Patterns Of Immune Cell Infiltration Research Articles

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.

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

BackgroundDermatomyositis (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.MethodsGene 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.ResultsInterferon-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.ConclusionOur 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.

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–receptor for advanced glycation end product signaling pathway. Based on protein–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.

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.

Novel prognostic model of complement and coagulation cascade-related genes correlates with immune environment and drug sensitivity in hepatocellular carcinoma

IntroductionHepatocellular carcinoma (HCC) is an immunogenic cancer characterized by high morbidity and mortality rates. The complement and coagulation systems are traditionally associated with the incidence of thrombotic complications and complement activation in cancer. However, the prognostic value of complement and coagulation-related factors (CCCR) in HCC remains undetermined. This study aims to construct a prognostic model based on the complement and coagulation cascades to evaluate its potential for immunotherapy and its relationship with drug sensitivity. Materials and methodsWe comprehensively investigated the expression profiles of CCCR genes using the TCGA, ICGC, and GTEx databases. Cox proportional hazards regression models were employed to assess prognostic value. ResultsThis study presents a novel prognostic model derived from the comprehensive analysis of nine CCCR genes (C1S, C6, C7, F11, F13B, F7, SERPINE1, SERPINF2, and SERPING1) to elucidate their correlation with the tumor immune environment and drug sensitivity in patients with HCC. Our model stratified patients into high- and low-risk groups based on distinct survival outcomes. The area under the curve (AUC) values of the risk score for one-, two-, and three-year survival rates were all greater than 0.660. Additionally, we analyzed immune cell infiltration patterns, revealing a strong correlation between CCCR gene expression and the immune microenvironment, including T cell and macrophage activity. Our findings also identified potential therapeutic targets, demonstrating differential drug sensitivity profiles between the risk groups. JAK1_8709_1718 was found to be more suitable for patients with low-risk HCC. ConclusionOur findings provide promising insights into the clinical relevance of CCCR genes as prognostic markers and therapeutic targets. This study underscores the significance of CCCR in HCC and paves the way for improved therapeutic strategies.

Single-nucleus sequencing unveils heterogeneity in renal cell carcinomas microenvironment: Insights into pathogenic origins and treatment-responsive cellular subgroups

BackgroundDifferent individuals with renal cell carcinoma (RCC) exhibit substantial heterogeneity in histomorphology, genetic alterations in the proteome, immune cell infiltration patterns, and clinical behavior. ObjectivesThis study aims to use single-nucleus sequencing on ten samples (four normal, three clear cell renal cell carcinoma (ccRCC), and three chromophobe renal cell carcinoma (chRCC)) to uncover pathogenic origins and prognostic characteristics in patients with RCC. MethodsBy using two algorithms, inferCNV and k-means, the study explores malignant cells and compares them with the normal group to reveal their origins. Furthermore, we explore the pathogenic factors at the gene level through Summary-data-based Mendelian Randomization and co-localization methods. Based on the relevant malignant markers, a total of 212 machine-learning combinations were compared to develop a prognostic signature with high precision and stability. Finally, the study correlates with clinical data to investigate which cell subtypes may impact patients’ prognosis. Results & conclusionTwo main origin tumor cells were identified: Proximal tubule cell B and Intercalated cell type A, which were highly differentiated in epithelial cells, and three gene loci were determined as potential pathogenic genes. The best malignant signature among the 212 prognostic models demonstrated high predictive power in ccRCC: (AUC: 0.920 (1-year), 0.920 (3-year) and 0.930 (5-year) in the training dataset; 0.756 (1-year), 0.828 (3-year), and 0.832 (5-year) in the testing dataset. In addition, we confirmed that LYVE1+ tissue-resident macrophage and TOX+ CD8 significantly impact the prognosis of ccRCC patients, while monocytes play a crucial role in the prognosis of chRCC patients.

Disulfidptosis-related gene SLC7A11 predicts prognosis and indicates tumor immune infiltration in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is closely associated with factors such as smoking and metabolic disorders. A unique form of cell death known as disulfidptosis, which is regulated by genes like SLC7A11, has emerged as an area of interest; however, its effect on the immune microenvironment in the context of cancer remains largely unexplored. The aim of this study was to analyze the immunoregulatory role of disulfidptosis-related genes in LUAD to unveil and underscore their significance in the process of immune regulation. This study examined the role of disulfidptosis-related genes in LUAD using data from The Cancer Genome Atlas (TCGA) with a particular focus on immune infiltration and the function of SLC7A11. The research employed a clustering analysis, survival analysis, and immune function assessment, integrating both bulk and single-cell RNA sequencing data, to gain a comprehensive understanding of disulfidptosis in LUAD. The analysis revealed three distinct LUAD clusters, each characterized by different survival rates and patterns of immune cell infiltration. Notably, high expression levels of SLC7A11 were associated with a poor prognosis and mechanisms of immune evasion. High SLC7A11 expression is correlated with a poor prognosis and immune evasion in LUAD. These results underscore the significant role of SLC7A11 in the progression of disulfidptosis and LUAD. This study sheds new light on the role of disulfidptosis in LUAD, particularly highlighting the immunoregulatory effects of SLC7A11. The findings suggest that targeting SLC7A11 could lead to the development of novel therapeutic strategies aimed at enhancing the response to immunotherapy in LUAD patients. To substantiate these results, further experimental validation is needed.

Unraveling Shared Diagnostic Biomarkers of Fibromyalgia in Ankylosing Spondylitis: Evidence from Comprehensive Bioinformatic Analysis and Experimental Validation.

Fibromyalgia (FM) is a commonly encountered disease featuring chronic generalized pain, sleep disorder, and physical fatigue. Ankylosing spondylitis (AS) causes chronic lumbodorsalgia involving the sacroiliac joint, often clinically complicated with FM. Nevertheless, the pathophysiology of FM secondary to AS is still lacking. Gene expression data of the whole blood in FM and AS patients were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were evaluated employing the "limma" package. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were implemented to explore common pathways. Weighted gene correlation network analysis (WGCNA) was adopted to screen key gene modules. Three machine learning algorithms were performed to refine the intersected genes. Single sample gene set enrichment analysis (ssGSEA) was applied to explore the relationships between hub genes and immune cells. The dependability of hub gene expressions in clinical blood specimens was verified by RT-PCR. Molecular docking was conducted to predict small molecular compounds targeting hub genes. DEG analysis screened 419 shared up-regulated and 179 shared down-regulated genes in FM and AS. A total of 143 common genes in positive modules of AS and FM were identified via WGCNA. Six key genes (CETN3, CACNA1E, OGT, QRFPR, SCOC, DIAPH1) were obtained by intersecting the WGCNA-derived shared genes and up-regulated DEGs. CETN3 and CACNA1E were refined as hub genes via three machine-learning algorithms and they showed excellent diagnostic value for FM and AS. However, ssGSEA exhibited different immune cell infiltration patterns in FM and AS. Gabapentin enacarbil was recognized as a potential therapeutic drug for AS-FM patients. This study reveals the shared hub genes in AS and FM. Meanwhile, these results were confirmed in clinical samples. CETN3 and CACNA1E may become potential diagnostic biomarkers and therapeutic targets for patients with AS complicated by FM.

Identification and validation of inflammatory subtypes in intrahepatic cholangiocellular carcinoma

BackgroundInflammation plays a critical role in tumor development. Inflammatory cell infiltration and inflammatory mediator synthesis cause changes in the tumor microenvironment (TME) in several cancers, especially in intrahepatic cholangiocellular carcinoma (ICC). However, methods to ascertain the inflammatory state of patients using reliable biomarkers are still being explored.MethodWe retrieved the RNA sequencing and somatic mutation analyses results and the clinical characteristics of 244 patients with ICC from published studies. We performed consensus clustering to identify the molecular subtypes associated with inflammation. We compared the prognostic patterns, clinical characteristics, somatic mutation profiles, and immune cell infiltration patterns across inflammatory subtypes. We performed quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) to confirm gene expression. We performed logistic regression analyses to construct a nomogram predicting the inflammatory status of patients with ICC.ResultsOur results confirmed that ICC can be categorized into an inflammation-high subtype (IHS) and an inflammation-low subtype (ILS). Patients from each group had distinct prognosis, clinical characteristics, and TME composition. Patients with ICC in the IHS group showed poorer prognosis owing to the immunosuppressive microenvironment and high frequency of KRAS and TP53 mutations. Cancer-associated fibroblast (CAF)-derived COLEC11 reduced myeloid inflammatory cell infiltration and attenuated inflammatory responses. The results of qRT-PCR and IHC experiments confirmed that COLEC11 expression levels were significantly reduced in tumor tissues compared to those in paracancerous tissues. Patients with ICC in the IHS group were more likely to respond to treatment with immune checkpoint inhibitors (ICIs) owing to their higher tumor mutational burden (TMB) scores, tumor neoantigen burden (TNB) scores, neoantigen counts, and immune checkpoint expression levels. Finally, we developed a nomogram to effectively predict the inflammatory status of patients with ICC based on their clinical characteristics and inflammatory gene expression levels. We evaluated the calibration, discrimination potential, and clinical utility of the nomogram.ConclusionThe inflammatory response in IHS is primarily induced by myeloid cells. COLEC11 can reduce the infiltration level of this group of cells, and myeloid inflammatory cells may be a novel target for ICC treatment. We developed a novel nomogram that could effectively predict the inflammatory state of patients with ICC, which will be useful for guiding individualized treatment plans.

Identification and experimental validation of KMO as a critical immune-associated mitochondrial gene in unstable atherosclerotic plaque

BackgroundThe heightened risk of cardiovascular and cerebrovascular events is associated with the increased instability of atherosclerotic plaques. However, the lack of effective diagnostic biomarkers has impeded the assessment of plaque instability currently. This study was aimed to investigate and identify hub genes associated with unstable plaques through the integration of various bioinformatics tools, providing novel insights into the detection and treatment of this condition.MethodsWeighted Gene Co-expression Network Analysis (WGCNA) combined with two machine learning methods were used to identify hub genes strongly associated with plaque instability. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was utilized to assess immune cell infiltration patterns in atherosclerosis patients. Additionally, Gene Set Variation Analysis (GSVA) was conducted to investigate the potential biological functions, pathways, and mechanisms of hub genes associated with unstable plaques. To further validate the diagnostic efficiency and expression of the hub genes, immunohistochemistry (IHC), quantitative real-time polymerase chain reaction (RT-qPCR), and enzyme-linked immunosorbent assay (ELISA) were performed on collected human carotid plaque and blood samples. Immunofluorescence co-staining was also utilized to confirm the association between hub genes and immune cells, as well as their colocalization with mitochondria.ResultsThe CIBERSORT analysis demonstrated a significant decrease in the infiltration of CD8 T cells and an obvious increase in the infiltration of M0 macrophages in patients with atherosclerosis. Subsequently, two highly relevant modules (blue and green) strongly associated with atherosclerotic plaque instability were identified. Through intersection with mitochondria-related genes, 50 crucial genes were identified. Further analysis employing least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithms revealed six hub genes significantly associated with plaque instability. Among them, NT5DC3, ACADL, SLC25A4, ALDH1B1, and MAOB exhibited positive correlations with CD8 T cells and negative correlations with M0 macrophages, while kynurenine 3-monooxygenas (KMO) demonstrated a positive correlation with M0 macrophages and a negative correlation with CD8 T cells. IHC and RT-qPCR analyses of human carotid plaque samples, as well as ELISA analyses of blood samples, revealed significant upregulation of KMO and MAOB expression, along with decreased ALDH1B1 expression, in both stable and unstable samples compared to the control samples. However, among the three key genes mentioned above, only KMO showed a significant increase in expression in unstable plaque samples compared to stable plaque samples. Furthermore, the expression patterns of KMO in human carotid unstable plaque tissues and cultured mouse macrophage cell lines were assessed using immunofluorescence co-staining techniques. Finally, lentivirus-mediated KMO silencing was successfully transduced into the aortas of high-fat-fed ApoE-/- mice, with results indicating that KMO silencing attenuated plaque formation and promoted plaque stability in ApoE-/- mice.ConclusionsThe results suggest that KMO, a mitochondria-targeted gene associated with macrophage cells, holds promise as a valuable diagnostic biomarker for assessing the instability of atherosclerotic plaques.

Characteristics of the immune environment in prostate cancer as an adjunct to immunotherapy.

The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the "ComBat" algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score. Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune-associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer-related and immune-related pathways such as pathways involving Toll-like receptors, T-cell receptors, JAK-STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer-relevant signaling pathways, which explain prostate cancer's poor prognosis. The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation

BackgroundLiver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15–20% develop recurrence post-transplant which tends to be aggressive. In this study, we examined the transcriptome profiles of patients with recurrent HCC to identify differentially expressed genes (DEGs), the involved pathways, biological functions, and potential gene signatures of recurrent HCC post-transplant using deep machine learning (ML) methodology.Materials and methodsWe analyzed the transcriptomic profiles of primary and recurrent tumor samples from 7 pairs of patients who underwent LT. Following differential gene expression analysis, we performed pathway enrichment, gene ontology (GO) analyses and protein-protein interactions (PPIs) with top 10 hub gene networks. We also predicted the landscape of infiltrating immune cells using Cibersortx. We next develop pathway and GO term-based deep learning models leveraging primary tissue gene expression data from The Cancer Genome Atlas (TCGA) to identify gene signatures in recurrent HCC.ResultsThe PI3K/Akt signaling pathway and cytokine-mediated signaling pathway were particularly activated in HCC recurrence. The recurrent tumors exhibited upregulation of an immune-escape related gene, CD274, in the top 10 hub gene analysis. Significantly higher infiltration of monocytes and lower M1 macrophages were found in recurrent HCC tumors. Our deep learning approach identified a 20-gene signature in recurrent HCC. Amongst the 20 genes, through multiple analysis, IL6 was found to be significantly associated with HCC recurrence.ConclusionOur deep learning approach identified PI3K/Akt signaling as potentially regulating cytokine-mediated functions and the expression of immune escape genes, leading to alterations in the pattern of immune cell infiltration. In conclusion, IL6 was identified to play an important role in HCC recurrence.

Genomic Insights into the Role of TOP Gene Family in Soft-Tissue Sarcomas: Implications for Prognosis and Therapy.

This study focuses on the role of topoisomerases (TOPs) in sarcomas (SARCs), highlighting TOPs' influence on sarcoma prognosis through mRNA expression, genetic mutations, immune infiltration, and DNA methylation analysis using transcriptase sequencing and other techniques. The findings indicate that TOP gene mutations correlate with increased inflammation, immune cell infiltration, DNA repair abnormalities, and mitochondrial fusion genes alterations, all of which negatively affect sarcoma prognosis. Abnormal TOP expression may independently affect sarcoma patients' survival. Cutting-edge genomic tools such as Oncomine, gene expression profiling interactive analysis (GEPIA), and cBio Cancer Genomics Portal (cBioPortal) are utilizedto explore the TOP gene family (TOP1/1MT/2A/2B/3A/3B) in soft-tissue sarcomas (STSs). This in-depth analysis reveals a notable upregulation of TOP mRNA in STS patients arcoss various SARC subtypes, French Federation Nationale des Centres de Lutte Contre le Cancer classification (FNCLCC) grades, and specific molecular profiles correlating with poorer clinical outcomes. Furthermore, this investigation identifies distinct patterns of immune cell infiltration, genetic mutations, and somatic copy number variations linked to TOP genes that inversely affect patient survival rates. These findings underscore the diagnostic and therapeutic relevance of the TOP gene suite in STSs.

Clinicopathologic and molecular landscape of invasive mucinous adenocarcinoma of the lung.

8037 Background: Invasive mucinous adenocarcinoma (IMA), accounting for 2–10% of lung adenocarcinomas, often mimics pneumonia with multicentric opacities in multiple lobes and both lungs. Notable for its microscopic skip lesions leading to tumor spread through air spaces (STAS), IMA is still under-researched in terms of its comprehensive clinical and genetic characteristics in large, multicenter studies. Methods: Primary lung cancer cases were obtained from the Tempus database, categorized into either IMA or non-IMA, and compared by patient, clinical, biopsy, and molecular characteristics. The normalization of RNA-seq data involved computing transcripts per million (TPM), performing log2 transformations, and adjusting for assay and batch effects. Significantly up- and down-regulated genes were defined as false discovery rate q-values < 0.05 and |log2(fold change)| > 0.5. Results: The study analyzed 18,857 cases, with 655 (3.5%) in the IMA group and 18,202 (96.5%) in the non-IMA group. The IMA group predominantly consisted of older individuals (median age 70 vs. 68; p<0.001), had fewer smokers (63% vs. 73%; p<0.001), and included more individuals of Hispanic or Latino ethnicity (4.3% vs. 2.7%; p=0.007). This group was more likely to be diagnosed with early-stage cancer and less likely to have stage 4 cancer at diagnosis (stage 1: 23% vs. 14%, stage 2: 18% vs. 7.3%, stage 3: 23% vs. 17%, stage 4: 36% vs. 62%; p<0.001). They exhibited significantly lower tumor mutational and neoantigen burdens (median 3.1 vs. 4.6 mutations/Mb and 6 vs. 9 neoantigens/Mb, respectively; both p<0.001), fewer positive PD-L1 statuses (27% vs. 58%; p<0.001), and different immune cell infiltration patterns (Table). Gene expression analysis revealed 416 upregulated and 258 downregulated genes in the IMA group compared to the non-IMA group. Conclusions: This analysis underscores the unique clinicopathologic and molecular characteristics of IMAs. An improved understanding of IMA’s biological behavior could lead to the development of more effective, personalized treatment strategies. [Table: see text]

Characterization of DNA damage repair (DDR) alterations and the tumor immune microenvironment (TIME) in advanced non-small cell lung cancer (NSCLC).

8603 Background: Exploiting the DDR pathway with synthetic lethality agents such as PARP inhibitors has been clinically validated in several cancers. In NSCLC, novel agents leveraging defects in the DDR pathway are being studied alone and in combination with checkpoint inhibitors. To expand on combination approaches and inform biomarker selected strategies, we analyzed DDR alteration prevalence and impact on the TIME in metastatic NSCLC. Methods: We retrospectively analyzed de-identified next-generation sequencing data from patients (pts) with metastatic NSCLC in the Tempus Database. Tumors were sequenced with the Tempus xT DNA and xR RNA assays. DDR pathway genes were defined as previously described (PMID 29617664). Pts were stratified based on the presence (DDRmt) or absence (DDRwt) of a pathogenic somatic alteration or copy number deletion in a DDR pathway gene. The associations of DDR alteration with immune cell infiltration predicted from gene expression patterns, PD-L1 from IHC, TMB (TMB-H, >10 mut/Mb), and microsatellite instability (MSI) were examined. Wilcoxon rank-sum and Pearson’s Chi-squared/Fisher's exact tests were used to assess statistical significance (p<0.05, q<0.05 for false discovery rate correction for multiple testing). Results: Across 14,127 samples, 5,255 (37%) were DDRmt and 8,872 (63%) were DDRwt. The median age at diagnosis was 67 (IQR 60-74). Current/former smokers (83%; n=10,445) had a higher prevalence of DDRmt (86% vs. 82%; p<0.001). The most prevalent DDRmt was SMARCA4 (18%), followed by PTEN (13%), BRCA2 (11%), ATM (9.0%), ERCC3 (6.6%), PMS2 (6.0%), and MUTYH (5.1%). TMB-H was more common (18% [n=931] vs. 11% [n=1001]; p<0.001) and median TMB was higher (5.4 vs. 4.6; p<0.001) in DDRmt pts compared to DDRwt. MSI-H was more prevalent in the DDRmt group (1.1% [n=57] vs. <0.1% [n=7]; p<0.001), while PD-L1 positivity was similar in DDRmt and DDRwt pts (55% [n=1918] vs. 58% [n=3314]; p=0.071). Compared to DDRwt, pts with DDRmt exhibited modest changes in immune cell infiltration patterns (Table). Conclusions: In this large, real-world cohort of pts with metastatic NSCLC, DDRmt was associated with distinct TIME patterns including high TMB, MSI-H, and modest changes in immune cell infiltrates. These findings warrant further studies to understand how DDR pathway alterations mediate changes in the TIME to inform novel combinatorial immunotherapeutic approaches in NSCLC.[Table: see text]

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.