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

Abstract

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. Supplementary Information The online version contains supplementary material available at 10.1007/s40291-020-00505-3.

A Tiny Viral Protein, SARS-CoV-2-ORF7b: Structural Features.

“…Some molecular partners of ORF7b-2 are known, albeit with different reliability and statistical significance, regardless of the knowledge of the spatiotemporal characteristics of where, how and when they perform their interactions and functions (111). The project by BioGrid curators could shed some light.…”

Multiple-Tissue and Multilevel Analysis on Differentially Expressed Genes and Differentially Correlated Gene Pairs for HFpEF

“…The PPI network was downloaded from STRING (version 11.0 3 ). We first downloaded the PPI network scored links between proteins from STRING (version 11.0, see text footnote 3) and reserved the interactions with scores above 900 at a confidence level ( Sharma and Colonna, 2021 ). The edges were removed such that the vertices did not comprise DEGs.…”

Understanding the SARS-CoV-2-Human Liver Interactome Using a Comprehensive Analysis of the Individual Virus-Host Interactions.

“…This suggests heterogeneity of networks. The differences in databases used to extract relationships are a common cause of conflicting results [39,40]. The relationships between the virus and the host occur at the molecular level, mainly through protein interactions.…”

“…The vast differences between databases make it extremely challenging to compare their data, particularly when the lack of experimental details obscures the nature of an interaction. What we often observe in the interactomics papers is an abnormal bloom of hub genes/proteins far beyond the needs of any biological network [40]. Therefore, the use of STRING, a platform that for each calculated interaction in a graph creates a specific knowledge base by querying thousands of scientific articles on PubMed, and BioGRID, a platform that archives only curated experimental data of the one-to-one interactions of SARS-CoV-2 proteins with the human proteome, are two indispensable tools to guarantee the best possible certainty of the data under analysis.…”