Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non-tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein-protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in ‘cell division’, the ‘proteinaceous extracellular matrix (ECM)’, ‘ECM structural constituents’ and ‘ECM-receptor interaction’, whereas downregulated genes were mainly enriched in ‘response to drugs’, ‘extracellular space’, ‘transcriptional activator activity’ and the ‘peroxisome proliferator-activated receptor signaling pathway’. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2-a, baculoviral inhibitor of apoptosis repeat-containing protein 5, cyclin-dependent kinase 1, G2/mitotic-specific cyclin-B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in ‘mitotic nuclear division’, ‘mid-body’, ‘protein binding’ and ‘cell cycle’. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.

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Gastric cancer (GC) is one of the most common cancers with high incidence and mortality worldwide. Recently, RNA-binding proteins (RBPs) have drawn more and more attention for its role in cancer pathophysiology. However, the function and clinical implication of RBPs in GC have not been fully elucidated. RNA sequencing data along with the corresponding clinical information of GC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA-binding proteins (DERBPs) between tumor and normal tissues were identified by “limma” package. Functional enrichment analysis and the protein-protein interaction (PPI) network were harnessed to explore the function and interaction of DERBPs. Next, univariate and multiple Cox regression were applied to screen prognosis-related hub RBPs and to construct a signature for GC. Meanwhile, a nomogram was built on the basis of the independent factors. A total of 296 DERBPs were found, and most of them mainly related to post-transcriptional regulation of RNA and ribonucleoprotein. A PPI network of DERBPs was constructed, consisting of 262 nodes and 2567 edges. A prognostic signature was built depending on 7 prognosis-related hub RBPs that could divide GC patients into high-risk and low-risk groups. Survival analysis showed that high-risk group had a worse prognosis compared with the low-risk group and the time-dependent receiver operating characteristic (ROC) curves suggested that signature existed moderate predictive capacities of survival for GC patients. Similar results were obtained from another independent set GSE62254, confirming the robustness of signature. Besides, the genetic variation and immune heterogeneity differences were identified between the high-risk and low-risk groups by bioinformatics methods. These findings would provide evidence of the effect of RBPs and offer a novel potential biomarker in prognostic prediction and clinical decision for GC.

Background: Gastric cancer (GC) is one of the most common cancers with high incidence and mortality worldwide. Recently, RNA-binding proteins (RBPs) have drawn more and more attention for its role in cancer pathophysiology. In this study, we aim to explore the function and clinical implication of RBPs in GC. Methods: RNA sequencing data along with the corresponding clinical information of GC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA-binding proteins (DERBPs) between tumor and normal tissues were identified by ‘limma’ package. Functional enrichment analysis and the protein-protein interaction (PPI) network were harnessed to explore the function and interaction of DERBPs. Next, Univariate and multiple Cox regression were applied to screen prognosis-related hub RBPs and to construct a signature for BC. Meanwhile, a nomogram was built based on the same RBPs. Results: A total of 296 DERBPs were found, and most of them mainly related to post-transcriptional regulation of RNA and ribonucleoprotein. A PPI network of DERBPs was constructed, consisting of 262 nodes and 2567 edges. A prognostic signature was built depended on seven prognosis-related hub RBPs that could divide GC patients into high- and low-risk groups. Survival analysis showed that the high-risk group had a worse prognosis compared to the low-risk group and the time-dependent receiver operating characteristic (ROC) curves suggested that the signature existed moderate predictive capacities of survival for GC patients. Similar results were obtained from another independent set GSE84437, confirming the robustness of signature. Calibration plots reported good consistency between overall survival (OS) prediction by nomogram and actual observation. Conclusion: The findings of this study would provide evidence of the effect of RBPs on GC as well as offering novel potential biomarkers in prognosis prediction and clinical decision for GC patients.