Necroptosis and immune infiltration in hypertrophic cardiomyopathy: novel insights from bioinformatics analyses.

Background: Hypertrophic Cardiomyopathy (HCM), a widespread genetic heart disorder, is largely associated with sudden cardiac fatality. Necroptosis, an emerging type of programmed cell death, plays a fundamental role in several cardiovascular diseases. Aim: This research utilized bioinformatics analysis to investigate necroptosis's implication in HCM. Methods: The study retrieved RNA sequencing datasets GSE130036 and GSE141910 from the Gene Expression Omnibus (GEO) database. It detected necroptosis-linked differentially expressed genes (NRDEGs) by reviewing both the gene set for necroptosis and the differently expressed genes (DEGs). The enriched signaling pathway of HCM was assessed using GSEA, while common DEGs were studied through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Concurrently, the Protein-Protein Interaction network (PPI) proved useful for identifying central genes. CIBERSORT facilitated evaluating the correlation between distinct immune cell-type prevalence and NRDEGs by analyzing immune infiltration patterns. Lastly, GSE141910 dataset validated the expression ranks of NRDEGs and immune-cell penetration. Results: The investigation disclosed significant enrichment and activation of the necroptosis pathway in HCM specimens. Seventeen diverse genes, including CYBB, BCL2, and JAK2 among others, were identified in the process. PPI network scrutiny classified nine of these genes as central genes. Results from GO and KEGG enrichment analyses showed substantial connections of these genes to pathways pertaining to the HIF-1 signaling track, necroptosis, and NOD-like receptor signaling process. Moreover, an imbalance in M2 macrophage cells in HCM samples was observed. Finally, CYBB, BCL2, and JAK2 emerged as vital genes and were validated using the GSE141910 dataset. Conclusion: These results indicate necroptosis as a probable underlying factor in HCM, with immune cell infiltration playing a part. Additionally, CYBB, BCL2, JAK2 could act as potential biomarkers for recognizing HCM. This information forms crucial insights into the basic mechanisms of HCM and could enhance its diagnosis and management.

Heavy metals and elderly kidney health: A multidimensional study through Enviro-target Mendelian Randomization.

Chronic Kidney Disease (CKD), closely linked to environmental factors, poses a significant public health challenge. This study, based on 529 triple-repeated measures from key national environmental pollution area and multiple gene-related public databases, employs various epidemiological and bioinformatics models to assess the impact of combined heavy metal exposure (Chromium [Cr], Cadmium [Cd], and Lead [Pb]) on early renal injury and CKD in the elderly. Introducing the novel Enviro-Target Mendelian Randomization method, our research explores the causal relationship between metals and CKD. The findings indicate a positive correlation between increased levels of metal and renal injury, with combined exposure caused renal damage more significantly than individual exposure. The study reveals that metals primarily influence CKD development through oxidative stress and metal ion resistance pathways, focusing on three related genes (SOD2, MPO, NQO1) and a transcription factor (NFE2L2). Metals were found to regulate oxidative stress levels in the body by increasing the expression of SOD2, MPO, NQO1, and decreasing NFE2L2, leading to CKD onset. Our research establishes a new causal inference framework linking environmental pollutants-pathways-genes-CKD, assessing the impact and mechanisms of metal exposure on CKD. Future studies with more extensive in vitro evidence and larger population are needed to validate.

Evaluation of secretome biomarkers in glioblastoma cancer stem cells: A bioinformatics analysis.

BACKGROUND: Glioblastoma (GBM) is a malignant brain tumor that frequently occurs alongside other central nervous system (CNS) conditions. The secretome of GBM cells contains a diverse array of proteins released into the extracellular space, influencing the tumor microenvironment. These proteins can serve as potential biomarkers for GBM due to their involvement in key biological processes, exploring the secretome biomarkers in GBM research represents a cutting-edge strategy with significant potential for advancing diagnostic precision, treatment monitoring, and ultimately improving outcomes for patients with this challenging brain cancer. AIM: This study was aimed to investigate the roles of secretome biomarkers and their pathwayes in GBM through bioinformatics analysis. METHODS AND RESULTS: Using data from the Gene Expression Omnibus and the Cancer Genome Atlas datasets-where both healthy and cancerous samples were analyzed-we used a quantitative analytical framework to identify differentially expressed genes (DEGs) and cell signaling pathways that might be related to GBM. Then, we performed gene ontology studies and hub protein identifications to estimate the roles of these DEGs after finding disease-gene connection networks and signaling pathways. Using the GEPIA Proportional Hazard Model and the Kaplan-Meier estimator, we widened our analysis to identify the important genes that may play a role in both progression and the survival of patients with GBM. In total, 890 DEGs, including 475 and 415 upregulated and downregulated were identified, respectively. Our results revealed that SQLE, DHCR7, delta-1 phospholipase C (PLCD1), and MINPP1 genes are highly expressed, and the Enolase 2 (ENO2) and hexokinase-1 (HK1) genes are low expressions. CONCLUSION: Hence, our findings suggest novel mechanisms that affect the occurrence of GBM development, growth, and/or establishment and may also serve as secretory biomarkers for GBM prognosis and possible targets for therapy. So, continued research in this field may uncover new avenues for therapeutic interventions and contribute to the ongoing efforts to combat GBM effectively.

Systematic analysis of the expression profiles and prognostic significance of the MED gene family in renal clear cell carcinoma.

The mediator complex (MED) family is a contributing factor in the regulation of transcription and proliferation of cells, and is closely associated with the development of various types of cancer. However, the significance of the expression levels and prognostic value of MED genes in kidney renal clear cell carcinoma (KIRC) have rarely been reported. The present study analyzed the expression and prognostic potential of MED genes in KIRC. The Search Tool for the Retrieval of Interacting Genes/Proteins was used to construct the protein-protein interaction network (PPI), the Assistant for Clinical Bioinformatics database was used to perform correlation analysis, GEPIA 2 was utilized to draw the Kaplan-Meier plot and analyze prognostic significance and the Tumor Immune Estimation Resource was used to assess the association of MED genes with the infiltration of immune cells in patients with KIRC. A total of 30 MED genes were identified, and among these genes, 11 were selected for the creation of a prognostic gene signature based on the results of a LASSO Cox regression analysis. Furthermore, according to univariate and multivariate analyses, MED7, MED16, MED21, MED25 and MED29 may be valuable independent predictive biomarkers for the prognosis of individuals with KIRC. Furthermore, there were significant differences in the expression levels of MED7, MED21 and MED25 in KIRC among different tumor grades. Additionally, patients with KIRC with high transcription levels of MED7, MED21 and MED29 had considerably longer overall survival times. The expression levels of MED genes were also linked to the infiltration of several immune cells. Overall, MED genes may have potential significance in predicting the prognosis of patients with KIRC.

Comprehensive transcriptomic analysis identifies SLC25A4 as a key predictor of prognosis in osteosarcoma.

Background: Osteosarcoma (OS) is highly malignant and prone to local infiltration and distant metastasis. Due to the poor outcomes of OS patients, the study aimed to identify differentially expressed genes (DEGs) in OS and explore their role in the carcinogenesis and progression of OS. Methods: RNA sequencing was performed to identify DEGs in OS. The functions of the DEGs in OS were investigated using bioinformatics analysis, and DEG expression was verified using RT-qPCR and Western blotting. The role of SLC25A4 was evaluated using gene set enrichment analysis (GSEA) and then investigated using functional assays in OS cells. Results: In all, 8353 DEGs were screened. GO and KEGG enrichment analyses indicated these DEGs showed strong enrichment in the calcium signaling pathway and pathways in cancer. Moreover, the Kaplan-Meier survival analysis showed ten hub genes were related to the outcomes of OS patients. Both SLC25A4 transcript and protein expression were significantly reduced in OS, and GSEA suggested that SLC25A4 was associated with cell cycle, apoptosis and inflammation. SLC25A4-overexpressing OS cells exhibited suppressed proliferation, migration, invasion and enhanced apoptosis. Conclusion: SLC25A4 was found to be significantly downregulated in OS patients, which was associated with poor prognosis. Modulation of SLC25A4 expression levels may be beneficial in OS treatment.

[Mechanism of electroacupuncture for vascular dementia based on proteomics].

OBJECTIVE: To explore the potential mechanism of electroacupuncture (EA) for vascular dementia (VD) using tandem mass tag (TMT) quantitative proteomics technology. METHODS: Among 80 male SPF SD rats, 78 rats which met the selection criteria through the Morris water maze test were selected and randomly divided into a sham surgery group (18 rats) and a surgery group (60 rats). VD model was established by four-vessel occlusion (4-VO) method in the surgery group, and 36 rats with successful modeling were randomly assigned to a model group (18 rats) and an EA group (18 rats). Each group was further divided into three subgroups based on intervention duration, with each subgroup containing 6 rats. Seven days after model establishment, the EA group received EA intervention at left and right "Sishencong" (EX-HN 1) and bilateral "Fengchi" (GB 20), with continuous wave at a frequency of 2 Hz and current intensity of 1 mA, daily for 30 min, with subgroups receiving EA for 7, 14, or 21 d respectively. Cognitive function before and after interventions was assessed using Morris water maze. Proteomic analysis was conducted on the optimal EA subgroup and corresponding sham surgery and model subgroups, identifying differentially expressed proteins and analyzing them through bioinformatics. Differentially expressed target proteins was performed using parallel reaction monitoring (PRM) and Western blot techniques. RESULTS: Compared to the sham surgery group, the model group exhibited prolonged escape latency and reduced number of platform crossings (P<0.01); compared with model group, the EA group showed reductions in escape latency and increased platform crossings after 7, 14, and 21 days of intervention (P<0.01, P<0.05). Compared to the 7 and 14-day intervention, the rats in the EA group of 21-day intervention showed the most significant improvements in reductions of escape latency and increased platform crossings (P<0.01, P<0.05), and was selected for further proteomic, PRM analyses, and Western blot validation. Compared to the sham surgery group, the model group displayed 71 differentially expressed proteins, with 50 up-regulated and 21 down-regulated proteins; compared to the model group, the EA group had 54 differentially expressed proteins, with 30 up-regulated and 24 down-regulated proteins. Functional enrichment and clustering analyses indicated that these proteins were primarily associated with cellular processes, metabolic processes, phagocytosis recognition, immune response, and regulation of extracellular matrix, etc. Enrichment was observed in the mammalian target of rapamycin (mTOR) signaling pathway and neurotrophic factors signaling pathways, involving glycogen synthase kinase 3ß (GSK3ß) and mitogen-activated protein kinase kinase 2 (Map2k2), with PRM and Western blot findings consistent with the proteomic results. Which meant that compared with the model group, the protein expression of GSK3ß and Map2k2 of hippocampus was increased in the EA group (P<0.01, P<0.05). CONCLUSION: EA at "Sishencong" (EX-HN 1) and "Fengchi" (GB 20) could improve cognitive function in VD rats, with the mechanism involving multiple targets and pathways, potentially related to GSK3ß, Map2k2 proteins, and the mTOR and neurotrophic factor signaling pathways.

Exploration of effective biomarkers for venous thrombosis embolism in Behçet's disease based on comprehensive bioinformatics analysis.

Behçet's disease (BD) is a multifaceted autoimmune disorder affecting multiple organ systems. Vascular complications, such as venous thromboembolism (VTE), are highly prevalent, affecting around 50% of individuals diagnosed with BD. This study aimed to identify potential biomarkers for VTE in BD patients. Three microarray datasets (GSE209567, GSE48000, GSE19151) were retrieved for analysis. Differentially expressed genes (DEGs) associated with VTE in BD were identified using the Limma package and weighted gene co-expression network analysis (WGCNA). Subsequently, potential diagnostic genes were explored through protein-protein interaction (PPI) network analysis and machine learning algorithms. A receiver operating characteristic (ROC) curve and a nomogram were constructed to evaluate the diagnostic performance for VTE in BD patients. Furthermore, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate potential underlying mechanisms. Finally, the efficacy of listed drugs was assessed based on the identified signature genes. The limma package and WGCNA identified 117 DEGs related to VTE in BD. A PPI network analysis then selected 23 candidate hub genes. Four DEGs (E2F1, GATA3, HDAC5, and MSH2) were identified by intersecting gene sets from three machine learning algorithms. ROC analysis and nomogram construction demonstrated high diagnostic accuracy for these four genes (AUC: 0.816, 95% CI: 0.723-0.909). Immune cell infiltration analysis revealed a positive correlation between dysregulated immune cells and the four hub genes. ssGSEA provided insights into potential mechanisms underlying VTE development and progression in BD patients. Additionally, therapeutic agent screening identified potential drugs targeting the four hub genes. This study employed a systematic approach to identify four potential hub genes (E2F1, GATA3, HDAC5, and MSH2) and construct a nomogram for VTE diagnosis in BD. Immune cell infiltration analysis revealed dysregulation, suggesting potential macrophage involvement in VTE development. ssGSEA provided insights into potential mechanisms underlying BD-induced VTE, and potential therapeutic agents were identified.

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

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

The EFNA4 gene is a potential prognostic biomarker in pancreatic cancer: a bioinformatics analysis.

Background: Pancreatic cancer is a highly aggressive malignancy with poor prognosis, and there is an urgent need to understand its molecular mechanisms for early diagnosis and treatment. Despite surgical resection being the only effective treatment, most patients are diagnosed at an advanced stage, missing the optimal window for therapy. Identifying novel biomarkers is crucial for prognostic assessment, treatment planning, and early intervention. Ephrin A4 (EFNA4), a member of the receptor tyrosine kinase family, is involved in vascular and epithelial development via regulation of cell migration and rejection. However, the role of EFNA4 in pancreatic cancer has not been reported. Therefore, our study aimed to clarify the role of EFNA4 in pancreatic cancer through bioinformatics analysis and vitro experiments. Methods: The expression of EFNA4 and its potential value as a diagnostic and prognostic biomarker in pancreatic cancer was analyzed using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Profiling Interactive Analysis (GEPIA) database. According to the expression level of EFNA4, patients were divided into high expression group and low expression group, and the correlation between overall survival (OS) and disease-free survival (DFS) with different expression levels of EFNA4 and clinical parameters were analyzed. Subsequently, reverse-transcription quantitative polymerase chain reaction (RT-qPCR) was performed to detect EFNA4 expression. The proliferation, invasion, and cloning ability of the cells were detected via Cell Counting Kit 8 (CCK8), Transwell, and plate cloning assays, respectively. Results: EFNA4 is highly expressed in pancreatic cancer, and upregulation of EFNA4 is associated with poor prognosis. In this study, EFNA4 expression was correlated with T stage and TNM (tumor-node-metastasis) stage of pancreatic cancer, and the median survival time and progression-free survival (PFS) were worse in those with high EFNA4 expression (394 days) than in those with low expression (525 days) [hazard ratio (HR): 1.47, 95% confidence interval (CI): 1.00-2.16, P=0.047]. In addition, EFNA4 was also found to be involved in the regulation of signal pathways such as cell adhesion, cyclic AMP, insulin secretion, pancreatic secretion, and protein digestion and absorption. In vitro experiments demonstrated that EFNA4 knockdown significantly inhibited the proliferation, cloning ability, and invasiveness of the PANC-1 and SW1990 pancreatic cancer cell lines. Conclusions: The abnormal expression of EFNA4 in pancreatic cancer is associated with poor prognosis. Knockout of EFNA4 gene could significantly inhibit the proliferation and invasion of pancreatic cancer cells. Therefore, EFNA4 may be one of the molecular targets for poor prognosis of patients with pancreatic cancer.

Progress based on a multi-omics research strategy in the biosynthesis and modernization of active ingredients of Herpetospermum pedunculosum seeds.

Herpetospermum pedunculosum seeds also known as Herpetospermum caudigerum Wall. is the mature seed of the Herpetospermum pedunculosum(Ser.) C. B. Clarke,Cucurbitaceae. Modern pharmacological studies have shown that H. pedunculosum has hepatoprotective, anti-inflammatory, anti-gout and antibacterial pharmacological activities. The biologically active chemical components include lignin compounds such as Herpetin, Herpetetrone, Herpetoriol and so on. The natural product displays considerable skeletal diversity and structural complexity, offering significant opportunities for novel drug discovery. Based on the multi-omics research strategy and the 'gene-protein-metabolite' research framework, the biosynthetic pathway of terpenoids and lignans in H. pedunculosum has has been elucidated at multiple levels. These approaches provide comprehensive genetic information for cloning and identification of pertinent enzyme genes. Furthermore, the application of multi-omics integrative approaches provides a scientific means to elucidate entire secondary metabolic pathways. We investigated the biosynthetic pathways of lignin and terpene components in H. pedunculosum and conducted bioinformatics analysis of the crucial enzyme genes involved in the biosynthetic process using genomic and transcriptomic data. We identified candidate genes for six key enzymes in the biosynthetic pathway. This review reports on the current literature on pharmacological investigations of H. pedunculosum, proposing its potential as an antidiabetic agent. Moreover, we conclude, for the first time, the identification of key enzyme genes potentially involved in the biosynthesis of active compounds in H. pedunculosum. This review provides a scientific foundation for the discovery of novel therapeutic agents from natural sources.

Apoptosis and NETotic cell death affect diabetic nephropathy independently: An study integrative study encompassing bioinformatics, machine learning, and experimental validation.

OBJECTIVE: Although programmed cell death (PCD) and diabetic nephropathy (DN) are intrinsically conneted, the interplay among various PCD forms remains elusive. In this study, We aimed at identifying independently DN-associated PCD pathways and biomarkers relevant to the related pathogenesis. METHODS: We acquired DN-related datasets from the GEO database and identified PCDs independently correlated with DN (DN-PCDs) through single-sample Gene Set Enrichment Analysis (ssGSEA) as well as, univariate and multivariate logistic regression analyses. Subsequently, applying differential expression analysis, weighted gene co-expression network analysis (WGCNA), and Mfuzz cluster analysis, we filtered the DN-PCDs pertinent to DN onset and progression. The convergence of various machine learning techniques ultimately spotlighted hub genes, substantiated through dataset meta-analyses and experimental validations, thereby confirming hub genes and related pathways expression consistencies. RESULTS: We harmonized four DN-related datasets (GSE1009, GSE142025, GSE30528, and GSE30529) post-batch-effect removal for subsequent analyses. Our differential expression analysis yielded 709 differentially expressed genes (DEGs), comprising 446 upregulated and 263 downregulated DEGs. Based on our ssGSEA as well as univariate and multivariate logistic regressions, apoptosis and NETotic cell death were appraised as independent risk factors for DN (Odds Ratio > 1, p < 0.05). Next, we further refined 588 apoptosis- and NETotic cell death-associated genes through WGCNA and Mfuzz analysis, resulting in the identification of 17 DN-PCDs. Integrating protein-protein interaction (PPI) network analyses, network topology, and machine learning, we pinpointed hub genes (e.g., IL33, RPL11, and CX3CR1) as significant DN risk factors with expression corroborating in subsequent meta-analyses and experimental validations. Our GSEA enrichment analysis discerned differential enrichments between DN and control samples within pathways such as IL2/STAT5, IL6/JAK/STAT3, TNF-α via NF-κB, apoptosis, and oxidative phosphorylation, with related proteins such as IL2, IL6, and TNFα, which we subsequently submitted to experimental verification. CONCLUSION: Innovatively stemming from from PCD interactions, in this study, we discerned PCDs with an independent impact on DN: apoptosis and NETotic cell death. We further screened DN evolution- and progression-related biomarkers, i.e. IL33, RPL11, and CX3CR1, all of which we empirically validated. This study not only poroposes a PCD-centric perspective for DN studies but also provides evidence for PCD-mediated immune cell infiltration exploration in DN regulation. Our results could motivate further exploration of DN pathogenesis, such as how the inflammatory microenvironment mediates NETotic cell death in DN regulation, representing a promising direction for future research.

Identification of high-risk non-synonymous SNPs (nsSNPs) in DNAH1 and DNAH17 genes associated with male infertility: a bioinformatics analysis.

Male infertility is a significant reproductive issue affecting a considerable number of couples worldwide. While there are various causes of male infertility, genetic factors play a crucial role in its development. We focused on identifying and analyzing the high-risk nsSNPs in DNAH1 and DNAH17 genes, which encode proteins involved in sperm motility. A total of 20 nsSNPs for DNAH1 and 10 nsSNPs for DNAH17 were analyzed using various bioinformatics tools including SIFT, PolyPhen-2, CADD, PhD-SNPg, VEST-4, and MutPred2. As a result, V1287G, L2071R, R2356W, R3169C, R3229C, E3284K, R4096L, R4133C, and A4174T in DNAH1 gene and C1803Y, C1829Y, R1903C, and L3595P in DNAH17 gene were identified as high-risk nsSNPs. These nsSNPs were predicted to decrease protein stability, and almost all were found in highly conserved amino acid positions. Additionally, 4 nsSNPs were observed to alter post-translational modification status. Furthermore, the interaction network analysis revealed that DNAH1 and DNAH17 interact with DNAH2, DNAH3, DNAH5, DNAH7, DNAH8, DNAI2, DNAL1, CFAP70, DNAI3, DNAI4, ODAD1, and DNAI7, demonstrating the importance of DNAH1 and DNAH17 proteins in the overall functioning of the sperm motility machinery. Taken together, these findings revealed the detrimental effects of identified high-risk nsSNPs on protein structure and function and highlighted their potential relevance to male infertility. Further studies are warranted to validate these findings and to elucidate the underlying mechanisms.

Screening of co-expressed genes in hypopharyngeal carcinoma with esophageal carcinoma based on RNA sequencing and Clinical Research.

To explore the hub comorbidity genes and potential pathogenic mechanisms of hypopharyngeal carcinoma with esophageal carcinoma, and evaluate their diagnostic value for hypopharyngeal carcinoma with co-morbid esophageal carcinoma. We performed gene sequencing on tumor tissues from 6 patients with hypopharyngeal squamous cell carcinoma with esophageal squamous cell carcinoma (hereafter referred to as "group A") and 6 patients with pure hypopharyngeal squamous cell carcinoma (hereafter referred to as "group B"). We analyzed the mechanism of hub genes in the development and progression of hypopharyngeal squamous cell carcinoma with esophageal squamous cell carcinoma through bioinformatics, and constructed an ROC curve and Nomogram prediction model to analyze the value of hub genes in clinical diagnosis and treatment. 44,876 genes were sequenced in 6 patients with group A and 6 patients with group B. Among them, 76 genes showed significant statistical differences between the group A and the group B.47 genes were expressed lower in the group A than in the group B, and 29 genes were expressed higher. The top five hub genes were GABRG2, CACNA1A, CNTNAP2, NOS1, and SCN4B. GABRG2, CNTNAP2, and SCN4B in the hub genes have high diagnostic value in determining whether hypopharyngeal carcinoma patients have combined esophageal carcinoma (AUC: 0.944, 0.944, 0.972). These genes could possibly be used as potential molecular markers for assessing the risk of co-morbidity of hypopharyngeal carcinoma combined with esophageal carcinoma.

Network-Based Transcriptome Analysis Reveals FAM3C as a Novel Potential Biomarker for Glioblastoma.

Glioblastoma (GBM) is the most common form of malignant primary brain tumor with a high mortality rate. The aim of the present study was to investigate the clinical significance of Family with Sequence Similarity 3, Member C, FAM3C, in GBM using bioinformatic-integrated analysis. First, we performed the transcriptomic integration analysis to assess the expression profile of FAM3C in GBM using several data sets (RNA-sequencing and scRNA-sequencing), which were obtained from TCGA and GEO databases. By using the STRING platform, we investigated FAM3C-coregulated genes to construct the protein-protein interaction network. Next, Metascape, Enrichr, and CIBERSORT databases were used. We found FAM3C high expression in GBM with poor survival rates. Further, we observed, via FAM3C coexpression network analysis, that FAM3C plays key roles in several hallmarks of cancer. Surprisingly, we also highlighted five FAM3C­coregulated genes overexpressed in GBM. Specifically, we demonstrated the association between the high expression of FAM3C and the abundance of the different immune cells, which may markedly worsen GBM prognosis. For the first time, our findings suggest that FAM3C not only can be a new emerging biomarker with promising therapeutic values to GBM patients but also gave a new insight into a potential resource for future GBM studies.

MEOX2 Participates in Hepatic Stellate Cells-Induced Liver Fibrosis by Regulating the PI3K/AKT Signaling Pathway.

BACKGROUND: Hepatic stellate cells (HSCs) serve as the crucial accelerating factor in the progression of liver fibrosis (LF). In contrast to HSCs, adult-derived human liver stem/progenitor cells (ADHLSCs) exhibit greater potency in terms of differentiation and proliferation, rendering them highly applicable in LF treatment. The objective of this study is to identify new therapeutic targets for LF by comparing differentially expressed genes (DEGs) between ADHLSCs and HSCs. METHODS: We investigated DEGs between ADHLSCs and HSCs using the GSE49995 dataset obtained from the Gene Expression Omnibus (GEO) database, aiming to identify new therapeutic targets for LF. Subsequently, we activated HSCs to delve deeper into the mesenchyme homeobox 2 (MEOX2), PH domain Leucine-rich repeat protein phosphatase (PHLPP), and Phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways in LF progression, employing platelet-derived growth factor (PDGF), and conducted infection with Overexpression (OE)-MEOX2 and shRNA-MEOX2 (sh-MEOX2) lentiviruses. Cell viability was assessed using the Cell Counting Kit-8 (CCK-8) assay, while cell proliferation was evaluated through 5-ethynyl-2'-deoxyuridine (EdU) staining and flow cytometry. Relative mRNA expression levels were determined via qPCR. Western blot analysis was performed to measure protein expression levels, and the regulatory role of MEOX2 was investigated using dual luciferase reporter assays. RESULTS: We identified 332 DEGs that were down-regulated and 201 DEGs that were up-regulated between ADHLSCs and HSCs. Notably, MEOX2 expression in ADHLSCs was significantly reduced. These DEGs primarily participated in the collagen-containing extracellular matrix and the PI3K/AKT signaling pathway. MEOX2 could inhibit cancer cell proliferation via the PI3K/AKT signaling pathway. Additionally, the JASRPAR2022 database predicted the target gene PHLPP of MEOX2. Our results indicated that OE-MEOX2 significantly inhibited HSCs' cell vitality and proliferation. Further analysis revealed that MEOX2 binds to PHLPP promoters, thereby up-regulating its transcription. This action led to the inhibition of p-AKT expression, consequently reducing HSC proliferation and slowing the progression of LF. CONCLUSIONS: MEOX2 up-regulates PHLPP expression and inhibits AKT phosphorylation, thereby reducing the cell activity and proliferation ability of HSCs and inhibiting the progression of LF.

Prognostic value of miR-223 for pregnancy outcomes in patients with in vitro fertilisation and intracytoplasmic sperm injection.

BACKGROUND: This study aimed to analyse the expression of microRNA-223 (miR-223) in embryo culture medium and its correlation with pregnancy outcomes. METHODS: Two hundred and two patients undergoing in vitro fertilisation/intracytoplasmic sperm injection (IVF/ICSI) were divided into clinical pregnancy group (n = 101) and non-pregnant group (n = 101). The baseline data, clinical indicators, and the expression level of miR-223 in the embryo medium were compared between the two groups. Logistic regression analysis was used to analyse the relationship between each index and the pregnancy outcome. Receiver operator characteristic curve was carried out to evaluate the differential ability of miR-223 in pregnancy status. Bioinformatics methods were used to identify the target genes of miR-223 and elucidate their functions. RESULTS: Compared with pregnancy group, the non-pregnancy group exhibited a reduction in miR-223 expression (p < 0.001). Multivariate analysis revealed that miR-223 reduction was an independent factor for pregnancy failure (p < 0.05). The ROC curve demonstrated the discriminative capability of miR-223 in distinguishing pregnancy and non-pregnancy. In addition, bioinformatics analysis indicated that the target genes of miR-223 were predominantly located in the endocytic vesicle membrane and were primarily enriched in adenosine monophosphate-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) signalling pathways. CONCLUSION: In this study, levels of miR-223 in the embryo culture medium predicted pregnancy outcomes in subjects undergoing IVF/ICSI. Low expression of miR-223 was a risk factor for adverse pregnancy outcomes in subjects.

In this study, 202 patients who underwent IVF/ICSI were retrospectively analysed and categorised into pregnant and non-pregnant groups based on their pregnancy status. The examination of embryo culture medium samples from both groups revealed that the non-pregnant group exhibited lower miR-223 expression compared to the pregnant group. Subsequent ROC analysis demonstrated the clinical relevance of miR-223 in effectively distinguishing between pregnant and non-pregnant states. Multi-factor analysis further established that the diminished expression of miR-223 independently influenced the likelihood of successful pregnancy.

Comprehensive bioinformatics analysis of integrator complex subunits: expression patterns, immune infiltration, and prognostic signature, validated through experimental approaches in hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is a common gastrointestinal malignancy with a high incidence and poor prognosis. The subunits of the integrator complex (INTS1-14) play a crucial role in regulating genes dependent on RNA Polymerase II, which may be associated with cancer. However, the role of INTSs in HCC remains unclear. This study aims to comprehensively analyze the clinical value and potential role of INTS family genes in HCC through systematic bioinformatics analysis. METHODS: We employed various public databases, including UALCAN, HPA, Kaplan-Meier Plotter, GEPIA2, TNMplot, STRING, TIMER, and TISIDB, to investigate the expression levels, clinicopathological correlations, diagnostic and prognostic value, genetic alterations, co-expression network, molecular targets, and immune infiltration of INTSs in HCC. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to investigate the biological functions of genes associated with INTSs. Furthermore, Western blot, real-time fluorescence quantitative reverse transcription polymerase chain reaction (RT-qPCR), and immunohistochemistry techniques were employed to assess the expression of relevant proteins and genes. The proliferation of HCC cells was evaluated using the CCK8 assay. RESULTS: We found that in HCC, there was a significant upregulation of INTSs at the transcriptional level, particularly INTS1, INTS4, INTS7, and INTS8. Additionally, the protein levels of INTS1 and INTS8 were notably elevated. The overexpression of these INTSs was strongly correlated with tumor stages in HCC patients. INTS1, INTS4, INTS7, and INTS8 exhibited significant diagnostic and prognostic value in HCC. Moreover, their expression was associated with immune infiltrations and activated status, including B cells, CD8 + T cells, CD4 + T cells, NK cells, macrophages, and dendritic cells. Functional predictions indicated that INTS1, INTS4, INTS7, and INTS8 were involved in various cancer-related signaling pathways, such as TRAIL, IFN-gamma, mTOR, CDC42, Apoptosis, and the p53 pathway. Furthermore, we observed a significant upregulation of INTS1, INTS4, INTS7, and INTS8 expression in HCC cell lines compared to normal liver cell lines. The level of INTS1 protein was higher in cancerous tissues compared to adjacent non-cancerous tissues (n = 16), and the suppression of INTS1 resulted in a significant decrease in the proliferation of Huh7 cells. CONCLUSION: These findings indicate the potential of INTS family genes as diagnostic biomarkers and therapeutic targets in HCC. Further research is needed to understand the underlying mechanisms and explore clinical applications.

Melatonin inhibits circadian gene DEC1 and TLR2/MyD88/NF-κB signaling pathway to alleviate renal injury in type 2 diabetic mice.

BACKGROUND: Diabetic Kidney Disease (DKD) is a complex disease associated with circadian rhythm and biological clock regulation disorders. Melatonin (MT) is considered a hormone with renal protective effects, but its mechanism of action in DKD is unclear. METHODS: We used the GSE151325 dataset from the GEO database for differential gene analysis and further explored related genes and pathways through GO and KEGG analysis and PPI network analysis. Additionally, this study used a type 2 diabetes db/db mouse model and investigated the role of melatonin in DKD and its relationship with clock genes through immunohistochemistry, Western blot, real-time PCR, ELISA, chromatin immunoprecipitation (ChIP), dual-luciferase reporter technology, and liposome transfection technology to study DEC1 siRNA. RESULTS: Bioinformatics analysis revealed the central position of clock genes such as CLOCK, DEC1, Bhlhe41, CRY1, and RORB in DKD. Their interaction with key inflammatory regulators may reveal melatonin's potential mechanism in treating diabetic kidney disease. Further experimental results showed that melatonin significantly improved the renal pathological changes in db/db mice, reduced body weight and blood sugar, regulated clock genes in renal tissue, and downregulated the TLR2/MyD88/NF-κB signaling pathway. We found that the transcription factor DEC1 can bind to the TLR2 promoter and activate its transcription, while CLOCK's effect is unclear. Liposome transfection experiments further confirmed the effect of DEC1 on the TLR2/MyD88/NF-κB signaling pathway. CONCLUSION: Melatonin shows significant renal protective effects by regulating clock genes and downregulating the TLR2/MyD88/NF-κB signaling pathway. The transcription factor DEC1 may become a key regulatory factor for renal inflammation and fibrosis by activating TLR2 promoter transcription. These findings provide new perspectives and directions for the potential application of melatonin in DKD treatment.

Bioinformatics study of the potential therapeutic effects of ginsenoside Rh3 in reversing insulin resistance.

Background: In recent years, the incidence of insulin resistance is increasing, and it can cause a variety of Metabolic syndrome. Ginsenosides have been clinically proven to improve fat metabolism and reduce insulin resistance, but their components and mechanism of action are still unclear. Objective: Ginsenoside, a bioactive compound derived from ginseng, exhibits significant potential in treating obesity, diabetes, and metabolic disorders. Despite evidence supporting its efficacy in ameliorating insulin resistance (IR) in obesity, the specific bioactive components and underlying mechanisms remain obscure. In this study, we endeavored to elucidate the potential molecular targets and pathways influenced by ginsenoside Rh3 (GRh3) to ameliorate IR in liver tissue. We employed a comprehensive approach that integrates system pharmacology and bioinformatics analysis. Materials and methods: Our methodology involved the identification of candidate targets for GRh3 and the profiling of differentially expressed genes (DEGs) related to IR in individuals with insulin resistance. The coalescence of candidate targets and DEGs facilitated the construction of a "GRh3-targets-disease" network for each tissue type, ultimately yielding 38 shared target genes. Subsequently, we conducted pathway enrichment analysis, established protein-protein interaction (PPI) networks, and identified hub targets among the GRh3 targets and IR-related DEGs. Additionally, we conducted animal experiments to corroborate the role of these hub targets in the context of GRh3. Results: Our investigation identified a total of 38 overlapping targets as potential candidates. Notably, our analysis revealed crucial hub targets such as EGFR, SRC, ESR1, MAPK1, and CASP3, alongside implicated signaling pathways, including those related to insulin resistance, the FoxO signaling pathway, the PPAR signaling pathway, and the IL-17 signaling pathway. This study establishes a robust foundation for the mechanisms underlying GRh3's efficacy in mitigating IR. Furthermore, these results suggest that GRh3 may serve as a representative compound within the ginsenoside family. Conclusion: This study elucidates the potential molecular targets and associated pathways through which GRh3 ameliorates IR, showcasing its multifaceted nature, spanning multiple targets, pathways, and mechanisms. These findings establish a robust foundation for subsequent experimental inquiries and clinical applications.