Identification of genes and pathways related to breast cancer metastasis in an integrated cohort

Abstract

Background Breast cancer is the most common malignant disease in women. Metastasis is the most common cause of death from this cancer. Screening genes related to breast cancer metastasis may help elucidate the mechanisms governing metastasis and identify molecular targets for antimetastatic therapy. The development of advanced algorithms enables us to perform cross‐study analysis to improve the robustness of the results. Materials and methods Ten data sets meeting our criteria for differential expression analyses were obtained from the Gene Expression Omnibus (GEO) database. Among these data sets, five based on the same platform were formed into a large cohort using the XPN algorithm. Differentially expressed genes (DEGs) associated with breast cancer metastasis were identified using the differential expression via distance synthesis (DEDS) algorithm. A cross‐platform method was employed to verify these DEGs in all ten selected data sets. The top 50 validated DEGs are represented with heat maps. Based on the validated DEGs, Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Protein interaction (PPI) networks were constructed to further illustrate the direct and indirect associations among the DEGs. Survival analysis was performed to explore whether these genes can affect breast cancer patient prognosis. Results A total of 817 DEGs were identified using the DEDS algorithm. Of these DEGs, 450 genes were validated by the second algorithm. Enriched KEGG pathway terms demonstrated that these 450 DEGs may be involved in the cell cycle and oocyte meiosis in addition to their functions in ECM‐receptor interaction and protein digestion and absorption. PPI network analysis for the proteins encoded by the DEGs indicated that these genes may be primarily involved in the cell cycle and extracellular matrix. In particular, several genes played roles in multiple signalling pathways and were related to patient survival. These genes were also observed to be targetable in the CTD2 database. Conclusions Our study analysed multiple cross‐platform data sets using two different algorithms, helping elucidate the molecular mechanisms and identify several potential therapeutic targets of metastatic breast cancer. In addition, several genes exhibited promise for applications in targeted therapy against metastasis in future research.

Comprehensive Analysis of Epigenetic Alternations of MicroRNA and DNA Methylation Contribute to Gene Expression and Prognosis in Lung Squamous Cell Carcinoma

“…Meanwhile, KEGG pathway analysis suggested that the signal pathways including cell cycle, homologous recombination, oocyte meiosis were signi cantly involved. Notably, these pathways have been shown to be related to the cancer's occurrence and development [29][30][31]. Our results suggested that the prognosis of LUSC may be improved by testing the identi ed DEMs and DMGs, and the LUSC development is a complex process involving various genes and proteins.…”

Identification of genes and pathways associated with menopausal status in breast cancer patients using two algorithms

“…These include Matrix Metallopeptidase 7 (MMP7), transcript factors of YAP1 (one of the most important effectors of the Hippo pathway) and FOXM1, fibroblast growth factor receptor 2 (FGFR2), Eukaryotic initiation factor 3B (EIF3B), Kinesin Family Members (Kif14, Kif4A, Kif23 and Kif2C), Cyclin Dependent Kinase 1 (CDK1), Cell division cycle proteins (CDCA3, CDCA5, CDCA7, CDCA8, CDCA20 and CDC25C) and Check point Kinase 1 (CHEK1). Some of these genes have also been found to be associated with breast cancer metastasis in our previous research [ 25 ].…”

Role of Ultrasound Imaging in the Prediction of TRIM67 in Brain Metastases From Breast Cancer

“…One study used RNA sequencing to probe the cellular origin and evolution of breast cancer in BRCA1 mutant carriers ( 25 ). Additionally, bioinformatics has reportedly been used to identify differentially expressed genes (DEGs) associated with breast cancer metastasis and provide potential therapeutic targets for metastatic breast cancer ( 26 ). Bioinformatics data combined with imaging findings can also play a crucial role in breast cancer diagnosis and prognosis.…”