Detection of lower levels of SNAP25 using multiple microarray systems and its functional significance in medulloblastoma

Screening Key Pathogenic Genes and Small Molecule Compounds for PNET

Primitive neuroectodermal tumors (PNET) are rare malignant tumors, but the mortality rate of the patients is extremely high. The aim of this study was to identify the hub genes and pathways involved in the pathogenesis of PNET and to screen the potential small molecule drugs for PNET. We extracted gene expression profiles from the Gene Expression Omnibus database and identified differentially expressed genes (DEGs) through Limma package in R. Two expression profiles (GSE14295 and GSE74195) were downloaded, including 33 and 5 cases separately. Four hundred sixty-eight DEGs (161 upregulated; 307 downregulated) were identified. Functional annotation and KEGG pathway enrichment of the DEGs were performed using DAVID and Kobas. Gene Ontology analysis showed the significantly enriched Gene Ontology terms included but not limited to mitosis, nuclear division, cytoskeleton, synaptic vesicle, syntaxin binding, and GABA A receptor activity. Cancer-related signaling pathways, such as DNA replication, cell cycle, and synaptic vesicle cycle, were found to be associated with these genes. Subsequently, the STRING database and Cytoscape were utilized to construct a protein-protein interaction and screen the hub genes, and we identified 5 hub genes (including CCNB1, CDC20, KIF11, KIF2C, and MAD2L1) as the key biomarkers for PNET. Finally, we identified potential small molecule drugs through CMap. Seven small molecule compounds, including trichostatin A, luteolin, repaglinide, clomipramine, lorglumide, vorinostat, and resveratrol may become potential candidates for PNET drugs.

DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations

Drug combination therapies are superior to monotherapy for cancer treatment in many ways. Identifying novel drug combinations by screening is challenging for the wet-lab experiments due to the time-consuming process of the enormous search space of possible drug pairs. Thus, computational methods have been developed to predict drug pairs with potential synergistic functions. Notwithstanding the success of current models, understanding the mechanism of drug synergy from a chemical-gene-tissue interaction perspective lacks study, hindering current algorithms from drug mechanism study. Here, we proposed a deep neural network model termed DTSyn (Dual Transformer encoder model for drug pair Synergy prediction) based on a multi-head attention mechanism to identify novel drug combinations. We designed a fine-granularity transformer encoder to capture chemical substructure-gene and gene-gene associations and a coarse-granularity transformer encoder to extract chemical-chemical and chemical-cell line interactions. DTSyn achieved the highest receiver operating characteristic area under the curve of 0.73, 0.78. 0.82 and 0.81 on four different cross-validation tasks, outperforming all competing methods. Further, DTSyn achieved the best True Positive Rate (TPR) over five independent data sets. The ablation study showed that both transformer encoder blocks contributed to the performance of DTSyn. In addition, DTSyn can extract interactions among chemicals and cell lines, representing the potential mechanisms of drug action. By leveraging the attention mechanism and pretrained gene embeddings, DTSyn shows improved interpretability ability. Thus, we envision our model as a valuable tool to prioritize synergistic drug pairs with chemical and cell line gene expression profile.

Prognostic Value of Long Non-Coding RNA Long Intergenic Non-Protein Coding RNA 1586 in Pancreatic Cancer

This study investigated the prognostic value of the long non-coding RNA (lncRNA) LINC01586 in pancreatic cancer. Three pancreatic cancer patients who received pancreaticoduodenectomies in our department in January 2019 were retrospectively selected. Cancer tissue and adjacent tissue samples were collected for high-throughput sequencing of lncRNAs. Among them, 221 lncRNAs were up-regulated and 235 were down-regulated. The expression of LINC01586 was decreased in pancreatic cancer patients (logFC = -3.308). An additional 74 tissue specimens were collected from pancreatic cancer patients from January 2011 to December 2016 for low-throughput validation. Patient samples were categorized into overexpression and low expression groups, based on the median LINC01586 expression level. The LINC01586 low expression group exhibited larger tumor size than the overexpression group (P < 0.001), while the low expression group exhibited a lower cancer-related survival rate than the overexpression group (one-year cancer-related survival rate: 55.6% vs. 89.2%, P < 0.001). Further analysis confirmed that low expression of LINC01586 was associated with poor prognosis for pancreatic cancer patients (OR = 0.169, 95% CI 0.066-0.437, P = 0.000). KEGG signaling pathway analysis was used to enrich LINC01586 target genes, and were mainly related to two metabolic pathways: insulin secretion (P = 0.011) and dopaminergic synapses (P = 0.0129), with SNAP25 as the core gene. The expression of LINC01586 and SNAP25were positively correlated in pancreatic cancer (R = 0.81 and P < 0.001). Together, our results indicate that LINC01586 may be used as a biomarker for prognosis predictions in pancreatic cancer patients, and its low expression is associated with poor prognosis.

Mining the potential prognostic value of synaptosomal-associated protein 25 (SNAP25) in colon cancer based on stromal-immune score

Background

Colon cancer is one of the deadliest tumors worldwide. Stromal cells and immune cells play important roles in cancer biology and microenvironment across different types of cancer. This study aimed to identify the prognostic value of stromal/immune cell-associated genes for colon cancer in The Cancer Genome Atlas (TCGA) database using bioinformatic technology.

Methods

The gene expression data and corresponding clinical information of colon cancer were downloaded from TCGA database. Stromal and immune scores were estimated based on the ESTIMATE algorithm. Sanger software was used to identify the differentially expressed genes (DEGs) and prognostic DEGs based on stromal and immune scores. External validation of prognostic biomarkers was conducted in Gene Expression Omnibus (GEO) database. Gene ontology (GO) analysis, pathway enrichment analysis, and gene set enrichment analysis (GSEA) were used for functional analysis. STRING and Cytoscape were used to assess the protein-protein interaction (PPI) network and screen hub genes. Quantitative real-time PCR (qRT-PCR) was used to validate the expression of hub genes in clinical tissues. Synaptosomal-associated protein 25 (SNAP25) was selected for analyzing its correlations with tumor-immune system in the TISIDB database.

Results

Worse overall survivals of colon cancer patients were found in high stromal score group (2963 vs. 1930 days, log-rank test P = 0.038) and high immune score group (2894 vs. 2230 days, log-rank test P = 0.076). 563 up-regulated and 9 down-regulated genes were identified as stromal-immune score-related DEGs. 70 up-regulated DEGs associated with poor outcomes were identified by COX proportional hazard regression model, and 15 hub genes were selected later. Then, we verified aquaporin 4 (AQP4) and SNAP25 as prognostic biomarkers in GEO database. qRT-PCR results revealed that AQP4 and SNAP25 were significantly elevated in colon cancer tissues compared with adjacent normal tissues (P = 0.003, 0.001). GSEA and TISIDB suggested that SNAP25 involved in cancer-related signaling pathway, immunity and metabolism progresses.

Conclusion

SNAP25 is a microenvironment-related and immune-related gene that can predict poor outcomes in colon cancer.

Proteomic Profiling of Retinoblastoma-Derived Exosomes Reveals Potential Biomarkers of Vitreous Seeding

Retinoblastoma (RB) is the most common tumor of the eye in early childhood. Although recent advances in conservative treatment have greatly improved the visual outcome, local tumor control remains difficult in the presence of massive vitreous seeding. Traditional biopsy has long been considered unsafe in RB, due to the risk of extraocular spread. Thus, the identification of new biomarkers is crucial to design safer diagnostic and more effective therapeutic approaches. Exosomes, membrane-derived nanovesicles that are secreted abundantly by aggressive tumor cells and that can be isolated from several biological fluids, represent an interesting alternative for the detection of tumor-associated biomarkers. In this study, we defined the protein signature of exosomes released by RB tumors (RBT) and vitreous seeding (RBVS) primary cell lines by high resolution mass spectrometry. A total of 5666 proteins were identified. Among these, 5223 and 3637 were expressed in exosomes RBT and one RBVS group, respectively. Gene enrichment analysis of exclusively and differentially expressed proteins and network analysis identified in RBVS exosomes upregulated proteins specifically related to invasion and metastasis, such as proteins involved in extracellular matrix (ECM) remodeling and interaction, resistance to anoikis and the metabolism/catabolism of glucose and amino acids.