bioinformatic analysis.

Methods

The latest RNA-Seq, DNA methylation data, and clinical data related to endometrial cancer were downloaded from the UCSC Xena dataset. The methylation-driven genes were selected, and then the risk score was obtained using “MethylMix” and “corrplot” R packages. The connection between methylated genes and the expression of screened driven genes were explored using “survminer” and “beeswarm” packages, respectively. Finally, the role of VTCN1in immune infiltration was analyzed using “CIBERSORT” package.

Results

In this study, 179 upregulated genes, and 311 downregulated genes were identified and found to be related to extracellular matrix organization, cell–cell junctions, and cell adhesion molecular binding. The methylation-driven gene VTCN1 was selected, and patients classified to the hypomethylation and high expression group displayed poor prognosis. The VTCN1 gene exhibited highest correlation coefficient between methylation and expression. More importantly, the hypomethylation of promoter of VTCN1 led to its high expression, thereby induce tumor development by inhibiting CD8+ T cell infiltration.

Conclusions

Overall, our study was the first to reveal the mechanism of endometrial cancer by assessing DNA methylation and immune infiltration via integrated bioinformatic analysis. In addition, we found a pivotal prognostic biomarker for the disease. Our study provides potential targets for the diagnosis and prognosis of endometrial cancer in the future.

A New Ferroptosis-Related lncRNA Signature Predicts the Prognosis of Bladder Cancer Patients

Background: Ferroptosis is closely related to the occurrence and development of cancer. An increasing number of studies have induced ferroptosis as a treatment strategy for cancer. However, the predictive value of ferroptosis-related lncRNAs in bladder cancer (BC) still need to be further elucidated. The purpose of this study was to construct a predictive signature based on ferroptosis-related long noncoding RNAs (lncRNAs) to predict the prognosis of BC patients.

Methods: We downloaded RNA-seq data and the corresponding clinical and prognostic data from The Cancer Genome Atlas (TCGA) database and performed univariate and multivariate Cox regression analyses to obtain ferroptosis-related lncRNAs to construct a predictive signature. The Kaplan-Meier method was used to analyze the overall survival (OS) rate of the high-risk and low-risk groups. Gene set enrichment analysis (GSEA) was performed to explore the functional differences between the high- and low-risk groups. Single-sample gene set enrichment analysis (ssGSEA) was used to explore the relationship between the predictive signature and immune status. Finally, the correlation between the predictive signature and the treatment response of BC patients was analyzed.

Results: We constructed a signature composed of nine ferroptosis-related lncRNAs (AL031775.1, AL162586.1, AC034236.2, LINC01004, OCIAD1-AS1, AL136084.3, AP003352.1, Z84484.1, AC022150.2). Compared with the low-risk group, the high-risk group had a worse prognosis. The ferroptosis-related lncRNA signature could independently predict the prognosis of patients with BC. Compared with clinicopathological variables, the ferroptosis-related lncRNA signature has a higher diagnostic efficiency, and the area under the receiver operating characteristic curve was 0.707. When patients were stratified according to different clinicopathological variables, the OS of patients in the high-risk group was shorter than that of those in the low-risk group. GSEA showed that tumor- and immune-related pathways were mainly enriched in the high-risk group. ssGSEA showed that the predictive signature was significantly related to the immune status of BC patients. High-risk patients were more sensitive to anti-PD-1/L1 immunotherapy and the conventional chemotherapy drugs sunitinib, paclitaxel, cisplatin, and docetaxel.

Conclusion: The predictive signature can independently predict the prognosis of BC patients, provides a basis for the mechanism of ferroptosis-related lncRNAs in BC and provides clinical treatment guidance for patients with BC.

Understanding the tumor-immune microenvironment in prostate cancer

PURPOSE OF REVIEW

This review aims to highlight recent advances in prostate cancer tumor-immune microenvironment research and summarize the state-of-the-art knowledge of immune checkpoint inhibitors in prostate cancer.

RECENT FINDINGS

Immune checkpoint inhibitors are the cornerstone of modern immunotherapy which have shown encouraging results across a spectrum of cancers. However, only limited survival benefit has been seen in patients with prostate cancer. Prostate cancer progression and its response to immunotherapies are strongly influenced by the tumor-immune microenvironment, whose feature can be summarized as low amounts of tumor-specific antigens, low frequency of tumor-infiltrating lymphocytes and high frequency of tumor-associated macrophages. To improve the therapeutic effect of immunotherapies, in recent years, many strategies have been applied, of which the most promising ones include the combination of multiple immunotherapeutic agents, the combination of an immunotherapeutic agent with other modalities in parallel or in sequential, and the development of biomarkers to find a subgroup of patients who may benefit the most from immunotherapeutic agents.

SUMMARY

The impact of immune content and specific immune cell types on prostate cancer biology is highly complex. Recent clinical trials have shed light on the optimal use of immunotherapies for prostate cancer.

Transcriptome sequencing and multi-plex imaging of prostate cancer microenvironment reveals a dominant role for monocytic cells in progression

BACKGROUND

Prostate cancer is caused by genomic aberrations in normal epithelial cells, however clinical translation of findings from analyses of cancer cells alone has been very limited. A deeper understanding of the tumour microenvironment is needed to identify the key drivers of disease progression and reveal novel therapeutic opportunities.

RESULTS

In this study, the experimental enrichment of selected cell-types, the development of a Bayesian inference model for continuous differential transcript abundance, and multiplex immunohistochemistry permitted us to define the transcriptional landscape of the prostate cancer microenvironment along the disease progression axis. An important role of monocytes and macrophages in prostate cancer progression and disease recurrence was uncovered, supported by both transcriptional landscape findings and by differential tissue composition analyses. These findings were corroborated and validated by spatial analyses at the single-cell level using multiplex immunohistochemistry.

CONCLUSIONS

This study advances our knowledge concerning the role of monocyte-derived recruitment in primary prostate cancer, and supports their key role in disease progression, patient survival and prostate microenvironment immune modulation.

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.

Immune Cell Landscape in Gastric Cancer

Background

The tumor-infiltrating immune cells are closely associated with the prognosis of gastric cancer (GC). This article is aimed at determining the composition change of immune cells and immune regulatory factors in GC and normal tissues, depicting their prognosis value in GC, and revealing the relationship between them and GC clinical parameters.

Methods

We used CIBERSORT to calculate the proportion of 22 immune cells in the GC or normal tissues; a t-test was applied to assess the expression difference of immune cells and immune regulatory factors in normal and GC tissues. The relationship of the immune cells, immune regulatory factors, and GC patients' clinical characteristics was assessed by univariate analysis.

Results

In this study, we found that the proportion of macrophages increased, while plasma cells and monocytes decreased in GC tissues. In these immune fractions, Tregs and naïve B cells were found to be correlated with GC patients' prognosis. Interestingly, the expression of immune regulatory factors was ambiguous with their classical function in GC tissues. For example, TIM-3, FOXP3, and CMTM6 were overexpressed, while CD27 and PD-1 were underexpressed in GC tissues. We also found that IDO1, PD-1, TIGIT, and TIM-3 were highly expressed in high-grade GC tissues, the HERC2 expression level was related to patients' gender, and the TIGIT expression level was sensitive to targeted therapy. Furthermore, our results suggested that the infiltration of Tregs and naive B cells was strongly correlated with the T stage, radiation therapy, targeted molecular therapy, and the expression levels of TIM-3 and FOXP3 in GC.

Conclusion

The expression pattern of tumor-infiltrating immune cells and immune regulatory factors was systematically depicted in the GC tumor microenvironment, indicating that individualized treatment based on the tumor-infiltrating immune cells and immune regulatory factors may be beneficial to GC patients.

The Landscape of Immune Cells Infiltrating in Prostate Cancer

Background

This study was to explore the infiltration pattern of immune cells in the prostate cancer (PCa) microenvironment and evaluate the possibility of specific infiltrating immune cells as potential prognostic biomarkers in PCa.

Methods

Infiltrating percentage of 22 immune cells were extracted from 27 normalized datasets by CIBERSORT algorithm. Samples with CIBERSORT p-value < 0.05 were subsequently merged and divided into normal or tumor groups. The differences of 22 immune cells between normal and tumor tissues were analyzed along with potential infiltrating correlations among 22 immune cells and Gleason grades. SNV data from TCGA was used to calculate the TMB score. A univariate and multivariate regression were used to evaluate the prognostic effects of immune cells in PCa.

Results

Ten immune cells with significant differences were identified, including seven increased and three decreased infiltrating immune cells from 190 normal prostate tissues and 537 PCa tissues. Among them, the percentage of infiltration of resting NK cells increased the most, whereas the percentage of infiltration of resting mast cells decreased the most. In normal tissues, CD8+ T cells had the strongest infiltrating correlation with monocytes, while activated NK cells and naive B cells were the highest in PCa tissues. Moreover, the infiltration of five immune cells was significantly associated with TMB score and mutations of immune gene change the infiltration of immune cells. The Area Under Curve (AUC) of the multivariate regression model for the five- and 10-year survival prediction of PCa reached 0.796 and 0.862. The validation cohort proved that the model was reproducible.

Conclusions

This study demonstrated that different infiltrating immune cells in prostate cancer, especially higher infiltrating M1 macrophages and neutrophils in PCa tissue, are associated with patients’ prognosis, suggesting that these two immune cells might be potential targets for PCa diagnosis and prognosis of treatment.

The Association between Immune Infiltration and Clinical Phenotypes and Prognosis of Prostate Cancer

Background:

Some evidences show that immune infiltration is closely related to the clinical outcomes in cancers such as colorectal cancer. However, previous studies have not explained the diversity of cell types that make up the immune response. In particular, although some studies and reviews have shown that immunotherapy is important for cancer treatment, few studies have elucidated the relationship between prostate cancer (PCa) phenotype and immune infiltration.

Objectives:

In this study, we analyzed whether different types of tumor-infiltrating immune cells would affect the clinical phenotypes and survival of PCa based on a deconvolution algorithm and annotated gene expression profiles.

Materials and Methods:

The 22 subsets of immune cells inferred by CIBERSORT and the infiltration abundance of 6 immune cells calculated by TIMER were used to determine the associations between them and the PCa traits and survival response. In addition, the survival tree models were constructed to classify PCa patients into four subtypes, and the traits and prognosis were compared among these subtypes.

Results:

As a result, we found that some PCa patients with high death risk lacking immune infiltration were related to the poor prognosis. For the cell subsets studied and subtypes analysis, a low proportion of mast resting cells and T-cells follicular helper exhibited the obvious association with poor outcome.

Conclusions:

In summary, our study suggested the differences in the cellular composition of the immune infiltrate in PCa, and these differences might be important determinants for PCa traits and prognosis.

High expression of stromal signatures correlated with macrophage infiltration, angiogenesis and poor prognosis in glioma microenvironment

Glioma is one of the most fatal tumors in central nervous system. Previous studies gradually revealed the association between tumor microenvironment and the prognosis of gliomas patients. However, the correlation between tumor-infiltrating immune cell and stromal signatures are unknown. In our study, we obtained gliomas samples from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA). The landscape of tumor infiltrating immune cell subtypes in gliomas was calculated by CIBERSORT. As a result, we found high infiltration of macrophages was correlated with poor outcome (P < 0.05). Then functional enrichment analysis of high/low macrophage-infiltrating groups was performed by GSEA. The results showed three gene sets includes 102 core genes about angiogenesis were detected in high macrophage-infiltrating group. Next, we constructed PPI network and analyzed prognostic value of 102 core genes. We found that five stromal signatures indicated poor prognosis which including HSPG2, FOXF1, KDR, COL3A1, SRPX2 (P < 0.05). Five stromal signatures were adopted to construct a classifier. The classifier showed powerful predictive ability (AUC = 0.748). Patients with a high risk score showed poor survival. Finally, we validated this classifier in TCGA and the result was consistent with CGGA. Our investigation of tumor microenvironment in gliomas may stimulate the new strategy in immunotherapy. Five stromal signature correlated with poor prognosis also provide a strong predator of gliomas patient outcome.

Identification of Immune Cell Landscape and Construction of a Novel Diagnostic Nomogram for Crohn's Disease

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.