Network-based survival-associated module biomarker and its crosstalk with cell death genes in ovarian cancer

Abstract

Ovarian cancer remains a dismal disease with diagnosing in the late, metastatic stages, therefore, there is a growing realization of the critical need to develop effective biomarkers for understanding underlying mechanisms. Although existing evidences demonstrate the important role of the single genetic abnormality in pathogenesis, the perturbations of interactors in the complex network are often ignored. Moreover, ovarian cancer diagnosis and treatment still exist a large gap that need to be bridged. In this work, we adopted a network-based survival-associated approach to capture a 12-gene network module based on differential co-expression PPI network in the advanced-stage, high-grade ovarian serous cystadenocarcinoma. Then, regulatory genes (protein-coding genes and non-coding genes) direct interacting with the module were found to be significantly overlapped with cell death genes. More importantly, these overlapping genes tightly clustered together pointing to the module, deciphering the crosstalk between network-based survival-associated module and cell death in ovarian cancer.

Stroma-associated master regulators of molecular subtypes predict patient prognosis in ovarian cancer

“…Indeed, we found that Mesenchymal or Immunoreactive MRs were detectable in the majority of TCGA samples (78%) at different levels of activation. Furthermore, there is an emerging consensus that network-based biomarker candidates, especially with sound biologic rationale, often exhibit high reproducibility and sensitivity in ovarian cancer 45 46 . The facts that stroma-associated MRs predicted clinical outcome and that Mesenchymal and Immunoreactive MRs differentially linked to opposite prognostic categories prompted us to deduce that the stromal composition and interplay might have a significant, if not major, contribution to the HGS-OvCa prognosis.…”

Revealing potential molecular targets bridging colitis and colorectal cancer based on multidimensional integration strategy

“…According to the functional modules we identified, 33 module pairs of UC and CRC were observed in total that significantly shared overlapping DEGs with a P value cutoff of 0.05 computed by MCODE algorithm ( Supplementary Table 4 ). Overlapping genes have been extensively recognized as versatile factors involved in a variety of phenotypically diverse disease states [ 14 , 15 ]. We therefore explored those overlapping DEGs to produce the first dimension explanation of the connections.…”

Predicting cancerlectins by the optimal g-gap dipeptides

“…They can be also used as a monitor when cells become cancerous, because cancerlectins can catch the instantaneous change of the glycosylated molecule, which distributes on the cells membrane, when the cell turns cancerous. In other words, cancerlectins can be the marker of tumor tissue-derived cells 9 10 11 12 . For example, Helix Pomatia agglutinin is an useful prognostic indicator in colorectal carcinoma 13 .…”