Biological Procedures Online最新文献

Background: Organotypic long-term cultivation of vascularized retina explants is a major challenge for application in drug development, drug screening, diagnostics and future personalized medicine. With this background, an assay and protocol for organotypic culture of vascularized retina explants in vitro with optimum tissue integrity preservation is developed and demonstrated.

Methods: Morphological, histologic and biochemical integrity as well as viability of vascularized retina explants are compared as function of cultivation time for differently structured nanotube scaffolds. In doing so, porcine retina explants obtained from a local slaughterhouse are employed as paradigm for vascularized retina.

Conclusions: We demonstrate that titania nanotube arrays are highly promising as culturing scaffold of vascularized retina explants in vitro due to highly tunable surface properties regarding biomedical signaling. The unprecedented maintenance of tissue integrity allows for screening of pharmacological drugs and disease mechanisms in an ex-vivo test-based culture system with reduced need for animal experiments.

Background: The HumanMethylationEPIC v2.0 BeadChip (EPIC v2.0) microarray is a widely used tool for genome-wide DNA methylation (DNAm) analysis, designed for high-quality human DNA with a recommended input of 250 ng. However, in clinical and forensic settings, DNA samples may be of low quality and/or quantity (highly fragmented and/or available in low amounts). This study assessed the performance of the EPIC v2.0 on DNA samples with various combinations of average DNA fragment size (350, 230, 165, and 95 bp) and DNA input amount (100, 50, 20, and 10 ng), compared to a paired control sample analyzed under optimal conditions (high-quality DNA and 250 ng DNA input).

Results: The best performance was obtained for samples with average DNA fragment size of 350 bp and 100 ng DNA input (~ 90% probe detection rate, r = 0.995, and median absolute beta value differences|Δβ| = 0.012 when compared with the control sample). Samples with lower average DNA fragment sizes and DNA input amount performed worse, with the lowest probe detection rate (~ 43%), r = 0.946, and the highest|Δβ| (0.038). Samples with average DNA fragment sizes of 95 bp and those with 165 bp at 10 ng DNA input failed to pass sample quality control (QC). CpG sites with intermediate DNAm values (β = 0.1-0.9) showed higher|Δβ| than the extreme DNAm values (β = 0-0.1, and β = 0.9-1). Finally, we assessed an application of DNAm by performing epigenetic age analysis, and observed mean absolute errors (MAEs) below 10 years for 350 bp samples across four epigenetic clocks.

Conclusions: Both DNA fragment size and DNA input amounts affect DNAm analysis on the EPIC v2.0, with the investigated DNA fragment size having a greater impact than the investigated DNA input amount. DNAm measurements were achieved with the EPIC v2.0 microarray down to an average DNA fragment size of 165 bp and a 20 ng DNA input. Highly fragmented DNA (95 bp) did not result in usable DNAm analysis as all samples failed QC. Overall, our study demonstrates the potential and limitations of EPIC v2.0 microarray with low quality and quantity DNA samples.

Objective: Gastric cancer (GCa) is a common malignancy where the epithelial-to-mesenchymal transition (EMT) pathway and lncRNAs are key to proliferation and metastasis. This study aimed to identify lncRNAs regulating the EMT pathway as therapeutic and diagnostic targets in GCa.

Methods: We utilized the TCGA-STAD dataset and differential expression gene (DEG) analysis was performed to identify overlapping genes between DEGs and the EMT pathway. The LASSO feature reduction algorithm and ROC curve analysis were applied to prioritize candidate biomarkers. Additionally, immunological and immunotherapy analyses were conducted to evaluate the significance of TIMP1 in GCa. Pearson correlation analysis was performed between TIMP1 and all differentially expressed lncRNAs, and the most strongly correlated candidates were selected. Experimental validation of the results was carried out using RT-qPCR with 25 cancerous and 25 adjacent normal tissue samples in GCa.

Results: DEG analysis identified 2,926 DEGs, among which 87 were associated with the EMT pathway. The LASSO algorithm reduced this list to 11 genes, and ROC curve analysis identified TIMP1 as a candidate biomarker in GCa. Higher TIMP1 expression was associated with improved response rates. Correlation analysis revealed that LINC01615 was most strongly co-regulated with TIMP1 in GCa. RT-qPCR confirmed that both TIMP1 and LINC01615 were significantly upregulated in tumor tissues compared to adjacent normal tissues, highlighting their potential as diagnostic biomarkers.

Conclusion: A strong correlation between TIMP1 and LINC01615 suggests their role in promoting GCa proliferation, invasion, and metastasis. TIMP1/LINC01615 axis regulates the EMT pathway and is a potential biomarker and therapeutic target in GCa.

We report a rare case of long-term survival in a 59-year-old male patient diagnosed with stage IIIB lung adenocarcinoma. Despite disease progression following initial chemotherapy, the patient achieved prolonged survival through a combination of chemotherapy and radiotherapy. This case highlights the potential for extended survival in advanced lung cancer with multimodal treatment approaches.

Background: The APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like) family plays a vital mutagenic role in diverse human malignancies. Nevertheless, the biological characteristics of APOBEC family members and their clinical significance in cancer have not been comprehensively explored. Our primary objective was to characterize the distribution and clinical relevance of APOBEC family members and their mutations across multiple cancer types, with a particular focus on thyroid carcinoma (THCA).

Methods: In this study, we employed an integrated, multi-faceted approach combining diverse statistical and bioinformatics methods. Data sources included whole-exome sequencing (WES), targeted next-generation sequencing (NGS), bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), Western blot assays, drug sensitivity analyses, and in vivo animal models.

Results: APOBEC mutations occupy a significant position in the mutation landscape of THCA. High APOBEC mutation enrichment scores were strongly associated with poor prognosis, immune evasion, and increased risk of malignant progression in THCA patients. These mutations contribute to tumor advancement and influence cellular differentiation within the tumor microenvironment. Additionally, we developed a prognostic APOBEC mutagenesis model using machine learning, which was validated across multiple THCA cohorts. In vitro and in vivo experiments demonstrated that APOBEC2 inhibition effectively suppressed tumor proliferation, metastasis, and glycolytic activity, while simultaneously enhancing immune activation and boosting the efficacy of immune checkpoint inhibitors.

Conclusion: Our study systematically characterizes APOBEC mutations across various cancer types and underscores their promise as biomarkers for aggressive tumor phenotypes, prognostic assessment, and immunotherapy response to THCA. These findings were accomplished through multi-omics sequencing and validated through comprehensive in vitro and in vivo experiments.

Immunosenescence has lately become a focal point in oncology investigations. Nevertheless, its significance in pancreatic adenocarcinoma (PAAD) remains to be elucidated. This research sought to examine the predictive value and treatment implications of immunosenescence-related genes in PAAD. We use the public datasets from TCGA and GEO to extract clinicopathological factors and RNA-sequencing data. Differentially expressed genes (DEGs) were ascertained, and then molecular subtypes were classified by consensus clustering. Functional enrichment analysis using KEGG and GO, GSEA, survival analysis, and the design of an immunosenescence-linked risk signature using Lasso-Cox regression is part of the analytical approach. We used TIMER, TISCH, etc., to further elaborate its relationship with the tumor immune microenvironment. Drug sensitivity analysis is conducted using GSCA. The independent prognostic gene MAFF's expression was validated utilizing GEPIA2, real-time quantitative PCR (RT-qPCR), and western blot (WB). We identify two immunosenescence-associated subtypes of PAADwith distinct gene expression profiles and clinical outcomes. The creation of a risk signature model utilizing IMSRGs reveals three genes-NCAM1, SESN1, and MAFF-that are significantly correlated with overall survival. The elevated-risk cohort, as identified by the risk assessment metric, correlates with poorer survival rates. Additionally, we uncover an association between the risk indicator and the tumor milieu, encompassing immune cell infiltration patterns and drug sensitivity profiles. Our results and published data show that MAFF levels in pancreatic cancer tissues markedly exceeds that of normal tissues and have a bad effect on prognosis. The study identifies a prognostic gene signature for PAAD, revealing its potential in personalized medicine and immunotherapy, and highlights the role of immunosenescence in cancer treatment.