Immune Infiltration Patterns Research Articles

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning

Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules, diverse signaling pathways, and multifaceted immune processes. N6-methyladenosine (m6A), a critical epigenetic modification, regulates various immune-metabolic and pathological pathways, though its precise role in TB pathogenesis remains largely unexplored. This study aims to identify m6A-associated genes implicated in TB, elucidate their mechanistic contributions, and evaluate their potential as diagnostic biomarkers and tools for molecular subtyping. Using TB-related datasets from the GEO database, this study identified differentially expressed genes associated with m6A modification. We applied four machine learning algorithms—Random Forest, Support Vector Machine, Extreme Gradient Boosting, and Generalized Linear Model—to construct diagnostic models focusing on m6A regulatory genes. The Random Forest algorithm was selected as the optimal model based on performance metrics (area under the curve [AUC] = 1.0, p < 0.01), and a clinical predictive model was developed based on these critical genes. Patients were stratified into distinct subtypes according to m6A gene expression profiles, followed by immune infiltration analysis across subtypes. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated the biological functions and pathways associated with the identified genes. Quantitative real-time PCR (RT-qPCR) was used to validate the expression of key m6A regulatory genes. Analysis of the GSE83456 dataset revealed four differentially expressed m6A-related genes—YTHDF1, HNRNPC, LRPPRC, and ELAVL1—identified as critical m6A regulators in TB through the Random Forest model. The diagnostic significance of these genes was further supported by a nomogram, achieving a high predictive accuracy (95% confidence interval [CI]: 0.87–0.94). Consensus clustering classified patients into two m6A subtypes with distinct immune profiles, as principal component analysis (PCA) showed significantly higher m6A scores in Group A than in Group B (p < 0.05). Immune infiltration analysis highlighted significant correlations between key m6A genes and specific immune cell infiltration patterns across subtypes. This study highlights the potential of key m6A regulatory genes as diagnostic biomarkers and immunotherapy targets for TB, supporting their role in TB pathogenesis. Future research should aim to further validate these findings across diverse cohorts to enhance their clinical applicability.

Functional profiling of murine glioma models highlights targetable immune evasion phenotypes

Cancer-intrinsic immune evasion mechanisms and pleiotropy are a barrier to cancer immunotherapy. This is apparent in certain highly fatal cancers, including high-grade gliomas and glioblastomas (GBM). In this study, we evaluated two murine syngeneic glioma models (GL261 and CT2A) as preclinical models for human GBM using functional genetic screens, single-cell transcriptomics and machine learning approaches. Through CRISPR genome-wide co-culture killing screens with various immune cells (cytotoxic T cells, natural killer cells, and macrophages), we identified three key cancer-intrinsic evasion mechanisms: NFκB signaling, autophagy/endosome machinery, and chromatin remodeling. Additional fitness screens identified dependencies in murine gliomas that partially recapitulated those seen in human GBM (e.g., UFMylation). Our single-cell analyses showed that different glioma models exhibited distinct immune infiltration patterns and recapitulated key immune gene programs observed in human GBM, including hypoxia, interferon, and TNF signaling. Moreover, in vivo orthotopic tumor engraftment was associated with phenotypic shifts and changes in proliferative capacity, with murine tumors recapitulating the intratumoral heterogeneity observed in human GBM, exhibiting propensities for developmental- and mesenchymal-like phenotypes. Notably, we observed common transcription factors and cofactors shared with human GBM, including developmental (Nfia and Tcf4), mesenchymal (Prrx1 and Wwtr1), as well as cycling-associated genes (Bub3, Cenpa, Bard1, Brca1, and Mis18bp1). Perturbation of these genes led to reciprocal phenotypic shifts suggesting intrinsic feedback mechanisms that balance in vivo cellular states. Finally, we used a machine-learning approach to identify two distinct immune evasion gene programs, one of which represents a clinically-relevant phenotype and delineates a subpopulation of stem-like glioma cells that predict response to immune checkpoint inhibition in human patients. This comprehensive characterization helps bridge the gap between murine glioma models and human GBM, providing valuable insights for future therapeutic development.

FCGBP functions as a tumor suppressor gene in head and neck squamous cell carcinoma

PurposeThe pathogenesis of head and neck squamous cell carcinoma (HNSCC) was complex and the overall survival was not satisfying. It was urgent to uncover novel molecules that play vital role in HNSCC for disease monitoring and drug development.MethodsDistinguished expression of FCGBP mRNA in HNSCC was analyzed by TCGA-HNSC and three GEO datasets, the relationship between FCGBP and clinical stage and survival was analyzed by GEPIA 2, the immune infiltration pattern analysis was conducted by TIMER 2.0, pathways affected by FCGBP was conducted by GSEA and GO/KEGG. In vitro experiments (including qRT-PCR, siRNA transfection, CCK8, transwell assay and flow cytometry) were conducted to confirm bioinformatic analysis.ResultsFCGBP was down-regulated in tumor samples compared with normal tissues at both mRNA and protein levels, and positively correlated with survival in HNSCC. Genes co-expressed with FCGBP were mainly enriched in immune-related biological processes and pathways. GSEA indicated that FCGBP was associated with activated immune reaction and inhibiting well-known pro-tumor pathways. GSE41613 validated FCGBP as an independent prognostic marker for HNSCC and FCGBP was down-regulated in HNSCC cell lines by qRT-PCR. Migration and invasion of SCC9 and CAL27 were enhanced by FCGBP-targeting siRNAs, the ratio of cytotoxic T lymphocytes were down-regulated while the ratio of myeloid-derived suppressor cells were increased by FCGBP-targeting siRNAs.ConclusionFCGBP was a tumor suppressor gene and was an independent prognostic marker for better survival. The underlying mechanism may be that FCGBP inhibited tumor migration and invasion and activated immune response against tumor cells.

Construction of molecular subtype and prognostic model for gastric cancer based on nucleus-encoded mitochondrial genes

Gastric cancer (GC) is a common digestive system cancer, characterized by a significant mortality rate. Mitochondria is an indispensable organelle in eukaryotic cells. It was previously revealed that a series of nucleus-encoded mitochondrial genes (NMG) mutations and dysfunctions potentially contribute to the initiation and progression of GC. However, the correlation between NMG mutations and survival outcomes for GC patients is still unclear. In this study, NMG expression profile and clinical information in GC samples were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Through consistent clustering and functional enrichment analysis, we have identified three NMG clusters and three gene clusters that are associated with patterns of immune cell infiltration. Prognostic genes were identified through Univariate Cox regression analysis. The principal component analysis was conducted to set up a scoring system. Subsequently, the Single‑cell RNA sequencing (scRNA-seq) data of GC patients and cancer cell drug sensitivity data were retrieved from the GEO database. Patients with high NMG scores exhibited increased microsatellite instability status and a heightened tumor mutation rate compared to those with low NMG scores. Survival analysis revealed that GC samples with high NMG scores could achieve a better prognosis. Additionally, These patients were observed to be more responsive to immunotherapy. Moreover, we delved into prognostic genes at the level of single cells, revealing that MRPL4 and MRPL37 exhibit high expression in epithelial cells, while TPM1 demonstrates high expression in tissue stem cells. Utilizing cancer cell drug sensitivity data from the Drug Sensitivity in Cancer (GDSC) database, we noted a heightened sensitivity to chemotherapy in the high NMG group. Furthermore, we discovered a significant enrichment of cuproptosis-related genes in clusters with high NMG scores. Consequently, employing the scoring system could facilitate the prediction of GC patients’ sensitivity to cuproptosis-induced therapy. Our study confirmed the potency of this scoring system as a therapeutic response biomarker for gastric cancer, potentially informing clinical treatment strategies.

Immune infiltration correlates with transcriptomic subtypes in primary estrogen receptor positive invasive lobular breast cancer

Understanding interplay of breast cancer and microenvironment is critical. Here we identified two transcriptomic subtypes and five immune infiltration patterns from RNA-seq and multiplex immunohistochemistry from 21 ER + /HER2- ILCs. We found proliferative subtype associated with increased suppressive immune infiltration, and defined a signature associated with lower proliferative, pro-inflammatory TAM infiltration, and improved survival in ER+ breast cancer. Our work identified genes related to ILC immune microenvironment and prognosis.

SPINK5 is a key regulator of eosinophil extracellular traps in head and neck squamous cell carcinoma

Enhanced infiltration of eosinophils is observed surrounding solid tumors. Some studies indicate that Eosinophil extracellular traps (EETs) play a crucial role in tumor progression and metastasis. However, its specific role in head and neck squamous cell carcinoma (HNSCC) remains unclear. This study established a gene set associated with eosinophil differentiation, chemotaxis, and EETs release from previous research. Employing bioinformatics techniques, the expression and biological significance of these genes in HNSCC were analyzed. Briefly, unsupervised clustering based on expression patterns of 133 EETs-related genes to classify TCGA-HNSCC patients. Immune cell infiltration patterns were assessed using “ImmuCellAI” package. A prognostic model was constructed using ten algorithms, with EETs-related gene sets as input features. Here, unsupervised clustering of samples into two types revealed worse prognosis for Cluster 1 (C1) patients after the first year. Cluster 2 (C2) exhibited higher ImmuneScore, but with a distinct immune cell infiltration pattern from the C1. Additionally, high eosinophil abundance only in the C2 had a positive prognostic impact. Serine peptidase inhibitor kazal type 5 (SPINK5) emerged as a potential key gene mediating the formation of EETs in HNSCC. EETs not only exhibit a positive correlation with diverse anti-cancer pathways but also demonstrate positive associations with processes such as proliferation, migration, and other critical pathways. The random survival forest (RSF) model was identified as the optimal eosinophil-related prognostic model. Collectively, this study elucidates the potential impact and mediating pathways of EETs on tumors, providing a reference for targeted therapy based on EETs-related genes.

Potential diagnostic markers and therapeutic targets for non-alcoholic fatty liver disease and ulcerative colitis based on bioinformatics analysis and machine learning.

Non-alcoholic fatty liver disease (NAFLD) and ulcerative colitis (UC) are two common health issues that have gained significant global attention. Previous studies have suggested a possible connection between NAFLD and UC, but the underlying pathophysiology remains unclear. This study investigates common genes, underlying pathogenesis mechanisms, identification of diagnostic markers applicable to both conditions, and exploration of potential therapeutic targets shared by NAFLD and UC. We obtained datasets for NAFLD and UC from the GEO database. The DEGs in the GSE89632 dataset of the NAFLD and GSE87466 of the UC dataset were analyzed. WGCNA, a powerful tool for identifying modules of highly correlated genes, was employed for both datasets. The DEGs of NAFLD and UC and the modular genes were then intersected to obtain shared genes. Functional enrichment analysis was conducted on these shared genes. Next, we utilize the STRING database to establish a PPI network. To enhance visualization, we employ Cytoscape software. Subsequently, the Cytohubba algorithm within Cytoscape was used to identify central genes. Diagnostic biomarkers were initially screened using LASSO regression and SVM methods. The diagnostic value of ROC curve analysis was assessed to detect diagnostic genes in both training and validation sets for NAFLD and UC. A nomogram was also developed to evaluate diagnostic efficacy. Additionally, we used the CIBERSORT algorithm to explore immune infiltration patterns in both NAFLD and UC samples. Finally, we investigated the correlation between hub gene expression, diagnostic gene expression, and immune infiltration levels. We identified 34 shared genes that were found to be associated with both NAFLD and UC. These genes were subjected to enrichment analysis, which revealed significant enrichment in several pathways, including the IL-17 signaling pathway, Rheumatoid arthritis, and Chagas disease. One optimal candidate gene was selected through LASSO regression and SVM: CCL2. The ROC curve confirmed the presence of CCL2 in both the NAFLD and UC training sets and other validation sets. This finding was further validated using a nomogram in the validation set. Additionally, the expression levels of CCL2 for NAFLD and UC showed a significant correlation with immune cell infiltration. This study identified a gene (CCL2) as a biomarker for NAFLD and UC, which may actively participate in the progression of NAFLD and UC. This discovery holds significant implications for understanding the progression of these diseases and potentially developing more effective diagnostic and treatment strategies.

An Innovative Telomere-associated Prognosis Model in AML: Predicting Immune Infiltration and Treatment Responsiveness.

To build an innovative telomere-associated scoring model to predict prognosis and treatment responsiveness in acute myeloid leukemia (AML). AML is a highly heterogeneous malignant hematologic disorder with a poor prognosis. While telomere maintenance is frequently observed in tumors, investigations into telomere-related genes (TRGs) in AML remain limited. This study aimed to identify prognostic TRGs using the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression, evaluate their predictive value, explore the association between TRG scores and immune cell infiltration, and assess the sensitivity of high-scoring AML patients to chemotherapeutic agents. Univariate Cox regression analysis was conducted on the TCGA cohort to identify prognostic TRGs and to develop the TRG scoring model using LASSO-Cox and multivariate Cox regression. Validation was performed on the GSE37642 cohort. Immune cell infiltration patterns were assessed through computational analysis, and the sensitivity to chemotherapeutic agents was evaluated. Thirteen prognostic TRGs were identified, and a seven-TRG scoring model (including NOP10, OBFC1, PINX1, RPA2, SMG5, MAPKAPK5, and SMN1) was developed. Higher TRG scores were associated with a poorer prognosis, as confirmed in the GSE37642 cohort, and remained an independent prognostic factor even after adjusting for other clinical characteristics. The high-score group was characterized by elevated infiltration of B cells, T helper cells, natural killer cells, tumor-infiltrating lymphocytes, regulatory T (Treg) cells, M2 macrophages, neutrophils, and monocytes, along with reduced infiltration of gamma delta T cells, CD4- T cells, and resting mast cells. Moreover, high infiltration of M2 macrophages and Tregs was associated with poor overall survival compared to low infiltration. Notably, high-risk AML patients were resistant to Erlotinib, Parthenolide, and Nutlin-3a, but sensitive to AC220, Midostaurin, and Tipifarnib. Additionally, using RT-qPCR, we observed significantly higher expression of two model genes, OBFC1 and SMN1, in AML tissues compared to control tissues. This innovative TRG scoring model demonstrates considerable predictive value for AML patient prognosis, offering valuable insights for optimizing treatment strategies and personalized medicine approaches. The identified TRGs and associated scoring models could aid in risk stratification and guide tailored therapeutic interventions in AML patients.

Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning.

Dermatomyositis (DM) is an autoimmune disease that primarily affects the skin and muscles. It can lead to increased mortality, particularly when patients develop associated malignancies or experience fatal complications such as pulmonary fibrosis. Identifying reliable biomarkers is essential for the early diagnosis and treatment of DM. This study aims to identify and validate pivotal diagnostic biomarker for DM through integrated bioinformatics analysis and clinical sample validation. Gene expression datasets GSE46239 and GSE142807 from the Gene Expression Omnibus (GEO) database were merged for analysis. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Advanced machine learning methods were utilized to further pinpoint hub genes. Weighted gene co-expression network analysis (WGCNA) was also conducted to discover key gene modules. Subsequently, we derived intersection gene from these methods. The diagnostic performance of the candidate biomarker was evaluated using analysis with dataset GSE128314 and confirmed by immunohistochemistry (IHC) in skin lesion biopsy specimens. The CIBERSORT algorithm was used to analyze immune cell infiltration patterns in DM, then the association between the hub gene and immune cells was investigated. Gene set enrichment analysis (GSEA) was performed to understand the biomarker's biological functions. Finally, the drug-gene interactions were predicted using the DrugRep server. Interferon-stimulated gene 15 (ISG15) was identified by intersecting DEGs, advanced machine learning-selected genes and key module genes from WGCNA. ROC analysis showed ISG15 had a high Area under the curve (AUC) of 0.950. IHC findings confirmed uniformly positive expression of ISG15, particularly in perivascular regions and lymphocytes, contrasting with universally negative expression in controls. Further analysis revealed that ISG15 is involved in abnormalities in various immune cells and inflammation-related pathways. We also predicted three drugs targeting ISG15, supported by molecular docking studies. Our study identifies ISG15 as a highly specific diagnostic biomarker for DM, ISG15 may be closely related to the pathogenesis of DM, demonstrating promising potential for clinical application.

Integrated multi-level omics profiling of disulfidptosis identifis SPAG4 as an innovative immunotherapeutic target in glioblastoma.

To investigate the association between disulfidptosis-related genes (DFRGs) and patient prognosis, while concurrently identifying potential therapeutic targets in glioblastoma (GBM). We retrieved RNA sequencing data and clinical characteristics of GBM patients from the TCGA database. We found there was a total of 6 distinct clusters in GBM, which was identified by the t-SNE and UMAP dimension reduction analysis. Prognostically significant genes in GBM were identified using the limma package, coupled with univariate Cox regression analysis. Machine learning algorithms were then applied to identify central genes. The CIBERSORT algorithm was utilized to assess the immunological landscape across different GBM subtypes. In vitro and in vivo experiments were conducted to investigate the role of SPAG4 in regulating the proliferation, invasion of GBM, and its effects within the immune microenvironment. 23 genes, termed DFRGs, were successfully identified, demonstrating substantial potential for establishing a prognostic model for GBM. Single cell analysis revealed a significant correlation between DFRGs and the progression of GBM. Utilizing individual risk scores derived from this model enabled the stratification of patients into two distinct risk groups, revealing significant variations in immune infiltration patterns and responses to immunotherapy. Utilizing the random survival forests algorithm, SPAG4 was identified as the gene with the highest prognostic significance within our model. In vitro studies have elucidated SPAG4's significant role in GBM pathogenesis, potentially through the regulation of fatty acid metabolism pathways. Our in vivo investigations using a subcutaneous xenograft model have confirmed SPAG4's influence on tumor growth and its capacity to modulate the immune microenvironment. Advanced research hints that SPAG4 might achieve immune evasion by increasing CD47 expression, consequently reducing phagocytosis. These findings highlight SPAG4 as a potential GBM therapeutic target and emphasize the complexity of the immune microenvironment in GBM progression.

The co-location of MARCO+ tumor-associated macrophages and CTSE+ tumor cells determined the poor prognosis in intrahepatic cholangiocarcinoma

Intra-tumor immune infiltration is a crucial element interacting with tumor cells in intrahepatic cholangiocarcinoma (ICC). However, its phenotype and related spatial structure remained elusive. To address these limitations, we undertook a comprehensive study combining spatial data (29,632 spots from six samples) and single-cell data (21,158 cells from 35 samples). We identified two distinct infiltration patterns: macrophage+ (characterized by CD68 and MARCO) and plasma cell+ (characterized by IGHG1 and JCHAIN). The macrophage+ and plasma cell+ signatures showed adverse and favorable roles in ICC patients’ survival, respectively. Notably, MARCO+ tumor-associated macrophage (TAM) was recognized as the main cell type in macrophage+ samples, indicating an immune-resistant microenvironment. Increased epithelial-mesenchymal transition activities, angiogenesis, and hypoxia were observed in MARCO+ TAM. The co-location of MARCO+ TAM and CTSE+ tumor cells was observed in spatial transcriptomics and bulk transcriptomics data, validated by multiplex immunofluorescence performed on twenty ICC samples. The co-location area exhibited similar protumorigenic pathways and suppressed immune response. CTSE exhibited associations with intrahepatic metastasis and vascular invasion. Both MARCO+ TAM and CTSE+ tumor cells were associated with worse survival and patients with high infiltration of two cell types displayed the worst survival. Within the co-location area, the galectin signaling pathway was most active in cell-cell communication, with LGALS9-CD44 identified as the main ligand-receptor pair. This study identified macrophage+ and plasma cell+ intra-tumor immune infiltration patterns and the co-location of MARCO+ TAM and CTSE+ tumor cells as contributors to immune resistance.

Machine learning-derived peripheral blood transcriptomic biomarkers for early lung cancer diagnosis: Unveiling tumor-immune interaction mechanisms.

Lung cancer continues to be the leading cause of cancer-related mortality worldwide. Early detection and a comprehensive understanding of tumor-immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor-immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in-house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in-house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor-immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T-cell infiltration, indicative of enhanced antitumor-immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator-activated receptor (PPAR), and Toll-like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning-derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor-immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor-immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.

Prognostic risk model of LIHC T-cells based on scRNA-seq and RNA-seq and the regulation of the tumor immune microenvironment

BackgroundT-cell-related genes play a crucial role in LIHC development. However, a reliable prognostic profile based on risk models of these genes has yet to be identified.MethodsSingle-cell datasets from both tumor and normal tissue samples were obtained from the GEO database. We identified T-cell marker genes and developed a genetic risk model using the TCGA-LIHC dataset, which was subsequently validated with an independent GEO dataset. We also explored the relationship between risk model predictions and immune responses.ResultsWe constructed a prognostic risk model using eight gene features identified through screening 860 T-cell marker genes via scRNA-seq and RNA-seq, which was subsequently integrated with the TCGA dataset. Its validity was independently confirmed using GEO and ICGC datasets. The TCGA dataset was stratified into high-risk and low-risk groups based on the risk model. Multivariate Cox regression analysis confirmed the risk score as an independent prognostic factor. GSEA indicated ribosomal transporter metabolism enrichment in the high-risk group and significant transcriptional activation in the low-risk group. ESTIMATE analysis showed higher ESTIMATE, immune, and stromal scores in the low-risk group, which also exhibited lower tumor purity than the high-risk group. Immunophenotyping revealed distinct patterns of immune cell infiltration and an immunosuppressive environment in the high-risk group.ConclusionsThis study introduces a T-cell marker-based prognostic risk model for LIHC patients. This model effectively predicted survival outcomes and immunotherapy effectiveness in LIHC patients, aligning with diverse immune responses and the distinct immunological profiles observed in the high-risk group.

Bioinformatics analysis of therapeutic targets for idiopathic pulmonary fibrosis and exploration of immune cell infiltration patterns

Idiopathic pulmonary fibrosis (IPF) is a severe progressive lung disease characterized by fibrotic changes in lung tissue, with limited treatment options. This study analyzed gene expression data from three gene expression omnibus datasets (GSE2052, GSE53845, and GSE110147) using R and LIMMA to identify differentially expressed genes (DEGs) in IPF samples. We identified 215 DEGs, comprising 106 upregulated and 109 downregulated genes. Weighted gene coexpression network analysis revealed five gene modules, with the module eigengene yellow showing the strongest correlation with IPF. Functional enrichment analysis of 40 consensus genes in this module indicated their significant involvement in extracellular matrix (ECM) organization. Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed pathways related to protein digestion, cell adhesion molecules, and the advanced glycation end product&amp;ndash;receptor for advanced glycation end product signaling pathway. Based on protein&amp;ndash;protein interaction network analysis, collagen type XV alpha 1 chain (COL15A1) and collagen type VI alpha 3 chain (COL6A3) were identified as upregulated hub genes in IPF, which were regulated by microRNAs and transcription factors. Immune cell infiltration analysis showed significant changes in immune cell populations in IPF samples, with increases in memory B cells, plasma cells, and M0 macrophages and decreases in CD8 T cells, and resting natural killer cells. Potential drugs targeting COL15A1 and COL6A3 were predicted using multiple databases, revealing compounds such as (+)-JQ1, aristolochic acid I, and dexamethasone with promising binding potential. These findings suggest that COL15A1 and COL6A3 are central hub genes in IPF, are associated with ECM organization and immune response, and serve as therapeutic targets for IPF.

Development of a prognostic model related to homologous recombination deficiency in glioma based on multiple machine learning.

Despite advances in neuro-oncology, treatments of glioma and tools for predicting the outcome of patients remain limited. The objective of this research is to construct a prognostic model for glioma using the Homologous Recombination Deficiency (HRD) score and validate its predictive capability for glioma. We consolidated glioma datasets from TCGA, various cancer types for pan-cancer HRD analysis, and two additional glioma RNAseq datasets from GEO and CGGA databases. HRD scores, mutation data, and other genomic indices were calculated. Using machine learning algorithms, we identified signature genes and constructed an HRD-related prognostic risk model. The model's performance was validated across multiple cohorts. We also assessed immune infiltration and conducted molecular docking to identify potential therapeutic agents. Our analysis established a correlation between higher HRD scores and genomic instability in gliomas. The model, based on machine learning algorithms, identified seven key genes, significantly predicting patient prognosis. Moreover, the HRD score prognostic model surpassed other models in terms of prediction efficacy across different cancers. Differential immune cell infiltration patterns were observed between HRD risk groups, with potential implications for immunotherapy. Molecular docking highlighted several compounds, notably Panobinostat, as promising for high-risk patients. The prognostic model based on the HRD score threshold and associated genes in glioma offers new insights into the genomic and immunological landscapes, potentially guiding therapeutic strategies. The differential immune profiles associated with HRD-risk groups could inform immunotherapeutic interventions, with our findings paving the way for personalized medicine in glioma treatment.

Upregulation of ARHGAP9 is correlated with poor prognosis and immune infiltration in clear cell renal cell carcinoma.

Rho GTPase activating protein (ARHGAP) family genes play critical roles in the onset and progression of human cancer. Rho GTPase activating protein 9 (ARHGAP9) is upregulated in various tumors. However, far too little attention has been paid to the prognostic value of ARHGAP9 and correlation with immune infiltration in clear cell renal cell carcinoma (ccRCC). Our aim is to evaluate the prognostic significance of ARHGAP9 expression and its correlation with immune infiltration in ccRCC. Transcriptional expression profiles of ARHGAP9 between ccRCC tissues and normal tissues were downloaded from The Cancer Genome Atlas. The ARHGAP9 protein expression was assessed by the Clinical Proteomic Tumor Analysis Consortium. Receiver operating characteristic curve was used to differentiate ccRCC from adjacent normal tissues. The Kaplan-Meier method was conducted to assess the effect of ARHGAP9 on survival. Protein-protein interaction networks were constructed by the STRING. Functional enrichment analyses were performed using the "ClusterProfiler" package. The immune infiltration patterns were evaluated via the tumor immune estimation resource 2.0 and Tumor-Immune System Interaction Database. ARHGAP9 expression was substantially higher in ccRCC tissues than in adjacent normal tissues. Increased ARHGAP9 mRNA expression was shown to be linked to high TNM stage and lymph node metastases. The diagnostic value of ARHGAP9 gene expression data was assessed using receiver operating characteristic curve analysis. The survival analysis module of GEPIA2 and the Kaplan-Meier plotter both showed ccRCC patients with high-ARHGAP9 had a worse prognosis than those with low-ARHGAP9. Correlation analysis indicated ARHGAP9 mRNA expression was significantly correlated with tumor purity and immune infiltrates. These findings demonstrate that upregulated ARHGAP9 indicates poor prognosis and immune infiltration in ccRCC. The current findings suggest that ARHGAP9 can be an effective biomarker and potential therapeutic strategy for ccRCC.

Exploring genetic and immune cell dynamics in systemic lupus erythematosus patients with Epstein-Barr virus infection via machine learning.

Epstein-Barr Virus (EBV) is a widespread virus implicated in various diseases, including Systemic Lupus Erythematosus (SLE). However, the specific genes and pathways altered in SLE patients with EBV infection remain unclear. We aimed to identify key genes and immune cells in SLE patients with EBV infection. The datasets of SLE (GSE50772 and GSE81622) or EBV infection (GSE85599 and GSE45918) were obtained from the Gene Expression Omnibus (GEO) database. Next, differential gene expression (DEGs) analysis were conducted to identify overlapping DEGs and then enrichment analysis was performed. Machine learning was applied to identify key genes. Validation was conducted using ROC curve analysis and expression level verification in test datasets and single-cell RNA sequencing. Immune cell infiltration patterns were analyzed using CIBERSORTx, and clinical data were reviewed for SLE patients. We identified 58 overlapping DEGs enriched in interferon-related pathways. Five overlapping DEGs (IFI27, TXK, RAPGEF6, PIK3IP1, PSENEN) were selected as key genes by machine learning algorithms, with IFI27 showing the highest diagnostic performance. The expression level of IFI27 was found higher in CD4 CTL, CD8 naïve and various B cell subsets of SLE patients with EBV infection. IFI27 showed significant correlation with B intermediate and CD4 CT. Clinical data showed lower CD4 T cell proportions in SLE patients with EBV infection. This study identifies IFI27 as a key gene for SLE patients with EBV infection, influencing CD4 CTL and B cell subtypes. These findings enhance the understanding of the molecular mechanisms linking SLE and EBV infection, providing potential targets for diagnostic and therapeutic strategies.

Integrative Analysis of Acupuncture Targets and Immune Genes in Diabetes, Diabetic Peripheral Neuropathy, and Adjunct Therapy of Cancer.

Acupuncture may help treat diabetes mellitus (DM), diabetic peripheral neuropathy (DPN), and adjunct therapy for cancer, but the biological mechanisms and immune-related genes involved are unclear; this study aims to clarify these aspects. Comprehensive gene expression analysis revealed differentially expressed genes (DEGs) among DM, DPN, and control samples. Key genes from WGCNA were intersected with DEGs and acupuncture targets. Inflammatory responses, immune processes, signaling pathways, immune cell infiltration, and microRNA-gene interactions were studied. Hub immune-related genes' dysregulation was analyzed for copy number variation and gene methylation. A pan-cancer nomogram model was created to predict survival based on various factors, linking hub genes to cancer properties. Our analysis found 3,217 and 2,191 DEGs in DM/control and DPN/DM comparisons, respectively, and identified 1,830 potential acupuncture targets. We pinpointed 21 key genes in DM and 43 in DPN, involved in inflammatory responses, immune processes, CAMKK2, and cAMP signaling pathways. Distinct immune cell infiltration patterns, including M0 and M2 macrophages, neutrophils, and follicular helper T cells, were noted. Further analysis revealed microRNAs and TF genes interacting with immune hub genes in both conditions. Dysregulation of eight hub immune-related genes was linked to copy number variation and gene methylation, correlating with cancer prognosis. Co-occurrence of single nucleotide variations and oncogenic mutations was observed in these genes. The pan-cancer nomogram model showed strong prognostic capabilities, and a significant association was found between the eight genes and cancer properties like angiogenesis, EMT, and cell cycle progression. Our findings underscore the pivotal roles of MAPK3, IL1RN, SOD2, CTSD, ESR1, SLC1A1, NPY, and CCR2 in the immune response mediated by acupuncture in the context of DM, DPN, and adjunct therapy for cancer.

Two-step consensus clustering approach to immune cell infiltration: An integrated exploration and validation of prognostic and immune implications in sarcomas

To conduct a comprehensive investigation of the sarcoma immune cell infiltration (ImmCI) patterns and tumoral microenvironment (TME). We utilized transcriptomic, clinical, and mutation data of sarcoma patients (training cohort) obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) server. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithms were applied to decipher the immune cell infiltration landscape and TME profiles of sarcomas. An unsupervised clustering method was utilized for classifying ImmCI clusters (initial clustering) and ImmCI-based differentially expressed gene-driven clusters (secondary clustering). Mortality rates and immune checkpoint gene levels was analyzed among the identified clusters. We calculated the ImmCI score through principal component analysis. The tumor immune dysfunction evaluation (TIDE) score was also employed to quantify immunotherapy efficacy between two ImmCI score groups. We further validated the biomarkers for ImmCI and gene-driven clusters via experimental verification and the accuracy of the ImmCI score in predicting survival outcomes and immunotherapy efficacy by external validation cohorts (testing cohort). We demonstrated that ImmCI cluster A and gene-driven cluster A, were beneficial prognostic biomarkers and indicators of immune checkpoint blockade response in sarcomas via in-silico and laboratory experiments. Additionally, the ImmCI score exhibited independent prognostic significance and was predictive of immunotherapy response. Our research underscores the clinical significance of ImmCI scores in identifying sarcoma patients likely to respond to immunotherapy.

Deciphering the role of heat shock protein HSPA1L: biomarker discovery and prognostic insights in Parkinson's disease and glioma

BackgroundHeat shock proteins (HSPs) play a critical role in cellular stress responses and have been implicated in numerous diseases, including Parkinson's disease (PD) and various cancers. Understanding the differential expression and functional implications of HSPs in these conditions is crucial for identifying potential therapeutic targets and biomarkers for diagnosis and prognosis. MethodsWe utilized combined datasets (GSE6613 and GSE72267) to identify and analyze the heat shock-related genes differentially expressed in PD. Gene Set Variation Analysis (GSVA) was performed to explore functional profiles, while LASSO regression was employed to screen potential PD biomarkers. In glioma, prognostic value, immune infiltration, and pathway enrichment associated with HSPA1L gene expression were assessed via Kaplan-Meier plots, ssGSEA, and enrichment analyses. ResultsIn PD, we identified 17 differentially expressed HSPs. Enrichment analysis revealed significant pathways related to protein homeostasis and cellular stress responses. LASSO regression pinpointed 12 genes, including HSPA1L, as significant markers for PD, with nomogram and calibration plots indicating predictive accuracy. Stratification based on HSPA1L expression in PD highlighted differentially active biological processes, immune responses, and metabolic disruptions. In the pan-cancer analysis, HSPA1L showed variable expression across cancer types and a significant correlation with patient survival and immune infiltration. In glioma, low HSPA1L expression was associated with worse overall survival, distinct immune infiltration patterns, and altered pathway activities. ConclusionThis integrative study reveals the substantial role of HSPs, especially HSPA1L, in the pathogenesis and prognosis of PD and glioma. Our findings offer new perspectives on the molecular mechanisms underlying these diseases and propose HSPA1L as a potential prognostic biomarker and a target for therapeutic intervention.