Identification of breast cancer prognostic modules based on weighted protein-protein interaction networks

LncRNA ENST869 Targeting Nestin Transcriptional Region to Affect the Pharmacological Effects of Chidamide in Breast Cancer Cells

Breast cancer is one of the leading threats to the health of women. It has the highest incidence and mortality in women worldwide. Although progress has been made in the development and application of anti-breast cancer drugs such as Chidamide and others, the occurrence of drug resistance limits the effective application of chemotherapies. The purpose of this study is to explore the role of LncRNA in the pharmacological effect of Chidamide in breast cancer therapy. The human breast cancer MCF-7 or MDA-MB-231 cells were used as the research cell models. The RNA library screening and high-throughput sequencing comparative analysis was conducted. The binding of LncRNA and its downstream target genes in RNA and protein levels was tested. The results showed that the expression of LncRNA ENST869 in cells treated with Chidamide increased significantly, as demonstrated by real-time PCR and cell viability assay. RNAplex analysis showed that LncRNA ENST869 and Nestin mRNA may interact. RNA interference and Western blot analysis indicated that LncRNA ENST869 could target and regulate the expression of Nestin. Luciferase assay and RNA-protein pulldown showed that LncRNA ENST869 affected Nestin transcription. There might be a highly active binding region of LncRNA ENST869 in regulating Nestin transcriptional activity within the site of 250 bp upstream of the transcription starting point of Nestin. In addition, LncRNA ENST869 did not directly interact with Nestin protein to affect its activity. In conclusion, our results demonstrated that LncRNA ENST869 could affect the function of Nestin in breast cancer cells treated with Chidamide. Nestin is a key player in influencing the pharmacological activity of Chidamide and an essential factor in drug resistance of breast cancer cells.

A network-based method for detecting cancer driver gene in transcriptional regulatory networks using the structure analysis of weighted regulatory interactions

Background:

The identification of genes that instigate cell anomalies and cause cancer in humans is an important field in oncology research. Abnormalities in these genes are transferred to other genes in the cell, disrupting its normal functionality. Such genes are known as cancer driver genes (CDGs). Various methods have been proposed for predicting CDGs, most of which are based on genomic data and computational methods. Some novel bioinformatic approaches have been developed.

Objective:

In this article, we propose a network-based algorithm, SalsaDriver (Stochastic approach for link-structure analysis to driver detection), which can calculate the receiving and influencing power of each gene using the stochastic analysis of regulatory interaction structures in gene regulatory networks.

Method:

First, regulatory networks related to breast, colon, and lung cancers were constructed using gene expression data and a list of regulatory interactions, the weights of which were then calculated using biological and topological features of the network. After that, the weighted regulatory interactions were used in the structure analysis of interactions achieved using two separate Markov chains on the bipartite graph taken from the main graph of the gene network and implementing the stochastic approach for link-structure analysis. The proposed algorithm categorizes higher-ranked genes as driver genes.

Results:

The proposed algorithm was compared with 24 other computational and network tools based on the F-measure value and the number of detected CDGs. The results were validated using four valid databases. The findings of this study show that SalsaDriver outperforms other methods and can identify a significant number of driver genes not identified using other methods.

Conclusion:

The SalsaDriver network-based approach is suitable for predicting CDGs and can be used as a complementary method along with other computational tools.

Integrated Bioinformatic Analysis Identifies Networks and Promising Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma

Chronic infection with hepatitis B virus (HBV) has long been recognized as a dominant hazard factor for hepatocellular carcinoma (HCC) and accounts for at least half of HCC instances globally. However, the underlying molecular mechanism of HBV-linked HCC has not been completely elucidated. Here, three microarray datasets, totally containing 170 tumoral samples and 181 adjacent normal tissues from the liver of patients suffering from HBV-related HCC assembled from the Gene Expression Omnibus (GEO) database, were subjected to integrated analysis of differentially expressed genes (DEGs). Subsequently, the analysis of function and pathway enrichment as well as the protein-protein interaction network (PPI) was performed. The ten hub genes screened out from the PPI network were further subjected to expression profile and survival analysis. Overall, 329 DEGs (67 upregulated and 262 downregulated) were identified. Ten DEGs with the highest degree of connectivity included cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), PDZ-binding kinase (PBK), abnormal spindle microtubule assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F (CENPF). Kaplan-Meier analysis unveiled that overexpression levels of KIF2C and TPX2 were relevant to both the poor overall survival and relapse-free survival. In summary, the hub genes validated in the present study may provide promising targets for the diagnosis, prognosis, and therapy of HBV-associated HCC. Additionally, our work uncovers various crucial biological components (e.g., extracellular exosome) and signaling pathways that participate in the progression of HCC induced by HBV, serving comprehensive knowledge of the mechanisms regarding HBV-related HCC.

Identification of key genes and pathways and therapeutic agents in cadmium-treated liver cells: A bioinformatics study

Evidence indicates that Cadmium (Cd) can accumulate in liver, which results in acute or chronic cell damage with unclear complex mechanisms. Thus, we aimed to explore the possible molecules and pathways by using bioinformatics methods Consequently, two datasets (GSE8865 and GSE31286) were retrieved and the differentially expressed genes (DEGs) were screened out. The intersection of the DEGs included seven up-regulated and forty-three down-regulated genes, which were mainly enriched in biological cell proliferation items, and were enriched in several metabolism-related pathways. Among the DEGs, several hub genes such as EGR1, FOSL1, ITGA2, EDN1, and IER3 were screened out through protein-protein interaction analysis. Interestingly, BW-B70C was predicted to be a potential agent for attenuating Cd-induced liver cell damage. The present study gave a novel insight into the mechanisms of Cd-induced liver cell damage or malignant transformation and identified several small agents that might be critical for Cd toxicity prevention and treatment.