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BACKGROUND: Fibromyalgia syndrome, as a common rheumatic disease, is related to central sensitization and immune abnormalities. However, the specific mechanism has not been elucidated, and there is a lack of specific diagnostic markers. Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE: To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods, such as weighted gene co-expression network analysis (WGCNA), and machine learning. METHODS: Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus (GEO) database. The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis. Least absolute shrinkage and selection operator (LASSO) and support vector machine -recursive feature elimination (SVM-RFE) machine learning algorithm were further used to identify hub biomarkers, and draw receiver operating characteristic curve (ROC) to evaluate the accuracy of diagnosing fibromyalgia syndrome. Finally, single sample gene set enrichment analysis (ssGSEA) and gene set enrichment analysis (GSEA) were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION: Eight down-regulated differentially expressed genes (DEGs) were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)| > 0 and P < 0.05. After WGCNA analysis, 497 genes were included in the module (MEdarkviolet) with the highest positive correlation (r=0.22, P=0.04), and 19 genes were included in the module (MEsalmon2) with the highest negative correlation (r=-0.41, P=6×10-5). After intersecting DEGs and the module genes of WGCNA, seven genes were obtained. Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm. After the intersection of the two, three core genes were identified, which were germinal center associated signaling and motility like, integrin beta-8, and carboxypeptidase A3. The areas under the ROC curve of the three core genes were 0.744, 0.739, and 0.734, respectively, indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome. The results of immune infiltration analysis showed that memory B cells, CD56 bright NK cells, and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group (P < 0.05), and were significantly positively correlated with the above three biomarkers (P < 0.05). The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways, mainly related to olfactory transduction pathway, neuroactive ligand- receptor interaction, and infection pathway. The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes, abnormal immune regulation, and multiple pathways imbalance. However, the interactions between these genes and immune cells, as well as their relationships with various pathways need to be further investigated. [ABSTRACT FROM AUTHOR]

BACKGROUND: At present, the pathogenesis of osteoarthritis is still unclear, and there is a lack of effective means to control the disease. Research on osteoarthritis is mostly concentrated in the field of immunity, and there are few studies in the field of oxidative stress. OBJECTIVE: To explore the roles of oxidative stress and immune infiltration in osteoarthritis and to predict related miRNAs and therapeutic agents. METHODS: The GSE55235 dataset (10 samples of osteoarthritis and 10 healthy control samples) and the GSE55457 dataset (10 samples of osteoarthritis and 10 healthy control samples) were obtained from the GEO database for merging to obtain their differentially expressed genes that were combined with oxidative stress genes to get the differentially expressed genes of oxidative stress. The differentially expressed genes of oxidative stress were analyzed for KEGG and GO enrichment, and the osteoarthritis pathways and biological processes were evaluated using GSEA enrichment analysis. The protein-protein interaction network was constructed using the STRING online website and Cytoscape software, and the Degree algorithm was run to get the key genes. The GSE1919 dataset was obtained from the GEO database as a validation dataset, and the key genes were analyzed by variance analysis and receiver operating characteristic curve analysis to get the core genes. In addition, immune infiltration was evaluated by CIBERSORT and the correlation between core genes and immune cells was explored. miRNA prediction of core genes was performed using TargetScan and target drugs were predicted using the DSigDB database. RESULTS AND CONCLUSION: Sixty-five differentially expressed genes and five core genes (IL1B, CXCL8, MYC, NFKBIA, JUN) associated with oxidative stress were identified. Enrichment analysis showed that differentially expressed genes associated with oxidative stress were concentrated in the pathways of oxidative stress, interleukin-17, osteoclast differentiation, fluid shear stress and atherosclerosis. The area under the receiver operating characteristic curve for the five core genes exceeded 0.85, indicating their excellent specificity and sensitivity in diagnosing bone and joint conditions, as well as their close association with immune cells. The predicted miRNA was has-miR-3937, and the therapeutic small-molecule drugs were metformin, ionomycin and celecoxib. To conclude, oxidative stress and immune infiltration exist in osteoarthritis, and immune infiltration is involved in activating oxidative stress. The core genes and predicted miRNAs can be used as novel markers for the diagnosis of osteoarthritis, and small molecule drugs are predicted to treat osteoarthritis. [ABSTRACT FROM AUTHOR]

Objective: Screening of hypertrophic cardiomyopathy-related signature genes and analysis of immune cell infiltration characteristics by bioinformatics methods. Methods: The dataset was downloaded from the GEO database, and multiple algorithms in R language were used to analyze differentially expressed genes, construct weighted gene co-expression networks to obtain clinical relevance module, screen core genes, identify signature genes, identify signature gene prognosis efficacy and perform functional enrichment to explore immune cell infiltration. Results: A total of 117 differential genes and a core module containing 32 genes were screened, and 19 core genes were obtained. Among them, ABCA5, DNAJC16, LGALS14, MGAT4A, SLC30A8 were identified as signature genes. Statistically significant changes in the levels of macrophages, eosinophils, and plasmacytoid dendritic cells in the plasma of patients with hypertrophic cardiomyopathy compared with healthy controls were observed (P<0.05). Conclusion: The characteristic genes ABCA5, DNAJC16, LGALS14, MGAT4A and SLC30A8 may serve as prognosis markers for hypertrophic cardiomyopathy, immune cells such as macrophages and eosinophils may be involved in the occurrence and development of hypertrophic cardiomyopathy. [ABSTRACT FROM AUTHOR]

Objective: To investigate relationship between expression of ADGRG5 and clinical prognosis and immune response in pancreatic adenocarcinoma (PAAD). Methods: ADGRG5 expression in PAAD and normal tissues were compared by Wilcoxon rank sum test. Diagnostic value of ADGRG5 was evaluated by ROC curve in PAAD. Kaplan-Meier method and Cox regression analysis were used to evaluate prognostic factors. Gene set enrichment analysis (GSEA) and immune infiltration analysis were applied to annotate biological function of ADGRG5. Results: ADGRG5 expression in PAAD was significantly higher than normal tissue (P=2.8e-32). ADGRG5 had significant diagnostic and prognostic ability for PAAD (AUC=0.866). High ADGRG5 expression predicted a good progress free interval (PFI) (P=0.01), and expression of ADGRG5 was independently associated with PFI (HR: 0.656, 95%CI: 0.433~0.972, P=0.035). ADGRG5 expression was related to regulation of immunomodulatory pathway and function of some types of immune infiltrating cells. Conclusion: Increased ADGRG5 may be a potential biomarker for PAAD diagnosis and prognosis, which affects prognosis of PAAD patients and significantly correlated with immune infiltration. [ABSTRACT FROM AUTHOR]

Objective: To analyze the relationship between cuprotosis-realted gene DLD expression in pan-cancer and prognosis, tumor microenvironment and tumor immune infiltration by bioinformatics methods combined with multiple databases. Methods: TCGA, GTEx, TISDB, GEO, UCSC, TARGET, TIMER2.0, GEPIA2, SangerBox, PrognoScan and related R software packages were used to analyze the difference of DLD expression between human cancer and adjacent normal tissues, inestigate relationship between prognosis, explore expression of DLD in different immune subtypes and molecular subtypes of human cancer, analyze correlation between DLD expression and tumor immune infiltration and tumor microenvironment, detecet relationship between DLD expression and 60 immune checkpoint genes. Results: There were differences in expression of DLD in different tumor tissues; low DLD expression in 6 tumors and high DLD expression in 3 tumors were significantly associated with better prognosis; 15 tumor immune subtypes, 11 molecular subtypes of tumors were significantly correlated with DLD expression; DLD expression was significantly correlated with immune infiltration in 37 cancer types; DLD expression was significantly associated with immune infiltration in 23 tumor stroma scores, 26 tumor immune scores and 25 tumor ESTIMATE scores; 60 immune checkpoint genes in 39 tumors were significantly associated with DLD expression. Conclusion: Expression of DLD in pan-cancer is related to tumor prognosis and immune invasion, DLD may be a potential predictor of prognosis in tumors, and may be an immunomodulator of tumors. [ABSTRACT FROM AUTHOR]

Objective To use machine learning algorithms to screen differentially apoptotic related genes (DARGS) in knee osteoarthritis, in order to further identify relevant biomarkers and to elucidate the role of apoptosis in the pathogenesis of knee osteoarthritis. Methods Firstly, the gene expression matrices of datasets GSE55235 and GSE169077 were downloaded and integrated from the GEO database. The differential analysis and WGCNA analysis on the integrated gene expression matrices were performed using R packages, respectively. Then, the two analysis result were intersected with apoptosis related genes obtained from the Genecards database to obtain DARGs, which were subjected to GO, KEGG, and GSEA enrichment analysis. Finally, the MCODE plugin in Cytoscape was used to screen the modules with the closest protein-protein interactions among DARGs. The Lasso algorithm was used to screen the hub gene biomarkers, and ROC curves were drawn in the osteoarthritis dataset GSE178557 for validation. The gene miRNA transcription factor drug regulatory network and immune infiltration analysis were performed on the hub gene biomarkers with good validation result. Results After screening, a total of 189 DARGs were obtained. After validation with a validation set, 7 reliable hub gene biomarkers were ultimately obtained. The miRNA regulatory network and transcription factor regulatory network of these genes were predicted. The potential drug and immune cell infiltration analysis result for hub gene biomarkers were obtained. Conclusion Apoptosis plays an important role in the pathogenesis of knee osteoarthritis. Seven hub gene biomarkers including TYROBP, COL14A1, CD74, LAMA4, COL5A1, MMP13, and IGFBP5 provide valuable clues for diagnosis and in-depth understanding of the mechanism of knee osteoarthritis. [ABSTRACT FROM AUTHOR]

Objective: To explore the role and clinical significance of mitochondria related reactive oxygen species (ROS) and glutathione (GSH) related genes in stroke based on bioinformatics, and to screen the key genes. Methods: Stroke related targets were obtained by gene expression omnibus (GEO) database, and mitochondria related ROS and GSH were obtained by FerrDb database. R language was used to evaluate the gene set scores and expression differences of mitochondria associated ROS and GSH related genes in stroke, as well as immune infiltration analysis, cluster analysis and post-cluster subgroup related feature differences. Results: The expression of mitochondrial related ROS and GSH gene sets was different between the normal group and the stroke group, and the expression levels of immune cells and inflammatory factors were significantly different between the two groups. Subgroup 1 and subgroup 2 were different in clinical phenotype, gene expression level, proportion of immune cells, level of inflammatory factors and protein enrichment. Conclusion: Mitochondria-related ROS and GSH genes play an important role in the immunity of stroke, and the different expression levels of their key genes may affect different immune pathways and clinical phenotypes. [ABSTRACT FROM AUTHOR]

Objective: To investigate clinical significance of WDR4 gene expression, prognosis and immune infiltration in lung adenocarcinoma(LUAD). Methods: Clinicopathological data and WDR4 mRNA expression data of 535 patients with LUAD were collected through TCGA database, WDR4 protein expression data of 111 patients with LUAD were collected through CPTAC database. Associations between WDR4 expression and various clinicopathological features, as well as with overall survival in LUAD were analyzed by R software. Diagnostic value of WDR4 expression in LUAD were assessed by ROC. Correlation between WDR4 expression and immune infiltration in tumor microenvironment in LUAD were investigated by Estimates and ssGSEA algorithms. Results: WDR4 mRNA expression in LUAD was significantly higher than that in normal lung tissues (all P<0.001). Protein expression of WDR4 in LUAD was also significantly up-regulated (P<0.05). Univariate analysis showed that expression of WDR4 was correlated with lymph node metastasis (P<0.05). Log-rank test showed that overall survival of patients with LUAD in WDR4 high expression group was significantly lower than that in low expression group (P=0.004). Multivariate Cox analysis revealed that T stage, lymph node metastasis and WDR4 mRNA expression remained independently associated with prognosis of LUAD patients. Death risk in patients with high expression of WDR4 mRNA was 1.478 times that of patients with low expression (95%CI: 1.041~2.097). ROC curve analysis showed that WDR4 mRNA expression predicted diagnosis of LUAD to high extent (AUC=0.912,95%CI: 0.885~0.935). High expression of WDR4 was negatively correlated with tumor microenvironment immune infiltration (all P<0.05). Conclusion: WDR4 expression in LUAD is significantly higher than that in normal lung tissue, and high WDR4 expression indicates poor prognosis in patients with LUAD. High expression of WDR4 inhibits immune infiltration in tumor microenvironment. WDR4 may be a novel potential biomarker for diagnosis and prognosis of patients with LUAD and therapeutic target. [ABSTRACT FROM AUTHOR]

Objective Immune infiltration, as well as their implications across various cancers, and to establish a clinical prognostic model based on the findings. Methods The expression profile and clinical information of endometrial cancer tissue and adjacent tissues in TCGA endometrial cancer database were used, the differential expression genes were screened out for analysis, and the mRNA co-expressed by LncRNA was analyzed by GO enrichment and KEGG signaling pathway.The differential genes between endometrial cancer and adjacent tissues were intersected with the basement membrane protein genes. The selected differentially expressed genes were combined with survival status and survival time to screen out Hub genes by Lasso-cox regression analysis. Multivariate Cox regression analysis was used to establish a prognostic model, and pan-cancer analysis and immunoinfiltration correlation analysis were further performed. Results Six basal membrane protein Hub genes, ADAMTS5, EVA1C, THBS4, CTSD, ITGAV and LAMA1, were identified as associated with the prognosis of patients with endometrial cancer, and found that the survival rate of patients decreased significantly with the increase of risk score. Pan-cancer analysis found that these 6 genes were significantly different in most cancer types, and high expression in GBMLGG (glioma), LGG (low-grade glioma of the brain), LAML (acute myeloid leukemia), UVM (Uveal melanoma), ACC (adrenal cortical cancer) and other cancers had poor prognosis. The potential role of these genes in tumor immunotherapy was also explored, and significant negative correlation was found with Th17 cells, Th2 cells, NK CD56 bright cells and other immune cells (P < 0.01), and significant positive correlation was found with Tcm, iDC, Eosinophils, aDC and other immune cells (P < 0.01). Conclusion Basal membrane protein gene has high clinical value in the diagnosis and prognosis of endometrial cancer, and can be used as a prognostic marker and potential therapeutic target for patients with endometrial cancer. [ABSTRACT FROM AUTHOR]

Objective This study employs bioinformatics methods to analyze the expression of Claudin 1 (CLDN1) across various types of cancer and its functional role in gastric cancer. Methods The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to evaluate the expression patterns of CLDN1 in various cancer types and their association with clinical pathology, patient prognosis, and immune characteristics. Western blotting, Polymerase Chain Reaction(PCR), and cell function assays were used to examine the expression and function of CLDN1 in gastric cancer cells. Results CLDN1 was significantly upregulated in 13 types of tumors and downregulated in 7, with a strong correlation to tumor stage and grade. It demonstrated good diagnostic potential and was associated with poor prognosis in 8 cancer types. Additionally, CLDN1 showed the highest mutation rate in lung squamous cell carcinoma. Immune analysis revealed significant correlations between CLDN1 expression and immune infiltration, immune checkpoint gene expression, tumor mutation burden, and microsatellite instability across most cancer types. CLDN1 was highly expressed in MKN45 cells, and its knockdown significantly reduced cell proliferation and migration. Conclusion CLDN1 exhibits significant expression heterogeneity across various cancers, with strong associations with diagnosis, prognosis, clinical characteristics, and immune profiling. It represents a potential diagnostic marker and therapeutic target in cancer treatment.

BACKGROUND: Rheumatoid arthritis is a chronic autoimmune disease. Early diagnosis is crucial for preventing disease progression and for effective treatment. Therefore, it is of significance to investigate the diagnostic characteristics and immune cell infiltration of rheumatoid arthritis. OBJECTIVE: Based on the Gene Expression Omnibus (GEO) database, to screen potential diagnostic markers of rheumatoid arthritis using machine learning algorithms and to investigate the relationship between the diagnostic characteristics of rheumatoid arthritis and immune cell infiltration in this pathology. METHODS: The gene expression datasets of synovial tissues related to rheumatoid arthritis were obtained from the GEO database. The data sets were merged using a batch effect removal method. Differential expression analysis and functional correlation analysis of genes were performed using R software. Bioinformatics analysis and three machine learning algorithms were used for the extraction of disease signature genes, and key genes related to rheumatoid arthritis were screened. Furthermore, we analyzed immune cell infiltration on all differentially expressed genes to examine the inflammatory state of rheumatoid arthritis and investigate the correlation between their diagnostic characteristics and infiltrating immune cells. RESULTS AND CONCLUSION: In both rheumatoid arthritis and normal synovial tissues, we identified 179 differentially expressed genes, with 124 genes upregulated and 55 genes down-regulated. Enrichment analysis revealed a significant correlation between rheumatoid arthritis and immune response. Three machine learning algorithms identified LRRC15 and MICB as potential biomarkers of rheumatoid arthritis. LRRC15 (area under the curve=0.964, 95% confidence interval: 0.924-0.992) and MICB (area under the curve=0.961, 95% confidence interval: 0.923-0.990) demonstrated strong diagnostic performance on the validation dataset. The infiltration of 13 types of immune cells was altered, with macrophages being the most affected. In rheumatoid arthritis, the majority of proinflammatory pathways in immune cell function were activated. Immunocorrelation analysis revealed that LRRC15 and MICB had the strongest correlation with M1 macrophages. To conclude, this study identified LRRC15 and MICB as potential diagnostic markers for rheumatoid arthritis, with strong diagnostic performance and significant correlation with immune cell infiltration. Machine learning and bioinformatics analysis deepened the understanding of immune infiltration in rheumatoid arthritis and provided new ideas for the diagnosis and treatment of rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

Objective To investigate the role of PSMA4 in the prognosis, diagnosis and immune infiltration of lung adenocarcinoma (LUAD), and explore its underlying mechanism. Methods The expression profiles and clinical data of LUAD patients were sourced from the Cancer Genome Atlas (TCGA) database. The expression level of PSMA4 in LUAD tissues (n=539) and normal tissues (n=59) were compared using the Wilcoxon rank-sum test. The expression levels of PSMA4 in LUAD tissues and normal tissues were validated by analyzing the GSE40791 and GSE10072 LUAD datasets obtained from the Gene Expression Omnibus (GEO) database. In addition, tumor and adjacent non-cancerous tissues were collected from 10 LUAD undergoing lung biopsy by fiberoptic bronchoscopy in Second Affiliated Hospital of Army Medical University from January to December 2023. The PSMA4 expression in above samples was further verified using RT-qPCR. RT-qPCR was performed to detect the expression of PSMA4 in lung cancer cells and normal lung epithelial cells. Functional enrichment analysis and immune cell infiltration analysis were conducted on the cells with high and low expression of PSMA4. Cox regression analysis and Kaplan-Meier (KM) survival analysis were used to determine the diagnostic and prognostic value of PSMA4 for LUAD, and a nomogram was constructed to predict the overall survival rate at different time points. Results The analysis of TCGA datasets, GSE40791, and GSE10072 LUAD data revealed that PSMA4 expression was significantly higher in LUAD tissues than in normal tissues (P < 0.01). RT-qPCR further confirmed that the expression of PSMA4 was obviously elevated in LUAD tissues and lung cancer cells than adjacent non-cancerous tissues and normal lung epithelial cells (P < 0.01). High PSMA4 expression could be regarded as a marker for LUAD diagnosis and poor prognosis, and was associated with reduced proportions of Tem cells, TFH cells, B cells, NK cells, Tcm cells, and mast cells in the tumor microenvironment. Conclusion PSMA4 presents significant diagnostic performance for LUAD, and is closely associated with the prognosis and immune infiltration of this malignancy.

Objective :Integrating genes and GEO database related cuproptosis chips, to analyze connection between cuproptosis genes, immune infiltration and myocardial infarction (Ml), construct risk prediction model, predict Western medicine and Chinese medicine, analyze miRNA-mRNA regulatory network, and to provides a new research direction for the future study of Mi-related cuproptosis mechanism and immune infiltration. Methods: By GEO database retrieval of MI related chips, standardized processing and Ml-related cuproptosis genes screening were performed, and immunoinfiltration analysis and quantification were performed based on the treated gene expression matrix, correlation between immune infiltrating cells and function was analyzed, as well as their differences in MI group and the control group. Cuproptosis genes that most related to MI in immune infiltration were screened out, and the risk model was constructed to analyze the risk probability of cuproptosis genes in MI. The Enrichr website and Coremine Medical database were used to predict cuproptosis-related genes in MI in Western medicine and traditional Chinese medicine. Finally, the upstream mirnas of FDX1 and SLC31A1 were predicted by miRTarBase, Starbase and Targetscan databases, and miRNA-mRNA regulatory networks were constructed. Results: Correlation of immune infiltration showed that Tfh cells and B cells had the strongest positive correlation (r=0.68), while regulatory T cells and iDC had the strongest negative correlation (r=-0.63); the difference analysis of immune infiltration showed that the differences among mast cells, NK cells and Thl cells in the MI group at the cellular level were the most significant (P<0.001) ; and the differences in APC co-inhibition and MHC I at the functional level were the most significant (P< 0.001). Six genes with the highest correlation between immune cells and immune function were screened out; ATP7A, DLD, FDX1, LIAS, LIPT1 and SLC31A1. Results of the risk model showed that the high levels of FDX1 and SLC31A1 were negatively correlated with the risk prediction of MI. A total of 21 Western medicines and 30 traditional medicines were predicted by database comparison. miRTarBase, Starbase and Targetscan databases predicted 9 upstream miRNAs of cuproptosis-related genes in MI, including has-miR- 122-5p. Conclusion:Tfh cells, B cells, para-inflammatory, type I interferon response and other related immune cells and functions may play important roles in pathogenesis and prognosis of ML FDX1 and SLC31A1 as the key genes of cuproptosis process, are negatively correlated with ML A total of 30 kinds of traditional Chinese medicines including Spirulina, sheep liver, Wen ezhu, Pian jianghuang and Yujin may have potential value in treatment of ML Finally, 9 miRNAs including has-miR-122-5p may play an important role in the regulation of cuproptosis in myocardial infarction. [ABSTRACT FROM AUTHOR]

Objective: To explore the role and relationship between oxidative stress and immune infiltration in idiopathic pulmonary fibrosis (IPF), and to predict the relevant therapeutic herbal medicines and active ingredients. Methods: GSE10667 gene expression profiles were downloaded from GEO database to obtain differential expression genes, differential expression of oxidative stress genes (DEOSGs) were identified in combination with oxidative stress genes. GSEA was used to evaluate the pathways and biological processes in IPF, and GO, KEGG and PPI network analysis were performed on DEOSGs. Candidate central genes were derived from PPI results and CytoHubba, and GSE110147 was validated as an independent group to identify central genes; in addition, the immune microenvironment of samples was evaluated using CIBERSORTF, and correlation between central gene levels and relative proportion of immune cells was explored; finally, therapeutic herbal medicines and components were predicted by central genes, and molecular docking verification was carried out. Results: A total of 51 DEOSGs, four central genes (ICAM-1, APOE, MMP-1, TGF- β2) were obtained; DEOSGs were mainly related to oxidative stress, immune response, etc; four central gene levels were closely correlated with 8 relative proportions of immune cells; therapeutic herbal medicines included 4 flavors such as Huangqi and Chuanxiong, and the active ingredients included 8 kinds of β-carotene, etc, the molecular docking results were stable. Conclusion: Oxidative stress and immune firing are exist in IPF, and oxidative stress may be recognized by immune cells or directly activate immune cells. [ABSTRACT FROM AUTHOR]

Objective: Using bioinformatics methods to study the immune infiltration mechanism of lupus nephritis (LN) and to explore potential target Chinese medicines, which can provide new directions for clinical treatment of LN. Methods: Gene expression profile microarray dataset of LN was downloaded from GEO database, differential expressed genes (DEGs) were screened using R software, and GO and KEGG enrichment analysis were performed on these DEGs. Protein interaction network analysis of DEGs was performed by applying STRING database, key genes were screened by using Cytoscape, core gene expression was validated by Nephroseq database, and the infiltration and correlation of 22 kinds of immune cells in LN were calculated by CIBERSORT deconvolution algorithm. Finally, herbal prediction of significantly enriched immune-related biological processes and key target genes were performed by Coremine Medical database. Results: A total of 367 LN-related DEGs were obtained, of which 253 were up-regulated and 114 were down-regulated. GO was mainly enriched in response to viruses, defense response to viruses, regulation of viral life cycle, regulation of viral processes, etc; KEGG was mainly enriched in S. aureus infection, COVID-19, influenza A, complement and coagulation cascade, pertussis, etc. Ten key genes were screened: IFIT3, OAS2, MX1, OAS1, RSAD2, XAF1, ISG15, IFIT1, ITGAM and PTPRC. Immune infiltration by CIBERSORT deconvolution algorithm showed that monocytes and initial B cells were highly expressed in LN, while memory B cells, naive CD4+T cells, follicular helper T cells and resting natural killer cells were lowly expressed in LN. Immune cell correlation analysis showed a positive correlation between initial B cells and plasma cells, a positive correlation between resting natural killer cells and resting dendritic cells, a negative correlation between monocytes and regulatory T cells, and a negative correlation between resting CD4+ memory T cells and monocytes. Coremine Medical predicted that Salvia miltiorrhiza, Panax notoginseng, Scutellaria baicalensis, ginseng, honey, monkey mushroom and peony were found to be most closely related to immunity in LN. Conclusion: The development and progression of LN are results of the combined involvement of multiple genes and pathways. Monocytes, memory B cells, initial CD4+T cells and initial B cells are most closely associated with LN. The predicted Salvia miltiorrhiza, Panax notoginseng, Scutellaria baicalensis, ginseng, honey, monkey mushroom and peony may be used as target herbs for the potential treatment of LN. [ABSTRACT FROM AUTHOR]

BACKGROUND: Rheumatoid arthritis is a chronic systemic autoimmune disease. It is important to study the immunological changes involved in it for diagnosis and treatment. OBJECTIVE: To identify immune-related biomarkers associated with rheumatoid arthritis utilizing bioinformatics techniques and examine alterations in immune cell infiltration as well as the relationship between immune cells and biomarkers. METHODS: Differential expression analysis was used to identify the immune-related genes that were up-regulated in rheumatoid arthritis based on the GEO and Immport databases. Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) enrichment analyses were used to investigate the possible function of these elevated genes. The immunological characteristic genes associated with rheumatoid arthritis were screened using least absolute shrinkage and selection operator (Lasso) and support vector machine recursive feature elimination (SVM-RFE). Independent datasets were used for difference validation, and the diagnostic performance was evaluated by plotting receiver operating characteristic curves for feature genes. Immune cell infiltration was used to analyze the differential profile of immune cells in rheumatoid arthritis and the correlation between the characterized genes and immune cells. In order to ascertain the causal relationship between monocytes and rheumatoid arthritis in immune cells, Mendelian randomization analysis was ultimately employed. RESULTS AND CONCLUSION: There were 39 upregulated differentially expressed genes in rheumatoid arthritis. The genes were primarily enriched in chemotaxis, cytokine activity, and immune receptor activity, according to GO enrichment analysis, while kEGG enrichment analysis revealed that the genes were considerably enriched in the tumor necrosis factor signaling pathway and peripheral leukocyte migration. Lasso and SVM-RFE identified five feature genes: CXCL13, SDC1, IGLC1, PLXNC1, and SLC29A3. Independent dataset validation of the feature genes found them to be similarly highly expressed in rheumatoid arthritis samples, with area under the curve values greater than 0.8 for all five feature genes in both datasets. Immune cell infiltration indicated that most immune cells, including natural killer cells and monocytes, exhibited increased levels of infiltration in rheumatoid arthritis samples. The correlation analysis revealed a significant positive correlation between memory B cells and immature B cells and these five feature genes. Correlation analysis showed that the five feature genes were positively correlated with memory B cells and immature B cells. The inverse variance weighting method revealed that monocytes were associated with the risk of developing rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To investigate the expression of eight subunits of chaperonin containing t-complex 1 (CCT) in thyroid cancer (TC) tissues and their correlation with TC staging, patient prognosis, immune cell infiltration, immune checkpoint expression, and chemotherapy drug sensitivity. Methods: Data from The Cancer Genome Atlas (TCGA) database were used to analyze the expression of each CCT subunit in TC tissues and para-cancerous tissues as well as their relationship with the prognosis of TC patients. The biological function of each CCT subunit was analyzed by the Gene Set Enrichment Analysis (GSEA). Data from the TCGA and TIMER2.0 databases were used to analyze the correlations between the expression of each CCT subunit and the tumor microenvironment, infiltration of immune cells, chemotherapeutic drug sensitivity, and immune checkpoint expression. Results: Database analysis showed high mRNA expression of CCT3, CCT7, and CCT8 and low mRNA expression of CCT1, CCT2, CCT5, and CCT6B in TC tissues (P < 0.01 or P < 0.001). CCT3, CCT6B, and CCT8 mRNA expression were correlated with T staging (P < 0.05 or P < 0.01); CCT6B mRNA expression was associated with lymph node metastasis (P < 0.01); CCT5 mRNA expression was associated with distant metastasis (P < 0.05); and CCT6B may serve as an independent prognostic biomarker for overall survival (OS) in TC patients. CCT subunit expression was primarily enriched in signaling pathways such as graft rejection, complement, and interferongamma.Low expression groups of CCT subunits demonstrated significantly higher TC stromal score, immune infiltration score and composite score than the high expression groups (P < 0.05 or P < 0.01 or P < 0.001), displaying a negative correlation between the expression of each CCT subunit and TC stromal, immune infiltration and composite scores (all P < 0.01). The mRNA expression of each CCT subunit was positively correlated with CD8+ T cell and macrophage infiltration (all P < 0.05), the mRNA expression of most CCT subunits (except CCT6B and CCT7) was positively correlated with neutrophil infiltration (P < 0.05 or P < 0.01 or P < 0.001), whereas the mRNA expression of CCT3, 4, 7, and 8 was negatively correlated with CD4+ T-cell infiltration (all P < 0.05). Compared to the low-expression group, patients with high mRNA expression of most CCT subunit had significantly higher IC50 for chemotherapeutic agents such as sorafenib, levatinib, darafenib, trametinib, vandetanib, and cabozantinib (all P < 0.05 or P < 0.01 or P < 0.001). CCT subunit expression in TC tissues was significantly correlated with the expression of PD-1, PD-L1, PD-L2, CTLA4, CD80, and CD86 (P < 0.05 or P < 0.01 or P < 0.001). Conclusion: CCT complexes may promote TC development by affecting the tumor microenvironment thus impacting patient prognosis, potentially serving as a target for the diagnosis and immunotherapy of refractory TC. [ABSTRACT FROM AUTHOR]

Copyright of Practical Oncology Journal is the property of Journal of Practical Oncology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To investigate diagnostic and prognostic value of actin-binding protein ANLN in clear cell renal cell carcinoma （ccRCC） and its relationship with tumor microenvironment. Methods: Gene expression data and clinical data for ccRCC were downloaded from The Cancer Genome Atlas （TCGA）. Relationship between ANLN expression and clinicopathological features was assessed by Wilcoxon rank sum test and Logistic regression. Receiver operating characteristic （ROC） curve was used to assess diagnostic value of ANLN expression in ccRCC. Kaplan-Meier and Cox regression analysis were used to investigate effect of ANLN expression on overall survival. Gene set enrichment analysis （GSEA） was used to identify signaling pathways associated with ANLN in ccRCC. Relationship between ANLN expression and immune infiltration was analyzed by ESTIMATE algorithm, tumor immune estimation resource （TIMER） and CIBERSORT algorithms. Relationship between ANLN and drug sensitivity was calculated using CellMiner database. Results: ANLN expression was significantly upregulated in ccRCC tissues. ANLN expression in ccRCC was correlated with clinicopathological features. ROC analysis showed that ANLN had a high diagnostic value in ccRCC. High ANLN expression was significantly associated with poor prognosis. Multivariate Cox regression analysis showed that high ANLN expression was an independent risk factor for overall survival in ccRCC patients. GSEA showed that ANLN was associated with multiple signaling pathways. In terms of immunity, ANLN was closely associated with tumor microenvironment, immune infiltration and immune checkpoint molecules in ccRCC. ANLN expression was negatively correlated with sensitivity of most antitumor drugs. Conclusion: ANLN is a potential diagnostic and prognostic biomarker and immunotherapeutic target for ccRCC. [ABSTRACT FROM AUTHOR]

Objective: To analyze the correlation between cuproptosis genes and immune infiltration during the occurrence and development of multiple sclerosis （MS）, and to predict the traditional Chinese medicine, to provide theoretical basis for the mechanism study of cuproptosis in MS immune infiltration and Chinese medicine to intervene in immune regulation. Methods: The gout chip of MS was downloaded from the GEO database and standardized; based on the processed data, immune cells and functions were extracted and quantified, and the correlation and differences of immune cells and functions were analyzed; at the same time, cuproptosis genes related to MS were screened out, a risk model was constructed, and enrichment analysis was carried out; the prediction of cuproptosis genes and immune-related biological processes were carried out. Results: ① Among immune cells, T follicular helper cell and B cell showed the strongest positive correlation; among the immune functions, parainflammation and typeⅠIFN reponse showed the strongest positive correlation; ②B cell, T helper cell and human leukocyte antigen were lowly expressed in MS patients, while major histocompatibility complex class Ⅰ, parainflammation and typeⅠIFN response were significantly expressed; ③ The cuproptosis genes associated with MS were SLC31A1, PDHA1, NLRP3, MTF1, GLS and DBT, of which DBT was the most likely risk factor for MS; ④The occurrence and development of MS involves biological processes such as IL-4 production, T-helper cell differentiation, and acute inflammatory response, which were related to pathways such as arginine biosynthesis, citrate cycle, and propanoate metabolism; ⑤Banxia, Danshen, Jianghuang and other traditional Chinese medicines may be used as potential molecular drug sources. Conclusion: The expression of cuproptosis gene is closely related to MS-related immune cells and functions, which can provide support for the basic research of MS. [ABSTRACT FROM AUTHOR]

Objective: To investigate the expression and clinical significance of CENPF in renal clear cell carcinoma （KIRC）. Methods: The UALCAN and HPA databases were applied to analyze the difference in expression of CENPF in KIRC compared with paracancerous tissue the relationship between CENPF gene and clinicopathological parameters and prognosis of KIRC patients were analyzed by the GEPIA database; the relationship between CENPF and immune infiltration level of KIRC were analyzed by the TIMER database; the UALCAN database was used to analyze the co-expression network of CENPF genes in TCGA KIRC patients, GO enrichment analysis and KEGG enrichment analysis were performed on the basis of the co-expression network. CCK-8, Transwell and clone formation assays were used to investigate the effect of CENPF down-regulated expression in renal cancer cell lines on the proliferation, migration and clone formation of ACHN and 786-O cells. Results: CENPF expression was significantly highly expressed in KIRC patients. High CENPF expression was negatively correlated with overall survival （OS） and disease-free survival （DFS）. The expression of CENPF in KIRC patients were significantly and positively correlated with tumor cell purity, macrophages, CD8+T cells, CD4+T cells, neutrophils and dendritic cells in tumor microenvironment. The correlation network analysis indicated that CENPF may be involved in the development of KIRC through the regulation of cell cycle, cell mitosis and DNA repair. In addition, CENPF mRNA expression was also increased in renal cancer cell lines. CCK-8 assay showed that CENPF knockdown inhibited the proliferation of ACHN and 786-O cells. Transwell assay results indicated that CENPF knockdown suppressed the migration of KIRC cells. The results of cell clonal formation experiment showed that interference with CENPF gene expression inhibited the clonal formation ability of 786-O cells. Conclusion: This study suggests that CENPF plays a key role in the progression of KIRC. CENPF has prospective clinical significance as a prognostic indicator and potential target of KIRC. [ABSTRACT FROM AUTHOR]

Copyright of Journal of China Medical University is the property of Journal of China Medical University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Early diagnosis and treatment of intervertebral disc degeneration is particularly important. Pyroptosis of nucleus pulposus cells plays an important role in the early process of intervertebral disc degeneration, but the role of pyroptosis-related molecules of nucleus pulposus cells in early intervertebral disc degeneration and related molecular markers are still unclear. OBJECTIVE: To explore the diagnostic and therapeutic value of differentially expressed genes related to pyroptosis during intervertebral disc degeneration. METHODS: Public datasets of the GEO database were integrated for differential analysis, and intersected with 33 pyroptosis-related genes previously reported. Using Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes, pathway enrichment analysis was performed in genes related to pyroptosis during intervertebral disc degeneration. Enrichment analysis of the intervertebral disc degeneration dataset was conducted using gene set enrichment analysis. To construct the immune cell spectrum in intervertebral disc degeneration samples, the correlation between the differentially expressed pyroptosis-related genes and immune cells was analyzed. Protein interaction networks were built. Screening of the Hub gene to identify key genes associated with intervertebral disc degeneration in protein interaction networks. The receiver operating characteristic curve of the differentially expressed pyroptosis-related genes was plotted, and the area under the curve was calculated. The clinical diagnostic value of target genes was explored. qRT-PCR was applied to verify the difference in the expression of pyroptosis-related genes between normal human nucleus pulposus cells and degenerated human nucleus pulposus cells with intervertebral discs. RESULTS AND CONCLUSION: (1) 4 426 differentially expressed genes associated with intervertebral disc degeneration were obtained, and 14 differentially expressed pyroptosis-related genes were obtained after the intersection. (2) Gene Ontology and Kyoto Encyclopedia of Genes and Genomes revealed important enrichment pathways, mainly related to inflammation, cell cycle, infection and NOD-like receptor pathway. Gene set enrichment analysis showed that pathways such as amino acid metabolism and P53 transcription regulation were significantly enriched in patients with intervertebral disc degeneration. Immune infiltration analysis suggested that the occurrence and development of intervertebral disc degeneration were closely related to immune cells. (3) A total of five Hub genes were screened, namely IL1-β, Caspase-1, AIM2, Caspase-5, and NLRC4. (4) Acquisition and identification of key biomarkers for intervertebral disc degeneration: After intersecting the two datasets of GEO with the pyroptosis-related gene, it was found that NLRP3 had significant expression differences, and the receiver operating characteristic curve showed that NLRP3 had clinical diagnostic significance. (5) qRT-PCR showed that the expression of Hub genes such as IL1-β, Caspase-5 and NLRC4 was significantly increased in the nucleus pulposus cells of intervertebral disc degeneration (P < 0.05), and there was no difference in Caspase-1, AIM2 and NLRP3 expression (P > 0.05). (6) It is suggested that cell pyroptosis plays an important mechanism in the occurrence and development of intervertebral disc degeneration, among which pyroptosis-related molecules NLRP3, IL1-β, Caspase-5 and NLRC4 have early diagnosis and treatment value. [ABSTRACT FROM AUTHOR]

Objective: Anoikis is a programmed cell death process that plays a crucial role in tumor metastasis. Lung adenocarcinoma frequently results in multi-organ metastasis, which significantly impacts patient prognosis. This study aims to identify new anoikis-related gene signature to predict metastasis and prognosis of lung adenocarcinoma. Methods: The study obtained gene expression profiles and clinical data of patients with metastatic and non-metastatic lung adenocarcinoma from the TCGA and GEO databases. Additionally, 293 genes related to anoikis were downloaded from the GeneCard database. Unsupervised cluster analysis was used to divide patients with metastatic lung adenocarcinoma into two tumor subtypes. The study assessed immunoinfiltration and immune cell function in two groups using the TIMER database and single sample gene set enrichment analysis (ssGSEA). A prognostic model of genes related to anoikis was constructed using the minimum absolute contraction and selection algorithm (LASSO) and Cox regression model, which was then validated using external data sets. The predictive power of the model was further evaluated using ROC curves and a nomogram. The study evaluated the differences in immunotherapy and drug therapy between high-risk and low-risk groups. Selective gene expression was verified using real-time quantitative fluorescent PCR (qRT-PCR), and immune cell infiltration was verified using multiple immunofluorescence histochemistry. Results: The two molecular subtypes exhibited significant differences in clinicopathological features, prognosis, and immune cell infiltration. Lasso and multivariate Cox regression identified three prognostic genes related to anoikis (TLE1, EIF2AK3 and BIRC3), and a risk model was constructed using these genes. The patients were categorized into high- and low-risk groups based on their median risk scores. The low-risk group exhibited higher overall survival (OS) time, immune activity, tumor mutation burden (TMB), and PD1/ PD-L1 expression, which is consistent with a better response to immune checkpoint inhibitors. The nomogram demonstrates the model's superior predictive value. The expression of three prognostic genes was higher in lung adenocarcinoma cell lines and tissue, as determined by qRT-PCR. Furthermore, this expression was positively correlated with the degree of immune cell infiltration. Conclusion: This study has established a prognostic risk model characterized by anoikis-related genes. The model has good prognostic value in patients with lung adenocarcinoma and can be used as a potential diagnostic marker and therapeutic target for evaluating patient prognosis. [ABSTRACT FROM AUTHOR]

Objective To analyze the correlation of interferon-induced protein with tetratricopeptide repeats 1 (IFIT1) with immune infiltration and prognosis of ovarian cancer (OC). Methods GEO database was employed to select the tumor immune related genes, and Kaplan-Meier and Prognoscan databases were used to identify the genes significantly associated with OC prognosis. The differential expression of IFIT1 between OC tissue and normal tissue were confirmed with GEPIA, Human Protein Atlas, and Timer databases. The expression level of IFIT1 in OC tissues with different grades and stages were analyzed in the UALCAN database. In addition, based on David database, GO enrichment analysis was used to analyze the interacting genes and proteins of IFIT1 in the String and Genemania databases. Timer and Tisidb databases verified the correlation between IFIT1 and immune cells mutually. Finally, after IFIT1 knockdown xenograft model was constructed based on lentiviral vector of IFIT1 shRNA, the tumor growth was observed in the transplanted nude mice, and infiltration of neutrophils was observed with immunohistochemical assay. Results FIT1, a tumor immune gene, selected from the GEO database, Kaplan-Meier and Prognoscan databases, was negatively correlated with the OC prognosis. GEPIA, Human Protein Atlas, Timer database, and UALCAN database indicated that the expression level of IFIT1 was significantly higher in the OC tissues than the normal ovarian tissues, and had no obvious correlation with tumor stage and grade. Analysis in String, Genemania, and David database found the interaction genes and proteins of IFIT1 were enriched in activation of 2'-5' oligonucleotide synthase, virus defense, and innate immunity, and other processes. The Timer database presented that IFIT1 was positively correlated with the infiltration of CD8+T cells, B cells, dendritic cells, neutrophils, and macrophage in OC, with neutrophils having the most significant correlation. Tisidb and GSCA also confirmed the positive correlation between IFIT1 and neutrophil infiltration in OC (P < 0.05). RT-qPCR and Western blotting confirmed that IFIT1 knockdown resulted in suppressed tumor growth in OC cells xenograft nude mice (P < 0.05) and reduced neutrophil infiltration in the xenograft tissues. Conclusion IFIT1 may influence the malignant progression of OC by promoting neutrophil infiltration.

Objective To explore the expression and clinical significance of ASXL3 in prostate cancer.Methods The data were obtained from multiple bioinformatics databases, including TCGA, GEPIA2, STRING and TIMER. R or online tools were used to analyze the expression of ASXL3 mRNA and its association with the prognosis, clinical features, and immune cell infiltration in prostate cancer. GO and KEGG pathway enrichment analyses were conducted using ASXL3 interacting proteins or expression-related genes. The expression of ASXL3 mRNA in prostate cancer cell lines was verified by using qRT-PCR.Results The expression level of ASXL3 mRNA in prostate cancer tissues was significantly lower compared to normal tissues. The high expression of ASXL3 mRNA was related to the better overall survival of prostate cancer patients, and the expression of ASXL3 mRNA was positively correlated with the infiltration of various immune cells. STRING database analysis revealed potential interactions of ASXL3 with BAP1, EZH2, ASXL2, and BRD4. ASXL3 mRNA expression was significantly lower in prostate cancer cell lines (LNCaP, C4-2, PC-3, DU145) than normal prostate stromal cell WPMY-1.Conclusion ASXL3 is down-regulated in prostate cancer tissues and is associated with overall survival, which may provide a novel prognostic biomarker and potential therapeutic target for prostate cancer.

Objective: To investigate the mechanism of endoplasmic reticulum Ca2+-translocating ATPa.se isoform 1 (ATP2A1) involved in influencing the biological behavior and immune infiltration of colorectal cancer (CRC). Methods: Tran-scriptome and immunohistochemistry (IHC) were u,sed to analyze the relationship between ATP2A1 expression in colorectal cancer tumors and the prognosis of colorectal cancer patients, and GO and KEGG were used to analyze the cellular functions and,signaling pathways that may be involved in the expression level of ATP2A1.The ESTIMATE algorithm was used to analyze the effect of ATP2A1 expression on the tumor microenvironment and the mutational load of tumors. The CCK8 proliferation assay and flow cell cycle assay were used to detect the mechanism of ATP2A1 involved in colorectal cancer proliferation, and the mechanism of ATP2A1 involved in colorectal cancer migration was analyzed by using .scratch wound healing assay, Transwell assay and Western blot assay.Results: Colorectal cancer patients were divided into two groups of high and low expression based on the median value of ATP2A1 expression. Kaplan-Meier curves and Cox regression analyses demonstrated that high ATP2A1 expression was a prognostic risk factor for colorectal cancer patients, and the results of the ESTIMATE algorithm demonstrated that ATP2A1 expression decreased the abundance of immune cell infiltration, increased the tumor mutational load (TMB) and tumor cell stem-ness. The results of CCK8 proliferation assay and cell cycle assay showed that knockdown of ATP2A1 expression caused S-phase blockage in rectal cancer cells and thus inhibited cell proliferation. The results of scratch wound healing assay, Transwell and Western blot assay showed that ATP2A1 could enhance the migration ability of colorectal cancer cells by promoting epithelial mesenchymal transition of colorectal cancer cells.Conclusion: ATP2A1 is highly expressed in colorectal cancer, which can promote the proliferation and migration of colorectal cancer cells, and affect the prognosis and immunotherapeutic efficacy of colorectal cancer patients. [ABSTRACT FROM AUTHOR]

Objective This study aims to develop a prognostic risk prediction model for gastric cancer based on m1A/m5C/m6A/m7G regulated genes and to investigate the relationship between this model and immunology. Methods The Cancer Genome Atlas （TCGA） gastric cancer dataset was utilized to identify m1A/m5C/m6A/m7G regulated genes with significant expression differences. A prognostic risk score （RS） model was constructed using univariate Cox regression analysis and the LASSO algorithm. The RS model was validated using the Kaplan‐Meier（K‐M） statistic and cell lines for RT‐qPCR biological validation. A nomogram model was created using univariate and multivariate Cox regression analyses. The CIBERSORT algorithm and ESTIMATE package were employed to conduct immune correlation analysis. Results A prognostic RS model based on eight methylation regulated genes was developed to classify patients with gastric cancer as high‐risk or low‐risk. These eight genes showed significant expression in gastric cancer cell lines （P < 0.05）. The TCGA‐gastric cancer training set and GSE62254‐validation set showed a substantial connection （P < 0.001） between overall survival rate （OS） and grouping status. The nomogram survival models accurately predicted 1‐year （C‐index = 0.703), 3‐year （C‐index = 0.729), and 5‐year （C‐index = 0.734） survival rates. Immune correlation analysis showed that compared to the low‐risk group, the high‐risk group had higher immune scores and higher expression of immune checkpoint‐related genes （P < 0.05）. Conclusion We created a reliable prognostic RS model based on m1A/m5C/m6A/m7G regulated genes that can predict gastric cancer prognosis and guide individualized immunotherapy decisions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Myelodysplastic syndrome has worse hazards of acute myeloid leukemia transformation, and some studies have revealed that immune infiltration plays a vital part in the two. Nevertheless, more studies are required to confirm the relationship between immune infiltration and related differentially expressed gene regulation. OBJECTIVE: To screen the differentially expressed genes with prognostic significance between myelodysplastic syndrome and acute myeloid leukemia by bioinformatics analysis and explore the possible roles and mechanisms among these differentially expressed genes and immune infiltration mechanisms in the occurrence and progression of diseases. METHODS: The differentially expressed genes were screened for bioinformatics analysis using the GEO datasets, and analyzed by DO, GO, KEGG and GSEA. The TCGA prognostic database was used to plot the K-M curves of differentially expressed genes and receiver operating characteristic curve analysis was applied to evaluate the clinical diagnostic performance. Finally, CIBERSORT analysis was used to intuitively demonstrate the correlation between critical prognostic genes and the distribution of immuno-infiltrated cells. RT-qPCR was employed to detect peripheral blood samples from healthy controls, myelodysplastic syndrome and acute myeloid leukemia patients so as to verify the crucial genes preliminarily. RESULTS AND CONCLUSION: (1) A total of 150 differentially expressed genes were obtained between myelodysplastic syndrome and acute myeloid leukemia, among which 16 genes were up-regulated and 134 were down-regulated. (2) The results of DO, GO, KEGG and GSEA analysis suggested that differentially expressed genes might promote the development of myelodysplastic syndrome to acute myeloid leukemia by regulating the immune response. CIBERSORT revealed the differences in immune infiltration between myelodysplastic syndrome and acute myeloid leukemia. The distribution of CD4+ T cells, monocytes, neutrophils and M1 macrophages decreased in acute myeloid leukemia patients. In contrast, the distribution of inflammatory suppressor cells M2 macrophages increased, suggesting that it may be related to the immunosuppression of acute myeloid leukemia. (3) K-M curve and receiver operating characteristic curve analysis of 150 differentially expressed genes screened out four genes relevant to immunity and prognosis with good diagnostic performance: MANSC1, FLT3, BMX and CXCR2. (4) The results of RT-qPCR exhibited that MANSC1, BMX and CXCR2 were low expressed, while FLT3 was highly expressed in acute myeloid leukemia patients. These findings verify that the differential expression of MANSC1, FLT3, BMX and CXCR2 in patients with myelodysplastic syndrome and acute myeloid leukemia is not only significantly correlated with the prognosis of patients but may also affect the occurrence and development of myelodysplastic syndrome and acute myeloid leukemia by regulating the immune infiltration of patients. They can be used as potential biomarkers and therapeutic targets of the transformation from myelodysplastic syndrome to acute myeloid leukemia, providing a new direction for clinical diagnosis and treatment of the transformation of myelodysplastic syndrome. [ABSTRACT FROM AUTHOR]

Objective: Bioinformatics methods was used to develop and validate a prognostic risk model of pyroptosis and immune-related genes in hepatocellular carcinoma, and to explore immune correlated features based on the risk scores to guide personalised immunotherapy. Methods: TCGA-LIHC and GSE14520 gene expression profiles were downloaded from TCGA and GEO official websites respectively, and immune-related genes were downloaded from INNATEDB and IMMPORT websites. TCGA as the training group and GEO as the test group, Cox regression analysis was performed on expression profile of pyroptosis gene in training group. Selected prognostic related genes for consensus clustering, and extracted the intersection genes between difference genes and immune-related genes. Univariate Cox and Lasso regression analysis were applied to the intersection genes to obtain the prognosis related risk model. High- and low-risk groups were divided according to the median risk of training group, and the feasibility of model was verified by the test group. Finally, the enrichment analysis (GO, KEGG, GSEA), immune correlation analysis and model gene, protein expression of training group were discussed. Results:Nine reliable risk model genes were screened (ICAM1, S100P, FABP3, CCL20, HRG, IGHM, SPP1, C4BPA, C6). GO enrichment analysis showed that risk differential genes mainly existed in various metabolic processes. KEGG enrichment analysis showed that risk differential genes were mainly involved in complement and coagulation cascade signal pathways. CSEA showed that high-risk group was closely related to the activation of carcinogenic pathway. High- and low-risk groups have significant differences in immune infiltration, immune related pathways and immune checkpoints. HPA database showed that there were significant differences in immunohistochemical expression of ICAM1, S100P and SPP1 model genes. Conclusion: The risk model composed of 9 genes may become a potential biomarker related to the occurrence, development and prognosis of hepatocellular carcinoma, and provide new clues for optimizing the diagnosis and treatment of hepatocellular carcinoma. [ABSTRACT FROM AUTHOR]

To construct a prognostic model of serine protease inhibitor (SERPIN) family-related genes in colon adenocarcinoma (COAD) was constructed by applying bioinformatics methods. Transcriptomic and clinical data of colon adenocarcinoma (COAD) were downloaded from the TCGA database and GEO database, and consistent clustering analysis of COAD patients was performed based on the expression of SERPINs family genes in the data; the data was randomly and equally divided into training set (Train) and validation set (Test). Based on the differential genes of the two subtypes, the COX regression and Lasso regression were used to construct a prognostic model using the Train set. The samples were divided into two groups of high and low risk according to the median risk score of the model, and survival curves were drawn for patients in the high and low risk groups; the predictive ability of the model was evaluated by ROC curves; the model was validated using the data from the Test group. Column line plots were constructed to assess the consistency between the predicted and actual values of patient survival rates. The ESTIMATE algorithm and C1BERSORT algorithms were utilized to assess the correlation between risk scores and tumor microenvironment (TME) as well as immune infiltration. The results showed that two subtypes were identified based on 34 SERPIN genes, and 436 prognosis-related staging differences were screened based on two subtypes. 1 1 prognosis-related genes were identified through Lasso regression to participate in the construction of the risk model, and the high and low risk groups distinguished by model scores had significant survival differences. In addition, the column line graphs could accurately predict 1-year, 3-year, and 5-year survival rates. Tumor microenvironment analysis and immune infiltration analysis showed poor immune activity in patients in the high-risk score group. In addition, the risk score model constructed by SERPIN family-related genes can predict the prognosis of COAD, which is beneficial to further guide the clinical diagnosis and treatment of COAD and improve the survival rate of patients. [ABSTRACT FROM AUTHOR]

Objective To identify inflammation-related genes in atrial fibrillation (AF) and explore the possible role and mechanism of these genes and infiltrating immune cells in the development of AF. Methods A series of bioinformatics analysis combined with machine learning algorithms to identify biomarkers of AF, the receiver operating characteristic (ROC) curves were used to verify the prediction and diagnostic value of key genes, and Spearman correlation analysis was used to clarify the correlation between key genes and infiltrating immune cells. Results 593 differential genes (| log2 (fold change, FC) |> 1, P<0.05), 7 immune cell subtypes (P<0.05) were selected, 190 immune-related differential genes were obtained, 3 biomarkers (IGF1, PTGS 2 and PPARG), and the correlation analysis showed that 3 markers were significantly associated with infiltrating immune cells (P < 0.05). Conclusion IGF1, PTGS2 and PPARG are inflammation-related genes of AF, which are speculated to be closely related to the process and pathway of immune cell infiltration. [ABSTRACT FROM AUTHOR]

Copyright of China Journal of Oral & Maxillofacial Surgery is the property of Shanghai Jiao Tong University, College of Stomatology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objective: To identify key ferroptosis-related biomarkers and investigate immune infiltration in meniscus injuries among individuals of different age groups. Methods: (1) We obtained our training dataset from the GEO database and analyzed gene expression matrices from the GSE191157 gene chip dataset. Simultaneously, we downloaded relevant genes associated with ferroptosis for further analysis. (2)Using the "limma" package, we conducted differential gene expression analysis between elderly and young patients with meniscus injuries and performed weighted gene co-expression network analysis (WGCNA) on all genes in the GSE191157 gene chip dataset. (3) We selected two color modules most correlated with meniscus injury and then intersected the genes in these modules with ferroptosis-related genes. (4)Subsequently, we conducted ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the intersected genes. (5) Using LASSO regression in machine learning, we analyzed the intersected genes related to ferroptosis in meniscus injuries among individuals of different ages and extracted core genes. (6) Finally, we conducted gene set enrichment analysis (GSEA) and single-sample gene set enrichment variation analysis (ssG-SEA) for immune infiltration and confirmed the correlation between core genes and ferroptosis in meniscus injuries. Results: A total of 2 517 differentially expressed genes were identified, and the intersection of these genes with ferroptosis-related genes yielded 61 genes. After WGCNA analysis, the blue and yellow modules were found to be most relevant to the disease, resulting in 46 intersected genes. Through LASSO regression analysis, four core genes (BEX1, MMP13, PTPN18, and SLC38A1) were selected. GSEA results indicated that ferroptosis in meniscus injuries is primarily associated with Toll-like receptor signaling, RNA transcription and splicing, mTOR signaling, and ECM-receptor interaction. Immune cell infiltration analysis revealed a significant correlation between core gene expression and immune cell infiltration. MMP13 exhibited a negative correlation with effector memory CD8 T cells, CD56dim natural killer cells, and central memory CD4 T cells, while SLC38A1, BEX1, and PTPN18 were positively correlated. Conclusion: This study identified four key genes associated with ferroptosis in meniscus injuries among individuals of different age groups, namely BEX1, MMP13, PTPN18, and SLC38A1. Immune cell infiltration analysis demonstrated a clear correlation between core gene expression and immune cell infiltration. [ABSTRACT FROM AUTHOR]

Objective: To analyze the expression and clinical significance of nuclear transcription factor Y subunit A (NFYA) in hepatocellular carcinoma (HCC) based on public databases and studies in vitro cell lines and tumor tissues. Methods: Transcriptome and clinical data of HCC patients were obtained and analyzed from the TCGA database. The expression of NFYA in HCC cell lines and patient tissues was validated through qRT-PCR and immunohistochemical staining. The correlation between NFYA expression levels and clinical-pathological features, prognosis, potential pathogenic mechanisms and immune cell infiltration was analyzed using univariate and multifactorial Cox regression analysis, Kaplan-Meier analysis, GO and KEGG analysis. Results: NFYA mRNA expression was significantly increased in HCC tissues compared to normal liver tissues, and this phenomenon was also confirmed in HCC cell lines. In the study cohort of HCC tissues, NFYA protein expression was significantly upregulated. NFYA mRNA expression level was correlated with tumor histological grade, T stage, and pathological stage. HCC patients with high NFYA expression had a worse prognosis. NFYA-related genes in HCC were mainly enriched in biological processes such as nuclear cleavage and cell cycle. The analysis of the tumor microenvironment suggested that high NFYA expression was associated with lower immune cell infiltration and stromal cell infiltration, and that NFYA expression was positively correlated with the expression of multiple immune checkpoint genes. Conclusion: NFYA is overexpressed and associated with poor prognosis in HCC patients and may serve as a potential prognostic marker. This activity of NFYA in HCC might be related to its influence on cell cycle and immune cell infiltration. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Modern Laboratory Medicine is the property of Journal of Modern Laboratory Medicine Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background and objective Transcription factor (TF) can bind specific sequences that either promotes or represses the transcription of target genes, and exerts important effects on tumorigenesis, migration, invasion. Staphylococcal nuclease-containing structural domain 1 (SND1), which is a transcriptional co-activator, is considered as a promising target for tumor therapy. However, its role in lung adenocarcinoma (LUAD) remains unclear. This study aims to explore the role of SND1 in LUAD. Methods Data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) database was obtained to explore the association between SND1 and the prognosis, as well as the immune cell infiltration, and subcellular localization in LUAD tissues. Furthermore, the functional role of SND1 in LUAD was verified in vitro. EdU assay, CCK-8 assay, flow cytometry, scratch assay, Transwell assay and Western blot were performed. Results SND1 was found to be upregulated and high expression of SND1 is correlated with poor prognosis of LUAD patients. In addition, SND1 was predominantly present in the cytoplasm of LUAD cells. Enrichment analysis showed that SND1 was closely associated with the cell cycle, as well as DNA replication, and chromosome segregation. Immune infiltration analysis showed that SND1 was closely associated with various immune cell populations, including T cells, B cells, cytotoxic cells and dendritic cells. In vitro studies demonstrated that silencing of SND1 inhibited cell proliferation, invasion and migration of LUAD cells. Besides, cell cycle was blocked at G1 phase by down-regulating SND1. Conclusion SND1 might be an important prognostic biomarker of LUAD and may promote LUAD cells proliferation and migration.

Objective: To reveal cell senescence related signals in breast cancer and their value in clinical outcome and immunotherapy response. Methods: Differential genes related to cell senescence were analyzed by DEseq2 package, TCGA-BRCA cohort was used as training set, and prognostic genes were screened by univariate Cox regression. Model was established by LASSO regression and multivariate Cox regression, and GSE20685-BRCA cohort was used as external validation set. Nomogram of TCGA-BRCA cohort was constructed and its prediction performance was tested by calibration curve and ROC curve. GSEA enrichment analysis was performed by grouping median risk score values of key prognostic genes of cell senescence. CIBERSORT deconvolution algorithm and ESTIMATE algorithm were used to analyze immune cell infiltration, immune microenvironment score and immune checkpoint gene expression of patients with high and low risk. Maftools package was used to observe mutations in genomes of patients with high and low risk. Results: Optimal prognostic model was constructed, including 5 genes WT1, IFNG, TP63, IGFBP6 and CPEB1, and verified by external validation set. Univariate and multivariate Cox regression analysis showed that the risk model was an independent prognostic factor for breast cancer patients. Based on risk score and clinical parameters, nomogram model of TCGA-BRCA cohort was successfully constructed, whose calibration curve fitted well and had positive benefits in guiding clinical decision making. Correlation analysis of immune cell infiltration, immune score, and gene expression at immune checkpoint suggested that high-risk patients showed upregulation of immunosuppressive phenotype. Conclusion: Prediction model of cell senescence related genes plays a positive role in guiding prognosis of breast cancer patients, and can be combined with specific immune checkpoint factors or tumor microenvironment as a biomarker to predict immune checkpoint inhibitor response. [ABSTRACT FROM AUTHOR]

Objective: Based on bioinformatics technology, gene set enrichment analysis (GSEA) and immune infiltration analysis of key differential genes were performed on the chip data of intrauterine growth restriction (IUGR), and the related effects of traditional Chinese medicine were predicted. Methods: GSE12216 chip data were obtained from Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were screened by the R software. Then, Gene Set Enrichment Analysis (GSEA) was used to analyze gene ontology (GO) functional and Kyoto encyclopedia of genes and genome (KEGG) for all expression data, and GO and KEGG of DEGs were analyzed by the R-package. The DEGs protein interaction network was constructed by STRING database, and the key genes were screened by Cytohubba; ROC analysis of key genes to judge the diagnostic value of key genes; contents and proportion of 24 immune cells in IUGR were obtained by single sample gene set enrichment analysis (ssGSEA); the correlation between key gene expression and immune cell infiltration was calculated by Spearman statistical analysis, and the immune related key genes were predicted by Coremine database. Results: Total 228 IUGR and DEGs of normal placenta were obtained; GSEA results showed that the gene expression changes of IUGR samples were mainly related to chemokines, receptors and lysosomal signal pathway; GO and KEGG analysis of DEGs showed that DEGs were mainly enriched in TNF signaling pathway, HIF-1 signaling pathway and JAK/STAT signaling pathway; Cytohubba screened 10 key genes: CSF2, IRAK1, PTGS2, FLT1, CHUK, CXCL12, FOS, LEP, CXCL2 and RELB; ROC analysis showed that these 10 genes had high diagnostic value; infiltration matrix analysis of immune cells revealed that natural killer T cells (NKTs) and monocytes appeared significantly altered in IUGR placentas. The correlation analysis between immune infiltration and key genes found that seven genes CSF2, IRAK1, PTGS2, CHUK, CXCL12, CXCL2 and RELB were correlated with immune infiltration. According to the prediction of traditional Chinese medicine, it was found that ginseng and Astragalus had the closest relationship with IUGR related immune infiltration. Conclusion: NKT cells and monocytes play an important role in the development and occurrence of IUGR; we identified seven genes that are involved with immune infiltration; Ginseng and Scutellaria baicalensis can be used as drug sources for the treatment of IUGR. [ABSTRACT FROM AUTHOR]

Objective：To analyze identification of copper death gene related subtypes， construction of prognosis model and influence of immune infiltration in osteosarcoma （OS） on basis of copper death gene. Methods：Survival and prognosis of OS associated copper death gene were analyzed combining by TARGET and GEO database. OS was divided into different subtypes of copper death by consistent clustering method. SSGSEA was used to analyze difference of immune cells in classification of copper death. Setting P value= 0.05 and q value=0.05， GO and KEGG enrichment analysis were performed on differential genes of copper death typing. Prognosis model was constructed according to results of Lasso regression analysis and cross validation， risk assessment analysis and ROC curve were used to evaluate accuracy of model prediction. Combined with clinical characteristics， nomograms were constructed to predict survival time of patients， and risk differences were analyzed. Immune cell infiltration and tumor microenvironment analysis were performed on OS samples. "pRRophetic" package in R software was used to analyze drug sensitivity of OS samples. Results：FDX1， GLS， DLAT and PDHB as high-risk genes for OS prognosis were identified. According to copper death classification of OS samples， OS could be divided into two types： CRGclusterA and CRGclusterB. CRGclusterA was associated with Th2 cells， and CRGclusterB was associated with Th1 cells. Most OS copper death genes were highly expressed in CRGclusterA. Immune cell infiltration analysis results showed that γδ T cells， resting mast cells and resting dendritic cells were positively correlated with risk score， while CD8 T cells were negatively correlated with risk score. Drug sensitivity analysis showed that OS showed higher sensitivity to Elesclomol and GW.441756. Conclusion：Two subtypes of CRGclusterA and CRGclusterB are identified in this study. Four high-risk prognostic genes FDX1， GLS， DLAT and PDHB are identified， providing new insights into prognostic evaluation and immunotherapy target candidates for OS. [ABSTRACT FROM AUTHOR]

Objective：To select the differential prognostic lactic acid metabolism-related genes (LRGs) of the head and neck squamous cell carcinoma (HNSCC) to construct the LRGs prognostic model of HNSCC, and to clarify the potential mechanism. Methods：The HNSCC gene expression and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) Databases, the LRGs were identified through GeneCards Database, and R software was used to screen out the LRGs of HNSCC； univariate Cox regression analysis was used to identify prognosis-related genes； two different subtypes were identified based on the prognostis-related LRGs； Kaplan-Meier (K-M) curve analysis was used to compare the prognosis of the patients between two groups； CIBERSORT algorithm was used to perform the immuno-correlation analysis between two groups；multivariate Cox regression analysis and LASSO regression analysis were used to construct the prognostic model； receiver operating characteristic curve (ROC) and K-M survival curve were used to assess the relationship between LRGs and survival and prognosis of the HNSCC patients. The prognostic model was validated by GSE27020, GSE41613, and GSE65858 datasets. The experiment were grouped based on risk score, and immune-related analysis and tumor score analysis were performed. Results：The TCGA Database differential analysis results showed that 1 196 LRGs were identified from HNSCC samples； univariate Cox regression analysis selected 27 differentially expressed genes (DEGs) associated with the prognosis of the HNSCC patients. Two different LRGs subtypes (Group 1 and Group 2) were identified according to the prognosis-related genes. The K-M survival curves results showed that the overall survival (OS) of the patients in Group 2 was significantly higher than that in Group 1, and the immune cell expression amount of the patients in Group 2 was also higher than that in group 1. The multivariate Cox regression and LASSO regression analysis results screened out 9 LRGs, including hypoxanthine phosphoribosyltransferase 1 (HPRT1), amyloid precursor protein (APP), glycogen phosphorylase L (PYGL), urokinase-type plasminogen activator (PLAU), cannabinoid receptor 2 (CNR2), stanniocalcin 2 (STC2), nucleotide binding oligomerization domain-like receptor protein 1 (NLRP1), integrin-linked kinase (ILK), and forkhead box B1 (FOXB1); the prognostic model was constructed. The K-M and ROC curve results indicated that the expression levels of above 9 genes were associated with the survival and prognosis of the HNSCC patients, providing good 1-year, 2-year, and 3-year survival prediction effect, and the area under ROC curve (AUC) values were all greater than 0. 650. Furthermore, the predictive ability of the prognosis model was validated in GSE27020, GSE41613, and GSE65858 datasets. The patients classified based on the risk scores had distinguishable immune statuses. Conclusion : The differentially expressed LRGs of HNSCC screened by bioinformatics methods are related to the survival and prognosis of the HNSCC patients； the prognostic model constructed by 9 LRGs can predict the survival status and treatment response of the HNSCC patients. [ABSTRACT FROM AUTHOR]

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Copyright of Journal Of Sichuan University (Natural Sciences Division) / Sichuan Daxue Xuebao-Ziran Kexueban is the property of Editorial Department of Journal of Sichuan University Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Synovium plays an important role in the development of osteoarthritis, and cuproptosis is a new type of programmed cell death recently discovered, up to now, there is no research on the mechanism of cuproptosis gene in osteoarthritis from synovial angle. OBJECTIVE: The synovial membrane was used as the entry point to explore the potential mechanism of the development of osteoarthritis from the perspective of cuproptosis. METHODS: The coincident osteoarthritis related chips were retrieved through Gene Expression Omnibus (GEO) database and standardized. Cuproptosis related genes were extracted and quantified based on the gene expression matrix after treatment. Random Forest model, Support Vector Machines model, Machine learning and Nomogram Model were used to construct disease prediction model to predict the risk of osteoarthritis. Then, consensus clustering algorithm, principal component analysis, single sample gene set enrichment analysis and immune infiltration were used to analyze the correlation of cuproptosis molecular subtypes with immune microenvironment and inflammatory factors. RESULTS AND CONCLUSION: (1) A risk prediction model based on cuproptosis characteristic gene was established for the first time. The disease prediction model constructed by three cuproptosis characteristic genes (DBT, LIPT1, FDX1) could predict the risk of osteoarthritis. (2) It is found for the first time that patients with osteoarthritis can be classified into two distinct subtypes of cuproptosis molecule (cluster A and cluster B). Cluster B is highly correlated with the imbalance of Th1/Th2 cell ratio, and has higher expression levels of interleukin-2, interleukin-4, and interleukin-5. [ABSTRACT FROM AUTHOR]

Objective: To explore pathogenesis, characteristic genes and immune infiltration of rheumatoid arthritis (RA) by bioinformatics and machine learning methods, and to find correlation between characteristic genes and immune cells. Methods: Chips related to RA were obtained from GEO database, gene differences were analyzed by R language, and GO and KEGG were enriched and analyzed; machine learning methods, namely LASSO regression and SVM-RFE were used to screen disease characteristic genes, ROC curve and sample chips were used to detect accuracy of characteristic genes, CIBERPORT algorithm was used to analyze immune infiltration of RA, and correlation between characteristic genes and immune cells was analyzed. Results: A total of 90 differential genes were obtained from GSE12021 and GSE55235, including 64 up-regulated and 26 down-regulated differentially expressed genes. A total of 209 main items were obtained by GO analysis, mainly involving leukocyte activation, lymphocyte activation, B-cell receptor signaling pathway, etc; KEGG analysis showed that chemokine signaling pathway, IL-17 signaling pathway, Toll-like receptor signaling pathway and PPAR signaling pathway were closely related to RA; ten key genes such as IGHM, SLAMF8, CXCL10, FNDC4, AIM2, EGR1 and AKR1B10 were screened by mechanical learning method. Disease characteristic genes were IGHM, SLAMF8, CXCL10, AIM2 and AKR1B10 by ROC curve and sample chip; immune infiltration results showed that there were significant differences between synovial tissues of RA and normal tissues in 9 kinds of immune cells, such as plasma cells, CD8 T cell, CD4 T cell and T cell follicle helper. By analyzing correlation between disease characteristic genes and immune cells, it was found that there were significant correlations between 5 characteristic genes and some immune cells. Conclusion: There is a significant correlation between characteristic genes and immune cells of RA, which provides a preliminary basis for follow-up in-depth study of targeted diagnosis and treatment of RA. [ABSTRACT FROM AUTHOR]

Objective: To explore potential role and mechanism of N6‑methyladenine (m6A) RNA methylation regulators in prognosis and tumor immune microenvironment of cervical cancer. Methods: Clinical and survival data as well as RNA sequencing data were acquired from TCGA and GEO database. Expressions and correlation among m6A RNA methylation regulators were analyzed by bioinformatics analysis and tissue validation experiments. LASSO Cox regression analysis was used to generate a prognostic risk signature based on m6A regulators. Two m6A-modification patterns were identified based on m6A regulators and their abundance of immune cell inflitration were analyzed by ssGSEA. Results: Compared with normal tissues, RBM15, IGF2BP2, IGF2BP3, FMR1, YTHDF1, METTL3 and YTHDF2 expressions were significantly up-regulated in cervical cancer tissues, while ZC3H13, METTL4, YTHDC1, FTO and ALKBH3 expressions were significantly down-regulated. Five m6A regulators HNRNPC, LRPPRC, METTL3, ELAVL1 and FMR1 were selected to establish prognostic risk signature, whose expressions in cervical cancer were verified by HPA database and qRT-PCR with good predictive value, and could be used as independent poor prognostic factor (AUC=0.734). Based on expressions of 25 m6A regulators, two m6A modification patterns m6Acluster A and m6Acluster B were determined. There were differences between two groups in biological functions, signal pathways and immune cells infiltration levels in tumor microenvironment. Conclusion: m6A RNA methylation regulators are closely related to prognosis and immune infiltration of cervical cancer, which provides guidance for immunotherapy strategy. [ABSTRACT FROM AUTHOR]