Characteristics of immune cell infiltration and associated diagnostic biomarkers in ulcerative colitis: results from bioinformatics analysis

Gas-propelled nanomotors alleviate colitis through the regulation of intestinal immunoenvironment-hematopexis-microbiota circuits

The progression of ulcerative colitis (UC) is associated with immunologic derangement, intestinal hemorrhage, and microbiota imbalance. While traditional medications mainly focus on mitigating inflammation, it remains challenging to address multiple symptoms. Here, a versatile gas-propelled nanomotor was constructed by mild fusion of post-ultrasonic CaO2 nanospheres with Cu2O nanoblocks. The resulting CaO2-Cu2O possessed a desirable diameter (291.3 nm) and a uniform size distribution. It could be efficiently internalized by colonic epithelial cells and macrophages, scavenge intracellular reactive oxygen/nitrogen species, and alleviate immune reactions by pro-polarizing macrophages to the anti-inflammatory M2 phenotype. This nanomotor was found to penetrate through the mucus barrier and accumulate in the colitis mucosa due to the driving force of the generated oxygen bubbles. Rectal administration of CaO2-Cu2O could stanch the bleeding, repair the disrupted colonic epithelial layer, and reduce the inflammatory responses through its interaction with the genes relevant to blood coagulation, anti-oxidation, wound healing, and anti-inflammation. Impressively, it restored intestinal microbiota balance by elevating the proportions of beneficial bacteria (e.g., Odoribacter and Bifidobacterium) and decreasing the abundances of harmful bacteria (e.g., Prevotellaceae and Helicobacter). Our gas-driven CaO2-Cu2O offers a promising therapeutic platform for robust treatment of UC via the rectal route.

MRGPRB2/X2 and the analogous effects of its agonist and antagonist in DSS-induced colitis in mice

The mast cell receptor Mrgprb2, a mouse orthologue of human Mrgprx2, is known as an inflammatory receptor and its elevated expression is associated with various diseases such as ulcerative colitis. We aimed to elucidate the role of Mrgprb2/x2 and the effect of its ligands on a chemically induced murine colitis model. We showed that in Mrgprb2-/- mice, there is a differential regulation of cytokine releases in the blood plasma and severe colonic damages after DSS treatment. Unexpectedly, we demonstrated that known Mrgprb2/x2 agonists (peptide P17, P17 analogues and CST-14) and antagonist (GE1111) similarly increased the survival rate of WT mice subjected to 4% DSS-induced colitis, ameliorated the colonic damages of 2.5% DSS-induced colitis, restored major protein mRNA expression involved in colon integrity, reduced CD68+ and F4/80+ immune cell infiltration and restored cytokine levels. Collectively, our findings highlight the eminent role of Mrpgrb2/x2 in conferring a beneficial effect in the colitis model, and this significance is demonstrated by the heightened severity of colitis with altered cytokine releases and inflammatory immune cell infiltration observed in the Mrgprb2 knockout mice. Elevated expression of Mrgprb2 in WT colitis murine models may represent the organism's adaptive protective mechanism since Mrgprb2 knockout results in severe colitis. On the other hand, both agonist and antagonist of Mrgprb2 analogously mitigated the severity of colitis in DSS-induced colitis model by altering Mrgprb2 expression, immune cell infiltration and inflammatory cytokine releases.

Large-scale phenotyping of patients with long COVID post-hospitalization reveals mechanistic subtypes of disease

One in ten severe acute respiratory syndrome coronavirus 2 infections result in prolonged symptoms termed long coronavirus disease (COVID), yet disease phenotypes and mechanisms are poorly understood1. Here we profiled 368 plasma proteins in 657 participants ≥3 months following hospitalization. Of these, 426 had at least one long COVID symptom and 233 had fully recovered. Elevated markers of myeloid inflammation and complement activation were associated with long COVID. IL-1R2, MATN2 and COLEC12 were associated with cardiorespiratory symptoms, fatigue and anxiety/depression; MATN2, CSF3 and C1QA were elevated in gastrointestinal symptoms and C1QA was elevated in cognitive impairment. Additional markers of alterations in nerve tissue repair (SPON-1 and NFASC) were elevated in those with cognitive impairment and SCG3, suggestive of brain-gut axis disturbance, was elevated in gastrointestinal symptoms. Severe acute respiratory syndrome coronavirus 2-specific immunoglobulin G (IgG) was persistently elevated in some individuals with long COVID, but virus was not detected in sputum. Analysis of inflammatory markers in nasal fluids showed no association with symptoms. Our study aimed to understand inflammatory processes that underlie long COVID and was not designed for biomarker discovery. Our findings suggest that specific inflammatory pathways related to tissue damage are implicated in subtypes of long COVID, which might be targeted in future therapeutic trials.

Identified S100A9 as a target for diagnosis and treatment of ulcerative colitis by bioinformatics analysis

Ulcerative colitis (UC) is a chronic, recurrent inflammatory bowel disease. UC confronts with severe challenges including the unclear pathogenesis and lack of specific diagnostic markers, demanding for identifying predictive biomarkers for UC diagnosis and treatment. We perform immune infiltration and weighted gene co-expression network analysis on gene expression profiles of active UC, inactive UC, and normal controls to identify UC related immune cell and hub genes. Neutrophils, M1 macrophages, activated dendritic cells, and activated mast cells are significantly enriched in active UC. MMP-9, CHI3L1, CXCL9, CXCL10, CXCR2 and S100A9 are identified as hub genes in active UC. Specifically, S100A9 is significantly overexpressed in mice with colitis. The receiver operating characteristic curve demonstrates the excellent performance of S100A9 expression in diagnosing active UC. Inhibition of S100A9 expression reduces DSS-induced colonic inflammation. These identified biomarkers associated with activity in UC patients enlighten the new insights of UC diagnosis and treatment.

Oral nanomedicine biointeractions in the gastrointestinal tract in health and disease

Oral administration is the preferred route of administration based on the convenience for and compliance of the patient. Oral nanomedicines have been developed to overcome the limitations of free drugs and overcome gastrointestinal (GI) barriers, which are heterogeneous across healthy and diseased populations. This review aims to provide a comprehensive overview and comparison of the oral nanomedicine biointeractions in the gastrointestinal tract (GIT) in health and disease (GI and extra-GI diseases) and highlight emerging strategies that exploit these differences for oral nanomedicine-based treatment. We introduce the key GI barriers related to oral delivery and summarize their pathological changes in various diseases. We discuss nanomedicine biointeractions in the GIT in health by describing the general biointeractions based on the type of oral nanomedicine and advanced biointeractions facilitated by advanced strategies applied in this field. We then discuss nanomedicine biointeractions in different diseases and explore how pathological characteristics have been harnessed to advance the development of oral nanomedicine.

Identification of cuproptosis-related asthma diagnostic genes by WGCNA analysis and machine learning

OBJECTIVE

Cuproptosis is the latest novel form of cell death. However, the relationship between asthma and cuproptosis is not fully understood.

METHODS

In this study, we screened differentially expressed cuproptosis-related genes from the Gene Expression Omnibus (GEO) database and performed immune infiltration analysis. Subsequently, patients with asthma were typed and analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was performed to calculate the module-trait correlations, and the hub genes of the intersection were taken to construct machine learning (XGB, SVM, RF, GLM). Finally, we used TGF-β to establish a BEAS-2B asthma model to observe the expression levels of hub genes.

RESULTS

Six cuproptosis-related genes were obtained. Immune-infiltration analysis shows that cuproptosis-related genes are associated with a variety of biological functions. We classified asthma patients into two subtypes based on the expression of cuproptosis-related genes and found significant Gene Ontology (GO) and immune function differences between the different subtypes. WGCNA selected 2 significant modules associated with disease features and typing. Finally, we identified TRIM25, DYSF, NCF4, ABTB1, CXCR1 as asthma biomarkers by taking the intersection of the hub genes of the 2 modules and constructing a 5-genes signature, which nomograph, decision curve analysis (DCA) and calibration curves, receiver operating characteristic curve (ROC) showed high efficiency in diagnosing the probability of survival of asthma patients. Finally, in vitro experiments have shown that DYSF and CXCR1 expression is up expressed in asthma.

CONCLUSIONS

Our study provides further directions for studying the molecular mechanism of asthma.

Biological, genetic and epigenetic markers in ulcerative colitis

In this review, we have summarized the existing knowledge of ulcerative colitis (UC) markers based on current literature, specifically, the roles of potential new biomarkers, such as circulating, fecal, genetic, and epigenetic alterations, in UC onset, disease activity, and in therapy response. UC is a complex multifactorial inflammatory disease. There are many invasive and non-invasive diagnostic methods in UC, including several laboratory markers which are employed in diagnosis and disease assessment; however, colonoscopy remains the most widely used method. Common laboratory abnormalities currently used in the clinical practice include inflammation-induced alterations, serum autoantibodies, and antibodies against bacterial antigens. Other new serum and fecal biomarkers are supportive in diagnosis and monitoring disease activity and therapy response; and potential salivary markers are currently being evaluated as well. Several UC-related genetic and epigenetic alterations are implied in its pathogenesis and therapeutic response. Moreover, the use of artificial intelligence in the integration of laboratory biomarkers and big data could potentially be useful in clinical translation and precision medicine in UC management.

Identification of genes critical for inducing ulcerative colitis and exploring their tumorigenic potential in human colorectal carcinoma

Ulcerative colitis (UC) is a chronic relapsing inflammatory bowel disease leading to continuous mucosal inflammation in the rectum extending proximally towards the colon. Chronic and/or recurrent UC is one of the critical predisposing mediators of the oncogenesis of human colorectal carcinoma (CRC). Perturbations of the differential expression of the UC-critical genes exert an intense impact on the neoplastic transformation of the affected tissue(s). Herein, a comprehensive exploration of the UC-critical genes from the transcriptomic profiles of UC patients was conducted to study the differential expression, functional enrichment, genomic alterations, signal transduction pathways, and immune infiltration level encountered by these genes concerning the oncogenesis of CRC. The study reveals that WFDC2, TTLL12, THRA, and EPHB3 play crucial roles as UC-CRC critical genes and are positively correlated with the molecular transformation of UC to CRC. Taken together, these genes can be used as potential biomarkers and therapeutic targets for combating UC-induced human CRC.

Role of Nox4 in Mitigating Inflammation and Fibrosis in Dextran Sulfate Sodium-Induced Colitis

BACKGROUND & AIMS

Fibrosis development in ulcerative colitis is associated directly with the severity of mucosal inflammation, which increases the risk of colorectal cancer. The transforming growth factor-β (TGF-β) signaling pathway is an important source of tissue fibrogenesis, which is stimulated directly by reactive oxygen species produced from nicotinamide adenine dinucleotide phosphate oxidases (NOX). Among members of the NOX family, NOX4 expression is up-regulated in patients with fibrostenotic Crohn's disease (CD) and in dextran sulfate sodium (DSS)-induced murine colitis. The aim of this study was to determine whether NOX4 plays a role in fibrogenesis during inflammation in the colon using a mouse model.

METHODS

Acute and recovery models of colonic inflammation were performed by DSS administration to newly generated Nox4-/- mice. Pathologic analysis of colon tissues was performed, including detection of immune cells, proliferation, and fibrotic and inflammatory markers. RNA sequencing was performed to detect differentially expressed genes between Nox4-/- and wild-type mice in both the untreated and DSS-treated conditions, followed by functional enrichment analysis to explore the molecular mechanisms contributing to pathologic differences during DSS-induced colitis and after recovery.

RESULTS

Nox4-/- mice showed increased endogenous TGF-β signaling in the colon, increased reactive oxygen species levels, intensive inflammation, and an increased fibrotic region after DSS treatment compared with wild-type mice. Bulk RNA sequencing confirmed involvement of canonical TGF-β signaling in fibrogenesis of the DSS-induced colitis model. Up-regulation of TGF-β signaling affects collagen activation and T-cell lineage commitment, increasing the susceptibility for inflammation.

CONCLUSIONS

Nox4 protects against injury and plays a crucial role in fibrogenesis in DSS-induced colitis through canonical TGF-β signaling regulation, highlighting a new treatment target.

Identification of diagnostic biomarks and immune cell infiltration in ulcerative colitis

We aimed to explore diagnostic biomarks and immune cell infiltration characteristics in ulcerative colitis (UC). We used the dataset GSE38713 as the training set and dataset GSE94648 as the test set. A total of 402 differentially expressed genes (DEGs) were obtained from GSE38713. Annotating, visualizing, and integrating discovery of these differential genes was performed using Gene Ontology (GO), Kyoto Gene and Genome Encyclopedia Pathway (KEGG), and Gene Set Enrichment Analysis (GSEA). Protein-protein interaction networks were constructed from the STRING database, and protein functional modules were identified using the CytoHubba plugin of Cytoscape. Random forest and LASSO regression were used to screen for UC-related diagnostic markers, and ROC curves were generated to validate their diagnostic value. The composition of 22 immune cells was analyzed, and the immune cell infiltration in UC was analyzed using CIBERSORT. Results: Seven diagnostic markers associated with UC were identified: TLCD3A, KLF9, EFNA1, NAAA,WDR4, CKAP4, and CHRNA1. Immune cell infiltration assessment revealed that macrophages M1, activated dendritic cells, and neutrophil cells infiltrated relatively more compared to normal control samples. Our results suggest a new functional feature of UC and suggest potential biomarkers for UC through comprehensive analysis of integrated gene expression data.

High Expression Level of BRD4 Is Associated with a Poor Prognosis and Immune Infiltration in Esophageal Squamous Cell Carcinoma

BACKGROUND

Bromodomain-containing protein 4 (BRD4) is a reader of histone acetylation and is associated with a variety of diseases.

AIM

To investigate the expression level of BRD4 in esophageal squamous cell carcinoma (ESCC), its prognostic value and its relationship with immune infiltration.

METHODS

The study included 94 ESCC patients from The Cancer Genome Atlas (TCGA) database and 179 ESCC patients from Affiliated Hospital 2 of Nantong University. The expression levels of proteins in tissue microarray were detected by immunohistochemistry. The prognostic factors were analyzed by Kaplan-Meier curve and univariate and multivariate cox regression. The ESTIMATE website was used to calculate the stromal, immune and ESTIMATE score. CIBERSORT was used to calculate the abundance of immune infiltrates. Spearman and Phi coefficient were used for correlation analysis. The TIDE algorithm was used to predict treatment response to immune checkpoint blockade.

RESULTS

BRD4 is up-regulated in ESCC, and high BRD4 expression level is associated with poor prognosis and adverse clinicopathological features. In addition, the monocyte count, systemic inflammatory-immunologic index, platelet-lymphocyte ratio, and monocyte-lymphocyte ratio in the BRD4 high expression level group were higher than in the low expression level group. Finally, we found that BRD4 expression level correlated with immune infiltration and that it was inversely correlated with infiltration of CD8 + T cells. Higher TIDE scores in the BRD4 high expression group than in the low expression group.

CONCLUSION

BRD4 is associated with poor prognosis and immune infiltration in ESCC, and may be a potential biomarker for prognosis and immunotherapy application.

WDR43 is a potential diagnostic biomarker and therapeutic target for osteoarthritis complicated with Parkinson’s disease

Osteoarthritis (OA) and Parkinson’s disease (PD) are on the rise and greatly impact the quality of individuals’ lives. Although accumulating evidence indicates a relationship between OA and PD, the particular interactions connecting the two diseases have not been thoroughly examined. Therefore, this study explored the association through genetic characterization and functional enrichment. Four datasets (GSE55235, GSE12021, GSE7621, and GSE42966) were chosen for assessment and validation from the Gene Expression Omnibus (GEO) database. Weighted Gene Co-Expression Network Analysis (WGCNA) was implemented to determine the most relevant genes for clinical features. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were carried out to explore the biological processes of common genes, and to display the interrelationships between common genes, the STRING database and the application Molecular Complex Detection Algorithm (MCODE) of Cytoscape software were leveraged to get hub genes. By intersecting the common genes with the differentially expressed genes (DEGs) acquired from GSE12021 and GSE42966, the hub genes were identified. Finally, we validated the diagnostic efficacy of hub genes and explored their correlation with 22 immune infiltrating cells. As a consequence, we discovered 71 common genes, most of which were functionally enriched in antigen processing and presentation, mitochondrial translation, the mRNA surveillance pathway, and nucleocytoplasmic transport. Furthermore, WDR43 was found by intersecting eight hub genes with 28 DEGs from the two validation datasets. Receiver Operating Characteristic (ROC) implied the diagnostic role of WDR43 in OA and PD. Immune infiltration research revealed that T-cell regulatory (Tregs), monocytes, and mast cells resting were associated with the pathogenesis of OA and PD. WDR43 may provide key insights into the relationship between OA and PD.

Circular RNA profile in Graves' disease and potential function of hsa_circ_0090364

Background

Graves' disease is a common autoimmune disease. Cytokines and their signalling pathways play a major part in the pathogenesis of Graves' disease; however, the underlying mechanism needs to be clarified.

Aims

The aim of this study was to explore whether circular RNAs participate in the immunological pathology of Graves' disease via cytokine-related signalling pathways.

Methods

Bioinformatics analysis was performed to identify differentially expressed circular RNAs and their targets and associated pathways. A total of three patients with Graves' disease and three sex- and age-matched healthy controls were enrolled for validation with microarray analysis and real-time quantitative PCR (qPCR). An additional 24 patients with Graves' disease and 24 gender- and age-matched controls were included for validation by real-time fluorescent qPCR. Flow cytometry and CCK8 assays were used to detect the apoptotic and proliferative levels of Jurkat cells (T lymphocytes) with the silenced expression of circRNA. ELISA was performed to detect the growth and apoptosis-related proteins. The competition mechanism of endogenous RNA was explored by real-time fluorescence qPCR.

Results

A total of 366 significantly differentially expressed circular RNAs were identified in the Graves' disease group compared to healthy controls. The level of hsa_circ_0090364 was elevated in Graves' disease patients and positively correlated with thyroid-stimulating hormone receptor antibodies. Further analyses suggested that hsa_circ_0090364 may regulate the JAK-STAT pathway via the hsa-miR-378a-3p/IL-6ST/IL21R axis to promote cell growth.

Conclusions

These results provide novel clues into the pathophysiological mechanisms of Graves' disease and potential targets for drug treatment.

Bioinformatics methods in biomarkers of preeclampsia and associated potential drug applications

Background

Preeclampsia is a pregnancy-related condition that causes high blood pressure and proteinuria after 20 weeks of pregnancy. It is linked to increased maternal mortality, organ malfunction, and foetal development limitation. In this view, there is a need critical to identify biomarkers for the early detection of preeclampsia. The objective of this study is to discover critical genes and explore medications for preeclampsia treatment that may influence these genes.

Methods

Four datasets, including GSE10588, GSE25906, GSE48424 and GSE60438 were retrieved from the Gene Expression Omnibus database. The GSE10588, GSE25906, and GSE48424 datasets were then removed the batch effect using the “sva” R package and merged into a complete dataset. The differentially expressed genes (DEGs) were identified using the “limma” R package. The potential small-molecule agents for the treatment of PE was further screened using the Connective Map (CMAP) drug database based on the DEGs. Further, Weight gene Co-expression network (WGNCA) analysis was performed to identified gene module associated with preeclampsia, hub genes were then identified using the logistic regression analysis. Finally, the immune cell infiltration level of genes was evaluated through the single sample gene set enrichment analysis (ssGSEA).

Results

A total of 681 DEGs (376 down-regulated and 305 up-regulated genes) were identified between normal and preeclampsia samples. Then, Dexamethasone, Prednisone, Rimexolone, Piretanide, Trazodone, Buflomedil, Scoulerin, Irinotecan, and Camptothecin drugs were screened based on these DEGs through the CMAP database. Two modules including yellow and brown modules were the most associated with disease through the WGCNA analysis. KEGG analysis revealed that the chemokine signaling pathway, Th1 and Th2 cell differentiation, B cell receptor signalling pathway and oxytocin signalling pathway were significantly enriched in these modules. Moreover, two key genes, PLEK and LEP were evaluated using the univariate and multivariate logistic regression analysis from the hub modules. These two genes were further validated in the external validation cohort GSE60438 and qRT-PCR experiment. Finally, we evaluated the relationship between immune cell and two genes.

Conclusion

In conclusion, the present study investigated key genes associated with PE pathogenesis that may contribute to identifying potential biomarkers, therapeutic agents and developing personalized treatment for PE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08937-3.

Identidication of novel biomarkers in non-small cell lung cancer using machine learning

Lung cancer is one of the leading causes of cancer-related deaths worldwide, and non-small cell lung cancer (NSCLC) accounts for a large proportion of lung cancer cases, with few diagnostic and therapeutic targets currently available for NSCLC. This study aimed to identify specific biomarkers for NSCLC. We obtained three gene-expression profiles from the Gene Expression Omnibus database (GSE18842, GSE21933, and GSE32863) and screened for differentially expressed genes (DEGs) between NSCLC and normal lung tissue. Enrichment analyses were performed using Gene Ontology, Disease Ontology, and the Kyoto Encyclopedia of Genes and Genomes. Machine learning methods were used to identify the optimal diagnostic biomarkers for NSCLC using least absolute shrinkage and selection operator logistic regression, and support vector machine recursive feature elimination. CIBERSORT was used to assess immune cell infiltration in NSCLC and the correlation between biomarkers and immune cells. Finally, using western blot, small interfering RNA, Cholecystokinin-8, and transwell assays, the biological functions of biomarkers with high predictive value were validated. A total of 371 DEGs (165 up-regulated genes and 206 down-regulated genes) were identified, and enrichment analysis revealed that these DEGs might be linked to the development and progression of NSCLC. ABCA8, ADAMTS8, ASPA, CEP55, FHL1, PYCR1, RAMP3, and TPX2 genes were identified as novel diagnostic biomarkers for NSCLC. Monocytes were the most visible activated immune cells in NSCLC. The knockdown of the TPX2 gene, a biomarker with a high predictive value, inhibited A549 cell proliferation and migration. This study identified eight potential diagnostic biomarkers for NSCLC. Further, the TPX2 gene may be a therapeutic target for NSCLC.

FABP6 Expression Correlates with Immune Infiltration and Immunogenicity in Colorectal Cancer Cells

Background

Immune checkpoint inhibitors (ICIs) have rapidly revolutionized colorectal cancer (CRC) treatment, but resistance caused by the heterogeneous tumor microenvironment (TME) still presents a challenge. Therefore, it is necessary to characterize TME immune infiltration and explore new targets to improve immunotherapy.

Methods

The compositions of 64 types of infiltrating immune cells and their relationships with CRC patient clinical characteristics were assessed. Differentially expressed genes (DEGs) between “hot” and “cold” tumors were used for functional analysis. A prediction model was constructed to explore the survival of CRC patients treated with and without immunotherapy. Finally, fatty acid-binding protein (FABP6) was selected for in vitro experiments, which revealed its roles in the proliferation, apoptosis, migration, and immunogenicity of CRC tissues and cell lines.

Results

The infiltration levels of several immune cells were associated with CRC tumor stage and prognosis. Different cell types showed the synergistic or antagonism infiltration patterns. Enrichment analysis of DEGs revealed that immune-related signaling was significantly activated in hot tumors, while metabolic process pathways were altered in cold tumors. In addition, the constructed model effectively predicted the survival of CRC patients treated with and without immunotherapy. FABP6 knockdown did not significantly alter tumor cell proliferation, apoptosis, and migration. FABP6 was negatively correlated with immune infiltration, and knockdown of FABP6 increased major histocompatibility complex (MHC) class 1 expression and promoted immune-related chemokine secretion, indicating the immunogenicity enhancement of tumor cells. Finally, knockdown of FABP6 could promote the recruitment of CD8+ T cells.

Conclusion

Collectively, we described the landscape of immune infiltration in CRC and identified FABP6 as a potential immunotherapeutic target for treatment.

An Immune-Related Prognostic Risk Model in Colon Cancer by Bioinformatics Analysis

Colon cancer is one of the leading malignancies with poor prognosis worldwide. Immune cell infiltration has a potential prognostic value for colon cancer. This study aimed to establish an immune-related prognostic risk model for colon cancer by bioinformatics analysis. A total of 1670 differentially expressed genes (DEGs), including 177 immune-related genes, were identified from The Cancer Genome Atlas (TCGA) dataset. A prognostic risk model was constructed based on six critical immune-related genes (C-X-C motif chemokine ligand 1 (CXCL1), epiregulin (EREG), C-C motif chemokine ligand 24 (CCL24), fatty acid binding protein 4 (FABP4), tropomyosin 2 (TPM2), and semaphorin 3G (SEMA3G)). This model was validated using the microarray dataset GSE35982. In addition, Cox regression analysis showed that age and clinical stage were correlated with prognostic risk scores. Kaplan–Meier survival analysis showed that high risk scores correlated with low survival probabilities in patients with colon cancer. Downregulated TPM2, FABP4, and SEMA3G levels were positively associated with the activated mast cells, monocytes, and macrophages M2. Upregulated CXCL1 and EREG were positively correlated with macrophages M1 and activated T cells CD4 memory, respectively. Based on these results, we can conclude that the proposed prognostic risk model presents promising novel signatures for the diagnosis and prognosis prediction of colon cancer. This model may provide therapeutic benefits for the development of immunotherapy for colon cancer.

Identification of a Novel Risk Model: A Five-Gene Prognostic Signature for Pancreatic Cancer

Objective. Biomarkers for pancreatic cancer (PCa) prognosis provide evidence for improving the survival outcome of this disease. This study aimed to identify a prognostic risk model based on gene expression profiling of microarray bioinformatics analysis. Methods. Prognostic immune genes in the TCGA-PAAD cohort were identified using the univariate Cox regression and Kaplan–Meier survival analysis. Multivariate Cox regression (stepAIC) was used to identify prognostic genes from the top 20 hub genes in the protein-protein interaction (PPI) network. A prognostic risk model was established and its performance in predicting the overall survival in PCa was validated in GSE62452. Gene mutations and infiltration immune cells in PCa tumors were analyzed using online databases. Results. Univariate Cox regression and Kaplan–Meier survival analyses identified 128 prognostic genes. Multivariate Cox regression (stepAIC) identified five prognostic genes (PLCG1, MET, TNFSF10, CXCL9, and TLR3) out of the 20 hub genes in the PPI network. A prognostic risk model was established using the signature of five genes. This model had moderate to high accuracies (AUC > 0.700) in predicting 3-year and 5-year overall survival in TCGA and GSE62452 cohorts. The Kaplan–Meier survival analysis showed that high-risk scores were correlated with poor survival outcomes in PCa ( $p<0.05$ ). Also, mutations in the five genes were related to poor survival. The five genes were related to multiple immune cells. Conclusions. The prognostic risk model was significantly correlated with the survival in PCa patients. This model modulated PCa tumor progression and prognosis by regulating immune cell infiltration.

Identification of NFASC and CHL1 as Two Novel Hub Genes in Endometriosis Using Integrated Bioinformatic Analysis and Experimental Verification

Background

Endometriosis (EMS) is a common and highly recurrent gynecological disease characterized by chronic pain and infertility. There are no definitive therapies for endometriosis since the pathogenesis remains undetermined. This study aimed to identify EMS-related functional modules and hub genes by integrated bioinformatics analysis.

Methods

Three endometriosis expression profiling series (GSE25628, GSE23339, and GSE7305) were obtained from Gene Expression Omnibus (GEO). The EMS-related module was constructed by weighted gene co-expression network analysis (WGCNA), followed by Gene Ontology (GO) enrichment analyses. Cytohubba and the MCODE plug-ins of Cytoscape were used to screen out the hub genes, which were verified via receiver operating characteristic (ROC) curves. Immunohistochemistry was performed to verify the protein expression of the hub genes in ectopic endometrial tissues. Moreover, CIBERSORT was used to analyze the relationship between the abundance of immune cells infiltration and the expression of hub genes.

Results

Among the 18 modules obtained, the darkmagenta module was identified as the EMS-related module, genes of which were significantly enriched to terms referring to cell migration and neurogenesis. NFASC and CHL1 were screened out and prioritized as hub genes through Cytoscape and confirmed to be differentially upregulated in ectopic endometrial samples. Finally, the expression of hub genes was related to the abundance of immune cells infiltration. The higher expression of NFASC or CHL1 correlated with increased M2 macrophages and decreased natural killer (NK) cells in ectopic lesions.

Conclusion

This study provided new insights into the molecular factors underlying the pathogenesis of endometriosis and provided a theoretical basis for the potential that the two hub genes, NFASC and CHL1, might be novel biomarkers and therapeutic targets in the future.

Identification of DTL as Related Biomarker and Immune Infiltration Characteristics of Nasopharyngeal Carcinoma via Comprehensive Strategies

Purpose

Although considerable progress has been made in basic and clinical research on nasopharyngeal carcinoma (NPC), the biomarkers of the progression of NPC have not been fully studied and described. This study was designed to identify potential novel biomarkers for NPC using integrated analyses and explore the immune cell infiltration in this pathological process.

Methods

Five GEO data sets were downloaded from gene expression omnibus database (GEO) and analysed to identify differentially expressed genes (DEGs), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. The four algorithms were adopted for screening of novel and key biomarkers for NPC, including random forest (RF) machine learning algorithm, least absolute shrinkage and selection operator (LASSO) logistic regression, support vector machine-recursive feature elimination (SVM-RFE), and weighted gene co-expression network analysis (WGCNA). Lastly, CIBERSORT was used to assess the infiltration of immune cells in NPC, and the correlation between diagnostic markers and infiltrating immune cells was analyzed.

Results

Herein, we identified 46 DEGs, and enrichment analysis results showed that DEGs and several kinds of signaling pathways might be closely associated with the occurrence and progression of NPC. DTL was recognized as NPC-related biomarker. DTL, also known as retinoic acid-regulated nuclear matrix-associated protein (RAMP), or DNA replication factor 2 (CDT2), is reported to be correlated with the cell proliferation, cell cycle arrest and cell invasion in hepatocellular carcinoma, breast cancer and gastric cancer. Immune infiltration analysis demonstrated that macrophages M0, macrophages M1 and T cells CD4 memory activated were linked to pathogenesis of NPC.

Conclusion

In summary, we adopted a comprehensive strategy to screen DTL as biomarkers related to NPC and explore the critical role of immune cell infiltration in NPC.

Screening of Potential Biomarkers in the Peripheral Serum for Steroid-Induced Osteonecrosis of the Femoral Head Based on WGCNA and Machine Learning Algorithms

Background. Steroid-induced osteonecrosis of the femoral head (SONFH) has produced a substantial burden of medical and social experience. However, the current diagnosis is still limited. Thus, this study is aimed at identifying potential biomarkers in the peripheral serum of patients with SONFH. Methods. The expression profile data of SONFH (number: GSE123568) was acquired from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in SONFH were identified and used for weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the biological functions. The protein-protein interaction (PPI) network and machine learning algorithms were employed to screen for potential biomarkers. Gene set enrichment analysis (GSEA), transcription factor (TF) enrichment analysis, and competing endogenous RNA (ceRNA) network were used to determine the biological functions and regulatory mechanisms of the potential biomarkers. Results. A total of 562 DEGs, including 318 upregulated and 244 downregulated genes, were identified between SONFH and control samples, and 94 target genes were screened based on WGCNA. Moreover, biological function analysis suggested that target genes were mainly involved in erythrocyte differentiation, homeostasis and development, and myeloid cell homeostasis and development. Furthermore, GYPA, TMCC2, and BPGM were identified as potential biomarkers in the peripheral serum of patients with SONFH based on machine learning algorithms and showed good diagnostic values. GSEA revealed that GYPA, TMCC2, and BPGM were mainly involved in immune-related biological processes (BPs) and signaling pathways. Finally, we found that GYPA might be regulated by hsa-miR-3137 and that BPGM might be regulated by hsa-miR-340-3p. Conclusion. GYPA, TMCC2, and BPGM are potential biomarkers in the peripheral serum of patients with SONFH and might affect SONFH by regulating erythrocytes and immunity.

Solute carrier family 12 member 8 (SLC12A8) is a potential biomarker and related to tumor immune cell infiltration in bladder cancer

The solute carrier family has been reported to play critical roles in the progression of several cancers; however, the relationship between solute carrier family 12 member 8 (SLC12A8) and bladder cancer (BC) has not been clearly confirmed. This study explores the prognostic value of SLC12A8 for BC and its correlation with immune cell infiltration. We found that the expression of SLC12A8 mRNA was significantly overexpressed in BC tissues compared with noncancerous tissues in multiple public databases, and the result was validated using real-time PCR and immunohistochemistry (IHC). The Kaplan-Meier method and Cox proportional hazards models were used to evaluate the prognostic value of SLC12A8 for BC. The high expression of SLC12A8 led to a shorter overall survival time and was an unfavorable prognostic biomarker for BC. The mechanisms of SLC12A8 promoting tumorigenesis were investigated by Gene Set Enrichment Analysis (GSEA). Moreover, the correlations of SLC12A8 expression with the tumor-infiltrating immune cells (TICs) in BC were explored using TIMER 2.0 and CIBERSORT. SLC12A8 was associated with CD4+ T cells, dendritic cells, neutrophils, and macrophages infiltration. The expression of SLC12A8 was positively correlated with crucial immune checkpoint molecules. In conclusion, SLC12A8 might be an unfavorable prognostic biomarker in BC related to tumor immune cell infiltration.

Monocyte-related gene biomarkers for latent and active tuberculosis

Monocytes are closely associated with tuberculosis (TB). Latent tuberculosis in some patients gradually develops into its active state. This study aimed to investigate the role of hub monocyte-associated genes in distinguishing latent TB infection (LTBI) from active TB. The gene expression profiles of 15 peripheral blood mononuclear cells (PBMCs) samples were downloaded from the gene expression omnibus (GEO) database, GSE54992. The monocyte abundance was high in active TB as evaluated by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The limma test and correlation analysis documented 165 differentially expressed monocyte-related genes (DEMonRGs) between latent TB and active TB. Functional annotation and enrichment analyses of the DEMonRGs using the database for annotation, visualization, and integration discovery (DAVID) tools showed enrichment of inflammatory response mechanisms and immune-related pathways. A protein-protein interaction network was constructed with a node degree ≥10. The expression levels of these hub DEMonRGs (SERPINA1, FUCA2, and HP) were evaluated and verified using several independent datasets and clinical settings. Finally, a single sample scoring method was used to establish a gene signature for the three DEMonRGs, distinguishing active TB from latent TB. The findings of the present study provide a better understanding of monocyte-related molecular fundamentals in TB progression and contribute to the identification of new potential biomarkers for the diagnosis of active TB.

High-altitude hypoxia exacerbates dextran sulfate sodium (DSS)-induced colitis by upregulating Th1 and Th17 lymphocytes

High altitude hypoxia (HAH) involves the pathogenesis of ulcerative colitis (UC) and gastrointestinal erosions. However, the mechanism of effects of HAH in colitis remains controversial. This study reports the immunomodulation mediated by HAH to enhancing the severity of UC in the mice model. BALB/c mice were used to establish the UC model by dextran sulfate sodium (DSS) compared to wild type mice. Mice groups were exposed to hypoxic conditions in a hypobaric chamber with an altitude of 5000 m for 7 days. Then, Spleen, mesenteric lymph nodes and colon tissues were collected. The activity of UC, the infiltration of the immune cells, and the released cytokines were investigated. Results showed that the severity of DSS-induced UC significantly increased in mice exposed to HAH. The analysis of pathological changes showed increased weight loss and decreased colon length accompanied by diarrhea and bloody feces in the hypobaric hypoxia group. Interestingly, the levels of inflammatory cytokines IL-17, TNF-α, and IFN-γ in the spleen and mesenteric lymph node showed a significant increase within the colon of the hypobaric hypoxia group. The population of Th 1 and Th 17 cells in the spleen was significantly increased in mice exposed to hypobaric hypoxia compared NC group. Suggesting that high altitude hypoxia enhances colitis in mice through activating the increase of inflammatory Th1 and Th17 lymphocytes. In conclusion, this study revealed that hypobaric hypoxia directly increases the severity of UC in the mice model via increasing the activity of inflammatory CD4+ Th1 and Th 17 lymphocytes.

Comprehensive analysis of pivotal biomarkers, immune cell infiltration and therapeutic drugs for steroid-induced osteonecrosis of the femoral head

Steroid-induced osteonecrosis of the femoral head (SONFH) is a progressive disease that leads to an increased disability rate. This study aimed to ascertain biomarkers, infiltrating immune cells, and therapeutic drugs for SONFH. The gene expression profile of the GSE123568 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified using the NetworkAnalyst platform. Functional enrichment, protein-protein interaction network (PPI), and module analyses were performed using Metascape tools. An immune cell abundance identifier was used to explore immune cell infiltration. Furthermore, hub genes were identified based on maximal clique centrality (MCC) evaluation using cytoHubba application and confirmed by qRT-PCR using clinical samples. Finally, the L1000 platform was used to determine potential drugs for SONFH treatment. The SONFH mouse model was used to determine the therapeutic effects of aspirin. In total, 429 DEGs were identified in SONFH samples. Functional enrichment analysis showed that these DEGs were enriched in myeloid leukocyte activation and osteoclast differentiation processes. A set of nine immune cell types was confirmed to be markedly different between the SONFH and control samples. All 10 hub genes were significantly highly expressed in the serum of SONFH patients, as shown by qRT-PCR. Finally, the therapeutic effect of aspirin on SONFH was examined in animal experiments. Taken together, our data revealed the hub genes and infiltrating immune cells in SONFH, and we also screened potential drugs for use in SONFH treatment.

C-X-C motif chemokine ligand 12: a potential therapeutic target in Duchenne muscular dystrophy

Duchenne muscular dystrophy (DMD) is an X-linked recessive disease caused by a mutant dystrophin protein. DMD patients undergo gradual progressive paralysis until death. Chronic glucocorticoid therapy remains one of the main treatments for DMD, despite the significant side effects. However, its mechanisms of action remain largely unknown. We used bioinformatics tools to identify pathogenic genes involved in DMD and glucocorticoid target genes. Two gene expression profiles containing data from DMD patients and healthy controls (GSE38417 and GSE109178) were downloaded for further analysis. Differentially expressed genes (DEGs) between DMD patients and controls were identified using GEO2R, and glucocorticoid target genes were predicted from the Pharmacogenetics and Pharmacogenomics Knowledge Base. Surprisingly, only one gene, CXCL12 (C-X-C motif chemokine ligand 12), was both a glucocorticoid target and a DEG. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, Gene Ontology term enrichment analysis, and gene set enrichment analysis were performed. A protein-protein interaction network was constructed and hub genes identified using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape. Enriched pathways involving the DEGs, including CXCL12, were associated with the immune response and inflammation. Levels of CXCL12 and its receptor CXCR4 (C-X-C motif chemokine receptor 4) were increased in X-linked muscular dystrophy (mdx) mice (DMD models) but became significantly reduced after prednisone treatment. Metformin also reduced the expression of CXCL12 and CXCR4 in mdx mice. In conclusion, the CXCL12-CXCR4 pathway may be a potential target for DMD therapy.

Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies

This study aims to explore biomarkers associated with vitiligo and analyze the pathological role of immune cell infiltration in the disease. We used the robust rank aggregation (RRA) method to integrate three vitiligo data sets downloaded from gene expression omnibus database, identify the differentially expressed genes (DEGs) and analyze the functional correlation. Then, the comprehensive strategy of combined weighted gene coexpression network analysis (WGCNA) and logical regression of the selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) machine learning algorithm are employed to screen and biomarkers associated with vitiligo. Finally, the immune cell infiltration of vitiligo was evaluated by CIBERSORT, and the correlation between biomarkers and infiltrating immune cells was analyzed. Herein, we identified 131 robust DEGs, and enrichment analysis results showed that robust DEGs and melanogenesis were closely associated with vitiligo development and progression. TYR, TYRP1, DCT and LARP7 were identified as vitiligo-related biomarkers. Immune infiltration analysis demonstrated that CD4 T Cell, CD8 T Cell, Tregs, NK cells, dendritic cells, and macrophages were involved in vitiligo’s pathogenesis. In summary, we adopted a comprehensive strategy to screen biomarkers related to vitiligo and explore the critical role of immune cell infiltration in vitiligo.

Abbreviations: TYR, Tyrosinase; TYRP1, Tyrosinase-related protein-1; DCT, dopachrome tautomerase; LARP7, La ribonucleoprotein domain family, member-7; RRA, robust rank aggregation; DEGs, differentially expressed genes; WGCNA, weighted gene coexpression network analysis; LASSO, logical regression of the selection operator; SVM-RFE, support vector machine recursive feature elimination; RF, random forest; GWAS, Genome-wide association study; FasL, Fas-Fas ligand; Tregs, T-regulatory cells; NK, natural killer; GEPCs, gene expression profiling chips; GO, gene ontology; GSEA, gene set enrichment analysis; FDR, false discovery rate; AUC, area under the curve; ROC, receiver-operating characteristic; BP, biological process; CC, cellular component; MF, molecular function.

Prediction of clinical prognosis in cutaneous melanoma using an immune-related gene pair signature

Cutaneous melanoma (CM) is a malignant and aggressive skin cancer that is the leading cause of skin cancer-related deaths. Increasing evidence shows that immunity plays a vital role in the prognosis of CM. In this study, we developed an immune-related gene pair (IRGP) signature to predict the clinical prognosis of patients with CM. Immune-related genes from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases were selected to construct the IRGPs, and patients with CM in these two cohorts were assigned to low- and high-risk subgroups. Moreover, we investigated the IRGPs and their individualized prognostic signatures using Kaplan-Meier survival analysis, univariate and multivariate Cox analyses, and analysis of immune cell infiltration in CM. A 41-IRGP signature was constructed from 2498 immune genes that could significantly predict the overall survival of patients with CM in both the TCGA and GEO cohorts. Immune infiltration analysis indicated that several immune cells, especially M1 macrophages and activated CD4 T cells, were significantly associated with the prognostic effect of the IRGP signature in patients with CM. Overall, the IRGP signature constructed in this study was useful for determining the prognosis of patients with CM and for providing further understanding of CM immunotherapy.