The gene expression profiling of hepatocellular carcinoma by a network analysis approach shows a dominance of intrinsically disordered proteins (IDPs) between hub nodes

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

Identification of Hub Genes and Analysis of Prognostic Values in Hepatocellular Carcinoma by Bioinformatics Analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most frequent cancers in the world. In this study, differentially expressed genes (DEGs) between tumor tissues and normal tissues were identified using the comprehensive analysis method in bioinformatics.

MATERIALS AND METHODS

We downloaded 3 mRNA expression profiles from the Gene Expression Omnibus database to identify DEGs between tumor tissues and adjacent normal tissues. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein-protein interaction network was performed to understand the function of DEGs. OncoLnc, which was linked to The Cancer Genome Atlas survival data, was used to investigate the prognostic values of hub genes. The expression of selected hub genes was validated by the quantitative real-time polymerase chain reaction.

RESULTS

A total of 235 DEGs, consisting of 36 upregulated and 199 downregulated genes, were identified between tumor tissue and normal tissue. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis results showed the upregulated DEGs to be significantly enriched in cell division, mid-body, ATP binding and oocyte meiosis pathways. The downregulated DEGs were mainly involved in epoxygenase P450 pathway, extracellular region, oxidoreductase activity and metabolic pathways. Ten hub genes, including Aurora kinase A, Cell division cycle 20, formiminotransferase cyclodeaminase, UBE2C, Cyclin B2, pituitary tumor-transforming gene 1, CDKN3, CKS1B, Topoisomerase-II alpha and KIF20A, were identified as the key genes in HCC. Survival analysis found the expression of hub genes to be significantly correlated with the survival of patients with HCC.

CONCLUSIONS

The present study identified hub genes and pathways in HCC that may be potential targets for diagnosis, treatment and prognostic prediction.

Evaluating the associations between human circadian rhythms and dysregulated genes in liver cancer cells

Network analysis is a useful approach in cancer biology as it provides information regarding the genes and proteins. In our previous study, a network analysis was performed on dysregulated genes in HepG2 cells, a hepatoblastoma cell line that lacks the viral infection, compared with normal hepatocytes, identifying the presence of 26 HUB genes. The present study aimed to identify whether these previously identified HUB genes participate in the network that controls the human circadian rhythms. The results of the present study demonstrated that 20/26 HUB genes were associated with the metabolic processes that control human circadian rhythms, which supports the hypothesis that a number of cancer types are dependent from circadian cycles. In addition, it was revealed that the CLOCK circadian regulator gene was associated, via cytoskeleton associated protein 5 (CKAP5), with the HUB genes of the HepG2 network, and that CKAP5 was associated with three other circadian genes (casein kinase 1ε, casein kinase 1δ and histone deacetylase 4) and 10 HepG2 genes (SH2 domain containing, ZW10 interacting kinetochore protein, aurora kinase B, cell division cycle 20, centromere protein A, inner centromere protein, mitotic arrest deficient 2 like 1, baculoviral IAP repeat containing 5, SPC24 NDC80 kinetochore complex component and kinesin family member 2C). Furthermore, the genes that associate the circadian system with liver cancer were demonstrated to encode intrinsically disordered proteins. Finally, the results of the present study identified the microRNAs involved in the network formed by the overlapping of HepG2 and circadian genes.