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Severe burns often have a high mortality rate due to sepsis, but the genetic and immune crosstalk between them remains unclear. In the present study, the GSE77791 and GSE95233 datasets were analysed to identify immune‐related differentially expressed genes (DEGs) involved in disease progression in both burns and sepsis. Subsequently, weighted gene coexpression network analysis (WGCNA), gene enrichment analysis, protein–protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, coexpression network analysis and clinical correlation analysis were performed. A total of 282 common DEGs associated with burns and sepsis were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the following enriched pathways in burns and sepsis: metabolic pathways; complement and coagulation cascades; legionellosis; starch and sucrose metabolism; and ferroptosis. Finally, six core DEGs were identified, namely, IL10, RETN, THBS1, FGF13, LCN2 and MMP9. Correlation analysis showed that some core DEGs were significantly associated with simultaneous dysregulation of immune cells. Of these, RETN upregulation was associated with a worse prognosis. The immune‐related genes and dysregulated immune cells in severe burns and sepsis provide potential research directions for diagnosis and treatment.

Ulcerative colitis (UC) is one of the high‐risk pathogenic factors for colorectal cancer (CRC). However, the shared gene and signalling mechanisms between UC and CRC remain unclear. The goal of this study was to delve more into the probable causal relationship between UC and CRC. CRC and UC datasets were downloaded from the Gene Expression Omnibus database. Using R software and Perl, differentially expressed genes (DEGs) in both UC and CRC tissues were re‐annotated and screened. The biological activities and signalling pathways involved in DEGs were investigated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The STRING database and Cytoscape software were used to construct the gene interaction network. A total of 384 DEGs were selected for further investigation, and functional analysis revealed that inflammatory and immunological responses were crucial in the development of the two diseases. Moreover, the top 15 key genes involved in the UC and CRC were screened using cytoHubba, including IL1B, CXCL10, CCL20, MMP9, ICAM1, CCL4, CXCR1, MMP3, TLR2, PTGS2, IL1RN, IL6, COL1A2, TIMP1 and CXCL1. The identification of these genes in the present study may provide a novel perspective for the prediction, prevention and personalized medicine of UC and CRC patients.

Background Keloid is a pathological dermatological condition that manifests as an overgrowth scar secondary to skin trauma. This study endeavored to excavate immune‐related signatures of keloid based on single‐cell RNA (scRNA) sequencing data and bulk RNA sequencing data. Method The keloid‐relevant scRNA sequencing dataset GSE163973 and bulk RNA sequencing dataset GSE113619 were mined from the GEO database. The “Seurat” R package was utilized for data quality control, cell clustering, and investigation of marker genes of each cell cluster. The “SingleR” package helped match the marker genes of the corresponding cluster to specific cell types. Moreover, the R package “Monocle” was deployed for pseudotemporal ordering analysis, and the “clusterProfiler” was applied for functional and pathway enrichment analysis. The immune‐related signatures were then identified, and potential targeted drugs were predicted via the DGIdb database. Verification of the immune‐related signatures in clinical validation samples was implemented by RT‐qPCR. Results Totally 23 cell clusters were screened and classified into 10 cell types based on the scRNA sequencing data. The keloid group had a significantly higher endothelial cell proportion than the control group. As enrichment analysis was applied in both differentially expressed genes (DEGs) of scRNA and bulk RNA sequencing data, we found they were enriched in multiple common immune‐related pathways and biological processes. Meanwhile, we acquired three immune‐related signatures (VCAM1, CALCRL, and HLA‐DPB1) by intersecting the above DEGs with immune‐related genes (IRGs). Then, we predicted 16 drugs potentially targeting the biomarkers through the DGIdb database. Finally, the outcome of RT‐qPCR of clinical validation samples further verified the results. Conclusion In conclusion, we analyzed the cell types and functional differences in the keloid through scRNA and bulk RNA sequencing data. We identified three immune‐related signatures (VCAM1, CALCRL, and HLA‐DPB1) in keloid, providing a basis for further in‐depth investigation of the molecular mechanisms of keloid and exploration of therapeutic targets.

Thyroid‐associated ophthalmopathy (TAO), the most common and severe manifestation of Graves' disease (GD), is a disfiguring and potentially blinding autoimmune disease. The high relapse rate (up to 20%) and substantial side effects of glucocorticoid treatment further decrease the life quality of TAO patients. To develop novel therapies, we amid to explore the immunopathogenesis of TAO. To identify the key immune‐related genes (IRGs) in TAO, we integrated the IRG expression profiles in thyrocytes from a GD patient set (GD vs healthy control) and a TAO patient set (TAO vs GD). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‐protein interaction (PPI) and receiver operating characteristic (ROC) curve analyses identified the leptin receptor (LEPR) gene as the key IRG in TAO immunopathogenesis. Gene set enrichment analysis (GSEA) suggested enrichment of the antigen presentation pathway in TAO patients with higher LEPR. Increased LEPR expression was validated in TAO orbital tissues, and weighted gene co‐expression network analysis (WGCNA) showed that cell adhesion processes were positively correlated with LEPR. Our study revealed that LEPR is a key gene in TAO immunopathogenesis and plays different roles in thyrocytes and orbital tissues. Our findings provide new insights into diagnostic and therapeutic biomarkers for TAO.

Plasma cells serve a crucial role in the human immune system and are important in tumor progression. However, the specific role of plasma cell immune-related genes (PCIGs) in tumor progression remains unclear. Therefore, the present study aimed to establish a risk assessment model for patients with lung adenocarcinoma (LUAD) based on PCIGs. The data used in the present study were obtained from The Cancer Genome Atlas and the Gene Expression Omnibus databases. After identifying nine PCIGs, a risk assessment model was constructed and a nomogram was developed for predicting patient prognosis. To explore the molecular mechanism and clinical significance, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, tumor microenvironment (TME) analysis and drug sensitivity prediction were performed. Furthermore, the accuracy of the model was validated using reverse transcription-quantitative PCR (RT-qPCR). The present study constructed a risk assessment model consisting of nine PCIGs. Kaplan-Meier survival curves indicated a worse prognosis in the high-risk subgroup (risk score ≥0.982) compared with that in the low-risk subgroup. The nomogram exhibited predictive value for survival prediction (area under the curve=0.727). GSEA enrichment analysis revealed enrichment of the focal adhesion and extracellular matrix-receptor interaction pathways in the high-risk group. Moreover, the high-risk group exhibited a higher TMB, as demonstrated by the TME analysis showing lower ESTIMATE scores. Drug sensitivity prediction facilitated potential drug selection. Subsequently, differential gene expression was validated in multiple LUAD cell lines using RT-qPCR. In conclusion, the risk assessment model based on nine PCIGs may be used to predict the prognosis and drug selection in patients with LUAD.

There is a correlation between tumors and immunity with the degree of immune cell infiltration in tumors being closely related to tumor growth and progression. Therefore, the present study identified immune-related prognostic genes and evaluated the immune infiltration level in lung adenocarcinoma (LUAD). This study performed Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis (GSEA) enrichment analyses on differential immune-associated genes. A risk model was created and validated using six immune-related prognostic genes. Reverse transcription-quantitative PCR was used to assess the prognostic gene expression in non-small cell lung cancer cells. Immune cell infiltration in LUAD was analyzed using the CIBERSORT method. Single sample GSEA was used to compare Tumor Immune Dysfunction and Exclusion (TIDE) scores between high and low-risk groups and to assess the activation of thirteen immune-related pathways. Multifactor Cox proportional hazards model analysis identified six prognostic risk genes (S100A16, FURIN, FGF2, LGR4, TNFRSF11A and VIPR1) to construct a risk model. The survival and receiver operating characteristic curves indicated that patients with higher risk scores had lower overall survival rates. The expression levels of prognostic genes S100A16, FURIN, LGR4, TNFRSF11A and VIPR1 were significantly increased in LUAD. B cells naive, plasma cells, T cells CD4 memory activated, T cells follicular helper, T cells regulatory, NK cells activated, macrophages M1, macrophages M2, and Dendritic cells resting cells showed elevated expression in LUAD. The prognostic genes were differentially associated with individual immune cells. Immune-related function scores, such as those for antigen presenting cell (APC) co-stimulation, APC co-inhibition, check-point, Cytolytic-activity, chemokine receptor, parainflammation, major histocompatibility complex-class-I, type-I-IFN-reponse and T-cell-co-inhibition, were higher in the high-risk group compared with the low-risk group. Furthermore, the TIDE score of the high-risk group was significantly lower than the low-risk group. This immune-related gene prognostic model has the potential to predict the prognosis of LUAD patients, supporting the development of a personalized clinical diagnosis and treatment plan.

Epithelial cells are associated with tumor immunity through interstitial transformation, yet the role of epithelial immune-related genes (EIGs) in this process remains unclear. Comprehending the mechanisms behind EIGs within lung squamous cell carcinoma (LUSC) may offer an explanation to these issues. The present study aimed to explore the biological role of EIGs in patients with LUSC. Based on data from the Gene Expression Omnibus and The Cancer Genome Atlas databases, a survival model and nomogram was established. This model and nomogram were used to study the mechanism of EIGs in LUSC and its medical significance by enrichment analysis, tumor microenvironment, immune cell infiltration and immune function correlation analysis. Finally, reverse transcription-quantitative PCR (RT-qPCR) and external dataset were used to assess the expression of the EIGs. The survival model was used to develop 4 EIGs as predictors for patient outcomes. Survival curves revealed that higher risk patients had more negative outcomes. This model and the nomogram developed based entirely on this model had an accurate prognosis predictive LUSC. The enrichment analysis indicated that pathways related to antigen processing and presentation, as well as Epstein-Barr virus infection, were prevalent in the high-risk populations. The research on immune infiltration demonstrated a notable rise in activated dendritic cells and neutrophils in the high-risk group. Furthermore, the results revealed that the high-risk populations are particularly susceptible to the effects of afureserpine, gefitinib and savolitinib. Finally, the outcomes of RT-qPCR were consistent with those of the bioinformatics analysis. In conclusion, the risk evaluation model and nomogram are effective in forecasting the prognosis and guiding drug selection for patients with LUSC. A worse prognosis in patients with high risk may be associated with certain viral infections and antigen processing and presentation.

Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune‐related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17‐IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17‐IRGP signature noticeably split patients into high‐ and low‐risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll‐like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17‐IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.

Hepatocellular carcinoma (HCC) is a typical inflammation-driven cancer. Chronically unresolved inflammation may remodel the immunosuppressive tumor microenvironment, which is rich in innate immune cells. The mechanisms via which HCC progresses through the evasion of the innate immune surveillance remain unclear. The present study thus aimed to identify key genes involved in HCC immunosuppression and to establish an innate immune risk signature, with the ultimate goal of obtaining new insight into effective immunotherapies. HCC and normal liver tissue mRNA expression and clinicopathological data were obtained from the Cancer Genome Atlas database. The immunosuppressive innate immune-related genes (IIRGs) in HCC were screened using integrated bioinformatics analyses. Gene expression was then validated using the Gene Expression Omnibus database and the Human Protein Atlas database, and tissues were obtained from patients with HCC who underwent surgery. In total, 3,676 genes were identified as differentially expressed mRNAs after comparing the HCC tissues with the normal liver tissues in TCGA. Gene Set Enrichment Analyses revealed 21 highly expressed IIRGs in HCC tissues. A survival analysis and Cox regression model were used to construct an innate immune risk signature, including three IIRGs: Collectin-12 (COLEC12), matrix metalloproteinase-12 (MMP12) and mucin-12 (MUC12) genes. Univariate and multivariate Cox analyses revealed that the signature of the three IIRGs was a robust independent risk factor in relation to the overall survival (OS) of patients with HCC. The expression of the three aforementioned IIRGs was confirmed through external validation. Moreover, COLEC12 and MMP12 expression significantly correlated with that of immune checkpoint molecules or immunosuppressive cytokines. The tumor immune dysfunction and exclusion tool predicted that the increased expression of the three IIRGs in patients with HCC was significantly associated with the efficacy of relatively poor immune checkpoint blockade therapy. Conclusively, a novel innate immune-related risk signature for patients with HCC was constructed and validated. This signature may be involved in immunosuppression, and may be used to predict a poor prognosis, functioning as a potential immunotherapeutic target for patients with HCC.

Background Triple‐negative breast cancer (TNBC) is the subtype of breast cancer with the worst prognosis, and it is highly heterogeneous. There is growing evidence that the tumor immune microenvironment (TIME) plays a crucial role in tumor development, maintenance, and treatment responses. Notably however, the full effects of the TIME on prognosis, TIME characteristics, and immunotherapy responses in TNBC patients have not been fully elucidated. Methods Gene Expression Omnibus and The Cancer Genome Atlas data were used to data analysis. Single‐cell sequencing and tissue microarray analysis were used to investigate gene expression. The concentrations and distributions of immune cell types were determined and analyzed using the CIBERSORT strategy. Tumor immune dysfunction and exclusion score and the IMvigor210 cohort were used to estimate the sensitivity of TNBC patients with different prognostic statuses to immune checkpoint treatment. Results Five immune‐related genes associated with TNBC prognosis (IL6ST, NR2F1, CKLF, TCF7L2, and HSPA2) was identified and a prognostic evaluation model was constructed based on those genes. The respective areas under the curve of the prognostic nomogram model at 3 and 5 years were 0.791 and 0.859. The group with a lower nomogram score, with a better prognosis survival status and clinical treatment benefit rate. Conclusion A prognostic model for TNBC that was closely related to the immune landscape and therapeutic responses was constructed. This model may help clinicians to make more precise and personalized treatment decisions pertaining to TNBC patients.