UPregulated single-stranded DNA-binding protein 1 induces cell chemoresistance to cisplatin in lung cancer cell lines

Cytoskeletal gene alterations linked to sorafenib resistance in hepatocellular carcinoma

BACKGROUND

Although sorafenib has been consistently used as a first-line treatment for advanced hepatocellular carcinoma (HCC), most patients will develop resistance, and the mechanism of resistance to sorafenib needs further study.

METHODS

Using KAS-seq technology, we obtained the ssDNA profiles within the whole genome range of SMMC-7721 cells treated with sorafenib for differential analysis. We then intersected the differential genes obtained from the analysis of hepatocellular carcinoma patients in GSE109211 who were ineffective and effective with sorafenib treatment, constructed a PPI network, and obtained hub genes. We then analyzed the relationship between the expression of these genes and the prognosis of hepatocellular carcinoma patients.

RESULTS

In this study, we identified 7 hub ERGs (ACTB, CFL1, ACTG1, ACTN1, WDR1, TAGLN2, HSPA8) related to drug resistance, and these genes are associated with the cytoskeleton.

CONCLUSIONS

The cytoskeleton is associated with sorafenib resistance in hepatocellular carcinoma. Using KAS-seq to analyze the early changes in tumor cells treated with drugs is feasible for studying the drug resistance of tumors, which provides reference significance for future research.

Predicting DNA binding protein-drug interactions based on network similarity

BACKGROUND

The study of DNA binding protein (DBP)-drug interactions can open a breakthrough for the treatment of genetic diseases and cancers. Currently, network-based methods are widely used for protein-drug interaction prediction, and many hidden relationships can be found through network analysis. We proposed a DCA (drug-cluster association) model for predicting DBP-drug interactions. The clusters are some similarities in the drug-binding site trimmers with their physicochemical properties. First, DBPs-drug binding sites are extracted from scPDB database. Second, each binding site is represented as a trimer which is obtained by sliding the window in the binding sites. Third, the trimers are clustered based on the physicochemical properties. Fourth, we build the network by generating the interaction matrix for representing the DCA network. Fifth, three link prediction methods are detected in the network. Finally, the common neighbor (CN) method is selected to predict drug-cluster associations in the DBP-drug network model.

RESULT

This network shows that drugs tend to bind to positively charged sites and the binding process is more likely to occur inside the DBPs. The results of the link prediction indicate that the CN method has better prediction performance than the PA and JA methods. The DBP-drug network prediction model is generated by using the CN method which predicted more accurately drug-trimer interactions and DBP-drug interactions. Such as, we found that Erythromycin (ERY) can establish an interaction relationship with HTH-type transcriptional repressor, which is fitted well with silico DBP-drug prediction.

CONCLUSION

The drug and protein bindings are local events. The binding of the drug-DBPs binding site represents this local binding event, which helps to understand the mechanism of DBP-drug interactions.

Cisplatin suppresses proliferation, migration and invasion of nasopharyngeal carcinoma cells in vitro by repressing the Wnt/β-catenin/Endothelin-1 axis via activating B cell translocation gene 1

PURPOSE

Nasopharyngeal carcinoma (NPC) is one of the most commonly diagnosed cancers worldwide with significantly high prevalence in Southern China. Chemoprevention of cancer with alkylating agent compounds could potentially reverse, suppress, or prevent cancer progression. Cisplatin (CIS) is an antineoplastic or cytotoxic platinum-based drug used for chemotherapy of different types of human cancers such as NPC. Nevertheless, the effects of CIS on the migration and invasion of human NPC cells and the underlying molecular mechanisms have not yet been fully scrutinized.

METHODS

In this work, we tested the effect of CIS on the proliferation, migration and invasion of NPC cells. The results exhibited that this drug exerts remarkable inhibitory effects on the proliferation, migration and invasion of NPC cells in a dose-dependent manner. Western blotting and real time RT-PCR were used for expression analyses.

RESULTS

We found that CIS treatment led to a dose-dependent inhibition of Endothelin-1 (ET1) expression, at protein as well as mRNA levels in NPC cells. CIS was also found to activate the expression of BTG1 in NPC cells. Moreover, mechanistic analyses revealed that CIS increased the expression of B cell translocation gene 1 (BTG1) to suppress the expression of ET1. Furthermore, we show that ET1 could not be induced in CIS-resistant cells with suppressed BTG1 expression, and subsequently demote the proliferation, migration and invasion of NPC cells.

CONCLUSIONS

These findings provided compelling evidence of the role of CIS in suppressing NPC metastasis and its underlying molecular mechanisms.

Differential Proteome Analysis of Extracellular Vesicles from Breast Cancer Cell Lines by Chaperone Affinity Enrichment

The complexity of human tissue fluid precludes timely identification of cancer biomarkers by immunoassay or mass spectrometry. An increasingly attractive strategy is to primarily enrich extracellular vesicles (EVs) released from cancer cells in an accelerated manner compared to normal cells. The Vn96 peptide was herein employed to recover a subset of EVs released into the media from cellular models of breast cancer. Vn96 has affinity for heat shock proteins (HSPs) decorating the surface of EVs. Reflecting their cells of origin, cancer EVs displayed discrete differences from those of normal phenotype. GELFrEE LC/MS identified an extensive proteome from all three sources of EVs, the vast majority having been previously reported in the ExoCarta database. Pathway analysis of the Vn96-affinity proteome unequivocally distinguished EVs from tumorigenic cell lines (SKBR3 and MCF-7) relative to a non-tumorigenic source (MCF-10a), particularly with regard to altered metabolic enzymes, signaling, and chaperone proteins. The protein data sets provide valuable information from material shed by cultured cells. It is probable that a vast amount of biomarker identities may be collected from established and primary cell cultures using the approaches described here.