Comprehensive Analysis of Fibroblast Growth Factor Receptor (FGFR) Family Genes in Breast Cancer by Integrating Online Databases and Bioinformatics

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Fibroblast growth factor receptor signalling dysregulation and targeting in breast cancer

Fibroblast Growth Factor Receptor (FGFR) signalling plays a critical role in breast embryonal development, tissue homeostasis, tumorigenesis and metastasis. FGFR, its numerous FGF ligands and signalling partners are often dysregulated in breast cancer progression and are one of the causes of resistance to treatment in breast cancer. Furthermore, FGFR signalling on epithelial cells is affected by signals from the breast microenvironment, therefore increasing the possibility of breast developmental abnormalities or cancer progression. Increasing our understanding of the multi-layered roles of the complex family of FGFRs, their ligands FGFs and their regulatory partners may offer novel treatment strategies for breast cancer patients, as a single agent or rational co-target, which will be explored in depth in this review.

Gene Regulation Network of Prognostic Biomarker YAP1 in Human Cancers: An Integrated Bioinformatics Study

Background: Yes-associated protein 1 (YAP1) is the main downstream effector of the Hippo signaling pathway, which is involved in tumorigenesis. This study aimed to comprehensively understand the prognostic performances of YAP1 expression and its potential mechanism in pan-cancers by mining databases.

Methods: The YAP1 expression was evaluated by the Oncomine database and GEPIA tool. The clinical significance of YAP1 expression was analyzed by the UALCAN, GEPIA, and DriverDBv3 database. Then, the co-expressed genes with YAP1 were screened by the LinkedOmics, and annotated by the Metascape and DAVID database. Additionally, by the MitoMiner 4.0 v tool, the YAP1 co-expressed genes were screened to obtain the YAP1-associated mitochondrial genes that were further enriched by DAVID and analyzed by MCODE for the hub genes.

Results: YAP1 was differentially expressed in human cancers. Higher YAP1 expression was significantly associated with poorer overall survival and disease-free survival in adrenocortical carcinoma (ACC), brain Lower Grade Glioma (LGG), and pancreatic adenocarcinoma (PAAD). The LinkedOmics analysis revealed 923 co-expressed genes with YAP1 in adrenocortical carcinoma, LGG and PAAD. The 923 genes mainly participated in mitochondrial functions including mitochondrial gene expression and mitochondrial respiratory chain complex I assembly. Of the 923 genes, 112 mitochondrial genes were identified by MitoMiner 4.0 v and significantly enriched in oxidative phosphorylation. The MCODE analysis identified three hub genes including CHCHD1, IDH3G and NDUFAF5.

Conclusion: Our findings showed that the YAP1 overexpression could be a biomarker for poor prognosis in ACC, LGG and PAAD. Specifically, the YAP1 co-expression genes were mainly involved in the regulation of mitochondrial function especially in oxidative phosphorylation. Thus, our findings provided evidence of the carcinogenesis of YAP1 in human cancers and new insights into the mechanisms underlying the role of YAP1 in mitochondrial dysregulation.