Construction and Analysis of Pseudogene-Related ceRNA Network in Breast Cancer – Scientific Reports
A groundbreaking study has been conducted to shed light on the pseudogene-related ceRNA network in breast cancer. The study, titled Construction and analysis of pseudogene-related ceRNA network in breast cancer, was recently published in Scientific Reports. Through a comprehensive three-step process, researchers screened for differentially expressed genes (DGEs) in breast cancer RNAseq datasets, constructed a ceRNA network using candidate genes and interacting miRNAs, and performed experimental validation in breast cancer patients and the MCF-7 cell line.
The study utilized raw RNAseq data from various datasets retrieved from the GEO database, including GSE113476, GSE99048, GSE110626, GSE183947, GSE166044, GSE166048, GSE165914, GSE175487, and GSE47462. The samples consisted of 163 normal breast tissues and 209 breast cancer tissues, which encompassed different breast cancer subtypes. Additionally, six breast cell lines were included in the analysis. To validate the findings, breast tumor and normal adjacent samples from the TCGA database, as well as breast mammary normal tissue from the GTEx database, were utilized.
To ensure the quality of the raw data, the FASTQC tool was employed to check for factors such as sequence per base quality and sequence length distribution. Reads with a Phred score lower than 30 were discarded, and the TRIMGALORE tool was used to exclude adapters and low-quality bases. The HISAT2 tool was utilized for aligning the data to the human reference genome GRCh38, with the annotation file obtained from the human genome browser at UCSC. Differentially expressed genes were identified using the Deseq2 package, and clustering was performed using the k-means method to assess co-expression.
To explore the potential functions of the differentially expressed genes, GO and KEGG pathway analysis were conducted using the BioPlanet platform. This analysis provided insights into the key biological functions and pathways associated with the candidate genes. Survival analysis, co-expression analysis, and ROC curve analysis were employed to identify hub genes. The RNAseq data with survival profiles of breast cancer patients were extracted from the CGA database, and correlation information was retrieved from ENCORI. Candidate gene-pseudogene pairs and interacting miRNAs were used to construct the ceRNA network.
The study unveiled a comprehensive ceRNA network, highlighting the intricate interactions between genes, pseudogenes, and miRNAs in breast cancer. The network analysis facilitated the identification of a core module within the ceRNA network, which was further investigated using clinical information. By comparing gene-miRNA-pseudogene axes in the core ceRNA module, researchers gained valuable insights into the potential implications of these interactions in breast cancer.
The findings of this study have significant implications for understanding the complexity of breast cancer and its potential therapeutic targets. By elucidating the pseudogene-related ceRNA network, researchers have opened new avenues for further investigation and exploration in the field of breast cancer research. This knowledge may ultimately contribute to the development of more targeted and effective treatment strategies for breast cancer patients.
The groundbreaking study published in Scientific Reports represents a significant step forward in our understanding of the molecular mechanisms underlying breast cancer. The comprehensive analysis of the pseudogene-related ceRNA network provides valuable insights into the complex interactions involved in breast cancer development and progression. With further research and validation, these findings have the potential to drive advances in diagnosing, treating, and ultimately preventing breast cancer, improving patient outcomes worldwide.
