The role of Piccolo in cancer treatment: relationship with EGFR and related therapies, and a marker for new targeted therapies

Frattini, Milo; Molinari, Francesca; Epistolio, Samantha

doi:10.21037/jtd.2017.10.38

Cited by 3 publications

(1 citation statement)

References 15 publications

(20 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This dysregulation can drive the transformation of normal cells into tumor cells, creating a favorable environment for sustained tumor cell survival [59]. Additionally, the protein encoded by PCLO is a component of the presynaptic cytoskeletal matrix, which is involved in establishing active synaptic zones and in synaptic vesicle trafficking [60]. Recent research has identified the expression level of PCLO as a prognostic biomarker for esophageal squamous cell carcinoma [61], and a notable high mutation frequency (47.9%) of PCLO has been observed in large Central European cohorts with gastric cancer [62].…”

Section: Differential Expression Analysis Revealed Significant Differ...mentioning

confidence: 99%

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Lu,

Zheng,

Hao

et al. 2024

Preprint

View full text Add to dashboard Cite

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses significant challenges to the effective diagnosis and treatment of ccRCC. To address this problem, we propose a deep reinforcement learning-based computational approach named RL-GenRisk to identify ccRCC risk genes. Distinct from traditional supervised models, RL-GenRisk frames the identification of ccRCC risk genes as a Markov decision process, combining the graph convolutional network and Deep Q-Network for risk gene identification. Moreover, a well-designed data-driven reward is proposed for mitigating the lim-itation of scant known risk genes. The evaluation demonstrates that RL-GenRisk outperforms existing methods in ccRCC risk gene identification. Additionally, RL-GenRisk identifies ten novel ccRCC risk genes. We successfully validated epidermal growth factor receptor (EGFR), corroborated through independent datasets and biological experimentation. This approach may also be used for other diseases in the future.

show abstract

Section: Differential Expression Analysis Revealed Significant Differ...mentioning

confidence: 99%

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Lu,

Zheng,

Hao

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Expression and clinical significance of five major genes in cutaneous melanoma based on TCGA database

Ren¹,

Ruan

2020

Human Pathology: Case Reports

View full text Add to dashboard Cite

Synaptic logistics: The presynaptic scaffold protein Piccolo a nodal point tuning synaptic vesicle recycling, maintenance and integrity

Garner

Ackermann

2023

Molecular and Cellular Neuroscience

View full text Add to dashboard Cite

The role of Piccolo in cancer treatment: relationship with EGFR and related therapies, and a marker for new targeted therapies

Cited by 3 publications

References 15 publications

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Expression and clinical significance of five major genes in cutaneous melanoma based on TCGA database

Synaptic logistics: The presynaptic scaffold protein Piccolo a nodal point tuning synaptic vesicle recycling, maintenance and integrity

Contact Info

Product

Resources

About