ABSTRACT
Many disease-related genes have been found to be associated with cancer diagnosis, which is useful for understanding the pathophysiology of cancer, generating targeted drugs, and developing new diagnostic and treatment techniques. With the development of the pan-cancer project and the ongoing expansion of sequencing technology, many scientists are focusing on mining common genes from The Cancer Genome Atlas (TCGA) across various cancer types. In this study, we attempted to infer pan-cancer associated genes by examining the microbial model organism Saccharomyces Cerevisiae (Yeast) by homology matching, which was motivated by the benefits of reverse genetics. First, a background network of protein-protein interactions and a pathogenic gene set involving several cancer types in humans and yeast were created. The homology between the human gene and yeast gene was then discovered by homology matching, and its interaction sub-network was obtained. This was undertaken following the principle that the homologous genes of the common ancestor may have similarities in expression. Then, using bidirectional long short-term memory (BiLSTM) in combination with adaptive integration of heterogeneous information, we further explored the topological characteristics of the yeast protein interaction network and presented a node representation score to evaluate the node ability in graphs. Finally, homologous mapping for human genes matched the important genes identified by ensemble classifiers for yeast, which may be thought of as genes connected to all types of cancer. One way to assess the performance of the BiLSTM model is through experiments on the database. On the other hand, enrichment analysis, survival analysis, and other outcomes can be used to confirm the biological importance of the prediction results. You may access the whole experimental protocols and programs at https://github.com/zhuyuan-cug/AI-BiLSTM/tree/master.
ABSTRACT
The simple, economic, rapid, and sensitive detection of lysozyme has an important significance for disease diagnosis since it is a potential biomarker. In this work, a new detection strategy for lysozyme was developed based on the change of the plasmon resonance light scattering (PRLS) signal of peptidoglycan stabilized gold nanoparticles (PGN-AuNPs). Peptidoglycan (PGN) was employed as a stabilizer to prepare PGN-AuNPs which have the properties of a uniform particle size, good stability, and a specific biological function. Due to the specific cleavage of lysozyme to PGN, a very simple specific and sensitive detection method for lysozyme was developed based on the PRLS signal of PGN-AuNPs after mixing with lysozyme for 1.5 h. The enhanced PRLS signals (ΔIPRLS, at 560 nm) increased linearly with increasing lysozyme in the range 5 nM to 1600 nM with the detection limit down to 2.32 nM (ΔIPRLS = 41.6397 + 0.5332c, R = 0.9961). When the PGN-AuNP based method was applied to assay lysozyme in authentic human serum samples, the recovery efficiency was 106.76-119.32% with the relative standard deviations in the range of 0.14-3.11%, showing good feasibility. The PGN-AuNP based method for lysozyme assay developed here is simple, rapid, selective, and sensitive, which is expected to provide a feasible new method for the diagnosis or prognosis of lysozyme-related diseases in a clinical setting.
