Gene Expression Profiling Methods Research Articles

FCER1G as a novel immune-associated blood biomarker in cardiogenic stroke

BackgroundCardioembolic stroke (CE) exhibits the highest recurrence rate and mortality rate among all subtypes of cerebral ischemic stroke (CIS), yet its pathogenesis remains uncertain. The immune system plays a pivotal role in the progression of CE. Growing evidence indicates that several immune-associated blood biomarkers may inform the causes of stroke. The study aimed to identify new immune-associated blood biomarkers in patients with CE and create an online predictive tool in distinguishing CE from noncardioembolic stroke (non-CE) in CIS. MethodsGene expression profiles that were publicly available were obtained from the Gene Expression Omnibus (GEO). The identification of differentially expressed genes (DEGs) was conducted using the Limma package. The hub module and hub genes were identified through the application of weighted gene coexpression network analysis (WGCNA). In order to identify potential diagnostic biomarkers for CE, both the random forest (RF) model and least absolute shrinkage and selection operator (LASSO) regression analysis were employed. Concurrently, the CIBERSORT algorithm was employed to evaluate the infiltration of immune cells in CE samples and examine the correlation between the biomarkers and the infiltrating immune cells. The diagnostic gene expression in blood samples was confirmed using qRT-PCR in a self-constructed dataset. Univariate and multiple logistic regression analyses were used to identify the risk factors for CE. Subsequently, the mathematical model of the nomogram was employed via Java's "Spring Boot" framework to develop the corresponding online tool, which was then deployed on a cloud server utilizing "nginx". ResultsEleven differentially expressed genes (DEGs) that were upregulated and seven DEGs that were downregulated were identified. Through bioinformatics analysis and clinical sample verification, it was discovered that Fc Fragment of IgE Receptor Ig (FCER1G) could serve as a novel potential blood biomarker for CE. FCER1G, along with other risk factors associated with CE, were utilized to develop a nomogram. The training and validation sets, which consisted of 65 CIS patients, yielded areas under the curve (AUCs) of 0.9722 and 0.9689, respectively. These results indicate a high level of precision in risk delineation by the nomogram. Furthermore, the associated online predictive platform has the potential to serve as a more efficacious and appropriate predictive instrument (https://www.origingenetic.com/CardiogenicStroke-FCER1G) for distinguishing between CE and non-CE. ConclusionBlood biomarker FCER1G has the potential to identify patients who are at a higher risk of cardioembolism and direct the search for occult AF.The utilization of this online tool is anticipated to yield significant implications in terms of distinguishing between CE and non-CE, as well as enhancing the optimization of treatment decision support.

Identification of tumor-antigen signatures and immune subtypes for messenger RNA vaccine selection in advanced clear cell renal cell carcinoma

BackgroundAdvanced clear cell renal cell carcinoma still lacks reliable diagnostic and prognostic biomarkers. Recently, tumor vaccines targeting specific molecules have been proposed as a promising treatment in mitigating tumor progression, which was rekindled under the background of the COVID-19 pandemic. However, the application of messenger RNA vaccine against advanced clear cell renal cell carcinoma antigens remains stagnant, and no subgroup of patients deemed suitable for vaccination has been extensively studied or validated. Our study aims to explore novel advanced clear cell renal cell carcinoma antigen signatures to select suitable patients for vaccination. MethodsGene expression profiles of advanced clear cell renal cell carcinoma samples and their corresponding clinical data were retrieved from The Cancer Genome Atlas. The least absolute shrinkage and selection operator model was applied to develop signatures to stratify patients with advanced clear cell renal cell carcinoma. Receiver operating characteristic analysis was used to compare the prognostic accuracy of each factor. Tumor Immune Estimation Resource was used to visualize the relationship between the proportion of antigen-presenting cells and the expression of filtered genes. The "CIBERSORT" and "WGCNA" R Packages were employed to ascertain disparities in immune infiltration levels between advanced clear cell renal cell carcinoma subgroups. The Search Tools for the Retrieval of Interacting Genes database and Cytoscape were used to construct the protein-protein interaction network. CCK-8 and colony formation assays were included in the invitro experiment. ResultsIn total, 244 potential tumor antigens were identified. Using the least absolute shrinkage and selection operator Cox regression, 21 tumor antigens were selected for developing a risk evaluation signature. The risk score signature can be a useful tool to predict the outcome of advanced clear cell renal cell carcinoma patients. According to the differential clinical, molecular, and immune-related genes, we divided advanced clear cell renal cell carcinoma patients into the immune “cold” subtype and immune “hot” subtype. By developing a logistic score, the immune subtype signature can better distinguish a patient more likely to be immune “cold” subtype or immune “hot” subtype.Interestingly, patients with high risk scores had a higher proportion of immune “hot” subtype than those with a low risk score. Furthermore, the prognostic value was significantly improved when combining risk score and immune subtype. Distinct immune landscapes and signal pathways were observed between different tumor subtypes. Finally, novel tumor antigens related to oxidative stress were identified. ConclusionThe tumor-antigens–based risk score and immune subtype signatures identified potentially effective neo-antigens for advanced clear cell renal cell carcinoma messenger RNA vaccine development, and patients with low risk scores and immune “cold” subtype tumors are more prone to benefit from messenger RNA vaccination. Furthermore, our study highlights the significant role of oxidative stress in determining the efficacy of the messenger RNA vaccine.

Identification of colon adenocarcinoma necroptosis subtypes and tumor antigens for the development of mRNA vaccines

BackgroundColon adenocarcinoma (COAD) is a serious public health issue due to high incidence and mortality rate. This study aimed to identify possible tumor antigens and necroptosis subtypes of COAD for the development of mRNA vaccines and the selection of appropriate patients for precision therapy. MethodsGene expression profiles and clinical information for COAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. We comprehensively studied the alterations in necroptosis-related genes (NRGs) using cBioPortal, and screened the hub NRGs associated with the prognosis of patients with COAD using Gene Expression Profiling Interactive Analysis 2. Consensuses clustering analysis was performed to identify necroptosis subtypes. Weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression modules of the NRGs. The necroptosis landscape of COAD was assessed using graph learning-based dimensionality reduction. Finally, a drug sensitivity analysis of the two necroptosis subtypes was performed. FindingsTwo tumor antigens, BLC-2-associated X protein (BAX) and interleukin 1 beta (IL1B) were identified based on their associations with prognosis of patients and antigen presenting cell infiltration. Two necroptosis subtypes (N1 and N2) were distinguished in patients with COAD, and they were characterized by their differential survival status and molecular expression levels of immune checkpoint proteins and immunogenetic cell death modulators. Furthermore, the necroptosis landscape of COAD indicated that individual patients had obvious heterogeneity. Co-expression modules were identified using WGCNA, and the hub NRGs were found to be involved in various immune processes. Drug sensitivity analysis indicated that there were significant differences in drug sensitivity between the N1 and N2 subtypes. Cell experiments suggested that both overexpression of BAX and IL1B promoted necroptosis of COAD cells and enhanced the cytotoxicity of CD8+ T cells. InterpretationBAX and IL1B are potential antigens for the development of anti-COAD mRNA vaccines, specifically for patients with the N2 subtype. Consequently, this study will guide the development of more effective immunotherapeutic approaches and the identification of appropriate patients.

Regulatory role of PDK1 via integrated gene analysis of mitochondria-immune response in periodontitis

AimsTo investigate the association between mitochondrial events and immune response in periodontitis and related regulatory genes. Main methodsGene expression profiles in gingival tissues were retrieved from the Gene Expression Omnibus. Mitochondria-immune response-related differentially expressed genes (MIR-DEGs) between the healthy and periodontitis samples were determined. WGCNA, GO, and KEGG were used to investigate the function and the enriched pathways of MIR-DEGs. The correlation between MIR-DEGs expression and clinical probing pocket depth was analyzed. The MIR-DEGs were further identified and verified in animal samples. A periodontitis model was established in C57BL/6 mice with silk ligation. Micro-computed tomography was used to assess alveolar bone loss. Western blot, quantitative real-time polymerase chain reaction, and immunohistochemical analyses further validated the differential expression of the MIR-DEGs. Key findingsA total of ten MIR-DEGs (CYP24A1, PRDX4, GLDC, PDK1, BCL2A1, CBR3, ARMCX3, BNIP3, IFI27, and UNG) were identified, the expression of which could effectively distinguish patients with periodontitis from the healthy controls. Enhanced immune response was detected in the periodontitis group with that in the healthy controls, especially in B cells. PDK1 was a critical MIR-DEG correlated with B cell immune response and clinical periodontal probing pocket depth. Both animal and clinical periodontal samples presented higher gene and protein expression of PDK1 than the control samples. Additionally, PDK1 colocalized with B cells in both animal and clinical periodontal tissues. SignificanceMitochondria participate in the regulation of the immune response in periodontitis. PDK1 may be the key mitochondria-related gene regulating B-cell immune response in periodontitis.

Abstract 6258: Inference of gene expression using fragmentation patterns from targeted high-depth sequencing of cell-free DNA

Abstract Cell-free DNA (cfDNA) circulating in blood plasma arises primarily from cellular chromatin fragmentation and release due to cell death. Studies have demonstrated that the assessment of fragmentomic features of cfDNA enables gene expression inference and tissue-of-origin classification with potential applications for noninvasive cancer detection. However, due to low depth of coverage of sites of interest, current whole genome sequencing (WGS) methods have not been able to infer expression of individual genes or limited gene sets. Therefore, we developed a targeted sequencing approach that enables individual gene expression inference from cfDNA fragmentation patterns based on transcriptional start-site gene activation probability (TSS-GAP). To assess the sensitivity of this method for cfDNA-inferred gene expression profiling, we applied TSS-GAP to blends of micrococcal nuclease (MNase)-digested DNA from a cancer cell line (LS180) and sorted peripheral blood mononuclear cells (PBMC), with LS180 DNA at varying concentrations (0.1-10%). This enabled the determination of the limit of detection (LoD), relative to healthy donor background levels, on the basis of the genes’ activation probabilities (TSS-GAP scores). We found 936 genes with high inferred expression in the LS180 cancer cell line and low inferred expression in non-cancer samples (i.e. CD4, CD14, and plasma cfDNA from 4 healthy donors) and used those for healthy donor background and LoD assessment. Using these gene-level background values as cutoffs, blends were assessed to determine the lowest blend level at which expression of the gene was able to be differentiated from background. Out of the 936 genes, 326 (34.8%) were detectable at a blend level of 0.1%, 59 (6.3%) at 0.3%, 31 (3.3%) at 1%, 19 (2.0%) at 3%, and 39 (4.2%) at 10%. In total, 416 genes could be detected by TSS-GAP at levels 1% of or lower above background, highlighting the potential of our method for detecting expression of certain genes even from a small fraction of cells contributing cfDNA to liquid biopsy samples. Further expansion of this work could consider the utility of such a method for circulating tumor DNA (ctDNA) detection, cfDNA tissue-of-origin classification, and deconvolution of cell types contributing to cfDNA found in blood. Citation Format: Emily Leff, Alexander Tseng, Victoria Cheung, Ehsan Tabari, Kimberly Walter, Alex Lovejoy, Cheng-Ho J. Lin. Inference of gene expression using fragmentation patterns from targeted high-depth sequencing of cell-free DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6258.

Exploration of the Key Genes Involved in Non-alcoholic Fatty Liver Disease and Possible MicroRNA Therapeutic Targets

BackgroundMicroRNAs (miRNAs) are promising therapeutic agents for nonalcoholic fatty liver disease (NAFLD). This study aimed to identify key genes/proteins involved in NAFLD pathogenesis and progression and to evaluate miRNAs influencing their expression. MethodsGene expression profiles from datasets GSE151158, GSE163211, GSE135251, GSE167523, GSE46300 and online databases were analyzed to identify significant NAFLD-related genes. Then, protein-protein interaction networks and module analysis identified hub genes/proteins, which were validated using real-time PCR in oleic acid-treated HepG2 cells. Functional enrichment analysis evaluated signaling pathways and biological processes. Gene-miRNA interaction networks identified miRNAs targeting critical NAFLD genes. ResultsThe most critical overexpressed hub genes/proteins included: TNF, VEGFA, TLR4, CYP2E1, ACE, SCD, FASN, SREBF2 and TGFB1 based on PPI network analysis, of which TNF, TLR4, SCD, FASN, SREBF2 and TGFB1 were up-regulated in oleic acid-treated HepG2 cells. Functional enrichment analysis for biological processes highlighted programmed necrotic cell death, lipid metabolic process response to reactive oxygen species, and inflammation. In Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, the highest adjusted P-value signaling pathways encompassed AGE-RAGE in diabetic complications, TNF and HIF-1 signaling pathways. In gene-miRNA network analysis, miR-16 and miR-124 were highlighted as the miRNAs exerting the most influence on important NAFLD-related genes. ConclusionIn silico analyses identified NAFLD therapeutic targets and miRNA candidates to guide further experimental investigation.

CD74 is associated with inflamed tumor immune microenvironment and predicts responsiveness to PD-1/CTLA-4 bispecific antibody in patients with solid tumors

IntroductionCadonilimab (AK104) is a first-in-class tetravalent bispecific antibody that targets both PD-1 and CTLA-4, showing a manageable safety profile and favorable clinical benefits. This study aimed to identify the biomarkers of clinical response and explore the immune response within the tumor microenvironment upon the AK104 therapy in advanced solid tumors.Material and methodsGene expression profiles of paired pre- and post-treatment tumor tissues from twenty-one patients were analyzed. The association of gene expression levels with either clinical efficacy or prognosis was evaluated and subsequently validated with published datasets using log-rank for Kaplan–Meier estimates. Comparative immune profile analyses of tumor microenvironment before and after AK104 treatment were conducted. The visualization of tumor-infiltrating lymphocytes was performed using multiplex immunohistochemistry. The predictive value of CD74 was further validated with protein expression by immunohistochemistry.ResultsBaseline CD74 gene expression was associated with favorable patient outcomes (overall survival [OS], HR = 0.33, 95% CI 0.11–1.03, p = 0.0463), which was further confirmed with the published datasets. Tumors with high CD74 gene expression at baseline were more likely to exhibit an immune-inflamed microenvironment. AK104 efficiently enhanced the infiltration of immune cells in the tumor microenvironment. Additionally, high CD74 protein expression (≥ 10% of the tumor area occupied by CD74 stained immune cells) at baseline was associated with better progressive-free survival (HR = 0.21, 95% CI 0.06–0.68, p = 0.0065) and OS (HR = 0.35, 95% CI 0.12–1.08, p = 0.0615).ConclusionsOur findings demonstrate that CD74 is a promising predictive biomarker for AK104 therapeutic response in advanced solid tumors.Trial registration number NCT03261011.

Systematic identification of key basement membrane related genes as potential new biomarkers in Alzheimer's disease

ObjectiveThe study aimed to identify biomarkers associated with basement membranes (BMs)-related genes (BMGs) in Alzheimer's disease (AD) and investigate their potential role in the progression of AD pathology. MethodsGene expression profiles were retrieved from Gene Expression Omnibus database. 222 human BMGs were collected from the relevant literature. Subsequently, the differentially expressed BMGs (DE-BMGs) were filtered, and the key DE-BMGs were identified using weighted gene correlation network analysis (WGCNA) and two machine learning algorithms. The expression levels, diagnostic values, clinical significances, enrichment analyses and regulatory networks of these candidate biomarkers were further examined. ResultsA total of 44 DE-BMGs were acquired by comparing AD temporal cortex with nondemented controls. Using WGCNA and machine learning, versiscan (VCAN), tissue inhibitor of metalloproteinase 1 (TIMP1), structural maintenance of chromosome 3 (SMC3), and laminin β2 (LAMB2) were ultimately identified as candidate biomarkers, and they were verified in a murine model. These biomarkers had high diagnostic value (area under the curve (AUC)＞0.8). The diagnostic value of the four gene combination was then evaluated in multiple databases, yielding AUCs ranging from 0.688 to 1. Furthermore, a meaningful correlation between these biomarkers and AD pathology progression was observed. Finally, comprehensive analyses involving Hallmark pathway enrichment, immune cell infiltration analysis, transcriptional regulatory, and competitive endogenous RNA networks indicated that key DE-BMGs closely correlated with oxidative stress and immune dysfunction. ConclusionOur study comprehensively identified four candidate BMGs and their combination model that play a crucial part in the diagnosis and pathogenesis of AD.

RADT-28. DISRUPTION OF BIOENERGETICS ENHANCES RADIO-SENSITIVITY OF PATIENT-DERIVED GLIOBLASTOMA TUMORSPHERES

Abstract INTRODUCTION Glioblastoma (GBM), the most common primary brain tumor, has been associated with poor prognosis despite various treatment modalities, the surgical resection accompanied by chemotherapy and radiotherapy. Although the importance of radiotherapy is well known, its combination with bioenergetics regulators has not been established in clinical practice. Here, we hypothesized that bioenergetics regulators improve the radio-sensitivity of GBM-tumorspheres (TS). METHODS Gene expression profiles of GBM patient-derived TSs were obtained from microarray. In vitro efficacies of the treatment were evaluated using clonogenic assay, 3D invasion assay, neurosphere-formation assay, and apoptosis-analysis. Protein expression was evaluated by western blot, and immunofluorescence was performed for detection of γH2AX foci. In vivo efficacies were confirmed using a mouse orthotopic-xenograft model. RESULTS GBM TSs with high-radioresistant or low-radioresistant were selected according to expression levels of radiation response associated genes. In order to select the bioenergetics regulators that significantly increased the radiotherapy effect of GBM-TSs, five bioenergetics regulators were combined which are oxidative-phosphorylation (OxPhos) inhibitor (IM156A), aldehyde-dehydrogenase inhibitor (gossypol) + OxPhos inhibitor (phenformin), fatty acid oxidation inhibitor (etomoxir), pentose-phosphate pathway inhibitor (dehydroepiandrosterone; DHEA) and glycolysis inhibitor (2-deoxy-D-glucose; 2DG). Amongst of these five bioenergetics regulators, DHEA and 2DG significantly augmented the therapeutic efficacies of ionizing radiation (IR) by reducing the survival fraction, stemness, and invasiveness in both high-radioresistant and low-radioresistant GBM TSs. IR combined with bioenergetics regulators (DHEA or 2DG) induced DNA damage response and apoptosis in low-radioresistant GBM TSs. In vivo experiments using a mouse orthotopic-xenograft model confirmed the tendency to prolong survival period and reduced the size in tumor after the combined treatment of 2DG with IR in low-radioresistant GBM TS. CONCLUSION These data proposes that the combination of bioenergetics regulators and radiotherapy is more effective than IR monotherapy itself. We expect that this combination regimen can strategically establish as a future treatment practice of GBM.

RNA-seq RNAaccess identified as the preferred method for gene expression analysis of low quality FFPE samples.

Clinical tumor tissues that are preserved as formalin-fixed paraffin-embedded (FFPE) samples result in extensive cross-linking, fragmentation, and chemical modification of RNA, posing significant challenges for RNA-seq-based gene expression profiling. This study sought to define an optimal RNA-seq protocol for FFPE samples. We employed a common RNA extraction method and then compared RNA-seq library preparation protocols including RNAaccess, RiboZero and PolyA in terms of sequencing quality and concordance of gene expression using FFPE and case-matched fresh-frozen (FF) triple-negative breast cancer (TNBC) tissues. We found that RNAaccess, a method based on exome capture, produced the most concordant results. Applying RNAaccess to FFPE gastric cancer tissues, we established a minimum RNA DV200 requirement of 10% and a RNA input amount of 10ng that generated highly reproducible gene expression data. Lastly, we demonstrated that RNAaccess and NanoString platforms produced highly concordant expression profiles from FFPE samples for shared genes; however, RNA-seq may be preferred for clinical biomarker discovery work because of the broader coverage of the transcriptome. Taken together, these results support the selection of RNA-seq RNAaccess method for gene expression profiling of FFPE samples. The minimum requirements for RNA quality and input established here may allow for inclusion of clinical FFPE samples of sub-optimal quality in gene expression analyses and ultimately increasing the statistical power of such analyses.

Identification of immunological characteristics and cuproptosis-related molecular clusters in Rheumatoid arthritis

BackgroundRheumatoid arthritis (RA) is a chronic autoimmune disease characterised by progressive articular damage, functional loss, and comorbidities. The relationship between cuproptosis, a form of programmed cell death, and RA remains unknown. Therefore, this study aimed to explore cuproptosis-related molecular clusters in RA. MethodsGene expression profiles of GSE93272 were downloaded from the Gene Expression Omnibus to identify the expression profiles of cuproptosis regulators and the immune infiltration characteristics of RA. The molecular clusters of cuproptosis-related genes and the related immune cell infiltration were explored. Cluster-specific differentially expressed genes were identified using the weighted gene co-expression network analysis. Further, an external dataset (GSE15573) was used, and an enzyme-linked immunosorbent assay was performed to validate the predictive efficiency. ResultsThirteen cuproptosis-related genes and activated immune responses were identified between patients with RA and controls. Immune infiltration revealed significant immunological heterogeneity in the two cuproptosis-related molecular clusters in RA. Functional enrichment indicated that Cluster1 and Cluster2 were predominantly enriched in the toll-like receptor signalling pathway and regulation of autophagy, respectively. Further, the performance of FAM96A and CGRRF1 genes in the external validation dataset was observed to be relatively satisfactory (area under the receiver operating characteristic curve = 0.687 and 0.674, respectively). Based on our serum samples, FAM96A and CGRRF1 both exhibited higher expression levels in patients with RA (p = 0.001; p = 0.000). ConclusionsOur study systematically illustrated the involvement of cuproptosis in the progression of RA, and explored the pathogenic mechanisms and novel therapeutic strategies for RA, targeting FAM96A and CGRRF1.

Identification of m7G-related hub biomarkers and m7G regulator expression pattern in immune landscape during the progression of osteoarthritis

BackgroundAccumulating evidence has shown that aberrant N7-methylguanosine (m7G) RNA methylation played an important role in the occurrence and development of cancer. However, knowledge of m7G modifications in inflammatory diseases is limited. Osteoarthritis (OA) is the most common arthritic disease with poor prognosis. Our research aimed to identify m7G-related hub biomarkers and investigate m7G regulator expression pattern in immune landscape of OA patients. MethodsGene expression profiles and their clinical information were obtained from the Gene Expression Omnibus (GEO) database, and differential analysis of 14 m7G-related regulators between elective OA and normal samples was performed. M7G-related hub genes for OA were mined based on single-sample gene set enrichment analysis (ssGSEA) and the random forest (RF) algorithm, and qRT-PCR was performed to confirm the abnormal expression of hub genes. Enrichment, protein–protein interaction (PPI), transcription factor (TF)-gene interaction and microRNA (miRNA)-gene coregulatory analysis based on m7G hub genes were performed. Then we predicted several candidate drugs related to m7G hub genes using DSigDB database. Moreover, we comprehensively evaluated m7G methylation patterns in OA samples and systematically correlated these modification patterns with the characteristics of immune cell infiltration. The m7G score was generated to quantify m7G methylation patterns for individual OA patients by the application of principal component analysis (PCA) algorithm. ResultsWe constructed an OA predictive model based on 4 m7G hub genes (SNUPN, METTL1, EIF4E2 and CYFIP1). Two m7G methylation patterns in OA were discovered to show distinct biological characteristics, and an m7G score were generated. M7G cluster A and a higher m7G score were found to be related to an inflamed phenotype. ConclusionsOur study was the first to comprehensively investigate the m7G methylation dysregulations in immune landscape during the progression of OA. These 4 m7G gene-related signatures can be used as novel OA biomarkers to predict the occurrence of OA. Evaluating the m7G methylation patterns of OA individuals will contribute to enhancing our cognition of immune infiltration characterization and guiding more effective immunotherapy strategies.

Identification of four-gene signature to diagnose osteoarthritis through bioinformatics and machine learning methods

BackgroundAlthough osteoarthritis (OA) is one of the most prevalent joint disorders, effective biomarkers to diagnose OA are still unavailable. This study aimed to acquire some key synovial biomarkers (hub genes) and analyze their correlation with immune infiltration in OA. MethodsGene expression profiles and clinical characteristics of OA and healthy synovial samples were retrieved from the Gene Expression Omnibus (GEO) database. Hub genes for OA were mined based on a combination of weighted gene co-expression network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) algorithms. A diagnostic nomogram model for OA prediction was developed based on the hub genes. Receiver operating characteristic curves (ROC) were performed to confirm the abnormal expression of hub genes in the experimemtal and validation datasets. qRT-PCR using patients’ samples were conducted as well. In addition, the infiltration level of 28 immune cells in the expression profile and their relationship with hub genes were analyzed using single-sample GSEA (ssGSEA). Results4 hub genes (ZBTB16, TNFSF11, SCRG1 and KDELR3) were obtained by WGCNA, lasso, SVM-RFE, RF algorithms as potential biomarkers for OA. The immune infiltration analyses revealed that hub genes were most correlated with regulatory T cell and natural killer cell. ConclusionA machine learning model to diagnose OA based on ZBTB16, TNFSF11, SCRG1 and KDELR3 using synovial tissue was constructed, providing theoretical foundation and guideline for diagnostic and treatment targets in OA.

Prognostic value of iron metabolism-related genes in bladder urothelial carcinoma

Abstract Objectives Iron metabolism-related genes (IMRGs) play important roles in the prognostic assessment of many tumours. However, IMRGs have not been reported as prognostic biomarkers in bladder urothelial carcinoma (BLCA). Methods Gene expression profiles and clinical data from BLCA patients were obtained from The Cancer Genome Atlas (TCGA) database. We used the DESeq2 package to screen for differentially expressed genes (DEGs). The predictive values of the differentially expressed IMRGs in BLCA patients were further evaluated using univariate Cox regression analysis. The risk-scoring model was constructed using the least absolute shrinkage and selection operator (LASSO) algorithm. The performance of this model for predicting the prognosis of BLCA patients in TCGA-BLCA cohort was assessed using Kaplan–Meier (K–M) and receiver operating characteristic (ROC) curves. This risk-scoring model was combined with the clinicopathological characteristics of BLCA patients in a multiple regression analysis, and a nomogram was constructed using the independent predictors identified. ROC analysis and calibration curves were adapted to test the predictive ability of the nomogram. Gene set enrichment analysis (GSEA) was used to identify potential molecular pathways and processes enriched by differential expression genes between risk groups. Finally, we explored the ability of the risk-scoring model to assess immune cell infiltration levels through a correlation analysis. Results Fourteen identified IMRGs with prognostic value were incorporated into the risk-scoring model. The ROC and K–M survival curves indicated that the model could effectively predict the overall survival (OS) outcomes of BLCA patients. The multiple regression analysis revealed that the risk-scoring model could be used as an independent prognostic factor for BLCA patients, and the associated nomogram could effectively predict the OS outcomes of BLCA patients. GSEA revealed that the DEGs between the risk groups were mainly involved in biological processes such as developmental process, cell cycle, mitosis, RHO GTPase reaction, DNA repair, and extracellular matrix regulation. The immune infiltration analysis showed that the infiltration levels of immune cells such as natural killer cells, memory T cells, effector T cells, Th2 cells, and macrophages differed significantly between the risk groups. Conclusions IMRGs screening revealed prognosis-associated genes. The prognostic model constructed could effectively predict the prognosis of BLCA patients, and the identified genes represent potential targets for BLCA treatment.

Transcriptome analysis of Ganoderma lingzhi (Agaricomycetes) response to Trichoderma hengshanicum infection.

Green mold caused by Trichoderma spp. has become one of the most serious diseases which threatening the production of Ganoderma lingzhi. To understand the possible resistance mechanism of the G. lingzhi response to T. hengshanicum infection, we examined the G. lingzhi transcript accumulation at 0, 12, and 24 h after T. hengshanicum inoculation. The gene expression analysis was conducted on the interaction between G. lingzhi and T. hengshanicum using RNA-seq and digital gene expression (DGE) profiling methods. Transcriptome sequencing indicated that there were 162 differentially expressed genes (DEGs) at three infection time points, containing 15 up-regulated DEGs and 147 down-regulated DEGs. Resistance-related genes thaumatin-like proteins (TLPs) (PR-5s), phenylalanine ammonia-lyase, and Beta-1,3-glucan binding protein were significantly up-regulated. At the three time points of infection, the heat shock proteins (HSPs) genes of G. lingzhi were down-regulated. The down-regulation of HSPs genes led to the inhibition of HSP function, which may compromise the HSP-mediated defense signaling transduction pathway, leading to G. lingzhi susceptibility. Pathway enrichment analyses showed that the main enriched pathways by G. lingzhi after infection were sphingolipid metabolism, ether lipid metabolism, and valine, leucine and isoleucine degradation pathway. Overall, the results described here improve fundamental knowledge of molecular responses to G. lingzhi defense and contribute to the design of strategies against Trichoderma spp.

Assessment of 2-Year Storage Conditions on Protein, RNA, and DNA in Unstained Human Tissue Sections, Including a Novel Multiplex Digital Gene Expression Profiling Method with Implications for Biobanking.

Background: Formalin-fixed, paraffin-embedded (FFPE) tissues are a valuable resource for clinical and basic science research. Paraffin blocks and the resulting unstained sections (USS) are often stored for years before being used. Previous studies have evaluated the effects of time, temperature, humidity, and inert gases on preservation of USS; however, no study has examined all four variables together. Methods: In the current work, we prospectively and blindly assessed time points from 0 to 24 months, room versus refrigerated temperature, and presence of a desiccant and/or nitrogen atmosphere on a variety of benign and malignant tissues from North America and Africa. End points included immunohistochemistry (IHC), in situ hybridization (ISH), extracted RNA and DNA quantity and quality, and messenger RNA performance in a novel, multiplexed digital gene expression profiling assay of both housekeeping and tumor-specific genes. Results: We found that using current methods of antigen retrieval, staining, and extraction, the end points of IHC, ISH, RNA, and DNA were well preserved under the various conditions tested, with implications that pre-embedding factors contribute to variability in subsequent tissue integrity. We also document that spectrophotometric estimations of nucleic acid concentrations were in general estimated to be higher than with fluorimetric methods, which may be pertinent to end assay development. We further describe a new multiplex assay, the PlexSet digital gene expression assay, suitable for evaluating RNA quality in FFPE tissues. Conclusion: Altogether, these results may provide helpful guidance with regard to approaches for long-term storage conditions for USS.

Prognostic biomarkers and immune cell infiltration characteristics in small cell lung cancer

BackgroundSmall cell lung cancer (SCLC) is a highly malignant and aggressive neuroendocrine tumor. With the rise of immunotherapy, it has provided a new direction for SCLC. However, due to the lack of prognostic biomarkers, the median overall survival of SCLC is still to be improved. This study aimed to explore novel biomarkers and tumor-infiltrating immune cell characteristics that may serve as potential diagnostic and prognostic markers in SCLC. MethodsGene expression profiles from patients with SCLC were downloaded from the Gene Expression Omnibus (GEO) database, and tumor microenvironment (TME) infiltration profile data were obtained using CIBERSORT. The robust rank aggregation (RRA) method was utilized to integrate three SCLC microarray datasets downloaded from the GEO database and identify robust differentially expressed genes (DEGs) between normal and tumor tissue samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to explore the functions of the robust DEGs. Subsequently, protein–protein interaction networks and key modules were constructed by Cytoscape, and hub genes were selected from the whole network using the plugin cytoHubba. Survival analysis of hub genes was performed by Kaplan–Meier plotter in 18 patients with extensive-stage SCLC. ResultsA total of 312 robust DEGs, including 55 upregulated and 257 downregulated genes, were screened from 129 SCLC tissue samples and 44 normal tissue samples. GO and KEGG enrichment analyses revealed that the robust DEGs were predominantly involved in human T-cell leukemia virus 1 infection, focal adhesion, complement and coagulation cascades, tumor necrosis factor (TNF) signaling pathway, and ECM-receptor interaction, which are closely associated with the development and progression of SCLC. Subsequently, three DEGs modules and six hub genes (ITGA10, DUSP12, PTGS2, FOS, TGFBR2, and ICAM1) were identified through screening with the Cytoscape plugins MCODE and cytoHubba, respectively. Immune cell infiltration analysis by the CIBERSORT algorithm revealed that resting memory CD4+ T cells were the predominant infiltrating immune cells in SCLC. In addition, Kaplan–Meier plotter revealed that the gene prostaglandin-endoperoxide synthase 2 (PTGS2) was a potential prognostic biomarker of SCLC. ConclusionsHub genes and tumor-infiltrating immune cells may be the molecular mechanisms underlying the development of SCLC, and this finding could contribute to the formulation of individualized immunotherapy strategies for SCLC.

Five hub genes contributing to the oncogenesis and trastuzumab-resistance in gastric cancer

BackgroundMonoclonal antibodies, as the targeted therapeutic strategies, provide huge clinical benefits for tumor patients. However, after undergoing several times treatment, patients developed drug resistance which is a major bottleneck in clinical cancer therapy. In this study, we aimed to explore the potential molecular mechanism of trastuzumab-resistant and cancer progression, and identify valuable diagnosis biomarkers for gastric cancer. Materials and methodsGene expression profiles and RNA-sequencing dataset of gastric cancer were acquired from Gene Expression Omnibus (GEO) dataset and The Cancer Genome Atlas (TCGA) dataset, respectively. The Differently expressed genes (DEGs) were screened by R programing language, and Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were adopted separately to analyze the function and pathway of DEGs. Subsequently, Search Tool for the Retrieval of Interacting (STRING) and Cytoscape was performed to establish the protein–protein interaction (PPI) network and to screen hub genes. The Receiver Operating Characteristic (ROC) curves were used to evaluate the diagnostic values of the hub genes. ResultsIntegrated analysis of TCGA-STAD (Stomach adenocarcinoma) and GEO databases identified 310 common DEGs. GSEA, GO and KEGG enrichment analysis revealed several crucial enriched oncological signatures and trastuzumab-resistant signaling pathways, which may help to explain the potential modulating mechanisms of trastuzumab-resistant. Based on the PPI network, 10 hub genes were screened and five genes (GNGT1, KRT7, KRT16, SOX9, TIMP1) were identified with good performance in the diagnosis of gastric cancer by ROC analysis. Furthermore, Kaplan-Meier analysis and log-rank test suggested that upregulation of KRT16 was correlated with overall survival in gastric cancer. ConclusionOverall, our study identified five hub genes that may play a critical role in promoting trastuzumab-resistant in gastric cancer, and would be a promising diagnostic and therapeutic biomarker for trastuzumab-resistant gastric cancer.

A chromatin mark by SETD7 regulates myocardial inflammation in obesity-related heart failure with preserved ejection fraction

Abstract Background Obesity is a major risk factor for heart failure with preserved ejection fraction (HFpEF). Post-translational modification of histones by chromatin modifying enzymes (CMEs) are emerging as pivotal regulators of gene transcription in cardiovascular disease. Purpose To investigate the role of chromatin remodelling in obese HFpEF (obHFpEF). Methods Gene expression profiling of CMEs (PCR array) was performed in left ventricular (LV) myocardial specimens from obHFpEF patients and age-matched control donors (n=8/group). Among myocardial CMEs, the methyltransferase SETD7 showed the highest variation in gene expression. Hence, we investigated the role of SETD7 and its chromatin mark H3K4me1 in a murine model of obHFpEF. Mice with cardiomyocyte-specific deletion of SETD7 (c-SETD7−/−) and control littermates (SETD7fl/fl) were subjected to high fat diet feeding and L-NAME treatment for 15 weeks to induce obHFpEF. Echocardiography and Treadmill exhaustion test were performed. ChIP-Seq datasets were employed to determine the biological pathways regulated by SETD7, whereas chromatin immunoprecipitation assays (ChIP) were performed to investigate SETD7/H3k4me1 enrichment on target gene promoters. SETD7 gain- and loss-of-function experiments were performed in cultured neonatal rat ventricular myocytes (NRVMs) exposed to palmitic acid (200μM) for 48h. Selective inhibition of SETD7 by (R)-PFI-2 was performed in skinned cardiomyocytes isolated from left ventricular specimens of obHFpEF patients. Passive stiffness, a main feature of HFpEF, was assessed before and after (R)-PFI-2 treatment. Results CMEs profiling showed SETD7 as the top-ranking transcript (fold change, 7.36, P<0.01) in myocardial specimens from obHFpEF patients as compared to controls. ChIP-Seq in CMs showed a strong enrichment of SETD7 and H3k4me1 on the promoter of NF-kB p65 gene, a master regulator of inflammation. SETD7 and H3k4me1 were upregulated in HFpEF vs. control mouse hearts, showed enrichment on NF-kB p65 promoter and were associated with IL-1β and IL-6 upregulation. In HFpEF mice, cardiomyocyte-specific deletion of SETD7 protected against LV hypertrophy, diastolic dysfunction (assessed by E/E' ratio) and lung congestion while improving exercise tolerance. At the molecular level, SETD7 deletion blunted H3K4me1 enrichment on p65 promoter thus preventing the upregulation of inflammatory genes and myocardial apoptosis. In cultured CMs exposed to PA, SETD7 inhibition by (R)-PFI-2 prevented H3k4me1-driven p65 upregulation, whereas SETD7 overexpression mimicked HFpEF features. Moreover, knockdown of NF-kB p65 prevented IL-1β/IL-6 transcription in SETD7-overexpressing CMs. Of clinical relevance, (R)-PFI-2 reduced passive stiffness in skinned CMs isolated from obHFpEF patients. Conclusions Our results unveil a new epigenetic mechanism underpinning inflammation in obHFpEF. Targeting SETD7 may represent a novel therapeutic approach to prevent HFpEF in obesity. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Swiss National Science Foundation

Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis.

ObjectiveDiabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.Materials and methodsGene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.ResultsA total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.ConclusionDUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.