Diprophylline inhibits non-small cell lung cancer A549 cell proliferation and migration, and promotes apoptosis, by downregulating PI3K signaling pathway

Heterogeneous network propagation with forward similarity integration to enhance drug-target association prediction

Identification of drug-target interaction (DTI) is a crucial step to reduce time and cost in the drug discovery and development process. Since various biological data are publicly available, DTIs have been identified computationally. To predict DTIs, most existing methods focus on a single similarity measure of drugs and target proteins, whereas some recent methods integrate a particular set of drug and target similarity measures by a single integration function. Therefore, many DTIs are still missing. In this study, we propose heterogeneous network propagation with the forward similarity integration (FSI) algorithm, which systematically selects the optimal integration of multiple similarity measures of drugs and target proteins. Seven drug-drug and nine target-target similarity measures are applied with four distinct integration methods to finally create an optimal heterogeneous network model. Consequently, the optimal model uses the target similarity based on protein sequences and the fused drug similarity, which combines the similarity measures based on chemical structures, the Jaccard scores of drug-disease associations, and the cosine scores of drug-drug interactions. With an accuracy of 99.8%, this model significantly outperforms others that utilize different similarity measures of drugs and target proteins. In addition, the validation of the DTI predictions of this model demonstrates the ability of our method to discover missing potential DTIs.

Analysis of Stemness and Prognosis of Subtypes in Breast Cancer Using the Transcriptome Sequencing Data

The stem characteristics of tumor cells have been proposed in theory very early, and we can use the signature of gene expression to speculate the stemness of tumor cells. However, systematic studies on the stemness of breast cancer as well as breast cancer subtypes, and the relationship between stemness and metastasis and prognosis, are still lacking. In the present research, using the transcriptome data of patients with breast cancer in the TCGA database, a stemness prediction model was utilized to derive the stemness of the patients’ tumors. We compared the stemness values among different subtypes and the differences with metastasis. COX regression was employed to evaluate the correlation between stemness value as well as prognosis. Using the Lasso-penalized Cox regression machine learning model, we obtained the gene signature of the basal subtype that is related to stemness and can also predict the prognosis of the patient. Patients can be stratified into two groups of high and low stemness, corresponding to good and poor prognosis. Based on further prediction of tumor infiltration by CIBERSORT and prediction of drug response by a connectivity map, we found that the difference in stemness between these two groups is associated with the activation of tumor-killing immune cells and drug response. Our findings can promote the understanding and research of subtypes of basal breast cancer and provide corresponding molecular markers for clinical detection and therapy.

Identification of metastasis and prognosis-associated genes for serous ovarian cancer

Serous ovarian cancer is one of the most fatal gynecological tumors with an extremely low 5-year survival rate. Most patients are diagnosed at an advanced stage with wide metastasis. The dysregulation of genes serves an important role in the metastasis progression of ovarian cancer. Differentially expressed genes (DEGs) between primary tumors and metastases of serous ovarian cancer were screened out in the gene expression profile of GSE73168 from Gene Expression Omnibus (GEO). Cytoscape plugin cytoHubba and weighted gene co-expression network analysis (WGCNA) were utilized to select hub genes. Univariate and multivariate Cox regression analyses were used to screen out prognosis-associated genes. Furthermore, the Oncomine validation, prognostic analysis, methylation mechanism, gene set enrichment analysis (GSEA), TIMER database analysis and administration of candidate molecular drugs were conducted for hub genes. Nine hundred and fifty-seven DEGs were identified in the gene expression profile of GSE73168. After using Cytoscape plugin cytoHubba, 83 genes were verified. In co-expression network, the blue module was most closely related to tumor metastasis. Furthermore, the genes in Cytoscape were analyzed, showing that the blue module and screened 17 genes were closely associated with tumor metastasis. Univariate and multivariate Cox regression revealed that the age, stage and STMN2 were independent prognostic factors. The Cancer Genome Atlas (TCGA) suggested that the up-regulated expression of STMN2 was related to poor prognosis of ovarian cancer. Thus, STMN2 was considered as a new key gene after expression validation, survival analysis and TIMER database validation. GSEA confirmed that STMN2 was probably involved in ECM receptor interaction, focal adhesion, TGF beta signaling pathway and MAPK signaling pathway. Furthermore, three candidate small molecule drugs for tumor metastasis (diprophylline, valinomycin and anisomycin) were screened out. The quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and western blot showed that STMN2 was highly expressed in ovarian cancer tissue and ovarian cancer cell lines. Further studies are needed to investigate these prognosis-associated genes for new therapy target.