Degree Of Immune Cell Infiltration Research Articles

The levels of immunosuppressive checkpoint protein PD-L1 and tumor-infiltrating lymphocytes were integrated to reveal the glioma tumor microenvironment.

In spite of significant strides in the realm of cancer biology and therapeutic interventions, the clinical prognosis for patients afflicted with glioblastoma (GBM) remains distressingly dismal. The tumor immune microenvironment (TIME), a crucial player in the progression, treatment response, and prognostic trajectory of glioma, warrants thorough exploration. Within this intricate microcosm, the immunosuppressive checkpoint protein PD-L1 and tumor-infiltrating lymphocytes (TILs) emerge as pivotal constituents, underscoring their potential role in deciphering glioma biology and informing treatment strategies. However, prognostic models based on the association between PD-L1 expression and TIL infiltration in the tumor immune microenvironment have not been established. The aim of this study was to explore TIME genes associated with PD-L1 expression and TIL invasion and to construct a risk score for predicting the overall survival (OS) of GBM patients based on these genes. The samples were separately classified according to the PD-L1 expression level and TIL score and TIME-related genes were identified using differential expression and weighted gene co-expression network analysis. The DEGs were subjected to least absolute contraction and selection operator (LASSO) -Cox regression to construct TIME associated risk score (TIMErisk). A TIMErisk was developed based on STEAP3 and CXCL13 genes. The STLEAP3 was demonstrated to be involved in glioma progression. The results showed that the patients in the high TIMErisk group had poor OS compared with subjects in the low TIMErisk group. The biological phenotypes associated with TIMErisk were analyzed in terms of functional enrichment, tumor immune profile, and tumor mutation profile. The results on tumor immune dysfunction and exclusion dysfunction (TIDE) score and immune surface score (IPS) showed that GBM patients with different TIME risks had different responses to immunotherapy. Tumor purity analysis indicated that PD-L1 and TIL scores were positively correlated with TIMErisk score and negatively correlated with tumor purity. These results show that the TIMErisk-based prognostic model had high predictive value for the prognosis and immune characteristics of GBM patients. Immunohistochemical staining images of patients in the high and low TIMErisk groups were analyzed, showing that the degree of immune cell infiltration was higher in the high TIMErisk group relative to the low TIMErisk group. The present study provides a basis for understanding glioma tumor microenvironment and a foundation for conducting comprehensive immunogenomic analysis.

Immune-Related Genes in the Pathogenesis of Atherosclerosis: Based on Sex Differences.

Atherosclerosis is still a global public problem with increasing incidence rate and mortality. It has been found that gender factors play an important role in the progression of atherosclerosis. However, few people explore gender related atherosclerosis at the level of genes and immune cells. The purpose of this study was to determine genetic and immune cell differences between male and female samples. This study aims to identify differential genes between male and female samples in the GSE43292 dataset. The focus will be on identifying immune-related genes (IRGs) among these differentially expressed genes. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis will be employed to explore the enrichment of IRGs in biological processes, molecular functions, cellular components, and pathways. Furthermore, a protein-protein interaction (PPI) network for the IRGs will be constructed using Cytoscape software. To estimate the degree of immune cell infiltration, single-sample gene set enrichment analysis (ssGSEA) will be conducted. Moreover, the identified IRGs will be validated using GSE28829 dataset. Finally, we validated in atherosclerotic mice. Seven IRGs (CCL13, IL1RN, FPR2, S100A8, CCL19, CXCL1, CXCL8) were identified as being overexpressed in male atherosclerosis. GO and KEGG analysis revealed that these IRGs are primarily enriched in inflammatory response pathways, cytokine signaling pathways, and cytokine- cytokine receptor interactions. Notably, when compared to females, there was a significant infiltration of immune cells in male specimens. Importantly, all seven IRGs demonstrated high diagnostic value in GSE28829 dataset. The use of animal samples supports our results. This study demonstrates the effectiveness of seven IRGs and reveal sex differences in atherosclerosis. Notably, there is a significant presence of immune cells within the atherosclerotic plaque of men compared to women. These findings have potential implications for the development of personalized treatment approaches targeting gender-related atherosclerosis.

Integration of scRNA-seq and bulk RNA-seq constructs a stemness-related signature for predicting prognosis and immunotherapy responses in hepatocellular carcinoma.

Cancer stem cells are associated with unfavorable prognosis in hepatocellular carcinoma (HCC). However, existing stemness-related biomarkers and prognostic models are limited. The stemness-related signatures were derived from taking the union of the results obtained by performing WGCNA and CytoTRACE analysis at the bulk RNA-seq and scRNA-seq levels, respectively. Univariate Cox regression and the LASSO were applied for filtering prognosis-related signatures and selecting variables. Finally, ten gene signatures were identified to construct the prognostic model. We evaluated the differences in survival, genomic alternation, biological processes, and degree of immune cell infiltration in the high- and low-risk groups. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) database was used to evaluate the protein expressions. A stemness-related prognostic model was constructed with ten genes including YBX1, CYB5R3, CDC20, RAMP3, LDHA, MTHFS, PTRH2, SRPRB, GNA14, and CLEC3B. Kaplan-Meier and ROC curve analyses showed that the high-risk group had a worse prognosis and the AUC of the model in four datasets was greater than 0.64. Multivariate Cox regression analyses verified that the model was an independent prognostic indicator in predicting overall survival, and a nomogram was then built for clinical utility in predicting the prognosis of HCC. Additionally, chemotherapy drug sensitivity and immunotherapy response analyses revealed that the high-risk group exhibited a higher likelihood of benefiting from these treatments. The novel stemness-related prognostic model is a promising biomarker for estimating overall survival in HCC.

Infiltration of tumor spheroids by activated immune cells

Recent years have seen a tremendous growth of interest in understanding the role that the adaptive immune system could play in interdicting tumor progression. In this context, it has been shown that the density of adaptive immune cells inside a solid tumor serves as a favorable prognostic marker across different types of cancer. The exact mechanisms underlying the degree of immune cell infiltration is largely unknown. Here, we quantify the temporal dynamics of the density profile of activated immune cells around a solid tumor spheroid. We propose a computational model incorporating immune cells with active, persistent movement and a proliferation rate that depends on the presence of cancer cells, and show that the model able to reproduce semi-quantitatively the experimentally measured infiltration profile. Studying the density distribution of immune cells inside a solid tumor can help us better understand immune trafficking in the tumor micro-environment, hopefully leading towards novel immunotherapeutic strategies.

Machine learning-based identification of cuproptosis-related markers and immune infiltration in severe community-acquired pneumonia.

Severe community-acquired pneumonia (SCAP) is one of the world's most common diseases and a major etiology of acute respiratory distress syndrome (ARDS). Cuproptosis is a novel form of regulated cell death that can occur in various diseases. Our study explored the degree of immune cell infiltration during the onset of severe CAP and identified potential biomarkers related to cuproptosis. Gene expression matrix was obtained from GEO database indexed GSE196399. Three machine learning algorithms were applied: The least absolute shrinkage and selection operator (LASSO), the random forest, and the support vector machine-recursive feature elimination (SVM-RFE). Immune cell infiltration was quantified by single-sample gene set enrichment analysis (ssGSEA) scoring. Nomogram was constructed to verify the applicability of using cuproptosis-related genes to predict the onset of severe CAP and its deterioration toward ARDS. Nine cuproptosis-related genes were differentially expressed between the severe CAP group and the control group: ATP7B, DBT, DLAT, DLD, FDX1, GCSH, LIAS, LIPT1, and SLC31A1. All 13 cuproptosis-related genes were involved in immune cell infiltration. A three-gene diagnostic model was constructed to predict the onset of severe CAP: GCSH, DLD, and LIPT1. Our study confirmed the involvement of the newly discovered cuproptosis-related genes in the progression of SCAP.

BASP1 expression is associated with poor prognosis and is correlated with immune infiltration in gastric cancer.

BASP1 is a membrane-bound protein that plays a promotional or inhibitory role in a variety of tumors; however, its role in gastric cancer (GC) and in the immune microenvironment has not been reported. The objectives of this study were to determine whether BASP1 is a valuable prognostic marker for GC and to explore its role in the immune microenvironment of GC. The expression level of BASP1 in GC was analyzed based on the TCGA dataset and further verified using GSE54129 and GSE161533 datasets, immunohistochemistry, and western blotting. The association between BASP1 and clinicopathological characteristics, as well as its predictive value, were examined using the STAD dataset. Cox regression analysis was performed to determine whether BASP1 can be used as an independent prognostic indicator for GC, and a nomogram was constructed to predict OS. The association between BASP1 and immune cell infiltration, immune checkpoints, and immune cell markers was confirmed by enrichment analysis, as well as analysis based on the TIMER and GEPIA databases. BASP1 was observed to be highly expressed in GC and was associated with a poor prognosis. The expression of BASP1 was positively correlated with the expression of immune checkpoints and immune cell markers, as well as immune cell infiltration. Thus, BASP1 may serve as a standalone prognostic indicator for GC. BASP1 is highly correlated with immune processes, and its expression is positively correlated with the degree of immune cell infiltration, immune checkpoints, and immune cell markers.

Comparison of immune microenvironment between primary and metastatic breast tumors.

1030 Background: Immune evasion is one of the mechanisms by which cancer cells gain the ability to metastasize from the primary tumor to distant sites. In triple-negative breast cancer (TNBC), metastatic tumors are shown to be more immunologically silent than primary tumors. As a result, there are varying degrees of responses to immunotherapy between early-stage and metastatic tumors. Positive clinical responses to immune checkpoint inhibitors (ICIs) are seen in patients with early-stage TNBC regardless of PD-L1 expression. In this study, we investigated the differences in the immune signatures of primary and metastatic breast cancer in a real-world patient population. Methods: We analyzed retrospective comprehensive genomic and immune profiling (CGIP) results from 529 breast tumors tested in the real-world clinical setting. Tumor specimens were classified as primary breast (PB), any lymph nodes (LN; regional and non-regional) or metastatic visceral (MV) sites. LN samples were chosen as positive controls due to expected elevated inflammatory signaling. PD-L1 (CPS positive ≥10) and TMB (high ≥10 Mut/Mb) were determined using IHC and DNA sequencing, respectively. mRNA expression signatures of tumor inflammation (TIGS, strong/moderate/weak) and expression of LAG3, TIGIT and TIM3 were determined by RNA-sequencing from a 395-gene panel. LAG3, TIGIT and TIM3 were selected due to emerging clinical trials in solid tumors. We used over-representation and proportion analysis using chi-squared test to determine the association of specimen sites to various genomic and immune correlates. Results: Among 529 cases, the median patient age was 63.2 years (25.5-93.5). The majority of patients were female (519, 98%), and the most common tumor histology was invasive ductal carcinoma (287, 54%). A total of 224 (42%), 72 (14%), and 232 (44%) of patients had specimens from PB, LN, MV, respectively, while 1 (0.19%) patient had an unknown source. Samples of PB harbored greater degree of immune infiltration, demonstrating higher TIGS score than MV (p=0.014). Primary lesions also demonstrated a greater proportion of PD-L1 positive than metastatic lesions (44% vs 21%, p < 0.001) and higher expressions of immune checkpoints such as TIGIT (p<0.001), LAG3 (p=0.037) and TIM3 (p<0.001). Conclusions: We found that non-lymph node breast cancer metastases harbor a less active immune response than primary breast lesions, showing a lower degree of immune cell infiltration and decreased expression of immune checkpoint markers. These findings support the notion that the immune microenvironment of breast cancer metastases is immunosuppressive and may exhibit a tempered response to ICIs. Therefore, combination treatments of ICIs with chemotherapy, targeted therapies or cancer vaccines may be promising therapeutic approaches to enhance the immune responses and potentially overcome resistance to ICIs in metastatic breast cancer.

B-arrestin deletion abrogates the innate immune response in murine CVB3 myocarditis

β-arrestins (βarrs), initially discovered as desensitizers of GPCR signaling, are now known to function as intracellular scaffolds and signaling transducers mediating a wide array of signaling cascades including immune responses to inflammatory stimuli. An important cause of immune cell infiltration and cardiac inflammation are viral pathogens such as enterovirus coxsackievirus B3 (CVB3) induced myocarditis. While viral myocarditis is a leading cause of dilated cardiomyopathy, the precise molecular mechanisms for the acute immune response to CVB3 infection are not well understood. Given the important role for βarrs in the acute immune response, we tested the hypothesis that βarrs are necessary for the activation of the acute immune response and mediate the progression and severity of cardiotropic CVB3-induced myocarditis in mice. Five to 7-week-old mice devoid of βarr1 (βarr1 KO) or βarr2 (βarr2 KO) were infected intraperitoneally with 1x103-1x104 plaque forming units (PFU) of CVB3 per animal. Wild type (WT) C57BL/6 mice infected with CVB3 were used as controls. The survival rate 7 days post infection (dpi) was significantly improved in male βarr1 KOs (80%) and βarr2 KOs (75%) compared to male WT control mice (35%, p=0.013). Female WT, βarr1 KO, and βarr2 KO mice showed similar survival rates 7 dpi., ranging from 70% to 80%. Early mortality among all groups before 5 dpi was associated with high viral titers (~6.0 ± 0.5 log10PFU/mg) in the liver compared to mortality after 5 dpi (~2.4 ± 1.7 log10PFU/mg) as determined by plaque assay. Viral titers in the heart (WT=3.7 ± 0.9 log10PFU/mg, βarr1KO=3.6 ± 1.3 log10PFU/mg, βarr2KO=3.5 ± 1.2log10PFU/mg) were similar among all groups suggesting that βarrs are not involved in viral replication. Morphometric analysis showed similar weight loss (~18%) across all groups at 7 dpi corroborated by viral cardiac PFU. Immunofluorescent staining of the heart with the pan-inflammatory marker, CD45, revealed high immune cell infiltration in infected WT mice (34.2% ± 4.5%), whereas immune cell infiltration was significantly blunted in infected βarr1 KO (6.0% ± 1.1%; p<0.0001) and βarr2 KO (8.5% ± 0.9%; p<0.0001) mice. The degree of immune cell infiltration in hearts of infected WT mice was directly correlated with an increase in heart viral titer, whereas immune cell infiltration remained blunted across a broad range of heart viral titers in both βarr1 and 2 KO mice. The blunted immune response in both βarr KOs compared to WT infected controls was consistent across multiple immune cell markers including CD4 for helper T cells and CD11b for innate immune cells. In conclusion, we demonstrate that βarrs are necessary for immune cell recruitment to the heart in CVB3 myocarditis and deletion of βarrs in male mice may be cardioprotective as indicated by an increase in survival rate. These data suggest that βarrs may serve as potential therapeutic targets in the treatment of acute viral myocarditis. This work was supported by the National Institutes of Health Grants HL056687 (to H.A.R.). This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Identification of immune-related genes contributing to head and neck squamous cell carcinoma development using weighted gene co-expression network analysis.

This study aimed to identify genes related to the degree of immune cell infiltration in head and neck squamous cell carcinoma (HNSCC), explore their new biological functions, and evaluate their diagnostic and prognostic value in HNSCC. Transcriptomic data from The Cancer Genome Atlas (TCGA) HNSCC dataset was used to screen differentially expressed genes between tumors and normal tissues, followed by weighted correlation network analysis (WGCNA) to identify immune-related modules. Differential gene expression, immune cell infiltration, and survival analyses were performed to screen key genes. The expression of these key genes was validated in Oncomine and gene expression omnibus (GEO) datasets and by immunohistochemistry (IHC). 1869 and 1578 genes were significantly upregulated and downregulated in HNSCC. WGCNA showed that the brown module was associated with the most significant number of immune-related genes. PPI network analysis demonstrated that PPL, SCEL, KRT4, KRT24, KRT78, KRT13, SPRR3, TGM3, CRCT1, and CRNN were key components in the brown module. Furthermore, the expression levels of KRT4, KRT78, KRT13, and SPRR3 in HNSCC correlated with infiltration levels of CD8+ T cells and macrophages. Survival analyses revealed that the expression of KRT78, KRT13, and SPRR3 in HNSCC correlated with overall survival (OS). The IHC assay indicated that KRT13 (p=.042), KRT78 (p < .001), and SPRR3 (p=.022) protein expression levels in HNSCC were significantly lower than in normal tissues. Analysis of GSE65858 and GSE41613 datasets showed that a worse OS was associated with low expression of KRT78 (p=.0086, and p=.005) and SPRR3 (p=.017, and p=.02). Our findings suggest that KRT4, KRT78, KRT13, and SPRR3 are related to the occurrence and development of HNSCC. Importantly, KRT78 and SPRR3 might serve as diagnostic and prognostic biomarkers of HNSCC.

Pan-cancer analysis and experimental validation of DTL as a potential diagnosis, prognosis and immunotherapy biomarker

BackgroundDTL has been found to be related with multiple cancers. However, comprehensive analyses, which identify the prediction value of DTL in diagnosis, prognosis, immune infiltration and treatment, have rarely been reported so far.MethodsCombined with the data online databases, the gene expression, gene mutation, function enrichment and the correlations with the immunity status and clinical indexes of DTL were analyzed. Expression of DTL and the degree of immune cell infiltration were examined by immunofluorescence (IF) and immunohistochemistry (IHC) and analyzed by statistical analysis. Furthermore, the influences of DTL on the cell cycle, cell proliferation and apoptosis were detected by live cell imaging, IF and flow cytometric (FC) analysis. Genomic stability assays were conducted by chromosome slide preparation.ResultsDTL was widely expressed in various cells and tissues, while it was overexpressed in tumor tissues except acute myeloid leukemia (LAML). Pan-cancer bioinformatics analysis showed that the expression of DTL was correlated with the prognosis, immunotherapy, and clinical indexes in various cancers. In addition, gene set enrichment analysis (GSEA) uncovered that DTL was enriched in oocyte meiosis, pyrimidine metabolism, the cell cycle, the G2M checkpoint, mTORC1 signaling and E2F targets. Furthermore, the overexpression of DTL, and its association with immune cell infiltration and clinical indexes in liver hepatocellular carcinoma (LIHC), bladder urothelial carcinoma (BLCA) and stomach adenocarcinoma (STAD) were verified in our study. It was also verified that overexpression of DTL could regulate the cell cycle, promote cell proliferation and cause genomic instability in cultured cells, which may be the reason why DTL plays a role in the occurrence, progression and treatment of cancer.ConclusionsCollectively, this study suggested that DTL is of clinical value in the diagnosis, prognosis and treatment of various cancers, and may be a potential biomarker in certain cancers.

Towards a consensus definition of immune exclusion in cancer.

The immune cell topography of solid tumors has been increasingly recognized as an important predictive factor for progression of disease and response to immunotherapy. The distribution pattern of immune cells in solid tumors is commonly classified into three categories - namely, "Immune inflamed", "Immune desert" and "Immune excluded" - which, to some degree, connect immune cell presence and positioning within the tumor microenvironment to anti-tumor activity. In this review, we look at the ways immune exclusion has been defined in published literature and identify opportunities to develop consistent, quantifiable definitions, which in turn, will allow better determination of the underlying mechanisms that span cancer types and, ultimately, aid in the development of treatments to target these mechanisms. The definitions of tumor immune phenotypes, especially immune exclusion, have largely been conceptual. The existing literature lacks in consistency when it comes to practically defining immune exclusion, and there is no consensus on a definition. Majority of the definitions use somewhat arbitrary cut-offs in an attempt to place each tumor into a distinct phenotypic category. Tumor heterogeneity is often not accounted for, which limits the practical application of a definition. We have identified two key issues in existing definitions of immune exclusion, establishing clinically relevant cut-offs within the spectrum of immune cell infiltration as well as tumor heterogeneity. We propose an approach to overcome these limitations, by reporting the degree of immune cell infiltration, tying cut-offs to clinically meaningful outcome measures, maximizing the number of regions of a tumor that are analyzed and reporting the degree of heterogeneity. This will allow for a consensus practical definition for operationalizing this categorization into clinical trial and signal-seeking endpoints.

A model based on immune-related lncRNA pairs and its potential prognostic value in immunotherapy for melanoma

A model based on long non-coding RNA (lncRNA) pairs independent of expression quantification was constructed to evaluate prognosis melanoma and response to immunotherapy in melanoma. RNA sequencing data and clinical information were retrieved and downloaded from The Cancer Genome Atlas and the Genotype-Tissue Expression databases. We identified differentially expressed immune-related lncRNAs (DEirlncRNAs), matched them, and used least absolute shrinkage and selection operator and Cox regression to construct predictive models. The optimal cutoff value of the model was determined using a receiver operating characteristic curve and used to categorize melanoma cases into high-risk and low-risk groups. The predictive efficacy of the model with respect to prognosis was compared with that of clinical data and ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data). Then, we analyzed the correlations of risk score with clinical characteristics, immune cell invasion, anti-tumor, and tumor-promoting activities. Differences in survival, degree of immune cell infiltration, and intensity of anti-tumor and tumor-promoting activities were also evaluated in the high- and low-risk groups. A model based on 21 DEirlncRNA pairs was established. Compared with ESTIMATE score and clinical data, this model could better predict outcomes of melanoma patients. Follow-up analysis of the model's effectiveness showed that patients in the high-risk group had poorer prognosis and were less likely to benefit from immunotherapy compared with those in the low-risk group. Moreover, there were differences in tumor-infiltrating immune cells between the high-risk and low-risk groups. By pairing the DEirlncRNA, we constructed a model to evaluate the prognosis of cutaneous melanoma independent of a specific level of lncRNA expression.

Identification and validation of neurotrophic factor-related gene signatures in glioblastoma and Parkinson's disease.

Glioblastoma multiforme (GBM) is the most common cancer of the central nervous system, while Parkinson's disease (PD) is a degenerative neurological condition frequently affecting the elderly. Neurotrophic factors are key factors associated with the progression of degenerative neuropathies and gliomas. The 2601 neurotrophic factor-related genes (NFRGs) available in the Genecards portal were analyzed and 12 NFRGs with potential roles in the pathogenesis of Parkinson's disease and the prognosis of GBM were identified. LASSO regression and random forest algorithms were then used to screen the key NFRGs. The correlation of the key NFRGs with immune pathways was verified using GSEA (Gene Set Enrichment Analysis). A prognostic risk scoring system was constructed using LASSO (Least absolute shrinkage and selection operator) and multivariate Cox risk regression based on the expression of the 12 NFRGs in the GBM cohort from The Cancer Genome Atlas (TCGA) database. We also investigated differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between risk groups. Finally, the accuracy of the model genes was validated using multi-omics mutation analysis, single-cell sequencing, QT-PCR, and HPA. We found that 4 NFRGs were more reliable for the diagnosis of Parkinson's disease through the use of machine learning techniques. These results were validated using two external cohorts. We also identified 7 NFRGs that were highly associated with the prognosis and diagnosis of GBM. Patients in the low-risk group had a greater overall survival (OS) than those in the high-risk group. The nomogram generated based on clinical characteristics and risk scores showed strong prognostic prediction ability. The NFRG signature was an independent prognostic predictor for GBM. The low-risk group was more likely to benefit from immunotherapy based on the degree of immune cell infiltration, expression of immune checkpoints (ICs), and predicted response to immunotherapy. In the end, 2 NFRGs (EN1 and LOXL1) were identified as crucial for the development of Parkinson's disease and the outcome of GBM. Our study revealed that 4 NFRGs are involved in the progression of PD. The 7-NFRGs risk score model can predict the prognosis of GBM patients and help clinicians to classify the GBM patients into high and low risk groups. EN1, and LOXL1 can be used as therapeutic targets for personalized immunotherapy for patients with PD and GBM.

Parkinson’s Disease Gene Biomarkers Screened by the LASSO and SVM Algorithms

Parkinson's disease (PD) is a common progressive neurodegenerative disorder. Various evidence has revealed the possible penetration of peripheral immune cells in the substantia nigra, which may be essential for PD. Our study uses machine learning (ML) to screen for potential PD genetic biomarkers. Gene expression profiles were screened from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) were selected for the enrichment analysis. A protein-protein interaction (PPI) network was built with the STRING database (Search Tool for the Retrieval of Interacting Genes), and two ML approaches, namely least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), were employed to identify candidate genes. The external validation dataset further tested the expression degree and diagnostic value of candidate biomarkers. To assess the validity of the diagnosis, we determined the receiver operating characteristic (ROC) curve. A convolution tool was employed to evaluate the composition of immune cells by CIBERSORT, and we performed correlation analyses on the basis of the training dataset. Twenty-seven DEGs were screened in the PD and control samples. Our results from the enrichment analysis showed a close association with inflammatory and immune-associated diseases. Both the LASSO and SVM algorithms screened eight and six characteristic genes. AGTR1, GBE1, TPBG, and HSPA6 are overlapping hub genes strongly related to PD. Our results of the area under the ROC (AUC), including AGTR1 (AUC = 0.933), GBE1 (AUC = 0.967), TPBG (AUC = 0.767), and HSPA6 (AUC = 0.633), suggested that these genes have good diagnostic value, and these genes were significantly associated with the degree of immune cell infiltration. AGTR1, GBE1, TPBG, and HSPA6 were identified as potential biomarkers in the diagnosis of PD and provide a novel viewpoint for further study on PD immune mechanism and therapy.

Identification of immunocell infiltrates and effective diagnostic biomarkers in laryngeal carcinoma.

Laryngeal cancer (LC) is a malignant tumor that occurs in the head and neck. Laryngeal cancer is one of the most common cancers of the neck and head, and its prognosis has always been poor. The incidence of LC increased gradually and showed an early rising trend. Laryngeal cancer is rarely studied in relation to immunity, Malignant tumors will change the state of the human body in various ways to adapt to their own survival and avoid the immune system. This study aims to explore the immune molecular mechanism of laryngeal cancer through bioinformatics analysis. The gene expression data was downloaded for 3 microarray datasets: GSE27020, GSE59102, and GSE51985. CIBERSORT algorithm was performed to evaluate immune cell infiltration in tissues between LC and healthy control (HC). Differentially expressed genes (DEGs) were screened. Functional correlation of DEGs were analyzed by Gene Ontology, Gene Set Enrichment Analysis and Kyoto encyclopedia of genes and genomes. Candidate biomarkers were identified by cytoHubba of Cytoscape. Spearman correlations between the above biomarkers and infiltrating immune cells were explored using R software analysis. The immune cell types of LC and HC were significantly different. Twenty-one DEGs were obtained by cross-screening. The function of DEGs is closely related to the number of immune cells. Five central genes (TNNT3, TNNI2, Desmin, matrix metallopeptidase 9 and cytotoxic T lymphocyte antigen 4) were screened. The HUB gene was demonstrated to have the ability to diagnose LC and HC with good specificity and sensitivity. The correlation between immune cells and biomarkers showed that hub gene was positively correlated with macrophages and dendritic cells, and negatively correlated with CD4 + T cell. TNNT3, TNNI2, Desmin, matrix metallopeptidase 9 and cytotoxic T lymphocyte antigen 4 can be used as diagnostic biomarker for LC. Macrophages, dendritic cells and CD4 + T cell may participate in the occurrence and development of LC.

Effects of spaceflight on the spleen and thymus of mice: Gene pathway analysis and immune infiltration analysis.

During space flight, the immune system function of the body is disrupted due to continuous weightlessness, radiation and other factors, resulting in an increased incidence of infectious diseases in astronauts. However, the effect of space flight on the immune system at the molecular level is unknown. The aim of this study was to identify key genes and pathways of spatial environmental effects on the spleen and thymus using bioinformatics analysis of the GEO dataset. Differentially expressed genes (DEGs) in the spleen and thymus of mice preflight and postflight were screened by comprehensive analysis of gene expression profile data. Then, GO enrichment analysis of DEGs was performed to determine the biological role of DEGs. A protein-protein interaction network was used to identify hub genes. In addition, transcription factors in DEGs were screened, and a TF-target regulatory network was constructed. Finally, immune infiltration analysis was performed on spleen and thymus samples from mice. The results showed that DEGs in the spleen and thymus are mainly involved in immune responses and in biological processes related to platelets. Six hub genes were identified in the spleen and 13 in the thymus, of which Ttr, Aldob, Gc and Fabp1 were common to both tissues. In addition, 5 transcription factors were present in the DEGs of the spleen, and 9 transcription factors were present in the DEGs of the thymus. The spatial environment can influence the degree of immune cell infiltration in the spleen and thymus. Our study bioinformatically analyzed the GEO dataset of spacefaring mice to identify the effects of the space environment on the immune system and the genes that play key roles, providing insights for the treatment of spaceflight-induced immune system disorders.

High Expression of SMO and GLI1 Genes with Poor Prognosis in Malignant Mesothelioma.

To investigate the value of SMO and GLI1 genes in the hedgehog pathway in malignant mesothelioma specimens. Further study on the expression and prognosis of SMO and GLI1 in malignant mesothelioma tissues and the relationship between the two and the molecular mechanisms of mesothelioma immunity and to further investigate the prognostic value of mesothelioma expression. Immunohistochemistry and RT-qPCR were applied to detect the expression of SMO and GLI1 proteins and mRNA in biopsy specimens and plasma cavity effusion specimens from malignant mesothelioma (n = 130) and benign mesothelial tissues (n = 50) and to analyze the clinicopathological significance and survival risk factors of SMO and GLI1 protein expression in mesothelioma. The mechanisms of mesothelioma cell expression and immune cell infiltration were investigated using bioinformatics methods. SMO and GLI1 in mesothelioma tissues detected high concordance between the diagnostic results of mesothelioma biopsy specimens and plasma cavity effusion specimens. The expression levels of SMO and GLI1 protein and mRNA in mesothelioma tissues were higher than those in benign mesothelioma tissues. The expression levels of SMO and GLI1 protein were correlated with the age, site, and asbestos exposure history of patients with mesothelioma. The expression levels of SMO and GLI1 protein were correlated with the expressions of ki67 and p53 (P < 0.05). SMO and GLI1 gene expression levels were negatively correlated with good prognosis in mesothelioma patients (P < 0.05). Cox proportional risk model indicated that protein expressions of invasion, lymph node metastasis, distant metastasis, staging, and genes were independent prognostic factors of mesothelioma. The GEPIA database showed the overall survival rate and the disease-free survival rate of mesothelioma patients in the high SMO and GLI1 expression groups; the UALCAN database analysis showed lower SMO expression levels in mesothelioma patients with more pronounced TP53 mutations (P = 0.001); GLI1 gene expression levels were strongly correlated with lymph node metastasis in mesothelioma patients (P = 0.009). Timer database analysis showed that the mechanism of immune cell infiltration was closely related to SMO and GLI1 expression. The degree of immune cell infiltration was strongly correlated with the prognosis of mesothelioma patients (P < 0.05). The expression levels of both SMO and GLI1 proteins were higher than those of normal mesothelial tissues, and the mRNA expression levels also changed in the same direction. SMO and GLI1 gene expressions in mesothelioma were negatively correlated with age, site of occurrence, and history of asbestos exposure. Positive expression of SMO and GLI1 was negatively correlated with patient survival. The Cox proportional risk model showed that gender, history of asbestos exposure, site of occurrence, SMO, and GLI1 were independent prognostic factors for mesothelioma. The mechanism of immune cell infiltration in mesothelioma is closely related to the gene expression of both and the survival prognosis of mesothelioma patients.

Elevated Fras1-related extracellular matrix 3 in isocitrate dehydrogenase 1-mutant astrocytoma World Health Organization grade 4 predicts favorable prognosis in glioma: A bioinformatic and experimental analysis

Background and Aim: World Health Organization (WHO) grade 4 glioma is a malignancy of the central nervous system characterized by refractoriness to treatment and a high mortality rate. Isocitrate dehydrogenase (IDH) mutation is a crucial molecular event for the classification of glioma and associated with prognosis and exploring genetic and molecular differences between IDH mutant and wildtype glioma is crucial. The aim of this study was to investigate the prognostic gene between IDH mutant and wildtype WHO grade 4 glioma and its functional significance. Materials and Methods: The mRNA expression profile data of WHO grade 4 glioma were downloaded from the Gene Expression Omnibus and Chinese Glioma Genome Atlas databases. Bioinformatic analysis was performed to identify the differentially expressed genes between IDH1-mutant and wildtype WHO grade 4 glioma. Survival analysis, functional enrichment analysis, immune cell infiltration evaluation, and in vitro experimental validation were conducted to evaluate the prognostic and functional significance of Fras1-related extracellular matrix 3 (FREM3). This study was approved by the Institutional Ethics Committee of Xinqiao Hospital, Third Military Medical University (approval No. 2021-Y068-01). Results: Elevated expression of FREM3 in IDH1-mutant WHO grade 4 astrocytoma predicted favorable prognosis in glioma. FREM3 was negatively associated with epithelial–mesenchymal transition (EMT), angiogenesis, and hypoxia; notably, low expression of FREM3 was associated with a higher degree of immune cell infiltration. In vitro experiments demonstrated that high FREM3 expression might attenuate the process of EMT and cellular proliferation in glioma. Conclusions: The gene FREM3 plays a major role in IDH1-mutant WHO grade 4 glioma and elevated FREM3 predicts a favorable prognosis of glioma. Further investigation on FREM3 is warranted to elucidate the mechanisms underlying the malignant evolution of glioma.

Identification of m7G Methylation-Related miRNA Signature Associated with Survival and Immune Microenvironment Regulation in Uterine Corpus Endometrial Carcinoma.

N7-methylguanosine (m7G) has been implicated in the development of cancer. The role of m7G-related miRNAs in the survival prediction of UCEC patients has not been investigated. Current research was the first to construct an m7G-related miRNA model to accurately predict the survival of patients with uterine corpus endometrial carcinoma (UCEC) and to explore immune cell infiltration and immune activity in the tumor microenvironment. RNA-seq data and clinical information of UCEC patients were derived from The Cancer Genome Atlas (TCGA) database. Using the TargetScan online database, we predicted miRNAs linked to the m7G-related genes and identified miRNAs which were significantly associated with the survival in UCEC patients and constructed a risk scoring model. The TCGA-UCEC cases were scored according to the risk model, and the high- and low-risk groups were divided by the median risk value. Gene enrichment analysis and immune cell infiltration and immune function analysis were performed using "clusterProfiler" and "GSVA" packages in R. The survival prediction model consisted of 9 miRNAs, namely, hsa-miR-1301, hsa-miR-940, hsa-miR-592, hsa-miR-3170, hsa-miR-876, hsa-miR-215, hsa-miR-934, hsa-miR-3920, and hsa-miR-216b. Survival of UCEC patients in the high-risk group was worse than that in the low-risk group (p < 0.001). The receiver operating characteristic (ROC) curve showed that the model had good predictive performance, and the area under the curve was 0.800, 0.690, and 0.705 for 1-, 3-, and 5-year survival predictions, respectively. There were differences in the degree of immune cell infiltration and immune activity between the low-risk and high-risk groups. The expression levels of the identified differentially expressed genes correlated with the susceptibility to multiple anticancer drugs. The survival prediction model constructed based on 9 m7G-related miRNAs had good predictive performance.

Classification and Prognosis Analysis of Pancreatic Cancer Based on DNA Methylation Profile and Clinical Information.

Pancreatic adenocarcinoma (PAAD) has a poor prognosis with high individual variation in the treatment response among patients; however, there is no standard molecular typing method for PAAD prognosis in clinical practice. We analyzed DNA methylation data from The Cancer Genome Atlas database, which identified 1235 differentially methylated DNA genes between PAAD and adjacent tissue samples. Among these, 78 methylation markers independently affecting PAAD prognosis were identified after adjusting for significant clinical factors. Based on these genes, two subtypes of PAAD were identified through consistent clustering. Fourteen specifically methylated genes were further identified to be associated with survival. Further analyses of the transcriptome data identified 301 differentially expressed cancer driver genes between the two PAAD subtypes and the degree of immune cell infiltration differed significantly between the subtypes. The 14 specific genes characterizing the unique methylation patterns of the subtypes were used to construct a Bayesian network-based prognostic prediction model for typing that showed good predictive value (area under the curve value of 0.937). This study provides new insight into the heterogeneity of pancreatic tumors from an epigenetic perspective, offering new strategies and targets for personalized treatment plan evaluation and precision medicine for patients with PAAD.