Immune Cell Infiltration Levels Research Articles

Tumor suppressor ACER1 correlates with prognosis and Immune Infiltration in head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) is notorious for poor prognoses, and effective biomarkers are urgently needed for early diagnosis of HNSCC patients. We investigate the role of alkaline ceramidase 1 (ACER1) and its relationship with immune infiltration in HNSCC. The differential expression and clinical prognostic significance of ACER1 in HNSCC patients are explored using bioinformatics methods and verified in human HNSCC samples. Genetic mutation, DNA methylation and drug sensitivity linked with ACER1 are examined. The potential biological function of ACER1 co-expression genes is assessed, and a series of functional assays are performed on ACER1in vitro. The results comprehensively reveal a relationship between ACER1 and immune infiltration in HNSCC patients. ACER1 expression is significantly downregulated in HNSCC tissues and closely correlated with better prognoses for HNSCC patients, and this prognostic significance is determined by distinct clinical characteristics. Genetic alteration and promoter hypomethylation of ACER1 are involved in progression of HNSCC, and ACER1 expression is significantly related to several drug sensitivities. Functional analysis shows that ACER1 co-expression genes are mainly enriched in the sphingolipid signaling pathway associated with inhibition of tumorigenesis, leading to better prognoses for HNSCC patients. In vitro, ACER1 overexpression inhibits proliferation and migration, induces apoptosis, and promotes adhesion of Fadu and SCC9 cells. In addition, high ACER1 expression is closely linked with infiltration levels of immune cells, and strongly associated with biomarkers of immune cells in HNSCC, suggesting the important role of ACER1 in regulating tumor immunity in HNSCC patients. In summary, ACER1 may be a useful indicator for diagnosis and prognosis, and may regulate immune infiltration in HNSCC patients, thus promising targeted immunotherapy for HNSCC.

Discovery of key molecular signatures for diagnosis and therapies of glioblastoma by combining supervised and unsupervised learning approaches.

Glioblastoma (GBM) is the most malignant brain cancer and one of the leading causes of cancer-related death globally. So, identifying potential molecular signatures and associated drug molecules are crucial for diagnosis and therapies of GBM. This study suggested GBM-causing ten key genes (ASPM, CCNB2, CDK1, AURKA, TOP2A, CHEK1, CDCA8, SMC4, MCM10, and RAD51AP1) from nine transcriptomics datasets by combining supervised and unsupervised learning results. Differential expression patterns of key genes (KGs) between GBM and control samples were verified by different independent databases. Gene regulatory network (GRN) detected some important transcriptional and post-transcriptional regulators for KGs. The KGs-set enrichment analysis unveiled some crucial GBM-causing molecular functions, biological processes, cellular components, and pathways. The DNA methylation analysis detected some hypo-methylated CpG sites that might stimulate the GBM development. From the immune infiltration analysis, we found that almost all KGs are associated with different immune cell infiltration levels. Finally, we recommended KGs-guided four repurposable drug molecules (Fluoxetine, Vatalanib, TGX221 and RO3306) against GBM through molecular docking, drug likeness, ADMET analyses and molecular dynamics simulation studies. Thus, the discoveries of this study could serve as valuable resources for wet-lab experiments in order to take a proper treatment plan against GBM.

Prognosis and immunotherapy significances of a cancer-associated fibroblasts-related gene signature in bladder urothelial carcinoma.

The biological significance of cancer-associated fibroblasts (CAFs) in bladder urothelial carcinoma (BUC) warrants further investigation. There is an urgent need to explore the predictive utility of CAF-related genes for prognosis in BUC. The transcriptome and clinical data of 407BUC patients in The Cancer Genome Atlas (TCGA) database were analyzed and a prognostic model was established. A total of 476 BUC cases from the E-MTAB-4321 database were used for validation. A risk model was constructed utilizing CAF-related genes through LASSO Cox regression, investigating its association with prognosis, gene mutations, immune cell infiltration, and drug sensitivity in BUC. We identified five CAF-related genes (EGFL6, NRSN2, SEMA3D, TM4SF1 and TPST1) in both the TCGA and E-MTAB-4321 datasets, and established a prognostic model using LASSO Cox regression. The high-risk group showed a significant correlation with poor survival. Furthermore, the low-risk group exhibited higher tumor mutational burden and lower levels of immune cell infiltration, and this model holds promise for guiding drug selection in BUC patients. These findings underscore the pivotal role of CAF-related genes in prognostic prediction for BUC patients. Clinical decision-making and tailored therapeutics stand to benefit from these results, providing a valuable reference for future research endeavors.

Discovery of five diagnostic biomarkers associated with immune cell infiltration in cases of acute myocardial infarction.

Acute myocardial infarction (AMI) remains one of the leading causes of mortality and morbidity worldwide. GSE61144 and GSE66360 were the sources of microarray gene expression profiles for acute myocardial infarction patients and were acquired from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). After merging the datasets, genes that were differentially expressed were chosen. A total of 234 genes were found to have different expression levels. Of these, 206 genes were upregulated, and 28 genes were downregulated. Five coexpression modules were identified by WGCNA, with the yellow module showing a high correlation with AMI (r=0.65, P=2.0e-15). Ninety-two hub genes were selected in the yellow module by setting a threshold of module membership (MM) greater than 0.8 and gene significance (GS) higher than 0.4. By overlapping these genes with the differentially expressed genes, 81 hub genes were obtained. Five key genes (C5AR1, CXCL1, CXCL2, FPR1, and P2RY13) were identified through PPI analysis. AMI patients exhibited elevated levels of immune cell infiltration, and immune scores in AMI samples were significantly positively correlated with all five key genes. Moreover, the expression levels of these five genes were higher in AMI patients. These five genes possessed area under the curve (AUC) values exceeding 0.8 for diagnosing AMI, thereby demonstrating their efficacy as diagnostic markers. C5AR1, CXCL1, CXCL2, FPR1, and P2RY13 have the potential to be useful biomarkers in diagnosing AMI and are linked to immune cell infiltration in AMI, opening up new avenues for future research into the pathogenesis of AMI.

TMEM173 is a biomarker of predicting prognosis, immune responses and therapeutic effect in human lung adenocarcinoma

The concerns on the function of Transmembrane protein 173 (TMEM173)-dependent innate immunity in prevention and management of cancers has recently been increased. The role of TMEM173 in predicting the prognosis and response to treatment in lung adenocarcinoma (LUAD) remain unclear. Our study revealed that TMEM173 expression was significantly differential in various tumors and the related prognosis was heterogeneous. Further investigation discovered that the expression level of TMEM173 in LUAD tissues was significantly decreased and high TMEM173 expression is associated with better overall survival in LUAD patients. TMEM173 was mainly enriched in immune response-regulating signaling pathway, T cell activation and cell cycle G2/M phase. Furthermore, it was found that TMEM173 expression was positively related to markers and infiltration levels of tumor-infiltrating immune cells. TMEM173 could predict response to targeted therapy, chemotherapy and immunotherapy in LUAD patients. In vitro TMEM173 knockdown decreased the percentage of G2 phase cells, contributing to the increased growth of lung cancer cells. These results implied that TMEM173 might be a prognostic biomarker and a potential target of precision therapy for LUAD patients.

Integrated analysis of bulk and single-cell RNA sequencing reveals the impact of nicotinamide and tryptophan metabolism on glioma prognosis and immunotherapy sensitivity

BackgroundNicotinamide and tryptophan metabolism play important roles in regulating tumor synthesis metabolism and signal transduction functions. However, their comprehensive impact on the prognosis and the tumor immune microenvironment of glioma is still unclear. The purpose of this study was to investigate the association of nicotinamide and tryptophan metabolism with prognosis and immune status of gliomas and to develop relevant models for predicting prognosis and sensitivity to immunotherapy in gliomas.MethodsBulk and single-cell transcriptome data from TCGA, CGGA and GSE159416 were obtained for this study. Gliomas were classified based on nicotinamide and tryptophan metabolism, and PPI network associated with differentially expressed genes was established. The core genes were identified and the risk model was established by machine learning techniques, including univariate Cox regression and LASSO regression. Then the risk model was validated with data from the CGGA. Finally, the effects of genes in the risk model on the biological behavior of gliomas were verified by in vitro experiments.ResultsThe high nicotinamide and tryptophan metabolism is associated with poor prognosis and high levels of immune cell infiltration in glioma. Seven of the core genes related to nicotinamide and tryptophan metabolism were used to construct a risk model, and the model has good predictive ability for prognosis, immune microenvironment, and response to immune checkpoint therapy of glioma. We also confirmed that high expression of TGFBI can lead to an increased level of migration, invasion, and EMT of glioma cells, and the aforementioned effect of TGFBI can be reduced by FAK inhibitor PF-573,228.ConclusionsOur study evaluated the effects of nicotinamide and tryptophan metabolism on the prognosis and tumor immune microenvironment of glioma, which can help predict the prognosis and sensitivity to immunotherapy of glioma.

Single-cell and bulk transcriptomic datasets enable the development of prognostic models based on dynamic changes in the tumor immune microenvironment in patients with hepatocellular carcinoma and portal vein tumor thrombus.

Hepatocellular carcinoma (HCC) patients exhibiting portal vein tumor thrombosis (PVTT) face a high risk of rapid malignant progression and poor outcomes, with this issue being compounded by a lack of effective treatment options. The integration of bulk RNA-sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) datasets focused on samples from HCC patients with PVTT has the potential to yield unprecedented insight into the dynamic changes in the tumor microenvironment (TME) and associated immunological characteristics in these patients, providing an invaluable tool for the reliable prediction of disease progression and treatment responses. scRNA-seq data from both primary tumor (PT) and PVTT cells were downloaded from the Gene Expression Omnibus (GEO) database, while the International Cancer Genome Consortium (ICGC) and Cancer Genome Atlas (TCGA) databases were used to access bulk RNA-seq datasets. scRNA-seq, clustering, GSVA enrichment, mutational profiling, and predictive immunotherapeutic treatment analyses were conducted using these data with the goal of systematically assessing the heterogeneity of PT and PVTT cells and establishing a model capable of predicting immunotherapeutic and prognostic outcomes in patients with HCC. These analyses revealed that PVTT cells exhibited patterns of tumor proliferation, stromal activation, and low levels of immune cell infiltration, presenting with immune desert and immune rejection-like phenotypes. PT cells, in contrast, were found to exhibit a pattern of immunoinflammatory activity. Core PVTT-associated genes were clustered into three patterns consistent with the tumor immune rejection and immune desert phenotypes. An established clustering model was capable of predicting tumor inflammatory stage, subtype, TME stromal activity, and patient outcomes. PVTT signature genes were further used to establish a risk model, with the risk scores derived from this model providing a tool to evaluate patient clinicopathological features including clinical stage, tumor differentiation, histological subtype, microsatellite instability status, and tumor mutational burden. These risk scores were also able to serve as an independent predictor of patient survival outcomes, responses to adjuvant chemotherapy, and responses to immunotherapy. In vitro experiments were used to partially validate the biological prediction results. These results offer new insight into the biological and immunological landscape of PVTT in HCC patients, By utilizing individual patient risk scores, providing an opportunity to guide more effective immunotherapeutic interventional efforts.

Kiss1 receptor knockout exacerbates airway hyperresponsiveness and remodeling in a mouse model of allergic asthma

BackgroundIn asthma, sex-steroids signaling is recognized as a critical regulator of disease pathophysiology. However, the paradoxical role of sex-steroids, especially estrogen, suggests that an upstream mechanism or even independent of estrogen plays an important role in regulating asthma pathophysiology. In this context, in our previous studies, we explored kisspeptin (Kp) and its receptor Kiss1R’s signaling in regulating human airway smooth muscle cell remodeling in vitro and airway hyperresponsiveness (AHR) in vivo in a mouse (wild-type, WT) model of asthma. In this study, we evaluated the effect of endogenous Kp in regulating AHR and remodeling using Kiss1R knockout (Kiss1R−/−) mice.MethodsC57BL/6J WT (Kiss1R+/+) and Kiss1R−/− mice, both male and female, were intranasally challenged with mixed-allergen (MA) and/or phosphate-buffered saline (PBS). We used flexiVent analysis to assess airway resistance (Rrs), elastance (Ers), and compliance (Crs). Following this, broncho-alveolar lavage (BAL) was performed for differential leukocyte count (DLC) and cytokine analysis. Histology staining was performed using hematoxylin and eosin (H&E) for morphological analysis and Masson’s Trichrome (MT) for collagen deposition. Additionally, lung sections were processed for immunofluorescence (IF) of Ki-67, α-smooth muscle actin (α-SMA), and tenascin-c.ResultsInterestingly, the loss of Kiss1R exacerbated lung function and airway contractility in mice challenged with MA, with more profound effects in Kiss1R−/− female mice. MA-challenged Kiss1R−/− mice showed a significant increase in immune cell infiltration and proinflammatory cytokine levels. Importantly, the loss of Kiss1R aggravated Th2/Th17 biased cytokines in MA-challenged mice. Furthermore, histology of lung sections from Kiss1R−/− mice showed increased collagen deposition on airway walls and mucin production in airway cells compared to Kiss1R+/+ mice. In addition, immunofluorescence analysis showed loss of Kiss1R significantly aggravated airway remodeling and subsequently AHR.ConclusionsThese findings demonstrate the importance of inherent Kiss1R signaling in regulating airway inflammation, AHR, and remodeling in the pathophysiology of asthma.

C6 and KLRG2 are pyroptosis subtype-related prognostic biomarkers and correlated with tumor-infiltrating lymphocytes in lung adenocarcinoma

Pyroptosis plays an important role in lung adenocarcinoma (LUAD). In this study, we aimed to explore the pyroptosis-related gene (PRG) expression pattern and to identify promising pyroptosis-related biomarkers to improve the prognosis of LUAD. The gene expression profiles and clinical information of LUAD patients were downloaded from the Cancer Genome Atlas (TCGA), and validation cohort information was extracted from the Gene Expression Omnibus database. Gene expression data were analyzed using the limma package and visualized using the ggplot2 package as well as the pheatmap package in R software. Functional enrichment analysis was also performed for the 44 differentially expressed PRGs (DEPRGs). Then, consensus clustering revealed pyroptosis-related tumor subtypes, and differentially expressed genes (DEGs) were screened according to the subtypes. Next, univariate Cox and multivariate Cox regression analyses were used to identify independent prognostic PRGs. After overlapping DEGs and the Lasso regression analysis-based prognostic genes, the predictive risk model was established and validated. Correlation analysis between PRGs and clinicopathological variables was also explored. Finally, the TIMER and TISIDB databases were used to further explore the correlation analysis between immune cell infiltration levels, the risk score, and clinicopathological variables in the predictive risk model. A total of 52 genes from the PubMed were identified as PRGs, and 44 of the 52 genes were pooled as DEPRGs. The most significant GO term was “collagen trimer” (P = 2.46E-13), and KEGG analysis results indicated that 44 DEPRGs were significantly enriched in Salmonella infection (P < 0.001). Then, consensus clustering analysis divided LUAD patients into two clusters, and a total of 79 DEGs were identified according to these cluster subtypes. Subsequently, univariate and multivariate Cox regression analyses were used to identify 12 genes that could serve as independent prognostic indicators and we also performed Lasso regression analysis and screened 23 DEGs. After overlapping 23 DEGs and 12 genes, only 4 (KLRG2, MAPK4, C6 and SFRP5) of 12 genes were selected for the further exploration of the prognostic pattern. Survival analysis results indicated that this risk model effectively predicted the prognosis (P < 0.001). Combined with the correlation analysis results between the 4 genes and clinicopathological variables, C6 and KLRG2 were screened as prognostic genes. In this study, we constructed a predictive risk model and identified two pyroptosis subtype-related gene expression patterns to improve the prognosis of LUAD. Understanding the subtypes of LUAD is helpful for accurately characterizing the LUAD and developing personalized treatment.

Multiple Machine Learning Models, Molecular Subtyping and Singlecell Analysis Identify PANoptosis-related Core Genes and their Association with Subtypes in Crohn's Disease.

PANoptosis plays an important role in many inflammatory diseases. However, there are no reports on the association between PANoptosis and CD. This study used five machine learning algorithms - least absolute shrinkage and selection operator, support vector machine, random forest, decision tree and Gaussian mixture models - to construct CD's PANoptosis signature. Unsupervised hierarchical clustering analysis was used to identify PANoptosis-associated subgroups of CD. Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted to compare the PANoptosis-associated subgroups of CD among the potential biological mechanisms. Single sample GSEA was used to assess immune microenvironmental differences among the subgroups. The potential role of PANoptosis in CD was further explored using single-cell RNA-Seq (scRNA-Seq) for PANoptosis scoring, differential analysis, pseudotime analysis, cellular communication analysis and weighted gene co-expression network analysis (WGCNA) analysis. CD's PANoptosis signature consisted of seven genes: CEACAM6, CHP2, PIK3R1, CASP10, PSMB1, PSMB8 and UBC. The PANoptosis signature in multiple cohorts had a strong ability to recognise CD. The levels of immune cell infiltration and the vigour of the immune responses significantly varied between the two subpopulations of CD associated with PANoptosis. Multiple lines of evidence from the GO, KEGG, GSEA, GSVA, scRNA-Seq and WGCNA analyses suggested that I-kappaB kinase/NF- kappaB signalling, mitogen-activated protein kinase (MAPK), leukocyte activation and leukocyte migration were mechanisms closely associated with PANoptosis in CD. This study is the first to construct a PANoptosis signature with excellent efficacy in recognising CD. PANoptosis may mediate the process of CD through inflammatory and immune mechanisms, such as NF- kappaB, MAPK and leukocyte migration.

Construction and multiple validations of a robust ferroptosis-related prognostic model in bladder cancer: A comprehensive study.

Ferroptosis is iron-dependent programmed cell death that inhibits tumor growth, particularly in traditional treatment-resistant tumors. Prognostic models constructed from ferroptosis-related genes are lacking; prognostic biomarkers remain insufficient. We acquired gene expression data and corresponding clinical information for bladder cancer (BC) samples from public databases. Ferroptosis-related genes from the ferroptosis database were screened for clinical predictive value. We validated gene expression differences between tumors and normal tissues through polymerase chain reaction and western blotting. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were conducted to explore signaling pathways affecting the overall survival of patients with BC. CIBERSORT was used to quantify the infiltration of 22 immune cell types. We identified 6 genes (EGFR, FADS1, ISCU, PGRMC1, PTPN6, and TRIM26) to construct the prognostic risk model. The high-risk group had a poorer overall survival than the low-risk group. Receiver operating characteristic curves demonstrated excellent predictive accuracy. The validation cohort and 3 independent datasets confirmed the models' general applicability and stability. BC tissues had elevated FADS1, PTPN6, and TRIM26 mRNA and protein levels and decreased ISCU levels. Enrichment analysis indicated that neurosecretory activity might be the main pathway affecting the overall survival. High- and low-risk groups had significantly different immune cell infiltration. Specific ferroptosis-related gene expression was associated with immune cell infiltration levels. The risk score was significantly correlated with patients' clinical characteristics. A novel, widely applicable risk model with independent predictive value for the prognosis of patients with BC was established; candidate molecules for future BC research were identified.

Pancancer analysis of NDUFA4L2 with focused role in tumor progression and metastasis of colon adenocarcinoma.

Colon adenocarcinoma (COAD) is a prevalent gastrointestinal malignant disease with a high mortality rate, and identification of novel prognostic biomarkers and therapeutic targets is urgently needed. Although NDUFA4L2 has high expressions in various tumors and affects tumor progression, its role in COAD remains unclear. The role of NDUFA4L2 in COAD was analyzed utilizing datasets available from public databases including The Cancer Genome Atlas, The Genotype-Tissue Expression (GTEx), Gene Expression Omnibus, Alabama Cancer Database (UALCAN), and The Human Protein Atlas databases. The prognostic value of NDUFA4L2 was determined using Kaplan-Meier analysis and Cox regression analysis. To investigate the possible mechanism underlying the role of NDUFA4L2 in COAD, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were employed. The correlation between NDUFA4L2 expression and immune cell infiltration levels was examined through single-sample gene set enrichment analysis (ssGSEA). The NDUFA4L2 expression levels in COAD patients and cell lines were validated through immunohistochemistry, immunofluorescence, qRT-PCR, and Western blot. Wound healing assay was also performed to evaluate the effect of NDUFA4L2 on COAD metastasis. Furthermore, the NDUFA4L2 mediated competing endogenous RNA (ceRNA) regulatory network was predicted and constructed through a variety of databases. The comprehensive pan-cancer analysis showed that NDUFA4L2 possesses diagnostic and prognostic value in many cancers, especially in COAD. GO-KEGG and GSEA analyses indicated that NDUFA4L2 was associated with multiple biological functions including epithelial-mesenchymal transition and adaptation to hypoxia. The ssGSEA analysis showed that NDUFA4L2 expression was associated with immune infiltration. In vitro experiments confirmed upregulation of NDUFA4L2 in COAD tissues and cell lines, and NDUFA4L2 overexpression significantly promoted migration of COAD cells. In addition, the C9orf139 /miR-194-3p axis was speculated as the possible upstream regulators of NDUFA4L2 in COAD. This study demonstrated that NDUFA4L2 upregulation was correlated with tumor progression, relapsed prognosis and aggressive migration of COAD, suggesting that NDUFA4L2 can act as an effective prognostic biomarker and a promising therapeutic target for COAD treatment.

Identification of necroptosis-related gene expression and the immune response in polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a common reproductive and endocrine disorder; however, the understanding of the pathogenesis of PCOS remains unclear. Necroptosis is a newly discovered mechanism of cell death, and it is closely related to reproductive endocrine-related diseases. This study aimed to investigate the hub necroptosis-related genes in PCOS patients and its correlation with immune cell infiltration by bioinformatics methods. The gene expression chip result matrix and the annotation matrix files of the GSE34526, GSE8157, and GSE5090 datasets were downloaded from the GEO database. We analyzed the expression and correlation of the necroptosis-related genes in all samples, constructed a diagnostic model based on all necroptosis-related genes and genes with significant differences, performed unsupervised clustering of samples and gene enrichment analysis, and evaluated the correlations between the hub gene and immune cell infiltration levels by the R packages GSVA and CIBERSORT. Finally, PPI networks were constructed using the Cytoscape software GeneMANIA plug-in, and the miRNA, transcription factors, RBP, and drugs were predicted. Necroptosis-related genes have important relationships in the development of PCOS and are potentially associated with immune infiltration in PCOS patients.

Bioinformatics analysis of effective biomarkers and immune infiltration in type 2 diabetes with cognitive impairment and aging

With the increasing prevalence of diabetes mellitus worldwide, type 2 diabetes mellitus (T2D) combined with cognitive impairment and aging has become one of the common and important complications of diabetes mellitus, which seriously affects the quality of life of the patients, and imposes a heavy burden on the patients’ families and the society. Currently, there are no special measures for the treatment of cognitive impairment and aging in type 2 diabetes mellitus. Therefore, the search for potential biological markers of type 2 diabetes mellitus combined with cognitive impairment and aging is of great significance for future precisive treatment. We downloaded three gene expression datasets from the GEO database: GSE161355 (related to T2D with cognitive impairment and aging), GSE122063, and GSE5281 (related to Alzheimer’s disease). Differentially expressed genes (DEGs) were identified, followed by gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed using the STRING database, and the top 15 hub genes were identified using the CytoHubba plugin in Cytoscape. Core genes were ultimately determined using three machine learning methods: LASSO regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Linear Discriminant Analysis (LDA). The diagnostic performance of these genes was assessed using ROC curve analysis and validated in an independent dataset (GSE5281). Regulatory genes related to ferroptosis were screened from the FerrDb database, and their biological functions were further explored through GO and KEGG enrichment analyses. Finally, the CIBERSORT algorithm was used to analyze immune cell infiltration, and the correlation between core genes and immune cell infiltration levels was calculated, leading to the construction of an mRNA-miRNA regulatory network. In the GSE161355 and GSE122063 datasets, 217 common DEGs were identified. GSEA analysis revealed their enrichment in the PI3K-PLC-TRK signaling pathway, TP53 regulation of metabolic genes pathway, Notch signaling pathway, among others. PPI network analysis identified 15 candidate core genes, and further selection using LASSO, LDA, and SVM-RFE machine learning algorithms resulted in 6 core genes: BCL6, TP53, HSP90AA1, CRYAB, IL1B, and DNAJB1. ROC curve analysis indicated that these genes had good diagnostic performance in the GSE161355 dataset, with TP53 and IL1B achieving an AUC of 0.9, indicating the highest predictive accuracy. BCL6, HSP90AA1, CRYAB, and DNAJB1 also had AUCs greater than 0.8, demonstrating moderate predictive accuracy. Validation in the independent dataset GSE5281 showed that these core genes also had good diagnostic performance in Alzheimer’s disease samples (AUC > 0.6). Ferroptosis-related analysis revealed that IL1B and TP53 play significant roles in apoptosis and immune response. Immune cell infiltration analysis showed that IL1B is significantly positively correlated with infiltration levels of monocytes and NK cells, while TP53 is significantly negatively correlated with infiltration levels of follicular helper T cells. The construction of the miRNA-mRNA regulatory network suggested that miR-150a-5p might play a key role in the regulation of T2D-associated cognitive impairment and aging by TP53. This study, by integrating bioinformatics and machine learning methods, identified BCL6, TP53, HSP90AA1, CRYAB, IL1B, and DNAJB1 as potential diagnostic biomarkers for T2D with cognitive impairment and aging, with a particular emphasis on the significance of TP53 and IL1B in immune cell infiltration. These findings not only enhance our understanding of the molecular mechanisms linking type 2 diabetes to cognitive impairment and aging, providing new targets for early diagnosis and treatment, but also offer new directions and targets for basic research.

Selenoprotein S (SELENOS) is a potential prognostic biomarker for brain lower grade glioma

BackgroundSelenium, an essential micronutrient, primarily exists as selenocysteine in various selenoproteins. Selenoprotein S (SELENOS) is crucial in the development of human cancer. This study aimed to explore the correlation between SELENOS gene expression and the prognosis of brain lower-grade glioma (LGG). MethodsSELENOS protein and mRNA expression in human normal and tumor tissues were explored through the HPA database. SELENOS expression differences between normal and tumor tissues, along with its prognostic significance in gliomas, were analyzed using the TCGA, GTEx datasets, while the CGGA dataset was used to further assess its prognostic potential in a Chinese cohort. The association between SELENOS expression and tumor immune infiltration was also assessed. Multivariate and univariate Cox models were used to screen for clinicopathological parameters associated with SELENOS expression. The GDSC datasets was utilized to explore the connection between SELENOS and chemotherapeutic responses in LGG. A protein-protein interaction network for SELENOS was created. SELENOS expression in LGG cell lines were determined by Western blotting and qRT-PCR, and its functions were ascertained by routine in vitro experiments. ResultsSELENOS was upregulated in 11 cancers and downregulated in 10 cancers relative to the corresponding normal tissues, and correlated significantly with the prognosis, especially for GBM, LGG and GBMLGG. Furthermore, It displayed a positive correlation with immune cell infiltration levels in LGG. Multivariate and Univariate Cox analyses confirmed that the impact of SELENOS on the prognosis of LGG is the combined result of factors such as age and tumor grade. The expression of SELENOS was significantly negatively correlated with temozolomide IC50 in LGG. We found that SELENOS interacts with 10 proteins, which are upregulated in LGG compared to human normal tissues. The expression of these interactors is positively correlated with SELENOS expression and LGG survival/prognosis. In vitro experiments confirmed the aberrant expression of SELENOS in LGG cell lines, and siRNA-mediated knockdown of SELENOS reduced the proliferation, viability, invasion and migration of LGG cells, and induced apoptosis. ConclusionsSELENOS is a potential prognostic marker and therapeutic target for LGG, and its low expression is associated with favorable prognosis in LGG.

Downregulation of ferroptosis-related ATF3 alleviates lupus nephritis progression

BackgroundThe role of ferroptosis in lupus nephritis (LN) is unclear. This study aimed to explore the effects of ferroptosis-related genes in LN through bioinformatics prediction and experimental validation. MethodsSample data were collected from the GEO dataset and divided into glomeruli and tubulointerstitium. We collected 382 ferroptosis-related genes. The intersection of ferroptosis-related genes with glomeruli and tubulointerstitium data, respectively, was performed. Machine learning methods (including unsupervised cluster typing and random forests) were operated to identify ferroptosis subtyping and ferroptosis important genes in LN. Immune infiltration and functional analysis were performed. The expression of ferroptosis important gene ATF3 was validated in vivo and in vitro. Results6 ferroptosis important genes common to glomeruli and tubulointerstitium were screened, including ATF3, CD44, CYBB, JUN, NCF2, and NNMT. ATF3 decreased in the LN group compared to the Control. Silencing ATF3 mitigated LPS/erastin-induced ferroptosis. Functional analysis showed that ATF3 was markedly enriched in the interferon-gamma-mediated signaling pathway, ECM-receptor interaction, and cell adhesion. In glomeruli, T cells regulatory (Tregs) infiltration decreased and Macrophages M1 levels increased with elevated ATF3 expression. Levels of immune cell infiltration were altered in different ferroptosis subtypes of LN glomeruli and tubulointerstitium. ConclusionsFerroptosis-related ATF3 levels decreased in LN. Inhibition of ATF3 might alleviate LN development by affecting the macrophage M1 and Treg cell infiltration. These implied that ATF3 might be a potential target for developing LN therapeutic strategies.

Prognostic value and anti-tumor immunity role of TMED9 in pan-cancer: a bioinformatics study.

Transmembrane p24 trafficking protein 9 (TMED9) belongs to the TMED family, and its overexpression frequently induces cancer. Studies have demonstrated the association between the overexpression of TMED9 and cancer development and proliferative migration in cancers such as ovarian cancer, hepatocellular carcinoma, and breast cancer. However, there has been no study investigating the clinical value, biological function, and anti-tumor immune effects of TMED9 from a pan-cancer perspective. The aim of this study is to evaluate the prognostic value and anti-tumor immunity role of TMED9 across pan-cancers. We utilized R language along with The Cancer Genome Atlas (TCGA), UCSC Xena (University of California, Santa Cruz Xena Browser), Human Protein Atlas (HPA), and other datasets to investigate TMED9 expression in various tumors. The association between high TMED9 expression and clinical prognosis and patient survival was examined using the Kaplan-Meier method, log-rank test, as well as univariate and multivariate Cox regression analyses. Tumor Immune Estimation Resource 2.0 (TIMER2.0) and various algorithms were employed to explore the relationship between TMED9 and the tumor microenvironment (TME). Additionally, the biological function of TMED9 in cancer was investigated through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) analyses. TMED9 was over-expressed in the majority of cancers. Patients exhibiting elevated TMED9 expression typically experienced diminished survival rates and unfavorable clinical outcomes. TMED9 played a role as a mediator in the aggressive phenotype of numerous tumors, actively engaging in various biological and signaling pathways linked to cancer development. TMED9 demonstrated the capacity to modulate the anti-tumor immune response in pan-cancer patients, exerting its influence on the infiltration levels of immune cells and cancer-associated fibroblasts (CAFs). TMED9 serves as a novel "cancer indicator" and "clinical prognostic marker", capable of reshaping the TME, impacting the immunotherapeutic response, and guiding precise treatments for cancers to a certain extent.

Prediction of Bladder Cancer Prognosis and Immune Microenvironment Assessment Using Machine Learning and Deep Learning Models

Bladder cancer (BCa) is a heterogeneous malignancy characterized by distinct immune subtypes, primarily due to differences in tumor-infiltrating immune cells and their functional characteristics. Therefore, understanding the tumor immune microenvironment (TIME) landscape in BCa is crucial for prognostic prediction and guiding precision therapy. In this study, we integrated 10 machine learning algorithms to develop an immune-related machine learning signature (IRMLS) and subsequently created a deep learning model to detect the IRMLS subtype based on pathological images. The IRMLS proved to be an independent prognostic factor for overall survival (OS) and demonstrated robust and stable performance(p<0.01). The high-risk group exhibited an immune-inflamed phenotype, associated with poorer prognosis and higher levels of immune cell infiltration. We further investigated the cancer immune cycle and mutation landscape within the IRMLS model, confirming that the high-risk group is more sensitive to immune checkpoint immunotherapy (ICI) and adjuvant chemotherapy with cisplatin(p=2.8e-10), docetaxel(p=8.8e-13), etoposide(p=1.8e-07), and paclitaxel(p=6.2e-13). In conclusion, we identified and validated a machine learning-based molecular characteristic, IRMLS, which reflects various aspects of the BCa biological process and offers new insights into personalized precision therapy for BCa patients.

LncRNA AF117829.1 is correlated with prognosis and immune infiltration and facilitates tumor progression by targeting OR7C1 in colorectal cancer.

Colorectal cancer (CRC) is one of the most common malignant tumors in the digestive system with a high incidence, a poor prognosis and an unsatisfactory therapeutic effect. Long non-coding RNAs (lncRNAs) play crucial roles in various biological processes related to tumor progression. Immune-related lncRNA gene AF117829.1 has been reported to participate in the construction of clinical predictive signature in CRC patients, suggesting that it may be involved in regulating the immune landscape and progression of CRC. However, the clinical and immunological significance and biological function of AF117829.1 in CRC remain unclear. In this study, we aim to explore the roles of AF117829.1 in CRC progression by bioinformatics analysis and experimental studies, thereby providing new targets for CRC treatment. This study collected data from The Cancer Genome Atlas (TCGA) database and explored the role of AF117829.1 in CRC by bioinformatics analysis. Cell-type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) methods estimated the immune infiltration. Gene Set Enrichment Analysis (GSEA) was performed to evaluate the enrichment of functional pathways and gene signatures. The biological functions and mechanism of AF117829.1 in CRC progression were validated using CRC tissues and in vitro experiments. In our study, high expression of AF117829.1 was found in pan-cancer including CRC and was positively associated with tumor (T) stage and tumor-node-metastasis (TNM) stage in CRC. The survival analysis results showed that CRC patients with high-AF117829.1 expression had significantly shorter overall survival (OS) time than those with low-AF117829.1 expression. Moreover, AF117829.1 expression was negatively associated with microsatellite instability (MSI) in colon adenocarcinoma (COAD). Subsequently, AF117829.1 expression was confirmed to be significantly associated with StromalScore, immune cell infiltration (ICI) levels and immune checkpoints (ICP) genes expression in CRC. The immunophenoscore (IPS) results indicated that immunotherapy could be more effective in CRC patients with low-AF117829.1 expression. Then we confirmed that AF117829.1 was highly expressed in CRC cell lines and tissues. Furthermore, our GSEA results showed that olfactory transduction-related signaling pathways were significantly enriched in the high-AF117829.1 expression group. Finally, in vitro experiments confirmed that AF117829.1 overexpression promoted the proliferation, migration and invasion of CRC cells by targeting olfactory receptor family 7 subfamily C member 1 (OR7C1). LncRNA AF117829.1 is closely related to the prognosis, immunological characteristics and immunotherapy response of CRC patients and promotes malignant progression of CRC by targeting OR7C1. Moreover, AF117829.1 may be a potential therapeutic target for CRC patients.

ERP44 could serve as a bridge mediating prognosis and immunity for glioma via single-cell and bulk RNA-sequencing

BackgroundThere was evidence that ERP44 played vital roles in a variety of cancers. However, currently, ERP44 was rarely mentioned in gliomas. Therefore, we firstly integrated proteomics, bulk, as well as single-cell RNA-sequencing (scRNA-seq) to study the possible functions of ERP44 in glioma patients. MethodsFrom online databases, we obtained bulk RNA-seq, scRNA-seq, and proteomic data of ERP44 in gliomas and verified the expression of ERP44 by qRT-PCR. Then, the Noman diagram, gene set enrichment analysis (GSEA), and univariate/multivariate Cox regression analysis were all carried out in turn. Further discussions were also conducted regarding tumor immunity and ERP44 expression. ResultsERP44 in glioma tissues was found to be considerably higher than that in normal tissues (P<0.05) in the TCGA dataset, as well as the verification of GSE50161, GSE4290, and qRT-PCR results. High ERP44 expression indicated poorer overall survival (OS) for glioma (P<0.05), and it might also be used to predict gliomas’ OS independently (P<0.05). In order to estimate these patients’ survival prognosis, a Noman chart was created with effectiveness. According to GSEA analysis, ERP44 might be implicated in five significant pathways in gliomas. The levels of immune cell infiltration of LGG, the tumor immune microenvironments, the immunological checkpoints of LGG, and GBM were all strongly linked with ERP44 in terms of tumor immunity (P<0.05). Further scRNA-seq analysis revealed that ERP44 could be expressed in various cell types, including T cells, Mono/Macrophages, and malignant cells. ConclusionsERP44 was an oncogenic gene in gliomas, serving as a bridge mediating prognosis and immunity.