LncRNAs KB-1836B5, LINC00566 and FAM27L are associated with the survival time of patients with ovarian cancer

Ontologies and Knowledge Graphs in Oncology Research

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

Identification of KEY lncRNAs and mRNAs Associated with Oral Squamous Cell Carcinoma Progression

Background:

Oral squamous cell carcinoma (OSCC) has been the sixth most common cancer worldwide. Emerging studies showed long non-coding RNAs to play a key role in human cancers. However, the molecular mechanisms underlying the initiation and progression of OSCC remained to be further explored.

Objective:

The present study aimed to identify differentially expressed lncRNAs and mRNAs in OSCC.

Methods:

GSE30784 was analyzed to identify differentially expressed lncRNAs and mRNAs in OSCC. Protein-protein interaction network and co-expression network analyses were performed to reveal the potential roles of OSCC related mRNAs and lncRNAs.

Results:

In the present study, we identified 21 up-regulated lncRNAs and 54 down-regulated lncRNAs in OSCC progression. Next, we constructed a lncRNA related co-expression network in OSCC, which included 692 mRNAs and 2193 edges. Bioinformatics analysis showed that lncRNAs were widely co-expressed with regulating type I interferon signaling pathway, extracellular matrix organization, collagen catabolic process, immune response, ECM-receptor interaction, Focal adhesion, and PI3K-Akt signaling pathway. A key network, including lncRNA C5orf66-AS1, C21orf15, LOC100506098, PCBP1-AS1, LOC284825, OR7E14P, HCG22, and FLG-AS1, was found to be involved in the regulation of immune response to tumor cell, Golgi calcium ion transport, negative regulation of vitamin D receptor signaling pathway, and glycerol- 3-phosphate catabolic process. Moreover, we found higher expressions of CYP4F29P, PCBP1- AS1, HCG22, and C5orf66-AS1, which were associated with shorter overall survival time in OSCC samples.

Conclusions:

Our analysis can provide novel insights to explore the potential mechanisms underlying OSCC progression.