Immune Infiltration Patterns Research Articles

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.

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients

Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model’s predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892–0.903, 0.710–0.894, and 0.850–0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.

Immune Infiltration Correlates with Transcriptomic Subtypes in Primary ER+ 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- invasive lobular breast cancers. The proliferative subtype associated with increased immune infiltration especially by immunosuppressive regulatory T-cells and macrophages. We also defined a TAM-Low signature, which associated with lower infiltration of proliferative, pro-inflammatory TAM, and improved outcome in patients with ER+ tumors.

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.

A prognostic biomarker of disulfidptosis constructed by machine learning framework model as potential reporters of pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD), known for its high lethality, has not been thoroughly explored in terms of its mechanisms and patterns of immune infiltration. Disulfidptosis, a newly identified mode of cell death, is likely associated with tumorigenesis and progression but remains poorly understood in PAAD at the genetic and mechanistic levels. MethodsSixteen PAAD samples from the GSE154778 scRNA-seq dataset were subjected to single-cell analysis. Disulfidptosis grouping and scores were established across various immune cell populations. Using the TCGA-PAAD database, LASSO regression was employed to construct prognostic markers linked to disulfidptosis. The performance of this model was assessed in both training and independent validation cohorts. Subsequent analyses explored the correlations between disulfidptosis scores, immune infiltration, and drug sensitivity. Cellular experiments further confirmed the significant positive relationship of the gene MET with disulfidptosis and its role in influencing the invasion and metastasis of PAAD. ResultsWGCNA identified Disulf-High and Disulf-Low as modules strongly correlated with disulfidptosis. Five prognostically significant genes were selected to construct prognostic models. Survival analysis demonstrated that the disulfidptosis-related biological model successfully achieved prognostic stratification in PAAD patients. Additionally, the disulfidptosis score was significantly correlated with both immune infiltration and drug sensitivity. Knockdown of the MET gene substantially inhibited cell multiplication and cell cycle progression in two PAAD cell lines, effects potentially induced by the activation of the PI3K/AKT signalling pathway in the tumour. ConclusionKey genes associated with disulfidptosis significantly correlate with immune infiltration and the development of PAAD. Biomarkers based on disulfidptosis present potential avenues for novel therapies and clinical treatments in PAAD.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients.

Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all>0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Integrated Single-cell and Bulk RNA Sequencing Analysis Cross Talk between Ferroptosis-related Genes and Prognosis in Oral Cavity Squamous Cell Carcinoma.

Ferroptosis is a new type of programmed apoptosis and plays an important role in tumour inhibition and immunotherapy. In this study, we aimed to explore the potential role of ferroptosis-related genes (FRGs) and the potential therapeutic targets in oral cavity squamous cell carcinoma (OCSCC). The transcription data of OCSCC samples were obtained from the Cancer Genome Atlas (TCGA) database as a training dataset. The prognostic FRGs were extracted by univariate Cox regression analysis. Then, we constructed a prognostic model using the least absolute shrinkage and selection operator (LASSO) and Cox analysis to determine the independent prognosis FRGs. Based on this model, risk scores were calculated for the OCSCC samples. The model's capability was further evaluated by the receiver operating characteristic curve (ROC). Then, we used the GSE41613 dataset as an external validation cohort to confirm the model's predictive capability. Next, the immune infiltration and somatic mutation analysis were applied. Lastly, single-cell transcriptomic analysis was used to identify the key cells. A total of 12 prognostic FRGs were identified. Eventually, 6 FRGs were screened as independent predictors and a prognostic model was constructed in the training dataset, which significantly stratified OCSCC samples into high-risk and low-risk groups based on overall survival. The external validation of the model using the GSE41613 dataset demonstrated a satisfactory predictive capability for the prognosis of OCSCC. Further analysis revealed that patients in the highrisk group had distinct immune infiltration and somatic mutation patterns from low-risk patients. Mast cell infiltrations were identified as prognostic immune cells and played a role in OCSCC partly through ferroptosis. We successfully constructed a novel 6 FRGs model and identified a prognostic immune cell, which can serve to predict clinical prognoses for OCSCC. Ferroptosis may be a new direction for immunotherapy of OCSCC.

PD-L1+ macrophage and tumor cell abundance and proximity to T cells in the pretreatment large B-cell lymphoma microenvironment impact CD19 CAR-T cell immunotherapy efficacy.

CD19-targeted chimeric antigen receptor T-cell (CAR-T) immunotherapy has transformed the management of relapsed/refractory large B-cell lymphoma (LBCL), yet durable remissions are observed in less than half of treated patients. The tumor microenvironment (TME) is a key and understudied factor impacting CD19 CAR-T therapy outcomes. Using NanoString nCounter transcriptome profiling (n = 24) and multiplex immunohistochemistry (mIHC, n = 15), we studied the TME in pretreatment biopsies from patients with LBCL undergoing CD19 CAR-T therapy. Patients who achieved complete response (CR) after CAR-T therapy demonstrated higher expression of genes associated with T-cell trafficking and function, whereas those who did not achieve CR had higher expression of genes associated with macrophages and T-cell dysfunction. Distinct patterns of immune infiltration and fibrosis in the TME were associated with CAR-T therapy outcomes, and these findings were corroborated using artificial intelligence-assisted image analyses. Patients who achieved CR had a lower proportion of the biopsy occupied by an interspersed immune infiltrate and a higher proportion of hypocellular/fibrotic regions. Furthermore, mIHC revealed lower density of CD4+ T cells and higher densities of both macrophages and tumor cells expressing PD-L1 in non-CR patients. Spatial analysis revealed that PD-1+ T cells were in close proximity to PD-L1+ macrophages or PD-L1+ tumor cells in patients who did not compared to those who did achieve CR after CAR-T therapy. These findings suggest that morphologic patterns in the TME and engagement of the PD-1/PD-L1 axis in pretreatment biopsies may impact CD19 CAR-T immunotherapy response in patients with LBCL.

Melatonin reverses EGFR-TKI therapeutic resistance by modulating crosstalk between circadian-related gene signature and immune infiltration patterns in patients with COVID-19 and lung adenocarcinoma

BackgroundPatients with lung cancer exhibit the poorest outcomes when infected with coronavirus disease 2019 (COVID-19). However, the potential impact of COVID-19 on the tumor microenvironment (TME) of lung adenocarcinoma (LUAD) remains unknown. MethodsExpression data and clinical information were sourced from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic, differentially expressed circadian-related genes (CRGs) were identified using multivariate Cox regression and LASSO regression analyses to establish an immune-related gene signature. The clinical value, immune landscape, somatic mutations, and drug sensitivity of high- and low-risk groups were assessed using Kaplan-Meier curves and immunotherapy cohorts. Finally, in vitro and in vivo experiments were conducted to elucidate the molecular function of melatonin in regulating the immune microenvironment and therapeutic resistance. ResultsThree circadian-related patterns and distinct CRGs clusters were identified based on the abnormal expression of 13 CRGs. Circadian genomic phenotypes were identified based on 13 circadian phenotype-related differentially expressed genes (DEGs). A CRGs risk signature was constructed; the high CRGs risk group displayed an immunosuppressive TME, poor survival, and therapy resistance. Melatonin reversed EGFR-tyrosine kinase inhibitor (EGFR-TKI) resistance by regulating immune cell infiltration into the TME, both in vitro and in vivo. ConclusionsThe investigation revealed crosstalk between CRGs signatures and immune infiltration patterns in LUAD and COVID-19. Melatonin acted as a promising agent to suppress the malignant features of lung cancer and enhance treatment sensitivity by modulating the TME.

The chemokine CCL14 is a potential biomarker associated with immune cell infiltration in lung adenocarcinoma

BackgroundChemokine ligand 14, which has a C–C motif (CCL14), mediates the immunological milieu around tumors. However, its role in the progression of lung adenocarcinoma (LUAD) is still unknown. Our objectives were to study the association between CCL14 and tumor-infiltrating immune cells (TIICs) as well as the predictive significance of CCL14 in LUAD.MethodsThe expression of CCL14 in LUAD was examined by using the Oncomine, The Cancer Genome Atlas (TCGA), The University of Alabama at Birmingham CANcer data analysis Portal (UALCAN), and Human Protein Atlas databases. To determine the prognostic significance of CCL14 in LUAD, researchers used the Kaplan‒Meier plotter and Gene Expression Profiling Interactive Analysis (GEPIA, version 2). We utilized TIMER and GEPIA2 to investigate the connection between CCL14 and TIICs. Gene set enrichment analysis (GSEA) was used to test for functional enrichment of genes. We used RT‒qPCR to measure CCL14 expression and Cell Counting Kit-8, Transwell, and wound healing assays to investigate the biological role of CCL14.ResultsThe prognosis of patients with LUAD was worse when CCL14 expression was low. Statistical analysis revealed that CCL14 mRNA expression was significantly greater in lung epithelial cells than in LUAD cell lines in vitro. Enhancing CCL14 expression reduced cell migration, invasion, and proliferation. The results of the immune infiltration research showed that CCL14 and TIICs were positively correlated. Different immune infiltration patterns associated with CCL14 were also shown by TIIC markers. According to GSEA, histone deacetylases, G2/M checkpoints, and Notch signaling pathways were associated with low CCL14 expression.ConclusionsCCL14 is anticipated to emerge as a prognostic marker and therapeutic target for LUAD due to its role in regulating TIICs, suggesting that it may be an antioncogene.

Spatial tumor immune microenvironment phenotypes in ovarian cancer

Immunotherapy has largely failed in ovarian carcinoma (OC), likely due to that the vast tumor heterogeneity and variation in immune response have hampered clinical trial outcomes. Tumor-immune microenvironment (TIME) profiling may aid in stratification of OC tumors for guiding treatment selection. Here, we used Digital Spatial Profiling combined with image analysis to characterize regions of spatially distinct TIME phenotypes in OC to assess whether immune infiltration pattern can predict presence of immuno-oncology targets. Tumors with diffuse immune infiltration and increased tumor-immune spatial interactions had higher presence of IDO1, PD-L1, PD-1 and Tim-3, while focal immune niches had more CD163 macrophages and a preliminary worse outcome. Immune exclusion was associated with presence of Tregs and Fibronectin. High-grade serous OC showed an overall stronger immune response and presence of multiple targetable checkpoints. Low-grade serous OC was associated with diffuse infiltration and a high expression of STING, while endometrioid OC had higher presence of CTLA-4. Mucinous and clear cell OC were dominated by focal immune clusters and immune-excluded regions, with mucinous tumors displaying T-cell rich immune niches.

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.

Comprehensive Analysis of Immune Cell Infiltration and M2-Like Macrophage Biomarker Expression Patterns in Atrial Fibrillation.

Macrophages play a crucial role in the progression of AF, closely linked to atrial inflammation and myocardial fibrosis. However, the functions and molecular mechanisms of different phenotypic macrophages in AF are not well understood. This study aims to analyze the infiltration characteristics of atrial immune cells in AF patients and further explore the role and molecular expression patterns of M2 macrophage-related genes in AF. This study integrates single-cell and large-scale sequencing data to analyze immune cell infiltration and molecular characterization of the LAA in patients with AF, using SR as a control group. CIBERSORT assesses immune cell types in LAA tissues; WGCNA identifies signature genes; cell clustering analyzes cell types and subpopulations; cell communication explores macrophage interactions; hdWGCNA identifies M2 macrophage gene modules in AF. AF biomarkers are identified using LASSO and Random Forest, validated with ROC curves and RT-qPCR. Potential molecular mechanisms are inferred through TF-miRNA-mRNA networks and single-gene enrichment analyses. Myeloid cell subsets varied considerably between the AF and SR groups, with a significant increase in M2 macrophages in the AF group. Signals of inflammation and matrix remodeling were observed in AF. M2 macrophage-related genes IGF1, PDK4, RAB13, and TMEM176B were identified as AF biomarkers, with RAB13 and TMEM176B being novel markers. A TF-miRNA-mRNA network was constructed using target genes, which are enriched in the PPAR signaling pathway and fatty acid metabolism. Over infiltration of M2 macrophages may be an important factor in the progression of AF. The M2 macrophage-related genes IGF1, RAB13, TMEM176B and PDK4 may regulate the progression of AF through the PPAR signaling pathway and fatty acid metabolism.

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.

Deciphering COPS5 influence on immune infiltration and prognosis in head and neck squamous cell carcinoma

Head and Neck Squamous Cell Carcinoma (HNSCC) is a widespread malignancy originating from the mucous epithelium of the oral cavity, pharynx, and larynx. Despite advances in diagnostic and therapeutic modalities, the prognosis of HNSCC remains challenging. This study investigates the intricate relationship among COPS5, immune infiltration patterns, and prognostic implications in HNSCC. Through comprehensive analyses of 519 HNSCC cases from TCGA and single-cell data from the GEO database, we utilize the CIBERSORT algorithm to discern immune cell dynamics influenced by COPS5 expression. Notably, Treg cells emerge as a central point in the interplay between COPS5 and immune modulation. Further analyses, encompassing differential gene expression, immune-related gene set enrichment, and protein-protein interaction networks, elucidate the molecular landscape associated with COPS5 in HNSCC. A prognostic risk model, incorporating CD27, TNFRSF4, FADD, and PSMD14, is formulated and validated across diverse datasets. The model demonstrates robust predictive power, underscoring its potential as a valuable prognostic tool. These genes, essential for immune regulation and cell cycle control, provide insights into the intricate mechanisms influencing HNSCC progression. In conclusion, this study not only reveals the impact of COPS5 on immune dynamics in HNSCC but also introduces a concise and effective prognostic model.

Single-cell and bulk RNA-seq unveils the immune infiltration landscape associated with cuproptosis in cerebral cavernous malformations

BackgroundCerebral cavernous malformations (CCMs) are vascular abnormalities associated with deregulated angiogenesis. Their pathogenesis and optimal treatment remain unclear. This study aims to investigate the molecular signatures of cuproptosis, a newly identified type of cell death, associated with CCMs development.MethodsBulk RNA sequencing (RNA-seq) from 15 CCM and 6 control samples were performed with consensus clustering and clustered to two subtypes based on expression levels of cuproptosis-related genes (CRGs). Differentially expressed genes and immune infiltration between subtypes were then identified. Machine learning algorithms including the least absolute shrinkage and selection operator and random forest were employed to screen for hub genes for CCMs associated with cuproptosis. Furthermore, Pathway enrichment and correlation analysis were used to explore the functions of hub genes and their association with immune phenotypes in CCMs. An external dataset was then employed for validation. Finally, employing the Cellchat algorithm on a single-cell RNA-seq dataset, we explored potential mechanisms underlying the participation of these hub genes in cell-cell communication in CCMs.ResultsOur study revealed two distinct CCM subtypes with differential pattern of CRG expression and immune infiltration. Three hub genes (BTBD10, PFDN4, and CEMIP) were identified and validated, which may significantly associate with CCM pathogenesis. These genes were found to be significantly upregulated in CCM endothelial cells (ECs) and were validated through immunofluorescence and western blot analysis. Single-cell RNA-seq analysis revealed the cellular co-expression patterns of these hub genes, particularly highlighting the high expression of BTBD10 and PFDN4 in ECs. Additionally, a significant co-localization was also observed between BTBD10 and the pivotal cuproptosis gene FDX1 in Mki67+ tip cells, indicating the crucial role of cuproptosis for angiogenesis in CCMs. The study also explored the cell-cell communication between subcluster of ECs expressing these hub genes and immune cells, particularly M2 macrophages, suggesting a role for these interactions in CCM pathogenesis.ConclusionThis study identifies molecular signatures linking cuproptosis to CCMs pathogenesis. Three hub genes—PFDN4, CEMIP, and BTBD10—may influence disease progression by modulating immunity. Further research is needed to understand their precise disease mechanisms and evaluate their potential as biomarkers or therapeutic targets for CCMs.

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]