Tumor hypoxia in immune infiltration and prognosis of bladder cancer.

Background: Bladder cancer (BC) is the sixth most common cancer and the ninth leading cause of cancer death among men in the world. Previous studies have shown that tumor hypoxia plays an important role in the occurrence and development of BC, but the role of tumor hypoxia in the prognosis and immune infiltration of BC remains unclear. Our aim was to perform a bioinformatics analysis combined with a clinical analysis to explore the roles of hypoxia in BC. Methods: We acquired datasets (GSE13507, GSE5287, and GSE1827) containing mRNA expression information from BC cohorts from the Gene Expression Omnibus (GEO) and measured the Hypoxia score using the Gene Set Variation Analysis (GSVA). Then we used X-tile method and log-rank test and Pearson's correlation test to analyze the relation among the Hypoxia score and the clinicopathological and immunological characteristics of BC and used stepwise Cox regression analysis to establish a Prognostic model. Results: Hypoxia was found to be closely associated with tumor grade, pathological type, invasion, and prognosis of BC in our study. Moreover, we determined that hypoxia was closely related to the infiltration abundance of multiple immune cells through a correlation analysis, and the tumor immune cell infiltration was further found to be significantly associated with the tumor grade and tumor type of BC. Furthermore, we constructed several models based on the Hypoxia score and tumor immune infiltration with C-indexes ranging from 0.703 and 0.888, which showed good performance in predicting the prognosis of BC. Conclusions: Our study showed that hypoxia plays an important role in the progression, prognosis, and tumor immune infiltration of BC. Our models based on hypoxia and tumor immune infiltration play a guiding role in the prognosis and treatment of BC patients.

Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm.

Systemic Lupus Erythematosus (SLE) is a multifaceted autoimmune disease that presents with a diverse array of clinical signs and unpredictable disease progression. Conventional diagnostic methods frequently fall short in terms of sensitivity and specificity, which can result in delayed diagnosis and less-than-optimal management. In this study, we introduce a novel approach for improving the identification of SLE through the use of gene-based predictive modelling and Stacked deep learning classifiers. The study proposes a new method for diagnosing SLE using Stacked Deep Learning Classifiers (SDLC) trained on Gene Expression Omnibus (GEO) database data. By combining transcriptomic data from GEO with clinical features and laboratory results, the SDLC model achieves a remarkable accuracy value of 0.996, outperforming traditional methods. Individual models within the SDLC, such as SBi-LSTM and ACNN, achieved accuracies of 92% and 95%, respectively. The SDLC's ensemble learning approach allows for identifying complex patterns in multi-modal data, enhancing accuracy in diagnosing SLE. This study emphasises the potential of deep learning methods, in conjunction with open repositories like GEO, to advance the diagnosis and management of SLE. Overall, this research shows strong performance and potential for improving precision medicine in managing SLE.

Bioinformatics and systems biology approach to identify the pathogenetic link of neurological pain and major depressive disorder.

Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and MDD.

Genome-Scale Investigation of the Regulation of azoR Expression in Escherichia coli Using Computational Analysis and Transposon Mutagenesis.

Bacterial azoreductases are enzymes that catalyze the reduction of ingested or industrial azo dyes. Although azoreductase genes have been well identified and characterized, the regulation of their expression has not been systematically investigated. To determine how different factors affect the expression of azoR, we extracted and analyzed transcriptional data from the Gene Expression Omnibus (GEO) resource, then confirmed computational predictions by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results showed that azoR expression was lower with higher glucose concentration, agitation speed, and incubation temperature, but higher at higher culture densities. Co-expression and clustering analysis indicated ten genes with similar expression patterns to azoR: melA, tpx, yhbW, yciK, fdnG, fpr, nfsA, nfsB, rutF, and chrR (yieF). In parallel, constructing a random transposon library in E. coli K-12 and screening 4320 of its colonies for altered methyl red (MR)-decolorizing activity identified another set of seven genes potentially involved in azoR regulation. Among these genes, arsC, relA, plsY, and trmM were confirmed as potential azoR regulators based on the phenotypic decolorization activity of their transposon mutants, and the expression of arsC and relA was confirmed, by qRT-PCR, to significantly increase in E. coli K-12 in response to different MR concentrations. Finally, the significant decrease in azoR transcription upon transposon insertion in arsC and relA (as compared to its expression in wild-type E. coli) suggests their probable involvement in azoR regulation. In conclusion, combining in silico analysis and random transposon mutagenesis suggested a set of potential regulators of azoR in E. coli.

Construction and evaluation of a metabolic correlation diagnostic model for diabetes based on machine learning algorithms.

BACKGROUND: Diabetes mellitus (DM) is a prevalent chronic disease marked by significant metabolic dysfunctions. Understanding its molecular mechanisms is vital for early diagnosis and treatment strategies. METHODS: We used datasets GSE7014, GSE25724, and GSE156248 from the GEO database to build a diagnostic model for DM using Random Forest (RF) and LASSO regression models. GSE20966 served as a validation cohort. DM patients were classified into two subtypes for functional enrichment analysis. Expression levels of key diagnostic genes were validated using quantitative real-time PCR (qRT-PCR) on Peripheral Blood Mononuclear Cells (PBMCs) from DM patients and healthy controls, focusing on CXCL12 and PPP1R12B with GAPDH as the internal control. RESULTS: After de-batching the datasets, we identified 131 differentially expressed genes (DEGs) between DM and control groups, with 70 up-regulated and 61 down-regulated. Enrichment analysis revealed significant down-regulation in the IL-12 signaling pathway, JAK signaling post-IL-12 stimulation, and the ferroptosis pathway in DM. Five genes (CXCL12, MXRA5, UCHL1, PPP1R12B, and C7) were identified as having diagnostic value. The diagnostic model showed high accuracy in both the training and validation cohorts. The gene set also enabled the subclassification of DM patients into groups with distinct functional traits. qRT-PCR results confirmed the bioinformatics findings, particularly the up-regulation of CXCL12 and PPP1R12B in DM patients. CONCLUSION: Our study pinpointed seven energy metabolism-related genes differentially expressed in DM and controls, with five holding diagnostic value. Our model accurately diagnosed DM and facilitated patient subclassification, offering new insights into DM pathogenesis.

Elucidating the role of TWIST1 in ulcerative colitis: a comprehensive bioinformatics and machine learning approach.

Background: Ulcerative colitis (UC) is a common and progressive inflammatory bowel disease primarily affecting the colon and rectum. Prolonged inflammation can lead to colitis-associated colorectal cancer (CAC). While the exact cause of UC remains unknown, this study aims to investigate the role of the TWIST1 gene in UC. Methods: Second-generation sequencing data from adult UC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and characteristic genes were selected using machine learning and Lasso regression. The Receiver Operating Characteristic (ROC) curve assessed TWIST1's potential as a diagnostic factor (AUC score). Enriched pathways were analyzed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). Functional mechanisms of marker genes were predicted, considering immune cell infiltration and the competing endogenous RNA (ceRNA) network. Results: We found 530 DEGs, with 341 upregulated and 189 downregulated genes. TWIST1 emerged as one of four potential UC biomarkers via machine learning. TWIST1 expression significantly differed in two datasets, GSE193677 and GSE83687, suggesting its diagnostic potential (AUC = 0.717 in GSE193677, AUC = 0.897 in GSE83687). Enrichment analysis indicated DEGs associated with TWIST1 were involved in processes like leukocyte migration, humoral immune response, and cell chemotaxis. Immune cell infiltration analysis revealed higher rates of M0 macrophages and resting NK cells in the high TWIST1 expression group, while TWIST1 expression correlated positively with M2 macrophages and resting NK cell infiltration. We constructed a ceRNA regulatory network involving 1 mRNA, 7 miRNAs, and 32 long non-coding RNAs (lncRNAs) to explore TWIST1's regulatory mechanism. Conclusion: TWIST1 plays a significant role in UC and has potential as a diagnostic marker. This study sheds light on UC's molecular mechanisms and underscores TWIST1's importance in its progression. Further research is needed to validate these findings in diverse populations and investigate TWIST1 as a therapeutic target in UC.

Influence of Pterostilbene on Gene Expression in Liver Cancer: An In Silico Analysis.

Background Liver cancer, in particular, is a serious threat to global health and has few viable treatments. One natural molecule that shows potential in cancer therapy is pterostilbene, especially for hepatocellular carcinoma (HCC). The molecular details of pterostilbene's interactions with liver cancer are uncovered in this study using an in silico method. Methodology This study determines the differentially expressed genes (DEGs) in HCC and the way pterostilbene affects them using data from Gene Expression Omnibus (GEO) datasets. To identify the intricate linkages and possible treatment targets, network pharmacology, protein-protein interaction (PPI) analysis, and pathway enrichment investigations were performed. Results The study revealed complex relationships between pterostilbene and liver cancer, identified important DEGs in HCC, and showed enriched pathways. Pterostilbene shows promise as a target for therapeutic approaches in HCC due to its modulation of important signaling pathways. Conclusions This work offers an extensive knowledge of pterostilbene's potential in liver cancer, despite intrinsic computational limitations. In addition to the importance of experimental validation, the pathways and DEGs that have been found provide insightful information for future investigation, highlighting the ongoing research that is necessary to create targeted therapeutics for HCC.

Role of dysregulated macrophage subpopulation ratios and functional changes in the development of coronary atherosclerosis.

Coronary heart disease is one of the most significant risk factors affecting human health worldwide. Its pathogenesis is intricate, with atherosclerosis being widely regarded as the leading cause. Aberrant lipid metabolism in macrophages is recognized as one of the triggering factors in atherosclerosis development. To investigate the role of macrophages in the formation of coronary artery atherosclerosis, we utilized single-cell data from wild-type mice obtained from the aortic roots and ascending aortas after long-term high-fat diet feeding, as deposited in GSE131776. Seurat software was employed to refine the single-cell data in terms of scale and cell types, facilitating the identification of differentially expressed genes. Through the application of differential expression genes, we conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analyses at 0, 8 and 16 weeks, aiming to uncover pathways with the most pronounced functional alterations as the high-fat diet progressed. The AddModuleScore function was employed to score the expression of these pathways across different cell types. Subsequently, macrophages were isolated and further subdivided into subtypes, followed by an investigation into intercellular communication within these subtypes. Subsequent to this, we induced THP-1 cells to generate foam cells, validating critical genes identified in prior studies. The results revealed that macrophages underwent the most substantial functional changes as the high-fat diet progressed. Furthermore, two clusters were identified as potentially playing pivotal roles in macrophage functional regulation during high-fat diet progression. Additionally, macrophage subtypes displayed intricate functionalities, with mutual functional counterbalances observed among these subtypes. The proportions of macrophage subtypes and the modulation of anti-inflammatory and pro-inflammatory functions played significant roles in the development of coronary artery atherosclerosis.

Expression of optineurin in multiple myeloma and its clinical significance / 西安交通大学学报(医学版)

【Objective】 To investigate the expression of optineurin (OPTN) in multiple myeloma (MM) and explore the mechanism and clinical value of OPTN gene in the occurrence and development of MM. 【Methods】 In this study, three gene expression omnibus (GEO) data sets were used to analyze the expression level of OPTN in MM. Clinical bone marrow samples of MM patients were collected. qRT-PCR was used to further verify the expression of OPTN in MM patients. The Kaplan-Meier survival curve and receiver operating characteristic (ROC) curve were used to analyze the value of OPTN in the prognosis and diagnosis of MM. At the same time, MM transcriptome data were downloaded from the Cancer Genome Atlas (TCGA) database. According to the median boundary of OPTN mRNA expression level, the MM patients were divided into OPTN high- and low-expression groups. In order to investigate the possible molecular mechanisms of OPTN in MM, gene set enrichment analysis (GSEA) was made after the differentially expressed genes were filtered using the limma package of the R language. 【Results】 The expression level of OPTN was significantly lower in MM tissues than in normal tissues (P<0.05). OPTN expression level was significantly correlated with International Staging System (ISS) in MM patients (P<0.05). ROC results showed that the expression level of OPTN could distinguish between normal and MM patients. Survival analysis showed that the overall survival (OS) of patients with low OPTN expression was significantly lower than that of patients with high OPTN expression (P<0.05). GO, KEGG and GSEA enrichment analyses indicated that OPTN might affect apoptosis and autophagy, and regulate cellular immune response by regulating Nod-like receptors, NF-κB, TNF and RAS/MAPK pathways. 【Conclusion】 Low expression of OPTN in MM is associated with poor prognosis of patients, and thus may be an important potential biomarker for the diagnosis and treatment of MM.

A prognostic signature for lung adenocarcinoma by five genes associated with chemotherapy in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer. Finding prognostic biomarkers is helpful in stratifying LUAD patients with different prognosis. METHODS: We explored the correlation of LUAD prognosis and genes associated with chemotherapy in LUAD and obtained data of LUAD patients from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Drug sensitivity data were acquired from the Genomics of Drug Sensitivity in Cancer (GDSC) database. Differential and enrichment analyses were used to screen the target genes utilizing limma and "clusterProfiler" packages. Then univariate and LASSO Cox analyses were used to select the prognosis-related genes. Survival analysis was used to estimate the overall survival (OS) of different groups. RESULTS: Twenty-three differentially expressed genes (DEGs) were screened between LUAD samples and healthy samples, and BTK, FGFR2, PIM2, CHEK1, and CDK1 were selected to construct a prognostic signature. The OS of patients in the high-risk group (risk score higher than 0.69) was worse than that in the low-risk group (risk score lower than 0.69). CONCLUSION: The risk score model constructed by five genes is a potential prognostic biomarker for LUAD patients.

Shared immune metabolic reprogramming between COVID-19 patients in the intensive care unit and ischemic stroke: a pilot study.

Introduction: To investigate shared gene signatures between COVID-19 and ischemic stroke. Methods: Combining the existing bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) data for COVID-19 to obtain a more comprehensive understanding of the role of dysregulated metabolism. Results: A total of 19 up-regulated differentially expressed genes (DEGs) and 24 down-regulated genes were selected for subsequent analyses. Nine genes were finally identified with the machine learning method as a potential diagnostic model in both ischemic stroke and COVID-19. In addition, the hub genes were related to both immune infiltration and metabolic pathways. Conclusions: Our study revealed the common molecular profile of COVID-19 and ischemic stroke.

Identifying Crucial Biomarkers in Osteoporosis and Ulcerative Colitis Through Bioinformatics Analysis of Co-expressed Genes.

Osteoporosis (OP) and ulcerative colitis (UC), prevalent immune diseases, exert a substantial socioeconomic impact globally. This study identifies biomarkers for these diseases, paving the way for in-depth research. Initially, the Gene Expression Omnibus (GEO) database was employed to analyze datasets GSE35958 and GSE87466. This analysis aimed to pinpoint co-expression differential genes (DEGs) between OP and UC. Subsequently, the Metascape database facilitated the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these DEGs' co-expression. For network construction and visualization, the STRING11.5 database along with Cytoscape 3.7.2 (Cytoscape Team, USA) were utilized to create a protein-protein interaction (PPI) network. Moreover, Cytoscape's cytoHubba plugin was instrumental in identifying the central genes, known as hub genes. In the datasets GSE35958 and GSE87466, 156 co-expressed DEGs were discovered. The PPI network, constructed using STRING11.5 and Cytoscape 3.7.2, comprises 96 nodes and 222 connections. Notably, seven hub genes were identified, namely COL6A1, COL6A2, BGN, NID1, PLAU, TGFB1, and PLAUR. These DEGs were predominantly enriched in pathways such as extracellular matrix organization and collagen-containing extracellular matrix, as per GO analysis. For diagnostic model construction and hub gene validation, datasets GSE56815 and GSE107499 from the GEO database were employed. The top five hub genes were validated. In conclusion, the hub genes identified in this study played a significant role in the early diagnosis, prevention, and treatment of OP and UC. Furthermore, they provide fresh insights into the underlying mechanisms of these diseases' development and progression.

Prognostic value and immune infiltration of ARMC10 in pancreatic adenocarcinoma via integrated bioinformatics analyses.

Background: Armadillo repeat-containing 10 (ARMC10) is involved in the progression of multiple types of tumors. Pancreatic adenocarcinoma (PAAD) is a lethal disease with poor survival and prognosis. Methods: We acquired the data of ARMC10 in PAAD patients from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) datasets and compared the expression level with normal pancreatic tissues. We evaluated the relevance between ARMC10 expression and clinicopathological factors, immune infiltration degree and prognosis in PAAD. Results: High expression of ARMC10 was relevant to T stage, M stage, pathologic stage, histologic grade, residual tumor, primary therapy outcome (P < 0.05) and related to lower Overall-Survival (OS), Disease-Specific Survival (DSS), and Progression-Free Interval (PFI). Gene set enrichment analysis showed that ARMC10 was related to methylation in neural precursor cells (NPC), G alpha (i) signaling events, APC targets, energy metabolism, potassium channels and IL10 synthesis. The expression level of ARMC10 was positively related to the abundance of T helper cells and negatively to that of plasmacytoid dendritic cells (pDCs). Knocking down of ARMC10 could lead to lower proliferation, invasion, migration ability and colony formation rate of PAAD cells in vitro. Conclusions: Our research firstly discovered ARMC10 as a novel prognostic biomarker for PAAD patients and played a crucial role in immune regulation in PAAD.

Identification and validation of a necroptosis-related gene prognostic signature for colon adenocarcinoma.

Background: Necroptosis is a novel programmed cell death pathway proposed in 2005, which is mainly activated by the tumor necrosis factor (TNF) family and mediates cellular disassembly via receptor interacting serine/threonine kinase 1 (RIPK1), receptor interacting serine/threonine kinase 3 (RIPK3) and mixed lineage kinase domain like pseudokinase (MLKL). We tried to analyze the relationship of necroptosis-related genes (NRGs) expression with colon adenocarcinoma (COAD) and propose potential therapeutic targets through immunological analysis. Methods: First, we evaluated the expression of NRGs in COAD patients and constructed a prognostic signature. The prognostic signature was validated using The Cancer Genome Atlas (TCGA)-COAD and GSE39582 datasets, respectively. And the Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis were used to evaluate the signature. Then we analyzed the enrichment of NRGs in the signature using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Finally, we analyzed the immunological characteristics of the COAD patients by single sample gene set enrichment analysis (ssGSEA) and predicted the possible immune checkpoints. Results: We constructed a prognostic signature with 8 NRGs (RIPK3, MLKL, TRAF2, CXCL1, RBCK1, CDKN2A, JMJD7-PLA2G4B and CAMK2B). The Kaplan-Meier analysis, ROC curves, and principal component analysis demonstrated good predictivity of the signature. In addition, we constructed a nomogram with good individualized predictive ability (C-index =0.772). The immunological analysis revealed that the prognosis of COAD was associated with autoimmune function, and we proposed 10 potential therapeutic targets. Conclusions: Overall, we constructed an NRGs prognostic signature and suggested potential therapeutic targets for the COAD treatment.

Identification of Novel Genes and Pathways of Ovarian Cancer Using a Comprehensive Bioinformatic Framework.

Ovarian cancer (OC) is a significant contributor to gynecological cancer-related deaths worldwide, with a high mortality rate. Despite several advances in understanding the pathogenesis of OC, the molecular mechanisms underlying its development and prognosis remain poorly understood. Therefore, the current research study aimed to identify hub genes involved in the pathogenesis of OC that could serve as selective diagnostic and therapeutic targets. To achieve this, the dataset GEO2R was used to retrieve differentially expressed genes. The study identified a total of five genes (CDKN1A, DKK1, CYP1B1, NTS, and GDF15) that were differentially expressed in OC. Subsequently, a network analysis was performed using the STRING database, followed by the construction of a network using Cytoscape. The network analyzer tool in Cytoscape predicted 276 upregulated and 269 downregulated genes. Furthermore, KEGG analysis was conducted to identify different pathways related to OC. Subsequently, survival analysis was performed to validate gene expression alterations and predict hub genes, using a p-value of 0.05 as a threshold. Four genes (CDKN1A, DKK1, CYP1B1, and NTS) were predicted as significant hub genes, while one gene (GDF15) was predicted as non-significant. The adjusted P values of said predicted genes are 2.85E - 07, 5.49E - 06, 4.28E - 07, 1.43E - 07, and 3.70E - 07 for CDKN1A, DKK1, NTS, GDF15, and CYP1B1 respectively; additionally 6.08, 5.76, 5.74, 5.01, and 4.9 LogFc values of the said genes were predicted in GEO data set. In a boxplot analysis, the expression of these genes was analyzed in normal and tumor cells. The study found that three genes were highly expressed in tumor cells, while two genes (CDKN1A and DKK1) were more elevated in normal cells. According to the boxplot analysis for CDKN1A, 50% of tumor cells ranged between approx 3.8 and 5, while 50% of normal cells ranged between approx 6.9 and 7.9, which is greater than tumor cells. This shows that in normal cells, the CYP1B1 has a high expression level according to the GEPIA boxplot; addtionally the boxplot for DKK1 indicated that 50% of tumor cells ranged between approx 0 and 0.5, which was less than that of normal cells which ranged between approx 0.3 and 0.9. It shows that DKK1 is highly expressed in normal genes. Overall, the current study provides novel insights into the molecular mechanisms underlying OC. The identified hub genes and drug candidate targets could potentially serve as alternative diagnostic and therapeutic options for OC patients. Further research is needed to investigate the clinical significance of these findings and develop effective interventions that can improve the prognosis of patients with OC.

Based on the Cancer Genome Atlas Database Development of a prognostic model of RNA binding protein in stomach adenocarcinoma.

The purpose of this study was to identify potential RNA binding proteins associated with the survival of gastric adenocarcinoma, as well as the corresponding biological characteristics and signaling pathways of these RNA binding proteins. RNA sequencing and clinical data were obtained from the cancer genome map (N = 32, T = 375) and the comprehensive gene expression database (GSE84437, N = 433). The samples in The Cancer Genome Atlas were randomly divided into a development group and a test group. A total of 1495 RNA binding protein related genes were extracted. Using nonparametric tests to analyze the difference of RNA binding protein related genes, 296 differential RNA binding proteins were obtained, 166 were up-regulated and 130 were down regulated. Twenty prognosis-related RNA binding proteins were screened using Cox regression, including 14 high-risk genes (hazard ratio > 1.0) and 6 low-risk genes (hazard ratio < 1.0). Seven RNA binding protein related genes were screened from the final prognostic model and used to construct a new prognostic model. Using the development group and test group, the model was verified with survival analysis, receiver operating characteristics curves and prognosis analysis curves. A prediction nomogram was finally developed and showed good prediction performance.

Prognostic analysis of uveal melanoma based on the characteristic genes of M2-type macrophages in the tumor microenvironment.

Uveal melanoma arises from stromal melanocytes and is the most prevalent primary intraocular tumor in adults. It poses a significant diagnostic and therapeutic challenge due to its high malignancy and early onset of metastases. In recent years, there has been a growing interest in the role of diverse immune cells in tumor cell development and metastasis. Using The Cancer Genome Atlas and the gene expression omnibus databases, and the CIBERSORT method, we investigated the topography of intra-tumor immune infiltration in uveal melanoma in this research. We evaluated the prognosis of uveal melanoma patients using the M2 macrophage immune cell infiltration score in conjunction with clinical tumor patient data. We built a prognostic model based on the distinctive genes of M2 macrophages and combined it with patients' clinical data in the database; we ran a survival prognostic analysis to authenticate the model's accuracy. The functional study revealed the importance of macrophage-associated genes in the development of uveal melanoma. Moreover, the reliability of our prediction model was verified by combining tumor mutational load, immune checkpoint, and drug sensitivity, respectively. Our study provides a reference for the follow-up study of uveal melanoma.

Integrated Analysis of miRNA and mRNA Regulation Network in Hypertension.

Hypertension is the most common chronic disease. Early diagnosis is helpful for early medical intervention. The miRNAs and the messenger RNAs (mRNAs) network may be valuable disease diagnosis markers. We aimed to explore the diagnostic value of the miRNA-mRNA network for hypertension patients. Data of miRNAs and mRNAs expression were obtained from the Gene Expression Omnibus database. The weighted gene co-expression network analysis was performed to screen hypertension-related gene modules, and these genes undergone functional enrichment analysis using "clusterProfiler" package. Differential expression analysis was applied on miRNAs expression profiles using "limma" package. TargetScanHuman and miRDB databases were used to select target mRNAs. Cytoscape software was used to visualize the miRNA-mRNA regulation network. P value < 0.05 was considered statistically significant after t test. There were 123 screened mRNAs which were enriched in 161 Gene ontology (GO) terms and 14 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Thirty-five differentially expressed miRNAs (DEMs) are found in the GSE75670. Totally 36 miRNA-mRNA pairs were obtained after the integrated analysis, and three mRNAs and the hsa-miRNA-5589-5p were identified as key joints. Hub genes, KIAA0513, ARID3A and LRPAP1, and key hsa-miRNA-5589-5p are potential diagnostic biomarkers for hypertension. Our findings are promising in the clinical application, conducive to early detection and prompt intervention of hypertension.

Extracellular matrix-based gene signature for predicting prognosis in colon cancer and immune microenvironment.

Background: The extracellular matrix (ECM) plays a vital role in progression, expansion, and prognosis of malignancies. In this study, we aimed to explore a novel ECM-based prognostic model for patients with colon cancer (CC). Methods: ECM-related genes were obtained from Molecular Signatures database. Differential expression analysis was performed using the CC dataset from The Cancer Genome Atlas (TCGA) database. Four ECM-related genes related to overall survival were identified using the Cox regression and LASSO analysis. Then an ECM-related signature was developed and verified in three independent CC cohorts (GSE33882, GSE39582 and GSE29621) from the Gene Expression Omnibus (GEO). A prognostic nomogram was developed incorporating the ECM-related gene signature with clinical risk factors. CIBERSORT was used to explore the immune cell infiltration level. Human Protein Atlas (HPA) database was utilized to validate the expression levels of identified prognostic ECM genes. Results: Four ECM-related genes (CXCL13, CXCL14, SFRP5 and THBS4) were identified to develop an ECM-based gene signature and demarcated CC patients into the high- and low-risk groups. In training and validation datasets, patients in the low-risk group had better overall survival outcomes than those in the high-risk group (log-rank P<0.001). In addition, ECM-related signature was significantly associated with consensus molecular subtype 4 (CMS4) as well as other known clinical risk factors such as a higher Tumor, Nodal Involvement, Metastasis (TNM) stage. Moreover, the risk score derived from the ECM-based gene signature could be utilized as an independent prognostic factor for CC patients. A nomogram including the ECM-related gene signature, age and stage was developed to serve clinical practice. CIBERSORT analysis showed immune cell infiltration was different between high- and low-risk groups. The immunohistochemical results derived from HPA indicated differential expression of prognosis-related ECM genes in CC and normal tissues. Conclusions: In the present study, a novel risk model based on ECM-signature could effectively reflect individual risk classification and provide potential therapeutic targets for CC patients. Moreover, the prognostic nomogram may help predict individualized survival.

Identification of biomarkers associated with macrophage infiltration in non-obstructive azoospermia using single-cell transcriptomic and microarray data.

Background: Non-obstructive azoospermia (NOA) is a common clinical cause of male infertility. Research suggests that macrophages are linked to testicular function; however, their involvement in NOA remains unknown. Methods: To evaluate the importance of macrophages infiltration in NOA and identify the macrophage-related biomarkers, the gene-expression microarray data GSE45885 and the single-cell transcriptomic data GSE149512 were utilized from the Gene Expression Omnibus (GEO). A single-sample gene set enrichment analysis (ssGSEA) was conducted to investigate immune cell proliferation. The Seurat package was used for the single-cell data analysis, and the limma package was used to identify the differentially expressed genes between the NOA and normal samples. Moreover, we conducted a weighted gene co-expression network analysis (WGCNA) to identify the macrophage-related key modules and genes, and conducted Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses for the functional exploration. To identify the macrophage-related biomarkers, we conducted least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) analyses. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to verify the marker genes present in NOA. Results: We confirmed that open reading frame 72 gene on chromosome 9 (C9orf72) [area under the curve (AUC) =0.861] and cartilage-associated protein (CRTAP) (AUC =0.917) were the hub genes of NOA, and the RT-qPCR analysis revealed the critical expression of both genes in NOA. Conclusions: Through the combination of tissue transcriptomic and single-cell RNA-sequencing analyses, we concluded that macrophage infiltration is significant in different subtypes of NOA, and we hypothesized that C9orf72 and CRTAP play critical roles in NOA due to their high expression in macrophages.