Comprehensive evaluation of genes related to basement membrane in hepatocellular carcinoma.

In all mammals, the basement membrane serves as a pivotal extracellular matrix. Hepatocellular carcinoma (HCC) is a challenge among numerous cancer types shaped by basement membrane-related genes (BMGs). Our research established an innovative prognostic model that is highly accurate in its prediction of HCC prognoses and immunotherapy efficacy to summarize the crucial role of BMGs in HCC. We obtained HCC transcriptome analysis data and corresponding clinical data from The Cancer Genome Atlas (TCGA). To augment our dataset, we incorporated 222 differentially expressed BMGs identified from relevant literature. A weighted gene coexpression network analysis (WGCNA) of 10158 genes demonstrated four modules that were connected to HCC. Additionally, 66 genes that are found at the intersection of BMGs and HCC-related genes were designated as hub HCC-related BMGs. MMP1, ITGA2, P3H1, and CTSA comprise the novel model that was engineered using univariate and multivariate Cox regression analysis. Furthermore, the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) datasets encouraged the BMs model's validity. The overall survival (OS) of individuals with HCC may be precisely predicted in the TCGA and ICGC databases utilizing the BMs model. A nomogram based on the model was created in the TCGA database at similar time, and displayed a favorable discriminating ability for HCC. Particularly, when compared to the patients at an elevated risk, the patients with a low-risk profile presented different tumor microenvironment (TME) and hallmark pathways. Moreover, we discovered that a lower risk score of HCC patients would display a greater response to immunotherapy. Finally, quantitative real-time PCR (qRT-PCR) experiments were used to verify the expression patterns of BMs model. In summary, BMs model demonstrated efficacy in prognosticating the survival probability of HCC patients and their immunotherapeutic responsiveness.

Fabrication of bFGF/polydopamine-loaded PEEK implants for improving soft tissue integration by upregulating Wnt/ß-catenin signaling.

The difficulties associated with polyetheretherketone (PEEK) implants and soft tissue integration for craniomaxillofacial bone repair have led to a series of complications that limit the clinical benefits. In this study, 3D printed multi-stage microporous PEEK implants coated with bFGF via polydopamine were fabricated to enhance PEEK implant-soft tissue integration. Multistage microporous PEEK scaffolds prepared by sulfonation of concentrated sulfuric acid were coated with polydopamine, and then used as templates for electrophoretic deposition of bFGF bioactive factors. Achieving polydopamine and bFGF sustained release, the composite PEEK scaffolds possessed good mechanical properties, hydrophilicity, protein adhesion properties. The in vitro results indicated that bFGF/polydopamine-loaded PEEK exhibited good biocompatibility to rabbit embryonic fibroblasts (REF) by promoting cell proliferation, adhesion, and migration. Ribonucleic acid sequencing (RNA-seq) revealed that bFGF/polydopamine-loaded PEEK implants significantly upregulated the expression of genes and proteins associated with soft tissue integration and activated Wnt/ß-catenin signaling in biological processes, but related expression of genes and proteins was significantly downregulated when the Wnt/ß-catenin signaling was inhibited. Furthermore, in vivo bFGF/polydopamine-loaded PEEK implants exhibited excellent performance in improving the growth and adhesion of the surrounding soft tissue. In summary, bFGF/polydopamine-loaded PEEK implants possess soft tissue integration properties by activating the Wnt/ß-catenin signaling, which have a potential translational clinical application in the future.

Combining bulk RNA-sequencing and single-cell RNA-sequencing data to reveal the immune microenvironment and metabolic pattern of osteosarcoma.

Background: Osteosarcoma (OS) is a kind of solid tumor with high heterogeneity at tumor microenvironment (TME), genome and transcriptome level. In view of the regulatory effect of metabolism on TME, this study was based on four metabolic models to explore the intertumoral heterogeneity of OS at the RNA sequencing (RNA-seq) level and the intratumoral heterogeneity of OS at the bulk RNA-seq and single cell RNA-seq (scRNA-seq) level. Methods: The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a glycolysis, pentose phosphate pathway (PPP), fatty acid oxidation (FAO) and glutaminolysis gene sets score. ConsensusClusterPlus was employed to cluster OS samples downloaded from the Target database. The scRNA-seq and bulk RNA-seq data of immune cells from GSE162454 dataset were analyzed to identify the subsets and types of immune cells in OS. Malignant cells and non-malignant cells were distinguished by large-scale chromosomal copy number variation. The correlations of metabolic molecular subtypes and immune cell types with four metabolic patterns, hypoxia and angiogenesis were determined by Pearson correlation analysis. Results: Two metabolism-related molecular subtypes of OS, cluster 1 and cluster 2, were identified. Cluster 2 was associated with poor prognosis of OS, active glycolysis, FAO, glutaminolysis, and bad TME. The identified 28608 immune cells were divided into 15 separate clusters covering 6 types of immune cells. The enrichment scores of 5 kinds of immune cells in cluster-1 and cluster-2 were significantly different. And five kinds of immune cells were significantly correlated with four metabolic modes, hypoxia and angiogenesis. Of the 28,608 immune cells, 7617 were malignant cells. The four metabolic patterns of malignant cells were significantly positively correlated with hypoxia and negatively correlated with angiogenesis. Conclusion: We used RNA-seq to reveal two molecular subtypes of OS with prognosis, metabolic pattern and TME, and determined the composition and metabolic heterogeneity of immune cells in OS tumor by bulk RNA-seq and single-cell RNA-seq.