Bioinformatic analysis reveals the key pathways and genes in early-onset breast cancer

CCNB1 is a novel prognostic biomarker and promotes proliferation, migration and invasion in Wilms tumor

BACKGROUND

Wilms tumour (WT) is a mixed type of embryonal tumour that usually occurs in early childhood. However, our knowledge of the pathogenesis or progression mechanism of WT is inadequate, and there is a scarcity of beneficial therapeutic strategies.

METHODS

High-throughput RNA sequencing was employed in this study to identify differentially expressed genes (DEGs) in clinical tumor samples and matching normal tissues. The STRING database was utilized to build a protein-protein interaction (PPI) network, and the Cytohubba method was used to identify the top 10 highly related HUB genes. Then, the key genes were further screened by univariate COX survival analysis. Subsequently, the XCELL algorithm was used to evaluate the tumour immune infiltration. RT-PCR, WB, and IF were used to verify the expression level of key genes in clinical tissues and tumour cell lines. Finally, the function of the key gene was further verified by loss-of-function experiments.

RESULTS

We initially screened 1612 DEGs, of which 1030 were up-regulated and 582 were down-regulated. The GO and KEGG enrichment analysis suggested these genes were associated with 'cell cycle', 'DNA replication'. Subsequently, we identified 10 key HUB genes, among them CCNB1 was strongly related to WT patients' overall survival. Multiple survival analyses showed that CCNB1 was an independent indicator of WT prognosis. Thus, we constructed a nomogram of CCNB1 combined with other clinical indicators. Single gene GSEA and immune infiltration analysis revealed that CCNB1 was associated with the degree of infiltration or activation status of multiple immune cells. TIDE analysis indicated that this gene was correlated with multiple key immune checkpoint molecules and TIDE scores. Finally, we validated the differential expression level of CCNB1 in an external gene set, the pan-cancer, clinical samples, and cell lines. CCNB1 silencing significantly inhibited the proliferation, migration, and invasive capabilities of WIT-49 cells, also, promoted apoptosis, and in turn induced G2 phase cell cycle arrest in loss-of-function assays.

CONCLUSION

Our study suggests that CCNB1 is closely related to WT progression and prognosis, and serves as a potential target.

Transcriptome Analysis of Bovine Rumen Tissue in Three Developmental Stages

Rumen development is a crucial physiological challenge for ruminants. However, the molecular mechanism regulating rumen development has not been clearly elucidated. In this study, we investigated genes involved in rumen development in 13 rumen tissues from three developmental stages (birth, youth, and adult) using RNA sequencing. We identified that 6,048 genes were differentially expressed among three developmental stages. Using weighted correlation network analysis, we found that 12 modules were significantly associated with developmental stages. Functional annotation and protein–protein interaction (PPI) network analysis revealed that CCNB1, CCNB2, IGF1, IGF2, HMGCL, BDH1, ACAT1, HMGCS2, and CREBBP involved in rumen development. Integrated transcriptome with GWAS information of carcass weight (CW), stomach weight (SW), marbling score (MS), backfat thickness (BFT), ribeye area (REA), and lean meat weight (LMW), we found that upregulated DEGs (fold change 0∼1) in birth–youth comparison were significantly enriched with GWAS signals of MS, downregulated DEGs (fold change >3) were significantly enriched with GWAS signals of SW, and fold change 0∼1 up/downregulated DEGs in birth–adult comparison were significantly enriched with GWAS signals of CW, LMW, REA, and BFT. Furthermore, we found that GWAS signals for CW, LMW, and REA were enriched in turquoise module, and GWAS signals for CW was enriched in lightgreen module. Our study provides novel insights into the molecular mechanism underlying rumen development in cattle and highlights an integrative analysis for illustrating the genetic architecture of beef complex traits.

Key Genes and Prognostic Analysis in HER2+ Breast Cancer

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes (CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

Identification of osteoporosis markers through bioinformatic functional analysis of serum proteome

Osteoporosis is a severe chronic skeletal disorder that increases the risks of disability and mortality; however, the mechanism of this disease and the protein markers for prognosis of osteoporosis have not been well characterized. This study aims to characterize the imbalanced serum proteostasis, the disturbed pathways, and potential serum markers in osteoporosis by using a set of bioinformatic analyses. In the present study, the large-scale proteomics datasets (PXD006464) were adopted from the Proteome Xchange database and processed with MaxQuant. The differentially expressed serum proteins were identified. The biological process and molecular function were analyzed. The protein-protein interactions and subnetwork modules were constructed. The signaling pathways were enriched. We identified 209 upregulated and 230 downregulated serum proteins. The bioinformatic analyses revealed a highly overlapped functional protein classification and the gene ontology terms between the upregulated and downregulated protein groups. Protein-protein interactions and pathway analyses showed a high enrichment in protein synthesis, inflammation, and immune response in the upregulated proteins, and cell adhesion and cytoskeleton regulation in the downregulated proteins. Our findings greatly expand the current view of the roles of serum proteins in osteoporosis and shed light on the understanding of its underlying mechanisms and the discovery of serum proteins as potential markers for the prognosis of osteoporosis.