โก Quick Summary
This study provides a comprehensive analysis of long QT syndrome (LQTS) associated genes in cancer, revealing their significant role in predicting patient outcomes. A robust prognostic model was developed, demonstrating that lower LQTS gene signature enrichment correlates with poorer prognosis across various cancer types.
๐ Key Details
- ๐ Dataset: 10,531 patients diagnosed with 33 types of cancer
- ๐งฉ Features used: LQTS gene signatures and clinical data
- โ๏ธ Technology: Machine learning approaches for prognostic model development
- ๐ Performance: Effective prediction of patient outcomes across diverse cancer types
๐ Key Takeaways
- ๐ฌ LQTS is a major manifestation of cardiotoxicity in cancer treatments.
- ๐ Prognostic model constructed using 17 LQTS gene signatures.
- ๐ก Lower enrichment levels of LQTS gene signatures indicate a poorer prognosis.
- ๐ Validation of the model was performed using transcriptome data from the GEO database.
- ๐บ๏ธ Nomogram developed to integrate clinical features with the prognostic model.
- ๐ค Machine learning approaches enhanced the robustness of the prognostic predictions.
- ๐ Study published in the Journal of Cellular and Molecular Medicine.
- ๐ Publication Year: 2024
๐ Background
Cancer remains a leading public health challenge globally, with treatment often complicated by cardiotoxicity associated with anti-cancer therapies. Long QT syndrome (LQTS) is a critical cardiac dysfunction that can arise from these treatments, necessitating a deeper understanding of its relationship with cancer to improve patient management and outcomes.
๐๏ธ Study
The study utilized transcriptomic sequencing data and clinical information from a substantial cohort of 10,531 cancer patients sourced from the TCGA database. The researchers aimed to elucidate the connection between LQTS and various cancer types, ultimately constructing a prognostic model based on LQTS gene signatures.
๐ Results
The findings indicated that patients with lower enrichment levels of LQTS gene signatures experienced a significantly poorer prognosis. The correlation between these enrichment levels and typical cancer-associated signaling pathways was observed across multiple cancer types. The machine learning-based prognostic model demonstrated its effectiveness in predicting patient outcomes, validated through various datasets.
๐ Impact and Implications
This research highlights the intricate relationship between LQTS and cancer pathways, paving the way for a feature-based clinical decision tool aimed at enhancing precision treatment in oncology. By integrating LQTS gene signatures into clinical practice, healthcare providers can potentially improve patient outcomes and tailor therapies more effectively.
๐ฎ Conclusion
The study underscores the importance of understanding the role of LQTS in cancer treatment and prognosis. The development of a robust prognostic model based on LQTS gene signatures represents a significant advancement in personalized cancer care. Continued research in this area is essential for refining treatment strategies and improving patient quality of life.
๐ฌ Your comments
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Comprehensive characterization of long QT syndrome-associated genes in cancer and development of a robust prognosis model.
Abstract
Cancer is the leading public health problem worldwide. However, the side effects accompanying anti-cancer therapies, particularly those pertaining to cardiotoxicity and adverse cardiac events, have been the hindrances to treatment progress. Long QT syndrome (LQTS) is one of the major clinic manifestations of the anti-cancer drug associated cardiac dysfunction. Therefore, elucidating the relationship between the LQTS and cancer is urgently needed. Transcriptomic sequencing data and clinic information of 10,531 patients diagnosed with 33 types of cancer was acquired from TCGA database. A pan-cancer applicative gene prognostic model was constructed based on the LQTS gene signatures. Meanwhile, transcriptome data and clinical information from various cancer types were collected from the GEO database to validate the robustness of the prognostic model. Furthermore, the expression level of transcriptomes and multiple clinical features were integrated to construct a Nomo chart to optimize the prognosis model. The ssGSEA analysis was employed to analysis the correlation between the LQTS gene signatures, clinic features and cancer associated signalling pathways. Our findings revealed that patients with lower LQTS gene signatures enrichment levels exhibit a poorer prognosis. The correlation of enrichment levels with the typical pathways was observed in multiple cancers. Then, based on the 17 LQTS gene signatures, we construct a prognostic model through the machine-learning approaches. The results obtained from the validation datasets and training datasets indicated that our prognostic model can effectively predict patient outcomes across diverse cancer types. Finally, we integrated this model with clinical features into a nomogram, demonstrating its potential as a valuable prognostic tool for cancer patients. Our study sheds light on the intricate relationship between LQTS and cancer pathways. A LQTS feature based clinic decision tool was developed aiming to enhance precision treatment of cancer.
Author: [‘Xu J’, ‘Wen Z’, ‘She Y’, ‘Li M’, ‘Shen X’, ‘Zhi F’, ‘Wang S’, ‘Jiang Y’]
Journal: J Cell Mol Med
Citation: Xu J, et al. Comprehensive characterization of long QT syndrome-associated genes in cancer and development of a robust prognosis model. Comprehensive characterization of long QT syndrome-associated genes in cancer and development of a robust prognosis model. 2024; 28:e70094. doi: 10.1111/jcmm.70094