Protocol for predicting peptides with anticancer and antimicrobial properties by a tri-fusion neural network.

Here, we describe the use of TriNet to predict peptides with anticancer and antimicrobial properties by a tri-fusion neural network. We detail the use of TriNet for both the offline Python script version and the online service, thereby demonstrating its convenience for users. In addition, we provide a detailed explanation of the training process of TriNet to enhance the understanding of researchers seeking to leverage deep learning techniques for peptide classification. For complete details on the use and execution of this protocol, please refer to Zhou et al.1.

Cyclin-Dependent Kinase 1 (CDK1) is Co-Expressed with CDCA5: Their Functions in Gastric Cancer Cell Line MGC-803.

BACKGROUND Gastric cancer (GC) is a worldwide malignancy and the molecular mechanism of the GC carcinogenesis has not been fully elucidated. Our previous study suggested CDCA5 played a role in GC development via regulating cell proliferation, migration, and apoptosis in GC cells. MATERIAL AND METHODS Here, we first carried out bioinformatics analysis and found cyclin-dependent kinase 1 (CDK1) was possibly associated with CDCA5 using STRING. Then, the expression levels of CDK1 and CDCA5 in cancer tissues were estimated through Oncomine and The Cancer Genome Atlas (TCGA) database. After that, functional experiments were exerted to detect the association of CDK1 and CDCA5. Finally, cell proliferation assay, colon formation assay, cell scratch assay, transwell migration and invasion assays were applied to explore the roles of CDK1 and CDCA5 in GC cells MGC-803. RESULTS CDK1 and CDCA5 were both upregulated and co-expressed in GC tissues. The expression of CDK1 and CDCA5 in MGC-803 was positively related. CDK1 or CDCA5 inhibition can suppress the proliferation, colon formation, migration, and invasion abilities of GC cells. CONCLUSIONS Co-expression of CDK1 and CDCA5 might confer cell proliferation, migration, and invasion abilities in GC cells, and this can provide some clues for further therapies of gastric tumors.

Association between potentially functional polymorphisms of chemokine family members and the survival of esophageal cancer patients in a Chinese population.

Background: The chemokine family plays an important role in the growth, invasion, and metastasis of tumors. However, most studies have only focused on a few genes or a few gene loci, and thus could not reveal the associations between functional polymorphisms of chemokine family members and tumor progression. This study aimed to determine the associations between single nucleotide polymorphisms (SNPs) of chemokine family members and the prognosis of esophageal cancer (EC). Methods: The Cox risk proportional model and log-rank test were used to analyze the associations of 16 potentially functional SNPs in 13 genes from the chemokine family with the survival of 729 Chinese patients with EC. Results: Prognostic analysis on the 16 SNPs showed that different genotypes of 5 SNPs were associated with patients' survival and the risk of death. Multivariate Cox regression analysis showed that the risk of death was higher in CCL26rs2302009 genotype A/C carriers than in A/A carriers and it was also higher in CX3CL1rs2239352 genotype T/T carriers than in C/C carriers. Stepwise Cox regression analysis showed that CCL26rs2302009 genotype A/C was an independent prognostic factor of EC, and its association with increased risk of death was stronger in patients who were ≤60 years old, female, with tumors located in the middle part of esophagus, with undifferentiated or poorly differentiated tumors, with early-stage pathologic type disease, with the longest diameter of tumor ≤5cm than in their counterparts. Conclusion: These findings suggest that CCL26rs2302009 may be a candidate biomarker for EC and its effect on death risk are associated with the histological grade, pathologic type, and the longest diameter of tumor.

Gene expression profile analysis of ENO1 knockdown in gastric cancer cell line MGC-803.

Gastric cancer (GC) is the third leading cause of cancer-associated mortality. In a previous study, we identified that α-enolase (ENO1) promoted cell migration in GC, but the underlying molecular mechanisms remain to be fully elucidated. In the present study, small interfering RNAs were identified to interfere with ENO1 expression. The cDNA expression profiling was performed using an Affymetrix mRNA array platform to identify genes that may be associated with ENO1 in human GC cell line MGC-803. The differentially expressed genes (DEGs) were identified using the reverse transcription-quantitative polymerase chain reaction, followed by a series of bioinformatic analyses. As a result, there were 448 DEGs, among which 183 (40.85%) were downregulated. The most significant functional terms for the DEGs were the nuclear lumen for cell components (P=2.83×10-4), transcription for biological processes (P=3.7×10-7) and transcription factor activity for molecular functions (P=1.16×104). In total, six significant pathways were enriched, including the most common cancer-associated forkhead box O signaling pathway (P=0.0077), microRNAs in cancer (P=0.0183) and the cAMP signaling pathway (P=0.0415). Furthermore, a network analysis identified three hub genes (HUWE1, PPP1CB and HSPA4), which were all involved in tumor metastasis. Taken together, the DEGs, significant pathways and hub genes identified in the present study shed some light on the molecular mechanisms of ENO1 involved in the pathogenesis of GC.