Immune Infiltration Characteristics Research Articles

Molecular Subtypes Defined by Cuproptosis-Associated Genes, Prognostic Model Development, and Tumor Immune Microenvironment Characterization in Adrenocortical Carcinoma.

This study aims to explore the role of cuproptosis-related genes in ACC, utilizing data from TCGA and GEO repositories, and to develop a predictive model for patient stratification. A cohort of 123 ACC patients with survival data was analyzed. RNA-seq data of 17 CRGs were examined, and univariate Cox regression identified prognostic CRGs. A cuproptosis-related network was constructed to show interactions between CRGs. Consensus clustering classified ACC into three subtypes, with transcriptional and survival differences assessed by PCA and survival analysis. Gene set variation analysis (GSVA) and ssGSEA evaluated functional and immune infiltration characteristics across subtypes. Differentially expressed genes (DEGs) were identified, and gene clusters were established. A risk score (CRG_score) was generated using LASSO and multivariate Cox regression, validated across datasets. Tumor microenvironment, stem cell index, mutation status, drug sensitivity, and hormone synthesis were examined in relation to the CRG_score. Protein expression of key genes was validated, and functional studies on ASF1B and NDRG4 were performed. Three ACC subtypes were identified with distinct survival outcomes. Subtype B showed the worst prognosis, while subtype C had the best. We identified 214 DEGs linked to cell proliferation and classified patients into three gene clusters, confirming their prognostic value. The CRG_score predicted patient outcomes, with high-risk patients demonstrating worse survival and possible resistance to immunotherapy. Drug sensitivity analysis suggested higher responsiveness to doxorubicin and etoposide in high-risk patients. This study suggests the potential prognostic value of CRGs in ACC. The CRG_score model provides a robust tool for risk stratification, with implications for treatment strategies.

Proteomic and phosphoproteomic landscape of localized prostate cancer unveils distinct molecular subtypes and insights into precision therapeutics

Building upon our previous investigation of genomic, epigenomic, and transcriptomic profiles of prostate cancer in China, we conducted a comprehensive analysis of proteomic and phosphoproteomic profiles of 82 tumor tissues and matched adjacent normal tissues from 41 Chinese patients with localized prostate cancer. We identified three distinct proteomic subtypes with significant difference in both molecular features and clinical prognosis. Notably, these proteomic subtypes exhibited a parallel degree of heterogeneity in the phosphoproteome, featuring unique metabolism, proliferation, and immune infiltration characteristics. We further demonstrated that a combination of proteins and phosphosites serves as the most effective biomarkers in prostate cancer to predict biochemical recurrence. Through an integrated multiomics analysis, we revealed mechanistic differences underlying different proteomic subtypes and highlighted the potential significance of Serine/arginine-rich splicing factor 1 (SRSF1) phosphorylation in promoting the malignant characteristics of prostate cancer cells. Our multiomics data provide valuable resources for understanding the molecular mechanisms of prostate cancer within the Chinese population, which have the potential to inform the development of personalized treatment strategies and enhance prognostic analyses for prostate cancer patients.

Multi-omics and Single Cell Sequencing Analyses Reveal Associations of Mitophagy-Related Genes Predicting Clinical Prognosis and Immune Infiltration Characteristics in Osteosarcoma.

Despite recent advances in clinical treatments, identifying high-risk osteosarcoma (OS) patients remains an unresolved clinical challenge. Mitophagy, a specialized form of cellular autophagy, selectively reduces the number of mitochondria or repairs their abnormal functions in response to external stress, thereby ensuring mitochondrial quality and maintaining mitochondrial function. Mitophagy plays a crucial role in cancer development, including processes such as mitochondrial repair, homeostasis maintenance, and tumor metabolism. However, its impact on OS has not yet been reported. In this study, we collected 58 mitophagy-related genes (MPRGs) from the TARGET and GEO databases and bioinformatically screened for those associated with OS prognosis. By LASSO-multivariable Cox regression algorithm, we subsequently developed a novel scoring system, the MPRG score, and validated its significance in predicting OS prognosis. Immune landscape analysis showed patients in the low MPRG group had a higher immune infiltration level than those in the high MPRG group. Drug sensitivity differences highlighted the potential need for alternative therapeutic strategies based on MPRG scoring system. The distribution characteristics of the MPRG signature in different cell subtypes of OS were explored by single-cell sequencing analyses. In vitro experiments further confirmed the abnormal expression of screened targets in OS. Our findings highlight the role of mitophagy in OS and its potential as a therapeutic target.

Identification of an Amino Acid Metabolism Reprogramming Signature for Predicting Prognosis, Immunotherapy Efficacy, and Drug Candidates in Colon Cancer.

Colon cancer ranked third among the most frequently diagnosed cancers worldwide. Amino acid metabolic reprogramming was related to the occurrence and development of colon cancer. We looked for the amino acid metabolism genes (AMGs) associated with amino acid metabolism from molecular signatures database as prognostic markers and constructed amino acid metabolism scoring model (AMS). According to AMS, the patients were divided into high AMS and low AMS groups, and the prognostic characteristics, molecular phenotypes, somatic cell mutation characteristics, immune cell infiltration characteristics, and immunotherapy effect of the two groups were systematically analyzed. Finally, the compounds targeting AMGs were also screened. We screen out 6 prognostic AMGs (P < 0.05) and construct an AMS model based on them. K-M curve indicated that OS in low AMS group was significantly higher than that in high group (P < 0.05), which were validated in multiple datasets. And different AMS groups had different molecular phenotypes, somatic cell mutation characteristics and immune cell infiltration characteristics. Low AMS group had a better effect for immunotherapy. In addition, we predicted potential therapeutic compounds that could bind to AMGs target proteins. AMS model can be used as a hierarchical tool to evaluate the prognosis, immune infiltration characteristics and immunotherapy response ability of colon cancer. And the compounds screened based on AMGs may become new anti-tumor drugs.

Highly expressed of BID indicates poor prognosis and mediates different tumor microenvironment characteristics in clear cell renal cell carcinoma

BackgroundStudies have found that BH3 interacting domain death agonist (BID) is closely related to the occurrence and development of many kinds of tumors. However, little attention has been paid to the situation of BID in clear cell renal cell carcinoma (ccRCC). So, our aim was to explore the effect of BID in ccRCC.MethodsSurvival analysis, ROC curve, correlation analysis and Cox regression analysis were executed to analyze the prognostic value and clinical correlation of BID in ccRCC. The risk prognosis model was constructed in the training cohort and further validated in the internal testing cohort, ICGC cohort, and GEO cohort. Transcriptome sequencing and immunohistochemical staining of clinical specimens were used to validate the results of bioinformatics analysis. The GSEA, ESTIMATE algorithm, CIBERSORT algorithm, ssGSEA, TIDE score, correlation and difference analysis were used to analyze the effects of BID on immune infiltration in tumor microenvironment (TME).ResultsBID was highly expressed in ccRCC tissues, which was verified by transcriptome sequencing and immunohistochemical staining of clinical specimens. Patients with high expression of BID had a worse prognosis. BID is an independent prognostic factor for ccRCC. The prognostic model based on BID can accurately predict the prognosis of patients in different cohorts. In addition, the expression levels of BID was closely related to immunomodulatory molecules such as PD-1, LAG3, and CTLA4. Enrichment analysis indicated that BID was significantly enriched in immune-related responses and cancer-related pathways. The change of BID expression mediates different characteristics of immune infiltration in TME.ConclusionsBID is highly expressed in ccRCC, which is a reliable biomarker of ccRCC prognosis. It is closely related to TME, and may be a potential target for immunotherapy in patients with ccRCC.

M6A-related bioinformatics analysis indicates that LRPPRC is an immune marker for ischemic stroke

Ischemic stroke (IS) is a common cerebrovascular disease whose pathogenesis involves a variety of immune molecules, immune channels and immune processes. 6-methyladenosine (m6A) modification regulates a variety of immune metabolic and immunopathological processes, but the role of m6A in IS is not yet understood. We downloaded the data set GSE58294 from the GEO database and screened for m6A-regulated differential expression genes. The RF algorithm was selected to screen the m6A key regulatory genes. Clinical prediction models were constructed and validated based on m6A key regulatory genes. IS patients were grouped according to the expression of m6A key regulatory genes, and immune markers of IS were identified based on immune infiltration characteristics and correlation. Finally, we performed functional enrichment, protein interaction network analysis and molecular prediction of the immune biomarkers. We identified a total of 7 differentially expressed genes in the dataset, namely METTL3, WTAP, YWHAG, TRA2A, YTHDF3, LRPPRC and HNRNPA2B1. The random forest algorithm indicated that all 7 genes were m6A key regulatory genes of IS, and the credibility of the above key regulatory genes was verified by constructing a clinical prediction model. Based on the expression of key regulatory genes, we divided IS patients into 2 groups. Based on the expression of the gene LRPPRC and the correlation of immune infiltration under different subgroups, LRPPRC was identified as an immune biomarker for IS. GO enrichment analyses indicate that LRPPRC is associated with a variety of cellular functions. Protein interaction network analysis and molecular prediction indicated that LRPPRC correlates with a variety of immune proteins, and LRPPRC may serve as a target for IS drug therapy. Our findings suggest that LRPPRC is an immune marker for IS. Further analysis based on LRPPRC could elucidate its role in the immune microenvironment of IS.

Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19

The death of coronavirus disease 2019 (COVID-19) is primarily due to from critically ill patients, especially from ARDS complications caused by SARS-CoV-2. Therefore, it is essential to contribute an in-depth understanding of the pathogenesis of the disease and to identify biomarkers for predicting critically ill patients at the molecular level. Immunogenic cell death (ICD), as a specific variant of regulatory cell death driven by stress, can induce adaptive immune responses against cell death antigens in the host. Studies have confirmed that both innate and adaptive immune pathways are involved in the pathogenesis of SARS-CoV-2 infection. However, the role of ICD in the pathogenesis of severe COVID-19 has rarely been explored. In this study, we systematically evaluated the role of ICD-related genes in COVID-19. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis based on ICD differentially expressed genes. The results showed that immune infiltration characteristics were altered in severe and non-severe COVID-19. In addition, we used multiple machine learning methods to screen for five risk genes (KLF5, NSUN7, APH1B, GRB10 and CD4), which are used to predict COVID-19 severity. Finally, we constructed a nomogram to predict the risk of severe COVID-19 based on the classification and recognition model, and validated the model with external data sets. This study provides a valuable direction for the exploration of the pathogenesis and progress of COVID-19, and helps in the early identification of severe cases of COVID-19 to reduce mortality.

Cuproptosis-Related Biomarkers and Characterization of Immune Infiltration in Sepsis.

Sepsis is a worldwide epidemic, with high morbidity and mortality.Cuproptosis is a form of cell death that is associated with a wide range of diseases. This study aimedto explore genes associated withcuproptosisin sepsis, construct predictive modelsandscreen for potential targets. The LASSO algorithm and SVM-RFE model has been analysedthe expression ofcuproptosis-related genes in sepsis and immune infiltration characteristics and identified the marker genes under a diagnostic model. Gene-drug networks, mRNA-miRNA networksandPPI networks were constructed to screen for potential biological targets. The expression of marker genes was validated based on the GSE57065 dataset. Consensus clustering method was used to classify sepsis samples. We found 381 genes associated with the development of sepsis anddiscovered significantly differentially expressedcuproptosis-related genes of 16 cell types in sepsis and immune infiltration with CD8/CD4 T cells being lower.NFE2L2, NLRP3, SLC31A1, DLD, DLAT, PDHB, MTF1, CDKN2A andDLST were identified as marker genes by the LASSO algorithm and the SVM-RFE model. AUC > 0.9 was constructed for PDHB and MTF1 alone respectively. The validation group data for PDHB (P=0.00099) and MTF1 (P=7.2e-14) were statistically significant. Consistent clustering analysis confirmed two subtypes.The C1 subtype may be more relevant to cellular metabolism and the C2 subtype has some relevance to immune molecules.The results of animal experiments showed that the gene expression was consistent with the bioinformatics analysis. Our study systematically explored the relationship between sepsis andcuproptosisand constructed a diagnostic model. And, severalcuproptosis-related genes may interfere with the progression of sepsis through immune cell infiltration.

Retracted: Identification of Diagnostic Biomarkers, Immune Infiltration Characteristics, and Potential Compounds in Rheumatoid Arthritis.

[This retracts the article DOI: 10.1155/2022/1926661.].

Multi-omics analysis of TLCD1 as a promising biomarker in pan-cancer.

Background: The TLC Domain Containing 1 (TLCD1) protein, a key regulator of phosphatidylethanolamine (PE) composition, is distributed across several cellular membranes, including mitochondrial plasma membranes. Existing research has revealed the impact of TLCD1 on the development of non-alcoholic fatty liver disease. However, there remains a gap in comprehensive pan-cancer analyses of TLCD1, and the precise role of TLCD1 in cancer patient prognosis and immunological responses remains elusive. This study aims to provide a comprehensive visualization of the prognostic landscape associated with TLCD1 across a spectrum of cancers, while shedding light on the potential links between TLCD1 expression within the tumor microenvironment and immune infiltration characteristics. Methods: TLCD1 expression data were obtained from GTEx, TCGA, and HPA data repositories. Multiple databases including TIMER, HPA, TISIDB, cBioPortal, GEPIA2, STRING, KEGG, GO, and CancerSEA were used to investigate the expression pattern, diagnostic and prognostic significance, mutation status, functional analysis, and functional status of TLCD1. In addition, we evaluated the relationship between TLCD1 expression and immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and immune-related genes in pan-cancer. Furthermore, the association of TLCD1 with drug sensitivity was analyzed using the CellMiner database. Results: We found that TLCD1 is generally highly expressed in pan-cancers and is significantly associated with the staging and prognosis of various cancers. Furthermore, our results also showed that TLCD1 was significantly associated with immune cell infiltration and immune regulatory factor expression. Using CellMiner database analysis, we then found a strong correlation between TLCD1 expression and sensitivity to anticancer drugs, indicating its potential as a therapeutic target. The most exciting finding is that high TLCD1 expression is associated with worse survival and prognosis in GBM and SKCM patients receiving anti-PD1 therapy. These findings highlight the potential of TLCD1 as a predictive biomarker for response to immunotherapy. Conclusion: TLCD1 plays a role in the regulation of immune infiltration and affects the prognosis of patients with various cancers. It serves as both a prognostic and immunologic biomarker in human cancer.

Novel Prognostic Model Construction of Tongue Squamous Cell Carcinoma Based on Apigenin-Associated Genes.

Clinical indexes are often selected as relevant factors for constructing prognostic models of tongue squamous cell carcinoma (TSCC) patients, while factors related to therapeutic targets are less frequently included. As Apigenin (API) shows anti-tumor properties in many tumors, in this study, we construct a novel prognostic model for TSCC patients based on Apigenin-associated genes through transcriptomic analysis. The effect of Apigenin (API) on the cell characteristics of TSCC cells was measured by several phenotype experiments. RNA-seq was executed to ensure differentially expressed genes (DEGs) in squamous cell carcinoma-9 (SCC-9) cells after API treatment. Furthermore, reverse transcription quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry were performed to verify the expression of API-related genes. Then, combined with the gene expression data and relevant individual information of TSCC samples acquired from The Cancer Genome Atlas (TCGA), an API-related model was built through Lasso regression and multivariate Cox regression. A receiver operating characteristic (ROC) curve and a nomogram and calibration curve were created to forecast patient outcomes to improve the clinical suitability of the API-related signature. The relationships between the two risk groups and function enrichment, immune infiltration characteristics, and drug susceptibility were analyzed. We demonstrated that API could inhibit the malignant behavior of TSCC cells. Among API-related genes, TSCC cells treated with API, compared to the control group, have higher levels of transmembrane protein 213 (TMEM213) and G protein-coupled receptor 158 (GPR158), and lower levels of caspase 14 (CASP14) and integrin subunit alpha 5 (ITGA5). An 7 API-associated gene model was built through Lasso regression and multivariate Cox regression that could direct TSCC prognostic status and tumor immune cell infiltration. In addition, we acquired 6 potential therapeutic agents for TSCC based on the prognostic model. Our research suggested the inhibition effect of API on TSCC cells and provided a novel prognostic model combined with therapeutic factors that can guide the prognosis of TSCC and clinical decision-making in TSCC.

Integrated single-cell and bulk characterization of branched chain amino acid metabolism-related key gene BCAT1 and association with prognosis and immunogenicity of clear cell renal cell carcinoma.

The relationship between clear cell renal cell carcinoma (ccRCC) and branched-chain amino acids (BCAA) metabolism has yet to be thoroughly explored. The BCAA metabolism-related clusters were constructed using non-negative matrix factorization (NMF). The features of BCAA metabolism in ccRCC were evaluated by building a prognostic model using least absolute shrinkage and selection operator (LASSO) regression algorithm. Real-time quantitative PCR (RT-qPCR) was employed to analyze differential expression of branched-chain amino acid transaminase 1 (BCAT1) between cancer and paracancer tissues and between different cell lines. Cell counting kit-8, wound healing and Transwell chamber assays were conducted to determine changes in proliferative and metastatic abilities of A498 and 786-O cells. Two BCAA metabolism-related clusters with distinct prognostic and immune infiltration characteristics were identified in ccRCC. The BCAA metabolic signature (BMS) was capable of distinguishing immune features, tumor mutation burden, responses to immunotherapy, and drug sensitivity among ccRCC patients. RT-qPCR revealed overexpression of BCAT1 in ccRCC tissues and cell lines. Additionally, single-gene RNA sequencing analysis demonstrated significant enrichment of BCAT1 in macrophages and tumor cells. BCAT1 played tumor-promoting role in ccRCC and was closely associated with immunosuppressive cells and checkpoints. BCAT1 promoted ccRCC cell proliferation and metastasis. The BMS played a crucial role in determining the prognosis, tumor mutation burden, responses to immunotherapy and drug sensitivity of ccRCC patients, as well as the immune cell infiltration features. BCAT1 was linked to immunosuppressive microenvironments and may offer new sights into ccRCC immunotherapeutic targets.

Progressions of the correlation between lipid metabolism and immune infiltration characteristics in gastric cancer and identification of BCHE as a potential biomarker.

Globally, gastric cancer (GC) is a category of prevalent malignant tumors. Its high occurrence and fatality rates represent a severe threat to public health. According to recent research, lipid metabolism (LM) reprogramming impacts immune cells' ordinary function and is critical for the onset and development of cancer. Consequently, the article conducted a sophisticated bioinformatics analysis to explore the potential connection between LM and GC. We first undertook a differential analysis of the TCGA queue to recognize lipid metabolism-related genes (LRGs) that are differentially expressed. Subsequently, we utilized the LASSO and Cox regression analyses to create a predictive signature and validated it with the GSE15459 cohort. Furthermore, we examined somatic mutations, immune checkpoints, tumor immune dysfunction and exclusion (TIDE), and drug sensitivity analyses to forecast the signature's immunotherapy responses. Kaplan-Meier (K-M) curves exhibited considerably longer OS and PFS (p<0.001) of the low-risk (LR) group. PCA analysis and ROC curves evaluated the model's predictive efficacy. Additionally, GSEA analysis demonstrated that a multitude of carcinogenic and matrix-related pathways were much in the high-risk (HR) group. We then developed a nomogram to enhance its clinical practicality, and we quantitatively analyzed tumor-infiltrating immune cells (TIICs) using the CIBERSORT and ssGSEA algorithms. The low-risk group has a lower likelihood of immune escape and more effective in chemotherapy and immunotherapy. Eventually, we selected BCHE as a potential biomarker for further research and validated its expression. Next, we conducted a series of cell experiments (including CCK-8 assay, Colony formation assay, wound healing assay and Transwell assays) to prove the impact of BCHE on gastric cancer biological behavior. Our research illustrated the possible consequences of lipid metabolism in GC, and we identified BCHE as a potential therapeutic target for GC. The LRG-based signature could independently forecast the outcome of GC patients and guide personalized therapy.

Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Unravels the Molecular Feature of Tumor-Associated Macrophage of Acute Myeloid Leukemia.

The association between acute myeloid leukemia (AML) and macrophage remains to be deeply explored. Gene expression profiles and clinical variable characteristics of AML patients were collected from TCGA, GEO, and TARGET databases. Consensus clustering was employed to construct the macrophage-related clusters. The macrophage-related index (MRI) was constructed using the LASSO and multivariate Cox analysis. The GSE71014 and TARGET datasets were utilized as external validation sets. Single-cell sequencing data for AML (GSE116256) was adopted to analyze modeled gene expression levels in cells. Two macrophage-related clusters with different prognostic and immune infiltration characteristics were constructed in AML. Cluster B had a poorer prognosis, more cancer-promoting pathway enrichment, and an immunosuppressive microenvironment. Relied on the MRI, patients of different groups showed different levels of immune infiltration, different mutations, and prognoses. LGALS1 and BCL2A1 may play roles in promoting cancer in AML, while ELANE may have a significant effect on suppressing cancer. Macrophage-related genes (MRGs) had significant impacts on the occurrence and progression of AML. MRI may better evaluate the prognosis and immune features of AML patients.

Crosstalk of cuproptosis-related subtypes, establishment of a prognostic signature, and immune infiltration characteristics in gastric cancer

BackgroundCuproptosis is a novel form of cellular demise that occurs through a unique pathway involving lipoylated proteins in the tricarboxylic acid (TCA) cycle and is closely linked to mitochondrial metabolism. Nevertheless, the comprehensive elucidation of the impact of carcinogenesis-associated genes (CRGs) on prognosis, tumor microenvironment (TME), and therapeutic response in patients with gastric cancer (GC) remains unclear. MethodsIn total, 1374 GC samples were gathered from three Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas database. The samples were then stratified into different subtypes through unsupervised clustering of the 13 CRG profiles. The CRG_score was developed to quantify CRG patterns of individual tumors. Subsequently, we investigated the associations among the various groups and clinicopathological features, immune infiltration features, TME mutation status, and response to immunotherapy. ResultsThe GC samples were divided into two clusters based on their distinct clinicopathological features, prognosis, and immune characteristics. Using LASSO and Cox regression analyses, 9 genes were identified for constructing a prognostic signature related to cuproptosis. The novel signature displayed outstanding durability and prognostic capability for the overall lifespan of individuals. Additionally, the expression levels of signature genes in GC tissues and adjacent normal tissues were tested by qRT-PCR. Moreover, we developed a remarkably dependable nomogram to enhance the practicality of the CRG_score in clinical settings. High tumor mutation burden, increased microsatellite instability-high, immune activation, along with good survival probability and increased immunoreactivity to immune checkpoint inhibitors, were distinguishing features of low CRG_scores. ConclusionsThe findings of this study revealed the possible impacts of CRGs on the TME, clinical and pathological characteristics, and outlook of patients with GC. This signature was strongly linked to the immune response against GC and has the potential to serve as a valuable tool for predicting patient prognosis.

Identification and validation of palmitoylation metabolism-related signature for liver hepatocellular carcinoma

BackgroundProtein S-palmitoylation is a reversible posttranslational modification widely involved in tumor progression. Nevertheless, the function of palmitoylation metabolism in prognosis and tumor microenvironment characteristics in liver hepatocellular carcinoma (LIHC) patients is not fully understood. MethodsmRNA and clinical data of LIHC patients were obtained from the TCGA and ICGC databases. Consensus clustering was used to construct palmitoylation metabolism-related clusters. Univariate Cox and Lasso regression analyses were employed to establish a palmitoylation metabolism-related signature (PMS). ssGSEA was applied to evaluate the immune cell score in each LIHC sample. Functional enrichments were accessed through GO, KEGG and GSVA. Drug sensitivity data were downloaded from the GDSC database. ResultsThree palmitoylation metabolism-related clusters with different prognostic and immune infiltration characteristics were constructed in LIHC. We identified PMS with distinct survival, clinical, and tumor immune microenvironment characteristics. The high PMS group had a poorer prognosis, higher infiltration of immunosuppressive cells and higher expression of immune checkpoints. ZDHHC20 exerted a tumor-promoting role in LIHC and was significantly associated with immunosuppressive cells and immunosuppressive checkpoints. Additionally, in HepG-2 and SMCC-7721 cells, si-ZDHHC20 boosted apoptosis but decreased proliferation and migration when compared to si-NC. ConclusionOur research revealed that PMS may accurately predict the prognosis and immune characteristics of LIHC patients. ZDHHC20 has significant clinical and immune relevance in LIHC and may contribute to the formulation of new targets for LIHC immunotherapy.

Identification of PANoptosis-related biomarkers and immune infiltration characteristics in psoriasis.

PANoptosis may play a vital role in psoriasis. We investigated the relationship between PANoptosis in psoriasis. Genes information was mainly obtained from GeneCards and the gene expression omnibus database. Genefunctions identification was based on gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Gene set enrichment analysis was used to identify enriched signaling pathways in psoriasis. We constructed PPI networks using the search tool for the retrieval of interacting genes database and Cytoscape and explored mRNA-miRNA, mRNA-TF, and mRNA-drug interaction networks. Receiver operating characteristic curves were performed to screen potential biomarkers among these hub genes. Immune cell infiltration was analyzed using the Pearson algorithm, and the correlation between immune-cell abundance and PANoptosis-related differentially expressed gene (PDGs) was investigated. We identified 10 PDGs, which were mainly involved in pyroptosis, cytokine-mediated signaling pathways, Salmonella infection and NOD-like receptor signaling pathway. The activated pathways were mostly proinflammatory and immunoregulatory pathways between immune cells. BAK1, CASP4, IL18, and IRF1 were identified as hub genes in the mRNA-miRNA network, and BAK1, IRF1, and PYCARD were hub genes in the mRNA-TF network. CASP1 was found to be the most targeted gene by drugs or molecular compounds. We found PDGs were positively associated with proinflammatory immune cell infiltration and negatively associated with anti-inflammatory or regulatory immune cells. We confirmed the role of PANoptosis in psoriasis for the first time and predicted hub genes and immune characteristics, which provides new ideas for further investigation of psoriasis on pathogenic mechanisms and therapeutic strategies.

Systematic analysis of cuproptosis-related genes in immunological characterization and predictive drugs in Alzheimer's disease.

This study aimed to make a systematic analysis of cuproptosis-related genes (CRGs) in immunological characterization and predictive drugs in Alzheimer's disease (AD) through bioinformatics and biological experiments. The molecular clusters related to CRGs and associated immune cell infiltrations in AD were investigated. The diagnostic models were constructed for AD and different AD subtypes. Moreover, drug prediction and molecular docking were also performed. Subsequently, a molecular dynamics (MD) simulation was conducted to further verify the findings. Finally, RT-qPCR validation was performed. The characterization of 12 AD-related CRGs was evaluated in AD, and a diagnostic model for AD showed a satisfying discrimination power based on five CRGs by LASSO regression analysis. The dysregulated CRGs and activated immune responses partially differed between patients with AD and healthy subjects. Furthermore, two molecular subtypes (clusters A and B) with different immune infiltration characteristics in AD were identified. Similarly, a diagnostic model for different AD subtypes was built with nine CRGs, which achieved a good performance. Molecular docking revealed the optimum conformation of CHEMBL261454 and its target gene CSNK1D, which was further validated by MD simulation. The RT-qPCR results were consistent with those of the comprehensive analysis. This study systematically elucidated the complex relationship between cuproptosis and AD, providing novel molecular targets for treatment and diagnosis biomarkers of AD.

Uncovering potential new biomarkers and immune infiltration characteristics in primary Sjögren's syndrome by integrated bioinformatics analysis.

Primary Sjögren's syndrome (pSS) is known as autoimmune disease characterized by damage to endocrine glands, such as the salivary and lacrimal glands. This study aimed to identify potential biomarkers for pSS using integrated bioinformatics analysis and explore the relationship between differentially expressed genes (DEGs) and immune infiltration. Three pSS datasets (GSE7451, GSE23117, and GSE40611) from the gene expression omnibus database were integrated. All the datasets were processed in R (version 4.0.3). A total of 16 immune cells and 13 immune functions were obtained. The top immune cell and immune function were "activated" dendritic cells and major histocompatibility complex class I. Correlation analysis showed the top correlation among 16 immune cells were B cells and tumor infiltrating lymphocytes, check-point and T cell co-stimulation, respectively. In comparisons of immune score, "activated" dendritic cells (.657 vs 594, P < .001), B cells (.492 vs 434, P = .004), macrophages (.631 vs 601, P = .010), inflammation-promoting (.545 vs 478, P < .001), Type I interferon Reponse (.728 vs 625, P < .001) and so on were higher in pSS than control group. In correlation analysis, the up-regulation of interferon induced protein with tetratricopeptide repeats 1 gene was strongly correlated with Type I interferon response with a correlation coefficient of .87. The receiver operating characteristic curve of 5 genes showed that the area under curve was.891. In the verification model, the area under curve was.881. In addition, disease ontology analysis supported the association between DEGs and pSS. In summary, pSS has a variety of DEGs in immune infiltration, which is worthy of the attention from clinicians.

Molecular Subtypes Based on PANoptosis Genes and Characteristics of Immune Infiltration in Cutaneous Melanoma.

Cutaneous melanoma (CM) remains the most life-threatening form of skin cancer. Further risk stratification and search for new prognostic targets for CM are of positive clinical significance. PANoptosis is defined as an inflammatory programmed cell death mediated by the PANoptosome complex and cannot be characterized by pyroptosis, apoptosis or necroptosis alone. Although PANoptosis is closely associated with many diseases including cancer, it has not been reported in CM. Combined with GTEx and TCGA database, we extracted 14 PANoptosis-related genes (PAGs). The molecular subtypes of CM were then analyzed by PAGs and their associations with the immune microenvironment and immunotherapy reactivity were analyzed. LASSO and univariate Cox analysis was performed on PAGs-related differentially expressed genes to establish PAGs characteristics that could effectively predict the prognosis of CM patients. Immune infiltration, tumor mutation burden analysis, immunotherapy response and drug sensitivity analysis were used to further analyze the causes of prognostic differences. Our study provides a new perspective on the role of PANoptosis in CM.