Leonhard Med, a trusted research environment for processing sensitive research data.

This paper provides an overview of the development and operation of the Leonhard Med Trusted Research Environment (TRE) at ETH Zurich. Leonhard Med gives scientific researchers the ability to securely work on sensitive research data. We give an overview of the user perspective, the legal framework for processing sensitive data, design history, current status, and operations. Leonhard Med is an efficient, highly secure Trusted Research Environment for data processing, hosted at ETH Zurich and operated by the Scientific IT Services (SIS) of ETH. It provides a full stack of security controls that allow researchers to store, access, manage, and process sensitive data according to Swiss legislation and ETH Zurich Data Protection policies. In addition, Leonhard Med fulfills the BioMedIT Information Security Policies and is compatible with international data protection laws and therefore can be utilized within the scope of national and international collaboration research projects. Initially designed as a "bare-metal" High-Performance Computing (HPC) platform to achieve maximum performance, Leonhard Med was later re-designed as a virtualized, private cloud platform to offer more flexibility to its customers. Sensitive data can be analyzed in secure, segregated spaces called tenants. Technical and Organizational Measures (TOMs) are in place to assure the confidentiality, integrity, and availability of sensitive data. At the same time, Leonhard Med ensures broad access to cutting-edge research software, especially for the analysis of human -omics data and other personalized health applications.

Correlation between Complement C1q A Chain (C1QA) and Macrophages in the Progression of Carotid Atherosclerosis.

Background: There is increasing evidence that macrophages are involved in the development of carotid atherosclerosis (CAS), but the specific mechanism is still unclear. We aimed to explore the key genes that play a regulatory role on macrophages in the progression of CAS. Methods: From 2021 August to 2023 August, GEO datasets GSE100927 and GSE43292 were downloaded and the key gene modules related to CAS were identified by weighted Gene co-expression network analysis (WGCNA). Kyoto Encyclopedia of Genes and Genes (KEGG) pathway analysis was performed on the genes of the key modules to identify common gene enrichment pathways. Differential expression analysis of pathway-related genes was performed by the "limma" package of R software. Case groups were categorized into high and low expression groups based on the expression levels of key genes, and ssGSEA immune infiltration analysis was performed. Results: The turquoise module of GSE100924 (threshold=12) and the brown module of GSE43292 (threshold=7) were obtained through WGCNA analysis. The analysis of KEGG showed that the differentially expressed genes in the turquoise and brown modules were co-enriched in the staphylococcus aureus infection signaling pathway. Differential expression analysis identified 18 common differentially expressed genes, all of which were highly expressed in the case group. C1QA is the gene of interest. According to ssGSEA analysis, the high expression group of C1QA showed a significant increase in the number of macrophages (GSE43292, P=0.0011; GSE100927, P=0.025). Conclusion: This study identified the key gene C1QA involved in regulating macrophage functional activity during the CAS process, providing new ideas for effective control of CAS.

Ribosome heterogeneity in development and disease.

Traditionally viewed as a fixed and homogeneous machinery for protein synthesis, the ribosome is increasingly recognized for its heterogeneity, as indicated by emerging studies highlighting the functional relevance of specialized ribosomes. However, whether ribosome heterogeneity is merely an outcome limited to specific conditions or a pervasive cellular phenomenon remains unclear, and existing evidence on the extensive existence of ribosome heterogeneity is scant. Here, we leveraged existing proteomic data and employed ribosome ratio-omics (RibosomeR), which comprehensively analyzes ribosome protein stoichiometry across various biological samples exhibiting distinct functions, developmental stages, and pathological states. Using the 80S monosome proteomic data, RibosomeR analysis unveils significant ribosome heterogeneity across different tissues, including fat, spleen, liver, kidney, heart, and skeletal muscles. Furthermore, examination of testes at various stages of spermatogenesis reveals distinct RibosomeR signatures during tissue development. Analysis of the whole cell proteomic data finds that RibosomeR undergoes dynamic changes during in vitro neuronal maturation, indicating functional associations with specific molecular aspects of neurodevelopment. In pathological contexts, RibosomeR signatures in gastric tumors demonstrate functional links to pathways associated with tumorigenesis. Additionally, dynamic alterations in RibosomeR are observed in macrophages following immune challenges. Collectively, our investigation across a diverse array of biological samples underscores the presence of ribosome heterogeneity, while previous studies observed functional aspects of ribosome specialization, in cellular function, development, and disease. The RibosomeR barcode serves as a valuable tool for elucidating these complexities.

Network pharmacology-based exploration of molecular mechanisms underlying therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure with spleen Qi deficiency syndrome.

BACKGROUND: Chronic heart failure is a complex clinical syndrome. The Chinese herbal compound preparation Jianpi Huatan Quyu recipe has been used to treat chronic heart failure; however, the underlying molecular mechanism is still not clear. AIM: To identify the effective active ingredients of Jianpi Huatan Quyu recipe and explore its molecular mechanism in the treatment of chronic heart failure. METHODS: The effective active ingredients of eight herbs composing Jianpi Huatan Quyu recipe were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. The target genes of chronic heart failure were searched in the Genecards database. The target proteins of active ingredients were mapped to chronic heart failure target genes to obtain the common drug-disease targets, which were then used to construct a key chemical component-target network using Cytoscape 3.7.2 software. The protein-protein interaction network was constructed using the String database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed through the Metascape database. Finally, our previously published relevant articles were searched to verify the results obtained via network pharmacology. RESULTS: A total of 227 effective active ingredients for Jianpi Huatan Quyu recipe were identified, of which quercetin, kaempferol, 7-methoxy-2-methyl isoflavone, formononetin, and isorhamnetin may be key active ingredients and involved in the therapeutic effects of TCM by acting on STAT3, MAPK3, AKT1, JUN, MAPK1, TP53, TNF, HSP90AA1, p65, MAPK8, MAPK14, IL6, EGFR, EDN1, FOS, and other proteins. The pathways identified by KEGG enrichment analysis include pathways in cancer, IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, calcium signaling pathway, cAMP signaling pathway, NF-kappaB signaling pathway, AMPK signaling pathway, etc. Previous studies on Jianpi Huatan Quyu recipe suggested that this Chinese compound preparation can regulate the TNF-α, IL-6, MAPK, cAMP, and AMPK pathways to affect the mitochondrial structure of myocardial cells, oxidative stress, and energy metabolism, thus achieving the therapeutic effects on chronic heart failure. CONCLUSION: The Chinese medicine compound preparation Jianpi Huatan Quyu recipe exerts therapeutic effects on chronic heart failure possibly by influencing the mitochondrial structure of cardiomyocytes, oxidative stress, energy metabolism, and other processes. Future studies are warranted to investigate the role of the IL-17 signaling pathway, PI3K-Akt signaling pathway, HIF-1 signaling pathway, and other pathways in mediating the therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure.

Protocol to search for genetic factors related to severe COVID-19 by analyzing publicly available genome-wide association studies.

COVID-19 casualties vary among different ancestral groups due to a variety of factors. Here, we present a protocol for analyzing publicly available genome-wide association studies (GWASs) to search for ancestry-specific genetic factors related to severe COVID-19. We describe steps for downloading and comparing two COVID-19 GWASs, calculating expression quantitative trait loci, and single-cell gene expression analysis. We demonstrate this approach using GWASs from Host Genetics Initiative; however, it is applicable to other databases such as the UK Biobank. For complete details on the use and execution of this protocol, please refer to Cheng et al.1.

Identification of prognostic RNA editing profiles for clear cell renal carcinoma.

Objective: Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer and currently lacks effective biomarkers. This research aims to analyze and identify RNA editing profile associated with ccRCC prognosis through bioinformatics and in vitro experiments. Methods: Transcriptome data and clinical information for ccRCC were retrieved from the TCGA database, and RNA editing files were obtained from the Synapse database. Prognostic models were screened, developed, and assessed using consistency index analysis and independent prognostic analysis, etc. Internal validation models were also constructed for further evaluation. Differential genes were investigated using GO, KEGG, and GSEA enrichment analyses. Furthermore, qPCR was performed to determine gene expression in human renal tubular epithelial cells HK-2 and ccRCC cells A-498, 786-O, and Caki-2. Results: An RNA editing-based risk score, that effectively distinguishes between high and low-risk populations, has been identified. It includes CHD3| chr17:7815229, MYO19| chr17:34853704, OIP5-AS1| chr15:41590962, MRI1| chr19:13883962, GBP4| chr1:89649327, APOL1| chr22:36662830, FCF1| chr14:75203040 edited sites or genes and could serve as an independent prognostic factor for ccRCC patients. qPCR results showed significant up-regulation of CHD3, MYO19, MRI1, APOL1, and FCF1 in A-498, 786-O, and Caki-2 cells, while the expression of OIP5-AS1 and GBP4 was significantly down-regulated. Conclusion: RNA editing site-based prognostic models are valuable in differentiating between high and low-risk populations. The seven identified RNA editing sites may be utilized as potential biomarkers for ccRCC.

Identification of Key Genes Associated with Polycystic Ovarian Syndrome and Endometrial and Ovarian Cancer through Bioinformatics.

Background: Polycystic ovary syndrome (PCOS), a common endocrine disorder, is linked to increased risks of endometrial cancer (EC) and ovarian cancer (OC). Our study utilises bioinformatics analysis to identify shared gene signatures and elucidate biological processes between EC and OC and PCOS. Aim: The objective of this research is to unveil the common molecular landscape shared by PCOS and EC and OC. Settings and Design: An observational computational bioinformatics analysis. Materials and Methods: Gene expression profiles for PCOS (GSE199225), EC (GSE215413) and OC (GSE174670) were obtained from the Gene Expression Omnibus database. Hub genes were identified through functional enrichment analysis and protein-protein interaction. Drug identification analyses were employed to find drugs targeting the hub genes. Results: Key hub genes linking PCOS and EC includes RECQL4, RAD54L, ATR, CHTF18, WDHD1, CDT1, PLK1, PKMYT1, RAD18 and RPL3; for PCOS and OC, they include HMOX1, TXNRD1, NQO1, GCLC, GSTP1, PRDX1, SOD1, GPX3, BOP1 and BYSL. Gene Ontology analysis revealed DNA metabolic process in PCOS and EC, while in PCOS and OC, it identified the removal of superoxide radicals. Kyoto Encyclopaedia of Genes and Genomes pathway analysis highlighted cell cycle in PCOS and EC and hepatocellular carcinoma in PCOS and OC. Potential drugs for PCOS and EC include quercetin, calcitriol and testosterone; for PCOS and OC, eugenol and 1-chloro-2,4-dinitrobenzene are identified. Conclusion: These findings offer insights into potential therapeutic targets and pathways linking PCOS with EC and OC, enhancing our understanding of the molecular mechanisms involved in these associations.

Panera: An innovative framework for surmounting uncertainty in microbial community modeling using pan-genera metabolic models.

Utilization of 16S rRNA data in constraint-based modeling to characterize microbial communities confronts a major hurdle of lack of species-level resolution, impeding the construction of community models. We introduce "Panera," an innovative framework designed to model communities under this uncertainty and yet perform metabolic inferences using pan-genus metabolic models (PGMMs). We demonstrated PGMMs' utility for comprehending the metabolic capabilities of a genus and in characterizing community models using amplicon data. The unique, adaptable nature of PGMMs unlocks their potential in building hybrid communities, combining genome-scale metabolic models (GSMMs) and PGMMs. Notably, these models provide predictions comparable to the standard GSMM-based community models, while achieving a nearly 46% reduction in error compared to the genus model-based communities. In essence, "Panera" presents a potent and effective approach to aid in metabolic modeling by enabling robust predictions of community metabolic potential when dealing with amplicon data, and offers insights into genus-level metabolic landscapes.

Defining the Functional Properties of Cyclopropane Fatty Acid Synthase from Pseudomonas aeruginosa PAO1.

Cyclopropane fatty acid synthases (CFAS) catalyze the conversion of unsaturated fatty acids to cyclopropane fatty acids (CFAs) within bacterial membranes. This modification alters the biophysical properties of membranes and has been correlated with virulence in several human pathogens. Despite the central role played by CFAS enzymes in regulating bacterial stress responses, the mechanistic properties of the CFAS enzyme family and the consequences of CFA biosynthesis remain largely uncharacterized in most bacteria. We report the first characterization of the CFAS enzyme from Pseudomonas aeruginosa (PA) - an opportunistic human pathogen with complex membrane biology that is frequently associated with antimicrobial resistance and high tolerance to various external stressors. We demonstrate that CFAs are produced by a single enzyme in PA and that cfas gene expression is upregulated during the transition to stationary phase and in response to oxidative stress. Analysis of PA lipid extracts reveal a massive increase in CFA production as PA cells enter stationary phase and help define the optimal membrane composition for in vitro assays. The purified PA-CFAS enzyme forms a stable homodimer and preferentially modifies phosphatidylglycerol lipid substrates and membranes with a higher content of unsaturated acyl chains. Bioinformatic analysis across bacterial phyla shows highly divergent amino acid sequences within the lipid binding domain of CFAS enzymes, perhaps suggesting distinct membrane binding properties among different orthologues. This work lays an important foundation for further characterization of CFAS in Pseudomonas aeruginosa and for examining the functional differences between CFAS enzymes from different bacteria.

Concepts and new developments in droplet-based single cell multi-omics.

Single cell sequencing technologies have become a fixture in the molecular profiling of cells due to their ease, flexibility, and commercial availability. In particular, partitioning individual cells inside oil droplets via microfluidic reactions enables transcriptomic or multi-omic measurements for thousands of cells in parallel. Complementing the multitude of biological discoveries from genomics analyses, the past decade has brought new capabilities from assay baselines to enable a deeper understanding of the complex data from single cell multi-omics. Here, we highlight four innovations that have improved the reliability and understanding of droplet microfluidic assays. We emphasize new developments that further orient principles of technology development and guidelines for the design, benchmarking, and implementation of new droplet-based methodologies.

Analysis of different expression RNA binding protein genes in mouse microglia cell from the brains of mice 72 h after subarachnoid hemorrhage or sham operation.

BACKGROUND: The prognosis of brain injury caused by subarachnoid hemorrhage (SAH) is poor. Previous studies showed that abnormal function of RBPs might be involved in brain injury, neuroinflammation and further affect microglia homeostasis. However, no studies have systematically analyzed the genome-wide abnormal expression of RBPs genes in microglia during SAH. METHODS: RNA-seq data of microglia from the SAH mouse group (SAH) and control sham-operated mouse group (sham) were downloaded from the GEO database in GSE167957, including four samples from the sham group and four samples from the SAH group for subsequent analysis.Utilizing GO and KEGG functional enrichment analyses, we conducted a comprehensive study of differentially expressed genes (DEGs), alternative splicing patterns, and co-expression networks to gain deeper insights into the differential expression of RNA-binding proteins (RBPs) and differential alternative splicing events (ASEs) between the SAH (subarachnoid hemorrhage) and sham groups. This analysis aimed to elucidate the potential mechanisms underlying the aberrant expression of RBPs in microglia during brain injury caused by SAH. RESULTS: ASEs and co-expression analyses of differentially expressed RBPs and differential ASEs were carried out in microglia in terms of gene expression. GO and KEGG functional enrichment analysis showed that aberrantly expressed RBPs such as Mcm7, Mtdh, SRSF3, and Hnrnpa2b1 may affect and regulate downstream Csnk1d, Uckl1 and other protein phosphorylation-related genes by alterative splicing. CONCLUSION: RBPs were aberrantly expressed in microglia during the development of brain injury secondary to SAH, regulating alterative splicing of downstream genes and influencing the progression of SAH brain injury in this study. This implies that RBPs are important for the identification of new therapeutic targets for brain injury after SAH.

Analysis of miR-497/195 cluster identifies new therapeutic targets in cervical cancer.

OBJECTIVE: miR-497/195, located at 17p13.1, is a highly conserved miRNA cluster whose abnormal expression is a key regulator of carcinogenesis. We performed a comprehensive analysis of the miR-497/195 cluster to determine its prognostic utility and role in cervical cancer (CC) using publicly available datasets. RESULTS: In silico analysis and validation revealed that this cluster is downregulated in CC. A total of 60 target genes of miR-497/195 cluster were identified as differentially expressed between normal and CC samples. ShinyGO, STRING, CytoHubba, Timer 2.0, HPA, and HCMBD were used for functional enrichment, PPIN network construction, hub gene identification, immune infiltration correlation, histopathological expression, and determination of the metastatic potential of miR-497/195 cluster and their target genes. PPIN analysis identified CCNE1, CCNE2, ANLN, RACGAP1, KIF23, CHEK1, CDC25A, E2F7, CDK1, and CEP55 as the top 10 hub genes (HGs). Furthermore, the upregulation of RECK, ATD5, and BCL2, downregulation of OSBPL3, RCAN3, and HIST1H3H effected overall survival of CC patients. We identified 6 targets (TFAP2A, CLSPN, RASEF, HIST1H3H, AKT3, and ITPR1) of miR-497/195 cluster to influence metastasis. In addition, 8 druggable genes and 38 potential drugs were also identified. Our study identified miR-497/195 cluster target genes and pathways that could be used for prognostic and therapeutic applications in CC.

Identification of key genes with abnormal RNA methylation modification and selected m6A regulators in ankylosing spondylitis.

BACKGROUND: N6-methyladenosine (m6A) has been identified as the most abundant modification of RNA molecules and the aberrant m6A modifications have been associated with the development of autoimmune diseases. However, the role of m6A modification in ankylosing spondylitis (AS) has not been adequately investigated. Therefore, we aimed to explore the significance of m6A regulator-mediated RNA methylation in AS. METHODS: The methylated RNA immunoprecipitation sequencing (meRIP-seq) and digital RNA sequencing (Digital RNA-seq) were conducted using the peripheral blood mononuclear cells from three AS cases and three healthy controls, to identify genes affected by abnormal RNA methylation. The genes associated with different peaks were cross-referenced with AS-related genes obtained from the GeneCards Suite. Subsequently, the expression levels of shared differentially expressed genes (DEGs) and key m6A regulators in AS were evaluated using data from 68 AS cases and 36 healthy controls from two data sets (GSE25101 and GSE73754). In addition, the results were validated through quantitative polymerase chain reaction (qPCR). RESULTS: The meRIP-seq and Digital RNA-seq analyses identified 28 genes with upregulated m6A peaks but with downregulated expression, and 52 genes with downregulated m6A peaks but with upregulated expression. By intersecting the genes associated with different peaks with 2184 AS-related genes from the GeneCards Suite, we identified a total of five shared DEGs: BCL11B, KAT6B, IL1R1, TRIB1, and ALDH2. Through analysis of the data sets and qPCR, we found that BCL11B and IL1R1 were differentially expressed in AS. Moreover, two key m6A regulators, WTAP and heterogeneous nuclear ribonucleoprotein C, were identified. CONCLUSIONS: In conclusion, the current study revealed that m6A modification plays a crucial role in AS and might hence provide a new treatment strategy for AS disease.

Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients.

OBJECTIVE: The activities and products of carbohydrate metabolism are involved in key processes of cancer. However, its relationship with hepatocellular carcinoma (HCC) is unclear. METHODS: The cancer genome atlas (TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases. Differentially expressed genes (DEGs) between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes (CRGs) to obtain differentially expressed CRGs (DE-CRGs). Then, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were applied to identify risk model genes, and HCC samples were divided into high/low-risk groups according to the median risk score. Next, gene set enrichment analysis (GSEA) was performed on the risk model genes. The sensitivity of the risk model to immunotherapy and chemotherapy was also explored. RESULTS: A total of 8 risk model genes, namely, G6PD, PFKFB4, ACAT1, ALDH2, ACYP1, OGDHL, ACADS, and TKTL1, were identified. Moreover, the risk score, cancer status, age, and pathologic T stage were strongly associated with the prognosis of HCC patients. Both the stromal score and immune score had significant negative/positive correlations with the risk score, reflecting the important role of the risk model in immunotherapy sensitivity. Furthermore, the stromal and immune scores had significant negative/positive correlations with risk scores, reflecting the important role of the risk model in immunotherapy sensitivity. Eventually, we found that high-/low-risk patients were more sensitive to 102 drugs, suggesting that the risk model exhibited sensitivity to chemotherapy drugs. The results of the experiments in HCC tissue samples validated the expression of the risk model genes. CONCLUSION: Through bioinformatic analysis, we constructed a carbohydrate metabolism-related risk model for HCC, contributing to the prognosis prediction and treatment of HCC patients.

Protocol for analysis of single-cell sequencing data by Seqtometry.

Seqtometry (sequencing-to-measurement) is an analytical platform for single-cell analysis based on direct profiling of gene expression and accessibility achieved by advanced scoring with gene signatures. Here, we present a protocol for single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) analysis using Seqtometry. We describe steps for preprocessing, imputation, scoring, and plotting, with extensions to large datasets and integration of multiple datasets. This protocol yields results in the form of biologically interpretable dimensions for direct identification and comprehensive characterization of specific cells. For complete details on the use and execution of this protocol, please refer to Kousnetsov et al.1.

Identification of differentially expressed genes, pathways, and immune infiltration in diabetes.

This study aimed to perform exhaustive bioinformatic analysis by using GSE29221 micro-array maps obtained from healthy controls and Type 2 Diabetes (T2DM) patients. Raw data are downloaded from the Gene Expression Omnibus database and processed by the limma package in R software to identify Differentially Expressed Genes (DEGs). Gene ontology functional analysis and Kyoto Gene Encyclopedia and Genome Pathway analysis are performed to determine the biological functions and pathways of DEGs. A protein interaction network is constructed using the STRING database and Cytoscape software to identify key genes. Finally, immune infiltration analysis is performed using the Cibersort method. This study has implications for understanding the underlying molecular mechanism of T2DM and provides potential targets for further research.

Identification of TFRC as a biomarker for pulmonary arterial hypertension based on bioinformatics and experimental verification.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a life-threatening chronic cardiopulmonary disease. However, there is a paucity of studies that reflect the available biomarkers from separate gene expression profiles in PAH. METHODS: The GSE131793 and GSE113439 datasets were combined for subsequent analyses, and batch effects were removed. Bioinformatic analysis was then performed to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) and a protein-protein interaction (PPI) network analysis were then used to further filter the hub genes. Functional enrichment analysis of the intersection genes was performed using Gene Ontology (GO), Disease Ontology (DO), Kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA). The expression level and diagnostic value of hub gene expression in pulmonary arterial hypertension (PAH) patients were also analyzed in the validation datasets GSE53408 and GSE22356. In addition, target gene expression was validated in the lungs of a monocrotaline (MCT)-induced pulmonary hypertension (PH) rat model and in the serum of PAH patients. RESULTS: A total of 914 differentially expressed genes (DEGs) were identified, with 722 upregulated and 192 downregulated genes. The key module relevant to PAH was selected using WGCNA. By combining the DEGs and the key module of WGCNA, 807 genes were selected. Furthermore, protein-protein interaction (PPI) network analysis identified HSP90AA1, CD8A, HIF1A, CXCL8, EPRS1, POLR2B, TFRC, and PTGS2 as hub genes. The GSE53408 and GSE22356 datasets were used to evaluate the expression of TFRC, which also showed robust diagnostic value. According to GSEA enrichment analysis, PAH-relevant biological functions and pathways were enriched in patients with high TFRC levels. Furthermore, TFRC expression was found to be upregulated in the lung tissues of our experimental PH rat model compared to those of the controls, and the same conclusion was reached in the serum of the PAH patients. CONCLUSIONS: According to our bioinformatics analysis, the observed increase of TFRC in the lung tissue of human PAH patients, as indicated by transcriptomic data, is consistent with the alterations observed in PAH patients and rodent models. These data suggest that TFRC may serve as a potential biomarker for PAH.

Pan-cancer analysis of SERPINE1 with a concentration on immune therapeutic and prognostic in gastric cancer.

The serine protease inhibitor clade E member 1 (SERPINE1) is a key modulator of the plasminogen/plasminase system and has been demonstrated to promote tumor progression and metastasis in various tumours. However, although much literature has explored the cancer-promoting mechanism of SERPINE1, the pan-cancer analyses of its predictive value and immune response remain unexplored. The differential expression, and survival analysis of SERPINE1 expression in multiple cancers were analysed using The Cancer Genome Atlas and Genotype-Tissue Expression database. Kaplan-Meier (K-M) plotter and survival data analysis were used to analyze the prognostic value of SERPINE1 expression, including overall survival (OS), disease-specific survival, disease-free interval and progression-free interval and investigated the relationship of SERPINE1 expression with microsatellite instability. We further analysed the correlation between the expression of SERPINE1 and immune infiltration. The Kyoto Encyclopaedia of Genes and Genomes pathway was used for enrichment analysis, and the Gene Set Enrichment Analysis (GSEA) database was used to perform pathway analysis. Finally, in vitro experiments demonstrated that knockdown or overexpression of SERPINE1 could alter the proliferation and migration of gastric cancer (GC) cells. The results indicated that SERPINE1 expression levels different significantly between cancer and normal tissues, meanwhile, it was highly expressed in various cancers. By analysing online data, it has been observed that the gene SERPINE1 exhibits heightened expression levels across a variety of human cancers, significantly impacting patient survival rates. Notably, the presence of SERPINE1 was strongly associated with decrease OS and disease-free survival in individuals diagnosed with GC. Furthermore, an observed link indicates that higher levels of SERPINE expression are associated with increased infiltration of immune cells in GC. Finally, in vitro experiments showed that knockdown or overexpression of SERPINE1 inhibited the growth, and migration, of GC cells. SERPINE1expression potentially represents a novel prognostic biomarker due to its significant association with immune cell infiltration in GC. This study shows that SERPINE1 is an oncogene that participates in regulating the immune infiltration and affecting the prognosis of patients in multiple cancers, especially in GC. These findings underscore the importance of further investigating the role of SERPINE1 in cancer progression and offer a promising direction for the development of new therapeutic strategies.

ITGB2 related to immune cell infiltration as a potential therapeutic target of inflammatory bowel disease using bioinformatics and functional research.

Inflammatory bowel disease (IBD) is a chronic systemic inflammatory condition regarded as a major risk factor for colitis-associated cancer. However, the underlying mechanisms of IBD remain unclear. First, five GSE data sets available in GEO were used to perform 'batch correction' and Robust Rank Aggregation (RRA) to identify differentially expressed genes (DEGs). Candidate molecules were identified using CytoHubba, and their diagnostic effectiveness was predicted. The CIBERSORT algorithm evaluated the immune cell infiltration in the intestinal epithelial tissues of patients with IBD and controls. Immune cell infiltration in the IBD and control groups was determined using the least absolute shrinkage selection operator algorithm and Cox regression analysis. Finally, a total of 51 DEGs were screened, and nine hub genes were identified using CytoHubba and Cytoscape. GSE87466 and GSE193677 were used as extra data set to validate the expression of the nine hub genes. CD4-naïve T cells, gamma-delta T cells, M1 macrophages and resting dendritic cells (DCs) are the main immune cell infiltrates in patients with IBD. Signal transducer and activator of transcription 1, CCR5 and integrin subunit beta 2 (ITGB2) were significantly upregulated in the IBD mouse model, and suppression of ITGB2 expression alleviated IBD inflammation in mice. Additionally, the expression of ITGB2 was upregulated in IBD-associated colorectal cancer (CRC). The silence of ITGB2 suppressed cell proliferation and tumour growth in vitro and in vivo. ITGB2 resting DCs may provide a therapeutic strategy for IBD, and ITGB2 may be a potential diagnostic marker for IBD-associated CRC.

Comprehensive analysis of hub genes associated with cisplatin-resistance in ovarian cancer and screening of therapeutic drugs through bioinformatics and experimental validation.

BACKGROUND: To identify key genes associated with cisplatin resistance in ovarian cancer, a comprehensive analysis was conducted on three datasets from the GEO database and through experimental validation. METHODS: Gene expression profiles were retrieved from the GEO database. DEGs were identified by comparing gene expression profiles between cisplatin-sensitive and resistant ovarian cancer cell lines. The identified genes were further subjected to GO, KEGG, and PPI network analysis. Potential inhibitors of key genes were identified through methods such as LibDock nuclear molecular docking. In vitro assays and RT-qPCR were performed to assess the expression levels of key genes in ovarian cancer cell lines. The sensitivity of cells to chemotherapy and proliferation of key gene knockout cells were evaluated through CCK8 and Clonogenic assays. RESULTS: Results showed that 12 genes influenced the chemosensitivity of the ovarian cancer cell line SKOV3, and 9 genes were associated with the prognosis and survival outcomes of ovarian cancer patients. RT-qPCR results revealed NDRG1, CYBRD1, MT2A, CNIH3, DPYSL3, and CARMIL1 were upregulated, whereas ERBB4, ANK3, B2M, LRRTM4, EYA4, and SLIT2 were downregulated in cisplatin-resistant cell lines. NDRG1, CYBRD1, and DPYSL3 knock-down significantly inhibited the proliferation of cisplatin-resistant cell line SKOV3. Finally, photofrin, a small-molecule compound targeting CYBRD1, was identified. CONCLUSION: This study reveals changes in the expression level of some genes associated with cisplatin-resistant ovarian cancer. In addition, a new small molecule compound was identified for the treatment of cisplatin-resistant ovarian cancer.