Collagen-containing Extracellular Matrix Research Articles

Differences in the cerebral amyloid angiopathy proteome in Alzheimer’s disease and mild cognitive impairment

Cerebral amyloid angiopathy (CAA) is characterized by amyloid beta (Aβ) deposition in cerebrovasculature. It is prevalent with aging and Alzheimer’s disease (AD), associated with intracerebral hemorrhage, and contributes to cognitive deficits. To better understand molecular mechanisms, CAA(+) and CAA(−) vessels were microdissected from paraffin-embedded autopsy temporal cortex of age-matched Control (n = 10), mild cognitive impairment (MCI; n = 4), and sporadic AD (n = 6) cases, followed by label-free quantitative mass spectrometry. 257 proteins were differentially abundant in CAA(+) vessels compared to neighboring CAA(−) vessels in MCI, and 289 in AD (p < 0.05, fold-change > 1.5). 84 proteins changed in the same direction in both groups, and many changed in the same direction among proteins significant in at least one group (p < 0.0001, R2 = 0.62). In CAA(+) vessels, proteins significantly increased in both AD and MCI were particularly associated with collagen-containing extracellular matrix, while proteins associated with ribonucleoprotein complex were significantly decreased in both AD and MCI. In neighboring CAA(−) vessels, 61 proteins were differentially abundant in MCI, and 112 in AD when compared to Control cases. Increased proteins in CAA(−) vessels were associated with extracellular matrix, external encapsulating structure, and collagen-containing extracellular matrix in MCI; collagen trimer in AD. Twenty two proteins were increased in CAA(−) vessels of both AD and MCI. Comparison of the CAA proteome with published amyloid-plaque proteomic datasets identified many proteins similarly enriched in CAA and plaques, as well as a protein subset hypothesized as preferentially enriched in CAA when compared to plaques. SEMA3G emerged as a CAA specific marker, validated immunohistochemically and with correlation to pathology levels (p < 0.0001; R2 = 0.90). Overall, the CAA(−) vessel proteomes indicated changes in vessel integrity in AD and MCI in the absence of Aβ, and the CAA(+) vessel proteome was similar in MCI and AD, which was associated with vascular matrix reorganization, protein translation deficits, and blood brain barrier breakdown.

Relationships between systemic sclerosis and atherosclerosis: screening for mitochondria-related biomarkers.

Patients with systemic sclerosis (SSc) are known to have higher incidence of atherosclerosis (AS). Mitochondrial injuries in SSc can cause endothelial dysfunction, leading to AS; thus, mitochondria appear to be hubs linking SSc to AS. This study aimed to identify the mitochondria-related biomarkers of SSc and AS. We identified common differentially expressed genes (DEGs) in the SSc (GSE58095) and AS (GSE100927) datasets of the Gene Expression Omnibus (GEO) database. Considering the intersection between genes with identical expression trends and mitochondrial genes, we used the least absolute shrinkage and selection operator (LASSO) as well as random forest (RF) algorithms to identify four mitochondria-related hub genes. Diagnostic nomograms were then constructed to predict the likelihood of SSc and AS. Next, we used the CIBERSORT algorithm to evaluate immune infiltration in both disorders, predicted the transcription factors for the hub genes, and validated these genes for the two datasets. A total of 112 genes and 13 mitochondria-related genes were identified; these genes were then significantly enriched for macrophage differentiation, collagen-containing extracellular matrix, collagen binding, antigen processing and presentation, leukocyte transendothelial migration, and apoptosis. Four mitochondria-related hub DEGs (IFI6, FSCN1, GAL, and SGCA) were also identified. The nomograms showed good diagnostic values for GSE58095 (area under the curve (AUC) = 0.903) and GSE100927 (AUC = 0.904). Further, memory B cells, γδT cells, M0 macrophages, and activated mast cells were significantly higher in AS, while the resting memory CD4+ T cells were lower and M1 macrophages were higher in SSc; all of these were closely linked to multiple immune cells. Gene set enrichment analysis (GSEA) showed that IFI6 and FSCN1 were involved in immune-related pathways in both AS and SSc; GAL and SGCA are related to mitochondrial metabolism pathways in both SSc and AS. Twenty transcription factors (TFs) were predicted, where two TFs, namely BRCA1 and PPARγ, were highly expressed in both SSc and AS. Four mitochondria-related biomarkers were identified in both SSc and AS, which have high diagnostic value and are associated with immune cell infiltration in both disorders. Hence, this study provides new insights into the pathological mechanisms underlying SSc and AS. The specific roles and action mechanisms of these genes require further clinical validation in SSc patients with AS.

Advanced Fe-COF-based photothermal cascade nano-generator for sterilization and promoting wound healing

Pharmacological treatments of diabetic ulcers remain a huge challenge due to the impaired angiogenesis and uncontrolled bacterial infection. Here, iminopyrazine-based Fe-covalent organic frameworks (Fe-COF) were synthesized via a subcomponent assembly strategy, and functionalized through covalent modification of L-arginine and encapsulated with photosensitizer IR780 and hyaluronic acid for enhancing diabetic wound healing. The functionalized Fe-COF (Fe-COF NRs) showed peroxidase-like activity and near-infrared (NIR)-controlled NO generation property, which can be used for synergistic photothermal/photodynamic antibacterial therapy and NO-based gas therapy. Treatment with Fe-COF NRs + NIR caused pathogenic elimination and enhanced angiogenesis, subsequently leading to accelerated wound healing in a rat model of infected diabetic wound. Mechanistically, the results from RNA sequencing analysis indicated that Fe-COF NRs treatment significantly induced the expression of genes involved in angiogenesis, but obviously inhibited the expression of proinflammatory cytokines. Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis revealed that collagen-containing extracellular matrix, hair follicle development, PI3K-Akt signaling pathway, wound healing and skin development were significantly enriched after Fe-COF NRs treatment. Taken together, Fe-COF NRs provide a novel therapeutic strategy for the treatment of infected diabetic wound.

Deciphering smooth muscle cell heterogeneity in atherosclerotic plaques and constructing model: a multi-omics approach with focus on KLF15/IGFBP4 axis

BackgroundRuptured atherosclerotic plaques often precipitate severe ischemic events, such as stroke and myocardial infarction. Unraveling the intricate molecular mechanisms governing vascular smooth muscle cell (VSMC) behavior in plaque stabilization remains a formidable challenge.MethodsIn this study, we leveraged single-cell and transcriptomic datasets from atherosclerotic plaques retrieved from the gene expression omnibus (GEO) database. Employing a combination of single-cell population differential analysis, weighted gene co-expression network analysis (WGCNA), and transcriptome differential analysis techniques, we identified specific genes steering the transformation of VSMCs in atherosclerotic plaques. Diagnostic models were developed and validated through gene intersection, utilizing the least absolute shrinkage and selection operator (LASSO) and random forest (RF) methods. Nomograms for plaque assessment were constructed. Tissue localization and expression validation were performed on specimens from animal models, utilizing immunofluorescence co-localization, western blot, and reverse-transcription quantitative-polymerase chain reaction (RT-qPCR). Various online databases were harnessed to predict transcription factors (TFs) and their interacting compounds, with determination of the cell-specific localization of TF expression using single-cell data.ResultsFollowing rigorous quality control procedures, we obtained a total of 40,953 cells, with 6,261 representing VSMCs. The VSMC population was subsequently clustered into 5 distinct subpopulations. Analyzing inter-subpopulation cellular communication, we focused on the SMC2 and SMC5 subpopulations. Single-cell subpopulation and WGCNA analyses revealed significant module enrichments, notably in collagen-containing extracellular matrix and cell-substrate junctions. Insulin-like growth factor binding protein 4 (IGFBP4), apolipoprotein E (APOE), and cathepsin C (CTSC) were identified as potential diagnostic markers for early and advanced plaques. Notably, gene expression pattern analysis suggested that IGFBP4 might serve as a protective gene, a hypothesis validated through tissue localization and expression analysis. Finally, we predicted TFs capable of binding to IGFBP4, with Krüppel-like family 15 (KLF15) emerging as a prominent candidate showing relative specificity within smooth muscle cells. Predictions about compounds associated with affecting KLF15 expression were also made.ConclusionOur study established a plaque diagnostic and assessment model and analyzed the molecular interaction mechanisms of smooth muscle cells within plaques. Further analysis revealed that the transcription factor KLF15 may regulate the biological behaviors of smooth muscle cells through the KLF15/IGFBP4 axis, thereby influencing the stability of advanced plaques via modulation of the PI3K-AKT signaling pathway. This could potentially serve as a target for plaque stability assessment and therapy, thus driving advancements in the management and treatment of atherosclerotic plaques.

Improvement of skin wound healing by giant salamander skin mucus gel wrapped with bone marrow mesenchymal stem cells via affecting integrin family molecules.

Traditional bandages, gauze, and cotton balls are increasingly insufficient for addressing complex war injuries characterized by severe bleeding and diverse wound conditions. The giant salamander, a species of high medical value, secretes a unique mucus when stimulated, which has potential applications in wound care. Giant salamander skin mucus gel dressing wrapped with bone marrow mesenchymal stem cells (BMSCs-GSSM-gel) was prepared and validated. Skin wound injury of rabbit and mouse models were established. Hematoxylin and Eosin, Masson's trichrome, and Sirius red staining were performed. The platelet aggregation rate and coagulation items were measured. Transcriptome sequencing was performed to find potential differential expression genes. Preparation and characterization of BMSCs-GSSM-gel were performed, and BMSCs-GSSM-gel particles with a diameter of about 200 nm were obtained. BMSCs-GSSM-gel accelerated wound healing in both rabbit and mouse models. BMSCs-GSSM-gel significantly promoted hemostasis via increasing platelet aggregation rate and fibrinogen, but decreasing activated partial thromboplastin time, thrombin time, and prothrombin time. BMSCs-GSSM-gel treatment significantly impacted several genes associated with cell adhesion, inflammatory response, collagen-containing extracellular matrix, and the positive regulation of cell migration based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Integrin Subunit Beta 4 (ITGB4), Integrin Subunit Alpha 3 (ITGA3), and Laminin Subunit Beta 3 (LAMB3) might be involved in the wound healing process by BMSCs-GSSM-gel. We proved the BMSCs-GSSM-gel greatly improved the skin wound healing, and it might play a crucial role in the application fields of skin damage repair.

Proteomic profiling points to the significance of adipocyte-derived extracellular vesicles in diet-induced obesity

Extracellular vesicles (EVs) are lipid-bilayer membrane-enclosed vesicles well known to mediate cell-cell communication to maintain normal physiological functions. The diverse cargo of EVs encompassing proteins, lipids and miRNAs represent potential biomarkers and therapeutic targets. However, the role of adipocyte-derived EVs (AdEVs) in obesity is still unknown. To address this, we performed proteomic profiling of AdEVs isolated from the C57BL/6 male mice that were fed either a normal chow diet (NCD) or a high-fat high-sucrose diet (HFHSD) for 12 weeks starting at 4 weeks of age. AdEVs were obtained, through ultracentrifugation, from the conditioned media of cultured adipocytes derived from gonadal adipose tissue. Nanoparticle tracking analysis (NTA) of AdEVs showed that adipocytes from mice fed HFHSD tended to release more AdEVs despite the comparable size of AdEVs relative to those of mice fed NCD. Subsequently, proteins were isolated, digested and labeled with TMTpro16-plex for proteomic analysis. Raw data were processed in Scaffold, and a general bioinformatic and functional analysis conducted in Ingenuity Pathway Analysis Qiagen (IPA) ( https://digitalinsights.qiagen.com/explore/ga-ipa?utm_source=QDI_GA_SA_IPA_1122_PF&amp;cmpid=QDI_GA_IPA ) and Srplot identified over 1100 proteins in AdEVs (protein threshold: 99%, minim peptide:2, peptide threshold: 95%, 0.00% FDR). Of these, 58 proteins were identified as upregulated while 41 proteins were downregulated in HFHSD-fed mice (Fold Change &gt; 2, adjusted p &lt; 0.05). With IPA analysis, the top 3 influenced pathways including mitochondrial dysfunction (27%), Sirtuin signaling pathway (22%), and Granzyme A signaling (38%) are predicted to be activated while the oxidative phosphorylation (47%), estrogen receptor signaling (15%) and neutrophil extracellular trap signaling pathway (14%) are predicted to be inhibited. The Gene Oncology (GO) functional enrichment analysis in Srplot revealed the involvement of identified differentially expressed proteins in various biological processes such as lipid metabolism, catabolic process processes, and cellular respiration. Experiments are underway to validate specific proteins involved in these biological processes. Furthermore, analysis of cellular components highlighted three notable terms including myelin sheath, collagen-containing extracellular matrix and organelle inner membrane. In conclusion, the present study elucidates the significance of AdEVs in the context of diet-induced obesity and underscores the potential role of mitochondrial dysfunction in the initiation and progression of obesity and its associated diseases. NIH Veterans Affairs. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Identifying critical genes and pathways of doxorubicin-induced cardiomyopathy via bioinformatics analysis.

The pathogenesis of doxorubicin (DOX) induced cardiomyopathy (DCM) is still uncertain. We aimed to identify the critical genes and pathways involved in DCM based on bioinformatics analysis. The GSE59672 and GSE23598 mice heart tissue microarray data were obtained from Gene Expression Omnibus (GEO) database. The "limma" package of R software was used to screen the differently expressed genes (DEGs). GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were performed on DEGs by using "clusterProfiler" package in R software. The PPI (Protein - Protein Interaction) network of DEGs constructed by STRING online database and thereby the top 15 hub genes selected by cytoHubba in Cytoscape software. The hub genes interaction was performed by GeneMANIA online database. The "Corrplot" R package was employed to assess hub genes correlation. Finally, a total of 492 and 501 DEGs were screened in GSE59672 and GSE23598 datasets, respectively. GO analyses revealed that DEGs were mainly involved in the regulation of extracellular matrix organization, metabolic process, regulation of collagen-containing extracellular matrix. KEGG pathway analyses indicated that DEGs were mainly involved in protein digestion and absorption, ECM-receptor interaction, phagosome, and p53 signaling pathway. Finally, the 8 hub genes were identified, including Col1a1, Col3a1, Col1a2, Col6a1, Ptprc, Tyrobp, Itgb2, and Ctss. The present study identified a series of key genes, including Col1a1, Col3a1, Col1a2, Col6a1, Ptprc, Tyrobp, Itgb2, and Ctss. In addition, important pathways were also discovered. The results of this study may provide a novel molecular mechanism and potential therapeutic targets for DCM.

Efficient preparation of high-purity and intact mesenchymal stem cell-derived extracellular vesicles.

Mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) have shown great promise for regeneration and immunomodulation. However, efficient and scalable methods for their preparation are still lacking. In this study, we present the adoption of a label-free technique known as "EXODUS" to isolate and purify MSC-EVs from the conditioned medium. Our findings indicate that EXODUS can rapidly isolate EVs from 10 mL of conditioned medium with a 5-fold higher yield compared to conventional approaches, including ultracentrifugation (UC) and polyethylene glycol precipitation (PEG) methods. Additionally, pre-storing the conditioned medium at 4°C for 1 week resulted in a ~2-fold higher yield of MSC-EVs compared to the freshly prepared medium. However, storing the purified EV particles at 4°C for 1 month led to a 2-fold reduction in particle concentration. Furthermore, we found that MSC-EVs isolated using EXODUS exhibit higher expression levels of EV markers such as Alix, Flotillin1, CD81, and TSG101 in comparison to PEG and UC methods. We also discovered that MSC-EVs isolated using EXODUS are enriched in response to cytokine, collagen-containing extracellular matrix, and calcium ion binding compared to PEG method and enriched in extracellular structure organization, extracellular matrix, and extracellular matrix structure constituents compared to UC. Finally, we demonstrated that MSC-EVs isolated using EXODUS exhibit greater potential in animal organ development, tissue development, and anatomical structure morphogenesis compared to the UC. These findings suggest that EXODUS is a suitable method for the large-scale preparation of high-quality MSC-EVs for various clinical applications.

Integrated analysis of differentially expressed genes and miRNA expression profiles in dilated cardiomyopathy

BackgroundAlthough dilated cardiomyopathy (DCM) is a prevalent form of cardiomyopathy, the molecular mechanisms underlying its pathogenesis and progression remain poorly understood. It is possible to identify and validate DCM-associated genes, pathways, and miRNAs using bioinformatics analysis coupled with clinical validation methods. MethodsOur analysis was performed using 3 mRNA datasets and 1 miRNA database. We employed several approaches, including gene ontology (GO) analysis, KEGG pathway enrichment analysis, protein-protein interaction networks analysis, and analysis of hub genes to identify critical genes and pathways linked to DCM. We constructed a regulatory network for DCM that involves interactions between miRNAs and mRNAs. We also validated the differently expressed miRNAs in clinical samples (87 DCM ，83 Normal) using qRT-PCR.The miRNAs' clinical value was evaluated by receiver operating characteristic curves (ROCs). Results78 differentially expressed genes (DEGs) and 170 differentially expressed miRNAs (DEMs) were associated with DCM. The top five GO annotations were collagen-containing extracellular matrix, cell substrate adhesion, negative regulation of cell differentiation, and inflammatory response. The most enriched KEGG pathways were the Neurotrophin signaling pathway, Thyroid hormone signaling pathway, Wnt signaling pathway, and Axon guidance. In the PPI network, we identified 10 hub genes, and in the miRNA-mRNA regulatory network, we identified 8 hub genes and 15 miRNAs. In the clinical validation, we found 13 miRNAs with an AUC value greater than 0.9. ConclusionOur research offers novel insights into the underlying mechanisms of DCM and has implications for identifying potential targets for diagnosis and treatment of this condition.

Multiplexed MRM-based proteomics for identification of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus

BackgroundType 2 diabetes mellitus (T2DM) increases the risk of coronary heart disease (CHD) by 2–4 fold, and is associated with endothelial dysfunction, dyslipidaemia, insulin resistance, and chronic hyperglycaemia. The aim of this investigation was to assess, by a multimarker mass spectrometry approach, the predictive role of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus.MethodsThe study considered 34 patients with both T2DM and CHD, 31 patients with T2DM and without CHD, and 30 patients without diabetes with a diagnosis of CHD. Plasma samples of subjects were analysed through a multiplexed targeted liquid chromatography mass spectrometry (LC-MS)-based assay, namely Multiple Reaction Monitoring (MRM), allowing the simultaneous detection of peptides derived from a protein of interest. Gene Ontology (GO) Analysis was employed to identify enriched GO terms in the biological process, molecular function, or cellular component categories. Non-parametric multivariate methods were used to classify samples from patients and evaluate the relevance of the analysed proteins’ panel.ResultsA total of 81 proteins were successfully quantified in the human plasma samples. Gene Ontology analysis assessed terms related to blood microparticles, extracellular exosomes and collagen-containing extracellular matrix. Preliminary evaluation using analysis of variance (ANOVA) of the differences in the proteomic profile among patient groups identified 13 out of the 81 proteins as significantly different. Multivariate analysis, including cluster analysis and principal component analysis, identified relevant grouping of the 13 proteins. The first main cluster comprises apolipoprotein C-III, apolipoprotein C-II, apolipoprotein A-IV, retinol-binding protein 4, lysozyme C and cystatin-C; the second one includes, albeit with sub-grouping, alpha 2 macroglobulin, afamin, kininogen 1, vitronectin, vitamin K-dependent protein S, complement factor B and mannan-binding lectin serine protease 2. Receiver operating characteristic (ROC) curves obtained with the 13 selected proteins using a nominal logistic regression indicated a significant overall distinction (p < 0.001) among the three groups of subjects, with area under the ROC curve (AUC) ranging 0.91–0.97, and sensitivity and specificity ranging from 85 to 100%.ConclusionsTargeted mass spectrometry approach indicated 13 multiple circulating proteins as possible biomarkers of cardiovascular damage progression associated with T2DM, with excellent classification results in terms of sensitivity and specificity.

Identification of prognostic biomarkers for hepatocellular carcinoma with vascular invasion.

Vascular invasion (VI) profoundly impacts the prognosis of hepatocellular carcinoma (HCC), yet the underlying biomarkers and mechanisms remain elusive. This study aimed to identify prognostic biomarkers for HCC patients with VI. Transcriptome data from primary HCC tissues and HCC tissues with VI were obtained through the Genome Expression Omnibus database. Differentially expressed genes (DEGs) in the two types of tissues were analyzed using functional enrichment analysis to evaluate their biological functions. We examined the correlation between DEGs and prognosis by combining HCC transcriptome data and clinical information from The Cancer Genome Atlas database. Univariate and multivariate Cox regression analyses, along with the least absolute shrinkage and selection operator (LASSO) method were utilized to develop a prognostic model. The effectiveness of the model was assessed through time-dependent receiver operating characteristic (ROC) curve, calibration diagram, and decision curve analysis. In the GSE20017 and GSE5093 datasets, a total of 83 DEGs were identified. Gene Ontology analysis indicated that these DEGs were predominantly associated with xenobiotic stimulus, collagen-containing extracellular matrix, and oxygen binding. Additionally, Kyoto Encyclopedia of Genes and Genomes analysis revealed that the DEGs were primarily involved in immune defense and cellular signal transduction. Cox and LASSO regression further identified 7 genes (HSPA8, ABCF2, EAF1, MARCO, EPS8L3, PLA3G1B, C6), which were used to construct a predictive model in the training cohort. We used X-tile software to calculate the optimal cut-off value to stratify HCC patients into low-risk and high-risk groups. Notably, the high-risk group exhibited poorer prognosis than the low-risk group (P < 0.001). The model demonstrated area under the ROC curve (AUC) values of 0.815, 0.730, and 0.710 at 1-year, 3-year, and 5-year intervals in the training cohort, respectively. In the validation cohort, the corresponding AUC values were 0.701, 0.571, and 0.575, respectively. The C-index of the calibration curve for the training and validation cohorts were 0.716 and 0.665. Decision curve analysis revealed the model's efficacy in guiding clinical decision-making. The study indicates that 7 genes may be potential prognostic biomarkers and treatment targets for HCC patients with VI.

Prognostic Significance of Iron Metabolism and Immune-Related Genes as Risk Markers in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly lethal liver cancer with significant heterogeneity, which poses challenges in predicting prognosis and treatment outcomes. The impact of iron metabolism and immune-related genes (IMRGs) on HCC patient prognoses remains elusive. We utilized The Cancer Genome Atlas (TCGA) dataset to obtain mRNA expression data and clinical information from HCC patients. Through the application of LASSO regression and univariate/multivariate Cox regression analyses, we identified five IMRGs significantly associated with survival of HCC patients. We constructed a prognostic model comprising these five genes. The model demonstrated excellent predictive performance, not only within TCGA dataset but also when validated using the Gene Expression Omnibus (GEO) dataset. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses presented significant variations in functional categories, such as apical plasma membrane and collagen-containing extracellular matrix. Several pathways, including the PI3K-AKT signaling pathway and the calcium signaling pathway, exhibited significant variations among HCC patients with varying prognoses (P < 0.05). Immune infiltration analysis indicated significantly lower levels of various immune cells, immune functions, and immune checkpoints, such as B cells, CD8+ T cells, and TILs, in the high-risk group (P < 0.05). Immunophenoscore results suggested that the low-risk group may exhibit a more favorable response to immune therapy. Furthermore, the CellMiner database predicted anti-tumor drugs significantly associated with prognostic genes (P < 0.001). In conclusion, our findings highlight the predictive role of IMRGs in prognosis and immune treatment of HCC, indicating that ADAMTS13, CRHBP, VIPR1, FCN3, and CLEC1B may serve as potential prognostic biomarkers for HCC.

Expression of interleukin-34 in tongue squamous cell carcinoma and its clinical implications

To investigate the expression of interleukin- 34 (IL-34) in tongue squamous cell carcinoma (TSCC) and its clinical implications. Serum IL-34 level was detected in 36 patients with TSCC and 36 healthy individuals using enzymelinked immunosorbent assay (ELISA). The expressions of IL-34 mRNA and protein levels in TSCC and adjacent tissues were examined in 41 patients using real-time fluorescence quantitative PCR (qRT-PCR) and immunohistochemistry (IHC), and their correlation with the clinicopathological features of the patients was further analyzed. Informatic analysis of the differentially expressed genes related with IL-34 in TSCC was carried out based on String database, LinkedOmics database and GEO database, and GO functional analysis and KEGG signaling pathway enrichment analysis were performed using Webgestalt database. The serum level of IL-34 was significantly lower in TSCC patients than in the healthy individuals (P < 0.001), and its expression level was also significantly lower in the tumor tissues than in the adjacent tissues (P < 0.001). The expression level of IL-34 in TSCC tissues was related with lymph node metastasis and TNM staging (P < 0.05), but not with age, gender, smoking, drinking, or tumor size (P > 0.05). Informatic analysis suggested that IL-34 had the strongest correlation with CSF1R and PTPRJ. IL-34 and its related genes in TSCC were enriched mainly in bone marrow cell differentiation, collagen-containing extracellular matrix, and cytokine binding and signal receptor activator activity. KEGG signaling pathway enrichment showed that IL-34 and the related differentially expressed genes were involved mainly in osteoclast differentiation, protein polysaccharide in cancer, and the MAPK signaling pathway. IL-34 is lowly expressed in TSCC and participates in the occurrence and progression of TSCC, and can be potentially used as a new diagnostic biomarker and therapeutic target for TSCC.

Molecular mechanism of colorectal cancer and screening of molecular markers based on bioinformatics analysis.

Genomics and bioinformatics methods were used to screen genes and molecular markers correlated with colorectal cancer incidence and progression, and their biological functions were analyzed. Differentially expressed genes were obtained using the GEO2R program following colorectal cancer chip data GSE44076 retrieval from the Gene Expression Omnibus gene expression comprehensive database. An online database (David) that combines annotation, visualization, and gene discovery was utilized for investigating genes. Pathway and protein analyses were performed via resources from the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Visual analysis of the KEGG pathway was carried out according to ClueGO and CluePedia to establish the PPI network of gene interaction between pathways; the genes with the highest connectivity were screened by the molecular complex detection analysis method as Hub genes in this study; gene expression was verified by GEPIA online analysis tool, and Kaplan-Meier survival curve was drawn for prognosis analysis. By analyzing GSE44076 microarray data, 86 genes were selected, and colorectal cancer tissues' upregulation was observed in 27 genes and downregulation in 59 ones. GO assessment revealed that the differentially expressed genes were basically correlated with retinol dehydrogenase activity, carbon dehydrogenase activity, collagen-containing extracellular matrix, anchored component of memory, and cellular hormone metabolic process. Moreover, the KEGG assessment revealed that the differential genes contained various signal pathways such as retinol metabolism, chemical carotenogenesis, and nitrogen metabolism. Through further analysis of the PPI protein network, 4 clusters were obtained, and 16 Hub genes were screened out by combining the degree of each gene. Through the analysis of each gene on the prognosis of colon cancer through the GEPIA online analysis website, it was found that the expression levels of AQP8, CXCL8, and ZG16 genes were remarkably associated with colon cancer prognosis (P < 0.05). Genomics and bioinformatics methods can effectively analyze the genes and molecular markers correlated with colorectal cancer incidence and progression, help to systematically clarify the molecular mechanism of 16 key genes in colorectal cancer development and progression, and provide a theoretically valid insight for the screening of diagnostic markers of colorectal cancer and the selection of accurate targets for drug therapy.

Identification of potential biomarkers and candidate smallmolecule drugs for heart failure via comprehensive gene microarray analysis

Purpose: To identify potential novel biomarkers and to explore new small-molecule drugs for heart failure (HF).&#x0D; Methods: The Gene Expression Omnibus (GEO) microarray datasets were downloaded for analyzing the differentially expressed genes (DEGs). Venn analysis was performed to calculate the overlapping genes which were then used for Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis using cluster Profiler in R package; a protein-protein interaction network (PPI) was constructed using STRING database. The hub genes were selected for small-molecule drug identification, while molecular docking of small-molecule drugs and hub genes was performed using CBdock2.&#x0D; Results: Upregulated and downregulated DEGs were obtained from GSE84796, GSE107569 and GSE116250 datasets, respectively. Eleven (11) overlapping genes, which were enriched in collagen fiber tissue, collagen-containing extracellular matrix and collagen fiber-related pathways, were also enriched in AGE-RAGE and relaxin signaling pathways. The PPI network of the DEGs was constructed, and five hub genes, with high connectivity, were significantly upregulated in HF. The five hub genes were ranked as MFAP4, LTBP2, THBS4, COL3A1 and COL1A1. Two targets (COL1A1 and COL3A1) matched potential drugs, and fostamatinib shared by the two targets had the greatest therapeutic value for HF.&#x0D; Conclusion: Five novel biomarkers and involved signaling pathways have been identified in HF via comprehensive microarray analyses. The results also show that fostamatinib might be a promising drug candidate for HF treatment

Novel cuproptosis-related lncRNAs can predict the prognosis of patients with multiple myeloma.

Cuproptosis-related long-stranded non-coding RNAs (lncRNAs) have several implications for the prognosis of multiple myeloma (MM). This research aimed to construct a prognostic risk model for MM patients and explore the potential signaling pathways in the risk group. Cuproptosis-related lncRNAs were obtained from the co-expression analysis of cuproptosis-related genes and lncRNAs. Subsequently, twelve cuproptosis-related lncRNAs were selected to construct a prognostic risk model of MM patients by the least absolute shrinkage and selection operator (LASSO) regression. Then, the clinical data of these patients were randomly divided into the training group and the testing group. Next, patients were divided into the low- and high-risk groups according to the median risk score. The Kaplan-Meier survival analysis was performed to clarify the prognostic differences between risk subtypes. Besides, the Cox analysis was conducted to identify whether the risk score can be used as an independent prognostic factor. In addition, the receiver operating characteristic (ROC) curve analysis and the concordance index (C-index) curve analysis were performed to elucidate the value of risk score as a prognostic indicator. Finally, the differential risk analysis and functional enrichment analysis were carried out to identify the potential signaling pathways in the low- and high-risk groups. The results demonstrated that the overall survival (OS) of patients in the high-risk group was shorter than that in the low-risk group. There were significant differences in the expression of genes in MM patients between the high- and low-risk groups. The Gene Ontology (GO) analysis results showed that the differentially expressed risk-related genes (DERGs) were mainly concentrated on the collagen-containing extracellular matrix. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results, the DERGs may be related to the neuroactive ligand-receptor interaction and mitogen-activated protein kinase (MAPK) signaling pathway, indicating that they may be involved in the progression of tumors. The findings of this study suggest that cuproptosis-related lncRNAs may be effective biomarkers for predicting the prognosis of MM patients, which is anticipated to contribute to the improvement of clinical outcomes.

Identification of key genes associated with poor prognosis and neoplasm staging in gastric cancer.

Gastric cancer (GC) is highly biologically and genetically heterogeneous disease with poor prognosis. Increasing evidence indicates that biomarkers can serve as prediction and clinical intervention. Therefore, it is vital to identify core molecules and pathways participating in the development of GC. In this study, GSE54129, GSE56807, GSE63089, and GSE118916 were used for identified overlapped 75 DEGs. GO and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed DEGs mainly enriched in biological process about collagen-containing extracellular matrix and collagen metabolic. Next, protein-protein interaction network was built and the hub gene was excavated. Clinicopathological features and prognostic value were also evaluated. Hub genes were shown as below, FN1, COL1A2, COL1A1, COL3A1, COL4A1, COL6A3, COL5A2, SPARC, PDGFRB, COL12A1. Those genes were upregulation in GC and related to the poor prognosis (except COL5A2, P = .73). What is more, high expression indicated worse T stage and tumor, node, metastasis stage in GC patients. Later, the results of 25 GC tumor specimens and 34 normal tissues showed that FN1, COL3A1, COL4A1, SPARC, COL5A2, and COL12A1 were significantly upregulated in cancer samples. Our study systematically explored the core genes and crucial pathways in GC, providing insights into clinical management and individual treatment.

COL3A1, COL5A1 and COL6A2 serve as potential molecular biomarkers for osteoarthritis based on weighted gene co‑expression network analysis bioinformatics analysis.

Osteoarthritis (OA) is a non-inflammatory degenerative joint disease, characterized by joint pain and stiffness. The prevalence of OA increases with age. However, the relationship between biomarkers [collagen type III α1 (COL3A1), COL5A1, COL6A2, COL12A1] and OA remains unclear. The OA subchondral bone dataset GSE51588 was downloaded from the GEO database, and the differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis was performed, and a protein-protein interaction network was constructed and further analyzed using Cytoscape and STRING. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and then Gene Set Enrichment Analysis (GSEA) was used to formulate the molecular functions and pathways based on the results of GO and KEGG analyses. Comparative Toxicogenomics Database and TargetScan were used to identify the hub-gene-related diseases and the microRNAs that regulated the central hub genes. Immunohistochemical staining was performed to confirm the expression of related proteins in OA and non-OA tissue samples. A total of 1,679 DEGs were identified. GO analysis showed that the DEGs were primarily enriched in the process of 'immune system', 'extracellular region', 'secretory granule', 'collagen-containing extracellular matrix', 'ECM-receptor, glycosaminoglycan binding' and 'systemic lupus erythematosus'. The results of GSEA were similar to those of GO and KEGG enrichment terms for DEGs. A total of 25 important modules were generated, and two core gene clusters and seven core genes were obtained (COL6A2, COL5A2, COL12A1, COL5A1, COL6A1, LUM and COL3A1). Core genes were expressed differentially between OA subchondral bone and normal tissue samples. The expression levels of COL3A1, COL5A1 and COL6A2 in OA subchondral bone tissue were higher compared with those in normal tissues, but COL12A1 expression was not significantly increased; all stained markers were highly expressed in surrounding tissues of immunohistochemical staining. In conclusion, COL3A1, COL5A1 and COL6A2 may be potential molecular biomarkers for OA.

An alpha-helix variant p.Arg156Pro in LMNA as a cause of hereditary dilated cardiomyopathy: genetics and bioinfomatics exploration

LMNA gene encodes lamin A/C protein which participates in the construction of nuclear lamina, the mutations of LMNA result in a wide variety of diseases known as laminopathies. LMNA-related dilated cardiomyopathy(LMNA-DCM) is one of the more common laminopathy which characterized by progressive heart failure and arrhythmia. However, the mutation features of LMNA-DCM are yet to be elucidated. Herein we described a dilated cardiomyopathy family carrying novel variant c.467G > C(p.Arg156Pro) of LMNA as heterozygous pathogenic variant identified by whole-exome sequencing. With the help of Alphafold2, we predicted mutant protein structure and found an interrupted α-helix region in lamin A/C. In the analysis of 49 confirmed pathogenic missense of laminopathies, Chi-square test showed the DCM phenotype was related to the α-helix region mutation (p < 0.017). After screening the differentially expressed genes (DEGs) in both mice models and human patients in Gene Expression Omnibus database, we found the variation of α-helix-coding region in LMNA caused abnormal transcriptomic features in cell migration, collagen-containing extracellular matrix, and PI3K-Akt signaling pathway. Subsequently we constructed (TF)-mRNA-microRNA (miRNA) regulatory network and identified 7 key genes (FMOD, CYP1B1, CA3, F2RL1, HAPLIN1, SNAP91, and KANSL1) as potential biomarkers or therapeutic targets in LMNA-DCM patients.

Comparative proteomic profiling of plasma exosomes in lung cancer cases of liver and brain metastasis

BackgroundMetastases within liver or the brain are the most common causes of mortality from lung cancer (LC). Predicting liver or brain metastases before having evidence from imaging of the tumors is challenging but important for early patient intervention. According to mounting evidence, exosomes circulating within blood may facilitate cancer spread by transporting certain proteins for target cells.MethodsUsing liquid chromatography–MS/MS, we investigated the plasma exosomes’ proteomic profiles derived from 42 metastatic LC patients [16 solitary liver metastasis (LM), together with 26 solitary brain metastasis (BM)] and 25 local advanced (LA) lung cancer cases without metastasis, together with five healthy controls (HC), assessing the LM and BM pathogenesis and find potential novel organ-designated proteomic biomarkers. Using ELISA assay, we verified the expression levels of three plasma exosomal protein biomarkers in 110 LC patients, including 40 solitary LM, 32 solitary BM and 38 LA, and 25 HC.ResultsIn total, 143 and 120 differentially expressed exosome-based proteins (DEEPs) were found to be dysregulated in LM and BM of lung cancer (LM-DEEPs, BM-DEEPs), compared for LA lung cancer samples, respectively. The bioinformatics analyses indicated the heterogeneity and homogeneity in LM-DEEPs and BM-DEEPs. They were primarily engaged within proteomic triggering cascade, ECM-receptor interaction, and the collagen-containing extracellular matrix. Regarding heterogeneity, LM-DEEPs primarily consisted of proteoglycans, lipoprotein, integrin, and heat shock protein, whereas the BM-DEEPs consisted of calcium-dependent/S100 proteins. Furthermore, small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC)-plasma-stemming exosome proteomics showed heterogeneity, which helped to explain some of the differences between SCLC and NSCLC's metastatic features. We also found that SELL and MUC5B could be used as diagnostic markers of BM, while APOH, CD81, and CCT5 could help diagnose LM in LC patients. Additionally, we demonstrated in a validation cohort that MUC5B and SELL could serve as biomarkers for diagnosing BM, and APOH could be a novel potential diagnostic biomarker of LM.ConclusionWe presented the comprehensive and comparative plasma-stemming exosomes’ proteomic profiles from cases of LC who had isolated liver and brain metastases for the first time. We also suggested several possible biomarkers and pathogenic pathways that might be a great starting point for future research on LC metastasis.