Immune Infiltration Patterns Research Articles

Prognostic analysis of patients with gastric cancer based on N6-methyladenosine modification patterns and tumor microenvironment characterization.

Cancers arise from genetic and epigenetic abnormalities that affect oncogenes and tumor suppressor genes, compounded by gene mutations. The N6-methyladenosine (m6A) RNA modification, regulated by methylation regulators, has been implicated in tumor proliferation, differentiation, tumorigenesis, invasion, and metastasis. However, the role of m6A modification patterns in the tumor microenvironment of gastric cancer (GC) remains poorly understood. In this study, we analyzed m6A modification patterns in 267 GC samples utilizing 31 m6A regulators. Using consensus clustering, we identified two unique subgroups of GC. Patients with GC were segregated into high- and low-infiltration cohorts to evaluate the infiltration proportions of the five prognostically significant immune cell types. Leveraging the differential genes in GC, we identified a "green" module via Weighted Gene Co-expression Network Analysis. A risk prediction model was established using the LASSO regression method. The "green" module was connected to both the m6A RNA methylation cluster and immune infiltration patterns. Based on "Module Membership" and "Gene Significance", 37 hub genes were identified, and a risk prediction model incorporating nine hub genes was established. Furthermore, methylated RNA immunoprecipitation and RNA Immunoprecipitation assays revealed that YTHDF1 elevated the expression of DNMT3B, which synergistically promoted the initiation and development of GC. We elucidated the molecular mechanism underlying the regulation of DNMT3B by YTHDF1 and explored the crosstalk between m6A and 5mC modification. m6A RNA methylation regulators are instrumental in malignant progression and the dynamics of tumor microenvironment infiltration of GC. Assessing m6A modification patterns and tumor microenvironment infiltration characteristics in patients with GC holds promise as a valuable prognostic biomarker.

Screening of Diagnostic Biomarkers and Immune Infiltration Characteristics Linking Rheumatoid Arthritis and Rosacea Based on Bioinformatics Analysis.

Both rheumatoid arthritis (RA) and rosacea represent common chronic systemic autoimmune conditions. Recent research indicates a heightened RA risk among individuals with rosacea. However, the molecular mechanisms linking these diseases remain largely unknown. This study aims to uncover shared molecular regulatory networks and immune cell infiltration patterns in both rosacea and RA. The gene expression profiles of RA (GSE12021, GSE55457), and the rosacea gene expression profile (GSE6591), were downloaded from Gene Expression Omnibus (GEO) databases, and obtained to screen differentially expressed genes (DEGs) by using "limma" package in R software. Various analyses including GO, KEGG, protein-protein interaction (PPI) network, and weighted gene co-expression network analyses (WGCNA) were conducted to explore potential biological functions and signaling pathways. CIBERSORT was used to assess the abundance of immune cells. Pearson coefficients were used to calculate the correlations between overlapped genes and the leukocyte gene signature matrix. Flow cytometry (FCM) analysis confirmed the most abundant immune cells detected in rheumatoid arthritis and rosacea. Receiver operator characteristic (ROC) analysis, enzyme-linked immunosorbent assay (ELISA), and qRT-PCR were used to confirm biomarkers and functions. Two hundred seventy-seven co-expressed DEGs were identified from these datasets. Functional enrichment analysis indicated that these DEGs were associated with immune processes and chemokine-mediated signaling pathways. Fourteen and 17 hub genes overlapped between cytoHubba and WGCNA were identified in RA and rosacea, respectively. Macrophages and dendritic cells were RA and rosacea's most abundant immune cells, respectively. The ROC curves demonstrated potential diagnostic values of CXCL10 and CCL27, showing higher levels in the serum of patients with RA or rosacea, and suggesting possible regulation in the densities and functions of macrophages and dendritic cells from RA and rosacea, which were validated by FCM and qRT-PCR. Importantly, our findings may contribute to the scientific basis for biomarkers and therapeutic targets for patients with RA and rosacea in the future.

Whole transcriptome landscape in HAPE under the stress of environment at high altitudes: new insights into the mechanisms of hypobaric hypoxia tolerance.

High altitude pulmonary edema (HAPE) is an idiopathic, noncardiogenic form of pulmonary edema that occurs at high altitudes. It is characterized by a severe clinical course and carries a significant mortality risk. Despite its clinical relevance, the molecular mechanisms underlying HAPE are not well understood. We conducted whole-transcriptome RNA sequencing on blood samples from 6 pairs of HAPE patients and healthy controls to identify differentially expressed (DE) mRNAs, miRNAs, circRNAs, lncRNAs, along with alternative splicing (AS) events, gene fusions, and novel transcripts. To explore the regulatory dynamics, we constructed ceRNA networks and analyzed immune cell infiltration patterns, further annotating the biological functions of these transcripts. For empirical validation, we selected five circRNAs from the ceRNA network and conducted RT-qPCR on 50 paired samples. Additionally, we assessed the correlations between circRNA expression levels and clinical data to evaluate their diagnostic potential. We observed 2,023 differentially expressed mRNAs (DEmRNAs), 84 DEmiRNAs, 200 DEcircRNAs, and 3,573 DElncRNAs. A total of 139 'A3SS' events, 103 'A5SS' events, 545 'MXE' events, 14 'RI' events, and 1,482 'SE' events were identified in the AS events analysis between the two groups. Two ceRNA networks were constructed. T cells, follicular helper, and Macrophages M1 cells exhibited the strongest positive correlation (R=0.82), while naive B cells and memory B cells demonstrated the strongest negative correlation (R=-0.62). In total, the expression of three circRNAs was significantly different in a larger cohort. Hsa_circ_0058497, hsa_circ_0081006, and hsa_circ_0083220 demonstrated consistent with the RNA sequencing results. These three circRNAs strongly correlate with clinical indicators and exhibit potential as diagnostic biomarkers. Finally, we verified five genes (CXCR4, HSD17B2, ANGPTL4, TIMP3, N4BP3) that were differentially expressed in endothelial cells under normoxia and hypoxia through bioinformatics and RT-qPCR analyses. This study elucidates the differential expression of coding and non-coding RNAs (ncRNAs) in HAPE, identifies new transcripts and genes, and enhances our understanding of the transcriptional characteristics of HAPE. Moreover, it highlights the potential role of circRNAs in advancing the diagnosis and treatment of HAPE.

Identification of M2 macrophage-related genes for establishing a prognostic model in pancreatic cancer: FCGR3A as key gene.

Pancreatic ductal adenocarcinoma (PDAC) has a rich and complex tumor immune microenvironment (TIME). M2 macrophages are among the most extensively infiltrated immune cells in the TIME and are necessary for the growth and migration of cancers. However, the mechanisms and targets mediating M2 macrophage infiltration in pancreatic cancer remain elusive. The M2 macrophage infiltration score of patients was assessed using the xCell algorithm. Using weighted gene co-expression network analysis (WGCNA), module genes associated with M2 macrophages were identified, and a predictive model was designed. The variations in immunological cell patterns, cancer mutations, and enrichment pathways between the cohorts with the high- and low-risk were examined. Additionally, the expression of FCGR3A and RNASE2, as well as their association with M2 macrophages were evaluated using the HPA, TNMplot, and GEPIA2 databases and verified by tissue immunofluorescence staining. Moreover, in vitro cell experiments were conducted, where FCGR3A was knocked down in pancreatic cancer cells using siRNA to analyze its effects on M2 macrophage infiltration, tumor proliferation, and metastasis. The prognosis of patients in high-risk and low-risk groups was successfully distinguished using a prognostic risk score model of M2 macrophage-related genes (p = 0.024). Between the high- and low-risk cohorts, there have been notable variations in immune cell infiltration patterns, tumor mutations, and biological functions. The risk score was linked to the manifestation of prevalent immunological checkpoints, immunological scores, and stroma values (all p < 0.05). In vitro experiments and tissue immunofluorescence staining revealed that FCGR3A can promote the infiltration or polarization of M2 macrophages and enhance tumor proliferation and migration. In this study, an M2 macrophage-related pancreatic cancer risk score model was established, and found that FCGR3A was correlated with tumor formation, metastasis, and M2 macrophage infiltration.

Constructed competitive endogenous RNA network and patterns of immune infiltration revealing the prognostic signature for cervical cancer.

Aim: To investigate the relationship between potential abnormal epigenetic modification and immune cell infiltration in patients with cervical carcinoma. Materials & methods: RNA expression profiles from The Cancer Genome Atlas database were used to explore the relationship between key biomarkers and tumor-infiltrating immune cells and for clinical specimen validation. Results: Two nomogram models were developed, one with specific ceRNA and the other based on biological markers of related tumor-infiltrating immune cells. Moreover, a key biomarker (RIPOR2), which was significantly relevant to CD8 T cells. Conclusion: RIPOR2 and CD8T cells play a crucial role in the development and progression of cervical carcinoma, suggesting their potential as markers for guiding future therapeutic strategies.

Expression, Prognostic Value, and Biological Function of CTHRC1 in Different Types of Gliomas: A Bioinformatic Analysis and Experiment Validation.

In recent years, abnormal expression of collagen triple helix repeat containing 1 (CTHRC1) has been found in some tumors, closely related to the poor prognosis of cancer patients. However, the clinical significance of CTHRC1 in gliomas is not completely understood. We investigated the expression, prognostic value, and potential biological function of CTHRC1 in different types of gliomas through bioinformatics analysis and experimental verification. Bioinformatics analysis revealed several key findings regarding the expression and clinical significance of CTHRC1 in gliomas. First, the analysis demonstrated a positive correlation between CTHRC1 expression and the World Health Organization (WHO) grading of gliomas, a relationship that was validated through immunohistochemistry experiments. In addition, a trend was observed in which CTHRC1 expression increased with the extent of glioma invasion, as supported by Western blot experiments. Subsequent bioinformatics analysis identified the mesenchymal subtype of gliomas as having the highest levels of CTHRC1 expression, a finding reinforced by immunohistochemical staining. Moreover, high CTHRC1 expression was associated with poor prognosis in gliomas and emerged as an independent prognostic factor, with varying impacts on prognosis between low-grade gliomas (LGGs) and glioblastoma (GBM) subgroups. Notably, comparative analysis unveiled distinct patterns of immune infiltration of CTHRC1 in LGG and GBM. Furthermore, alterations in copy number variations and DNA methylation were identified as potential mechanisms underlying elevated CTHRC1 levels in gliomas. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that CTHRC1 and its associated genes mainly function in the extracellular matrix and participate in tumor-related signaling pathways. The CTHRC1 has shown significant clinical utility as a prognostic marker and mesenchymal subtype marker of glioma.

Necroptosis and immune infiltration in hypertrophic cardiomyopathy: novel insights from bioinformatics analyses.

Hypertrophic Cardiomyopathy (HCM), a widespread genetic heart disorder, is largely associated with sudden cardiac fatality. Necroptosis, an emerging type of programmed cell death, plays a fundamental role in several cardiovascular diseases. This research utilized bioinformatics analysis to investigate necroptosis's implication in HCM. The study retrieved RNA sequencing datasets GSE130036 and GSE141910 from the Gene Expression Omnibus (GEO) database. It detected necroptosis-linked differentially expressed genes (NRDEGs) by reviewing both the gene set for necroptosis and the differently expressed genes (DEGs). The enriched signaling pathway of HCM was assessed using GSEA, while common DEGs were studied through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Concurrently, the Protein-Protein Interaction network (PPI) proved useful for identifying central genes. CIBERSORT facilitated evaluating the correlation between distinct immune cell-type prevalence and NRDEGs by analyzing immune infiltration patterns. Lastly, GSE141910 dataset validated the expression ranks of NRDEGs and immune-cell penetration. The investigation disclosed significant enrichment and activation of the necroptosis pathway in HCM specimens. Seventeen diverse genes, including CYBB, BCL2, and JAK2 among others, were identified in the process. PPI network scrutiny classified nine of these genes as central genes. Results from GO and KEGG enrichment analyses showed substantial connections of these genes to pathways pertaining to the HIF-1 signaling track, necroptosis, and NOD-like receptor signaling process. Moreover, an imbalance in M2 macrophage cells in HCM samples was observed. Finally, CYBB, BCL2, and JAK2 emerged as vital genes and were validated using the GSE141910 dataset. These results indicate necroptosis as a probable underlying factor in HCM, with immune cell infiltration playing a part. Additionally, CYBB, BCL2, JAK2 could act as potential biomarkers for recognizing HCM. This information forms crucial insights into the basic mechanisms of HCM and could enhance its diagnosis and management.

Molecular Pathogenesis and Histological Variability of Dermatofibrosarcoma Protuberans

Dermatofibrosarcoma protuberans (DFSP) is a kind of infrequent tumor of the soft tissues distinguished by its gradual proliferation and a preference for regional reoccurrence, despite its limited capacity for metastasis. At the molecular level, a significant majority of DFSP cases manifest the t (17;22) (q22; q13) chromosomal translocation, resulting in the fusion of the collagen type I alpha chain (COL1A1) gene and platelet-derived growth factor B chain (PDGFB) gene. This fusion results in PDGFB overexpression, consequently activating the PDGF Receptor-beta (PDGFR-β). The activated PDGFR-β serves as a central nexus for numerous intracellular signaling pathways, initiating cascades including Phosphatidylinositol 3-kinase (PI3K)/Akt, Mitogen-Activated Protein Kinase (MAPK)/extracellular signal-regulated kinase (ERK), and STATs pathways. These cascades collectively enhance DFSP cell growth, proliferation, and invasiveness. Histologically, DFSP tumors are predominantly fibroblastic and display distinct immune cell infiltration patterns in their microenvironment. Elevated expression of CD4+ naïve cells, CD4+ Th2 cells, CD8+ central memory T cells, B cells, and macrophages is evident, while levels of CD4+ central memory T cells and eosinophils are diminished. Immunohistochemical analyses further highlight that most DFSP cells exhibit positive cytoplasmic CD34 expression, whereas factor XIIIa and α-smooth muscle actin are notably absent. Clinically, DFSP presents in diverse subtypes, each characterized by its unique histological profile. Prominent subtypes encompass classic DFSP, fibrosarcomatous DFSP (FS-DFSP), pigmented DFSP, giant cell fibroblastoma, myxoid DFSP, atrophic DFSP, and the granular cell variant. This review aspires to deliver an exhaustive insight into DFSP, accentuating its molecular underpinnings and clinical presentation, with the ultimate goal of advancing our understanding and management of this distinct dermatological entity.

The predictive efficacy of programmed cell death in immunotherapy of melanoma: A comprehensive analysis of gene expression data for programmed cell death biomarker and therapeutic target discovery.

In this study, genes linked to prognosis in skin cutaneous melanoma (SKCM) involved in programmed cell death (PCD) were identified and confirmed and prognostic models based on these genes were constructed. Acquisition and analysis of clinical data and RNA sequencing information from The Cancer Genome Atlas-SKCM (TCGA-SKCM) and Sangerbox databases, gene expression data for 477 tumor samples and 2 normal samples were successfully gathered. The patients were separated into two clusters based on consensus clustering of PCD-related genes, with Cluster A having greater tumor purity, ESTIMATE score, immune score, and matrix score, and Cluster B having a significantly distinct pattern of immune cell infiltration. The use of gene set enrichment analysis and weighted correlation network analysis showed significant associations between certain genes and factors such as tumor mutation burden, age, stage, grade, and tumor subtype. Finally, based on the 12 genes selected by Least Absolute Shrinkage and Selection Operator regression analysis (STAT3, IRF2, SLC7A11, ZEB1, LIPT1, PML, GCH1, GYS1, ABCC1, XBP1, TFAP2C, NOX4), a prognostic model of PGD-related genes was constructed. The effectiveness of the model's prognostic value was confirmed through survival analysis, time-dependent receiver operating characteristic curve, single-factor Cox regression analysis, and nomogram. We also verified the relationship between the GCH1 and MKI67 expression by wet experiment. This model has high prediction accuracy in SKCM patients and can provide a reference for clinical treatment.

Identification of immune cell infiltration pattern in diabetic nephropathy

Identification of immune cell infiltration pattern in diabetic nephropathy

Bioinformatics-led discovery of liver-specific genes and macrophage infiltration in acute liver injury.

Acute liver injury (ALI) is an important global health concern, primarily caused by widespread hepatocyte cell death, coupled with a complex immune response and a lack of effective remedies. This study explores the underlying mechanisms, immune infiltration patterns, and potential targets for intervention and treatment ALI. The datasets of acetaminophen (APAP), carbon tetrachloride (CCl4), and lipopolysaccharide (LPS)-induced ALI were obtained from the GEO database. Differentially expressed genes (DEGs) were individually identified using the limma packages. Functional enrichment analysis was performed using KEGG, GO, and GSEA methods. The overlapping genes were extracted from the three datasets, and hub genes were identified using MCODE and CytoHubba algorithms. Additionally, PPI networks were constructed based on the String database. Immune cell infiltration analysis was conducted using ImmuCellAI, and the correlation between hub genes and immune cells was determined using the Spearman method. The relationship between hub genes, immune cells, and biochemical indicators of liver function (ALT, AST) was validated using APAP and triptolide (TP) -induced ALI mouse models. Functional enrichment analysis indicated that all three ALI models were enriched in pathways linked to fatty acid metabolism, drug metabolism, inflammatory response, and immune regulation. Immune analysis revealed a significant rise in macrophage infiltration. A total of 79 overlapping genes were obtained, and 10 hub genes were identified that were consistent with the results of the biological information analysis after screening and validation. Among them, Clec4n, Ms4a6d, and Lilrb4 exhibited strong associations with macrophage infiltration and ALI.

Identification of novel biomarkers and immune infiltration characteristics of ischemic stroke based on comprehensive bioinformatic analysis and machine learning

BackgroundIschemic stroke (IS) is one of most common causes of disability in adults worldwide. However, there is still a lack of effective and reliable diagnostic markers and therapeutic targets in IS. Furthermore, immune cell dysfunction plays an important role in the pathogenesis of IS. Hence, in-depth research on immune-related targets in progressive IS is urgently needed. MethodsExpression profile data from patients with IS were downloaded from the Gene Expression Omnibus (GEO) database. Then, differential expression analysis and weighted gene coexpression network analysis (WGCNA) were performed to identify the significant modules and differentially expressed genes (DEGs). Key genes were obtained and used in functional enrichment analyses by overlapping module genes and DEGs. Next, hub candidate genes were identified by utilizing three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine–recursive feature elimination (SVM-RFE). Subsequently, a diagnostic model was constructed based on the hub genes, and receiver operating characteristic (ROC) curves were constructed to validate the performances of the predictive models and candidate genes. Finally, the immune cell infiltration landscape of IS was explored with the CIBERSORT deconvolution algorithm. ResultsA total of 40 key DEGs were identified based on the intersection of the DEGs and module genes, and we found that these genes were mainly enriched in the regulation of lipolysis in adipocytes, neutrophil extracellular trap formation and complement and coagulation cascades. Based on the results from three advanced machine learning algorithms, we obtained 7 hub candidate genes (ABCA1, ARG1, C5AR1, CKAP4, HMFN0839, SDCBP and TLN1) as diagnostic biomarkers of IS and developed a reliable nomogram with high predictive performance (AUC = 0.987). In addition, immune cell infiltration dysregulation was implicated in IS, and compared with those in the normal group, IS patients had increased fractions of gamma delta T cells, monocytes, M0 macrophages, M2 macrophages and neutrophils and clearly lower percentages of naive B cells, CD8 T cells, CD4+ memory T cells, follicular helper T cells, regulatory T cells (Tregs) and resting dendritic cells. Furthermore, correlation analysis indicated a significant correlation between the hub genes and immune cells in progressive IS. ConclusionIn conclusion, our study identified 7 hub genes as diagnostic biomarkers and established a reliable model to predict the occurrence of IS. Meanwhile, we explored the immune cell infiltration pattern and investigated the relationship between candidate genes and immune cells in the pathogenesis of IS. Hence, our study provides new insights into the diagnosis and treatment of IS.

Identification of potential immune-related hub genes in Parkinson's disease based on machine learning and development and validation of a diagnostic classification model.

Parkinson's disease is the second most common neurodegenerative disease in the world. However, current diagnostic methods are still limited, and available treatments can only mitigate the symptoms of the disease, not reverse it at the root. The immune function has been identified as playing a role in PD, but the exact mechanism is unknown. This study aimed to search for potential immune-related hub genes in Parkinson's disease, find relevant immune infiltration patterns, and develop a categorical diagnostic model. We downloaded the GSE8397 dataset from the GEO database, which contains gene expression microarray data for 15 healthy human SN samples and 24 PD patient SN samples. Screening for PD-related DEGs using WGCNA and differential expression analysis. These PD-related DEGs were analyzed for GO and KEGG enrichment. Subsequently, hub genes (dld, dlk1, iars and ttd19) were screened by LASSO and mSVM-RFE machine learning algorithms. We used the ssGSEA algorithm to calculate and evaluate the differences in nigrostriatal immune cell types in the GSE8397 dataset. The association between dld, dlk1, iars and ttc19 and 28 immune cells was investigated. Using the GSEA and GSVA algorithms, we analyzed the biological functions associated with immune-related hub genes. Establishment of a ceRNA regulatory network for immune-related hub genes. Finally, a logistic regression model was used to develop a PD classification diagnostic model, and the accuracy of the model was verified in three independent data sets. The three independent datasets are GES49036 (containing 8 healthy human nigrostriatal tissue samples and 15 PD patient nigrostriatal tissue samples), GSE20292 (containing 18 healthy human nigrostriatal tissue samples and 11 PD patient nigrostriatal tissue samples) and GSE7621 (containing 9 healthy human nigrostriatal tissue samples and 16 PD patient nigrostriatal tissue samples). Ultimately, we screened for four immune-related Parkinson's disease hub genes. Among them, the AUC values of dlk1, dld and ttc19 in GSE8397 and three other independent external datasets were all greater than 0.7, indicating that these three genes have a certain level of accuracy. The iars gene had an AUC value greater than 0.7 in GES8397 and one independent external data while the AUC values in the other two independent external data sets ranged between 0.5 and 0.7. These results suggest that iars also has some research value. We successfully constructed a categorical diagnostic model based on these four immune-related Parkinson's disease hub genes, and the AUC values of the joint diagnostic model were greater than 0.9 in both GSE8397 and three independent external datasets. These results indicate that the categorical diagnostic model has a good ability to distinguish between healthy individuals and Parkinson's disease patients. In addition, ceRNA networks reveal complex regulatory relationships based on immune-related hub genes. In this study, four immune-related PD hub genes (dld, dlk1, iars and ttd19) were obtained. A reliable diagnostic model for PD classification was developed. This study provides algorithmic-level support to explore the immune-related mechanisms of PD and the prediction of immune-related drug targets.

Exploration of the biomarkers of comorbidity of psoriasis with inflammatory bowel disease and their association with immune infiltration.

There was evidence that significant bidirectional associations between psoriasis and inflammatory bowel diseases (IBDs), which influences management strategy of the patients, so the investigation on the mechanisms by which these two diseases co-occur is important. The Gene Expression Omnibus (GEO) database was used to download gene expression profiles of psoriasis and IBD. The differentially expressed genes (DEGs) between disease and health control groups for each data set were calculated, and Venn diagram was used to obtain for intersection. We performed Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on the intersection, followed by developing a protein-protein interaction network and module construction, and identified hub genes by cytoHubba. Thereafter, least absolute shrinkage and selection operator algorithms was used to identify the co-biomarkers of psoriasis and IBD from the top 50 hub genes. The biomarkers were used to construct a screening model, the discriminatory capacity of which was verified by receiver operating characteristic (ROC) curves. CIBERSORT algorithm was utilized to estimate the compositional patterns of immune cell infiltration in biomarkers of psoriasis and IBD. Spearman rank correlation analysis was used to further evaluate the correlation between the identified biomarkers and immune cells. A total of 271 shared DEGs were screened. The GO and KEGG enrichment analysis indicated that the shared DEGs were mainly enriched in response to lipopolysaccharide, secretory granule lumen, cytokine activity, and interleukin (IL)-17 signaling pathway. Fifty genes such as IL1B, IL6, were identified as hub genes, based on which, FOS, IFI44, MMP9, MNDA, PTGS2, S100A9, and STAT1 were identified as biomarkers of psoriasis. CCL20, CD274, CTGF, CXCL1, CXCL10, CXCL2, CXCL9, FCGR3B, FOS, GBP1, GZMB, IFI27, IFI6, IL1RN, ISG15, ISG20, LCN2, LILRB2, MMP12, MMP7, S100A8, TLR8, and TNFSF13B were identified as biomarkers of IBD. FOS was the common biomarker of psoriasis and IBD. Screening models were validated in the validation data set (Psoriasis: area under the curve (AUC)=1.000, IBD: AUC=0.870). Immunocyte infiltration analysis showed the macrophages cells, mast cells resting, and T cells CD4 memory activated have the common characteristics in psoriasis and IBD. FOS may play a key role in the occurrence and development of psoriasis complicated with IBD and macrophages cells may be an entrance for treating this comorbidity.

Prognostic Value and the Immune Microenvironment-associated Role of SMAD4 in Pancreatic Adenocarcinoma.

SMAD4 is a well-known cancer suppressor gene that regulates cell proliferation, differentiation, autoimmunity, pluripotency, and immune responses in pancreatic adenocarcinoma (PAAD). We herein investigated a novel involvement of SMAD4 within the PAAD microenvironment. Transcriptome data, derived from The Cancer Genome Atlas and Genotype-Tissue Expression Using TIMER 2.0, CIBERSORT, and ImmuCellAI, were used to identify the immune cell infiltration pattern of PAAD. We then knocked-down SMAD4 in the PANC-1 cell line and acquired RNA-seq data through the Illumina microarray technology. Kyoto Encyclopedia of Genes and Genomes was utilized along with Gene Ontology enrichment analyses, and protein-protein interaction network analysis to screen for genes that were differentially expressed. We constructed a miRNA-mRNA regulatory network and analyzed SMAD4 copy number variation (CNV) data. In PAAD, decreased levels of SMAD4 expression were found to be connected to an unfavorable prognosis. There was a significantly higher infiltration level of DC cells, CD8+ T cells, TgD, Tc, and Tex cells and a lower level of B cells and Th2 in the SMAD4-high group. The expression of SMAD4 and immune cell infiltration including CD8+ T cells, myeloid dendritic cells, neutrophils, and macrophages are significantly positively correlated. DEGs were found enriched in the hypoxia response pathway. The six hypoxia-related genes exhibited a significant correlation with immune cell infiltration and survival rates. SMAD4 CNV levels were associated with MSI, stemness, infiltration of immune cells, and survival rates. A statistically significant correlation was found between SMAD4 expression and immune cell infiltration. SMAD4 could mediate hypoxia response in pancreatic cancer. The CNV levels of SMAD4 were associated with prognosis. SMAD4 has potential as a prognostic biomarker and provides a new orientation for the immunotherapy of PAAD.

Whole stromal fibroblast signature is linked to specific chemokine and immune infiltration patterns and to improved survival in NSCLC

ABSTRACT Cancer associated fibroblasts (CAF) are known to orchestrate multiple components of the tumor microenvironment, whereas the influence of the whole stromal-fibroblast compartment is less understood. Here, an extended stromal fibroblast signature was investigated to define its impact on immune cell infiltration. The lung cancer adenocarcinoma (LUAD) data set of the cancer genome atlas (TCGA) was used to test whole stroma signatures and cancer-associated fibroblast signatures for their impact on prognosis. 3D cell cultures of the NSCLC cancer cell line A549 together with the fibroblast cell line SV80 were used in combination with infiltrating peripheral blood mononuclear cells (PBMC) for in-vitro investigations. Immune cell infiltration was assessed via flow cytometry, chemokines were analyzed by immunoassays and RNA microarrays. Results were confirmed in specimens from NSCLC patients by flow cytometry or immunohistochemistry as well as in the TCGA data set. The TCGA analyses correlated the whole stromal-fibroblast signature with an improved outcome, whereas no effect was found for the CAF signatures. In 3D microtumors, the presence of fibroblasts induced infiltration of B cells and CD69+CD4+ T cells, which was linked to an increased expression of CCL13 and CXCL16. The stroma/lymphocyte interaction was confirmed in NSCLC patients, as stroma-rich tumors displayed an elevated B cell count and survival in the local cohort and the TCGA data set. A whole stromal fibroblast signature was associated with an improved clinical outcome in lung adenocarcinoma and in vitro and in vivo experiments suggest that this signature increases B and T cell recruitment via induction of chemokines.

Cellular crosstalk of macrophages and therapeutic implications in non-small cell lung cancer revealed by integrative inference of single-cell transcriptomics.

Introduction: Non-small cell lung cancer (NSCLC) exhibits heterogeneity with diverse immune cell infiltration patterns that can influence tumor cell behavior and immunotherapy. A comprehensive characterization of the tumor microenvironment can guide precision medicine. Methods: Here, we generated a single-cell atlas of 398170 cells from 52 NSCLC patients, and investigated the imprinted genes and cellular crosstalk for macrophages. Subsequently, we evaluated the effect of tumor cells on macrophages and verified the expression of marker genes using co-culture experiments, flow cytometry and RT-qPCR assays. Results: Remarkable macrophage adaptability to NSCLC environment was observed, which contributed to generating tumor-associated macrophages (TAMs). We identified 5 distinct functional TAM subtypes, of which the majority were SELENOP-positive macrophages, with high levels of SLC40A1 and CCL13. The TAMs were also involved in mediating CD8+ T cell activity and form intercellular interaction with cancer cells, as indicated by receptor-ligand binding. Indirect coculture of tumor cells SPC-A1 and THP-1 monocytes, produced M2-like TAMs that highly expressed several markers of SELENOP-positive macrophages. The abundance of this type TAMs seemed to be associated with poorer overall survival rates [hazard ratio (HR) = 1.34, 95% confidence interval (CI) = 0.98-1.83, p = 0.068] based on deconvolution of TCGA-LUAD dataset. Discussion: In summary, we provided a high-resolution molecular resource of TAMs, and displayed the acquired properties in the tumor microenvironment. Dynamic crosstalk between TAMs and tumor cells via multiple ligand-receptor pairs were revealed, emphasizing its role in sustaining the pro-tumoral microenvironment and its implications for cancer therapy.

Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in breast cancer.

Breast cancer (BC) is now the most common type of cancer in women. Disulfidptosis is a new regulation of cell death (RCD). RCD dysregulation is causally linked to cancer. However, the comprehensive relationship between disulfidptosis and BC remains unknown. This study aimed to explore the predictive value of disulfidptosis-related genes (DRGs) in BC and their relationship with the TME. This study obtained 11 disulfidptosis genes (DGs) from previous research by Gan etal. RNA sequencing data of BC were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO) databases. First, we examined the effect of DG gene mutations and copy number changes on the overall survival of breast cancer samples. We then used the expression profile data of 11 DGs and survival data for consensus clustering, and BC patients were divided into two clusters. Survival analysis, gene set variation analysis (GSVA) and ss GSEA were used to compare the differences between them. Subsequently, DRGs were identified between the clusters used to perform Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a prognosis model. Finally, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. CCK-8 and a colony assay obtained by knocking down genes and gene sequencing were used to validate the model. Two DG clusters were identified based on the expression of 11DGs. Then, 225 DRGs were identified between them. RS, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-RS shows a better prognosis and higher immunotherapy response than high-RS. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. CCK-8 and colony assay obtained by knocking down genes have demonstrated that the knockdown of high-risk genes in the RS model significantly inhibited cell proliferation. This study elucidates the potential relationship between disulfidptosis-related genes and breast cancer and provides new guidance for treating breast cancer.

A novel prognostic signature and immune microenvironment characteristics associated with disulfidptosis in papillary thyroid carcinoma based on single-cell RNA sequencing.

Background: Disulfidptosis is a newly discovered form of regulated cell death. The research on disulfidptosis and tumor progression remains unclear. Our research aims to explore the relationship between disulfidptosis-related genes (DRGs) and the clinical outcomes of papillary thyroid carcinoma (PTC), and its interaction on the tumor microenvironment. Methods: The single-cell RNA seq data of PTC was collected from GEO dataset GSE191288. We illustrated the expression patterns of disulfidptosis-related genes in different cellular components in thyroid cancer. LASSO analyses were performed to construct a disulfidptosis associated risk model in TCGA-THCA database. GO and KEGG analyses were used for functional analyses. CIBERSORT and ESTIMATE algorithm helped with the immune infiltration estimation. qRT‒PCR and flow cytometry was performed to validate the hub gene expression and immune infiltration in clinical samples. Results: We clustered PTC scRNA seq data into 8 annotated cell types. With further DRGs based scoring analyses, we found endothelial cells exhibited the most relationship with disulfidptosis. A 4-gene risk model was established based on the expression pattern of DRGs related endothelial cell subset. The risk model showed good independent prognostic value in both training and validation dataset. Functional enrichment and genomic feature analysis exhibited the significant correlation between tumor immune infiltration and the signature. The results of flow cytometry and immune infiltration estimation showed the higher risk scores was related to immuno-suppressive tumor microenvironment in PTC. Conclusion: Our study exhibited the role of disulfidptosis based signature in the regulation of tumor immune microenvironment and the survival of PTC patients. A 4-gene prognostic signature (including SNAI1, STC1, PKHD1L1 and ANKRD37) was built on the basis of disulfidptosis related endothelial cells. The significance of clinical outcome and immune infiltration pattern was validated robustly.

Bioinformatics analysis of immune cell infiltration patterns and potential diagnostic markers in atherosclerosis

This study aimed to investigate efficient diagnostic markers and molecular mechanisms of atherosclerosis and to analyze the role of immune infiltration through bioinformatics analysis. Expression profile datasets (GSE28829 and GSE43292) of patients with atherosclerosis and healthy controls were downloaded from the GEO database. Glutamine (GLN) metabolism-associated genes were obtained from the Molecular Signatures Database (MSigDB). The limma package in R was used to identify differentially expressed genes (DEGs). Significant modules were filtered using Weighted Gene Co-expression Network Analysis (WGCNA). MSigDB sets were subjected to Gene Set Enrichment Analysis and Gene Set Variation Analysis. The biological functions of DEGs were examined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. STRING and Cytoscape software were used to identify hub genes and functional modules through protein–protein interaction (PPI) network analysis. The xCell software was adopted to assess the composition patterns of immune and stromal cells. Correlation analyses were performed for key genes and immune cell subtypes. We identified 308 DEGs and GLN-associated genes. Functional enrichment analysis showed that these genes were strongly enriched in muscle contract, muscle tissue development, cutile fiber, mycobacterial, and actin binding. Enriched KEGG pathways comprised dilated cardiomyopathy, hypertrophic cardiomyopathy, and the cAMP signaling pathway. In the PPI network analysis, 27 genes were identified as hub genes. The area under the curve (AUC) values of 27 biomarkers were relatively high, indicating high diagnostic values. The atherosclerosis group exhibited a markedly higher degree of infiltration than the control group. This study identified 27 GLN-associated genes as potential biomarkers for the diagnosis of atherosclerosis. It provides a new perspective on immune responses that facilitates exploration of the molecular mechanisms of atherosclerosis.