BACKGROUND: Pulmonary sarcomatoid carcinoma (PSC) is a highly invasive pulmonary malignancy with an extremely poor prognosis. The results of previous studies suggest that ubiquitin-specific peptidase 9X (USP9X) contributes to the progression of numerous types of cancer. Nevertheless, there is little knowledge about the molecular mechanisms and functions of USP9X in the metastasis of PSC.
METHODS: Immunohistochemistry and western blotting were used to detect USP9X expression levels in PSC tissues and cells. Wound healing, transwell, enzyme-linked immunosorbent assay (ELISA), tube formation, and aortic ring assays were used to examine the function and mechanism of USP9X in the metastasis of PSC.
RESULTS: Expression of USP9X was markedly decreased and significantly correlated with metastasis and prognosis of patients with PSC. Then we revealed that USP9X protein levels were negatively associated with the levels of epithelial-mesenchymal transition (EMT) markers and the migration of PSC cells. It was confirmed that USP9X in PSC cells reduced VEGF secretion and inhibited tubule formation of human umbilical vein endothelial cells (HUVEC) in vitro. USP9X was detected to downregulate MMP9. Meanwhile, MMP9 was positively related to EMT, angiogenesis and was negatively related to immune infiltration in the public databases. USP9X was significantly negatively associated with the expression of MMP9, EMT markers, CD31, and positively associated with CD4, and CD8 in PSC tissues.
CONCLUSIONS: The present study reveals the vital role of USP9X in regulating EMT, angiogenesis and immune infiltration and inhibiting metastasis of PSC via downregulating MMP9, which provides a new effective therapeutic target for PSC.

 The incidence of Alzheimer\'s disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis‑related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

DLAT has been recognized as a cuproptosis-related gene that is crucial for cuproptosis in earlier research. The study is to look at how DLAT affects individuals with low-grade glioma\'s prognosis and immune infiltration. The Genotype-Tissue Expression (GTEx) database and the TCGA database were used in this work to download RNAseq data in TPM format. DLAT was found to be overexpressed in LGG by comparing DLAT expression levels between LGG and normal brain tissue, and the expression of DLAT was verified by immunohistochemistry and semi-quantitative analysis. Then, the functional enrichment analysis revealed that the biological functional pathways and possible signal transduction pathways involved were primarily focused on extracellular matrix organization, transmembrane transporter complex, ion channel complex, channel activity, neuroactive ligand-receptor interaction, complement and coagulation cascades, and channel activity. The level of immune cell infiltration by plasmacytoid dendritic cells and CD8 T cells was subsequently evaluated using single-sample gene set enrichment analysis, which showed that high DLAT expression was inversely connected with that level of infiltration. The link between the methylation and mRNA transcription of DLAT was then further investigated via the MethSurv database, and the results showed that DLAT\'s hypomethylation status was linked to a poor outcome. Finally, by evaluating the prognostic value of DLAT using the Cox regression analysis and Kaplan-Meier technique, a column line graph was created to forecast the overall survival (OS) rate at 1, 3, and 5 years after LGG identification. The aforementioned results demonstrated that high DLAT expression significantly decreased OS and DSS, and that overexpression of DLAT in LGG was significantly linked with WHO grade, IDH status, primary therapy outcome, overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) events. DLAT was discovered as a separate predictive sign of OS in the end. DLAT might thus represent a brand-new predictive biomarker.

BACKGROUND: Accurate regulation of gene expression is crucial for normal development and function of cells. The prognostic significance and potential carcinogenic mechanisms of the related gene JARID2 in OSCC are not yet clear, but existing research has indicated a significant association between the two.
METHODS: The relationship between the expression of the JARID2 gene in tumor samples of OSCC patients and clinical pathological factors was analyzed using immunohistochemistry experiments and RT-qPCR analysis. Based on the clinical pathological data of patients, bioinformatics analysis was conducted using public databases to determine the function of JARID2 in OSCC. Knockdown OSCC cell lines were constructed, and the impact of JARID2 on the biological behavior of OSCC cell lines was assessed through CCK-8, wound healing assay, and transwell analysis.
RESULTS: Immunohistochemistry experiments confirmed the correlation between JARID2 and the prognosis of OSCC patients, while RT-qPCR experiments demonstrated its expression levels in tissue and cells. CKK-8 experiments, wound healing assays, and Transwell experiments indicated that knocking down JARID2 had a negative impact on the proliferation, invasion, and migration of OSCC cells. Bioinformatics analysis results showed that the expression of JARID2 in OSCC is closely associated with patient gene co-expression, gene function enrichment, immune infiltration, and drug sensitivity.
CONCLUSIONS: Our study indicates that JARID2 is a novel prognostic biomarker and potential therapeutic target for OSCC.

UNASSIGNED: Chronic obstructive pulmonary disease (COPD) stands as a predominant cause of global morbidity and mortality. This study aims to elucidate the relationship between pyroptosis-related genes (PRGs) and COPD diagnosis in the context of immune infiltration, ultimately proposing a PRG-based diagnostic model for predicting COPD outcomes.
UNASSIGNED: Clinical data and PRGs of COPD patients were sourced from the GEO database. The \"ConsensusClusterPlus\" package was employed to generate molecular subtypes derived from PRGs that were identified through differential expression analysis and LASSO Cox analysis. A diagnostic signature including eight genes (CASP4, CASP5, ELANE, GPX4, NLRP1, GSDME, NOD1and IL18) was also constructed. Immune cell infiltration calculated by the ESTIMATE score, Stroma scores and Immune scores were also compared on the basis of pyroptosis-related molecular subtypes and the risk signature. We finally used qRT - PCR to detect the expression levels of eight genes in COPD patient and normal.
UNASSIGNED: The diagnostic model, anchored on eight PRGs, underwent validation with an independent experimental cohort. The area under the receiver operating characteristic (ROC) curves (AUC) for the diagnostic model showcased values of 0.809, 0.765, and 0.956 for the GSE76925, GSE8545, and GSE5058 datasets, respectively. Distinct expression patterns and clinical attributes of PRGs were observed between the comparative groups, with functional analysis underscoring a disparity in immune-related functions between them.
UNASSIGNED: In this study, we developed a potential as diagnostic biomarkers for COPD and have a significant role in modulating the immune response. Such insights pave the way for novel diagnostic and therapeutic strategies for COPD.

UNASSIGNED: One of the primary reasons for tumor invasion and metastasis is anoikis resistance. Biochemical recurrence (BCR) of prostate cancer (PCa) serves as a harbinger of its distant metastasis. However, the role of anoikis in PCa biochemical recurrence has not been fully elucidated.
UNASSIGNED: Differential expression analysis was used to identify anoikis-related genes based on the TCGA and GeneCards databases. Prognostic models were constructed utilizing LASSO regression, univariate and multivariate Cox regression analyses. Moreover, Gene Expression Omnibus datasets (GSE70770 and GSE46602) were applied as validation cohorts. Gene Ontology, KEGG and GSVA were utilized to explore biological pathways and molecular mechanisms. Further, immune profiles were assessed using CIBERSORT, ssGSEA, and TIDE, while anti-cancer drugs sensitivity was analyzed by GDSC database. In addition, gene expressions in the model were examined using online databases (Human Protein Atlas and Tumor Immune Single-Cell Hub).
UNASSIGNED: 113 differentially expressed anoikis-related genes were found. Four genes (EEF1A2, RET, FOSL1, PCA3) were selected for constructing a prognostic model. Using the findings from the Cox regression analysis, we grouped patients into groups of high and low risk. The high-risk group exhibited a poorer prognosis, with a maximum AUC of 0.897. Moreover, larger percentage of immune infiltration of memory B cells, CD8 Tcells, neutrophils, and M1 macrophages were observed in the high-risk group than those in the low-risk group, whereas the percentage of activated mast cells and dendritic cells in the high-risk group were lower. An increased TIDE score was founded in the high-risk group, suggesting reduced effectiveness of ICI therapy. Additionally, the IC50 results for chemotherapy drugs indicated that the low-risk group was more sensitive to most of the drugs. Finally, the genes EEF1A2, RET, and FOSL1 were expressed in PCa cases based on HPA website. The TISCH database suggested that these four ARGs might contribute to the tumor microenvironment of PCa.
UNASSIGNED: We created a risk model utilizing four ARGs that effectively predicts the risk of BCR in PCa patients. This study lays the groundwork for risk stratification and predicting survival outcomes in PCa patients with BCR.

BACKGROUND: Overexpression of SLC16A3 can contribute to the development of various tumors by regulating metabolism, but a systematic analysis of SLC16A3 in bladder cancer (BC) has been rarely reported.
METHODS: We used the BC datasets from public databases to investigate SLC16A3 expression in BC. We first analysed the relationship between SLC16A3 expression and clinical characteristics of 412 bladder cancer patients. After that, gene function analyses and immunocorrelation analyses of SLC16A3 were conducted with the R package. For immunotherapy effect and drug sensitivity analysis, we also used the R package. We also analysed the relation between SLC16A3 expression and 20 m6A modification key genes. Finally, we determined the expression localization of SLC16A3 in bladder cancer by single-cell sequencing analysis using 3,115 BC cells. We further detected the expression of SLC16A3/MCT4 on BC samples by reversed transcriptionquantitative polymerase chain reaction and immunohistochemistry.
RESULTS: The SLC16A3 was overexpressed in BC cells, including epithelial cells (p＜0.001). The high SLC16A3 expression level of patients with BC was significantly related to poor prognosis (p＝0.044), and we established a reliable prognosis model for BC patients. Statistically significant associations between SLC16A3 and m6A modification (ALKBH5) gene (p＜0.001), key genes in aerobic glycolysis, M2 macrophage infiltration (p＝0.0058), and immune checkpoint regulation were observed.
CONCLUSIONS: Overexpression of SLC16A3 is an independent prognostic factor in patients with BC. SLC16A3 may influence the immune infiltration of BC by regulating BC metabolism and m6A methylation, which ultimately can lead to the progress of BC. For the detection and therapy of BC, SLC16A3 may be a potent therapeutic target for BC.

OBJECTIVE: Cyclin-dependent kinase 1 (CDK1) regulates the cell cycle and is highly expressed in most tumors. CDK1 expression has been associated with poor disease prognosis. This study aimed to identify the prognostic value of CDK1 in pan-cancer and investigate the association between CDK1 expression and immune cell infiltration.
METHODS: CDK1 expression and its correlation with prognosis in pan-cancer were analyzed using online databases. Immune infiltration was assessed by ESTIMATE and CIBERSORT algorithms. We then evaluated the relationship between CDK1 expression and tumor mutational burden (TMB), microsatellite instability (MSI), or tumor-infiltrating immune cells. In addition, we performed the co-expression analysis of immune-related genes and GO analysis with CDK1 expression in pan-cancer. Finally, we compared the CDK1 expression profile with the immune-related genes in 30 pairs of clinical gastrointestinal tumor samples.
RESULTS: Our analysis demonstrated overexpression of CDK1 in most tumor tissues, especially in gastrointestinal tumors. The high expression of CDK1 was associated with poor overall survival, disease-specific survival, disease-free interval, and progression-free interval in kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), pancreatic adenocarcinoma (PAAD), prostate adenocarcinoma (PRAD), and sarcoma (SARC). Besides, CDK1 expression was significantly associated with TMB in 22 cancer types and MSI in 8 cancer types as well as greater frequencies of MSI-high (MSI-H) status and high tumor mutational burden (TMB-H) in uterine corpus endometrial carcinoma (UCEC), stomach adenocarcinoma (STAD), sarcoma (SARC), rectum adenocarcinoma (READ), mesothelioma (MESO), head and neck squamous cell carcinoma (HNSC), and colon adenocarcinoma (COAD). In addition, CDK1 expression correlated with immune cell infiltrating levels, such as M0, M1, or M2 macrophages, memory CD4 T cells, T follicular helper cells, and naive B cells. Our data showed that CDK1 was remarkably correlated with 47 immune-related and immune checkpoint genes in many cancer types. Furthermore, CDK1 was up-regulated in gastrointestinal tumor samples, especially in gastric cancer and intestinal cancer. CDK1 was positively correlated with IDO1 in gastric cancer and PD-1 in intestinal cancer.
CONCLUSIONS: Taken together, our data demonstrated the roles of CDK1 in oncogenesis and metastasis in pan-cancer. Thus, CDK1 is a potential prognostic biomarker and a target for tumor immunotherapy.

BACKGROUND: Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN.
METHODS: The gene chip data were retrieved from the GEO database using the search term \' diabetic nephropathy \'. The \' limma \' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package \'WGCNA\' was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the \'ClusterProfiler\' software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the \'ConsensusClusterPlus \'R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients.
RESULTS: Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P < 0.05).
CONCLUSIONS: Our study provides new insights into the role of immune-related biomarkers in DN tubulointerstitial injury and provides potential targets for early diagnosis and treatment of DN patients. Seven different genes ( AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1, FSTL1 ), as promising sensitive biomarkers, may affect the progression of DN by regulating immune inflammatory response. However, further comprehensive studies are needed to fully understand their exact molecular mechanisms and functional pathways in DN.

BACKGROUND: Parkinson\'s disease (PD) is the fastest growing neurological disease. Currently, there is no disease-modifying therapy to slow the progression of the disease. Danggui buxue decoction (DBD) is widely used in the clinic because of its therapeutic effect. However, little is known about the molecular mechanism of DBD against PD. This study intends to explore the possible molecular mechanisms involved in DBD treatment of PD based on network pharmacology, and provide potential research directions for future research.
METHODS: Firstly, the active components and target genes of DBD were screened from the traditional Chinese medicine systems pharmacology (TCMSP), DrugBank and UniProt database. Secondly, target genes of PD were identified from the (GEO) dataset, followed by identification of common target genes of DBD and PD. Thirdly, analysis of protein-protein interaction (PPI), functional enrichment and diagnosis was performed on common target genes, followed by correlation analysis between core target genes, immune cell, miRNAs, and transcription factors (TFs). Finally, molecular docking between core target genes and active components, and real-time PCR were performed.
RESULTS: A total of 72 common target genes were identified between target genes of DBD and target genes of PD. Among which, 11 target genes with potential diagnostic value were further identified, including TP53, AKT1, IL1B, MMP9, NOS3, RELA, MAPK14, HMOX1, TGFB1, NOS2, and ERBB2. The combinations with the best docking binding were identified, including kaempferol-AKT1/HMOX1/NOS2/NOS3, quercetin-AKT1/ERBB2/IL1B/HMOX1/MMP9/TP53/NOS3/TGFB1. Moreover, IL1B and NOS2 respectively positively and negatively correlated with neutrophil and Type 1 T helper cell. Some miRNA-core target gene regulatory pairs were identified, such as hsa-miR-185-5p-TP53/TGFB1/RELA/MAPK14/IL1B/ERBB2/AKT1 and hsa-miR-214-3p-NOS3. These core target genes were significantly enriched in focal adhesion, TNF, HIF-1, and ErbB signaling pathway.
CONCLUSIONS: Diagnostic TP53, AKT1, IL1B, MMP9, NOS3, RELA, MAPK14, HMOX1, TGFB1, NOS2, and ERBB2 may be considered as potential therapeutic targets of DBD in the treatment of PD.