Transcriptomics in the Study of Antiviral Innate Immunity.

Transcriptomics is an extremely important area of molecular biology and is a powerful tool for studying all RNA molecules in an organism. Conventional transcriptomic technologies include microarrays and RNA sequencing, and the rapid development of single-cell sequencing and spatial transcriptomics in recent years has provided an enormous scope for research in this field. This chapter describes the application, significance, and experimental procedures of a variety of transcriptomic technologies in antiviral natural immunity.

Inhibition mechanisms of perchlorate on the photosynthesis of cyanobacterium Synechocystis sp. PCC6803: Insights from physiology and transcriptome analysis.

Perchlorate (ClO4-) is a type of novel persistent inorganic pollutant that has gained increasing attention because of its high solubility, poor degradability, and widespread distribution. However, the impacts of perchlorate on aquatic autotrophs such cyanobacterium are still unclear. Herein, Synechocystis sp. PCC6803 (Synechocystis) was used to investigate the response mechanisms of perchlorate on cyanobacterium by integrating physiological and transcriptome analyses. Physiological results showed that perchlorate mainly damaged the photosystem of Synechocystis, and the inhibition degree of photosystem II (PSII) was severer than that of photosystem I (PSI). When the exposed cells were moved to a clean medium, the photosynthetic activities were slightly repaired but still lower than in the control group, indicating irreversible damage. Furthermore, perchlorate also destroyed the cellular ultrastructure and induced oxidative stress in Synechocystis. The antioxidant glutathione (GSH) content and the superoxide dismutase (SOD) enzyme activity were enhanced to scavenge harmful reactive oxygen (ROS) in Synechocystis. Transcriptome analysis revealed that the genes associated with "photosynthesis" and "electron transport" were significantly regulated. For instance, most genes related to PSI (e.g., psaf, psaJ) and the "electron transport chain" were upregulated, whereas most genes related to PSII (e.g., psbA3, psbD1, psbB, and psbC) were downregulated. Additionally, perchlorate also induced the expression of genes related to the antioxidant system (sod2, gpx, gst, katG, and gshB) to reduce oxidative damage. Overall, this study is the first to investigate the impacts and mechanisms of cyanobacterium under perchlorate stress, which is conducive to assessing the risk of perchlorate in aquatic environments.

Study on the underlying mechanism of Huachansu Capsule induced cardiotoxicity of normal rat by integrating transcriptomics, metabolomics and network toxicology.

ETHNOPHARMACOLOGICAL RELEVANCE: Huachansu Capsule (HCSc) is a simple enteric-coated capsule refined from the skin of the dried toad, a traditional medicinal herb. It has been used clinically for many years to treat a variety of malignant tumors with remarkable efficacy. To date, a number of main components of HCSc have been reported to be cardiotoxic, but the specific mechanism of cardiotoxicity is still unknown. AIM OF THE STUDY: The aim of this study was to elucidate the possible cardiotoxic symptoms caused by high-doses of HCSc and to further reveal the complex mechanisms by which it causes cardiotoxicity. MATERIALS AND METHODS: UPLC-Q-Exactive Orbitrap MS and network toxicology were used to identify and predict the potential toxic components, related signaling pathways. Then, we used acute and sub-acute toxicity experiments to reveal the apparent phenomenon of HCSc-induced cardiotoxicity. Finally, we combined transcriptomics and metabolomics to elucidate the potential mechanism of action, and verified the putative mechanism by molecular docking, RT-qPCR, and Western blot. RESULTS: We found 8 toad bufadienolides components may be induced cardiac toxicity HCSc main toxic components. Through toxicity experiments, we found that high dose of HCSc could increase a variety of blood routine indexes, five cardiac enzymes, heart failure indexes (BNP), troponin (cTnI and cTnT), heart rate and the degree of heart tissue damage, while low-dose of HCSc had no such changes. In addition, by molecular docking, found that 8 kinds of main toxic components and cAMP, AMPK, IL1ß, mTOR all can be a very good combination, especially in the cAMP. Meanwhile, RT-qPCR and Western blot results showed that HCSc could induce cardiotoxicity by regulating a variety of heart-related differential genes and activating the cAMP signaling pathway. CONCLUSIONS: In this study, network toxicology, transcriptomics and metabolomics were used to elucidate the complex mechanism of possible cardiotoxicity induced by high-dose HCSc. Animal experiments, molecular docking, Western blot and RT-qPCR experiments were also used to verify the above mechanism. These findings will inform further mechanistic studies and provide theoretical support for its safe clinical application.

Lens Regeneration: The Application of iSyTE and In Silico Approaches to Evaluate Gene Expression in Lens Organoids.

Several protocols have been established for the generation of lens organoids from embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and other cells with regenerative potential in humans or various animal models. It is important to examine how well the regenerated lens organoids reflect lens biology, in terms of its development, homeostasis, and aging. Toward this goal, the iSyTE database (integrated Systems Tool for Eye gene discovery; https://research.bioinformatics.udel.edu/iSyTE/ ), a bioinformatics resource tool that contains meta-analyzed gene expression data in wild-type lens across different embryonic, postnatal, and adult stages, can serve as a resource for comparative analysis. This article outlines the approaches toward effective use of iSyTE to gain insights into normal gene expression in the mouse lens, enriched expression in the lens, and differential gene expression in select mouse gene-perturbation cataract/lens defects models, which in turn can be used to evaluate expression of key lens-relevant genes in lens organoids by transcriptomics (e.g., RNA-sequencing (RNA-seq), microarrays, etc.) or other downstream methods (e.g., RT-qPCR, etc.).

Comparing Gene Expression Between Planktonic and Biofilm Cells of Foodborne Bacterial Pathogens Through RT-qPCR.

Like most microorganisms, important foodborne pathogenic bacteria, such as Salmonella enterica, Listeria monocytogenes, and several others as well, can attach to surfaces, of either abiotic or biotic nature, and create biofilms on them, provided the existence of supportive environmental conditions (e.g., permissive growth temperature, adequate humidity, and nutrient presence). Inside those sessile communities, the enclosed bacteria typically present a gene expression profile that differs from the one that would be displayed by the same cells growing planktonically in liquid media (free-swimming cells). This altered gene expression has important consequences on cellular physiology and behavior, including stress tolerance and induction of virulence. In this chapter, the methodology to use reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to monitor and comparatively quantify expression changes in preselected genes of bacteria between planktonic and biofilm growth modes is presented.

Transcriptomic profiling of human mesenchymal stem cells using a pulsed electromagnetic-wave motion bioreactor system for enhanced osteogenic commitment and therapeutic potentials.

Traditional bioreactor systems involve the use of three-dimensional (3D) scaffolds or stem cell aggregates, limiting the accessibility to the production of cell-secreted biomolecules. Herein, we present the use a pulse electromagnetic fields (pEMFs)-assisted wave-motion bioreactor system for the dynamic and scalable culture of human bone marrow-derived mesenchymal stem cells (hBMSCs) with enhanced the secretion of various soluble factors with massive therapeutic potential. The present study investigated the influence of dynamic pEMF (D-pEMF) on the kinetic of hBMSCs. A 30-min exposure of pEMF (10V-1Hz, 5.82 G) with 35 oscillations per minute (OPM) rocking speed can induce the proliferation (1 × 105 â†’ 4.5 × 105) of hBMSCs than static culture. Furthermore, the culture of hBMSCs in osteo-induction media revealed a greater enhancement of osteogenic transcription factors under the D-pEMF condition, suggesting that D-pEMF addition significantly boosted hBMSCs osteogenesis. Additionally, the RNA sequencing data revealed a significant shift in various osteogenic and signaling genes in the D-pEMF group, further suggesting their osteogenic capabilities. In this research, we demonstrated that the combined effect of wave and pEMF stimulation on hBMSCs allows rapid proliferation and induces osteogenic properties in the cells. Moreover, our study revealed that D-pEMF stimuli also induce ROS-scavenging properties in the cultured cells. This study also revealed a bioactive and cost-effective approach that enables the use of cells without using any expensive materials and avoids the possible risks associated with them post-implantation.

Transcriptome wide analysis of MADS box genes in Crocus sativus and interplay of CstMADS19-CstMADS26 in orchestrating apocarotenoid biosynthesis.

Flowers of Crocus sativus L. are immensely important not only for arrangement of floral whorls but more because each floral organ is dominated by a different class of specialized compounds. Dried stigmas of C. sativus flowers form commercial saffron, and are known to accumulate unique apocarotenoids like crocin, picrocrocin and safranal. Inspite of being a high value crop, the molecular mechanism regulating flower development in Crocus remains largely unknown. Moreover, it would be very interesting to explore any co-regulatory mechanism which controls floral architecture and secondary metabolic pathways which exist in specific floral organs. Here we report transcriptome wide identification of MADS box genes in Crocus. A total of 39 full length MADS box genes were identified among which three belonged to type I and 36 to type II class. Phylogeny classified them into 11 sub-clusters. Expression pattern revealed some stigma up-regulated genes among which CstMADS19 encoding an AGAMOUS gene showed high expression. Transient over-expression of CstMADS19 in stigmas of Crocus resulted in increased crocin by enhancing expression of pathway genes. Yeast one hybrid assay demonstrated that CstMADS19 binds to promoters of phytoene synthase and carotenoid cleavage dioxygenase 2 genes. Yeast two hybrid and BiFC assays confirmed interaction of CstMADS19 with CstMADS26 which codes for a SEPALATA gene. Co-overexpression of CstMADS19 and CstMADS26 in Crocus stigmas enhanced crocin content more than was observed when genes were expressed individually. Collectively, these findings indicate that CstMADS19 functions as a positive regulator of stigma based apocarotenoid biosynthesis in Crocus.

Expression profiling and single nucleotide polymorphism of mitogen-activated protein kinase kinase kinase 8 MAP3K8 in white muscovy ducks (Cairina moschata).

A previous study on ovarian and hypothalami transcriptome analysis in white Muscovy duck revealed that MAP3K8 gene participated in MAPK signaling pathway that influence egg production. Additionally, MAP3K8 was predicted as a target gene of miRNA-509-3p that promotes the secretion of oestradiol which is an important hormone in egg ovulation. This suggested that MAP3K8 might have a functional role in the reproductive performance "egg production" of white Muscovy ducks. Herein, we focused on expression level of MAP3K8 in reproductive and non-reproductive tissues of highest (HP) and lowest (LP) egg producing white Muscovy ducks and identified the polymorphism in MAP3K8 and its association with three egg production traits; Age at first egg (AFE), number of eggs at 300 days (N300D) and 59 weeks (N59W). The results of expression level indicated that mRNA of MAP3K8 was significantly (p < 0.01) expressed in the oviduct than in the ovary and hypothalamus. Seven synonymous SNPs were detected, and association analysis showed that g.148303340 G>A and g.148290065 A>G were significantly (p < 0.05) associated with N300D and N59W. The results of this study might serve as molecular marker for marker-assisted selection of white Muscovy ducks for egg production.

MRPL13 is a metastatic and prognostic marker of breast cancer: A silico analysis accompanied with experimental validation.

BACKGROUND: Although progress has been made in accurate diagnosis and targeted treatments, breast cancer (BC) patients with metastasis still present a grim prognosis. With the continuous emergence and development of new personalized and precision medicine targeting specific tumor biomarkers, there is an urgent need to find new metastatic and prognostic biomarkers for BC patients. METHODS: We were dedicated to identifying genes linked to metastasis and prognosis in breast cancer through a combination of in silico analysis and experimental validation. RESULTS: A total of 25 overlap differentially expressed genes were identified. Ten hub genes (namely MRPL13, CTR9, TCEB1, RPLP0, TIMM8B, METTL1, GOLT1B, PLK2, PARL and MANBA) were identified and confirmed. MRPL13, TCEB1 and GOLT1B were shown to be associated with the worse overall survival (OS) and were optionally chosen for further verification by western blot. Only MRPL13 was found associated with cell invasion, and the expression of MRPL13 in metastatic BC was significantly higher than in primary BC. CONCLUSION: We proposed MRPL13 could be a potential novel biomarker for the metastasis and prognosis of breast cancer.

Evaluation of Gene Expression Profiling by QuantiGene™ 2.0 RNA Assay.

QuantiGene™ 2.0 technique could be used to investigate the gene expression signature of the immune system senescence and thus to understand the molecular mechanism involved in the defects of the immune response during aging.QuantiGene™ 2.0 technique is a multiplex platform allowing the simultaneous analysis of several target RNA molecules (up to 80) present in a single sample. QuantiGene Assays use an accurate method for multiplexed or for single gene expression quantitation. QuantiGene 2.0 uses magnetic beads which are dyed internally with two fluorescence dyes, exhibiting a unique spectral signal and providing specificity and multiplexing capability of the technique. QuantiGene Assays incorporate branched-DNA technology for gene expression profiling.Branched-DNA system is responsible for the high sensitivity of the system. In fact, it permits to detect low levels of mRNA molecules. This branched-DNA system allows for the direct measurement of RNA transcripts by using signal amplification rather than target amplification. The assay protocol is spread over 2 days. First, immune cells are lysed to release the target RNA, which is incubated with oligonucleotide probe set targeted with beads capable to hybridize with the target RNA. Signal amplification is performed by sequential hybridization of the branched-DNA pre-amplifier, amplifier, and label probe molecules. The last step involves the incubation with Streptavidin-conjugated R-phycoerythrin. The fluorescent reporter generates a signal directly proportional to the levels of RNA molecules present in the cells. Luminex instrument evaluates the median intensity of fluorescence, which is proportional to the number of RNA target molecules present in the cells.

Learning Enhancer-Gene associations from Bulk Transcriptomic and Epigenetic Sequencing Data with STITCHIT.

To reveal gene regulation mechanisms, it is essential to understand the role of regulatory elements, which are possibly distant from gene promoters. Integrative analysis of epigenetic and transcriptomic data can be used to gain insights into gene-expression regulation in specific phenotypes. Here, we discuss STITCHIT, an approach to dissect epigenetic variation in a gene-specific manner across many samples for the identification of regulatory elements without relying on peak calling algorithms. The obtained genomic regions are then further refined using a regularized linear model approach, which can also be used to predict gene expression. We illustrate the use of STITCHIT using H3k27ac ChIP-seq and RNA-seq data from the International Human Epigenome Consortium (IHEC).

Identification of necroptosis genes and characterization of immune infiltration in non-alcoholic steatohepatitis.

BACKGROUND: The most common progressive form of non-alcoholic fatty liver disease (NAFLD) is non-alcoholic steatohepatitis (NASH), which is characterized by the development of cirrhosis, and requires liver transplantation. We screened for the differentially expressed necroptosis-related genes in NASH in this study, and analyzed immune infiltration through microarray and bioinformatics analysis to identify potential biomarkers, and explore the molecular mechanisms involved in NASH. METHODS: The GSE24807 microarray dataset of NASH patients and healthy controls was downloaded, and we identified the differentially expressed genes (DEGs). Necroptosis-related differential genes (NRDEGs) were extracted from these DEGs, and functionally annotated by enrichment analyses. The core genes were obtained by constructing gene co-expression networks using weighted gene co-expression network analysis (WGCNA). Finally, the transcription factor (TF) regulatory network and the mRNA-miRNA network were constructed, and the infiltrating immune cell populations were analyzed with CIBERSORT. RESULTS: We identified six necroptosis-related genes (CASP1, GLUL, PYCARD, IL33, SHARPIN, and IRF9), and they are potential diagnostic biomarkers for NASH. In particular, PYCARD is a potential biomarker for NAFLD progression. Analyses of immune infiltration showed that M2 macrophages, γδ T cells, and T follicular helper cells were associated with the immune microenvironment of NASH, which is possibly regulated by CASP1, IL33, and IRF9. CONCLUSIONS: We identified six necroptosis-related genes in NASH, which are also potential diagnostic biomarkers. Our study provides new insights into the molecular mechanisms and immune microenvironment of NASH.

Single cell RNA sequencing provides novel cellular transcriptional profiles and underlying pathogenesis of presbycusis.

Age-related hearing loss (ARHL) or presbycusis is associated with irreversible progressive damage in the inner ear, where the sound is transduced into electrical signal; but the detailed mechanism remains unclear. Here, we sought to determine the potential molecular mechanism involved in the pathogeneses of ARHL with bioinformatics methods. A single-cell transcriptome sequencing study was performed on the cochlear samples from young and aged mice. Detection of identified cell type marker allowed us to screen 18 transcriptional clusters, including myeloid cells, epithelial cells, B cells, endothelial cells, fibroblasts, T cells, inner pillar cells, neurons, inner phalangeal cells, and red blood cells. Cell-cell communications were analyzed between young and aged cochlear tissue samples by using the latest integration algorithms Cellchat. A total of 56 differentially expressed genes were screened between the two groups. Functional enrichment analysis showed these genes were mainly involved in immune, oxidative stress, apoptosis, and metabolic processes. The expression levels of crucial genes in cochlear tissues were further verified by immunohistochemistry. Overall, this study provides new theoretical support for the development of clinical therapeutic drugs.

scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets.

Single-cell RNA sequencing (scRNA-seq) technologies can generate transcriptomic profiles at a single-cell resolution in large patient cohorts, facilitating discovery of gene and cellular biomarkers for disease. Yet, when the number of biomarker genes is large, the translation to clinical applications is challenging due to prohibitive sequencing costs. Here, we introduce scPanel, a computational framework designed to bridge the gap between biomarker discovery and clinical application by identifying a sparse gene panel for patient classification from the cell population(s) most responsive to perturbations (e.g. diseases/drugs). scPanel incorporates a data-driven way to automatically determine a minimal number of informative biomarker genes. Patient-level classification is achieved by aggregating the prediction probabilities of cells associated with a patient using the area under the curve score. Application of scPanel to scleroderma, colorectal cancer, and COVID-19 datasets resulted in high patient classification accuracy using only a small number of genes (<20), automatically selected from the entire transcriptome. In the COVID-19 case study, we demonstrated cross-dataset generalizability in predicting disease state in an external patient cohort. scPanel outperforms other state-of-the-art gene selection methods for patient classification and can be used to identify parsimonious sets of reliable biomarker candidates for clinical translation.

B-cell-specific signatures reveal novel immunophenotyping and therapeutic targets for hepatocellular carcinoma.

BACKGROUND: Immunotherapy presents both promises and challenges in treating hepatocellular carcinoma (HCC) due to its complex immunological microenvironment. The role of B cells, a key part of the immune system, remains uncertain in HCC. AIM: To identify B-cell-specific signatures and reveal novel immunophenotyping and therapeutic targets for HCC. METHODS: Using the Tumor Immune Single-cell Hub 2 database, we identified B-cell-related genes (BRGs) in HCC. Gene enrichment analysis was performed to explore the possible collaboration between B cells and T cells in HCC. We conducted univariate Cox regression analysis using The Cancer Genome Atlas liver HCC collection dataset to find BRGs linked to HCC prognosis. Subsequently, least absolute shrinkage and selection operator regression was utilized to develop a prognostic model with 11 BRGs. The model was validated using the International Cancer Genome Consortium dataset and GSE76427. RESULTS: The risk score derived from the prognostic model emerged as an independent prognostic factor for HCC. Analysis of the immune microenvironment and cell infiltration revealed the immune status of various risk groups, supporting the cooperation of B and T cells in suppressing HCC. The BRGs model identified new molecular subtypes of HCC, each with distinct immune characteristics. Drug sensitivity analysis identified targeted drugs effective for each HCC subtype, enabling precision therapy and guiding clinical decisions. CONCLUSION: We clarified the role of B cells in HCC and propose that the BRGs model offers promising targets for personalized immunotherapy.

Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.

Introduction: Heart failure (HF) and kidney failure (KF) are closely related conditions that often coexist, posing a complex clinical challenge. Understanding the shared mechanisms between these two conditions is crucial for developing effective therapies. Methods: This study employed transcriptomic analysis to unveil molecular signatures and novel biomarkers for both HF and KF. A total of 2869 shared differentially expressed genes (DEGs) were identified in patients with HF and KF compared to healthy controls. Functional enrichment analysis was performed to explore the common mechanisms underlying these conditions. A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. These genes were further analyzed using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA), with their diagnostic values validated in both training and validation sets. Molecular docking studies were conducted. Additionally, immune cell infiltration and correlation analyses were performed to assess the relationship between immune responses and the identified biomarkers. Results: The functional enrichment analysis indicated that the common mechanisms are associated with cellular homeostasis, cell communication, cellular replication, inflammation, and extracellular matrix (ECM) production, with the PI3K-Akt signaling pathway being notably enriched. The PPI network revealed two key protein clusters related to the cell cycle and inflammation. CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. Additionally, docking studies with CDK2 and CCND1 were performed to evaluate potential drug candidates. Immune cell infiltration and correlation analyses highlighted the immune microenvironment, and that CDK2 and CCND1 are associated with immune responses in HF and KF. Discussion: This study identifies CDK2 and CCND1 as novel biomarkers linking cell cycle regulation and inflammation in heart and kidney failure. These findings offer new insights into the molecular mechanisms of HF and KF and present potential targets for diagnosis and therapy.

Comprehensive analysis of stearoyl-coenzyme A desaturase in prostate adenocarcinoma: insights into gene expression, immune microenvironment and tumor progression.

Prostate adenocarcinoma (PRAD) is a prevalent global malignancy which depends more on lipid metabolism for tumor progression compared to other cancer types. Although Stearoyl-coenzyme A desaturase (SCD) is documented to regulate lipid metabolism in multiple cancers, landscape analysis of its implications in PRAD are still missing at present. Here, we conducted an analysis of diverse cancer datasets revealing elevated SCD expression in the PRAD cohort at both mRNA and protein levels. Interestingly, the elevated expression was associated with SCD promoter hypermethylation and genetic alterations, notably the L134V mutation. Integration of comprehensive tumor immunological and genomic data revealed a robust positive correlation between SCD expression levels and the abundance of CD8+ T cells and macrophages. Further analyses identified significant associations between SCD expression and various immune markers in tumor microenvironment. Single-cell transcriptomic profiling unveiled differential SCD expression patterns across distinct cell types within the prostate tumor microenvironment. The Gene Ontology and Kyoto Encyclopedia of Genes and Genome analyses showed that SCD enriched pathways were primarily related to lipid biosynthesis, cholesterol biosynthesis, endoplasmic reticulum membrane functions, and various metabolic pathways. Gene Set Enrichment Analysis highlighted the involvement of elevated SCD expression in crucial cellular processes, including the cell cycle and biosynthesis of cofactors pathways. In functional studies, SCD overexpression promoted the proliferation, metastasis and invasion of prostate cancer cells, whereas downregulation inhibits these processes. This study provides comprehensive insights into the multifaceted roles of SCD in PRAD pathogenesis, underscoring its potential as both a therapeutic target and prognostic biomarker.

Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.

Objective: To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in chronic HBV infection (CBI) patients. Methods: Five public sequencing datasets were collected from the GEO database. Gene differential expression and LASSO analyses identified genes linked to OP and CBI. Machine learning algorithms (random forests, support vector machines, and gradient boosting machines) further filtered these genes. The best diagnostic model was chosen based on accuracy and Kappa values. A nomogram model based on HRGs was constructed and assessed for reliability. OP patients were divided into two chronic HBV-related clusters using non-negative matrix factorization. Differential gene expression analysis, Gene Ontology, and KEGG enrichment analyses explored the roles of these genes in OP progression, using ssGSEA and GSVA. Differences in immune cell infiltration between clusters and the correlation between HRGs and immune cells were examined using ssGSEA and the Pearson method. Results: Differential gene expression analysis of CBI and combined OP dataset identified 822 and 776 differentially expressed genes, respectively, with 43 genes intersecting. Following LASSO analysis and various machine learning recursive feature elimination algorithms, 16 HRGs were identified. The support vector machine emerged as the best predictive model based on accuracy and Kappa values, with AUC values of 0.92, 0.83, 0.74, and 0.7 for the training set, validation set, GSE7429, and GSE7158, respectively. The nomogram model exhibited AUC values of 0.91, 0.79, and 0.68 in the training set, GSE7429, and GSE7158, respectively. Non-negative matrix factorization divided OP patients into two clusters, revealing statistically significant differences in 11 types of immune cell infiltration between clusters. Finally, intersecting the HRGs obtained from LASSO analysis with the HRGs identified three genes. Conclusion: This study successfully identified HRGs and developed an efficient diagnostic model based on HRGs, demonstrating high accuracy and strong predictive performance across multiple datasets. This research not only offers new insights into the complex relationship between OP and CBI but also establishes a foundation for the development of early diagnostic and personalized treatment strategies for chronic HBV-related OP.

Identification of common signature genes and pathways underlying the pathogenesis association between nonalcoholic fatty liver disease and heart failure.

Background: Non-alcoholic fatty liver disease (NAFLD) and heart failure (HF) are related conditions with an increasing incidence. However, the mechanism underlying their association remains unclear. This study aimed to explore the shared pathogenic mechanisms and common biomarkers of NAFLD and HF through bioinformatics analyses and experimental validation. Methods: NAFLD and HF-related transcriptome data were extracted from the Gene Expression Omnibus (GEO) database (GSE126848 and GSE26887). Differential analysis was performed to identify common differentially expressed genes (co-DEGs) between NAFLD and HF. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were conducted to explore the functions and regulatory pathways of co-DEGs. Protein-protein interaction (PPI) network and support vector machine-recursive feature elimination (SVM-RFE) methods were used to screen common key DEGs. The diagnostic value of common key DEGs was assessed by receiver operating characteristic (ROC) curve and validated with external datasets (GSE89632 and GSE57345). Finally, the expression of biomarkers was validated in mouse models. Results: A total of 161 co-DEGs were screened out in NAFLD and HF patients. GO, KEGG, and GSEA analyses indicated that these co-DEGs were mainly enriched in immune-related pathways. PPI network revealed 14 key DEGs, and SVM-RFE model eventually identified two genes (CD163 and CCR1) as common key DEGs for NAFLD and HF. Expression analysis revealed that the expression levels of CD163 and CCR1 were significantly down-regulated in HF and NAFLD patients. ROC curve analysis showed that CD163 and CCR1 had good diagnostic values for HF and NAFLD. Single-gene GSEA suggested that CD163 and CCR1 were mainly engaged in immune responses and inflammation. Experimental validation indicated unbalanced macrophage polarization in HF and NAFLD mouse models, and the expression of CD163 and CCR1 were significantly down-regulated. Conclusion: This study identified M2 polarization impairment characterized by decreased expression of CD163 and CCR1 as a common pathogenic pathway in NAFLD and HF. The downregulation of CD163 and CCR1 may reflect key pathological changes in the development and progression of NAFLD and HF, suggesting their potential as diagnostic and therapeutic targets.

Construction and validation of a pancreatic cancer prognostic model based on genes related to the hypoxic tumor microenvironment.

BACKGROUND: Pancreatic cancer is one of the most lethal malignancies, characterized by poor prognosis and low survival rates. Traditional prognostic factors for pancreatic cancer offer inadequate predictive accuracy, often failing to capture the complexity of the disease. The hypoxic tumor microenvironment has been recognized as a significant factor influencing cancer progression and resistance to treatment. This study aims to develop a prognostic model based on key hypoxia-related molecules to enhance prediction accuracy for patient outcomes and to guide more effective treatment strategies in pancreatic cancer. AIM: To develop and validate a prognostic model for predicting outcomes in patients with pancreatic cancer using key hypoxia-related molecules. METHODS: This pancreatic cancer prognostic model was developed based on the expression levels of the hypoxia-associated genes CAPN2, PLAU, and CCNA2. The results were validated in an independent dataset. This study also examined the correlations between the model risk score and various clinical features, components of the immune microenvironment, chemotherapeutic drug sensitivity, and metabolism-related pathways. Real-time quantitative PCR verification was conducted to confirm the differential expression of the target genes in hypoxic and normal pancreatic cancer cell lines. RESULTS: The prognostic model demonstrated significant predictive value, with the risk score showing a strong correlation with clinical features: It was significantly associated with tumor grade (G) (b P < 0.01), moderately associated with tumor stage (T) (a P < 0.05), and significantly correlated with residual tumor (R) status (b P < 0.01). There was also a significant negative correlation between the risk score and the half-maximal inhibitory concentration of some chemotherapeutic drugs. Furthermore, the risk score was linked to the enrichment of metabolism-related pathways in pancreatic cancer. CONCLUSION: The prognostic model based on hypoxia-related genes effectively predicts pancreatic cancer outcomes with improved accuracy over traditional factors and can guide treatment selection based on risk assessment.