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Lokeshwar SD, Lopez M, Sarcan S, Aguilar K, Morera DS, Shaheen DM, Lokeshwar BL, Lokeshwar VB. Molecular Oncology of Bladder Cancer from Inception to Modern Perspective. Cancers (Basel) 2022; 14:cancers14112578. [PMID: 35681556 PMCID: PMC9179261 DOI: 10.3390/cancers14112578] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/19/2022] [Accepted: 05/20/2022] [Indexed: 02/05/2023] Open
Abstract
Within the last forty years, seminal contributions have been made in the areas of bladder cancer (BC) biology, driver genes, molecular profiling, biomarkers, and therapeutic targets for improving personalized patient care. This overview includes seminal discoveries and advances in the molecular oncology of BC. Starting with the concept of divergent molecular pathways for the development of low- and high-grade bladder tumors, field cancerization versus clonality of bladder tumors, cancer driver genes/mutations, genetic polymorphisms, and bacillus Calmette-Guérin (BCG) as an early form of immunotherapy are some of the conceptual contributions towards improving patient care. Although beginning with a promise of predicting prognosis and individualizing treatments, "-omic" approaches and molecular subtypes have revealed the importance of BC stem cells, lineage plasticity, and intra-tumor heterogeneity as the next frontiers for realizing individualized patient care. Along with urine as the optimal non-invasive liquid biopsy, BC is at the forefront of the biomarker field. If the goal is to reduce the number of cystoscopies but not to replace them for monitoring recurrence and asymptomatic microscopic hematuria, a BC marker may reach clinical acceptance. As advances in the molecular oncology of BC continue, the next twenty-five years should significantly advance personalized care for BC patients.
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Affiliation(s)
- Soum D. Lokeshwar
- Department of Urology, Yale University School of Medicine, New Haven, CT 06520, USA;
| | - Maite Lopez
- Departments of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA 30912, USA; (M.L.); (S.S.); (K.A.); (D.S.M.)
| | - Semih Sarcan
- Departments of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA 30912, USA; (M.L.); (S.S.); (K.A.); (D.S.M.)
- Department of Urology, University Hospital Schleswig-Holstein, Campus Lübeck, 23562 Lübeck, Germany
| | - Karina Aguilar
- Departments of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA 30912, USA; (M.L.); (S.S.); (K.A.); (D.S.M.)
| | - Daley S. Morera
- Departments of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA 30912, USA; (M.L.); (S.S.); (K.A.); (D.S.M.)
| | - Devin M. Shaheen
- Yale School of Nursing, Yale University, New Haven, CT 06520, USA;
| | - Bal L. Lokeshwar
- Georgia Cancer Center, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA 30912, USA
- Research Service, Charlie Norwood VA Medical Center, Augusta, GA 30904, USA
- Correspondence: (B.L.L.); (V.B.L.)
| | - Vinata B. Lokeshwar
- Departments of Biochemistry and Molecular Biology, Medical College of Georgia, Augusta University, 1410 Laney Walker Blvd., Augusta, GA 30912, USA; (M.L.); (S.S.); (K.A.); (D.S.M.)
- Correspondence: (B.L.L.); (V.B.L.)
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Network-based protein-protein interaction prediction method maps perturbations of cancer interactome. PLoS Genet 2021; 17:e1009869. [PMID: 34727106 PMCID: PMC8610286 DOI: 10.1371/journal.pgen.1009869] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 11/23/2021] [Accepted: 10/09/2021] [Indexed: 01/09/2023] Open
Abstract
The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.
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