Project description:Loop-mediated isothermal amplification assays are currently limited to one target per reaction in the absence of melting curve analysis, molecular probes or restriction enzyme digestion. Here, we demonstrate multiplexing of five targets in a single fluorescent channel using digital LAMP and the machine learning-based method amplification curve analysis, resulting in a classification accuracy of 91.33% on 54 186 positive amplification events.
Project description:Digital transcriptomics with pyrophosphatase based ultra-high throughput DNA sequencing of di-tags provides high sensitivity and cost-effective gene expression profiling. Sample preparation and handling are greatly simplified compared to Serial Analysis of Gene Expression (SAGE). We compare DeepSAGE and LongSAGE data and demonstrate greater power of detection and multiplexing of samples derived from potato. The transcript analysis revealed a great abundance of up-regulated potato transcripts associated with stress in dormant potatoes compared to harvest. Importantly, many transcripts were detected that cannot be matched to known genes, but is likely to be part of the abiotic stress-response in potato.
Project description:Current histopathological diagnosis involves human expert interpretation of stained images for diagnosis. This process is prone to inter-observer variability, often leading to low concordance rates amongst pathologists across many types of tissues. Further, since structural features are mostly just defined for epithelial alterations during tumor progression, the use of associated stromal changes is limited. Here we sought to examine whether digital analysis of commonly used hematoxylin and eosin-stained images could provide precise and quantitative metrics of disease from both epithelial and stromal cells. We developed a convolutional neural network approach to identify epithelial breast cells from their microenvironment. Second, we analyzed the microenvironment to further observe different constituent cells using unsupervised clustering. Finally, we categorized breast cancer by the combined effects of stromal and epithelial inertia. Together, the work provides insight and evidence of cancer association for interpretable features from deep learning methods that provide new opportunities for comprehensive analysis of standard pathology images.
Project description:Semi-quantitative scoring is a method that is widely used to estimate the quantity of proteins on chromogen-labelled immunohistochemical (IHC) tissue sections. However, it suffers from several disadvantages, including its lack of objectivity and the fact that it is a time-consuming process. Our aim was to test a recently established artificial intelligence (AI)-aided digital image analysis platform, Pathronus, and to compare it to conventional scoring by five observers on chromogenic IHC-stained slides belonging to three experimental groups. Because Pathronus operates on grayscale 0-255 values, we transformed the data to a seven-point scale for use by pathologists and scientists. The accuracy of these methods was evaluated by comparing statistical significance among groups with quantitative fluorescent IHC reference data on subsequent tissue sections. The pairwise inter-rater reliability of the scoring and converted Pathronus data varied from poor to moderate with Cohen's kappa, and overall agreement was poor within every experimental group using Fleiss' kappa. Only the original and converted that were obtained from Pathronus original were able to reproduce the statistical significance among the groups that were determined by the reference method. In this study, we present an AI-aided software that can identify cells of interest, differentiate among organelles, protein specific chromogenic labelling, and nuclear counterstaining after an initial training period, providing a feasible and more accurate alternative to semi-quantitative scoring.
Project description:<p>Cancer cells exhibit metabolic plasticity to meet oncogene-driven dependencies while coping with nutrient availability. A better understanding of how systemic metabolism impacts the accumulation of metabolites that reprogram the tumor microenvironment and drive cancer could facilitate development of precision nutrition approaches. Using the Hi-MYC prostate cancer mouse model, we demonstrated that an obesogenic high-fat diet rich in saturated fats accelerates the development of c-MYC-driven invasive prostate cancer through metabolic rewiring. Although c-MYC modulated key metabolic pathways, interaction with an obesogenic high-fat diet was necessary to induce glycolysis and lactate accumulation in tumors. These metabolic changes were associated with augmented infiltration of CD206+ and PD-L1+ tumor-associated macrophages and FOXP3+ regulatory T cells, as well as with the activation of transcriptional programs linked to disease progression and therapy resistance. Lactate itself also stimulated neoangiogenesis and prostate cancer cell migration, which were significantly reduced following treatment with the lactate dehydrogenase inhibitor FX11. In prostate cancer patients, high saturated fat intake and increased body mass index were associated with tumor glycolytic features that promote the infiltration of M2-like tumor-associated macrophages. Finally, upregulation of lactate dehydrogenase, indicative of a lactagenic phenotype, was associated with a shorter time to biochemical recurrence in independent clinical cohorts. This work identifies cooperation between genetic drivers and systemic metabolism to hijack the tumor microenvironment and promote prostate cancer progression through oncometabolite accumulation. This sets the stage for the assessment of lactate as a prognostic biomarker and supports strategies of dietary intervention and direct lactagenesis blockade in treating advanced prostate cancer.</p><p><br></p><p><strong>Murine prostate assays</strong> are reported in the current study <strong>MTBLS3317</strong>.</p><p><strong>Murine serum assays</strong> are reported in <a href='https://www.ebi.ac.uk/metabolights/MTBLS3316' rel='noopener noreferrer' target='_blank'><strong>MTBLS3316</strong></a>.</p>
Project description:Background5-Methylcytidine (m5C) methylation is an emerging epigenetic modification in recent years, which is associated with the development and progression of various cancers. However, the prognostic value of m5C regulatory genes and the correlation between m5C methylation and the tumor microenvironment (TME) in prostate cancer remain unknown.MethodsIn the current study, the genetic and transcriptional alterations and prognostic value of m5C regulatory genes were investigated in The Cancer Genome Atlas and Gene Expression Omnibus datasets. Then, an m5C prognostic model was established by LASSO Cox regression analysis. Gene set variation analyses (GSVA), gene set enrichment analysis (GSEA), clinical relevance, and TME analyses were conducted to explain the biological functions and quantify the TME scores between high-risk and low-risk subgroups. m5C regulatory gene clusters and m5C immune subtypes were identified using consensus unsupervised clustering analysis. The Cell-type Identification By Estimating Relative Subsets of RNA Transcripts algorithm was used to calculate the contents of immune cells.ResultsTET3 was upregulated at transcriptional levels in PCa compared with normal tissues, and a high TET3 expression was associated with poor prognosis. An m5C prognostic model consisting of 3 genes (NSUN2, TET3, and YBX1) was developed and a nomogram was constructed for improving the clinical applicability of the model. Functional analysis revealed the enrichment of pathways and the biological processes associated with RNA regulation and immune function. Significant differences were also found in the expression levels of m5C regulatory genes, TME scores, and immune cell infiltration levels between different risk subgroups. We identified two distinct m5C gene clusters and found their correlation with patient prognosis and immune cell infiltration characteristics. Naive B cells, CD8+ T cells, M1 macrophages and M2 macrophages were obtained and 2 m5C immune subtypes were identified. CTLA4, NSUN6, TET1, and TET3 were differentially expressed between immune subtypes. The expression of CTLA4 was found to be correlated with the degree of immune cell infiltration.ConclusionsOur comprehensive analysis of m5C regulatory genes in PCa demonstrated their potential roles in the prognosis, clinical features, and TME. These findings may improve our understanding of m5C regulatory genes in the tumor biology of PCa.
Project description:The tumor immune microenvironment (TIME) consists of multiple cell types that contribute to the heterogeneity and complexity of prostate cancer (PCa). In this study, we sought to understand the gene-expression signature of patients with primary prostate tumors by investigating the co-expression profiles of patient samples and their corresponding clinical outcomes, in particular "disease-free months" and "disease reoccurrence". We tested the hypothesis that the CXCL13-CXCR5 axis is co-expressed with factors supporting TIME and PCa progression. Gene expression counts, with clinical attributes from PCa patients, were acquired from TCGA. Profiles of PCa patients were used to identify key drivers that influence or regulate CXCL13-CXCR5 signaling. Weighted gene co-expression network analysis (WGCNA) was applied to identify co-expression patterns among CXCL13-CXCR5, associated genes, and key genetic drivers within the CXCL13-CXCR5 signaling pathway. The processing of downloaded data files began with quality checks using NOISeq, followed by WGCNA. Our results confirmed the quality of the TCGA transcriptome data, identified 12 co-expression networks, and demonstrated that CXCL13, CXCR5 and associated genes are members of signaling networks (modules) associated with G protein coupled receptor (GPCR) responsiveness, invasion/migration, immune checkpoint, and innate immunity. We also identified top canonical pathways and upstream regulators associated with CXCL13-CXCR5 expression and function.
Project description:Prostate cancer (PCa) is the second most common cause of cancer death in American men. Metastatic castration-resistant prostate cancer (mCRPC) is the most lethal form of PCa and preferentially metastasizes to the bones through incompletely understood molecular mechanisms. Herein, we processed RNA sequencing data from patients with mCRPC (n = 60) and identified 14 gene clusters (modules) highly correlated with mCRPC bone metastasis. We used a novel combination of weighted gene co-expression network analysis (WGCNA) and upstream regulator and gene ontology analyses of clinically annotated transcriptomes to identify the genes. The cyan module (M14) had the strongest positive correlation (0.81, p = 4 × 10-15) with mCRPC bone metastasis. It was associated with two significant biological pathways through KEGG enrichment analysis (parathyroid hormone synthesis, secretion, and action and protein digestion and absorption). In particular, we identified 10 hub genes (ALPL, PHEX, RUNX2, ENPP1, PHOSPHO1, PTH1R, COL11A1, COL24A1, COL22A1, and COL13A1) using cytoHubba of Cytoscape. We also found high gene expression for collagen formation, degradation, absorption, cell-signaling peptides, and bone regulation processes through Gene Ontology (GO) enrichment analysis.
Project description:Semaphorin 4A (SEMA4A) belonged to a family of membrane-bound proteins that were initially recognized as a kind of axon guidance factors in nervous system. It was preferentially expressed on immune cells and has been proven to play a prominent role in immune function and angiogenesis. In this study, we found that SEMA4A was highly expressed in prostate cancer (PCa) tissues and correlated with Gleason scores and distant metastasis. SEMA4A could induce Epithelial-mesenchymal transition (EMT) of PCa cells and consequently promote invasion by establishing a positive loop with IL-10 in stromal cells. In vivo experiments showed more dissemination in mice injected with SEMA4A-overexpressing cells in mouse models and both the number and size of lung metastases were significantly increased in SEMA4A-overexpressing tumors. SEMA4A depletion by genetic means prevents lung metastasis in PCa xenograft models. Our data suggest a crucial role of SEMA4A in PCa and blocking SEMA4A-IL-10 axis represents an attractive approach to improving therapeutic outcomes.
Project description:Prostate cancer is one of the most common malignancies in males wherein 1 in 8 men are diagnosed with this disease in their lifetime. The urgency to find novel therapeutic interventions is associated with high treatment resistance and mortality rates associated with castration-resistant prostate cancer. Anoikis is an apoptotic phenomenon for normal epithelial or endothelial cells that have lost their attachment to the extracellular matrix (ECM). Tumor cells that lose their connection to the ECM can die via apoptosis or survive via anoikis resistance and thus escaping to distant organs for metastatic progression. This review discusses the recent advances made in our understanding of the signaling effectors of anoikis in prostate cancer and the approaches to translate these mechanistic insights into therapeutic benefits for reducing lethal disease outcomes (by overcoming anoikis resistance). The prostate tumor microenvironment is a highly dynamic landscape wherein the balance between androgen signaling, cell lineage changes, epithelial-mesenchymal transition (EMT), extracellular matrix interactions, actin cytoskeleton remodeling as well as metabolic changes, confer anoikis resistance and metastatic spread. Thus, these mechanisms also offer unique molecular treatment signatures, exploitation of which can prime prostate tumors to anoikis induction with a high translational significance.