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基于代谢相关基因对的signature 可预测骨肉瘤患者的总生存期。

Signature based on metabolic-related gene pairs can predict overall survival of osteosarcoma patients.

机构信息

Department of Orthopaedic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR China.

Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, PR China.

出版信息

Cancer Med. 2021 Jul;10(13):4493-4509. doi: 10.1002/cam4.3984. Epub 2021 May 28.

DOI:10.1002/cam4.3984
PMID:34047495
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8267140/
Abstract

BACKGROUND

Osteosarcoma is a tumour of malignant origin in children and adolescents. Recent progression indicates that it is necessary to develop new therapies to improve the patient's prognosis rather than strengthen anti-tumour chemotherapy. Researchers recently realised that cancer is a kind of disease with a metabolic disorder, and metabolic reprogramming is becoming a new cancer hallmark. Hence, our study's primary purpose is to explore the value of genes related to osteosarcoma metabolism.

METHODS

From public databases, three osteosarcoma datasets with adequate clinical information were obtained. Besides, the IMvigor dataset through the 'IMvigor' package as a supplement was downloaded, the metabolic-related genes were identified, and these genes were used to construct the metabolic-related gene pairs (MRGP). Based on the prognosis-related MRGP, two molecular subtypes were identified. There are significant differences in the metabolic characteristics between the two molecular subtypes. Subsequently, the MRGP signature is constructed using the least absolute shrinkage and selection operator regression method. Finally, use SubMap analysis to evaluate the response of patients in the MRPG signature group to immunotherapy.

RESULTS

The MRGP signature can reliably predict overall survival in patients with osteosarcoma. The MRGP signature is also associated with osteosarcoma patients' metastatic status and can be used for subsequent risk classification of metastatic patients. The immunotherapy is more likely to benefit the patients in the MRGP low-risk group.

CONCLUSION

Metabolic-related gene pairs signature can assess the prognosis of patients with osteosarcoma.

摘要

背景

骨肉瘤是儿童和青少年的一种恶性起源肿瘤。最近的进展表明，有必要开发新的疗法来改善患者的预后，而不是加强抗肿瘤化疗。研究人员最近意识到，癌症是一种代谢紊乱的疾病，代谢重编程正在成为癌症的一个新标志。因此，我们的研究主要目的是探索与骨肉瘤代谢相关的基因的价值。

方法

从公共数据库中获取了三个具有充足临床信息的骨肉瘤数据集。此外，还通过“IMvigor”包下载了 IMvigor 数据集作为补充，鉴定了与代谢相关的基因，并将这些基因用于构建代谢相关基因对（MRGP）。基于与预后相关的 MRGP，鉴定出了两个分子亚型。这两个分子亚型在代谢特征上存在显著差异。随后，使用最小绝对收缩和选择算子回归方法构建了 MRGP 特征。最后，使用 SubMap 分析评估了 MRPG 特征组患者对免疫治疗的反应。

结果

MRGP 特征可可靠地预测骨肉瘤患者的总生存期。MRGP 特征还与骨肉瘤患者的转移状态相关，并可用于对转移性患者进行后续风险分类。免疫治疗更有可能使 MRGP 低风险组的患者受益。

结论

代谢相关基因对特征可评估骨肉瘤患者的预后。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/6da652a960d5/CAM4-10-4493-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/901b4175124f/CAM4-10-4493-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/f40b1701419f/CAM4-10-4493-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/c8f5c09fb5d9/CAM4-10-4493-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/febaeb5792a6/CAM4-10-4493-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/e4470cc975f0/CAM4-10-4493-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/79fe043faf65/CAM4-10-4493-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/b408f0df09bd/CAM4-10-4493-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/a3392f8555e9/CAM4-10-4493-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/6da652a960d5/CAM4-10-4493-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/901b4175124f/CAM4-10-4493-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/f40b1701419f/CAM4-10-4493-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/c8f5c09fb5d9/CAM4-10-4493-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/febaeb5792a6/CAM4-10-4493-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/e4470cc975f0/CAM4-10-4493-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/79fe043faf65/CAM4-10-4493-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/b408f0df09bd/CAM4-10-4493-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/a3392f8555e9/CAM4-10-4493-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9af6/8267140/6da652a960d5/CAM4-10-4493-g008.jpg

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