Identification of potential immune-related prognostic biomarkers of lung cancer using gene co-expression network analysis

Objective The objective of this study was to identify new carcinogenetic hub genes and develop the integration of differentially expressed genes to predict the prognosis of lung cancer.Methods GSE139032 microarray data packages were downloaded from the Gene Expression Omnibus for planning,testing,and review of data.We identified KRT6C,LAMC2,LAMB3,KRT6A,and MYEOV from a key module for validation.Results We found that the five genes were related to a poor prognosis,and the expression levels of these genes were associated with tumor stage.Furthermore,Kaplan-Meier plotter showed that the five hub genes had better prognostic values.The mean levels of methylation in lung adenocarcinoma(LUAD)were significantly lower than those in healthy lung tissues for the hub genes.However,gene set enrichment analysis(GSEA)for single hub genes showed that all of them were immune-related.Conclusion Our findings demonstrated that KRT6C,LAMC2,LAMB3,KRT6A,and MYEOV are all candidate diagnostic and prognostic biomarkers for LUAD.They may have clinical implications in LUAD patients not only for the improvement of risk stratification but also for therapeutic decisions and prognosis prediction.

Logistic Weighted Profile-Based Bi-Random Walk for Exploring MiRNA-Disease Associations

MicroRNAs(miRNAs)exert an enormous influence on cell differentiation,biological development and the onset of diseases.Because predicting potential miRNA-disease associations(MDAs)by biological experiments usually requires considerable time and money,a growing number of researchers are working on developing computational methods to predict MDAs.High accuracy is critical for prediction.To date,many algorithms have been proposed to infer novel MDAs.However,they may still have some drawbacks.In this paper,a logistic weighted profile-based bi-random walk method(LWBRW)is designed to infer potential MDAs based on known MDAs.In this method,three networks(i.e.,a miRNA functional similarity network,a disease semantic similarity network and a known MDA network)are constructed first.In the process of building the miRNA network and the disease network,Gaussian interaction profile(GIP)kernel is computed to increase the kernel similarities,and the logistic function is used to extract valuable information and protect known MDAs.Next,the known MDA matrix is preprocessed by the weighted K-nearest known neighbours(WKNKN)method to reduce the number of false negatives.Then,the LWBRW method is applied to infer novel MDAs by bi-randomly walking on the miRNA network and the disease network.Finally,the predictive ability of the LWBRW method is confirmed by the average AUC of 0.9393(0.0061)in 5-fold cross-validation(CV)and the AUC value of 0.9763 in leave-one-out cross-validation(LOOCV).In addition,case studies also show the outstanding ability of the LWBRW method to explore potential MDAs.

Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs) represent an efficient way to dissect functional pathways for revealing novel protein functions. In this review, we illustrate the technological evolution for the large-scale and precise identification of PPIs toward higher physiologically relevant accuracy. These techniques aim at improving the efficiency of complex pull-down, the signal specificity and accuracy in distinguishing specific PPIs, and the accuracy of identifying physiological relevant PPIs. A newly developed streamline proteomic approach for mapping the binary relationship of PPIs in a protein complex is introduced.

Biochemical reactions in metabolite-protein interaction

Active endogenous metabolites regulate the viability of cells. This process is controlled by a series ofinteractions between small metabolites and large proteins. Previously, several studies had reported thatmetabolite regulates the protein functions, such as diacylglycerol to protein kinase C, lactose regulationof the lac repressor, and HIF-1α stabilization by 2-hydroxyglutarate. However, decades old traditionalbiochemical methods are insufficient to systematically investigate the bio-molecular reactions for a high-throughput discovery. Here, we have reviewed an update on the recently developed chemical proteomicscalled activity-based protein profiling （ABPP）. ABPP is able to identify proteins interacted eithercovalently or non-covalently with metabolites significantly. Thus, ABPP will facilitate the characteriza-tion of specific metabolite regulating; proteins in human disease progression.