In silico analysis of the potential mechanism of telocinobufagin on breast cancer MCF-7 cells

Telocinobufagin Has Antitumor Effects in Non-Small-Cell Lung Cancer by Inhibiting STAT3 Signaling

Non-small-cell lung carcer (NSCLC), the main histological subtype of lung cancer, is responsible for significant morbidity and mortality worldwide. Telocinobufagin, an active compound of the Chinese traditional medicine ChanSu, has antitumor effects, but its mechanism of action remains unknown. Therefore, we investigated the effect of telocinobufagin on NSCLC growth and metastasis and its possible mechanism of action, in vitro and in vivo. Cell proliferation, migration, and apoptosis were measured by methyl thiazol tetrazolium assay, colony formation, 5-ethynyl-2'-deoxyuridine incorporation, Transwell migration, wound healing, and flow cytometry analysis. A mouse xenograft model was used to evaluate tumor formation in vivo. Telocinobufagin was found to suppress proliferation and metastasis and induce apoptosis in human NSCLC cells. Moreover, telocinobufagin was able to significantly inhibit STAT3 phosphorylation at tyrosine 705 (Y⁷⁰⁵) and its downstream targets. Additionally, telocinobufagin also impaired the IL-6-induced nuclear translocation of STAT3. Consistent with the in vitro experiments, telocinobufagin reduced the A549 xenograft tumor burden and the levels of P-STAT3^Y705, MCL1, BCL2, and cleaved PARP1 in vivo. These results support telocinobufagin as a promising STAT3 signaling inhibitor candidate for the treatment of NSCLC patients.

Identification of a 6-gene signature for the survival prediction of breast cancer patients based on integrated multi-omics data analysis

Purpose

To identify a gene signature for the prognosis of breast cancer using high-throughput analysis.

Methods

RNASeq, single nucleotide polymorphism (SNP), copy number variation (CNV) data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into training set or verification set. Genes related to breast cancer prognosis and differentially expressed genes (DEGs) with CNV or SNP were screened from training set, then integrated together for feature selection of identify robust biomarkers using RandomForest. Finally, a gene-related prognostic model was established and its performance was verified in TCGA test set, Gene Expression Omnibus (GEO) validation set and breast cancer subtypes.

Results

A total of 2287 prognosis-related genes, 131 genes with amplified copy numbers, 724 gens with copy number deletions, and 280 genes with significant mutations screened from Genomic Variants were closely correlated with the development of breast cancer. A total of 120 candidate genes were obtained by integrating genes from Genomic Variants and those related to prognosis, then 6 characteristic genes (CD24, PRRG1, IQSEC3, MRGPRX, RCC2, and CASP8) were top-ranked by RandomForest for feature selection, noticeably, several of these have been previously reported to be associated with the progression of breast cancer. Cox regression analysis was performed to establish a 6-gene signature, which can stratify the risk of samples from training set, test set and external validation set, moreover, the five-year survival AUC of the model in the training set and validation set was both higher than 0.65. Thus, the 6-gene signature developed in the current study could serve as an independent prognostic factor for breast cancer patients.

Conclusion

This study constructed a 6-gene signature as a novel prognostic marker for predicting the survival of breast cancer patients, providing new diagnostic/prognostic biomarkers and therapeutic targets for breast cancer patients.

Differentially expressed gene profile and relevant pathways of the traditional Chinese medicine cinobufotalin on MCF‑7 breast cancer cells

Cinobufotalin is a chemical compound extracted from the skin of dried bufo toads that may have curative potential for certain malignancies through different mechanisms; however, these mechanisms remain unexplored in breast cancer. The aim of the present study was to investigate the antitumor mechanism of cinobufotalin in breast cancer by using microarray data and in silico analysis. The microarray data set GSE85871, in which cinobufotalin exerted influences on the MCF‑7 breast cancer cells, was acquired from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) were analyzed. Subsequently, protein interaction analysis was conducted, which clarified the clinical significance of core genes, and Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to analyze cinobufotalin‑related pathways. The Connectivity Map (CMAP) database was used to select existing compounds that exhibited curative properties similar to those of cinobufotalin. A total of 1,237 DEGs were identified from breast cancer cells that were treated with cinobufotalin. Two core genes, SRC proto‑oncogene non‑receptor tyrosine kinase and cyclin‑dependent kinase inhibitor 2A, were identified as serving a vital role in the onset and development of breast cancer, and their expression levels were markedly reduced following cinobufotalin treatment as detected by the microarray of GSE85871. It also was revealed that the 'neuroactive ligand‑receptor interaction' and 'calcium signaling' pathways may be crucial for cinobufotalin to perform its functions in breast cancer. Conducting a matching search in CMAP, miconazole and cinobufotalin were indicated to possessed similar molecular mechanisms. In conclusion, cinobufotalin may serve as an effective compound for the treatment of a subtype of breast cancer that is triple positive for the presence of estrogen, progesterone and human epidermal growth factor receptor‑2 receptors, and its mechanism may be related to different pathways. In addition, cinobufotalin is likely to exert its antitumor influences in a similar way as miconazole in MCF‑7 cells.

Identification of putative drugs for gastric adenocarcinoma utilizing differentially expressed genes and connectivity map

Gastric adenocarcinoma (GAC) is a challenging disease with dim prognosis even after surgery; hence, novel treatments for GAC are in urgent need. The aim of the present study was to explore new potential compounds interfering with the key pathways related to GAC progression. The differentially expressed genes (DEGs) between GAC and adjacent tissues were identified from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database. Connectivity Map (CMap) was performed to screen candidate compounds for treating GAC. Subsequently, pathways affected by compounds were overlapped with those enriched by the DEGs to further identify compounds which had anti-GAC potential. A total of 843 DEGs of GAC were identified. Via Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, 13 pathways were significantly enriched. Moreover, 78 compounds with markedly negative correlations with DEGs were revealed in CMap database (P<0.05 and Enrichment <0). Subpathways of cell cycle and p53 signaling pathways, and core genes of these compounds, cyclin B1 (CCNB1) and CDC6, were identified. This study further revealed seven compounds that may be effective against GAC; in particular methylbenzethonium chloride and alexidine have never yet been reported for GAC treatment. In brief, the candidate drugs identified in this study may provide new options to improve the treatment of patients with GAC. However, the biological effects of these drugs need further investigation.

Novel drug candidates for treating esophageal carcinoma: A study on differentially expressed genes, using connectivity mapping and molecular docking

Patients with esophageal carcinoma (ESCA) have a poor prognosis and high mortality rate. Although standard therapies have had effect, there is an urgent requirement to develop novel options, as increasing drug tolerance has been identified in clinical practice. In the present study, differentially expressed genes (DEGs) of ESCA were identified in The Cancer Genome Atlas and Genotype-Tissue Expression databases. Functional and protein-protein interaction (PPI) analyses were performed. The Connectivity Map (CMAP) was selected to predict drugs for the treatment of ESCA, and their target genes were acquired from the Search Tool for Interactions of Chemicals (STITCH) by uploading the Simplified Molecular-Input Line-Entry System structure. Additionally, significant target genes and ESCA-associated hub genes were extracted using another PPI analysis, and the corresponding drugs were added to construct a network. Furthermore, the binding affinity between predicted drug candidates and ESCA-associated hub genes was calculated using molecular docking. Finally, 827 DEGs (|log₂ fold-change|≥2; q-value <0.05), which are principally involved in protein digestion and absorption (P<0.005), the plasminogen-activating cascade (P<0.01), as well as the ‘biological regulation’ of the Biological Process, ‘membrane’ of the Cellular Component and ‘protein binding’ of the Molecular Function categories, were obtained. Additionally, 11 hub genes were obtained from the PPI network (all degrees ≥30). Furthermore, the 15 first screen drugs were extracted from CMAP (score <−0.85) and the 9 second screen drugs with 70 target genes were extracted from STITCH. Furthermore, another PPI analysis extracted 51 genes, and apigenin, baclofen, Prestwick-685, menadione, butyl hydroxybenzoate, gliclazide and valproate were selected as drug candidates for ESCA. Those molecular docking results with a docking score of >5.52 indicated the significance of apigenin, Prestwick-685 and menadione. The results of the present study may lead to novel drug candidates for ESCA, among which Prestwick-685 and menadione were identified to be significant new drug candidates.

Identification of potential drugs for diffuse large b-cell lymphoma based on bioinformatics and Connectivity Map database

Diffuse large B-cell lymphoma (DLBCL) is the most main subtype in non-Hodgkin lymphoma. After chemotherapy, about 30% of patients with DLBCL develop resistance and relapse. This study was to identify potential therapeutic drugs for DLBCL using the bioinformatics method. The differentially expressed genes (DEGs) between DLBCL and non-cancer samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were analyzed using the Database for Annotation, Visualization, and Integrated Discovery. The R software package (SubpathwayMiner) was used to perform pathway analysis on DEGs affected by drugs found in the Connectivity Map (CMap) database. Protein-protein interaction (PPI) networks of DEGs were constructed using the Search Tool for the Retrieval of Interacting Genes online database and Cytoscape software. In order to identify potential novel drugs for DLBCL, the DLBCL-related pathways and drug-affected pathways were integrated. The results showed that 1927 DEGs were identified from TCGA and GEO. We found 54 significant pathways of DLBCL using KEGG pathway analysis. By integrating pathways, we identified five overlapping pathways and 47 drugs that affected these pathways. The PPI network analysis results showed that the CDK2 is closely associated with three overlapping pathways (cell cycle, p53 signaling pathway, and small cell lung cancer). The further literature verification results showed that etoposide, rinotecan, methotrexate, resveratrol, and irinotecan have been used as classic clinical drugs for DLBCL. Anisomycin, naproxen, gossypol, vorinostat, emetine, mycophenolic acid and daunorubicin also act on DLBCL. It was found through bioinformatics analysis that paclitaxel in the drug-pathway network can be used as a potential novel drug for DLBCL.