Activated Memory CD4 Research Articles

Integrated machine learning and Mendelian randomization reveal PALMD as a prognostic biomarker for nonspecific orbital inflammation

Background: Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration. Methods: PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149. Results: Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI. Conclusions: This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

Screening COPD-Related Biomarkers and Traditional Chinese Medicine Prediction Based on Bioinformatics and Machine Learning.

To employ bioinformatics and machine learning to predict the characteristics of immune cells and genes associated with the inflammatory response and ferroptosis in chronic obstructive pulmonary disease (COPD) patients and to aid in the development of targeted traditional Chinese medicine (TCM). Mendelian randomization analysis elucidates the causal relationships among immune cells, genes, and COPD, offering novel insights for the early diagnosis, prevention, and treatment of COPD. This approach also provides a fresh perspective on the use of traditional Chinese medicine for treating COPD. R software was used to extract COPD-related data from the Gene Expression Omnibus (GEO) database, differentially expressed genes were identified for enrichment analysis, and WGCNA was used to pinpoint genes within relevant modules associated with COPD. This analysis included determining genes linked to the inflammatory response in COPD patients and analyzing their correlation with ferroptosis. Further steps involved filtering core genes, constructing TF-miRNA‒mRNA network diagrams, and employing three types of machine learning to predict the core miRNAs, key immune cells, and characteristic genes of COPD patients. This process also delves into their correlations, single-gene GSEA, and diagnostic model predictions. Reverse inference complemented by molecular docking was used to predict compounds and traditional Chinese medicines for treating COPD; Mendelian randomization was applied to explore the causal relationships among immune cells, genes, and COPD. We identified 2443 differential genes associated with COPD through the GEO database, along with 8435 genes relevant to WGCNA and 1226 inflammation-related genes. A total of 141 genes related to the inflammatory response in COPD patients were identified, and 37 core genes related to ferroptosis were selected for further enrichment analysis and analysis. The core miRNAs predicted for COPD include hsa-miR-543, hsa-miR-181c, and hsa-miR-200a, among others. The key immune cells identified were plasma cells, activated memory CD4 T cells, gamma delta T cells, activated NK cells, M2 macrophages, and eosinophils. Characteristic genes included EGF, PLG, PTPN22, and NR4A1. A total of 78 compounds and 437 traditional Chinese medicines were predicted. Mendelian randomization analysis revealed a causal relationship between 36 types of immune cells and COPD, whereas no causal relationship was found between the core genes and COPD. A definitive causal relationship exists between immune cells and COPD, while the prediction of core miRNAs, key immune cells, characteristic genes, and targeted traditional Chinese medicines offers novel insights for the early diagnosis, prevention, and treatment of COPD.

Multi-dataset identification of innovative feature genes and molecular mechanisms in keratoconus.

This study aimed to identify feature genes and explore the molecular mechanisms of keratoconus (KC). We downloaded data files from NCBI GEO public database. The Limma package was used for differential expression analysis of gene profiles. Lasso regression was used to identify the feature genes. The CIBERSORT algorithm was used to infer the proportion of immune-infiltrating cells and analyse the correlation between gene expression levels and immune cells. Related transcription factors and miRNAs of key genes were predicted using the Cistrome DB and Mircode databases. Analysis of expression differences in disease genes was based on the GeneCards database. The CMap was used to analyse targeted therapeutic drugs. IHC was performed to verify the expression levels of ATOH7 and MYRF in corneas. Exactly 593 upregulated and 473 downregulated genes were identified. Lasso regression analysis identified ATOH7, DBNDD1, RNF217-AS1, ARL11, MYRF and SNORA74B as feature genes for KC. All key genes were correlated with immune infiltration and the levels of activated memory CD4+ T cells and plasma cells were significantly increased. miRNA, IRF and STAT families were correlated to feature genes. The expression levels of key genes were significantly correlated to KC-related genes. Entinostat, ochratoxin-a, diphencyprone and GSK-3-inhibitor-II were predicted as potential KC medications. The expression of MYRF was significantly higher in the KC samples, contrary to the expression of ATOH7. KC is related to both immune infiltration and genetic factors. MYRF and ATOH7 were newly identified and verified feature genes of KC.

Construction of a prognostic model for disulfidptosis-related long noncoding RNAs in R0 resected hepatocellular carcinoma and analysis of their impact on malignant behavior

BackgroundDisulfidptosis is an emerging form of cellular death resulting from the binding of intracellular disulfide bonds to actin cytoskeleton proteins. This study aimed to investigate the expression and prognostic significance of hub disulfidptosis-related lncRNAs (DRLRs) in R0 resected hepatocellular carcinoma (HCC) as well as their impact on the malignant behaviour of HCC cells.MethodsA robust signature for R0 resected HCC was constructed using least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression and was validated in an independent internal validation cohort to predict the prognosis of R0 HCC patients. Comprehensive bioinformatics analysis was performed on the hub DRLRs (KDM4A-AS1, MKLN1-AS, and TMCC1-AS1), followed by experimental validation using quantitative real-time polymerase chain reaction (qRT‒PCR) and cellular functional assays.ResultsThe signature served as an independent prognostic factor applicable to R0 HCC patients across different age groups, tumour stages, and pathological characteristics. Gene Ontology (GO) and gene set enrichment analysis (GSEA) revealed hub pathways associated with this signature. The high-risk group presented an increased abundance of M0 macrophages and activated memory CD4 T cells as well as elevated macrophage and major histocompatibility complex (MHC) class I expression. High-risk R0 HCC patients also presented increased tumour immune dysfunction and exclusion scores (TIDEs), mutation frequencies, and tumour mutational burdens (TMBs). Drug sensitivity analysis revealed that high-risk patients were more responsive to drugs, including GDC0810 and osimertinib. High expression levels of the three hub DRLRs were detected in R0 HCC tissues and HCC cell lines. Functional assays revealed that the three hub DRLRs enhanced HCC cell proliferation, migration, and invasion.ConclusionsA signature was constructed on the basis of three DRLRs, providing novel insights for personalized precision therapy in R0 HCC patients.Graphical

Identification and analysis of key genes related to efferocytosis in colorectal cancer

The impact of efferocytosis-related genes (ERGs) on the diagnosis of colorectal cancer (CRC) remains unclear. In this study, efferocytosis-associated biomarkers for the diagnosis of CRC were identified by integrating data from transcriptome sequencing and public databases. Finally, the expression of biomarkers was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Our study may provide a reference for CRC diagnosis.BackgroundIt has been shown that some efferocytosis related genes (ERGs) are associated with the development of cancer. However, it is still uncertain how ERGs may influence the diagnosis of colorectal cancer (CRC).MethodsIn our study, the CRC cohorts were gained from transcriptome sequencing and the gene expression omnibus (GEO) database (GSE71187). Efferocytosis related biomarkers with diagnostic utility for CRC were identified through combining differentially expressed analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. Then, infiltration abundance of immune cells between CRC and control was evaluated. The regulatory networks (including mRNA-miRNA-lncRNA and miRNA/transcription factors (TF)-mRNA networks) were created. Finally, the expression of biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR).ResultsThere were 3 biomarkers (ELMO3, P2RY12, and PDK4) related diagnosis for CRC patients gained. ELMO3 was highly expressed in CRC group, while P2RY12 and PDK4 was lowly expressed. Besides, the infiltrating abundance of 3 immune cells between CRC and control groups was significantly differential, namely activated CD4 memory T cells, macrophages M0, and resting mast cells. We then constructed a mRNA-miRNA-lncRNA network containing 3 mRNAs, 33 miRNAs, and 22 lncRNAs, and a miRNA/TF-mRNA network including 3 mRNAs, 33 miRNAs, and 7 TFs. Additionally, RT-qPCR results revealed that the expression trends of all biomarkers were consistent with the transcriptome sequencing data and GSE71187.ConclusionTaken together, this study provides three efferocytosis related biomarkers (ELMO3, P2RY12, and PDK4) for diagnosis of CRC, providing a scientific reference for further studies of CRC.

Identification and Validation of IFI44 as a Novel Biomarker for Primary Sjögren's Syndrome.

Primary Sjögren's syndrome (pSS) is an autoimmune condition marked by lymphocyte infiltration in the exocrine glands. Our study aimed to identify a novel biomarker for pSS to improve its diagnosis and treatment. The gene expression profiles of pSS were obtained from the Gene Expression Omnibus (GEO) database. The specific differentially expressed genes (DEGs) were screened by the Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest (RF), and Recursive Feature Elimination with Support Vector Machines (SVM-RFE). A biomarker was picked out based on correlation and diagnostic performance, the connection between the biomarker and clinical traits and immune infiltrating cells was explored, and the biomarker's protein expression level in the serum of pSS patients was detected by enzyme-linked immunosorbent assay (ELISA). The competitive endogenous RNA (ceRNA) network regulated by the biomarker was predicted to verify the reliability of the biomarker in diagnosing pSS. IFI44, XAF1, GBP1, EIF2AK2, IFI27, and IFI6 showed prominent diagnostic ability, with the high accuracy (AUC = 0.859) and significance (R ≥ 0.8) of IFI44 within the training dataset. IFI44 strongly exhibited a negative correlation with resting NK cells, macrophages M0, and eosinophils, and a positive correlation with activated dendritic cells, naive B cells, and activated CD4 memory T cells. Furthermore, IFI44 was significantly positively correlated with clinical traits such as IgG, SSA, SSB, ANA, and ESSDAI, with its protein expression level in the serum of pSS patients being notably elevated compared to controls (p < 0.001). Finally, the ceRNA regulatory network showed that hsa-miR-944, hsa-miR-9-5p, hsa-miR-126-5p, and hsa-miR-335-3p were significantly targeted IFI44, suggesting that IFI44 may serve as a dependable biomarker for pSS. In this study, we dug out IFI44 as a biomarker for pSS, systematically studied the potential regulatory mechanism of IFI44, and verified its reliability as a biomarker for pSS.

Single-cell sequencing analysis revealed that WDR72 was a novel cancer stem cells related gene in gastric cancer

BackgroundCancer stem cells (CSCs) are pivotal in tumor resistance to chemotherapy and gastric cancer's rapid proliferation and metastasis. We aimed to explore the CSCs-related genes in gastric cancer epithelial cells. MethodsThe mRNA expression profile and single-cell sequencing data of gastric cancer were downloaded from the public database. ResultsWe identified WDR72 as a CSCs-related gene in gastric cancer epithelial cells. WDR72 was highly expressed in gastric cancer tissues, and high expression of WDR72 was associated with inferior prognosis of patients. WDR72 expression had a significant negative correlation with the infiltration of CD8 + T cells and activated memory CD4 + T cells. PD-L1 expression was significantly reduced in gastric cancer patients with high WDR72 expression. WDR72 was correlated with IC50 of multiple small-molecule drugs. ConclusionWe identified a novel CSCs-related gene in gastric cancer epithelial cells, WDR72, which was highly expressed in patients with high stemness scores.

Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning.

Non-specific Orbital Inflammation (NSOI) is a chronic idiopathic condition marked by extensive polymorphic lymphoid infiltration in the orbital area. The integration of metabolic and immune pathways suggests potential therapeutic roles for C-peptide and G protein-coupled receptor 146 (GPR146) in diabetes and its sequelae. However, the specific mechanisms through which GPR146 modulates immune responses remain poorly understood. Furthermore, the utility of GPR146 as a diagnostic or prognostic marker for NSOI has not been conclusively demonstrated. We adopted a comprehensive analytical strategy, merging differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) datasets GSE58331 and GSE105149 with immune-related genes from the ImmPort database. Our methodology combined LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) for feature selection, followed by Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to explore gene sets co-expressed with GPR146, identifying a significant enrichment in immune-related pathways. The tumor microenvironment's immune composition was quantified using the CIBERSORT algorithm and the ESTIMATE method, which confirmed a positive correlation between GPR146 expression and immune cell infiltration. Validation of GPR146 expression was performed using the GSE58331 dataset. Analysis identified 113 DEGs associated with GPR146, with a significant subset showing distinct expression patterns. Using LASSO and SVM-RFE, we pinpointed 15 key hub genes. Functionally, these genes and GPR146 were predominantly linked to receptor ligand activity, immune receptor activity, and cytokine-mediated signaling. Specific immune cells, such as memory B cells, M2 macrophages, resting mast cells, monocytes, activated NK cells, plasma cells, and CD8+ T cells, were positively associated with GPR146 expression. In contrast, M0 macrophages, naive B cells, M1 macrophages, activated mast cells, activated memory CD4+ T cells, naive CD4+ T cells, and gamma delta T cells showed inverse correlations. Notably, our findings underscore the potential diagnostic relevance of GPR146 in distinguishing NSOI. Our study elucidates the immunological signatures associated with GPR146 in the context of NSOI, highlighting its prognostic and diagnostic potential. These insights pave the way for GPR146 to be a novel biomarker for monitoring the progression of NSOI, providing a foundation for future therapeutic strategies targeting immune-metabolic pathways.

Identification of the differentially expressed activated memory CD4+ T-cells-related genes and ceRNAs in oral lichen planus

BackgroundOral lichen planus (OLP) is a common chronic oral mucosal disease with 1.4 % malignant transformation rate, and its etiology especially immune pathogenesis remains unclear. This study was aimed at investigating the immune cells related molecular underlying the pathophysiology of OLP through bioinformatics analysis. MethodsThe dataset GSE52130 obtained from the Gene Expression Omnibus (GEO) database was conducted a comprehensive analysis in this study. The CIBERSORTx was used for investigating immune cells infiltration. The gene set enrichment analysis (GSEA) and gene ontology (GO) enrichment were performed for exploring the biological functions and gene annotation. The protein-protein interactions (PPI) were constructed by STRING database and visualized by Cytoscape software. The cytohubba plugin was utilized for screening hub genes. The receiver operating characteristic (ROC) was performed for evaluating diagnostic value of hub genes. The miRNAs, lncRNAs and drugs were respectively predicted by NetworkAnalyst, miRTarbase, ENCORI, and DGIdb database. ResultsThis study identified 595 differentially expressed genes (DEGs). The GSEA indicated keratinization, innate immune system and biological oxidation were involved in OLP. GO analysis showed extracellular matrix and keratinocyte were mainly enriched. And we found the activated memory CD4+ T cells were lowly infiltrated in OLP. We identified 101 activated memory CD4+ T-cells-related DEGs. Three hub genes (APP, IL1B, TF) were selected. APP and IL1B were significantly up-regulated, whereas TF was down-regulated in OLP. The three hub genes show high diagnostic value in OLP. Additionally, they were involved in MAPK signal, NF-kappaB signal and iron metabolism in OLP. What's more, NEAT1/XIST - miR - 15a - 5p/miR - 155–5p - APP/IL1B signal axis was focused in competing endogenous RNA (ceRNA) network. In addition, 35 drugs were predicted for OLP. ConclusionThree activated memory CD4+ T-cells-related DEGs were identified by integrative analysis. It may provide novel insight into the pathogenesis of OLP and suggest potential therapeutic targets for OLP.

NGEF is a potential prognostic biomarker and could serve as an indicator for immunotherapy and chemotherapy in lung adenocarcinoma.

Neuronal guanine nucleotide exchange factor (NGEF) plays a key role in several cancers; however, its role in lung adenocarcinoma (LUAD) remains unclear. The aim of this study was to evaluate the efficacy of NGEF as a prognostic biomarker and potential therapeutic target for LUAD. NGEF expression data for multiple cancers and LUAD were downloaded from multiple databases. The high- and low-NGEF expression groups were constructed based on median NGEF expression in LUAD samples, and then performed Kaplan-Meier survival analysis. Differentially expressed genes (DEGs) from the two NGEF expression groups were screened and applied to construct a protein-protein interaction network. The primary pathways were obtained using gene set enrichment analysis. The associations between NGEF expression and clinical characteristics, immune infiltration, immune checkpoint inhibitors (ICIs), sensitivity to chemotherapy, and tumor mutation burden (TMB) were investigated using R. Levels of NGEF expression in the lung tissue was validated using single-cell RNA sequencing, quantitative polymerase chain reaction (qPCR), immunohistochemical staining, and western blot analysis. The expression of NGEF mRNA was upregulated in multiple cancers. mRNA and protein expression levels of NGEF were higher in patients with LUAD than in controls, as validated using qPCR and western blot. High NGEF expression was an independent prognostic factor for LUAD and was associated with advanced tumor stage, large tumor size, more lymph node metastasis, and worse overall survival (OS). A total of 182 overlapping DEGs were screened between The Cancer Genome Atlas and GSE31210, among which the top 20 hub genes were identified. NGEF expression was mainly enriched in the pathways of apoptosis, cell cycle, and DNA replication. Moreover, elevated NGEF expression were associated with a high fraction of activated memory CD4+ T cells and M0 macrophages; elevated expression levels of the ICIs: programmed cell death 1 and programmed cell death 1 ligand 1 expression; higher TMB; and better sensitivity to bortezomib, docetaxel, paclitaxel, and parthenolide, but less sensitivity to axitinib and metformin. NGEF expression is upregulated in LUAD and is significantly associated with tumor stages, OS probability, immune infiltration, immunotherapy response, and chemotherapy response. NGEF may be a potential diagnostic and prognostic biomarker and therapeutic target in LUAD.

Development of a Risk Model and Genotyping Patterns Based on Disulfidptosis-Related lncRNAs to Predict Prognosis and Immune Landscape in Osteosarcoma.

Osteosarcoma (OS) is the most prevalent orthopedic malignancy with a dismal prognosis. Disulfidptosis-related lncRNAs (DRLncs) may be related to the progression of OS, but their potential molecular regulatory role is still unclear. Based on the data collected from The Cancer Genome Atlas (TCGA), we conducted correlation analysis and the univariate Cox analysis to screen prognosis-related DRLncs, followed by developing genotyping patterns and corresponding classifier. Subsequently, the survival analysis, enrichment analysis, drug sensitivity analysis and immune infiltration analysis were performed. Afterward, multivariate Cox regression was used to construct a risk model, which was further validated by the receiver operating characteristic (ROC) curve. The aberrant expression of hub DRLncs in OS was validated using the Reverse Transcription Polymerase Chain Reaction (RT-qPCR) assay. We identified 262 DRLncs and eleven prognosis-related DRLncs through filtering. We then constructed two distinct expression patterns of prognosis-related DRLncs and developed a classifier. We obtained 393 differentially expressed genes (DEGs) between different subtypes, which were significantly enriched in biological processes related to the extracellular matrix, integrin binding, focal adhesion, and Wnt signaling pathways. Through immune infiltration analysis, the activated CD4 memory T cells, resting natural killer (NK) cells, M1 macrophages, and resting dendritic cells (DC) were observed to exhibit different abundance in distinct subtypes. In the drug sensitivity analysis, tamoxifen showed a promising effect for drug-resistant OS. Furthermore, we identified five hub DRLncs and constructed a risk model. The RT-qPCR confirmed the aberrant expression of five hub DRLncs in OS. The present study identified DRLncs in OS, and conducted a comprehensive investigation of DRLncs-related expression patterns, survival status, immune landscape and drug sensitivity to reveal the difference in prognostic, pharmacological and immunological phenotype characteristics between distinct subtypes. Additionally, we developed a risk model to predict the prognosis, and constructed a genotyping classifier to predict the above phenotype characteristics in OS.

Four centrosome-related genes to predict the prognosis and drug sensitivity of patients with colon cancer.

As the primary microtubule organizing center in animal cells, centrosome abnormalities are involved in human colon cancer. To explore the role of centrosome-related genes (CRGs) in colon cancer. CRGs were collected from public databases. Consensus clustering analysis was performed to separate the Cancer Genome Atlas cohort. Univariate Cox and least absolute shrinkage selection operator regression analyses were performed to identify candidate prognostic CRGs and construct a centrosome-related signature (CRS) to score colon cancer patients. A nomogram was developed to evaluate the CRS risk in colon cancer patients. An integrated bioinformatics analysis was conducted to explore the correlation between the CRS and tumor immune microenvironment and response to immunotherapy, chemotherapy, and targeted therapy. Single-cell transcriptome analysis was conducted to examine the immune cell landscape of core prognostic genes. A total of 726 CRGs were collected from public databases. A CRS was constructed, which consisted of the following four genes: TSC1, AXIN2, COPS7A, and MTUS1. Colon cancer patients with a high-risk signature had poor survival. Patients with a high-risk signature exhibited decreased levels of plasma cells and activated memory CD4+ T cells. Regarding treatment response, patients with a high-risk signature were resistant to immunotherapy, chemotherapy, and targeted therapy. COPS7A expression was relatively high in endothelial cells and fibroblasts. MTUS1 expression was high in endothelial cells, fibroblasts, and malignant cells. We constructed a centrosome-related prognostic signature that can accurately predict the prognosis of colon cancer patients, contributing to the development of individualized treatment for colon cancer.

Identification and validation of potential diagnostic signature and immune cell infiltration for HIRI based on cuproptosis-related genes through bioinformatics analysis and machine learning.

Cuproptosis has emerged as a significant contributor in the progression of various diseases. This study aimed to assess the potential impact of cuproptosis-related genes (CRGs) on the development of hepatic ischemia and reperfusion injury (HIRI). The datasets related to HIRI were sourced from the Gene Expression Omnibus database. The comparative analysis of differential gene expression involving CRGs was performed between HIRI and normal liver samples. Correlation analysis, function enrichment analyses, and protein-protein interactions were employed to understand the interactions and roles of these genes. Machine learning techniques were used to identify hub genes. Additionally, differences in immune cell infiltration between HIRI patients and controls were analyzed. Quantitative real-time PCR and western blotting were used to verify the expression of the hub genes. Seventy-five HIRI and 80 control samples from three databases were included in the bioinformatics analysis. Three hub CRGs (NLRP3, ATP7B and NFE2L2) were identified using three machine learning models. Diagnostic accuracy was assessed using a receiver operating characteristic (ROC) curve for the hub genes, which yielded an area under the ROC curve (AUC) of 0.832. Remarkably, in the validation datasets GSE15480 and GSE228782, the three hub genes had AUC reached 0.904. Additional analyses, including nomograms, decision curves, and calibration curves, supported their predictive power for diagnosis. Enrichment analyses indicated the involvement of these genes in multiple pathways associated with HIRI progression. Comparative assessments using CIBERSORT and gene set enrichment analysis suggested elevated expression of these hub genes in activated dendritic cells, neutrophils, activated CD4 memory T cells, and activated mast cells in HIRI samples versus controls. A ceRNA network underscored a complex regulatory interplay among genes. The genes mRNA and protein levels were also verified in HIRI-affected mouse liver tissues. Our findings have provided a comprehensive understanding of the association between cuproptosis and HIRI, establishing a promising diagnostic pattern and identifying latent therapeutic targets for HIRI treatment. Additionally, our study offers novel insights to delve deeper into the underlying mechanisms of HIRI.

Prognostic Cell Death Index for Lung Adenocarcinoma: A Comprehensive Transcriptome-Based Analysis of Twelve Programmed Cell Death Pattern Genes.

Lung adenocarcinoma (LUAD) is a prominent contributor to global cancer mortality, characterized by constrained prognosis. This study aimed to develop a novel prognostic indicator, the Cell Death Index (CDI), utilizing twelve programmed cell death (PCD) pattern genes, to predict the immune infiltration and prognosis in LUAD patients. We collected PCD-related genes and identified prognostic PCD genes in the Cancer Genome Atlas (TCGA)-LUAD dataset, and made rigorous validation in the Clinical Proteomic Tumor Analysis Consortium (CPTAC)-LUAD cohorts. CDI was calculated using a multivariable Cox regression model. Functional enrichment and tumor microenvironment were evaluated. Drug sensitivity prediction and nomogram development were performed to assess CDI's potential value. The results revealed 10 PCD genes (ERO1A, CDK5R1, TRIM6, DNASE2B, ITPRIP, MRGPRX2, FGA, NDUFA13, NLRP2, and CD68) significantly associated with LUAD prognosis. The CDI was constructed and showed high accuracy in predicting patient survival with C-index values of 0.801 and 0.794 in the prognosis cohort and validation cohort, respectively. CDI is also indicative of variations in biological functions, tumor microenvironment, and immune cell infiltration including neutrophils, activated mast cells, activated dendritic cells, M0 macrophages, resting natural killer cells, γδT cells, and activated memory CD4+T cells. Furthermore, drug sensitivity analysis indicated potential targeted strategies. The CDI, based on PCD genes, serves as a robust prognostic tool for LUAD, offering profound insights into tumor biology, immune response, and personalized treatment strategies. This study underscores the pivotal role of PCD mechanisms in LUAD pathogenesis and identifies potential therapeutic targets.

Exploring the autophagy-related pathogenesis of active ulcerative colitis.

The pathogenesis of ulcerative colitis (UC) is complex, and recent therapeutic advances remain unable to fully alleviate the condition. To inform the development of novel UC treatments, bioinformatics was used to explore the autophagy-related pathogenesis associated with the active phase of UC. The GEO database was searched for UC-related datasets that included healthy controls who met the screening criteria. Differential analysis was conducted to obtain differentially expressed genes (DEGs). Autophagy-related targets were collected and intersected with the DEGs to identiy differentially expressed autophagy-related genes (DEARGs) associated with active UC. DEARGs were then subjected to KEGG, GO, and DisGeNET disease enrichment analyses using R software. Differential analysis of immune infiltrating cells was performed using the CiberSort algorithm. The least absolute shrinkage and selection operator algorithm and protein-protein interaction network were used to narrow down the DEARGs, and the top five targets in the Dgree ranking were designated as core targets. A total of 4822 DEGs were obtained, of which 58 were classified as DEARGs. SERPINA1, BAG3, HSPA5, CASP1, and CX3CL1 were identified as core targets. GO enrichment analysis revealed that DEARGs were primarily enriched in processes related to autophagy regulation and macroautophagy. KEGG enrichment analysis showed that DEARGs were predominantly associated with NOD-like receptor signaling and other signaling pathways. Disease enrichment analysis indicated that DEARGs were significantly linked to diseases such as malignant glioma and middle cerebral artery occlusion. Immune infiltration analysis demonstrated a higher presence of immune cells like activated memory CD4 T cells and follicular helper T cells in active UC patients than in healthy controls. Autophagy is closely related to the active phase of UC and the potential targets obtained from the analysis in this study may provide new insight into the treatment of active UC patients.

Abstract 3807: CXCL9:SPP1 macrophage polarity as a prognostic biomarker for localized colorectal cancer after surgical resection

Abstract Background: There is an unmet need for additive predictive biomarkers for the recurrence of stage II/III colorectal cancer (CRC) after surgery. As recent studies showing the potential prognostic role of macrophage polarity defined by CXCL9 and SPP1, instead of conventional M1 and M2, we tried to evaluate the prognostic role of this biomarker in real-world practice setting, delineating biologic mechanisms. Method: The RNA sequencing data of 273 tumors derived from patietns with stage II/III colorectal cancer who underwent surgical resection in 5 Korean cancer-centers were available for analysis. We initially divided two subgroups based on the ratio of expression level of CXCL9 per SPP1 expressions (CS ratio) and evaluated prognostic outcomes and compared immune cell proportions by deconvolutional method between subgroups. Result: The CS ratio was higher in consensus molecular subtype (CMS)2 or CMS3 than others, and no significant differences were observed according to sidedness, stage, microsatellite instability status and medical centers. Total 69 (25,3%) patients were classified as low level of CS ratio group, and there were no significant differences in baseline characteristics, including age, sex, CMS, sidedness, stage, microsatellite instability status and medical centers. With a median follow-up of 58.2 months, the low CS ratio group showed poor 5-year recurrence-free survival (50.6 vs. 71.1%, HR=2.27, P = 0.002) and 5-year overall survival (69.7% vs. 85.9%, HR=1.75, P = 0.033). These trends were consistent across the subgroups. In the multivariate analyses including the potential prognostic factors (P &amp;lt; 0.1 in the univariate analyses), low CS ratio was significantly associated with poorer for RFS (adjusted HR = 1.96, P = 0.011) and OS (adjusted HR = 1.81, P = 0.002). These trends were consistent in the TCGA dataset (5 years OS: 69.7% vs. 85.9%, HR=1.98, P = 0.011). The low CS ratio group showed upregulated WNT signaling, and extracellular matrix organization pathway. Immune cell deconvolutional method exhibited the low CS ratio group showed more proportion of neutrophils, monocytes, whereas CD8+T cells, activated memory CD4+T cells were decreased, compared to high CS ratio group. Conclusions: These results suggest that CS ratio could be an applicable prognostic biomarker in locally advanced colorectal cancer after surgical resection. Based on this classification, more advanced treatment strategy may be required for low CS ratio group to improve surgical outcomes. Citation Format: Yeong Hak Bang, Ji Hye Choi, Hyunsu Kim, Kyunghee Park, Dae Hee Pyo, Yong Beom Cho, Woong-Yang Park. CXCL9:SPP1 macrophage polarity as a prognostic biomarker for localized colorectal cancer after surgical resection [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3807.

Tumor-infiltrating immune cells and survival in head and neck squamous cell carcinoma: a retrospective computational study

The immune infiltration profiles of the tumor microenvironment have effects on the prognosis of head and neck squamous cell carcinoma (HNSCC). Whereas, HNSCC is a heterogeneous group of tumors, but past work has not taken this into consideration. Herein, we investigate the associations between survival and the function of immune cells in different tumorigenic sites of HNSCC. 1149 samples of HNSCC were collected from publicly accessible databases. Based on gene expression data, CIBERSORTx was applied to determine the proportion of 22 immune cell subpopulations. In the Cox regression model, the associations between overall survival, disease-free survival, and immune cells were examined, modeling gene expression and immune cell proportion as quartiles. Consensus cluster analysis was utilized to uncover immune infiltration profiles. Regardless of tumor sites, CD8+ T cells and activated CD4 memory T cells were associated with favorable survival, while eosinophils were the opposite. The survival of the hypopharynx, oral cavity, and larynx subsites was somewhat affected by immune cells, while the survival of the oropharynx subsite potentially was the most impacted. High expression of TIGIT, CIITA, and CXCR6 was linked to better survival, mainly in the oropharynx subsite. Immune cell clusters with four distinct survival profiles were discovered, of which the cluster with a high CD8+ T cell content had a better prognosis. The immune-infiltration pattern is related to the survival of HNSCC to varying degrees depending on the tumor sites; forthcoming studies into immune-mediated infiltration profiles will lay the groundwork for treating HNSCC with precision therapy.

Screening of Genes Associated with Immune Infiltration of Discoid Lupus Erythematosus Based on Weighted Gene Co-expression Network Analysis.

Discoid lupus erythematosus (DLE) is a disorder of the immune system commonly seen in women of childbearing age. The pathophysiology and aetiology are still poorly understood, and no cure is presently available. Therefore, there is an urgent need to explore the underlying molecular mechanisms, as well as search for new therapeutic targets. Gene expression data from skin biopsies samples of DLE patients and healthy controls were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between DLE and healthy control samples were identified by differential expression analysis. Samples were analysed using CIBERSORT to examine the proportion of immune infiltration. Weighted gene co-expression network analysis was used to screen for the module most relevant to immune infiltration. Candidate genes were uploaded to the TRRUST database to obtain the potential transcription factors regulating these genes. Protein-protein interaction (PPI) analysis was performed to obtain the hub genes most associated with immune infiltration among the candidate genes. A total of 273 DEGs were identified between the DLE and healthy control samples. The results of immunoinfiltration analysis showed that the abundances of resting memory CD4 T cells, activated memory CD4 T cells and M1 macrophages were significantly higher, while those of resting infiltration of plasma cells, regulatory T cells and dendritic cells were lower in DLE samples than in healthy control samples. Correlation analysis showed that ISG15, TRIM22, XAF1, IFIT2, OAS2, OAS3, OAS1, IFI44, IFI6, BST2, IFIT1 and MX2 were negatively correlated with the abundances of plasma cells, T-cell regulatory cells and resting dendritic cells and positively correlated with activated memory CD4 T cells and M1 macrophages. Our study shows that these hub genes may regulate DLE via immune-related pathways mediated by the infiltration of these immune cells.

Pan-cancer analysis reveals potential immunological and prognostic roles of METTL7A in human cancers

Methyltransferase-like protein 7A (METTL7A) is an m6A RNA methyltransferase that has been linked to cancer prognosis and drug resistance. However, a comprehensive analysis of METTL7A is lacking. The expression of METTL7A, prognostic performance, correlation with microsatellite instability (MSI), tumor mutational burden (TMB), and immune infiltration was investigated in The Cancer Genome Atlas (TCGA). Immunohistochemistry staining was applied to detect METTL7A in 6 tumors. METTL7A was significantly decreased in 19 cancers in TCGA including LUAD. Alterations of METTL7A include amplification and mutation, and epigenetic alterations revealed increased promoter methylation may result in down-regulation of METTL7A in LUAD. We also found that METTL7A was linked to both TMB and MSI in LUAD. METTL7A was increasingly correlated with invasive immune cells, while being negatively associated with Macrophages M0, Mast cells activated, activated memory CD4 T cells, CD8 T cells, and follicular helper T cells in several tumors. Additionally, METTL7A showed similar correlation with immune therapy-related genes across cancers. Our biological validation found that the protein levels of METTL7A were down-regulated in breast cancer (BRCA), endometrioid cancer (UCEC), colon cancer (COAD), prostate cancer (PRAD), and kidney clear cell carcinoma (KIRC), as detected by immunohistochemistry staining. Overall, our work indicates that METTL7A may serve as promising diagnostic and prognostic indicator of LUAD, and our work sheds light on the potential immunological and prognostic roles of METTL7A in human cancers.