Lessons learned from a lncRNA odyssey for two genes with vascular functions, DLL4 and TIE1

Cervical cancer cell-derived Tie1 expression via PI3K/AKT signaling pathway promotes tumor progression

BACKGROUND

Tie1 orphan receptor has become a focus of research, Tie1 can form a polymer with Tie2, regulate the Ang/Tie2 pathway and play a vital role in pathological angiogenesis and tumor progression, the function of Tie1 has remained uncertain in the progression of cervical cancer (CC). Here, we investigated the functional influences of Tie1 overexpress on CC in vitro and in vivo.

METHODS

We used Immunohistochemistry (IHC) analysis to detect the relative expression of Tie1 in CC, and we analyzed its connection with the overall survival (OS) and progression free survival (PFS)of CC patients. To prove the role of Tie1 in cell proliferation and metastatic, Tie1 expression in CC cell lines was upregulated by lentivirus.

RESULTS

The high expression of Tie1 in tumor cells of cervical cancer tissues is significantly correlated with FIGO stage, differentiated tumors, tumors with diameters, deep stromal invasion. We found that cell progression was promoted in Tie1-overexpress CC cell lines in vivo and in vitro. Tie1 potentially exerts a commanding influence on the expression of markers associated with epithelial-mesenchymal transition (EMT) and the PI3K/AKT signaling pathway.

CONCLUSIONS

Our research indicates that Tie1 is highly connected to CC progression as it may play a role in the EMT process through the PI3K/AKT signaling pathway.

FGGA-lnc: automatic gene ontology annotation of lncRNA sequences based on secondary structures

The study of long non-coding RNAs (lncRNAs), greater than 200 nucleotides, is central to understanding the development and progression of many complex diseases. Unlike proteins, the functionality of lncRNAs is only subtly encoded in their primary sequence. Current in-silico lncRNA annotation methods mostly rely on annotations inferred from interaction networks. But extensive experimental studies are required to build these networks. In this work, we present a graph-based machine learning method called FGGA-lnc for the automatic gene ontology (GO) annotation of lncRNAs across the three GO subdomains. We build upon FGGA (factor graph GO annotation), a computational method originally developed to annotate protein sequences from non-model organisms. In the FGGA-lnc version, a coding-based approach is introduced to fuse primary sequence and secondary structure information of lncRNA molecules. As a result, lncRNA sequences become sequences of a higher-order alphabet allowing supervised learning methods to assess individual GO-term annotations. Raw GO annotations obtained in this way are unaware of the GO structure and therefore likely to be inconsistent with it. The message-passing algorithm embodied by factor graph models overcomes this problem. Evaluations of the FGGA-lnc method on lncRNA data, from model and non-model organisms, showed promising results suggesting it as a candidate to satisfy the huge demand for functional annotations arising from high-throughput sequencing technologies.