Hepatocellular carcinoma (HCC) is a common human malignancy with high mortality and dismal prognosis. A growing number of novel targets underlying HCC pathophysiology have been detected using microarray high throughput screening platforms. This study carried out bioinformatics analysis to explore underlying biomarkers in HCC and assessed the potential action of the miR-193b-3p/CDK1 signaling pathway in HCC progression. A total of 241 common differentially expressed genes (DEGs) were screened from GSE33294, GSE104310, and GSE144269. Functional analysis results implicated that DEGs are significantly associated with "cell cycle," "cell division," and "proliferation." The protein-protein interaction network analysis extracted ten hub genes from common DEGs. Ten hub genes were significantly overexpression in HCC tissues. Kaplan-Meier survival analysis revealed that 10 hub genes were linked with a poorer prognosis in HCC patients. Functional assays showed that CDK1 knockdown repressed HCC cell proliferation and migration. Luciferase reporter assay showed that miR-193b-3p could target CDK1 3' untranslated region, and miR-193b-3p negatively modulated CDK1. Enforced CDK1 expression attenuated miR-193b-3p-modulated suppressive actions on HCC cell proliferation and migration. To summarize, we performed a comprehensive bioinformatics analysis and identified 10 hub genes linked to the prognosis in HCC patients. Functional analysis revealed that CDK1, negatively regulated by miR-193b-3p, may act as an oncogene to promote HCC cell proliferation and migration and may predict poor prognosis of HCC patients. However, the role of CDK1/miR-193b-3p may still require further investigation.

Gastrointestinal (GI) cancers are a set of diverse diseases affecting many parts/ organs. The five most frequent GI cancer types are esophageal, gastric cancer (GC), liver cancer, pancreatic cancer, and colorectal cancer (CRC); together, they give rise to 5 million new cases and cause the death of 3.5 million people annually. We provide information about molecular changes crucial to tumorigenesis and the behavior and prognosis. During the formation of cancer cells, the genomic changes are microsatellite instability with multiple chromosomal arrangements in GC and CRC. The genomically stable subtype is observed in GC and pancreatic cancer. Besides these genomic subtypes, CRC has epigenetic modification (hypermethylation) associated with a poor prognosis. The pathway information highlights the functions shared by GI cancers such as apoptosis; focal adhesion; and the p21-activated kinase, phosphoinositide 3-kinase/Akt, transforming growth factor beta, and Toll-like receptor signaling pathways. These pathways show survival, cell proliferation, and cell motility. In addition, the immune response and inflammation are also essential elements in the shared functions. We also retrieved information on protein-protein interaction from the STRING database, and found that proteins Akt1, catenin beta 1 (CTNNB1), E1A binding protein P300, tumor protein p53 (TP53), and TP53 binding protein 1 (TP53BP1) are central nodes in the network. The protein expression of these genes is associated with overall survival in some GI cancers. The low TP53BP1 expression in CRC, high EP300 expression in esophageal cancer, and increased expression of Akt1/TP53 or low CTNNB1 expression in GC are associated with a poor prognosis. The Kaplan Meier plotter database also confirmed the association between expression of the five central genes and GC survival rates. In conclusion, GI cancers are very diverse at the molecular level. However, the shared mutations and protein pathways might be used to understand better and reveal diagnostic/prognostic or drug targets.

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.