ABSTRACT: Metabolic reprogramming is one of the emerging hallmarks of cancer cells. Various factors, such as signaling proteins (S), miRNA, and transcription factors (TFs), may play important roles in altering the metabolic status in cancer cells by interacting with metabolic enzymes either directly or via protein-protein interactions (PPIs). Therefore, it is important to understand the coordination among these cellular pathways, which may provide better insight into the molecular mechanism behind metabolic adaptations in cancer cells. In this study, we have designed a cervical cancer-specific supra-interaction network where signaling pathway proteins, TFs, and microRNAs (miRs) are connected to metabolic enzymes via PPIs to investigate novel molecular targets and connections/links/paths regulating the metabolic enzymes. Using publicly available omics data and PPIs, we have developed a Hidden Markov Model (HMM)-based mathematical model yielding 94, 236, and 27 probable links/paths connecting signaling pathway proteins, TFs, and miRNAs to metabolic enzymes, respectively, out of which 83 paths connect to six common metabolic enzymes (RRM2, NDUFA11, ENO2, EZH2, AKR1C2, and TYMS). Signaling proteins (e.g., PPARD, BAD, GNB5, CHECK1, PAK2, PLK1, BRCA1, MAML3, and SPP1), TFs (e.g., KAT2B, ING1, MED1, ZEB1, AR, NCOA2, EGR1, TWIST1, E2F1, ID4, RBL1, ESR1, and HSF2), and miR (e.g., mir-147a, mir-593-5p, mir-138-5p, mir-16-5p, and mir-15b-5p) were found to regulate two key metabolic enzymes, EZH2 and AKR1C2, with altered metabolites (L-lysine and tetrahydrodeoxycorticosterone, THDOC) status in cervical cancer. We believe, the biology-based approach of our system will pave the way for future studies, which could be aimed toward identifying novel signaling, transcriptional, and post-transcriptional regulators of metabolic alterations in cervical cancer.
