CBNplot: Bayesian network plots for enrichment analysis

Identification of endocrine disrupting chemicals targeting NTD-related hub genes during pregnancy via in silico analysis

Neural tube defects (NTDs) represent severe congenital malformations of the central nervous system with multifactorial etiology, involving intricate gene-environment interactions that remain incompletely characterized. Endocrine disrupting chemicals (EDCs) are exogenous substances with hormone-disrupting properties that are ubiquitous in our surroundings. These chemicals pose a significant threat to human health, contributing to a range of diseases. Pregnant women are particularly vulnerable to the effects of EDCs, as these substances can traverse the placental barrier and impact the development of both the placenta and fetus. This study utilized placental and fetal transcriptome data to identify hub genes associated with NTDs during pregnancy. By leveraging the Comparative Toxicogenomics Database (CTD), we predicted the EDCs targeting these hub genes and performed molecular docking to assess their interactions. Our findings revealed four hub genes (CTSC, FCER1G, ITGB2, and LYVE1) in NTDs, with 72 EDCs identified as their targets. Molecular docking demonstrated that atrazine, bisphenol A (BPA) and diuron exhibited stable affinity with the proteins encoded by hub genes. These findings provide new insights into the environmental endocrine disruptors that affect the development of NTDs during pregnancy.

Differential Expression of Disulfidptosis-Related Genes in Spinal Cord Injury and Their Role in the Immune Microenvironment

Spinal cord injury (SCI) often results in severe sensory, motor, and autonomic dysfunction, with limited treatment options due to complex underlying mechanisms. Disulfidptosis, a recently discovered form of cell death driven by disulfide bond accumulation, has been linked to various diseases, but its role in SCI remains unexplored. This study investigates the involvement of disulfidptosis-related genes (DRGs) in SCI to identify potential diagnostic markers and therapeutic targets. Using SCI datasets from the Gene Expression Omnibus (GEO), we conducted differential gene expression analysis, identifying key disulfidptosis-related differentially expressed genes (DRDEGs). Further analysis through gene set enrichment (GSEA) and Bayesian pathway enrichment highlighted significant involvement in pathways such as NF-κB, PI3K/Akt, and MAPK, with an emphasis on nephrin family interactions. Three core DRDEGs-HK2, Map3k8, and S100a6-were identified, and a diagnostic model built on these genes demonstrated strong predictive performance (AUC: 0.896 in training, 0.850 in validation). Additionally, real-time PCR (qRT-PCR) in an animal model validated the elevated expression of these DRDEGs in SCI samples. This research provides novel insights into disulfidptosis in SCI, suggesting these genes as promising targets for improved diagnostic and therapeutic strategies.

Modulation of brain immune microenvironment and cellular dynamics in systemic inflammation

Background: Sepsis-associated encephalopathy (SAE) is a severe complication of sepsis, affecting approximately 70% of patients, leading to increased mortality and long-term cognitive impairments among survivors. However, there is a lack of comprehensive studies on the development of SAE, especially related to the cellular communication networks in the brain microenvironment. Methods: We evaluated the impact of myeloid cells on the brain's immune microenvironment through glial cell alterations using bulk and single-cell transcriptomics data from human and mouse models and validated this with correlative experiments. We also developed the DeconvCellLink R package to study neuroinflammation-associated cellular interaction networks. A dynamic brain immune microenvironment map showing temporal alterations in brain cellular network during systemic inflammatory reactions was constructed using time-series data. Results: While brain cellular alterations differed between human and animal models, a highly conserved set of sepsis-associated genes regulating immune microenvironment signalling was identified. The dynamic alterations in cellular interaction networks and cytokines revealed brain immune cells' temporal response to systemic inflammation. We also found that valproic acid could mitigate sepsis-induced neuroinflammation by regulating glial cell balance and modulating the neuroimmune microenvironment. Conclusion: Through dynamic cellular communication networks, the study revealed that, immune dysregulation in the inflamed brain in SAE involves overactivation of innate immunity, with neutrophils playing a crucial role, providing a scientific framework for developing novel therapeutic strategies and offering new insights into the mechanisms underlying sepsis-induced brain dysfunction.

ACSL5 mediates macrophage infiltration and lipid metabolism in erythrotelangiectasia rosacea via potential pathogenic mechanisms and therapeutic targets

Rosacea, an inflammatory skin disorder with complex pathogenesis, remains poorly understood. Through integrative bioinformatics and experimental approaches, we identified 304 differentially expressed genes in erythrotelangiectasia rosacea (ETR), primarily enriched in lipid metabolism pathways. Support vector machine (SVM), linear regression analyses and network analysis revealed ACADVL and ACSL5 as potential therapeutic targets. Immunological profiling demonstrated distinctive immune cell infiltration, with elevated M0 and M1 macrophages in patients with ETR. Immunofluorescence validation confirmed significant ACSL5 upregulation and increased M1 macrophage infiltration in the rosacea mouse model. The co-localization of ACSL5 with M1 macrophage markers suggests a mechanistic link between lipid metabolism and inflammatory responses. These findings provide new insights into ETR pathogenesis and highlight ACSL5 as a promising therapeutic target for inflammatory skin disorders.

Minimum uncertainty as Bayesian network model selection principle

BACKGROUND

Bayesian Network (BN) modeling is a prominent methodology in computational systems biology. However, the incommensurability of datasets frequently encountered in life science domains gives rise to contextual dependence and numerical irregularities in the behavior of model selection criteria (such as MDL, Minimum Description Length) used in BN reconstruction. This renders model features, first and foremost dependency strengths, incomparable and difficult to interpret. In this study, we derive and evaluate a model selection principle that addresses these problems.

RESULTS

The objective of the study is attained by (i) approaching model evaluation as a misspecification problem, (ii) estimating the effect that sampling error has on the satisfiability of conditional independence criterion, as reflected by Mutual Information, and (iii) utilizing this error estimate to penalize uncertainty with the novel Minimum Uncertainty (MU) model selection principle. We validate our findings numerically and demonstrate the performance advantages of the MU criterion. Finally, we illustrate the advantages of the new model evaluation framework on real data examples.

CONCLUSIONS

The new BN model selection principle successfully overcomes performance irregularities observed with MDL, offers a superior average convergence rate in BN reconstruction, and improves the interpretability and universality of resulting BNs, thus enabling direct inter-BN comparisons and evaluations.

Molecular subtyping of renal clear cell carcinoma based on prognostic RASSF family genes and validation of C1QL1 as a key prognostic marker

RASSF family proteins play crucial roles in mitosis, apoptosis, cell migration, adhesion, and immune functions, primarily acting as tumor suppressors. Their roles in renal clear cell carcinoma (ccRCC) are not fully understood. We analyzed the expression and prognostic significance of RASSF genes in 573 ccRCC samples from TCGA and GEO, identifying two molecular subtypes with distinct characteristics. We developed a RAS score to assess prognosis and molecular status and investigated the key gene C1QL1 through in vitro assays. Four RASSF genes were identified as associated with prognosis and progression in ccRCC. Based on their co-expression, we defined two patient subtypes, one with poorer prognosis. A higher RAS score correlated with advanced disease and worse outcomes but indicated a favorable response to specific inhibitors, supporting personalized treatment strategies. Additionally, VHL mutations may cause abnormal SFMBT1 expression, leading to high C1QL1 levels, which promote tumor progression via the YAP-EMT pathway. Our findings highlight the potential of RASSF-based molecular subtypes and scoring systems in personalizing ccRCC treatment. Understanding C1QL1's role may facilitate the development of novel therapeutic approaches.

Decoding the cytokine code for heart failure based on bioinformatics, machine learning and Bayesian networks

BACKGROUND

Despite maximal pharmacological treatment guided by clinical guidelines, the prognosis of heart failure (HF) remains poor, posing a significant public health burden. This necessitates uncovering novel pathological and cardioprotective pathways. Targeting cytokines presents a promising therapeutic strategy for HF, yet their intricate mechanisms in HF progression remain obscure.

METHODS

HF datasets were obtained from the GEO database. Cytokine-related genes were identified through WGCNA and the CytReg database. GO and KEGG enrichment analyses were conducted using the clusterProfiler package. Reactome pathway enrichment analysis and Bayesian regulatory network construction were performed using the CBNplot package. Key genes were identified via LASSO regression and RF algorithms, with diagnostic accuracy evaluated by ROC curves. Potential therapeutic drugs were predicted using the DSigDB database, and immune cell infiltration was assessed with the CIBERSORT package.

RESULTS

We identified 13 cytokine-related genes associated with HF. Enrichment analyses indicated these genes mediate inflammatory responses and immune cell recruitment. Bayesian network analysis revealed two cytokine regulatory chains: IL34-CCL5-CCL4 and IL34-CCL5-CXCL12. Machine learning algorithms identified five key cytokine genes: CCL4, CCL5, CXCL12, CXCL14, and IL34. The DSigDB database predicted 47 potential therapeutic drugs, including Proscillaridin. Immune infiltration analysis showed significant differences in seven immune cell types between HF and healthy samples.

CONCLUSION

Our study provides insights into cytokines' molecular mechanisms in HF pathophysiology and highlights potential immunomodulatory strategies, gene therapies, and candidate drugs. Future research should validate these findings in clinical settings to develop effective HF therapies.

The microenvironment cell index is a novel indicator for the prognosis and therapeutic regimen selection of cancers

BACKGROUND

It is worthwhile to establish a prognostic prediction model based on microenvironment cells (MCs) infiltration and explore new treatment strategies for triple-negative breast cancer (TNBC).

METHODS

The xCell algorithm was used to quantify the cellular components of the TNBC microenvironment based on bulk RNA sequencing (bulk RNA-seq) data. The MCs index (MCI) was constructed using the least absolute shrinkage and selection operator Cox (LASSO-Cox) regression analysis. Single-cell RNA sequencing (scRNA-seq), spatially resolved transcriptomics (SRT), and multiplex immunofluorescence (mIF) staining analyses verified MCI. The mechanism of action of the MCI was investigated in tumor-bearing mice.

RESULTS

MCI consists of the six types of MCs, which can precisely predict the prognosis of the TNBC patients. scRNA-seq, SRT, and mIF analyses verified the existence and proportions of these cells. Furthermore, combined with the spatial distribution characteristics of the six types of MCs, an MCI-enhanced (MCI-e) model was constructed, which could predict the prognosis of the TNBC patients more accurately. More importantly, inhibition of the insulin signaling pathway activated in the cancer cells of the MCIhigh the TNBC patients significantly prolonged the survival time of tumor-bearing mice.

CONCLUSIONS

Overall, our results demonstrate that MCs infiltration can be exploited as a novel indicator for the prognosis and therapeutic regimen selection of the TNBC patients.

Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework

BACKGROUND

Abnormal programmed cell death (PCD) plays a central role in spermatogenic dysfunction. However, the molecular mechanisms and biomarkers of PCD in patients with nonobstructive azoospermia (NOA) remain unclear.

METHODS

The genetic conditions of NOA patients were analysed using bulk transcriptomic, single-cell transcriptomic, single nucleotide polymorphism (SNP), and clinical data from multiple centres. A total of 675 machine learning methods were applied to construct models from 12 different PCDs and to screen for distinctive genes. A new PCDscore system was created to measure the degree of PCD in patients. Using the NOA mouse model, TUNEL, qRT-PCR, Western blotting, and immunohistochemistry (IHC) were utilized to validate the PCD status in NOA testes and the expression levels of hub PCD-related genes (PCDRGs). Mouse testicular samples were used for sequencing of the whole transcriptome. The sequencing results were used to evaluate the correlation between PCD scores and expression of hub genes.

RESULTS

A PCDscore system was built using 12 characteristic PCDRGs chosen by machine learning. PCD scores correlated with gene interaction and immune activity changes. Leydig, Sertoli, and T cells were prominent in cell interactions with PCDscore changes. PCDscore in the NOA mouse testis was increased. Among the 12 PCDRGs, BCL2L14, GGA1, GPX4, PHKG2, and SLC39A8 were strongly linked to spermatogenesis. BCL2L14, GGA1, GPX4, and PHKG2 strongly correlated with PCD statuses. The changes in the expression of these genes may be due to the effects of SNPs, which may lead to the male reproductive system disorders.

CONCLUSIONS

Our study provides new insights into PCD-related mechanisms in NOA patients via multiomics and proposes reliable models for the diagnosis of NOA via the use of PCD biomarkers. A deeper understanding of these mechanisms may aid in the clinical diagnosis and treatment of NOA.

Prognostic prediction and immune checkpoint profiling in glioma patients through neddylation-associated features

BACKGROUND

Gliomas are the most common primary malignant tumours of the central nervous system, and neddylation may be a potential target for the treatment of gliomas. Our study analysed neddylation's potential role in gliomas of different pathological types and its correlation with immunotherapy.

METHODS

Genes required for model construction were sourced from existing literature, and their expression data were extracted from the TCGA and CGGA databases. LASSO regression was employed to identify genes associated with the prognosis of glioma patients in TCGA and to establish a clinical prognostic model. Biological changes in glioma cell lines following intervention with hub genes were evaluated using the CCK-8 assay and transwell assay. The genes implicated in the model construction were validated across various cell lines using Western blot. We conducted analyses to examine correlations between model scores and clinical data, tumor microenvironments, and immune checkpoints. Furthermore, we investigated potential differences in molecular functions and mechanisms among different groups.

RESULTS

We identified 249 genes from the Reactome database and analysed their expression profiles in the TCGA and CGGA databases. After using LASSO-Cox, four genes (BRCA1, BIRC5, FBXL16 and KLHL25, p < 0.05) with significant correlations were identified. We selected FBXL16 for validation in in vitro experiments. Following FBXL16 overexpression, the proliferation, migration, and invasion abilities of glioma cell lines all showed a decrease. Then, we constructed the NEDD Index for gliomas. The nomogram indicated that this model could serve as an independent prognostic marker. Analysis of the tumour microenvironment and immune checkpoints revealed that the NEDD index was also correlated with immune cell infiltration and the expression levels of various immune checkpoints.

CONCLUSION

The NEDD index can serve as a practical tool for predicting the prognosis of glioma patients, and it is correlated with immune cell infiltration and the expression levels of immune checkpoints.

Augmenting antitumor efficacy of Th17-derived Th1 cells through IFN-γ-induced type I interferon response network via IRF7

The importance of CD4+ T cells in cancer immunotherapy has gained increasing recognition. Particularly, a specific subset of CD4+ T cells coexpressing the T helper type 1 (Th1) and Th17 markers has demonstrated remarkable antitumor potential. However, the underlying mechanisms governing the differentiation of these cells and their subsequent antitumor responses remain incompletely understood. Single-cell RNA sequencing (scRNA-seq) data reanalysis demonstrated the presence of Th171 cells within tumors. Subsequent trajectory analysis found that these Th171 cells are initially primed under Th17 conditions and then converted into IFN-γ-producing cells. Following the in vivo differentiation trajectory of Th171 cells, we successfully established in vitro Th171 cell culture. Transcriptomic profiling has unveiled a substantial resemblance between in vitro-generated Th171 cells and their tumor-infiltrating counterparts. Th171 cells exhibit more potent antitumor responses than Th1 or Th17 cells. Additionally, Th171chimeric antigen receptor T (CAR-T) cells eradicate solid tumors more efficiently. Importantly, Th171 cells display an early exhaustion phenotype while retaining stemness. Mechanistically, Th171 cells migrate faster and accumulate more in tumors in an extracellular matrix protein 1 (ECM1)-dependent manner. Furthermore, we show that IFN-γ up-regulated IRF7 to promote the type I interferon response network and ECM1 expression but decreased the exhaustion status in Th171 cells. Taken together, our findings position Th171 cells as a great candidate for improving targeted immunotherapies in solid malignancies.

VCAN in the extracellular matrix drives glioma recurrence by enhancing cell proliferation and migration

Introduction

Gliomas are the most prevalent primary malignant intracranial tumors, characterized by high rates of therapy resistance, recurrence, and mortality. A major factor contributing to the poor prognosis of gliomas is their ability to diffusely infiltrate surrounding and even distant brain tissues, rendering complete total resection almost impossible and leading to frequent recurrences. The extracellular matrix (ECM) plays a key role in the tumor microenvironment and may significantly influence glioma progression, recurrence, and therapeutic response.

Methods

In this study, we first identified the ECM and the Versican (VCAN), a key ECM protein, as critical contributors to glioma recurrence through a comprehensive analysis of transcriptomic data comparing recurrent and primary gliomas. Using single-cell sequencing, we revealed heterogeneous distribution patterns and extensive intercellular communication among ECM components. External sequencing and immunohistochemical (IHC) staining further validated that VCAN is significantly upregulated in recurrent gliomas and is associated with poor patient outcomes.

Results

Functional assays conducted in glioma cell lines overexpressing VCAN demonstrated that VCAN promotes cell proliferation and migration via the PI3K/Akt/AP-1 signaling pathway. Furthermore, inhibiting the PI3K/Akt pathway effectively blocked VCAN-mediated glioma progression.

Conclusion

These findings provide valuable insights into the mechanisms underlying glioma recurrence and suggest that targeting both VCAN and the PI3K/Akt pathway could represent a promising therapeutic strategy for managing recurrent gliomas.

Proteomic and Phosphoproteomic Profiling of Matrix Stiffness-Induced Stemness-Dormancy State Transition in Breast Cancer Cells

The dormancy of cancer stem cells is a major factor leading to drug resistance and a high rate of late recurrence and mortality in estrogen receptor-positive (ER+) breast cancer. Previously, we demonstrated that a stiffer matrix induces tumor cell dormancy and drug resistance, whereas a softened matrix promotes tumor cells to exhibit a stem cell state with high proliferation and migration. In this study, we present a comprehensive analysis of the proteome and phosphoproteome in response to gradient changes in matrix stiffness, elucidating the mechanisms behind cell dormancy-induced drug resistance. Overall, we found that antiapoptotic and membrane transport processes may be involved in the mechanical force-induced dormancy resistance of ER+ breast cancer cells. Our research provides new insights from a holistic proteomic and phosphoproteomic perspective, underscoring the significant role of mechanical forces stemming from the stiffness of the surrounding extracellular matrix as a critical regulatory factor in the tumor microenvironment.

Singular Value Decomposition-Driven Non-negative Matrix Factorization with Application to Identify the Association Patterns of Sarcoma Recurrence

Sarcomas are malignant tumors from mesenchymal tissue and are characterized by their complexity and diversity. The high recurrence rate making it important to understand the mechanisms behind their recurrence and to develop personalized treatments and drugs. However, previous studies on the association patterns of multi-modal data on sarcoma recurrence have overlooked the fact that genes do not act independently, but rather function within signaling pathways. Therefore, this study collected 290 whole solid images, 869 gene and 1387 pathway data of over 260 sarcoma samples from UCSC and TCGA to identify the association patterns of gene-pathway-cell related to sarcoma recurrences. Meanwhile, considering that most multi-modal data fusion methods based on the joint non-negative matrix factorization (NMF) model led to poor experimental repeatability due to random initialization of factorization parameters, the study proposed the singular value decomposition (SVD)-driven joint NMF model by applying the SVD method to calculate initialized weight and coefficient matrices to achieve the reproducibility of the results. The results of the experimental comparison indicated that the SVD algorithm enhances the performance of the joint NMF algorithm. Furthermore, the representative module indicated a significant relationship between genes in pathways and image features. Multi-level analysis provided valuable insights into the connections between biological processes, cellular features, and sarcoma recurrence. In addition, potential biomarkers were uncovered, while various mechanisms of sarcoma recurrence were identified from an imaging genetic perspective. Overall, the SVD-NMF model affords a novel perspective on combining multi-omics data to explore the association related to sarcoma recurrence.

Identification of RNMT as an immunotherapeutic and prognostic biomarker: From pan-cancer analysis to lung squamous cell carcinoma validation

BACKGROUND

Dysregulation of RNA guanine-7 methyltransferase (RNMT) plays a crucial role in the tumor progression and immune responses. However, the detailed role of RNMT in pan-cancer is still unknown.

METHODS

Bulk transcriptomic data of pan-cancer were obtained from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases. Single-cell transcriptomic and proteomics data of lung squamous cell carcinoma (LUSC) were analyzed in the Tumor Immune Single-cell Hub 2 (TISCH2) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases, respectively. The correlation between RNMT expression and cancer prognosis was analyzed by Cox proportional hazards regression and Kaplan-Meier analyses. The correlation of RNMT expression with common immunoregulators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), and DNA methyltransferase (DNMT) was analyzed. Additionally, the correlation between RNMT expression and immune infiltration level was evaluated. A total of 1287 machine learning combinations were used to construct prognostic models for LUSC. qRT-PCR and Western blot were used to validate the bioinformatics findings of RNMT upregulation in LUSC.

RESULTS

RNMT was widely expressed across different cancers, with significant correlation to prognosis in cancers such as kidney chromophobe (KICH) (p = 0.0033, HR = 7.12), liver hepatocellular carcinoma (LIHC) (p = 0.01, HR = 1.41), and others. Notably, RNMT participates in the regulation of the tumor microenvironment. RNMT expression positively correlated with immune cell expression (Spearman's rank correlation, p < 0.05). Moreover, RNMT expression was strongly associated with immunoregulators, TMB, MSI, MMR, and DNMT in most cancer types. Notably, RNMT expression displayed excellent prognostic and immunological performance in LUSC. The expression of RNMT was mainly enriched in B cells of LUSC tissues. qRT-PCR and Western blot verified the high expression of RNMT in LUSC.

CONCLUSION

RNMT expression widely correlated with prognosis and immune infiltration in various tumors, especially LUSC. The RNMT detection may provide a new idea for future tumor immune studies and treatment strategies.

Identification of key metabolism-related genes and pathways in spontaneous preterm birth: combining bioinformatic analysis and machine learning

Background

Spontaneous preterm birth (sPTB) is a global disease that is a leading cause of death in neonates and children younger than 5 years of age. However, the etiology of sPTB remains poorly understood. Recent evidence has shown a strong association between metabolic disorders and sPTB. To determine the metabolic alterations in sPTB patients, we used various bioinformatics methods to analyze the abnormal changes in metabolic pathways in the preterm placenta via existing datasets.

Methods

In this study, we integrated two datasets (GSE203507 and GSE174415) from the NCBI GEO database for the following analysis. We utilized the "Deseq2" R package and WGCNA for differentially expressed genes (DEGs) analysis; the identified DEGs were subsequently compared with metabolism-related genes. To identify the altered metabolism-related pathways and hub genes in sPTB patients, we performed multiple functional enrichment analysis and applied three machine learning algorithms, LASSO, SVM-RFE, and RF, with the hub genes that were verified by immunohistochemistry. Additionally, we conducted single-sample gene set enrichment analysis to assess immune infiltration in the placenta.

Results

We identified 228 sPTB-related DEGs that were enriched in pathways such as arachidonic acid and glutathione metabolism. A total of 3 metabolism-related hub genes, namely, ANPEP, CKMT1B, and PLA2G4A, were identified and validated in external datasets and experiments. A nomogram model was developed and evaluated with 3 hub genes; the model could reliably distinguish sPTB patients and term labor patients with an area under the curve (AUC) > 0.75 for both the training and validation sets. Immune infiltration analysis revealed immune dysregulation in sPTB patients.

Conclusion

Three potential hub genes that influence the occurrence of sPTB through shadow participation in placental metabolism were identified; these results provide a new perspective for the development and targeting of treatments for sPTB.

Cross-talk between oxidative stress and lipid metabolism regulators reveals molecular clusters and immunological characterization in polycystic ovarian syndrome

BACKGROUND

Changes in the oxidative stress and lipid metabolism (OSLM) pathways play important roles in polycystic ovarian syndrome (PCOS) pathogenesis and development. Consequently, a systematic analysis of genes related to OSLM was conducted to identify molecular clusters and explore new biomarkers that are helpful for the diagnostic of PCOS.

METHODS

Gene expression and clinical data from 22 PCOS women and 14 normal women were obtained from the GEO database (GSE34526, GSE95728, and GSE106724). Consensus clustering identified OSLM-related molecular clusters, and WGCNA revealed co-expression patterns. The immune microenvironment was quantitatively assessed utilizing the CIBERSORT algorithm. Multiple machine learning models and connectivity map analyses were subsequently applied to explore potential biomarkers for PCOS, and nomograms were employed to develop a predictive multigene model of PCOS. Finally, the OSLM status of PCOS and the hub genes expression profiles were preliminarily verified using TUNEL, qRT‒PCR, western blot, and IHC assays in a PCOS mouse model.

RESULTS

19 differential expression genes (DEGs) related to OSLM were identified. Based on 19 DEGs that were strongly influenced by OSLM, PCOS patients were stratified into two distinct clusters, designated Cluster 1 and Cluster 2. Distinct differences in the immune cell proportions existed in normal and two PCOS clusters. The random forest showed the best results, with the least cross-entropy and the utmost AUC (cross-entropy: 0.111 AUC: 0.960). Among the 19 OSLM-related genes, CXCR1, ACP5, CEACAM3, S1PR4, and TCF7 were identified by a Bayesian network and had a good fit with PCOS disease risk by the nomogram (AUC: 0.990 CI: 0.968-1.000). TUNEL assays revealed more severe DNA damage within the ovarian granule cells of PCOS mice than in those of normal mice (P < 0.001). The RNA and protein expression levels of the five hub genes were significantly elevated in PCOS mice, which was consistent with the results of the bioinformatics analyses.

CONCLUSION

A novel predictive model was constructed for PCOS patients and five hub genes were identified as potential biomarkers to offer novel insights into clinical diagnostic strategies for PCOS.

Leveraging a neutrophil-derived PCD signature to predict and stratify patients with acute myocardial infarction: from AI prediction to biological interpretation

BACKGROUND

Programmed cell death (PCD) has recently been implicated in modulating the removal of neutrophils recruited in acute myocardial infarction (AMI). Nonetheless, the clinical significance and biological mechanism of neutrophil-related PCD remain unexplored.

METHODS

We employed an integrative machine learning-based computational framework to generate a predictive neutrophil-derived PCD signature (NPCDS) within five independent microarray cohorts from the peripheral blood of AMI patients. Non-negative matrix factorization was leveraged to develop an NPCDS-based AMI subtype. To elucidate the biological mechanism underlying NPCDS, we implemented single-cell transcriptomics on Cd45+ cells isolated from the murine heart of experimental AMI. We finally conducted a Mendelian randomization (MR) study and molecular docking to investigate the therapeutic value of NPCDS on AMI.

RESULTS

We reported the robust and superior performance of NPCDS in AMI prediction, which contributed to an optimal combination of random forest and stepwise regression fitted on nine neutrophil-related PCD genes (MDM2, PTK2B, MYH9, IVNS1ABP, MAPK14, GNS, MYD88, TLR2, CFLAR). Two divergent NPCDS-based subtypes of AMI were revealed, in which subtype 1 was characterized as inflammation-activated with more vibrant neutrophil activities, whereas subtype 2 demonstrated the opposite. Mechanically, we unveiled the expression dynamics of NPCDS to regulate neutrophil transformation from a pro-inflammatory phase to an anti-inflammatory phase in AMI. We uncovered a significant causal association between genetic predisposition towards MDM2 expression and the risk of AMI. We also found that lidoflazine, isotetrandrine, and cepharanthine could stably target MDM2.

CONCLUSION

Altogether, NPCDS offers significant implications for prediction, stratification, and therapeutic management for AMI.

Deciphering the role of zinc homeostasis in the tumor microenvironment and prognosis of prostate cancer

BACKGROUND

Dysregulation of zinc homeostasis is widely recognized as a hallmark feature of prostate cancer (PCa) based on the compelling clinical and experimental evidence. Nevertheless, the implications of zinc dyshomeostasis in PCa remains largely unexplored.

METHODS

In this research, the zinc homeostasis pattern subtype (ZHPS) was constructed according to the profile of zinc homeostasis genes. The identified subtypes were assessed for their immune functions, mutational landscapes, biological peculiarities and drug susceptibility. Subsequently, we developed the optimal signature, known as the zinc homeostasis-related risk score (ZHRRS), using the approach won out in multifariously machine learning algorithms. Eventually, clinical specimens, Bayesian network inference and single-cell sequencing were used to excavate the underlying mechanisms of MT1A in PCa.

RESULTS

The zinc dyshomeostasis subgroup, ZHPS2, possessed a markedly worse prognosis than ZHPS1. Moreover, ZHPS2 demonstrated a more conspicuous genomic instability and better therapeutic responses to docetaxel and olaparib than ZHPS1. Compared with traditional clinicopathological characteristics and 35 published signatures, ZHRRS displayed a significantly improved accuracy in prognosis prediction. The diagnostic value of MT1A in PCa was substantiated through analysis of clinical samples. Additionally, we inferred and established the regulatory network of MT1A to elucidate its biological mechanisms.

CONCLUSIONS

The ZHPS classifier and ZHRRS model hold great potential as clinical applications for improving outcomes of PCa patients.

Emodin-8-O-β-D-glucopyranoside-induced hepatotoxicity and gender differences in zebrafish as revealed by integration of metabolomics and transcriptomics

BACKGROUND

Emodin-8-O-β-D-glucopyranoside (Em8G) is an active ingredient of traditional Chinese medicine Rhei Radix et Rhizoma and Polygonum multiflorum Thunb.. And it caused hepatotoxicity, while the underlying mechanism was not clear yet.

PURPOSE

We aimed to explore the detrimental effects of Em8G on the zebrafish liver through the metabolome and transcriptome integrated analysis.

STUDY DESIGN AND METHODS

In this study, zebrafish larvae were used in acute toxicity tests to reveal the hepatotoxicity of Em8G. Adult zebrafish were then used to evaluate the gender differences in hepatotoxicity induced by Em8G. Integration of transcriptomic and metabolomic analysis was used further to explore the molecular mechanisms underlying gender differences in hepatotoxicity.

RESULTS

Our results showed that under non-lethal concentration exposure conditions, hepatotoxicity was observed in Em8G-treated zebrafish larvae, including changes in liver transmittance, liver area, hepatocyte apoptosis and hepatocyte vacuolation. Male adult zebrafish displayed a higher Em8G-induced hepatotoxicity than female zebrafish, as demonstrated by the higher mortality and histopathological alterations. The results of transcriptomics combined with metabolomics showed that Em8G mainly affected carbohydrate metabolism (such as TCA cycle) in male zebrafish and amino acid metabolism (such as arginine and proline metabolism) in females, suggesting that the difference of energy metabolism disorder may be the potential mechanism of male and female liver toxicity induced by Em8G.

CONCLUSIONS

This study provided the direct evidence for the hepatotoxicity of Em8G to zebrafish models in vivo, and brought a new insight into the molecular mechanisms of Em8G hepatotoxicity, which can guide the rational application of this phytotoxin. In addition, our findings revealed gender differences in the hepatotoxicity of Em8G to zebrafish, which is related to energy metabolism and provided a methodological reference for evaluating hepatotoxic drugs with gender differences.

Pan-cancer analysis for the prognostic and immunological role of CD47: interact with TNFRSF9 inducing CD8 + T cell exhaustion

PURPOSE

The research endeavors to explore the implications of CD47 in cancer immunotherapy effectiveness. Specifically, there is a gap in comprehending the influence of CD47 on the tumor immune microenvironment, particularly in relation to CD8 + T cells. Our study aims to elucidate the prognostic and immunological relevance of CD47 to enhance insights into its prospective utilities in immunotherapeutic interventions.

METHODS

Differential gene expression analysis, prognosis assessment, immunological infiltration evaluation, pathway enrichment analysis, and correlation investigation were performed utilizing a combination of R packages, computational algorithms, diverse datasets, and patient cohorts. Validation of the concept was achieved through the utilization of single-cell sequencing technology.

RESULTS

CD47 demonstrated ubiquitous expression across various cancer types and was notably associated with unfavorable prognostic outcomes in pan-cancer assessments. Immunological investigations unveiled a robust correlation between CD47 expression and T-cell infiltration rather than T-cell exclusion across multiple cancer types. Specifically, the CD47-high group exhibited a poorer prognosis for the cytotoxic CD8 + T cell Top group compared to the CD47-low group, suggesting a potential impairment of CD8 + T cell functionality by CD47. The exploration of mechanism identified enrichment of CD47-associated differentially expressed genes in the CD8 + T cell exhausted pathway in multiple cancer contexts. Further analyses focusing on the CD8 TCR Downstream Pathway and gene correlation patterns underscored the significant involvement of TNFRSF9 in mediating these effects.

CONCLUSION

A robust association exists between CD47 and the exhaustion of CD8 + T cells, potentially enabling immune evasion by cancer cells and thereby contributing to adverse prognostic outcomes. Consequently, genes such as CD47 and those linked to T-cell exhaustion, notably TNFRSF9, present as promising dual antigenic targets, providing critical insights into the field of immunotherapy.

Exploring the NRF2-TP53 Signaling Network Through Machine Learning and Pan-Cancer Analysis: Identifying Potential targets for Cancer Prognosis Related to Oxidative Stress

Oxidative stress (OXS) is closely related to tumor prognosis and immune response, while TP53 integrated with NRF2 is closely associated with the regulation of cancer-related OXS. Hence, constructing a TP53-NRF2 integrated OXS signature of pan-cancer is essential in predicting survival prognosis and facilitating cancer drug treatment. The pan-cancer analysis acquired the Cancer Genome Atlas (TCGA) transcriptome sequencing data from UCSC Xena, which consisted of 33 cancer types (n = 10 440). The Random Forest, Lasso regression, and Cox regression analyses are used to construct an OXS score based on 25 OXS genes. Following this, based on the OXS signature, patients are categorized into low- and high-risk groups. The disparities between the two cohorts regarding survival prognosis, immune infiltration, and drug sensitivity are delved deeply. The expression level of genes is confirmed using immunohistochemistry. The prognosis of pan-cancer patients is adequately predicted by the OXS signature with the assistance of the machine-learning algorithm. A highly accurate nomogram is developed by combining the OXS signature and clinical features. The presence of immune cells indicated that the OXS signature can be associated with the critical pathways of immunotherapy for all types of cancer, and BCL2 showed promising results. Distinct inter-group differences are observed in the OXS signature for frequently utilized antineoplastic medications in clinical settings, including first-line drugs suggested in the guidelines. In summary, by conducting a thorough analysis of OXS genes, a new model based on OXSscore is successfully developed. This model can predict the clinical prognosis and drug sensitivity of pan-cancer with high accuracy. Potential stars in the field of cancer-related anti-OXS may include drugs that target BCL2.

Exploring the role of mitochondrial-associated and peripheral neuropathy genes in the pathogenesis of diabetic peripheral neuropathy

BACKGROUND

Diabetic peripheral neuropathy (DPN) is a prevalent and serious complication of diabetes mellitus, impacting the nerves in the limbs and leading to symptoms like pain, numbness, and diminished function. While the exact molecular and immune mechanisms underlying DPN remain incompletely understood, recent findings indicate that mitochondrial dysfunction may play a role in the advancement of this diabetic condition.

METHODS

Two RNA transcriptome datasets (codes: GSE185011 and GSE95849), comprising samples from diabetic peripheral neuropathy (DPN) patients and healthy controls (HC), were retrieved from the Gene Expression Omnibus (GEO) database hosted by the National Center for Biotechnology Information (NCBI). Subsequently, differential expression analysis and gene set enrichment analysis were performed. Protein-protein interaction (PPI) networks were constructed to pinpoint key hub genes associated with DPN, with a specific emphasis on genes related to mitochondria and peripheral neuropathy disease (PND) that displayed differential expression. Additionally, the study estimated the levels of immune cell infiltration in both the HC and DPN samples. To validate the findings, quantitative polymerase chain reaction (qPCR) was employed to confirm the differential expression of selected genes in the DPN samples.

RESULTS

This research identifies four hub genes associated mitochondria or PN. Furthermore, the analysis revealed increased immune cell infiltration in DPN tissues, particularly notable for macrophages and T cells. Additionally, our investigation identified potential drug candidates capable of regulating the expression of the four hub genes. These findings were corroborated by qPCR results, reinforcing the credibility of our bioinformatics analysis.

CONCLUSIONS

This study provides a comprehensive overview of the molecular and immunological characteristics of DPN, based on both bioinformatics and experimental methods.

SHP-1 inhibition targets leukaemia stem cells to restore immunosurveillance and enhance chemosensitivity by metabolic reprogramming

Leukaemia stem cells (LSCs) in acute myeloid leukaemia present a considerable treatment challenge due to their resistance to chemotherapy and immunosurveillance. The connection between these properties in LSCs remains poorly understood. Here we demonstrate that inhibition of tyrosine phosphatase SHP-1 in LSCs increases their glycolysis and oxidative phosphorylation, enhancing their sensitivity to chemotherapy and vulnerability to immunosurveillance. Mechanistically, SHP-1 inhibition leads to the upregulation of phosphofructokinase platelet (PFKP) through the AKT-β-catenin pathway. The increase in PFKP elevates energy metabolic activities and, as a consequence, enhances the sensitivity of LSCs to chemotherapeutic agents. Moreover, the upregulation of PFKP promotes MYC degradation and, consequently, reduces the immune evasion abilities of LSCs. Overall, our study demonstrates that targeting SHP-1 disrupts the metabolic balance in LSCs, thereby increasing their vulnerability to chemotherapy and immunosurveillance. This approach offers a promising strategy to overcome LSC resistance in acute myeloid leukaemia.

Quantified pathway mutations associate epithelial-mesenchymal transition and immune escape with poor prognosis and immunotherapy resistance of head and neck squamous cell carcinoma

BACKGROUND

Pathway mutations have been calculated to predict the poor prognosis and immunotherapy resistance in head and neck squamous cell carcinoma (HNSCC). To uncover the unique markers predicting prognosis and immune therapy response, the accurate quantification of pathway mutations are required to evaluate epithelial-mesenchymal transition (EMT) and immune escape. Yet, there is a lack of score to accurately quantify pathway mutations.

MATERIAL AND METHODS

Firstly, we proposed Individualized Weighted Hallmark Gene Set Mutation Burden (IWHMB, https://github.com/YuHongHuang-lab/IWHMB ) which integrated pathway structure information and eliminated the interference of global Tumor Mutation Burden to accurately quantify pathway mutations. Subsequently, to further elucidate the association of IWHMB with EMT and immune escape, support vector machine regression model was used to identify IWHMB-related transcriptomic features (IRG), while Adversarially Regularized Graph Autoencoder (ARVGA) was used to further resolve IRG network features. Finally, Random walk with restart algorithm was used to identify biomarkers for predicting ICI response.

RESULTS

We quantified the HNSCC pathway mutation signatures and identified pathway mutation subtypes using IWHMB. The IWHMB-related transcriptomic features (IRG) identified by support vector machine regression were divided into 5 communities by ARVGA, among which the Community 1 enriching malignant mesenchymal components promoted EMT dynamically and regulated immune patterns associated with ICI responses. Bridge Hub Gene (BHG) identified by random walk with restart was key to IWHMB in EMT and immune escape, thus, more predictive for ICI response than other 70 public signatures.

CONCLUSION

In summary, the novel pathway mutation scoring-IWHMB suggested that the elevated malignancy mediated by pathway mutations is a major cause of poor prognosis and immunotherapy failure in HNSCC, and is capable of identifying novel biomarkers to predict immunotherapy response.

Identifying OGN as a Biomarker Covering Multiple Pathogenic Pathways for Diagnosing Heart Failure: From Machine Learning to Mechanism Interpretation

BACKGROUND

The pathophysiologic heterogeneity of heart failure (HF) necessitates a more detailed identification of diagnostic biomarkers that can reflect its diverse pathogenic pathways.

METHODS

We conducted weighted gene and multiscale embedded gene co-expression network analysis on differentially expressed genes obtained from HF and non-HF specimens. We employed a machine learning integration framework and protein-protein interaction network to identify diagnostic biomarkers. Additionally, we integrated gene set variation analysis, gene set enrichment analysis (GSEA), and transcription factor (TF)-target analysis to unravel the biomarker-dominant pathways. Leveraging single-sample GSEA and molecular docking, we predicted immune cells and therapeutic drugs related to biomarkers. Quantitative polymerase chain reaction validated the expressions of biomarkers in the plasma of HF patients. A two-sample Mendelian randomization analysis was implemented to investigate the causal impact of biomarkers on HF.

RESULTS

We first identified COL14A1, OGN, MFAP4, and SFRP4 as candidate biomarkers with robust diagnostic performance. We revealed that regulating biomarkers in HF pathogenesis involves TFs (BNC2, MEOX2) and pathways (cell adhesion molecules, chemokine signaling pathway, cytokine-cytokine receptor interaction, oxidative phosphorylation). Moreover, we observed the elevated infiltration of effector memory CD4+ T cells in HF, which was highly related to biomarkers and could impact immune pathways. Captopril, aldosterone antagonist, cyclopenthiazide, estradiol, tolazoline, and genistein were predicted as therapeutic drugs alleviating HF via interactions with biomarkers. In vitro study confirmed the up-regulation of OGN as a plasma biomarker of HF. Mendelian randomization analysis suggested that genetic predisposition toward higher plasma OGN promoted the risk of HF.

CONCLUSIONS

We propose OGN as a diagnostic biomarker for HF, which may advance our understanding of the diagnosis and pathogenesis of HF.

Pyroptosis-Related Gene Signature Predicts Prognosis and Response to Immunotherapy and Medication in Pediatric and Young Adult Osteosarcoma Patients

Purpose

Pyroptosis, a new form of inflammatory programmed cell death, has recently gained attention. However, the impact of the expression levels of pyroptosis-related genes (PRGs) on the overall survival (OS) of osteosarcoma patients remains unclear. This study aims to investigate the impact of the expression levels of PRGs on the OS of pediatric and young adult patients with osteosarcoma.

Patients and Methods

Transcriptome matrix datasets of normal muscle or skeletal tissues from the Genotype-Tissue Expression (GTEx) project and osteosarcoma specimen the National Cancer Institute's (NCI) Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database were used to identify pyroptosis-related genes (PRGs) associated with prognosis. The National Center for Biotechnology Information's (NCBI) GSE21257 dataset was employed to validate the predictive value of the pyroptosis-related signature (PRS). Additionally, reverse transcription polymerase chain reaction (RT-qPCR) experiment was performed in normal and osteosarcoma cell lines.

Results

The study identified 18 differentially expressed PRGs (DEPRGs) between normal muscle or skeletal tissues and tumor samples. Multiple machine learning techniques were used to select PRGs, resulting in the identification of four hub PRGs. A PRS-score was calculated for each sample based on the expression of these four hub PRGs, and samples were categorized into low and high PRS-score level groups. It was confirmed that metastatic status and PRS-score level are independent prognostic predictors. A nomogram model for predicting OS of osteosarcoma patients was constructed. Single-cell RNA-sequencing data display the expression patterns of the hub PRGs. RT-qPCR data results were found to be consistent with the differential expression analysis performed on TARGET and GTEx samples.

Conclusion

The study developed a novel pyroptosis-related gene signature that can stratify pediatric and young adult osteosarcoma patients into different risk groups, thus predicting their response to immunotherapy and chemotherapy.

A link between mitochondrial damage and the immune microenvironment of delayed onset muscle soreness

BACKGROUND

Delayed onset muscle soreness (DOMS) is a self-healing muscle pain disorder. Inflammatory pain is the main feature of DOMS. More and more researchers have realized that changes in mitochondrial morphology are related to pain. However, the role of mitochondria in the pathogenesis of DOMS and the abnormal immune microenvironment is still unknown.

METHODS

Mitochondria-related genes and gene expression data were obtained from MitoCarta3.0 and NCBI GEO databases. The network of mitochondrial function and the immune microenvironment of DOMS was constructed by computer algorithm. Subsequently, the skeletal muscle of DOMS rats was subjected to qPCR to verify the bioinformatics results. DOMS and non-DOMS histological samples were further studied by staining and transmission electron microscopy.

RESULTS

Bioinformatics results showed that expression of mitochondria-related genes was changed in DOMS. The results of qPCR showed that four hub genes (AMPK, PGC1-α, SLC25A25, and ARMCX1) were differentially expressed in DOMS. These hub genes are related to the degree of skeletal muscle immune cell infiltration, mitochondrial respiratory chain complex, DAMPs, the TCA cycle, and mitochondrial metabolism. Bayesian network inference showed that IL-6 and PGC1-α may be the main regulatory genes of mitochondrial damage in DOMS. Transmission electron microscopy revealed swelling of skeletal muscle mitochondria and disorganization of myofilaments.

CONCLUSIONS

Our study found that skeletal muscle mitochondrial damage is one of the causes of inflammatory factor accumulation in DOMS. According to the screened-out hub genes, this study provides a reference for follow-up clinical application.

Optimal treatment for post-MI heart failure in rats: dapagliflozin first, adding sacubitril-valsartan 2 weeks later

Background

Based on previous research, both dapagliflozin (DAPA) and sacubitril-valsartan (S/V) improve the prognosis of patients with heart failure (HF). Our study aims to investigate whether the early initiation of DAPA or the combination of DAPA with S/V in different orders would exert a greater protective effect on heart function than that of S/V alone in post-myocardial infarction HF (post-MI HF).

Methods

Rats were randomized into six groups: (A) Sham; (B) MI; (C) MI + S/V (1st d); (D) MI + DAPA (1st d); (E) MI + S/V (1st d) + DAPA (14th d); (F) MI + DAPA (1st d) + S/V (14th d). The MI model was established in rats via surgical ligation of the left anterior descending coronary artery. Histology, Western blotting, RNA-seq, and other approaches were used to explore the optimal treatment to preserve the heart function in post-MI HF. A daily dose of 1 mg/kg DAPA and 68 mg/kg S/V was administered.

Results

The results of our study revealed that DAPA or S/V substantially improved the cardiac structure and function. DAPA and S/V monotherapy resulted in comparable reduction in infarct size, fibrosis, myocardium hypertrophy, and apoptosis. The administration of DAPA followed by S/V results in a superior improvement in heart function in rats with post-MI HF than those in other treatment groups. The administration of DAPA following S/V did not result in any additional improvement in heart function as compared to S/V monotherapy in rats with post-MI HF. Our findings further suggest that the combination of DAPA and S/V should not be administered within 3 days after acute myocardial infarction (AMI), as it resulted in a considerable increase in mortality. Our RNA-Seq data revealed that DAPA treatment after AMI altered the expression of genes related to myocardial mitochondrial biogenesis and oxidative phosphorylation.

Conclusions

Our study revealed no notable difference in the cardioprotective effects of singular DAPA or S/V in rats with post-MI HF. Based on our preclinical investigation, the most effective treatment strategy for post-MI HF is the administration of DAPA during the 2 weeks, followed by the addition of S/V to DAPA later. Conversely, adopting a therapeutic scheme whereby S/V was administered first, followed by later addition of DAPA, failed to further improve the cardiac function compared to S/V monotherapy.

Construction of an oxidative stress-related lncRNAs signature to predict prognosis and the immune response in gastric cancer

Oxidative stress, as a characteristic of cellular aerobic metabolism, plays a crucial regulatory role in the development and metastasis of gastric cancer (GC). Long noncoding RNAs (lncRNAs) are important regulators in GC development. However, research on the prognostic patterns of oxidative stress-related lncRNAs (OSRLs) and their functions in the immune microenvironment is currently insufficient. We identified the OSRLs signature (DIP2A-IT1, DUXAP8, TP53TG1, SNHG5, AC091057.1, AL355001.1, ARRDC1-AS1, and COLCA1) from 185 oxidative stress-related genes in The Cancer Genome Atlas (TCGA) cohort via random survival forest and Cox analyses, and the results were subsequently validated in the Gene Expression Omnibus (GEO) dataset. The patients were divided into high- and low-risk groups by the risk score of the OSRLs signature. Longer overall survival was detected in the low-risk group than in the high-risk group in both the TCGA cohort (P < 0. 001, HR = 0.43, 95% CI 0.31-0.62) and the GEO cohort (P = 0.014, HR = 0.67, 95% CI 0.48-0.93). Next, multivariate Cox analysis identified that the risk model was an independent prognostic characteristic (HR > 1, P = 0.005), and time-dependent receiver operating characteristic (ROC) curve analysis and nomogram analysis were utilized to evaluate the predictive ability of the risk model. Next, gene set enrichment analysis revealed that the immune-related pathway, Wnt/[Formula: see text]-catenin signature, mammalian target of rapamycin complex 1 signature, and cytokine‒cytokine receptor interaction was enriched. High-risk patients were more responsive to CD200, TNFSF4, TNFSF9, and BTNL2 immune checkpoint blockade. The results of qRT‒PCR further proved the accuracy of our bioinformatic analysis. Overall, our study identified a novel OSRLs signature that can serve as a promising biomarker and prognostic indicator, which provides a personalized predictive approach for patient prognosis evaluation and treatment.

Machine learning approach combined with causal relationship inferring unlocks the shared pathomechanism between COVID-19 and acute myocardial infarction

Background

Increasing evidence suggests that people with Coronavirus Disease 2019 (COVID-19) have a much higher prevalence of Acute Myocardial Infarction (AMI) than the general population. However, the underlying mechanism is not yet comprehended. Therefore, our study aims to explore the potential secret behind this complication.

Materials and methods

The gene expression profiles of COVID-19 and AMI were acquired from the Gene Expression Omnibus (GEO) database. After identifying the differentially expressed genes (DEGs) shared by COVID-19 and AMI, we conducted a series of bioinformatics analytics to enhance our understanding of this issue.

Results

Overall, 61 common DEGs were filtered out, based on which we established a powerful diagnostic predictor through 20 mainstream machine-learning algorithms, by utilizing which we could estimate if there is any risk in a specific COVID-19 patient to develop AMI. Moreover, we explored their shared implications of immunology. Most remarkably, through the Bayesian network, we inferred the causal relationships of the essential biological processes through which the underlying mechanism of co-pathogenesis between COVID-19 and AMI was identified.

Conclusion

For the first time, the approach of causal relationship inferring was applied to analyzing shared pathomechanism between two relevant diseases, COVID-19 and AMI. Our findings showcase a novel mechanistic insight into COVID-19 and AMI, which may benefit future preventive, personalized, and precision medicine.

Graphical abstract

Use of machine learning-based integration to develop a monocyte differentiation-related signature for improving prognosis in patients with sepsis

BACKGROUND

Although significant advances have been made in intensive care medicine and antibacterial treatment, sepsis is still a common disease with high mortality. The condition of sepsis patients changes rapidly, and each hour of delay in the administration of appropriate antibiotic treatment can lead to a 4-7% increase in fatality. Therefore, early diagnosis and intervention may help improve the prognosis of patients with sepsis.

METHODS

We obtained single-cell sequencing data from 12 patients. This included 14,622 cells from four patients with bacterial infectious sepsis and eight patients with sepsis admitted to the ICU for other various reasons. Monocyte differentiation trajectories were analyzed using the "monocle" software, and differentiation-related genes were identified. Based on the expression of differentiation-related genes, 99 machine-learning combinations of prognostic signatures were obtained, and risk scores were calculated for all patients. The "scissor" software was used to associate high-risk and low-risk patients with individual cells. The "cellchat" software was used to demonstrate the regulatory relationships between high-risk and low-risk cells in a cellular communication network. The diagnostic value and prognostic predictive value of Enah/Vasp-like (EVL) were determined. Clinical validation of the results was performed with 40 samples. The "CBNplot" software based on Bayesian network inference was used to construct EVL regulatory networks.

RESULTS

We systematically analyzed three cell states during monocyte differentiation. The differential analysis identified 166 monocyte differentiation-related genes. Among the 99 machine-learning combinations of prognostic signatures constructed, the Lasso + CoxBoost signature with 17 genes showed the best prognostic prediction performance. The highest percentage of high-risk cells was found in state one. Cell communication analysis demonstrated regulatory networks between high-risk and low-risk cell subpopulations and other immune cells. We then determined the diagnostic and prognostic value of EVL stabilization in multiple external datasets. Experiments with clinical samples demonstrated the accuracy of this analysis. Finally, Bayesian network inference revealed potential network mechanisms of EVL regulation.

CONCLUSIONS

Monocyte differentiation-related prognostic signatures based on the Lasso + CoxBoost combination were able to accurately predict the prognostic status of patients with sepsis. In addition, low EVL expression was associated with poor prognosis in sepsis.

Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5

Sepsis is a disease with a very high mortality rate, mainly involving an immune-dysregulated response due to bacterial infection. Most studies are currently limited to the whole blood transcriptome level; however, at the single cell level, there is still a great deal unknown about specific cell subsets and disease markers. We obtained 29 peripheral blood single-cell sequencing data, including 66,283 cells from 10 confirmed samples of sepsis infection and 19 healthy samples. Cells related to the sepsis phenotype were identified and characterized by the "scissor" method. The regulatory relationships of sepsis-related phenotype cells in the cellular communication network were clarified using the "cell chat" method. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) were used to identify sepsis signature genes of diagnostic value. External validation was performed using multiple datasets from the GEO database (GSE28750, GSE185263, GSE57065) and 40 clinical samples. Bayesian algorithm was used to calculate the regulatory network of LILRA5 co-expressed genes. The stability of atenolol-targeting LILRA5 was determined by molecular docking techniques. Ultimately, action trajectory and survival analyses demonstrate the effectiveness of atenolol-targeted LILRA5 in treating patients with sepsis. We successfully identified 1215 healthy phenotypic cells and 462 sepsis phenotypic cells. We focused on 447 monocytes of the sepsis phenotype. Among the cellular communications, there were a large number of differences between these cells and other immune cells showing a significant inflammatory phenotype compared to the healthy phenotypic cells. Together, the three machine learning algorithms identified the LILRA5 marker gene in sepsis patients, and validation results from multiple external datasets as well as real-world clinical samples demonstrated the robust diagnostic performance of LILRA5. The AUC values of LILRA5 in the external datasets GSE28750, GSE185263, and GSE57065 could reach 0.875, 0.940, and 0.980, in that order. Bayesian networks identified a large number of unknown regulatory relationships for LILRA5 co-expression. Molecular docking results demonstrated the possibility of atenolol targeting LILRA5 for the treatment of sepsis. Behavioral trajectory analysis and survival analysis demonstrate that atenolol has a desirable therapeutic effect. LILRA5 is a marker gene in sepsis patients, and atenolol can stably target LILRA5.

A novel zinc metabolism-related gene signature to predict prognosis and immunotherapy response in lung adenocarcinoma

Background

Zinc is a key mineral element in regulating cell growth, development, and immune system. We constructed the zinc metabolism-related gene signature to predict prognosis and immunotherapy response for lung adenocarcinoma (LUAD).

Methods

Zinc metabolism-associated gene sets were obtained from Molecular Signature Database. Then, the zinc metabolism-related gene signature (ZMRGS) was constructed and validated. After combining with clinical characteristics, the nomogram for practical application was constructed. The differences in biological pathways, immune molecules, and tumor microenvironment (TME) between the different groups were analyzed. Tumor Immune Dysfunction and Exclusion algorithm (TIDE) and two immunotherapy datasets were used to evaluate the immunotherapy response.

Results

The signature was constructed according to six key zinc metabolism-related genes, which can well predict the prognosis of LUAD patients. The nomogram also showed excellent prediction performance. Functional analysis showed that the low-risk group was in the status of immune activation. More importantly, the lower risk score of LUAD patients showed a higher response rate to immunotherapy.

Conclusion

The state of zinc metabolism is closely connected to prognosis, tumor microenvironment, and response to immunotherapy. The zinc metabolism-related signature can well evaluate the prognosis and immunotherapy response for LUAD patients.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure

The etiologies and pathogenesis of dilated cardiomyopathy (DCM) with heart failure (HF) remain to be defined. Thus, exploring specific diagnosis biomarkers and mechanisms is urgently needed to improve this situation. In this study, three gene expression profiling datasets (GSE29819, GSE21610, GSE17800) and one single-cell RNA sequencing dataset (GSE95140) were obtained from the Gene Expression Omnibus (GEO) database. GSE29819 and GSE21610 were combined into the training group, while GSE17800 was the test group. We used the weighted gene co-expression network analysis (WGCNA) and identified fifteen driver genes highly associated with DCM with HF in the module. We performed the least absolute shrinkage and selection operator (LASSO) on the driver genes and then constructed five machine learning classifiers (random forest, gradient boosting machine, neural network, eXtreme gradient boosting, and support vector machine). Random forest was the best-performing classifier established on five Lasso-selected genes, which was utilized to select out NPPA, OMD, and PRELP for diagnosing DCM with HF. Moreover, we observed the up-regulation mRNA levels and robust diagnostic accuracies of NPPA, OMD, and PRELP in the training group and test group. Single-cell RNA-seq analysis further demonstrated their stable up-regulation expression patterns in various cardiomyocytes of DCM patients. Besides, through gene set enrichment analysis (GSEA), we found TGF-β signaling pathway, correlated with NPPA, OMD, and PRELP, was the underlying mechanism of DCM with HF. Overall, our study revealed NPPA, OMD, and PRELP serving as diagnostic biomarkers for DCM with HF, deepening the understanding of its pathogenesis.

Stemness Analysis Uncovers That The Peroxisome Proliferator-Activated Receptor Signaling Pathway Can Mediate Fatty Acid Homeostasis In Sorafenib-Resistant Hepatocellular Carcinoma Cells

Hepatocellular carcinoma (HCC) stem cells are regarded as an important part of individualized HCC treatment and sorafenib resistance. However, there is lacking systematic assessment of stem-like indices and associations with a response of sorafenib in HCC. Our study thus aimed to evaluate the status of tumor dedifferentiation for HCC and further identify the regulatory mechanisms under the condition of resistance to sorafenib. Datasets of HCC, including messenger RNAs (mRNAs) expression, somatic mutation, and clinical information were collected. The mRNA expression-based stemness index (mRNAsi), which can represent degrees of dedifferentiation of HCC samples, was calculated to predict drug response of sorafenib therapy and prognosis. Next, unsupervised cluster analysis was conducted to distinguish mRNAsi-based subgroups, and gene/geneset functional enrichment analysis was employed to identify key sorafenib resistance-related pathways. In addition, we analyzed and confirmed the regulation of key genes discovered in this study by combining other omics data. Finally, Luciferase reporter assays were performed to validate their regulation. Our study demonstrated that the stemness index obtained from transcriptomic is a promising biomarker to predict the response of sorafenib therapy and the prognosis in HCC. We revealed the peroxisome proliferator-activated receptor signaling pathway (the PPAR signaling pathway), related to fatty acid biosynthesis, that was a potential sorafenib resistance pathway that had not been reported before. By analyzing the core regulatory genes of the PPAR signaling pathway, we identified four candidate target genes, retinoid X receptor beta (RXRB), nuclear receptor subfamily 1 group H member 3 (NR1H3), cytochrome P450 family 8 subfamily B member 1 (CYP8B1) and stearoyl-CoA desaturase (SCD), as a signature to distinguish the response of sorafenib. We proposed and validated that the RXRB and NR1H3 could directly regulate NR1H3 and SCD, respectively. Our results suggest that the combined use of SCD inhibitors and sorafenib may be a promising therapeutic approach.