Immune Infiltration Patterns Research Articles

Identification of m6A regulator-mediated RNA methylation modification patterns and key immune-related genes involved in atrial fibrillation.

The role of m6A in the regulation of the immune microenvironment in atrial fibrillation (AF) remains unclear. This study systematically evaluated the RNA modification patterns mediated by differential m6A regulators in 62 AF samples, identified the pattern of immune cell infiltration in AF and identified several immune-related genes associated with AF. A total of six key differential m6A regulators between healthy subjects and AF patients were identified by the random forest classifier. Three distinct RNA modification patterns (m6A cluster-A, -B and -C) among AF samples were identified based on the expression of 6 key m6A regulators. Differential infiltrating immune cells and HALLMARKS signaling pathways between normal and AF samples as well as among samples with three distinct m6A modification patterns were identified. A total of 16 overlapping key genes were identified by weighted gene coexpression network analysis (WGCNA) combined with two machine learning methods. The expression levels of the NCF2 and HCST genes were different between controls and AF patient samples as well as among samples with the distinct m6A modification patterns. RT-qPCR also proved that the expression of NCF2 and HCST was significantly increased in AF patients compared with control participants. These results suggested that m6A modification plays a key role in the complexity and diversity of the immune microenvironment of AF. Immunotyping of patients with AF will help to develop more accurate immunotherapy strategies for those with a significant immune response. The NCF2 and HCST genes may be novel biomarkers for the accurate diagnosis and immunotherapy of AF.

Cytoskeleton regulator RNA expression on cancer-associated fibroblasts is associated with prognosis and immunotherapy response in bladder cancer

BackgroundDysregulation of long noncoding RNAs (lncRNAs) has been reported to be associated with multiple tumors where they act as tumor suppressors or accelerators. The lncRNA CYTOR was identified as an oncogene involved in many cancers, such as gastric cancer, colorectal cancer, hepatocellular carcinoma, and renal cell carcinoma. However, the role of CYTOR in bladder cancer (BCa) has rarely been reported. MethodsUsing cancer datasets from The Cancer Genome Atlas (TCGA) program, we analyzed the association between CYTOR expression and prognostic value, oncogenic pathways, antitumor immunity and immunotherapy response in BCa. The influence of CYTOR on the immune infiltration pattern in the urothelial carcinoma microenvironment was further verified in our dataset. Single-cell analysis revealed the role of CYTOR in the tumor microenvironment (TME) of BCa. Finally, we evaluated the expression of CYTOR in BCa in the Peking University First Hospital (PKU–BCa) dataset and its correlation with the malignant phenotype of BCa in vitro and in vivo. ResultsThe results indicated that CYTOR was highly expressed in multiple cancer samples, including BCa, and increased CYTOR expression contributed to poor overall survival (OS). Additionally, elevated CYTOR expression was significantly correlated with clinicopathological features of BCa, such as female sex, advanced TNM stage, high histological grade and non-papillary subtype. Functional characterization revealed that CYTOR may be involved in immune-related pathways and the epithelial mesenchymal transformation (EMT) process. Moreover, CYTOR had a significant association with infiltrating immune cells, including M2 macrophages and regulatory T cells (Tregs). CYTOR facilitates the crosstalk between cancer-associated fibroblasts (CAFs) and macrophages, and mediates M2 polarization of macrophages. Correlation analysis revealed a positive correlation between CYTOR expression and programmed cell death-1 (PD-1)/programmed death ligand 1 (PD-L1)/expression and other targets for specific immunotherapy in BCa, which are recognized to predict the efficacy of immunotherapy. ConclusionsThese results suggest that CYTOR serves as a potential biomarker for predicting survival outcome, TME cell infiltration characteristics and immunotherapy response in BCa.

The prognostic value of the GPAT/AGPAT gene family in hepatocellular carcinoma and its role in the tumor immune microenvironment.

Liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related death worldwide. Hepatocellular carcinoma accounts for an estimated 90% of all liver cancers. Many enzymes of the GPAT/AGPAT family are required for the synthesis of triacylglycerol. Expression of AGPAT isoenzymes has been reported to be associated with an increased risk of tumorigenesis or development of aggressive phenotypes in a variety of cancers. However, whether members of the GPAT/AGPAT gene family also influence the pathophysiology of HCC is unknown. Hepatocellular carcinoma datasets were obtained from the TCGA and ICGC databases. Predictive models related to the GPAT/AGPAT gene family were constructed based on LASSO-Cox regression using the ICGC-LIRI dataset as an external validation cohort. Seven immune cell infiltration algorithms were used to analyze immune cell infiltration patterns in different risk groups. IHC, CCK-8, Transwell assay, and Western blotting were used for in vitro validation. Compared with low-risk patients, high-risk patients had shorter survival and higher risk scores. Multivariate Cox regression analysis showed that risk score was a significant independent predictor of overall survival (OS) after adjustment for confounding clinical factors (p < 0.001). The established nomogram combined risk score and TNM staging to accurately predict survival at 1, 3, and 5 years in patients with HCC with AUC values of 0.807, 0.806, and 0.795, respectively. This risk score improved the reliability of the nomogram and guided clinical decision-making. In addition, we comprehensively analyzed immune cell infiltration (using seven algorithms), response to immune checkpoint blockade, clinical relevance, survival, mutations, mRNA expression-based stemness index, signaling pathways, and interacting proteins related to the three core genes of the prognostic model (AGPAT5, LCLAT1, and LPCAT1). We also performed preliminary validation of the differential expression, oncological phenotype, and potential downstream pathways of the three core genes by IHC, CCK-8, Transwell assay, and Western blotting. These results improve our understanding of the function of GPAT/AGPAT gene family members and provide a reference for prognostic biomarker research and individualized treatment of HCC.

Identification of a prognostic cuproptosis-related signature in hepatocellular carcinoma

BackgroundCuproptosis is a new type of copper-induced cell death that is characterized by the aggregation of lipoylated tricarboxylic acid (TCA) cycle proteins. However, its role in hepatocellular carcinoma (HCC) remains unclear. The goal of this research is to develop a cuproptosis-related signature predicting the prognosis of HCC.MethodsThe cuproptosis-related genes were defined using Pearson correlation coefficients. LASSO-Cox regression analysis was used to evaluate the prognostic values of cuproptosis-related genes to construct a cuproptosis-related prognostic model. The immune microenvironment analysis was performed by “ssGSEA” to reveal the associated immune cell infiltration patterns with the cuproptosis-related genes signature. The expression levels of one of the prognostic genes PDXK were then verified in HCC samples by Western Blot and immunohistochemistry. The potential roles of target genes in cuproptosis were further explored during in-vitro experiments.ResultsA total of 136 cuproptosis-related genes were discovered using Pearson correlation analysis in HCC. A cuproptosis-related signature that included 5 cuproptosis-related genes (PDXK, HPN, SLC25A28, RNFT1, CLEC3B) was established in the TCGA-LIHC training cohort. TCGA validation cohort and another two external validation cohorts confirmed the robustness of the signature’s predictive value. Moreover, a nomogram using the risk score was created to best predict the survival of HCC patients. The immune microenvironment analysis revealed distinct immune infiltrations patterns between different risk groups based on the signature model. Furthermore, the upregulation of PDXK was confirmed in HCC tumor tissues in 30 clinical HCC specimens. The knockdown of PDXK reduced the proliferation, migration and invasion of HCC cells. Besides, the expression of PDXK was upregulated after the induction of cuproptosis by elesclomol–CuCL2, which could be suppressed when pretreated with a copper ion chelator. And PDXK deficiency increased the sensitivity of HCC cells to cuproptosis inducer.ConclusionOur study identified a new cuproptosis-related gene signature that could predict the prognosis of HCC patient. Besides, the upregulated PDXK could promote the proliferation and metastasis of HCC. And PDXK deficiency facilities cuproptosis in HCC. Therefore, these fundings highlighted that PDXK might serve as a potential diagnostic and therapeutic target for HCC.

Single-cell transcriptomics-based multidisease analysis revealing the molecular dynamics of retinal neurovascular units under inflammatory and hypoxic conditions

The retinal neurovascular unit (NVU) is paramount to maintaining the homeostasis of the retina and determines the progression of various diseases, including diabetic retinopathy (DR), glaucoma, and retinopathy of prematurity (ROP). Although some studies have investigated these diseases, a combined analysis of disease-wide etiology in the NUV at the single-cell level is lacking. Herein, we constructed an atlas of the NVU under inflammatory and hypoxic conditions by integrating single-cell transcriptome data from retinas from wild-type, AireKO, and NdpKO mice. Based on the heterogeneity of the NVU structure and transcriptome diversity under normal and pathological conditions, we discovered two subpopulations of Müller cells: Aqp4hi and Aqp4lo cells. Specifically, Aqp4lo cells expresses phototransduction genes and represent a special type of Müller cell distinct from Aqp4hi cells, classical Müller cells. AireKO mice exhibit experimental autoimmune uveitis (EAU) with severe damage to the NVU structure, mainly degeneration of Aqp4hi cells. NdpKO mice exhibited familial exudative vitreoretinopathy (FEVR), with damage to the endothelial barrier, endothelial cell tight junction destruction and basement membrane thickening, accompanied by the reactive secretion of proangiogenic factors by Aqp4hi cells. In both EAU and FEVR, Aqp4hi cells are a key factor leading to NVU damage, and the mechanism by which they are generated is regulated by different transcription factors. By studying the pattern of immune cell infiltration in AireKO mice, we constructed a regulatory loop of “inflammatory cells/NVU - monocytes - APCs - Ifng+ T cells”, providing a new target for blocking the inflammatory cascade. Our elucidation of the cell-specific molecular changes, cell–cell interactions and transcriptional mechanisms of the retinal NVU provides new insights to support the development of multipurpose drugs to block or even reverse NVU damage.

Role of mitochondrial metabolic disorder and immune infiltration in diabetic cardiomyopathy: new insights from bioinformatics analysis

BackgroundDiabetic cardiomyopathy (DCM) is one of the common cardiovascular complications of diabetes and a leading cause of death in diabetic patients. Mitochondrial metabolism and immune-inflammation are key for DCM pathogenesis, but their crosstalk in DCM remains an open issue. This study explored the separate roles of mitochondrial metabolism and immune microenvironment and their crosstalk in DCM with bioinformatics.MethodsDCM chip data (GSE4745, GSE5606, and GSE6880) were obtained from NCBI GEO, while mitochondrial gene data were downloaded from MitoCarta3.0 database. Differentially expressed genes (DEGs) were screened by GEO2R and processed for GSEA, GO and KEGG pathway analyses. Mitochondria-related DEGs (MitoDEGs) were obtained. A PPI network was constructed, and the hub MitoDEGs closely linked to DCM or heart failure were identified with CytoHubba, MCODE and CTD scores. Transcription factors and target miRNAs of the hub MitoDEGs were predicted with Cytoscape and miRWalk database, respectively, and a regulatory network was established. The immune infiltration pattern in DCM was analyzed with ImmuCellAI, while the relationship between MitoDEGs and immune infiltration abundance was investigated using Spearman method. A rat model of DCM was established to validate the expression of hub MitoDEGs and their relationship with cardiac function.ResultsMitoDEGs in DCM were significantly enriched in pathways involved in mitochondrial metabolism, immunoregulation, and collagen synthesis. Nine hub MitoDEGs closely linked to DCM or heart failure were obtained. Immune analysis revealed significantly increased infiltration of B cells while decreased infiltration of DCs in immune microenvironment of DCM. Spearman analysis demonstrated that the hub MitoDEGs were positively associated with the infiltration of pro-inflammatory immune cells, but negatively associated with the infiltration of anti-inflammatory or regulatory immune cells. In the animal experiment, 4 hub MitoDEGs (Pdk4, Hmgcs2, Decr1, and Ivd) showed an expression trend consistent with bioinformatics analysis result. Additionally, the up-regulation of Pdk4, Hmgcs2, Decr1 and the down-regulation of Ivd were distinctly linked to reduced cardiac function.ConclusionsThis study unraveled the interaction between mitochondrial metabolism and immune microenvironment in DCM, providing new insights into the research on potential pathogenesis of DCM and the exploration of novel targets for medical interventions.

Identification of a novel immune-related gene signature for prognosis and the tumor microenvironment in patients with uveal melanoma combining single-cell and bulk sequencing data.

Uveal melanoma (UVM) is the most invasive intraocular malignancy in adults with a poor prognosis. Growing evidence revealed that immune-related gene is related to tumorigenesis and prognosis. This study aimed to construct an immune-related prognostic signature for UVM and clarify the molecular and immune classification. Based on The Cancer Genome Atlas (TCGA) database, single-sample gene set enrichment (ssGSEA) and hierarchical clustering analysis were performed to identify the immune infiltration pattern of UVM and classify patients into two immunity clusters. Then, we proposed univariate and multivariate Cox regression analysis to identify immune-related genes that related to overall survival (OS) and validated in the Gene Expression Omnibus (GEO) external validation cohort. The molecular and immune classification in the immune-related gene prognostic signature defined subgroups were analyzed. The immune-related gene prognostic signature was constructed based on S100A13, MMP9, and SEMA3B genes. The prognostic value of this risk model was validated in three bulk RNA sequencing datasets and one single-cell sequencing dataset. Patients in the low-risk group had better OS than those in the high-risk group. The receiver-operating characteristic (ROC) analysis revealed its strong predictive ability for UVM patients. Lower expression of immune checkpoint genes was presented in the low-risk group. Functional studies showed that S100A13 knockdown via siRNA inhibited UVM cell proliferation, migration, and invasion in vitro, with the increased expression of reactive oxygen species (ROS) related markers in UVM cell lines. The immune-related gene prognostic signature is an independent predictive factor for the survival of patients with UVM and provides new information about cancer immunotherapy in UVM.

Novel diagnostic biomarkers related to immune infiltration in Parkinson's disease by bioinformatics analysis.

Parkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers. Three substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers. A total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905). By affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.

Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy.

Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME. Senescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR. Three senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed. In conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy.

GBP2 promotes clear cell renal cell carcinoma progression through immune infiltration and regulation of PD‑L1 expression via STAT1 signaling.

Guanylate‑binding protein 2 (GBP2) has been widely studied in cancer, however, its potential role in clear cell renal cell carcinoma (ccRCC) is not fully elucidated. The present study aimed to explore the effect of GBP2 on tumor progression and its possible underlying molecular mechanisms in ccRCC. The Cancer Genome Atlas, Gene Expression Omnibus, Cancer Cell Line Encyclopedia databases, and several bioinformatics analysis tools, such as Gene Expression Profiling Interactive Analysis 2, Kaplan‑Meier plotter, UALCAN, LinkedOmics, Metascape, GeneMANIA and Tumor Immune Estimation Resource, were used to characterize the functional relationship between GBP2 and ccRCC. Focusing on the association between GBP2 and programmed death ligand 1 (PD‑L1) invitro, the regulatory mechanism was investigated by knockdown and overexpression of GBP2 in Caki‑1 and 786‑O cells using reverse transcription‑quantitative PCR, western blotting and co‑immunoprecipitation techniques. The results indicated that GBP2 was commonly upregulated in ccRCC, correlating with worse prognosis. In addition, GBP2 expression levels were positively associated with different patterns of immune cell infiltration, suggesting that the GBP2 gene regulates PD‑L1 expression via the signal transducer and activator of transcription 1 (STAT1) pathway. The present study suggested that GBP2 regulates tumor immune infiltration and promotes tumor immune escape through PD‑L1 expression, revealing a potential immunotherapeutic target for ccRCC.

Exploring the pathogenesis and immune infiltration in dilated cardiomyopathy complicated with atrial fibrillation by bioinformatics analysis.

Atrial fibrillation (AF) is a serious complication of dilated cardiomyopathy (DCM), which increases the risk of thromboembolic events and sudden death in DCM patients. However, the common mechanism of DCM combined with AF remains unclear. This study aims to explore the molecular mechanism and analyze immune infiltration in DCM complicated with AF through comprehensive bioinformatics analysis. The gene expression datasets of DCM (GSE141910) and AF (GSE41177 and GSE79768) were obtained from the Gene Expression Omnibus database. Gene enrichment analyses were performed after screening the common differentially expressed genes (DEGs) of DCM and AF. Protein-protein interaction network was constructed in the STRING database and visualized in Cytoscape software, which helped to further screen the central functional modules of DEGs and hub genes. In addition, ImmuCellAI algorithm was performed to estimate immune infiltration patterns, and Spearman correlation was conducted to investigate the correlation between the abundance of multiple immune cells and the expression levels of hub immune-related genes after obtaining hub immune-related genes from the ImmPort database. The hub immune-related genes expression and immune infiltration patterns were additionally verified in the validation datasets (GSE57338, GSE115574, and GSE31821). The diagnostic effectiveness of hub immune-related genes was evaluated through Receiver Operator Characteristic Curve analysis. A total of 184 common DEGs in DCM and AF were identified for subsequent analyses. The functions of hub genes were significantly associated with immune responses. We identified 7 hub immune-related genes (HLA-DRA, LCK, ITK, CD48, CD247, CD3D, and IL2RG) and a spectrum of immune cell subsets including Monocyte, Neutrophil, and follicular helper T (Tfh) cells were found to be concurrently dysregulated in both DCM and AF. 7 hub immune-related genes were predominantly favorably correlated with Tfh cells and were primarily negatively correlated with Neutrophil infiltrations in DCM and AF. CD48+CD3D were verified to diagnose DCM and AF with excellent sensitivity and specificity, showing favorable diagnostic value. Our study reveals that immune cells (Tfh cells) disorders caused by hub immune-related genes (CD48 and CD3D) may be the common pathogenesis of DCM combined with AF, which lays a foundation for further immune mechanism research.

Cuproptosis-related lncRNA signatures: Predicting prognosis and evaluating the tumor immune microenvironment in lung adenocarcinoma.

Cuproptosis, a unique kind of cell death, has implications for cancer therapy, particularly lung adenocarcinoma (LUAD). Long non-coding RNAs (lncRNAs) have been demonstrated to influence cancer cell activity by binding to a wide variety of targets, including DNA, RNA, and proteins. Cuproptosis-related lncRNAs (CRlncRNAs) were utilized to build a risk model that classified patients into high-and low-risk groups. Based on the CRlncRNAs in the model, Consensus clustering analysis was used to classify LUAD patients into different subtypes. Next, we explored the differences in overall survival (OS), the tumor immune microenvironment (TIME), and the mutation landscape between different risk groups and molecular subtypes. Finally, the functions of LINC00592 were verified through in vitro experiments. Patients in various risk categories and molecular subtypes showed statistically significant variations in terms of OS, immune cell infiltration, pathway activity, and mutation patterns. Cell experiments revealed that LINC00592 knockdown significantly reduced LUAD cell proliferation, invasion, and migration ability. The development of a trustworthy prediction model based on CRlncRNAs may significantly aid in the assessment of patient prognosis, molecular features, and therapeutic modalities and may eventually be used in clinical applications.

Identification of lymph node metastasis-related genes and patterns of immune infiltration in colon adenocarcinoma.

Colon adenocarcinoma(COAD) is one of the most common tumors of the digestive tract. Lymph node metastasis (LNM) is a well-established prognostic factor for COAD. The mechanism of COAD lymph node metastasis in immunology remains unknown. The identification of LNM-related biomarkers of COAD could help in its treatment. Thus, the current study was aimed to identify key genes and construct a prognostic signature. Gene expression and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes were calculated by using R software. GO functional and KEGG pathway enrichment analysis were processed. The CIBERSORT algorithm was used to assess immune cell infiltration. STRING database was used to screen key genes and constructed a protein-protein interaction network (PPI network). The LASSO-Cox regression analysis was performed based on the components of the PPI network. The correlation analysis between LNM-related signature and immune infiltrating cells was then investigated. TISIDB was used to explore the correlation between the abundance of immunomodulators and the expression of the inquired gene. In total, 394 differentially expressed genes were identified. After constructing and analyzing the PPI network, 180 genes were entered into the LASSO-Cox regression model, constructing a gene signature. Five genes(PMCH, LRP2, NAT1, NKAIN4, and CD1B) were identified as LNM-related genes of clinical value. Correlation analysis revealed that LRP2 and T follicular helper cells (R=0.34, P=0.0019) and NKAIN4 and T follicular helper cells (R=0.23, P=0.041) had significant correlations. Immunologic analysis revealed that LRP2 and NKAIN4 are potential coregulators of immune checkpoints in COAD. In general, this study revealed the key genes related to lymph node metastasis and prognostic signature. Several potential mechanisms and therapeutic and prognostic targets of lymph node metastasis were also demonstrated in COAD.

A meta-validated immune infiltration-related gene model predicts prognosis and immunotherapy sensitivity in HNSCC

BackgroundTumor microenvironment (TME) is of great importance to regulate the initiation and advance of cancer. The immune infiltration patterns of TME have been considered to impact the prognosis and immunotherapy sensitivity in Head and Neck squamous cell carcinoma (HNSCC). Whereas, specific molecular targets and cell components involved in the HNSCC tumor microenvironment remain a twilight zone.MethodsImmune scores of TCGA-HNSCC patients were calculated via ESTIMATE algorithm, followed by weighted gene co-expression network analysis (WGCNA) to filter immune infiltration-related gene modules. Univariate, the least absolute shrinkage and selection operator (LASSO), and multivariate cox regression were applied to construct the prognostic model. The predictive capacity was validated by meta-analysis including external dataset GSE65858, GSE41613 and GSE686. Model candidate genes were verified at mRNA and protein levels using public database and independent specimens of immunohistochemistry. Immunotherapy-treated cohort GSE159067, TIDE and CIBERSORT were used to evaluate the features of immunotherapy responsiveness and immune infiltration in HNSCC.ResultsImmune microenvironment was significantly associated with the prognosis of HNSCC patients. Total 277 immune infiltration-related genes were filtered by WGCNA and involved in various immune processes. Cox regression identified nine prognostic immune infiltration-related genes (MORF4L2, CTSL1, TBC1D2, C5orf15, LIPA, WIPF1, CXCL13, TMEM173, ISG20) to build a risk score. Most candidate genes were highly expressed in HNSCC tissues at mRNA and protein levels. Survival meta-analysis illustrated high prognostic accuracy of the model in the discovery cohort and validation cohort. Higher proportion of progression-free outcomes, lower TIDE scores and higher expression levels of immune checkpoint genes indicated enhanced immunotherapy responsiveness in low-risk patients. Decreased memory B cells, CD8+ T cells, follicular helper T cells, regulatory T cells, and increased activated dendritic cells and activated mast cells were identified as crucial immune cells in the TME of high-risk patients.ConclusionsThe immune infiltration-related gene model was well-qualified and provided novel biomarkers for the prognosis of HNSCC.

Immune infiltration patterns and identification of new diagnostic biomarkers GDF10, NCKAP5, and RTKN2 in non-small cell lung cancer

This study aimed to identify potential biomarkers for non-small cell lung cancer (NSCLC) and analyze the role of immune cell infiltration in NSCLC. R software was used to screen differentially expressed genes (DEGs) from NSCLC datasets obtained from the Gene Expression Omnibus (GEO) database, and functional correlation analysis was performed. The machine learning algorithms were used to screen the potential biomarkers of NSCLC. The diagnostic values were assessed through receiver operating characteristic (ROC) curves. The protein and mRNA expression levels of potential biomarkers were verified based on the Human Protein Atlas (HPA) database and qRT-PCR. CIBERSORT was used to evaluate the infiltration of immune cells in NSCLC tissues, and the correlation between potential biomarkers and infiltrated immune cell was analyzed. Finally, specific siRNAs were utilized to reduce the GDF10, NCKAP5, and RTKN2 expression in A549 and H1975 cells. The proliferation ability of A549 and H1975 cells was detected by MTT assay. A total of 848 upregulated DEGs and 1308 downregulated DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that the DEGs were mainly related to cell division. Disease ontology (DO) enrichment analysis showed that the diseases with these DEGs were mainly lung diseases, including NSCLC. In addition,three potential biomarkers were identified: GDF10, NCKAP5, and RTKN2. Immune cell infiltration analysis showed that resting NK cells, activated dendritic cells, and Tregs may be involved in the pathogenesis of NSCLC. Meanwhile, GDF10, NCKAP5, and RTKN2 were negatively correlated with Tregs and naïve B cells but were positively correlated with activated dendritic cells and resting NK cells. Immunohistochemical staining showed that the expression of GDF10, NCKAP5, and RTKN2 in the lung tissue of patients with NSCLC was lower than that of normal lung tissue. qRT-PCR also confirmed that the mRNA expression of three biomarkers in NSCLC cell lines A549 and H1975 were significantly lower than those in human normal lung epithelial cells BEAS-2B. An MTT assay showed that GDF10, NCKAP5, and RTKN2 knockdown significantly promoted the proliferation of A549 and H1975 cells. The in vitro experiments showed that GDF10, NCKAP5, and RTKN2 played the inhibitory effects on NSCLC cell lines proliferation. Hence, GDF10, NCKAP5, and RTKN2 can be used as diagnostic biomarkers for NSCLC.

Modification of m5C regulators in sarcoma can guide different immune infiltrations as well as immunotherapy.

Recent studies have found that 5-methylcytosine (m5C) modulators are associated with the prognosis and treatment of cancer. However, the relevance of m5C modulators in sarcoma prognosis and the tumour microenvironment is unclear. We selected 15 m5C regulators and performed unsupervised clustering to identify m5C modification patterns and differentially expressed genes associated with the m5C phenotype in The Cancer Genome Atlas (TCGA) sarcomas. The extent of immune cell infiltration in different clustering groups was explored using single-sample gene set enrichment analysis and estimation algorithms. A principal component analysis algorithm-based m5C scoring protocol was performed to assess the m5C modification patterns of individual tumors. We identified two distinct m5C modification patterns in the TCGA sarcoma cohort, which possess different clinical outcomes and biological processes. Tumour microenvironment analysis revealed two groups of immune infiltration patterns highly consistent with m5C modification patterns, classified as immune inflammatory and immune desert types. We constructed m5C scores and found that high m5C scores were closely associated with leiomyosarcoma and other subtypes, and were associated with poorer prognosis, lower PD-L1 expression, and poorer immunotherapy outcomes. The best application was validated against the m5C database. We constructed an m5C score for sarcoma based on the TCGA database and identified a poorer prognosis in the high m5c score group. The stability and good prognostic predictive power of the m5C score was verified by an external database. We found that sarcomas in the low m5C score group may have a better response to immunotherapy.

Genome-wide exploration of a pyroptosis-related gene module along with immune cell infiltration patterns in bronchopulmonary dysplasia.

Pyroptosis plays a crucial role in bronchopulmonary dysplasia (BPD) and is associated with various lung injury illnesses. However, the function of pyroptosis-related genes (PRGs) in BPD remains poorly understood. The gene expression omnibus (GEO) database was searched for information on genes associated with BPD. Twenty-five BPD-related DE-PRGs were identified, all of which were closely associated with pyroptosis regulation and immunological response. LASSO and SVM-RFE algorithms identified CHMP7, NLRC4, NLRP2, NLRP6, and NLRP9 among the 25 differentially expressed PRGs as marker genes with acceptable diagnostic capabilities. Using these five genes, we also generated a nomogram with excellent predictive power. Annotation enrichment analyses revealed that these five genes may be implicated in BPD and numerous BPD-related pathways. In addition, the ceRNA network showed an intricate regulatory link based on the marker genes. In addition, CIBERSORT-based studies revealed that alterations in the immunological microenvironment of BPD patients may be associated with the marker genes. We constructed a diagnostic nomogram and gave insight into the mechanism of BPD. Its diagnostic value for BPD must be evaluated in further research before it can be used in clinical practice.

Regulation of RNA methylation and immune infiltration patterns by m5C regulators in head and neck squamous cell carcinoma

Regulation of RNA methylation and immune infiltration patterns by m5C regulators in head and neck squamous cell carcinoma

Machine learning screening for Parkinson's disease-related cuproptosis-related typing development and validation and exploration of personalized drugs for cuproptosis genes.

Parkinson's disease (PD) is a common, degenerative disease of the nervous system that is characterized by the death of dopaminergic neurons in the substantia nigra densa (SNpc). There is growing evidence that copper (Cu) is involved in myelin formation and is involved in cell death through modulation of synaptic activity as well as neurotrophic factor-induced excitotoxicity. This study aimed to explore potential cuproptosis-related genes (CRGs) and immune infiltration patterns in PD and the development of Cu chelators relevant for PD treatment. The PD datasets GSE7621, GSE20141, and GSE49036 were downloaded from the Gene Expression Omnibus (GEO) database. The consensus clustering method was used to classify the specimens of PD. Using weighted gene co-expression network analysis (WGCNA) and random forest (RF) tree model, support vector machine (SVM) learning model, extreme gradient boosting (XGBoost) model, and general linear model (GLM) algorithms to screen disease progression-related models, the column charts were created to verify the accuracy of these CRGs in predicting PD progression. Single sample genomic enrichment analysis (ssGSEA) was used to estimate the correlation between genes associated with copper poisoning and genes associated with immune cells and immune function. Molecular docking was used to verify interactions with copper chelating agents associated with cuproptosis for PD treatment. Through ssGSEA, we identified three copper poisoning related genes ATP7A, NFE2L2 and MTF1, which are related to immune cells in PD. We also verified that LAGASCATRIOL can bind to NFE2L2 through molecular docking. Consistent cluster analysis identified two subtypes, among which C2 subtype was just enriched in PD. And to more accurately diagnose PD progression, patients can benefit from a feature map based on these genes. CRGs such as NFE2L2, MTF1, and ATP7B were identified to be associated with the pathogenesis of PD and provide a possible new direction for the treatment of PD, which needs further in-depth study.

Prognostic and immunological role of acetaldehyde dehydrogenase 1B1 in human tumors: A pan-cancer analysis.

Acetaldehyde dehydrogenases (ALDH) 1B1 is associated with a poor prognosis in pancreatic cancer, colorectal cancer, and osteosarcoma. Overexpression of ALDH also impairs tumor immunity. However, it is unclear how ALDH1B1 is associated with patient prognosis and immune infiltration in different cancer types. This is an original research based on bioinformatics analysis. In this study, we investigated the expression and prognostic value of ALDH1B1 in pan-cancer specimens using several databases, including GEPIA2 and Kaplan-Meier Plotter. The GEPIA2 and TIMER2 databases were used to explore correlations between ALDH1B1 expression and immune infiltration in cancers, especially head and neck squamous cell carcinoma (HNSC) and stomach adenocarcinoma (STAD). Finally, the expression of ALDH1B1 was validated by qPCR and immunohistochemistry. The expression of ALDH1B1 differed in most cancers compared to normal tissue controls. ALDH1B1 has an important impact on the prognosis different cancer types, and the high expression of ALDH1B1 is inversely associated with survival in patients with HNSC. A significant positive correlation was identified between ALDH1B1 expression in HNSC and immune infiltration. The poor prognosis associated with high expression of ALDH1B1 may be related to the promotion of M2 polarization of tumor-associated macrophages. Furthermore, markers of immune cell infiltration, such as exhausted T cells and regulatory T cells showed different patterns of ALDH1B1-associated immune infiltration. ALDH1B1 can serve as a prognostic biomarker in pan-cancer types and is correlated with immune infiltration.