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Results for “Immune infiltration”

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At least 19 recordsLinked to original sources

PubMed2026

HLA class I expression and tumor immune infiltration together shape colon cancer immune contexture.

BACKGROUND: The relative contribution of HLA class I molecules-including classical HLA class Ia (HLA-A, -B, -C) and non-classical HLA class Ib (HLA-E, -F, -G)-to shaping the tumor immune microenvironment in colon cancer remains insufficiently defined. We investigated how their expression patterns relate to immune infiltration and clinical outcome. METHODS: In a retrospective cohort of 280 colon cancers, we assessed HLA class Ia and class Ib expression and quantified CD45-positive immune cell infiltration by immunohistochemistry. These features were correlated with clinicopathological variables, microsatellite instability (MSI) status, and previously established genomic immune signatures. RESULTS: High HLA class Ia expression and increased CD45-positive cell infiltration were each associated with improved overall, disease-specific, and progression-free survival. CD45-positive density correlated strongly with Immunologic Constant of Rejection scores. HLA class Ia loss was more frequent in advanced stages and in MSI-H tumors. Among HLA class Ib molecules, only HLA-E expression was associated with favorable disease-specific and progression-free survival. Integrative analysis identified three immune phenotypes with distinct prognostic profiles; tumors characterized by high HLA class Ia expression, low HLA class Ib expression, and high CD45-positive infiltration had the best outcomes. CONCLUSION: Colon cancer immunogenicity is shaped by coordinated patterns of HLA class I expression and immune infiltration. Integrating HLA class Ia/Ib expression with immune cell density provides a refined stratification of tumor immune phenotypes and may support personalized immunotherapeutic decision-making.

Antigen presentation↗

PubMed2024

Identification of autophagy-related genes as potential biomarkers correlated with immune infiltration in bipolar disorder: a bioinformatics analysis.

BACKGROUND: Bipolar disorder (BPD) is a kind of manic and depressive phase alternate episodes of serious mental illness, and it is correlated with well-documented cortical brain abnormalities. Emerging evidence supports that autophagy dysfunction in neuronal system contributes to pathophysiological changes in neurological disease. However, the role of autophagy in bipolar disorder has rarely been elucidated. This study aimed to identify the autophagy-related gene as a potential biomarker Correlated to immune infiltration in BPD. METHODS: The microarray dataset GSE23848 and autophagy-related genes (ARGs) were downloaded. Differentially expressed genes (DEGs) between normal and BPD samples were screened using the R software. Machine learning algorithms were performed to screen the significant candidate biomarker from autophagy-related differentially expressed genes (ARDEGs). The correlation between the screened ARDEGs and infiltrating immune cells was explored through correlation analysis. RESULTS: In this study, the autophagy pathway was abundantly enriched and activated in BPD, as indicated by Pathway enrichment analysis. We identified 16 ARDEGs in BPD compared to the normal group. A signature of 4 ARDEGs (ERN1, ATG3, CTSB, and EIF2AK3) was screened. ROC analysis showed that the above genes have good diagnostic performance. In addition, immune correlation analysis considered that the above four genes significantly correlated with immune cells in BPD. CONCLUSIONS: Autophagy - immune cell axis mediates pathophysiological changes in BPD. Four important ARDEGs are prospective to be potential biomarkers associated with immune infiltration in BPD and helpful for the prediction or diagnosis of BPD.

Bipolar Disorder↗

PubMed2026

Zhiling Jiangya decoction treats hypertension in rats: An integrative study of network pharmacology, immune infiltration, molecular simulation, and 16S rDNA sequencing.

OBJECTIVE: This study integrated network pharmacology, immune infiltration analysis, molecular docking, molecular dynamics simulation, ADMET prediction, 16S rDNA sequencing, and rat experiments to elucidate the potential mechanisms underlying the antihypertensive effects of Zhiling Jiangya Decoction (ZLJYD). METHODS: Active compounds and their potential targets were screened from the PubChem, TCMSP, NovoPro, and SwissTargetPrediction databases. Hypertension-related targets were retrieved from the OMIM and GeneCards databases, and overlapping targets were identified. The STRING database and Cytoscape 3.10.1 software were used to construct a protein-protein interaction network and a herb-component-target-disease network. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed to identify the key biological processes and signaling pathways involved. Using the CIBERSORT algorithm combined with correlation analysis, we investigated the association between key targets and immune cell infiltration. Molecular docking, molecular dynamics simulations, and ADMET predictions were performed to assess the binding stability and pharmacokinetic properties of the main compounds with their corresponding targets. Finally, the antihypertensive efficacy of ZLJYD was validated using a spontaneously hypertensive rat model, and alterations in gut microbiota were analyzed using 16S rDNA sequencing. RESULTS: A total of 123 active compounds and 267 hypertension-related targets of ZLJYD were identified. Enrichment analysis revealed that these targets were primarily associated with the PI3K-Akt signaling pathway and lipid and atherosclerosis pathways. Immune infiltration analysis suggested that the therapeutic effects of ZLJYD may involve the regulation of follicular helper T cells, naïve B cells, and naïve CD4⁺ T cells. Molecular docking and dynamics simulations supported the stable binding of key compounds to their target proteins, while ADMET predictions indicated favorable pharmacokinetic properties and safety profiles. Rat experiments demonstrated that ZLJYD significantly reduced blood pressure in spontaneously hypertensive rats, partially alleviated gut microbiota dysbiosis, and altered microbial community structure and phylogenetic diversity. CONCLUSION: This study systematically elucidates the potential mechanisms underlying the antihypertensive effects of ZLJYD through multiple components, targets, and pathways, particularly immune regulation and gut microbiota remodeling. These findings provide mechanistic insights into its potential therapeutic application.

16S rDNA sequencing↗

PubMed2023

MYH16 upregulation is associated with lung adenocarcinoma aggressiveness and immune infiltration.

Myosin heavy chain 16 (MYH16) may significantly affect cell cycle progression. Nevertheless, there is a lack of evidence about the clinical relevance of MYH16 upregulation in pan cancers, including lung adenocarcinoma (LUAD). MYH16 expression patterns were evaluated in various bioinformatics databases using The Cancer Genome Atlas data set. Clinical and pathological factor data were employed to risk-stratify patients. The Kaplan-Meier plotter approach was used to estimate survival rates. Tumor immune infiltration was explored via the TIMER tool, and gene set enrichment analysis (GSEA) was used to identify the pathways involved in MYH16 upregulation. The results showed that MYH16 was abnormally upregulated in pan cancers, including LUAD. MYH16 expression induction in LUAD was found to be related to the tumor stage. Furthermore, MYH16 upregulation was correlated with LUAD development and worse overall survival, particularly in women. Notably, MYH16 overexpression in LUAD tissues corresponded to the amount of immune infiltration in the tumor. Additionally, univariate Cox hazard regression analysis revealed that MYH16 may be an independent prognostic indicator for LUAD. Furthermore, a nomogram was constructed according to MYH16 expression and clinical characteristics. BMP6 expression deficiency may be a key factor contributing to MYH16 upregulation in LUAD. Finally, GSEA demonstrated that MYH16 might mediate meiosis and gene silencing through RNA signaling pathways. This study, for the first time, showed that MYH16 upregulation in LUAD is associated with various risk factors, increased cancer aggressiveness, enhanced infiltration of tumor immune cells, and reduced survival rates.

Female↗

PubMed2025

m6A regulator-based molecular classification and hub genes associated with immune infiltration characteristics and clinical outcomes in diffuse gliomas.

BACKGROUND: m6A methylation modification is a new regulatory mechanism involved in tumorigenesis and tumor-immunity interaction. However, its impact on glioma immune microenvironment and clinical outcomes remains unclear. METHODS: Comprehensive expression profiles of 18 m6A regulators were used to identify molecular subtypes exhibiting distinct m6A modification patterns in 1673 glioma samples sourced from public datasets. A multi-genes signature was constructed for predicting clinical outcomes and response to immunotherapy in glioma patients. Immunohistochemistry and cellular experiments were performed for validation. RESULTS: Two m6A subtypes of gliomas were identified. The m6A-low-risk subtype was characterized by paucity of immune infiltrates; While the m6A-high-risk subtype had higher abundances of multiple immune cells including lymphocyte and macrophage as well as increased expression of PD-L1, corresponding to an immunosuppressive phenotype. The m6A-high-risk subtype had poorer survival than the m6A-low-risk subtype in both the glioblastoma and lower grade gliomas cohorts. Eight m6A-related hub genes of high prognostic significances were identified and selected for developing a scoring signature termed as m6Ascore. Elevated m6Ascore indicated worse survival for glioma patients under standard care, but showed enhanced response to immunotherapy. Moreover, we demonstrated that overexpression of FTO, a m6A demethylase, inhibited the expressions of m6A-related hub genes (PTX3, SPAG4), impaired glioma cell viability and reduced macrophage chemotaxis. CONCLUSION: This work develops an immune- and clinical-relevant m6A subtyping and a scoring model, which enhances our understanding of the role of m6A modification in regulating immune infiltration microenvironment in gliomas and helps to identify patients who are more likely to benefit from immunotherapy.

Humans↗

PubMed2024

Comprehensive analysis of the expression, prognostic, and immune infiltration for COL4s in stomach adenocarcinoma.

BACKGROUND: Collagen (COL) genes, play a key role in tumor invasion and metastasis, are involved in tumor extracellular matrix (ECM)-receptor interactions and focal adhesion pathways. However, studies focusing on the diagnostic value of the COL4 family in stomach adenocarcinoma (STAD) are currently lacking. METHODS: The TCGA database was employed to retrieve the clinical features and RNA sequencing expression profiles of patients with STAD. We conducted an investigation to examine the expression disparities between STAD and adjacent normal tissues. Kaplan-Meier survival analysis was utilized to assess their prognostic significance, while Spearman correlation analysis was employed to determine their association with immune checkpoint genes and immunomodulatory molecules. Furthermore, GO and KEGG analyses were performed on the COL4s-related genes, revealing potential biological pathways through gene set enrichment analysis (GSEA). Subsequently, we explored the extent of immune infiltration of the COL4 family in STAD using the TIMER database. Lastly, the expression levels of the COL4 family in STAD were further validated through quantitative PCR (qPCR) and western blot techniques. RESULTS: The expression levels of COL4A1/2 were significantly upregulated, while COL4A5/6 were conspicuously downregulated in STAD. The survival analysis revealed that the upregulated COL4s indicated poorer overall survival, first progression and post-progression survival outcomes. Additionally, our findings demonstrated a positive correlation between the expressions of COL4A1/2/3/4 and the infiltration of immune cells, including CD8 + T cells, dendritic cells, macrophages, neutrophils and CD4 + T cells. Further correlation analysis uncovered a favorable association between the expression of COL4A1/2/3/4 and various crucial immunomodulatory molecules, immunological checkpoint molecules, and chemokines. Quantitative PCR analysis confirmed that the expression patterns of COL4A1/3/4/6 genes aligned with the finding from the TCGA database. However, gastric cancer cells exhibited downregulation of COL4A2. Consistently, the protein level of COL4A1 was elevated, whereas the protein level of COL4A2 was reduced in the gastric cancer cell lines. CONCLUSION: COL4s could potentially serve as biomarkers for diagnosing and predicting the prognosis of STAD.

Stomach Neoplasms↗

PubMed2026

Comprehensive Analysis of Differentially Expressed Genes and Immune Infiltration in Burn Injury: Key Biomarkers and Pathways.

BACKGROUND: Burn injuries trigger complex immune responses and gene expression changes, impacting wound healing and systemic inflammation. Understanding these changes is crucial for identifying biomarkers and therapeutic targets. METHODS: We analyzed two gene expression omnibus datasets (wound tissue [GSE8056] and blood [GSE37069]) to identify differentially expressed genes (DEGs) in burn injury samples versus controls. Immune cell proportions were assessed using CIBERSORT. Functional enrichment analyses (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes) and protein-protein interaction networks were constructed to identify key genes and pathways. RESULTS: We identified 1170 upregulated and 1227 downregulated DEGs. Gene Ontology analysis revealed enrichment in neutrophil activation, inflammatory response, and extracellular matrix organization. Kyoto Encyclopedia of Genes and Genomes analysis highlighted cytokine-cytokine receptor interaction, TNF, and IL-17 signaling pathways. Immune infiltration analysis showed significant changes in neutrophils, macrophages (M1/M2), and T-cell subsets. Protein-protein interaction network analysis identified five hub genes: JUN, STAT1, Bcl2, MMP9, and TLR2. CONCLUSIONS: This study provides a comprehensive bioinformatic analysis of gene expression and immune responses in burn injuries. The identified DEGs, hub genes, and pathways offer insights into the immune response mechanisms and suggest potential targets for diagnostic and therapeutic interventions in burn injury management.

Burns↗

PubMed2026

New insights into diagnostic values and mechanisms of ferroptosis associated with immune infiltration in diabetic kidney disease.

The pathogenesis of diabetic kidney disease (DKD) is complex and closely related to ferroptosis and immune dysregulation, but the relevance is unclear. The present study investigates the potential mechanisms of ferroptosis-related genes (FRGs) in DKD and their relationship with the immune-inflammatory response. It searches for new diagnostic biomarkers to help diagnose and treat DKD. Four Gene Expression Omnibus (GEO) datasets, GSE30528, GSE30529 and GSE30122 as the test set, and GSE96804 for validation, were analyzed. FRGs were obtained from GeneCards, and 47 ferroptosis-related differentially expressed genes (FRDEGs) were identified by intersecting with DKD-related differentially expressed genes. Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, Gene Set Enrichment Analysis and Gene Set Variation Analysis, revealed that these FRDEGs are primarily associated with ferroptosis, hypoxia response and immune inflammation. Subsequently, the weighted gene co-expression network analysis (WGCNA) was employed to expand the ferroptosis-related gene network, and intersection of the 47 FRDEGs with key WGCNA module genes yielded 10 key genes. Based on the 10 key genes, the least absolute shrinkage and selection operator and support vector machine algorithms identified three hub genes [chemokine ligand 5 (CCL5), forkhead box C1 (FOXC1) and lactotransferrin (LTF)] for DKD diagnosis. Receiver operating characteristic curves confirmed their diagnostic value, with FOXC1 and LTF validated in the independent dataset. Immune infiltration analysis via CIBERSORT revealed eight immune cell types with significantly different infiltration levels between the DKD and control group in the integrated GEO datasets. Notably, both LTF and CCL5 showed a significant positive correlation with gamma delta T cells (γδT). Quantitative PCR results confirmed differential expression of the three hub genes in the DKD group, with elevated expression observed in DKD mice following intervention with rosiglitazone and hyperoside.

bioinformatics analysis↗

PubMed2026

RECQL correlates with immune infiltration and serves as a prognostic biomarker and therapeutic predictor in gastric cancer.

BACKGROUND: RecQ-like helicase (RECQL), a member of the RecQ-like DNA helicase family, plays a crucial role in maintaining genomic stability. However, its relevance in gastric cancer (GC) has not been fully investigated. This study aimed to explore the clinical significance, biological functions, and potential role of RECQL in the tumor immune microenvironment of GC through comprehensive bioinformatics analyses and in vitro experiments. METHODS: Weighted gene co-expression network analysis (WGCNA), differential expression analysis, and least absolute shrinkage and selection operator (LASSO) regression were performed using public datasets [The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD), GSE150290] to identify key genes associated with GC progression. Subsequently, key pathways were identified through functional enrichment analysis, while immune infiltration and spatial transcriptomic analyses were conducted to characterize RECQL expression and its association with the tumor immune microenvironment. Finally, the effects of RECQL knockdown on the biological function of GC cells were assessed through Cell Counting Kit-8 (CCK-8), colony formation, scratch, and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assays. RESULTS: RECQL was significantly upregulated in GC tissues and correlated with advanced clinical stage and poor prognosis. Gene set enrichment analysis (GSEA) revealed a strong association between high RECQL expression and DNA repair pathway. Immune infiltration analysis indicated significant enrichment of M2 macrophages in the high-RECQL group, along with upregulation of immune checkpoint molecules including PDCD1, CTLA4, and CD274. Spatial transcriptomics further demonstrated co-localization of RECQL with myeloid cell-enriched regions in tumor parenchymal areas. Furthermore, in vitro experimental results indicated that RECQL was highly expressed in GC cell lines, and its knockdown effectively inhibited the viability, proliferation, and migration capabilities of HGC-27 cells, while enhancing their apoptosis. CONCLUSIONS: RECQL serves as a promising biomarker and potential therapeutic target in GC.

DNA repair↗

PubMed2025

Lactate dehydrogenase a is a crucial biomarker that affects the prognosis, chemotherapy effect, and immune infiltration of breast cancer.

PURPOSE: Lactate dehydrogenase A (LDHA) is a key node in tumor growth, metabolism, and invasion and is upregulated across multiple cancers. However, the molecular mechanisms by which LDHA influences breast cancer (BC) remain unclear. We analyzed public datasets and an institutional cohort to clarify the relationship between LDHA and BC, with the aim of informing future therapeutic strategies. PATIENTS AND METHODS: Using The Cancer Genome Atlas (TCGA), we assessed LDHA expression in BC and examined its associations with tumor mutational burden (TMB), immune cell infiltration, immune checkpoint molecules, and drug sensitivity. We integrated multiple databases and used Kaplan-Meier analyses to evaluate prognostic value. To explore biological functions of LDHA, we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). We then analyzed BC patients receiving neoadjuvant chemotherapy (NAC) at Harbin Medical University Cancer Hospital to test the relationship between serum lactate dehydrogenase (LDH) and pathological complete response (pCR). Finally, we used National Health and Nutrition Examination Survey (NHANES) data to examine the association between serum LDH and mortality. RESULTS: LDHA was upregulated in BC tissues, and higher expression was significantly associated with worse overall survival (OS), recurrence-free survival (RFS), and distant metastasis-free survival (DMFS). Functional analyses indicated enrichment of metabolic pathways, such as glycolysis. LDHA expression correlated with multiple immune cell populations, suggesting involvement in the tumor immune microenvironment. Lower LDHA expression was associated with greater sensitivity to several chemotherapeutic agents. In our institutional cohort, patients with lower serum LDH were more likely to achieve pCR, and LDH was an independent predictor of pCR. In NHANES, elevated serum LDH was linked to increased mortality risk. CONCLUSION: Our findings suggest that LDHA expression and serum LDH levels are promising prognostic biomarkers for survival and may predict chemotherapy response in BC patients. These results highlight the clinical relevance of LDHA-mediated metabolic pathways. However, as a correlational study, our findings warrant further validation through functional experiments to confirm LDHA's role as a potential therapeutic target.

Humans↗

PubMed2025

Construction of a prognostic model for gastric cancer based on immune infiltration and microenvironment, and exploration of MEF2C gene function.

BACKGROUND: Advanced gastric cancer (GC) exhibits a high recurrence rate and a dismal prognosis. Myocyte enhancer factor 2c (MEF2C) was found to contribute to the development of various types of cancer. Therefore, our aim is to develop a prognostic model that predicts the prognosis of GC patients and initially explore the role of MEF2C in immunotherapy for GC. METHODS: Transcriptome sequence data of GC was obtained from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO) and PRJEB25780 cohort for subsequent immune infiltration analysis, immune microenvironment analysis, consensus clustering analysis and feature selection for definition and classification of gene M and N. Principal component analysis (PCA) modeling was performed based on gene M and N for the calculation of immune checkpoint inhibitor (ICI) Score. Then, a Nomogram was constructed and evaluated for predicting the prognosis of GC patients, based on univariate and multivariate Cox regression. Functional enrichment analysis was performed to initially investigate the potential biological mechanisms. Through Genomics of Drug Sensitivity in Cancer (GDSC) dataset, the estimated IC50 values of several chemotherapeutic drugs were calculated. Tumor-related transcription factors (TFs) were retrieved from the Cistrome Cancer database and utilized our model to screen these TFs, and weighted correlation network analysis (WGCNA) was performed to identify transcription factors strongly associated with immunotherapy in GC. Finally, 10 patients with advanced GC were enrolled from Sun Yat-sen University Cancer Center, including paired tumor tissues, paracancerous tissues and peritoneal metastases, for preparing sequencing library, in order to perform external validation. RESULTS: Lower ICI Score was correlated with improved prognosis in both the training and validation cohorts. First, lower mutant-allele tumor heterogeneity (MATH) was associated with lower ICI Score, and those GC patients with lower MATH and lower ICI Score had the best prognosis. Second, regardless of the T or N staging, the low ICI Score group had significantly higher overall survival (OS) compared to the high ICI Score group. For its mechanisms, consistently, for Camptothecin, Doxorubicin, Mitomycin, Docetaxel, Cisplatin, Vinblastine, Sorafenib and Paclitaxel, all of the IC50 values were significantly lower in the low ICI Score group compared to the high ICI Score group. As a result, based on univariate and multivariate Cox regression, ICI Score was considered to be an independent prognostic factor for GC. And our Nomogram showed good agreement between predicted and actual probabilities. Based on CIBERSORT deconvolution analysis, there was difference of immune cell composition found between high and low ICI Score groups, probably affecting the efficacy of immunotherapy. Then, MEF2C, a tumor-related transcription factor, was screened out by WGCNA analysis. Higher MEF2C expression is significantly correlated with a worse OS. Moreover, its higher expression is also negatively correlated with tumor mutation burden (TMB) and microsatellite instability (MSI), but positively correlated with several immunosuppressive molecules, indicating MEF2C may exert its influence on tumor development by upregulating immunosuppressive molecules. Finally, based on transcriptome sequencing data on 10 paired tumor tissues from Sun Yat-sen University Cancer Center, MEF2C expression was significantly lower in paracancerous tissues compared to tumor tissues and peritoneal metastases, and it was also lower in tumor tissues compared to peritoneal metastases, indicating a potential positive association between MEF2C expression and tumor invasiveness. CONCLUSIONS: Our prognostic model can effectively predict outcomes and facilitate stratification GC patients, offering valuable insights for clinical decision-making. The identified transcription factor MEF2C can serve as a biomarker for assessing the efficacy of immunotherapy for GC.

Humans↗

PubMed2024

ZEB family is a prognostic biomarker and correlates with anoikis and immune infiltration in kidney renal clear cell carcinoma.

BACKGROUND: Zinc finger E-box binding homEeobox 1 (ZEB1) and ZEB2 are two anoikis-related transcription factors. The mRNA expressions of these two genes are significantly increased in kidney renal clear cell carcinoma (KIRC), which are associated with poor survival. Meanwhile, the mechanisms and clinical significance of ZEB1 and ZEB2 upregulation in KIRC remain unknown. METHODS: Through the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database, expression profiles, prognostic value and receiver operating characteristic curves (ROCs) of ZEB1 and ZEB2 were evaluated. The correlations of ZEB1 and ZEB2 with anoikis were further assessed in TCGA-KIRC database. Next, miRTarBase, miRDB, and TargetScan were used to predict microRNAs targeting ZEB1 and ZEB2, and TCGA-KIRC database was utilized to discern differences in microRNAs and establish the association between microRNAs and ZEBs. TCGA, TIMER, TISIDB, and TISCH were used to analyze tumor immune infiltration. RESULTS: It was found that ZEB1 and ZEB2 expression were related with histologic grade in KIRC patient. Kaplan-Meier survival analyses showed that KIRC patients with low ZEB1 or ZEB2 levels had a significantly lower survival rate. Meanwhile, ZEB1 and ZEB2 are closely related to anoikis and are regulated by microRNAs. We constructed a risk model using univariate Cox and LASSO regression analyses to identify two microRNAs (hsa-miR-130b-3p and hsa-miR-138-5p). Furthermore, ZEB1 and ZEB2 regulate immune cell invasion in KIRC tumor microenvironments. CONCLUSIONS: Anoikis, cytotoxic immune cell infiltration, and patient survival outcomes were correlated with ZEB1 and ZEB2 mRNA upregulation in KIRC. ZEB1 and ZEB2 are regulated by microRNAs.

Humans↗

PubMed2025

Analysis of ferroptosis-related genes in cerebral ischemic stroke via immune infiltration and single-cell RNA-sequencing.

Ischemic stroke (IS) represents a harmful neurological disorder with limited treatment options. Ferroptosis accounts for the iron-dependent, nonapoptotic cell death pattern, which shows the feature of fatal lipid ROS accumulation. Nonetheless, ferroptosis-related biomarkers for identifying IS early are currently lacking. The present study focused on investigating the possible ferroptosis-related biomarkers for IS and analyzing their effects on immune infiltration. Altogether five hub differentially expressed ferroptosis-related genes (DEFRGs) were identified from the relevant databases. Additionally, single-cell RNA-sequencing (seq) analysis was conducted for the comprehensive mapping of cell populations based on the IS database. These five hub DEFRGs were analyzed using gene set enrichment analysis, miRNA prediction, and single-cell RNA-seq analysis. A transient middle cerebral artery occlusion mouse model was constructed. We also adopted bioinformatics methods combined with western blot, changes to mitochondria, hematoxylin & eosin staining, Nissl staining, ROS fluorescence staining, immunohistochemistry, and quantitative real-time polymerase chain reaction (qRT-PCR) to show the involvement of ferroptosis in IS progression. The results revealed that nuclear factor erythroid-derived 2-like 2 (Nfe2l2) was the potential candidate biomarker for IS diagnosis, and ferroptosis may be suppressed via the Nfe2l2/HO-1 pathway. Thus, drug targeting Nfe2l2 can shed novel lights on IS treatment.

Ferroptosis↗

PubMed2026

Comprehensive analysis suggests CRIF1 is a potential target in breast cancer associated with prognosis and immune infiltration.

BACKGROUND: CRIF1 is a multifunctional factor that regulates cell biological processes such as the cell cycle, cell proliferation, and energy metabolism, and it is a new molecule that contributes to the poor prognosis of many malignancies. However, its involvement in breast cancer development is not fully known. MATERIALS AND METHODS: To investigate the relationship between CRIF1 expression, prognosis, and clinical characteristics using The Cancer Genome Atlas (TCGA-BRCA). The relationship between CRIF1 expression and the immunological microenvironment was investigated using CIBERSORT, ESTIMATE. Breast tissue and CRIF1 expression were validated by IHC. A tiny interfering plasmid was designed to transiently transfect breast cancer cell lines, and proliferation-related functional tests were carried out. The effect of sh CRIF1 on tumor formation was confirmed using a subcutaneous tumor experiment in naked mice. RESULTS: We discovered that CRIF1 was highly elevated in breast cancer tissues and associated with a poor prognosis. CRIF1 stimulates breast cancer cell proliferation, migration, and invasion. Knockdown decreased PI3K/AKT/mTOR signaling, which boosted autophagy activity. Immune infiltration research revealed that patients with high CRIF1 expression had higher CD8+ T cell expression but reduced macrophage M2 expression. CONCLUSION: Upregulation of CRIF1 in breast cancer cells enhances malignant behavior, which may be mediated by PI3K/AKT/mTOR signaling and is linked to cellular autophagy.

Humans↗

PubMed2025

Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration.

OBJECTIVE: Diagnosis of hepatocellular carcinoma (HCC) remains challenging for clinicians. Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers. MATERIALS AND METHODS: In this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms, such as least absolute shrinkage and selection operator, to screen for reliable diagnostic genes. Disease Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis enrichment analyses were performed to explore differential gene functions and disease pathways. CIBERSORT was performed to calculate the immune cell infiltration of HCC and the correlation between diagnostic genes and immune cells. Cell experiments were performed to evaluate the function of R-spondin 3 (RSPO3) in HCC cells. Immunohistochemical staining was used to evaluate the protein expression of CD138, CD206 and iNOS. RESULTS: The results indicated that extracellular matrix protein 1 (ECM1), Niemann-Pick C1-Like 1 (NPC1L1) and RSPO3 were down-regulated in HCC compared with the normal group (p < 0.05), which was validated in clinical tissue samples. Moreover, ECM1, NPC1L1 and RSPO3 had high diagnostic values (AUC > 0.75) for HCC in both training and test groups. Immuno-infiltration analysis revealed that ECM1 and RSPO3 were highly positively correlated with neutrophil and macrophage M2 levels, whereas they were negatively correlated with Tregs. RSPO3-si affected cell proliferation and apoptosis in HCC. Furthermore, RSPO3 exhibited a positive correlation with tumour progression, the proportion of plasma cells and M2 macrophages in mice, while showing a negative association with M1 macrophages. CONCLUSION: The present study identified ECM1, NPC1L1 and RSPO3 as new diagnostic biomarkers for HCC based on normal and diseased samples from HCC, meanwhile the pro-oncogenic function of RSPO3 and its regulation on immune infiltration have been confirmed.

Carcinoma, Hepatocellular↗

PubMed2025

A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involved in the pathogenesis of COPD. From the perspective of predictive, preventive, and personalized medicine (PPPM), clarifying the role of disulfidptosis in the development of COPD could provide a opportunity for primary prediction, targeted prevention, and personalized treatment of the disease. METHODS: We analyzed the expression profiles of DRGs and immune cell infiltration in COPD patients by using the GSE38974 dataset. According to the DRGs, molecular clusters and related immune cell infiltration levels were explored in individuals with COPD. Next, co-expression modules and cluster-specific differentially expressed genes were identified by the Weighted Gene Co-expression Network Analysis (WGCNA). Comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB), we constructed the ptimal machine learning model. RESULTS: DE-DRGs, differential immune cells and two clusters were identified. Notable difference in DRGs, immune cell populations, biological processes, and pathway behaviors were noted among the two clusters. Besides, significant differences in DRGs, immune cells, biological functions, and pathway activities were observed between the two clusters.A nomogram was created to aid in the practical application of clinical procedures. The SVM model achieved the best results in differentiating COPD patients across various clusters. Following that, we identified the top five genes as predictor genes via SVM model. These five genes related to the model were strongly linked to traits of the individuals with COPD. CONCLUSION: Our study demonstrated the relationship between disulfidptosis and COPD and established an optimal machine-learning model to evaluate the subtypes and traits of COPD. DRGs serve as a target for future predictive diagnostics, targeted prevention, and individualized therapy in COPD, facilitating the transition from reactive medical services to PPPM in the management of the disease.

Pulmonary Disease, Chronic Obstructive↗

PubMed2025

The correlation of DPM1 overexpression with immune infiltration and poor prognosis in hepatocellular carcinoma.

BACKGROUND: The DPM1 gene, crucial for glycosylation processes, has shown abnormal expression in various cancers, raising interest in its potential oncogenic role and as a biomarker in hepatocellular carcinoma (HCC). METHODS: Transcriptomic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. DPM1 expression levels were compared between HCC tissues and adjacent normal tissues. Clinical correlations were assessed using statistical analyses, including survival analysis and multivariate Cox regression. Immune microenvironment profiling was conducted to evaluate associations between DPM1 expression and immune cell infiltration patterns. RESULTS: Elevated DPM1 levels were associated with advanced tumor stages (P < 0.001), higher pathologic T stage (P < 0.001), increased histologic grade (P < 0.001), tumor positivity (P < 0.001), tissue inflammation (P < 0.001), and elevated alpha-fetoprotein levels (AFP > 400 ng/mL, P < 0.05). Multivariate Cox regression analysis identified DPM1 as an independent prognostic factor for reduced overall survival (HR = 1.990, 95% CI 1.390-2.848). Immunological analysis revealed that DPM1 expression was positively correlated with T helper cells (R = 0.268, P < 0.001) and Th2 cells (R = 0.295, P < 0.001), and negatively correlated with plasmacytoid dendritic cells (R=-0.291, P < 0.001) and cytotoxic cells (R=-0.284, P < 0.001). CONCLUSIONS: DPM1 serves as a promising prognostic biomarker in HCC, with its expression correlating with unfavorable clinical outcomes and immune landscape alterations. Future studies should further validate DPM1's impact on ferroptosis and immune evasion in HCC, and explore its potential as a therapeutic target.

DPM1↗

PubMed2026

Predicting diagnostic gene biomarkers associated with immune infiltration in patients with diabetes.

Diabetes is a global public health problem with various complications, which can lead to disability and mortality. This study identified potential diagnostic markers for diabetes and explored the immunometabolic mechanisms in the pathological process. The gene expression of 17 diabetes cases and 16 normal controls were obtained from the Gene Expression Omnibus (GEO) database. The "limma" package was employed for screening differentially expressed genes (DEGs). Gene functions and enriched pathways of DEGs were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Candidate key genes were screened using the least absolute shrinkage and selection operator (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analysis. The diagnostic effectiveness of identified markers was further verified via the receiver operating characteristic (ROC) curve. The compositional patterns of immune cell infiltration and signaling pathway enrichment associated with key genes were explored via single sample Gene Set Enrichment Analysis (ssGSEA) and GSEA analysis, respectively. Possible miRNAs interacting with key genes were predicted via miRcode database. B2M, FTL, SH3BGRL3, and SOD2 were recognized as diagnostic markers for diabetes based on LASSO regression and the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm. Analysis of immune cell infiltration demonstrated that the four key genes were related to B cells, neutrophils, macrophages, and CD8+ T cells. The diagnostic value of B2M, FTL, and SOD2 for diabetes was higher than that of SH3BGRL3 according to the ROC curve. Validation experiments indicated that the mRNA expression of B2M and FTL was increased in liver tissues of diabetic mice. B2M and FTL can act as diagnostic markers for diabetes and contribute to new understandings of the disease's molecular mechanisms.

Humans↗