Bladder Cancer and Artificial Intelligence

Laurie, Mark A.; Zhou, Steve R.; Islam, Md Tauhidul; Shkolyar, Eugene; Xing, Lei; Liao, Joseph C.

doi:10.1016/j.ucl.2023.07.002

Cited by 3 publications

(1 citation statement)

References 84 publications

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“…Nevertheless, owing to the diversity in both cancer detection targets and AI methodologies, there is a wide variety of these AI tools in the market. 201–208 These targets range from detecting protein biomarkers to cell-free DNA or a combination of these indicators. In crafting AI models, different approaches are adopted, including the use of datasets from case-control studies or databases from real-life cancer screenings.…”

Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

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Section: Challenges and Future Perspectivesmentioning

confidence: 99%

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Khorsandi,

Rezayat,

Sezen

et al. 2024

J. Mater. Chem. B

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Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence

Shkolyar,

Zhou,

Carlson

et al. 2024

Nat Rev Urol

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Revealing the mechanisms of RAC3 in tumor aggressiveness, the immunotherapy response, and drug resistance in bladder cancer

Gao,

Qiu,

Zheng

et al. 2024

Front. Oncol.

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BackgroundBladder cancer (BLCA) is a prevalent urinary tract malignancy with a high propensity for recurrence and chemoresistance. The molecular mechanisms underlying its progression and response to therapy have not been fully elucidated.MethodsWe conducted a multifaceted analysis, integrating immunohistochemical (IHC) staining, bioinformatics evaluation using TCGA and CCLE databases, and in vitro assays using the BLCA cell lines 5637 and T24. RAC3 expression was assessed relative to clinical and pathological features. Functional enrichment analyses and gene set enrichment analysis (GSEA) were performed to identify associated biological processes and pathways. The impacts of RAC3 on cell proliferation, migration, invasion, and the immune microenvironment were evaluated using siRNA knockdown, CCK-8, Transwell, wound healing and colony formation assays.ResultsElevated RAC3 expression was significantly correlated with an advanced tumor stage, lymph node metastasis, and poor prognosis for BLCA patients. The functional enrichment analysis implicated RAC3 in immune cell infiltration and immune checkpoint mechanisms. Notably, RAC3 knockdown significantly reduced the proliferative, migratory, and invasive capabilities of BLCA cells. These effects were reversed by the overexpression of RAC3. Additionally, RAC3 expression was linked to chemoresistance, with high RAC3 expression predicting resistance to certain therapeutic agents. The TIDE algorithm indicated that RAC3 expression could be a predictive biomarker for the immunotherapy response.ConclusionRAC3 was identified as a potential therapeutic target and biomarker of BLCA, as its expression significantly influenced tumor progression, the immune response, and chemosensitivity. Targeting RAC3 may provide a novel strategy for the management of BLCA, particularly for patients resistant to conventional therapies. Further research is essential to elucidate the detailed mechanisms of RAC3 in BLCA and explore its clinical application in precision medicine.

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Bladder Cancer and Artificial Intelligence

Cited by 3 publications

References 84 publications

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like?

Optimizing cystoscopy and TURBT: enhanced imaging and artificial intelligence

Revealing the mechanisms of RAC3 in tumor aggressiveness, the immunotherapy response, and drug resistance in bladder cancer

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