Analysis of mRNA‑lncRNA and mRNA‑lncRNA-pathway co‑expression networks based on WGCNA in developing pediatric sepsis

The role of lncRNAs in sepsis-induced acute lung injury: Molecular mechanisms and therapeutic potential

Sepsis, a life-threatening syndrome, results from a dysregulated immune and hemostatic response, contributing to acute lung injury (ALI) and its progression into acute respiratory distress syndrome (ARDS). The development of septic ALI is complex, involving excessive inflammatory mediator production that damages endothelial and epithelial cells, leading to vascular leakage, edema, and vasodilation-key factors in ALI pathogenesis. Long noncoding RNAs (lncRNAs), over 200 nucleotides in length, play critical roles in various biological processes, including sepsis regulation. They exhibit both promotive and inhibitory effects, influencing sepsis progression and resolution. Despite their significance, comprehensive reviews detailing lncRNA involvement in sepsis-induced ALI remain limited. This review aims to address this gap by summarizing the diverse functions of lncRNAs in septic ALI, emphasizing their potential in diagnosis and treatment. Furthermore, we will explore the molecular mechanisms underlying lncRNA involvement, particularly their miRNA-dependent regulatory pathways. Understanding these interactions may provide novel insights into therapeutic strategies for sepsis-induced ALI.

Oxiracetam ameliorates neurological function after traumatic brain injury through competing endogenous RNA regulatory network

RATIONALE

Oxiracetam (ORC) has been demonstrated to improve neurological function resulting from traumatic brain injury (TBI).

OBJECTIVES

This study aims to explore the precise molecular mechanism of ORC in the treatment of TBI.

METHODS

TBI rat model was established and treated with ORC. Modified Garcia score, rotarod test and HE staining were employed to evaluate the neuroprotective effects of ORC. Subsequently, RNA-seq was conducted on the hippocampus of sham, TBI and ORC rats to identify differential expression (DE) lncRNAs and mRNAs. Functional analysis of DE lncRNAs and mRNAs was performed. The real-time quantitative polymerase chain reaction (qRT-PCR) was used to determine the expression of DE lncRNAs and DE mRNAs. Western blot was performed to explore important pathway in ceRNA networks.

RESULTS

ORC has been demonstrated to effectively improve neurological function in TBI rats. A total of 10 ORC-treated DE lncRNAs and 61 DE mRNAs were obtained. A co-expression network comprising 79 lncRNA-mRNA pairs associated with the treatment of ORC was constructed. Furthermore, an lncRNA-miRNA-mRNA regulated ceRNA network was constructed, comprising 15 mRNAs, 41 miRNAs and 10 lncRNAs. Functional enrichment, qRT-PCR, and Western blot analysis showed that ORC improve neurological function of TBI rats by regulating multiple signaling pathways, including the JAK-STAT/PI3K-Akt pathway, as well as affecting the expression of key genes Prlr, Cdkn1a, and Cldn1.

CONCLUSION

Our study reveals the mechanism of ORC therapy in TBI rats, which mainly relies on the regulation of the JAK-STAT/PI3K-Akt pathway and the influence on the expression of key genes Prlr, Cdkn1a, and Cldn1.

Identification of lncRNAs in peripheral blood mononuclear cells associated with sepsis immunosuppression based on weighted gene co-expression network analysis

BACKGROUND

Sepsis-induced immunosuppression involves complex molecular mechanisms, including dysregulated long noncoding RNAs (lncRNAs), which remain poorly understood.

OBJECTIVE

We aimed to identify immunosuppression-related lncRNAs and their functional pathways in sepsis. Methods: Using weighted gene coexpression network analysis (WGCNA), we analyzed lncRNA profiles from peripheral blood mononuclear cells (PBMCs) of three sepsis patients and three healthy controls. Key modules linked to immunosuppression were validated via RT-PCR and external datasets. Pathway enrichment and protein interaction networks were employed to prioritize mechanisms.

RESULTS

A sepsis-associated module containing 4,193 lncRNAs revealed three immunosuppression-related pathways: Th17 cell differentiation, cytokine-cytokine receptor interactions, and cancer-related proteoglycan signaling. Protein-protein interaction networks identified three central genes (SLFN12, ICOS, IKZF2) and their linked lncRNAs (ENSG00000267074, lnc-ICOSLG-1, lnc-IKZF2-7), all significantly downregulated in sepsis patients.

CONCLUSION

 Our findings highlight novel lncRNA-regulated pathways in sepsis-induced immunosuppression, providing potential targets for improved diagnosis and therapy.

Heterogeneity of Neutrophils and Immunological Function in Neonatal Sepsis: Analysis of Molecular Subtypes Based on Hypoxia-Glycolysis-Lactylation

Objective: Hypoxia-glycolysis-lactylation (HGL) may play a crucial role in neonatal sepsis (NS). This study aims to identify HGL marker genes in NS and explore immune microenvironment among NS subtypes. Materials and Methods: The gene expression dataset GSE69686, comprising 64 NS cases and 85 controls, was selected for analysis. Based on the screened HGL-related marker genes, diagnostic prediction models were constructed using nine machine learning algorithms, and molecular subtypes of NS were identified through consensus clustering. Subsequently, the heterogeneity of biological functions and immune cell infiltration among the different subtypes was analyzed. Finally, the marker genes and lactylation were validated using the GSE25504 dataset, clinical samples, and mouse neutrophil, respectively. Results: MERTK, HK3, PGK1, and STAT3 were identified and validated as marker genes, and the diagnostic prediction model for NS constructed using the support vector machine (SVM) algorithm exhibited optimal predictive performance. Based on gene expression patterns, two distinct NS subtypes were identified. Functional enrichment analysis highlighted significant immune-related pathways, while immune infiltration analysis revealed differences in neutrophil proportions between the subtypes. Furthermore, the expression levels of marker genes were positively correlated with neutrophil infiltration. Importantly, the experimental validation results were consistent with the findings from the bioinformatics analysis. Conclusion: This study identified the distinct NS subtypes and their associated marker genes. These findings will contribute to elucidating the disease's heterogeneity and establishing appropriate personalized therapeutic approaches.

The Regulation of Messenger RNAs and Biological Pathways by Long Non-Coding RNAs and Circular RNAs in Ischemic Stroke

Our aim was to evaluate the regulation of messenger RNAs (mRNAs) and biological pathways by long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) in ischemic stroke. We employed weighted gene co-expression network analysis (WGCNA) to construct two co-expression networks for mRNAs with circRNAs and lncRNAs, respectively, to investigate their association with ischemic stroke. We compared the overlap of mRNAs and biological pathways in the stroke-associated modules of the two networks. Furthermore, we validated the association of key non-coding RNAs with the risk of ischemic stroke and poor prognosis using quantitative real-time polymerase chain reaction. Ischemic stroke patients exhibited lower eigengene expression in the turquoise module associated with lncRNAs and mRNAs, as well as in the turquoise, red, and greenyellow modules associated with circRNAs and mRNAs in ischemic stroke. In the lncRNA-mRNA network and circRNA-mRNA network, we observed a significant overlap of the 5126 mRNAs (P < 0.001) and 51 biological pathways (P < 0.001), respectively. Among the ten key non-coding RNAs, lnc-TPRG1-AS1, lnc-GUK1, and hsa_circ_RELL1 were significantly increased (P < 0.05), while hsa_circ_ZBTB20 and hsa_circ_ERBB2 were significantly decreased (P < 0.05) in ischemic stroke. Additionally, ischemic stroke patients with poor functional outcome had significantly lower levels of hsa_circ_ZBTB20 and hsa_circ_ERBB2 compared to those with favorable prognosis (P < 0.05). Our findings suggest lncRNAs and circRNAs display similar biological functions in ischemic stroke. Key non-coding RNAs may be associated with the risk and clinical prognosis of ischemic stroke. These results warrant further validation in the future studies.

Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune microenvironment in sepsis

Objectives

N7-methylguanosine (m7G) modification is closely related to the occurrence of human diseases, but its roles in sepsis remain unclear. This study aimed to explore the patterns of lethality-related m7G regulatory factor-mediated RNA methylation modification and immune microenvironment regulatory features in sepsis.

Methods

Three sepsis-related datasets (E-MTAB-4421 and E-MTAB-4451 as training sets and GSE185263 as a validation set) were collected, and differentially expressed m7G-related genes were analyzed between survivors and non-survivors. Lethality-related m7G signature genes were then screened using machine learning methods, followed by the construction of a survival recognition model. Additionally, differences in immune cell distribution were determined and differentially expressed genes (DEGs) between different subtypes were analyzed. Weighted gene co-expression network analysis (WGCNA) was used to select important modules and related hub genes.

Results

In total, 10 differentially expressed m7G-related genes were identified between the survivors and non-survivors, and after further analysis, EIF4G3, EIF4E3, NSUN2, NUDT4, and GEMIN5 were identified as the optimal lethality-related m7G genes. A survival status diagnostic model was then constructed with a combined AUC of 0.678. Fifteen types of immune cells were significantly different between survivors and non-survivors. Sepsis samples were classified into two subtypes, with 22 types of immune cells showing significant differences. Subsequently, 1707 DEGs were identified between the two subtypes, which were significantly enriched in 91 GO terms and 16 KEGG pathways. Finally, the green module with |correlation| > 0.3 was found to be closely related to the subtypes and survival status; further, the top10 hub genes were obtained.

Conclusion

The constructed survival status diagnostic model based on the five lethality-related m7G signature genes may help predict the survival status of patients, and the 10 hub genes obtained may be potential therapeutic targets for sepsis.

Integration WGCNA with LC-MS data for evaluating the processing status and transformation rules of Ligustri Lucidi Fructus: A novel strategy for evaluating the processing technology of traditional Chinese medicines

Ligustri Lucidi Fructus (LLF) is a traditional Chinese medicine (TCM) to treat hepatopathy and osteopathy. Wine-processed LLF (WLLF) was much more widely used than raw LLF (RLLF) in clinical practice, however, there is no consensus on processing time. To investigate the processing status of WLLF and transformation rules during processing, a UHPLC-Q-Orbitrap-MS method combined with data-independent acquisition (DIA) mode was firstly established and 227 compounds were identified or tentatively identified. Subsequently, a novel strategy using integration weighted gene co-expression network analysis (WGCNA) with LC-MS data was proposed. A total of 73 differential metabolites were screened out between RLLF and WLLF (wine steaming for 18 h). Meanwhile, the concentration of 11 differential compounds for WLLF was quantified. Finally, correlations between compounds were analyzed by WGCNA and the top five compounds negatively correlated with salidroside were validated, revealing that G13, specnuezhenide, oleuropein, acteoside, and neonuzhenide could be transformed into salidroside and its analogues during processing, respectively. The results indicated that our proposed strategy could be effectively employed to evaluate the processing status of TCMs.

Bioinformatics data combined with single-cell analysis reveals patterns of immunoinflammatory infiltration and cell death in melanoma

BACKGRUOND

Melanoma is a common cancer in dermatology, but its molecular mechanisms remain poorly explained.

AIM

Utilizing single-cell analytics and bioinformatics, the work sought to discover the immunological infiltration and cellular molecular mechanisms of melanoma.

METHODS

Melanoma genes databases were downloaded from GeneCards, and gene expression profiles were chosen from the Gene Expression Omnibus (GSE244889). Establishing and analyzing protein-protein interaction networks for functional enrichment made use of the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. The process assesses the immunological cell infiltration variations between normal and malignant samples by Immune Cell AI software program. Different cell type differences were clarified by cell quality control, filtration, removal of batch effects and cell clustering analysis using single cell analysis techniques.

RESULTS

Using a variety of machine learning techniques, 20 differentially expressed hub genes were found; among these, TP53, HSP90AB1, HSPA4, RHOA, CCND1, CYCS, PPARG, NFKBIA, CAV1, ANXA5, ENO1, ITGAM, YWHAZ, RELA, SOD1, and VDAC1 were found to be significantly significant. The results of enrichment analysis demonstrated that immune response and inflammatory response were strongly associated with melanoma. Animal mitophagy, ferroptosis, the PI3K-Akt signaling pathway, and the HIF-1 signaling pathway were the primary signaling pathways implicated. Cells of immunity, T-cells, lymphocytes, B-cells, NK-cells, monocytes, and macrophages were shown to be significantly infiltrated in melanoma patients, according to analysis. Single cell analysis also demonstrated that ferroptosis is a significant mechanism of cell death that contributes to the advancement of melanoma and that macrophages are important in the disease.

CONCLUSION

In summary, different immune cell infiltrations-particularly macrophages-have a significant impact on the onset and course of melanoma, and our findings may help direct future investigations into melanoma macrophages.

Multiomic molecular patterns of lipid dysregulation in a subphenotype of sepsis with higher shock incidence and mortality

BACKGROUND

Lipids play a critical role in defense against sepsis. We sought to investigate gene expression and lipidomic patterns of lipid dysregulation in sepsis.

METHODS

Data from four adult sepsis studies were analyzed and findings were investigated in two external datasets. Previously characterized lipid dysregulation subphenotypes of hypolipoprotein (HYPO; low lipoproteins, increased mortality) and normolipoprotein (NORMO; higher lipoproteins, lower mortality) were studied. Leukocytes collected within 24 h of sepsis underwent RNA sequencing (RNAseq) and shotgun plasma lipidomics was performed.

RESULTS

Of 288 included patients, 43% were HYPO and 57% were NORMO. HYPO patients exhibited higher median SOFA scores (9 vs 5, p = < 0.001), vasopressor use (67% vs 34%, p = < 0.001), and 28-day mortality (30% vs 16%, p = 0.004). Leukocyte RNAseq identified seven upregulated lipid metabolism genes in HYPO (PCSK9, DHCR7, LDLR, ALOX5, PLTP, FDFT1, and MSMO1) vs. NORMO patients. Lipidomics revealed lower cholesterol esters (CE, adjusted p = < 0.001), lysophosphatidylcholines (LPC, adjusted p = 0.001), and sphingomyelins (SM, adjusted p = < 0.001) in HYPO patients. In HYPO patients, DHCR7 expression strongly correlated with reductions in CE, LPC, and SM (p < 0.01), while PCSK9, MSMO1, DHCR7, PLTP, and LDLR upregulation were correlated with low LPC (p < 0.05). DHCR7, ALOX5, and LDLR correlated with reductions in SM (p < 0.05). Mortality and phenotype comparisons in two external datasets (N = 824 combined patients) corroborated six of the seven upregulated lipid genes (PCSK9, DHCR7, ALOX5, PLTP, LDLR, and MSMO1).

CONCLUSION

We identified a genetic lipid dysregulation signature characterized by seven lipid metabolism genes. Five genes in HYPO sepsis patients most strongly correlated with low CE, LPC, and SMs that mediate cholesterol storage and innate immunity.

Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning

PURPOSE

Mitochondria are involved in septic shock and inflammatory response syndrome, which severely affects the life security of patients. It is necessary to recognize and explore the immune-mitochondrial genes in septic shock.

METHODS

The GSE57065 dataset was acquired from the Gene Expression Omnibus (GEO) database and filtered by limma and the weighted correlation network analysis (WGCNA) to identify mitochondrial-related differentially expressed genes (MitoDEGs) in septic shock. The function of MitoDEGs was analyzed using the Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), respectively. The Protein-Protein Interaction (PPI) network composed of MitoDEGs was established using Cytoscape. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO) were used to identify diagnostic MitoDEGs, which were validated using receiver operating characteristic (ROC) analysis and Quantitative Real-time Reverse Transcription Polymerase Chain Reaction (qRT-PCR). Furthermore, the infiltration of immunocytes was analyzed using CIBERSORT, and the correlation between diagnostic MitoDEGs and immunocytes was explored using Spearman.

RESULTS

A total of 44 MitoDEGs were filtered, and functional enrichment analysis showed they were associated with mitochondrial function, and the PPI network had 457 nodes and 547 edges. Four diagnostic genes, MitoDEGs, PGS1, C6orf136, THEM4, and EPHX2, were identified by three machine learning algorithms, and qRT-PCR results obtained similar expression levels as bioinformatics analysis. Furthermore, the diagnostic model constructed by the diagnostic genes had fine diagnostic efficacy. Immunocyte infiltration analysis showed that activated immunocytes were abundant and correlated with hub genes, with neutrophils accounting for the largest proportion in septic shock.

CONCLUSIONS

In this study, we recognized four immune-mitochondrial key genes (PGS1, C6orf136, THEM4, and EPHX2) in septic shock and designed a novel gene diagnosis model that provided a new and meaningful way for the diagnosis of septic shock.

Long noncoding RNAs and metabolic memory associated with continued progression of diabetic retinopathy

Progression of diabetic retinopathy resists arrest even after institution of intensive glycemic control, suggesting a "metabolic memory" phenomenon, but the mechanism responsible for this phenomenon is still elusive. Gene expression and biological processes can also be regulated by long noncoding RNAs (LncRNAs), the RNAs with >200 nucleotides and no open reading frame for translation, and several LncRNAs are aberrantly expressed in diabetes. Our aim was to identify retinal LncRNAs that fail to reverse after termination of hyperglycemia. Microarray analysis was performed on retinal RNA from streptozotocin-induced diabetic rats in poor glycemic control for 8 months, followed by in good glycemic control (blood glucose >400 mg/dL), or for 4 months, with four additional months of good glycemic control (blood glucose <150 mg/dL). Differentially expressed LncRNAs and mRNAs were identified through Volcano filtering, and their functions were predicted using gene ontology and pathway enrichment analyses. Compared with age-matched normal rats, rats in continuous poor glycemic control had >1479 differentially expressed LncRNAs (710 downregulated, 769 upregulated), and among those, 511 common LncRNAs had similar expression in Diab and Rev groups (139 downregulated, 372 upregulated). Gene Ontology/pathway analysis identified limited LncRNAs in biological processes, but analysis based on biological processes/molecular function revealed >350 genes with similar expression in Diab and Rev groups; these genes were mainly associated with stress response, cell death, mitochondrial damage and cytokine production. Thus, identifying retinal LncRNAs and their gene targets that do not benefit from termination of hyperglycemia have potential to serve as therapeutic targets to slow down the progression of diabetic retinopathy.

Paediatric systemic inflammatory response syndrome (SIRS) and the development of patient-specific therapy: ethical perspectives through experts' opinions

Background

Research for personalised therapies concerning the Systemic Inflammatory Response Syndrome (SIRS) in children involves the utilisation of OMICS technologies and Artificial Intelligence (AI).

Methods

To identify specific ethical challenges through the perspective of healthcare professionals, we conducted 10 semi-structured interviews. The development of interview questions for the interviews was preceded by a systematic review of the scientific literature. To address the complexities of paediatric emergency research, informed consent, and data processing, experts with expertise in paediatric intensive care, computer science, and medical law were sought. After the transcription and anonymisation, the analysis followed established guidelines for qualitative content and thematic analysis.

Results

Interviewees highlighted the intricacies of managing consent in personalised SIRS research due to the large amount and complexity of information necessary for autonomous decision-making. Thus, instruments aimed at enhancing the understanding of legal guardians and to empowering the child were appreciated and the need for specific guidelines and establishing standards was expressed. Medical risks were estimated to be low, but the challenges of securing anonymisation and data protection were expected. It was emphasised that risks and benefits cannot be anticipated at this stage. Social justice issues were identified because of possible biases within the research population. Our findings were analysed using current ethical and legal frameworks for research with a focus on the particularities of the patient group and the emergency background. In this particular context, experts advocated for an enabling approach pertaining to AI in combination with OMICS technologies.

Conclusion

As with every new technological development, ethical and legal challenges cannot be foreseen for SIRS-personalised treatment. Given this circumstance, experts emphasised the importance of extending the ethics-legal discourse beyond mere restrictions. The organisation of supervision should be reconsidered and not limited only to the precautionary principle, which per se was seen as impeding both the medical progress and clinical flexibility. It was noted that the establishment and monitoring of guidelines were emergent and should evolve through an interdisciplinary discourse. Therefore, it was recommended to enhance the qualifications of physicians in the field of computer science, impart ethics training to AI developers, and involve experts with expertise in medical law and data protection.

Integrated analysis reveals NLRC4 as a potential biomarker in sepsis pathogenesis

Sepsis remains a significant global health burden and contributor to mortality, yet the precise molecular mechanisms underlying the immune response are not fully elucidated. To gain insight into this issue, we performed a comprehensive analysis using a variety of techniques including bulk RNA sequencing, single-cell RNA sequencing, and enzyme-linked immunosorbent assay (ELISA). We performed enrichment analysis of differentially expressed genes in sepsis and healthy individuals by utilizing Gene Ontology (GO) analysis and indicated significant enrichment of immune-related response. Following Weighted Gene Co-Expression Network Analysis (WGCNA) and protein-protein interaction analysis (PPI) were used to identify key immune-related hub genes and validated by ELISA to show that NLRC4 is highly expressed in sepsis. Additionally, an analysis of scRNA-seq data from newly diagnosed sepsis, sepsis diagnosis at 6 hours, and healthy samples demonstrates a significant increase in both the expression levels and proportions of NLRC4 in sepsis monocytes and neutrophils. In addition, using pySCENIC we identified upstream transcription factors that regulate NLRC4. Our study provides valuable insights into the identification of NLRC4 in peripheral blood as a potential candidate gene for the diagnosis and treatment of sepsis.

Structure of POU2AF1 recombinant protein and it affects the progression and treatment of liver cancer based on WGCNA and molecular docking analysis

Hepatocellular carcinoma, also referred to as HCC, is the most frequent form of primary liver cancer. It is anticipated that the discovery of the molecular pathways related with HCC would open up new possibilities for the treatment of HCC.WGCNA (Weighted gene co-expression network analysis) and molecular docking analysis were used to study the structural characteristics of POU2AF1 recombinant protein and its interaction with related proteins. Normal samples were placed in one group, and tumor samples were placed in another group inside the GEO database. We continued our investigation of the DEGs by performing an enrichment analysis using GO and KEGG. The GSCA platform is utilized in the process of doing an analysis of the connection between gene expression and medication sensitivity. In the end, the core target and the active molecule were both given the green light for a molecular docking investigation. POU2AF1 is being considered as a possible therapeutic target for HCC, and the results of our work have presented novel concepts for the treatment of HCC.

Post-Transcriptional Regulator RBM47 Stabilizes FBXO2 mRNA to Advance Osteoarthritis Development: WGCNA Analysis and Experimental Validation

Osteoarthritis (OA) is a common chronic joint degenerative disease and a major cause of disability in the elderly. However, the current intervention strategies cannot effectively improve OA, and the pathogenesis of OA remains elusive. The present study identified RNA binding motif protein 47 (RBM47) as an upstream modulator of key dysregulation gene co-expression module based on weighted gene co-expression network analysis (WGCNA) analysis and least absolute shrinkage and selection operator (Lasso) modeling. Subsequently, data from real-time quantitative PCR and western blot analysis revealed that RBM47 was upregulated in OA models in vivo and in vitro compared with normal controls. Functional analysis results from the MTT assay, flow cytometry, evaluation of LDH activities and inflammatory mediators, and western blot analysis of extracellular matrix (ECM) proteins, showed that RBM47 knockdown significantly alleviated inflammation, apoptosis, and ECM degradation in interleukin 1β (IL-1β)-treated chondrocytes. Mechanistically, RBM47 bound to F box only protein 2 (FBXO2) and stabilized FBXO2 messenger RNA (mRNA) to promote the phosphorylation of signal transducer and activator of transcription 3 (STAT3) in chondrocytes. Results from the recovery assay showed that the re-activation of STAT3 signaling by overexpressing FBXO2 or STAT3 counteracted the alleviating effect of RBM47 downregulation on IL-1β-induced inflammation, apoptosis, and ECM degradation. Altogether, our findings illustrate that RBM47 stabilizes FBXO2 mRNA to advance OA development by activating STAT3 signaling, which enhances our understanding of the molecular regulatory mechanisms underlying the development of OA.

The effect of LNCRNA SHANK3 on the malignant development of gastric cancer cells by regulating the miR-4530/MNX1

Gastric cancer (GC) has become the first malignant tumor with highest incidence rate and mortality of cancer in China, finding therapeutic targets for gastric cancer is of great significant for improving the survival rate of patients with GC. Recently, many of studies have shown that LncRNAs is involved in multiple biological progresses in the development of GC. This study, we screened for abnormally high expression of LncSHANK3 in GC through the TCGA database, and found that LncSHANK3 sponge adsorbs miR-4530, further competing with MNX1 and binding to miR-4530. We demonstrated the interaction between LncSHANK3 and miR-4530 through luciferase reporting analysis, with miR-4530 negatively regulating MNX1.Through CCK8, colony formation, transwell, and wound healing assays, it was found that LncSHANK3 affects the occurrence of GC through cell proliferation, migration and invasion. In conclusion, LncSHANK3/miR-4530/MNX1 axis is a potential mechanism for the treatment of GC.

S100A9 exacerbates sepsis-induced acute lung injury via the IL17-NFκB-caspase-3 signaling pathway

BACKGROUND

Sepsis-induced acute lung injury (ALI) is associated with considerable morbidity and mortality in critically ill patients. S100A9, a key endothelial injury factor, is markedly upregulated in sepsis-induced ALI; however, its specific mechanism of action has not been fully elucidated.

METHODS

The Gene Expression Omnibus database transcriptome data for sepsis-induced ALI were used to screen for key differentially expressed genes (DEGs). Using bioinformatics analysis methods such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network analyses, the pathogenesis of sepsis-induced ALI was revealed. Intratracheal infusion of lipopolysaccharide (LPS, 10 mg/kg) induced ALI in wild-type (WT) and S100A9 knockout mice. Multiomics analyses (transcriptomics and proteomics) were performed to investigate the potential mechanisms by which S100A9 exacerbates acute lung damage. Hematoxylin-eosin, Giemsa, and TUNEL staining were used to evaluate lung injury and cell apoptosis. LPS (10 μg/mL)-induced murine lung epithelial MLE-12 cells were utilized to mimic ALI and were modulated by S100A9 lentiviral transfection. The impact of S100A9 on cell apoptosis and inflammatory responses were identified using flow cytometry and PCR. The expression of interleukin (IL)-17-nuclear factor kappa B (NFκB)-caspase-3 signaling components was identified using western blotting.

RESULTS

Six common DEGs (S100A9, S100A8, IFITM6, SAA3, CD177, and MMP9) were identified in the six datasets related to ALI in sepsis. Compared to WT sepsis mice, S100A9 knockout significantly alleviated LPS-induced ALI in mice, with reduced lung structural damage and inflammatory exudation, decreased exfoliated cell and protein content in the lung lavage fluid, and reduced apoptosis and necrosis of pulmonary epithelial cells. Transcriptomic analysis revealed that knocking out S100A9 significantly affected 123 DEGs, which were enriched in immune responses, defense responses against bacteria or lipopolysaccharides, cytokine-cytokine receptor interactions, and the IL-17 signaling pathway. Proteomic analysis revealed that S100A9 knockout alleviated muscle contraction dysfunction and structural remodeling in sepsis-induced ALI. Multiomics analysis revealed that S100A9 may be closely related to interferon-induced proteins with tetratricopeptide repeats and oligoadenylate synthase-like proteins. LPS decreased MLE12 cell activity, accompanied by high expression of S100A9. The expression of IL-17RA, pNFκB, and cleaved-caspase-3 were increased by S100A9 overexpression and reduced by S100A9 knockdown in LPS-stimulated MLE12 cells. S100A9 knockdown decreases transcription of apoptosis-related markers Bax, Bcl and caspase-3, alleviating LPS-induced apoptosis.

CONCLUSIONS

S100A9 as a key biomarker of sepsis-induced acute lung injury, and exacerbates lung damage and epithelial cell apoptosis induced by LPS via the IL-17-NFκB-caspase-3 signaling pathway.

Identification of HK3 as a promising immunomodulatory and prognostic target in sepsis-induced acute lung injury

BACKGROUND

Sepsis is a life-threatening global disease with a significant impact on human health. Acute lung injury (ALI) has been identified as one of the primary causes of mortality in septic patients. This study aimed to identify candidate genes involved in sepsis-induced ALI through a comprehensive approach combining bioinformatics analysis and experimental validation.

METHODS

The datasets GSE65682 and GSE32707 obtained from the Gene Expression Omnibus database were merged to screen for sepsis-induced ALI related differentially expressed genes (DEGs). Functional enrichment and immune infiltration analyses were conducted on DGEs, with the construction of protein-protein interaction (PPI) networks to identify hub genes. In vitro and in vivo models of sepsis-induced ALI were used to study the expression and function of hexokinase 3 (HK3) using various techniques including Western blot, real-time PCR, immunohistochemistry, immunofluorescence, Cell Counting Kit-8, Enzyme-linked immunosorbent assay, and flow cytometry.

RESULTS

The results of bioinformatics analysis have identified HK3, MMP9, and S100A8 as hub genes with diagnostic and prognostic significance for sepsis-induced ALI. The HK3 has profound effects on sepsis-induced ALI and exhibits a correlation with immune regulation. Experimental results showed increased HK3 expression in lung tissue of septic mice, particularly in bronchial and alveolar epithelial cells. In vitro studies demonstrated upregulation of HK3 in lipopolysaccharide (LPS)-stimulated lung epithelial cells, with cytoplasmic localization around the nucleus. Interestingly, following the knockdown of HK3 expression, lung epithelial cells exhibited a significant decrease in proliferation activity and glycolytic flux, accompanied by an increase in cellular inflammatory response, oxidative stress, and cell apoptosis.

CONCLUSIONS

It was observed for the first time that HK3 plays a crucial role in the progression of sepsis-induced ALI and may be a valuable target for immunomodulation and therapy.Bioinformatics analysis identified HK3, MMP9, and S100A8 as hub genes with diagnostic and prognostic relevance in sepsis-induced ALI. Experimental findings showed increased HK3 expression in the lung tissue of septic mice, particularly in bronchial and alveolar epithelial cells. In vitro experiments demonstrated increased HK3 levels in lung epithelial cells stimulated with LPS, with cytoplasmic localization near the nucleus. Knockdown of HK3 expression resulted in decreased proliferation activity and glycolytic flux, increased inflammatory response, oxidative stress, and cell apoptosis in lung epithelial cells.

Identification of hub genes and pathogenesis in Kawasaki disease based on bioinformatics analysis

BACKGROUND

The aim of this study was to explore new biomarkers of Kawasaki disease (KD) and provide evidence for clinical diagnosis and treatment.

MATERIALS AND METHODS

Gene Expression Omnibus (GEO) datasets GSE68004 and GSE73461 were downloaded, and the differentially expressed genes (DGEs) were taken, along with DEGs enrichment analysis and protein interaction network. Finally, five algorithms in CytoHubba plug-in were applied to obtain hub genes.

RESULTS

In this study, 32 Co-DEGs were identified, and these genes mainly participated in neutrophil degranulation, neutrophil activation involved in immune response, and negative regulation of cytokine production involved in immune response; meanwhile, they were primarily enriched in starch and sucrose metabolism, fatty acid metabolism, autophagy and apoptosis, ferroptosis, and other pathways. Combined with the results of PPI and CytoHubba, 13 key genes were selected as follows: S100A12, HK3, HP, MMP9, MCEMP1, PYGL, ARG1, HIST2H2AA, ANXA3, HIST2H2AC, HIST2H2AA3, GYG1, DYSF.

CONCLUSIONS

These 13 key genes may mediate the occurrence and development of KD through various processes such as immune regulation, inflammatory response, glucose metabolism, autophagy, and apoptosis, which provide valuable references for the diagnosis and treatment of KD.

Identification of important modules and biomarkers in tuberculosis based on WGCNA

Background

Tuberculosis (TB) is a significant public health concern, particularly in China. Long noncoding RNAs (lncRNAs) can provide abundant pathological information regarding etiology and could include candidate biomarkers for diagnosis of TB. However, data regarding lncRNA expression profiles and specific lncRNAs associated with TB are limited.

Methods

We performed ceRNA-microarray analysis to determine the expression profile of lncRNAs in peripheral blood mononuclear cells (PBMCs). Weighted gene co-expression network analysis (WGCNA) was then conducted to identify the critical module and genes associated with TB. Other bioinformatics analyses, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and co-expression networks, were conducted to explore the function of the critical module. Finally, real-time quantitative polymerase chain reaction (qPCR) was used to validate the candidate biomarkers, and receiver operating characteristic analysis was used to assess the diagnostic performance of the candidate biomarkers.

Results

Based on 8 TB patients and 9 healthy controls (HCs), a total of 1,372 differentially expressed lncRNAs were identified, including 738 upregulated lncRNAs and 634 downregulated lncRNAs. Among all lncRNAs and mRNAs in the microarray, the top 25% lncRNAs (3729) and top 25% mRNAs (2824), which exhibited higher median expression values, were incorporated into the WGCNA. The analysis generated 16 co-expression modules, among which the blue module was highly correlated with TB. GO and KEGG analyses showed that the blue module was significantly enriched in infection and immunity. Subsequently, considering module membership values (>0.85), gene significance values (>0.90) and fold-change value (>2 or < 0.5) as selection criteria, the top 10 upregulated lncRNAs and top 10 downregulated lncRNAs in the blue module were considered as potential biomarkers. The candidates were then validated in an independent validation sample set (31 TB patients and 32 HCs). The expression levels of 8 candidates differed significantly between TB patients and HCs. The lncRNAs ABHD17B (area under the curve [AUC] = 1.000) and ENST00000607464.1 (AUC = 1.000) were the best lncRNAs in distinguishing TB patients from HCs.

Conclusion

This study characterized the lncRNA profiles of TB patients and identified a significant module associated with TB as well as novel potential biomarkers for TB diagnosis.

Deciphering the Hypoxia-immune interface in esophageal squamous carcinoma: a prognostic network model

Introduction

The rapid progress and poor prognosis of the exercise of esophageal squamous cell carcinoma (ESCA) bring great challenges to the treatment. Hypoxia in the tumor microenvironment has become a key factor in the pathogenesis of tumors. However, due to the lack of clear therapeutic targets, hypoxia targeted therapy of ESCA is still in the exploratory stage.

Methods

To bridge this critical gap, we mined a large number of gene expression profiles and clinical data on ESCA from public databases. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed. We next delved into the relationship between hypoxia and apoptotic cell interactions. Meanwhile, using LASAS-Cox regression, we designed a robust prognostic risk score, which was subsequently validated in the GSE53625 cohort. In addition, we performed a comprehensive analysis of immune cell infiltration and tumor microenvironment using cutting-edge computational tools.

Results

Hypoxia-related genes were identified and classified by WGCNA. Functional enrichment analysis further elucidated the mechanism by which hypoxia affected the ESCA landscape. The results of the interaction analysis of hypoxia and apoptotic cells revealed their important roles in driving tumor progression. The validation results of the prognostic risk score model in the GSE53625 cohort obtained a good area under the receiver operating characteristic (ROC) curve, and the risk score was independently verified as a significant predictor of ESCA outcome. The results of immune cell infiltration and tumor microenvironment analysis reveal the profound impact of immune cell dynamics on tumor evolution.

Conclusion

Overall, our study presents a pioneering hypoxiacentered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice.

Multilevel omics for the discovery of biomarkers in pediatric sepsis

Severe sepsis causes organ dysfunction and continues to be the leading reason for pediatric death worldwide. Early recognition of sepsis could substantially promote precision treatment and reduce the risk of pediatric death. The host cellular response to infection during sepsis between adults and pediatrics could be significantly different. A growing body of studies focused on finding markers in pediatric sepsis in recent years using multi-omics approaches. This narrative review summarized the progress in studying pediatric sepsis biomarkers from genome, transcript, protein, and metabolite levels according to the omics technique that has been applied for biomarker screening. It is most likely not a single biomarker could work for precision diagnosis of sepsis, but a panel of markers and probably a combination of markers detected at multi-levels. Importantly, we emphasize the importance of group distinction of infectious agents in sepsis patients for biomarker identification, because the host response to infection of bacteria, virus, or fungus could be substantially different and thus the results of biomarker screening. Further studies on the investigation of sepsis biomarkers that were caused by a specific group of infectious agents should be encouraged in the future, which will better improve the clinical execution of personalized medicine for pediatric sepsis.

The role of pyroptosis-related genes in the diagnosis and subclassification of sepsis

Pyroptosis is a new form of programmed cell death recognized as crucial in developing sepsis. However, there is limited research on the mechanism of pyroptosis-related genes in sepsis-related from the Gene Expression Omnibus (GEO) database and standardized. The expression levels of pyroptosis-related genes were extracted, and differential expression analysis was conducted. A prediction model was constructed using random forest (RF), support vector machine (SVM), weighted gene co-expression new analysis (WGCNA), and nomogram techniques to assess the risk of sepsis. The relationship between pyroptosis-related subgroups and the immune microenvironment and inflammatory factors was studied using consistent clustering algorithms, principal component analysis (PCA), single-sample genomic enrichment analysis (ssGSEA), and immune infiltration. A risk prediction model based on 3 PRGs has been constructed and can effectively predict the risk of sepsis. Patients with sepsis can be divided into two completely different subtypes of pyroptosis-related clusters. Cluster B is highly correlated with the lower proportion of Th17 celld and has lower levels of expression of inflammatory factors. This study utilizes mechanical learning methods to further investigate the pathogenesis of sepsis, explore potential biomarkers, provide effective molecular targets for its diagnosis and treatment of sepsis.

Analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric sepsis by integrating bioinformatics and machine learning

BACKGROUND

Pediatric sepsis is a complicated condition characterized by life-threatening organ failure resulting from a dysregulated host response to infection in children. It is associated with high rates of morbidity and mortality, and rapid detection and administration of antimicrobials have been emphasized. The objective of this study was to evaluate the diagnostic biomarkers of pediatric sepsis and the function of immune cell infiltration in the development of this illness.

METHODS

Three gene expression datasets were available from the Gene Expression Omnibus collection. First, the differentially expressed genes (DEGs) were found with the use of the R program, and then gene set enrichment analysis was carried out. Subsequently, the DEGs were combined with the major module genes chosen using the weighted gene co-expression network. The hub genes were identified by the use of three machine-learning algorithms: random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator. The receiver operating characteristic curve and nomogram model were used to verify the discrimination and efficacy of the hub genes. In addition, the inflammatory and immune status of pediatric sepsis was assessed using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT). The relationship between the diagnostic markers and infiltrating immune cells was further studied.

RESULTS

Overall, after overlapping key module genes and DEGs, we detected 402 overlapping genes. As pediatric sepsis diagnostic indicators, CYSTM1 (AUC = 0.988), MMP8 (AUC = 0.973), and CD177 (AUC = 0.986) were investigated and demonstrated statistically significant differences (P < 0.05) and diagnostic efficacy in the validation set. As indicated by the immune cell infiltration analysis, multiple immune cells may be involved in the development of pediatric sepsis. Additionally, all diagnostic characteristics may correlate with immune cells to varying degrees.

CONCLUSIONS

The candidate hub genes (CD177, CYSTM1, and MMP8) were identified, and the nomogram was constructed for pediatric sepsis diagnosis. Our study could provide potential peripheral blood diagnostic candidate genes for pediatric sepsis patients.

The roles of FLOT1 in human diseases (Review)

FLOT1, a scaffold protein of lipid rafts, is involved in several biological processes, including lipid raft protein‑-dependent or clathrin‑independent endocytosis, and the formation of hippocampal synapses, amongst others. Increasing evidence has shown that FLOT1 can function as both a cancer promoter and cancer suppressor dependent on the type of cancer. FLOT1 can affect the occurrence and development of several types of cancer by affecting epithelial‑mesenchymal transition, proliferation of cancer cells, and relevant signaling pathways, and is regulated by long intergenic non‑coding RNAs or microRNAs. In the nervous system, overexpression or abnormally low expression of FLOT1 may lead to the occurrence of neurological diseases, such as Alzheimer's disease, Parkinson's disease, major depressive disorder and other diseases. Additionally, it is also associated with dilated cardiomyopathy, pathogenic microbial infection, diabetes‑related diseases, and gynecological diseases, amongst other diseases. In the present review, the structure and localization of FLOT1, as well as the physiological processes it is involved in are reviewed, and then the upstream and downstream regulation of FLOT1 in human disease, particularly in different types of cancer and neurological diseases are discussed, with a focus on potentially targeting FLOT1 for the clinical treatment of several diseases.

Identification of cuproptosis-related asthma diagnostic genes by WGCNA analysis and machine learning

OBJECTIVE

Cuproptosis is the latest novel form of cell death. However, the relationship between asthma and cuproptosis is not fully understood.

METHODS

In this study, we screened differentially expressed cuproptosis-related genes from the Gene Expression Omnibus (GEO) database and performed immune infiltration analysis. Subsequently, patients with asthma were typed and analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was performed to calculate the module-trait correlations, and the hub genes of the intersection were taken to construct machine learning (XGB, SVM, RF, GLM). Finally, we used TGF-β to establish a BEAS-2B asthma model to observe the expression levels of hub genes.

RESULTS

Six cuproptosis-related genes were obtained. Immune-infiltration analysis shows that cuproptosis-related genes are associated with a variety of biological functions. We classified asthma patients into two subtypes based on the expression of cuproptosis-related genes and found significant Gene Ontology (GO) and immune function differences between the different subtypes. WGCNA selected 2 significant modules associated with disease features and typing. Finally, we identified TRIM25, DYSF, NCF4, ABTB1, CXCR1 as asthma biomarkers by taking the intersection of the hub genes of the 2 modules and constructing a 5-genes signature, which nomograph, decision curve analysis (DCA) and calibration curves, receiver operating characteristic curve (ROC) showed high efficiency in diagnosing the probability of survival of asthma patients. Finally, in vitro experiments have shown that DYSF and CXCR1 expression is up expressed in asthma.

CONCLUSIONS

Our study provides further directions for studying the molecular mechanism of asthma.

Autophagy-related LncRNA PRDM10-DT responds to UVB radiation in keratinocytes

Ultraviolet (UV)-B radiation is a major environmental risk factor that is responsible for the development and progression of many skin disorders. Autophagy is the process of degradation and recycling of damaged cytoplasmic organelles, macromolecular aggregates, and long-lived proteins. Previously, we found that the autophagy inducer apigenin restored UVB-impaired autophagy and the cellular response by downregulating the expression of autophagy-related genes such as ATG5. To explore long noncoding RNAs (lncRNAs) involved in regulating these autophagy-related genes, in this study, we assessed the expression profiles of lncRNAs and mRNAs using a microarray in human epidermal keratinocytes (HEKs) treated with or without apigenin after UVB radiation. The expression levels of 80 selected autophagy-related genes and related lncRNAs were confirmed by quantitative real-time polymerase chain reaction (qRT‒PCR). The lncRNA PRDM10-DT was proposed to regulate IRGM based on the ceRNA and coexpression pattern and was demonstrated to be involved in autophagy regulation, proliferation and migration of HEKs by qRT‒PCR, Western blotting, colony formation and scratch wound assays, respectively. These findings suggest an autophagy-related lncRNA in response to UVB radiation that promotes the proliferation and migration of HEKs through inducing autophagy by competing microRNAs for IRGM.

LncRNA RARA-AS1 could serve as a novel prognostic biomarker in pan-cancer and promote proliferation and migration in glioblastoma

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cancer progression and are potential biomarkers for diagnosis and treatment. This study investigates the role of RARA Antisense RNA 1 (RARA-AS1) in cancer and its implications for diagnosis and treatment. Various bioinformatics tools were conducted to analyze the expression patterns, immune-related functions, methylation, and gene expression correlations of RARA-AS1, mainly including the comparisons of different subgroups and correlation analyses between RARA-AS1 expression and other factors. Furthermore, we used short hairpin RNA to perform knockdown experiments, investigating the effects of RARA-AS1 on cell proliferation, invasion, and migration in glioblastoma. Our results revealed that RARA-AS1 has distinct expression patterns in different cancers and exhibits notable correlation with prognosis. Additionally, RARA-AS1 is highly correlated with certain immune checkpoints and mismatch repair genes, indicating its potential role in immune infiltration and related immunotherapy. Further analysis identified potential effective drugs for RARA-AS1 and demonstrated its potential RNA binding protein (RBP) mechanism in glioblastoma. Besides, a series of functional experiments indicated inhibiting RARA-AS1 could decrease cell proliferation, invasion, and migration of glioblastoma cell lines. Finally, RARA-AS1 could act as an independent prognostic factor for glioblastoma patients and may serve as a promising therapeutic target. All in all, Our study provides a comprehensive understanding of the functions and implications of RARA-AS1 in pan-cancer, highlighting it as a promising biomarker for survival. It is also an independent risk factor affecting prognosis in glioblastoma and an important factor affecting proliferation and migration in glioblastoma, setting the stage for further mechanistic investigations.

Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm

Sepsis, a disease caused by severe infection, has a high mortality rate. At present, there is a lack of reliable algorithmic models for biomarker mining and diagnostic model construction for sepsis. Programmed cell death (PCD) has been shown to play a vital role in disease occurrence and progression, and different PCD-related genes have the potential to be targeted for the treatment of sepsis. In this paper, we analyzed PCD-related genes in sepsis. Implicated PCD processes include apoptosis, necroptosis, ferroptosis, pyroptosis, netotic cell death, entotic cell death, lysosome-dependent cell death, parthanatos, autophagy-dependent cell death, oxeiptosis, and alkaliptosis. We screened for diagnostic-related genes and constructed models for diagnosing sepsis using multiple machine-learning models. In addition, the immune landscape of sepsis was analyzed based on the diagnosis-related genes that were obtained. In this paper, 10 diagnosis-related genes were screened for using machine learning algorithms, and diagnostic models were constructed. The diagnostic model was validated in the internal and external test sets, and the Area Under Curve (AUC) reached 0.7951 in the internal test set and 0.9627 in the external test set. Furthermore, we verified the diagnostic gene via a qPCR experiment. The diagnostic-related genes and diagnostic genes obtained in this paper can be utilized as a reference for clinical sepsis diagnosis. The results of this study can act as a reference for the clinical diagnosis of sepsis and for target discovery for potential therapeutic drugs.

Diagnostic and prognostic value of autophagy-related key genes in sepsis and potential correlation with immune cell signatures

Background: Autophagy is involved in the pathophysiological process of sepsis. This study was designed to identify autophagy-related key genes in sepsis, analyze their correlation with immune cell signatures, and search for new diagnostic and prognostic biomarkers. Methods: Whole blood RNA datasets GSE65682, GSE134347, and GSE134358 were downloaded and processed. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify autophagy-related key genes in sepsis. Then, key genes were analyzed by functional enrichment, protein-protein interaction (PPI), transcription factor (TF)-gene and competing endogenous RNA (ceRNA) network analysis. Subsequently, key genes with diagnostic efficiency and prognostic value were identified by receiver operating characteristic (ROC) curves and survival analysis respectively. The signatures of immune cells were estimated using CIBERSORT algorithm. The correlation between significantly different immune cell signatures and key genes was assessed by correlation analysis. Finally, key genes with both diagnostic and prognostic value were verified by RT-qPCR. Results: 14 autophagy-related key genes were identified and their TF-gene and ceRNA regulatory networks were constructed. Among the key genes, 11 genes (ATIC, BCL2, EEF2, EIF2AK3, HSPA8, IKBKB, NLRC4, PARP1, PRKCQ, SH3GLB1, and WIPI1) had diagnostic efficiency (AUC > 0.90) and 5 genes (CAPN2, IKBKB, PRKCQ, SH3GLB1 and WIPI1) were associated with survival prognosis (p-value < 0.05). IKBKB, PRKCQ, SH3GLB1 and WIPI1 had both diagnostic and prognostic value, and their expression were verified by RT-qPCR. Analysis of immune cell signatures showed that the abundance of neutrophil, monocyte, M0 macrophage, gamma delta T cell, activated mast cell and M1 macrophage subtypes increased in the sepsis group, while the abundance of resting NK cell, resting memory CD4+ T cell, CD8+ T cell, naive B cell and resting dendritic cell subtypes decreased. Most of the key genes correlated with the predicted frequencies of CD8+ T cells, resting memory CD4+ T cells, M1 macrophages and naive B cells. Conclusion: We identified autophagy-related key genes with diagnostic and prognostic value in sepsis and discovered associations between key genes and immune cell signatures. This work may provide new directions for the discovery of promising biomarkers for sepsis.

Epigenome-wide methylation study identified two novel CpGs associated with T2DM risk and a network of co-methylated CpGs capable of patient's classifications

Prevention of Type 2 diabetes mellitus (T2DM) pandemic needs markers that can precisely predict the disease risk in an individual. Alterations in DNA methylations due to exposure towards environmental risk factors are widely sought markers for T2DM risk prediction. To identify such individual DNA methylation signatures and their effect on disease risk, we performed an epigenome-wide association study (EWAS) in 844 Indian individuals of Indo-European origin. We identified and validated methylation alterations at two novel CpG sites in MIR1287 (cg01178710) and EDN2-SCMH1 (cg04673737) genes associated with T2DM risk at the epigenome-wide-significance-level (P < 1.2 × 10-7). Further, we also replicated the association of two known CpG sites in TXNIP, and CPT1A in the Indian population. With 535 EWAS significant CpGs (P < 1.2 × 10-7) identified in the discovery phase samples, we created a co-methylation network using weighted correlation network analysis and identified four modules among the CpGs. We observed that methylation of one of the module associates with T2DM risk factors (e.g. BMI, insulin and C-peptide) and can be used as markers to segregate T2DM patients with good glycemic control (e.g. low HbA1c) and dyslipidemia (low HDL and high TG) from the other patients. Additionally, an intronic SNP (rs6503650) in the JUP gene, a member of the same module, associated with methylation at all the 14 hub CpG sites of that module as methQTL. Our network-assisted EWAS is the first to systematically explore DNA methylation variations conferring risks to T2DM in Indians and use the identified risk CpG sites for patient segregation with different clinical outcomes. These findings can be useful for better stratification of patients to improve the clinical management and treatment effects.

Down-regulation of Jun induces senescence through destabilizing chromatin in osteoarthritis chondrocytes

OBJECTIVE

Osteoarthritis (OA) is the most common degenerative joint disease leading to disability worldwide. Cellular senescence is considered to be a fundamental pathogenic mechanism in the development of OA and has attracted increasing attention. However, regulatory mechanisms underlying chondrocyte senescence in OA remain unclear.

METHODS

Bioinformatic methods were used to screen key genes. Immunohistochemistry and the quantitative reverse transcription polymerase chain reaction were used to evaluate gene expression. RNA intervention experiments were performed to explore the functions of key genes.

RESULTS

We used 494 aging-associated genes provided by the Aging Atlas to identify the co-expression modules associated with age and OA. Thirty age-associated differentially expressed genes (ASDEGs) were identified. Using cytoHubba in Cytoscape, we identified Jun as the hub-ASDEG for OA chondrocytes. We confirmed the downregulation of Jun in OA rats and senescent chondrocytes by immunohistochemistry and quantitative reverse transcription polymerase chain reaction, respectively. Inhibition of proliferation and accelerated senescence were observed in chondrocytes treated with siRNA against Jun. Mechanistically, we observed micronuclei formation and reduced expression of H3K9me3 and heterochromatin protein 1gamma in siRNA-Jun-treated chondrocytes, indicating that destabilization of chromatin occurred during this treatment.

CONCLUSION

Jun plays a crucial role in OA development and causes senescence by destabilizing chromatin in chondrocytes. These findings provide new insights into OA progression and suggest promising therapeutic targets.

LC-MS/MS-Based Serum Metabolomics and Transcriptome Analyses for the Mechanism of Augmented Renal Clearance

Augmented Renal Clearance (ARC) refers to the increased renal clearance of circulating solute in critically ill patients. In this study, the analytical research method of transcriptomics combined with metabolomics was used to study the pathogenesis of ARC at the transcriptional and metabolic levels. In transcriptomics, 534 samples from 5 datasets in the Gene Expression Omnibus database were analyzed and 834 differential genes associated with ARC were obtained. In metabolomics, we used Ultra-Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry to determine the non-targeted metabolites of 102 samples after matching propensity scores, and obtained 45 differential metabolites associated with ARC. The results of the combined analysis showed that purine metabolism, arginine biosynthesis, and arachidonic acid metabolism were changed in patients with ARC. We speculate that the occurrence of ARC may be related to the alteration of renal blood perfusion by LTB4R, ARG1, ALOX5, arginine and prostaglandins E2 through inflammatory response, as well as the effects of CA4, PFKFB2, PFKFB3, PRKACB, NMDAR, glutamate and cAMP on renal capillary wall permeability.

Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis

Background

To identify differentially expressed lipid metabolism-related genes (DE-LMRGs) responsible for immune dysfunction in sepsis.

Methods

The lipid metabolism-related hub genes were screened using machine learning algorithms, and the immune cell infiltration of these hub genes were assessed by CIBERSORT and Single-sample GSEA. Next, the immune function of these hub genes at the single-cell level were validated by comparing multiregional immune landscapes between septic patients (SP) and healthy control (HC). Then, the support vector machine-recursive feature elimination (SVM-RFE) algorithm was conducted to compare the significantly altered metabolites critical to hub genes between SP and HC. Furthermore, the role of the key hub gene was verified in sepsis rats and LPS-induced cardiomyocytes, respectively.

Results

A total of 508 DE-LMRGs were identified between SP and HC, and 5 hub genes relevant to lipid metabolism (MAPK14, EPHX2, BMX, FCER1A, and PAFAH2) were screened. Then, we found an immunosuppressive microenvironment in sepsis. The role of hub genes in immune cells was further confirmed by the single-cell RNA landscape. Moreover, significantly altered metabolites were mainly enriched in lipid metabolism-related signaling pathways and were associated with MAPK14. Finally, inhibiting MAPK14 decreased the levels of inflammatory cytokines and improved the survival and myocardial injury of sepsis.

Conclusion

The lipid metabolism-related hub genes may have great potential in prognosis prediction and precise treatment for sepsis patients.

Identification and Evaluation of Hub Long Noncoding RNAs and mRNAs in High Fat Diet Induced Liver Steatosis

Nonalcoholic fatty liver disease (NAFLD) is considered the most prevalent chronic liver disease, but the understanding of the mechanism of NAFLD is still limited. The aim of our study was to explore hub lncRNAs and mRNAs and pathological processes in high-fat diet (HFD)-induced and lycopene-intervened liver steatosis. We analyzed the gene profiles in the GSE146627 dataset from the Gene Expression Omnibus (GEO) database to identify differentially expressed lncRNAs and mRNAs, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were utilized for functional enrichment analysis. We found that the turquoise, blue, brown, yellow, green, and black modules were significantly correlated with NAFLD. Functional enrichment analysis revealed that some hub lncRNAs (Smarca2, Tacc1, Flywch1, and Mef2c) might be involved in the regulation of the inflammatory and metabolic pathways (such as TNF signaling, metabolic, mTOR signaling, MAPK signaling, and p53 signaling pathways) in NAFLD. The establishment of an NAFLD mouse model confirmed that lycopene supply attenuated hepatic steatosis in HFD-induced NAFLD. Our analysis revealed that the inflammatory and metabolic pathways may be crucially involved in the pathogenesis of NAFLD, and hub lncRNAs provide novel biomarkers, therapeutic ideas, and targets for NAFLD. Moreover, lycopene has the potential to be a phytochemical for the prevention of HFD-induced liver steatosis.

Diagnostic and predictive values of pyroptosis-related genes in sepsis

Background

Sepsis is an organ dysfunction syndrome caused by the body's dysregulated response to infection. Yet, due to the heterogeneity of this disease process, the diagnosis and definition of sepsis is a critical issue in clinical work. Existing methods for early diagnosis of sepsis have low specificity.

Aims

This study evaluated the diagnostic and predictive values of pyroptosis-related genes in normal and sepsis patients and their role in the immune microenvironment using multiple bioinformatics analyses and machine-learning methods.

Methods

Pediatric sepsis microarray datasets were screened from the GEO database and the differentially expressed genes (DEGs) associated with pyroptosis were analyzed. DEGs were then subjected to multiple bioinformatics analyses. The differential immune landscape between sepsis and healthy controls was explored by screening diagnostic genes using various machine-learning models. Also, the diagnostic value of these diagnosis-related genes in sepsis (miRNAs that have regulatory relationships with genes and related drugs that have regulatory relationships) were analyzed in the internal test set and external test.

Results

Eight genes (CLEC5A, MALT1, NAIP, NLRC4, SERPINB1, SIRT1, STAT3, and TLR2) related to sepsis diagnosis were screened by multiple machine learning algorithms. The CIBERSORT algorithm confirmed that these genes were significantly correlated with the infiltration abundance of some immune cells and immune checkpoint sites (all P<0.05). SIRT1, STAT3, and TLR2 were identified by the DGIdb database as potentially regulated by multiple drugs. Finally, 7 genes were verified to have significantly different expressions between the sepsis group and the control group (P<0.05).

Conclusion

The pyroptosis-related genes identified and verified in this study may provide a useful reference for the prediction and assessment of sepsis.

Analysis of bulk RNA-seq data from sepsis patients reveals sepsis-associated lncRNAs and targeted cell death-related genes contributing to immune microenvironment regulation

Background

Sepsis is a life-threatening organ dysfunction syndrome that leads to the massive death of immune cells. Long non-coding RNAs (lncRNAs) have been reported to exert key regulatory roles in cells. However, it is unclear how lncRNAs regulate the survival of immune cells in the occurrence and development of sepsis.

Methods

In this study, we used blood whole transcriptome sequencing data (RNA-seq) from normal controls (Hlty) and patients with uncomplicated infection (Inf1 P), sepsis (Seps P), and septic shock (Shock P), to investigate the fraction changes of immune cell types, expression pattern of cell death-related genes, as well as differentially expressed lncRNAs. Association network among these factors was constructed to screen out essential immune cell types, lncRNAs and their potential targets. Finally, the expression of lncRNAs and cell death genes in sepsis patients were validated by qRT-PCR.

Results

In this study, we found fifteen immune cell types showed significant fraction difference between Hlty and three patient groups. The expression pattern of cell death-related genes was also dysregulated in Hlty compared with patient groups. Co-expression network analysis identified a key turquoise module that was associated with the fraction changes of immune cells. We then identified differentially expressed lncRNAs and their potential targets that were tightly associated with the immune cell dysregulation in sepsis. Seven lncRNAs, including LINC00861, LINC01278, RARA-AS1, RP11-156P1.3, RP11-264B17.3, RP11-284N8.3 and XLOC_011309, as well as their co-expressed cell death genes, were finally identified, and we validated two lncRNAs (LINC00861 and LINC01278) and four mRNA targets using qRT-PCR in sepsis samples.

Conclusion

The global analysis of cell death-related genes in the occurrence and development of sepsis was carried out for the first time, and its expression regulation mode was displayed. The expression pattern of sepsis-associated lncRNAs were analyzed and identified, and the lncRNAs were significantly related to the change of immune cell proportion. We highlight the important roles of lncRNAs and their potential targets in the regulation of immune cell fraction changes during sepsis progression. The identified lncRNAs and their target genes may become new biomarkers and therapeutic targets of sepsis.

Transcription Factors and ncRNAs Associated with CYP3A Expression in Human Liver and Small Intestine Assessed with Weighted Gene Co-Expression Network Analysis

CYP3A4, CYP3A5, and CYP3A7, which are located in a multigene locus (CYP3A), play crucial roles in drug metabolism. To understand the highly variable hepatic expression of CYP3As, regulatory network analyses have focused on transcription factors (TFs). Since long non-coding RNAs (lncRNAs) likely contribute to such networks, we assessed the regulatory effects of both TFs and lncRNAs on CYP3A expression in the human liver and small intestine, main organs of CYP3A expression. Using weighted gene co-expression network analysis (WGCNA) of GTEx v8 RNA expression data and multiple stepwise regression analysis, we constructed TF-lncRNA-CYP3A co-expression networks. Multiple lncRNAs and TFs displayed robust associations with CYP3A expression that differed between liver and small intestines (LINC02499, HNF4A-AS1, AC027682.6, LOC102724153, and RP11-503C24.6), indicating that lncRNAs contribute to variance in CYP3A expression in both organs. Of these, HNF4A-AS1 had been experimentally demonstrated to affect CYP3A expression. Incorporating ncRNAs into CYP3A expression regulatory network revealed additional candidate TFs associated with CYP3A expression. These results serve as a guide for experimental studies on lncRNA-TF regulation of CYP3A expression in the liver and small intestines.

Comparative transcriptomics reveals unique pine wood decay strategies in the Sparassis latifolia

Sparassis latifolia is a valuable edible mushroom, growing on fresh pine wood sawdust substrate. However, the mechanistic bases are poorly understood. The gene expression profiles of S. latifolia were analyzed from submerged cultures with fresh pine wood sawdust substrate for different time (0 h, 1 h, 6 h, 1 day, 5 days, and 10 days, respectively). The total number of differentially expressed genes (DEGs) identified under pine sawdust inducing was 2,659 compared to 0 h (CK). And 1,073, 520, 385, 424, and 257 DEGs were identified at the five time points, respectively. There were 34 genes in common at all inoculated time points, including FAD/NAD(P)-binding domain-containing protein, glucose methanol choline (GMC) oxidoreductase, flavin-containing monooxygenase, and taurine catabolism dioxygenase. Weighted gene co-expression analysis (WGCNA) was then used to compare the molecular characteristics among the groups and identified that the blue module had the highest correlation with the time induced by pine wood sawdust. There were 102 DEGs out of 125 genes in the blue model, which were most enriched in nitronate monooxygenase activity, dioxygenase activity, and oxidation-reduction process GO terms (p < 0.05), and peroxisome in KEGG pathway. This may provide clues into mechanisms that S. latifolia can grow on fresh pine wood sawdust substrate.

Integrated analysis of lncRNA-mediated ceRNA network involved in immune regulation in the spleen of Meishan piglets

Meishan pigs are a famous local pig breed in China, with high fertility and early sexual maturity, and stronger immunity compared to other breeds. The spleen is the largest lymphoid organ in pigs and performs essential functions, such as those relating to immunity and haematopoiesis. The invasion of many pathogenic microorganisms in pigs is associated with spleen damage. Long non-coding RNAs participate in a broad range of biological processes and have been demonstrated to be associated with splenic immune regulation. However, the expression network of mRNAs and lncRNAs in the spleen of Meishan pigs remains unclear. This study collected spleen tissues from Meishan piglets at three different ages as a model, and mRNA and lncRNA transcripts were profiled for each sample. Additionally, 1,806 differential mRNAs and 319 differential lncRNAs were identified. A complicated interaction between mRNAs and lncRNAs was identified via WGCNA, demonstrating that lncRNAs are a crucial regulatory component in mRNA. The results show that the modules black and red have similar mRNA and lncRNA transcription patterns and are mainly involved in the process of the immune defense response. The core genes (DHX58 and IFIT1) and key lncRNAs (TCONS-00002102 and TCONS-00012474) of piglet spleen tissue were screened using the ceRNA network. The expression of these genes is related to the immune response of pigs. Our research may contribute to a further understanding of mRNA and lncRNA expression in the spleen of piglets, and provide new ideas to improve the disease resistance of piglets.

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

Weighted Gene Coexpression Network Analysis Identified IL2/STAT5 Signaling Pathway as an Important Determinant of Peri-Implantitis

Objective

Peri-implantitis (PI) is one of the main reasons for dental implant failure. Until now, the etiology and pathogenesis of PI remain unclear.

Methods

In this study, we used differentially expressed genes (DEGs) analysis and gene function enrichment analysis to assess the expression profile of peri-implant bone tissue and gingiva in PI public data from the Gene Expression Omnibus (GEO) database. Then, we used gingival tissues from patients with PI and healthy individual to construct gene coexpression networks to reveal the biological functions of the genes in PI using RNA sequencing data. Afterward, key gene modules were selected to reveal the critical biological process or signaling pathway using Hallmark's gene enrichment and expression analysis of the related pathway members in PI.

Results

DEGs were enriched in the formation of cellular responses to external stimuli in bone tissue. Cytokine production, lymphocyte activation, immune response-regulating signaling pathway, and blood vessel development were the top GO biology process or pathways of the DEGs in gingival tissue. Weighted gene coexpression network analysis (WGCNA) of RNA-seq data was used to assess the results of correlation analysis between modules and traits and correlation analysis between modules and functions. kMEpurple, kMEgreen, and kMEred modules were selected as the key gene modules. Signaling pathways and gene expression analysis were performed on selected modules, such as IL2/STAT5 signaling pathway, TNFα signaling pathway via NFκB, and angiogenesis were enriched in kMEpurple module. Hedgehog signaling pathway, Wnt β-catenin signaling pathway, and IL2/STAT5 signaling pathway were enriched in kMEgreen module. Peroxisome, IL2/STAT5 signaling pathway, and epithelial-mesenchymal transformation process were enriched in kMEred module. All the enrichment results of key modules contained IL2/STAT5 signaling pathway. Conclusion. Differential gene and enrichment analysis based on public data showed differences in gene expression patterns and biological process between bone and gingival tissues in PI. This spatial-temporal heterogeneity is reflected in the formation of cellular responses to external stimuli, which was enriched in bone tissue, but cytokine production, lymphocyte activation, immune response regulating signaling pathway, and blood vessel development were enriched in gingival tissue. WGCNA and Hallmark gene sets enrichment analysis of the gingival tissue expression profile and showed that IL2-mediated activation of immune cells could be a critical mechanism in PI. As a new clinical treatment alternative, we suggest that IL2/STAT5 pathway blockers could be helpful in the treatment of PI.

Identification of crucial lncRNAs and mRNAs in liver regeneration after portal vein ligation through weighted gene correlation network analysis

BACKGROUND

Portal vein ligation (PVL)-induced liver hypertrophy increases future liver remnant (FLR) volume and improves resectability of large hepatic carcinoma. However, the molecular mechanism by which PVL facilitates liver hypertrophy remains poorly understood.

METHODS

To gain mechanistic insight, we established a rat PVL model and carried out a comprehensive transcriptome analyses of hepatic lobes preserving portal blood supply at 0, 1, 7, and 14-day after PVL. The differentially expressed (DE) long-non coding RNAs (lncRNAs) and mRNAs were applied to conduct weighted gene co-expression network analysis (WGCNA). LncRNA-mRNA co-expression network was constructed in the most significant module. The modules and genes associated with PVL-induced liver hypertrophy were assessed through quantitative real-time PCR.

RESULTS

A total of 4213 DElncRNAs and 6809 DEmRNAs probesets, identified by transcriptome analyses, were used to carry out WGCNA, by which 10 modules were generated. The largest and most significant module (marked in black_M6) was selected for further analysis. Gene Ontology (GO) analysis of the module exhibited several key biological processes associated with liver regeneration such as complement activation, IL-6 production, Wnt signaling pathway, autophagy, etc. Sixteen mRNAs (Notch1, Grb2, IL-4, Cops4, Stxbp1, Khdrbs2, Hdac2, Gnb3, Gng10, Tlr2, Sod1, Gosr2, Rbbp5, Map3k3, Golga2, and Rev3l) and ten lncRNAs (BC092620, AB190508, EF076772, BC088302, BC158675, BC100646, BC089934, L20987, BC091187, and M23890) were identified as hub genes in accordance with gene significance value, module membership value, protein-protein interaction (PPI) and lncRNA-mRNA co-expression network. Furthermore, the overexpression of 3 mRNAs (Notch1, Grb2 and IL-4) and 4 lncRNAs (BC089934, EF076772, BC092620, and BC088302) was validated in hypertrophic liver lobe tissues from PVL rats and patients undergoing hepatectomy after portal vein embolization (PVE).

CONCLUSIONS

Microarray and WGCNA analysis revealed that the 3 mRNAs (Notch1, Grb2 and IL-4) and the 4 lncRNAs (BC089934, EF076772, BC092620 and BC088302) may be promising targets for accelerating liver regeneration before extensive hepatectomy.

Diagnostic model based on bioinformatics and machine learning to distinguish Kawasaki disease using multiple datasets

Background

Kawasaki disease (KD), characterized by systemic vasculitis, is the leading cause of acquired heart disease in children. Herein, we developed a diagnostic model, with some prognosis ability, to help distinguish children with KD.

Methods

Gene expression datasets were downloaded from Gene Expression Omnibus (GEO), and gene sets with a potential pathogenic mechanism in KD were identified using differential expressed gene (DEG) screening, pathway enrichment analysis, random forest (RF) screening, and artificial neural network (ANN) construction.

Results

We extracted 2,017 DEGs (1,130 with upregulated and 887 with downregulated expression) from GEO. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses showed that the DEGs were significantly enriched in innate/adaptive immune response-related processes. Subsequently, the results of weighted gene co-expression network analysis and DEG screening were combined and, using RF and ANN, a model with eight genes (VPS9D1, CACNA1E, SH3GLB1, RAB32, ADM, GYG1, PGS1, and HIST2H2AC) was constructed. Classification results of the new model for KD diagnosis showed excellent performance for different datasets, including those of patients with KD, convalescents, and healthy individuals, with area under the curve values of 1, 0.945, and 0.95, respectively.

Conclusions

We used machine learning methods to construct and validate a diagnostic model using multiple bioinformatic datasets, and identified molecules expected to serve as new biomarkers for or therapeutic targets in KD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12887-022-03557-y.

Involvement of plasma lncRNA GSEC in sepsis discrimination and prognosis, and its correlation with macrophage cell inflammation and proliferation

BACKGROUND

Despite the dysregulation and function of G-quadruplex-forming sequence containing lncRNA (GSEC) have been widely reported in human cancers, there are few available data revealing its role in sepsis.

OBJECTIVE

To assess the expression and function of GSEC in the development of sepsis and its potential molecular mechanism.

MATERIALS AND METHODS

A total of 78 sepsis patients, 55 non-sepsis intensive care unit patients, and 42 healthy individuals were enrolled in this study. The expression of GSEC was evaluated in plasma and macrophage cells with polymerase chain reaction. The inflammation response of sepsis patients and macrophage cells was analyzed with an enzyme-linked immunosorbent assay. The diagnostic and prognostic value of GSEC in sepsis patients were estimated by receiver operator curve (ROC) and Cox analysis. The molecular mechanism underlying the function of GSEC was investigated in RAW264.7 cell with luciferase reporter assay and cell transfection.

RESULTS

Significant upregulation of GSEC was observed in sepsis patients' plasma, which could discriminate sepsis patients from healthy and non-sepsis individuals. Upregulation of GSEC was positively correlated with inflammation cytokine levels and adverse prognosis of sepsis patients. In vitro, GSEC was found to modulate the expression level of miR-873-3p, which mediated the regulatory effect of GSEC on the inflammation and proliferation of RAW264.7.

CONCLUSION

Upregulated GSEC could serve as a biomarker of sepsis pathogenesis and development. GSEC regulates the inflammation and proliferation of macrophage cells through modulating miR-873-3p.

Identification of a Four-Gene Signature for Diagnosing Paediatric Sepsis

Aim

Early diagnosis of paediatric sepsis is crucial for the proper treatment of children and reduction of hospitalization and mortality. Biomarkers are a convenient and effective method for diagnosing any disease. However, huge differences among the studies reporting biomarkers for diagnosing sepsis have limited their clinical application. Therefore, in this study, we aimed to evaluate the diagnostic value of key genes involved in paediatric sepsis based on the data of the Gene Expression Omnibus database.

Methods

We used the GSE119217 dataset to identify differentially expressed genes (DEGs) between patients with and without paediatric sepsis. The most relevant gene modules of paediatric sepsis were screened through the weighted gene coexpression network analysis (WGCNA). Common genes (CGs) were found between DEGs and WGCNA. Genes with a potential diagnostic value in paediatric sepsis were selected from the CGs using least absolute shrinkage and selection operator regression and support vector machine recursive feature elimination. The principal component analysis, receiver operating characteristic curves, and C-index were used to verify the diagnostic value of the identified genes in six other independent sepsis datasets. Subsequently, a meta-analysis of the selected genes was performed to evaluate the value of these genes as biomarkers in paediatric sepsis.

Results

A total of 41 CGs were selected from the GSE119217 dataset. A four-gene signature composed of ANXA3, CD177, GRAMD1C, and TIGD3 effectively distinguished patients with paediatric sepsis from those in the control group. The signature was verified using six other independent datasets. In addition, the meta-analysis results showed that the pooled sensitivity, specificity, and area under the curve values were 1.00, 0.98, and 1.00, respectively.

Conclusion

The four-gene signature can be used as new biomarkers to distinguish patients with paediatric sepsis from healthy individuals.

Effect of circRNA_FOXO3 rs12196996 polymorphism and FOXO3 rs2232365 polymorphism on survival rate and severity of intensive care unit-acquired sepsis

The expression of circRNA_FOXO3 was found to be positively associated with the expression of Forkhead Box O3 (FOXO3), which is targeted and regulated by miR-23a. Polymorphisms in rs12196996 and rs2232365 have been reported in various diseases. In this study, we recruited intensive care unit (ICU)-acquired sepsis patients and grouped them according to their genotypes of rs12196996 and rs2232365. Quantitative real-time PCR was performed to analyze the expression of circRNA_FOXO3, FOXO3 mRNA, and miR-23a. ELISA was carried out to evaluate the abundance of cytokines and luciferase assay was used to explore the inhibitory role of miR-23a on circRNA_FOXO3 and FOXO3. Accordingly, we found that rs12196996 GG and rs2232365 AA were significantly correlated with prolonged survival of ICU-acquired sepsis patients. Rs12196996 GG and rs2232365 AA were also correlated with increased level of miR-23a, IL-10 and decreased level of TNF, IL-2, IFN, IL-6 and IL-1β in the peripheral blood cell samples of patients with ICU-acquired sepsis. The luciferase activity of wild-type (WT) circRNA_FOXO3 and FOXO3 were severely reduced by miR-23a. MiR-23a precursors could effectively suppress the expression of circRNA_FOXO3 and FOXO3 in the cells. Moreover, LPS-induced cell viability loss and dysregulation of cytokines were effectively restored by the knockdown of FOXO3 or circRNA_FOXO3 siRNA in the cells. This study revealed that the minor allele of rs12196996 polymorphism and rs2232365 polymorphism collaboratively contributed to the increased survival and suppressed severity of ICU-acquired sepsis.

LPIN1 Is a Regulatory Factor Associated With Immune Response and Inflammation in Sepsis

Objectives

Sepsis is a clinical disease that is typically treated in the intensive care unit, and the complex pathophysiology under this disease has not been thoroughly understood. While ferroptosis is involved in inflammation and infection, its effect in sepsis is still unknown. The study aimed to identify ferroptosis-related genes in sepsis, providing translational potential therapeutic targets.

Methods

The dataset GSE65682 was used to download the sample source from the Gene Expression Omnibus (GEO) database. Consensus weighted gene co-expression network analysis (WGCNA) was performed to find suspected modules of sepsis. The differentially expressed genes (DEGs) most significantly associated with mortality were intersected with those altered by lipopolysaccharide (LPS) treatment and were further analyzed for the identification of main pathways of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The related pathway markers were further verified by qPCR.

Results

A total of 802 blood samples with sepsis were included for WGCNA, which identified 21 modules. Intersected with ferroptosis databases and LPS treatment groups, we identified two ferroptosis-related genes: PEBP1 and LPIN1. Only LPIN1 contributes to a poor outcome. Then, 205 DEGs were further identified according to the high or low LPIN1 expression. Among them, we constructed a gene regulatory network with several transcriptional factors using the NetworkAnalyst online tool and identified that these genes mostly correlate with inflammation and immune response. The immune infiltration analysis showed that lower expression of LPIN1 was related to macrophage infiltration and could be an independent predictor factor of the survival status in sepsis patients. Meanwhile, the multivariate Cox analysis showed that LPIN1 had a significant correlation with survival that was further verified by in vitro and in vivo experiments.

Conclusion

In conclusion, LPIN1 could become a reliable biomarker for patient survival in sepsis, which is associated with immune and inflammation status.

The long non-coding RNA ASMTL-AS1 promotes hepatocellular carcinoma progression by sponging miR-1343-3p that suppresses LAMC1 (laminin subunit gamma 1)

Long non-coding RNAs (lncRNAs) are critical regulators of hepatocellular carcinoma (HCC) carcinogenesis and development. We aimed to identify the function of the lncRNA ASMTL-AS1 during HCC malignancy. The expression of ASMTL-AS1, miR-1343-3p, and LAMC1 (laminin subunit gamma 1) was assessed in HCC tissues and cells. Cell Counting Kit-8 (CCK8) and Transwell migration assays were performed to determine the effect of ASMTL-AS1 on HCC cell proliferation and migration. Cell apoptosis was identified by detecting Bax and Bcl-2 protein expression using Western blotting, and a xenograft assay was performed to investigate tumor growth in vivo. The interplay between miR-1343-3p and ASMTL-AS1 or LAMC1 was verified through luciferase reporter and RNA immunoprecipitation assays. ASMTL-AS1 and LAMC1 were highly expressed in HCC tissues and cells, whereas miR-1343-3p showed low expression. Clinically, miR-1343-3p expression in HCC tissues showed a negative correlation with ASMTL-AS1 or LAMC1 expression. Functional assays demonstrated that ASMTL-AS1 silencing suppressed HCC cell proliferation and migration and increased cell apoptosis. More interestingly, ASMTL-AS1 sponged miR-1343-3p and miR-1343-3p to target the 3'-UTR of LAMC1, thereby interfering with the malignant behavior of HCC cells. In conclusion, ASMTL-AS1 acts as a carcinogen in HCC through competing endogenous RNA (ceRNA) activity in the miR-1343-3p/LAMC1 axis. Our findings demonstrate that regulating ASMTL-AS1/miR-1343-3p/LAMC1-mediated HCC cell malignancy might be an effective method to interfere with HCC progression.

Long noncoding RNAs Colorectal Neoplasia Differentially Expressed and taurine-upregulated gene 1 are downregulated in sepsis and positively regulate each other to suppress the apoptosis of cardiomyocytes

Long noncoding RNAs (lncRNAs) Colorectal Neoplasia Differentially Expressed (CRNDE) and taurine-upregulated gene 1 (TUG1) play similar roles in sepsis, indicating the existence of the crosstalk between them. Sepsis is a major cause of injuries in heart, which are related to high mortality rates. This study was therefore carried out to analyze the potential crosstalk between CRNDE and TUG1 in sepsis, with a focus on sepsis-induced cell apoptosis in heart. Expression of CRNDE and TUG1 was analyzed with RT-qPCR. Correlations between them were analyzed by Pearson’s correlation coefficient. CRNDE and TUG1 were overexpressed in cardiomyocytes to determine the relationship between them. The roles of CRNDE and TUG1 in regulating the apoptosis of cardiomyocytes were explored by cell apoptosis assay. We found that both CRNDE and TUG1 were downregulated in sepsis. In cardiomyocytes, LPS treatment resulted in the downregulation of CRNDE and TUG1. Overexpression of CRNDE and TUG1 in cardiomyocytes increased the expression levels of each other. Under lipopolysaccharide (LPS) treatment, decreased apoptosis rates of cardiomyocytes were observed after CRNDE and TUG1 overexpression. CRNDE and TUG1 co-overexpression showed a stronger effect. In conclusion, CRNDE and TUG1 are downregulated in sepsis and they positively regulate each other to suppress the apoptosis of cardiomyocytes.