Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China; La Trobe Institute for Molecular Science, La Trobe University, Melbourne, VIC, 3086, Australia.
Genomics Research Platform, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, VIC, 3086, Australia; School of Animal and Veterinary Science, The University of Adelaide, Adelaide, SA 5371, Australia; School of BioSciences, The University of Melbourne, Melbourne, VIC, 3010, Australia.
Comput Biol Med. 2024 Apr;172:108233. doi: 10.1016/j.compbiomed.2024.108233. Epub 2024 Feb 28.
Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies.
Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining.
Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle.
The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.
癌症恶病质是一种严重的代谢综合征，其特征是骨骼肌萎缩。目前临床上缺乏对癌症恶病质的有效干预手段。恶病质机制的研究主要基于临床前动物模型，并且随着下一代测序技术的广泛应用，越来越多的消瘦小鼠肌肉高通量转录组数据集可用。
将十项不同研究的消瘦小鼠肌肉转录组数据集进行组合，通过七种属性加权模型进行挖掘，该模型分析了类别变量和数值变量。通过属性加权算法确定了癌症恶病质的转录组特征，并将其用于评估十一种模式发现模型的性能。该特征用于寻找治疗癌症恶病质的最佳药物组合（药物再利用），并通过文献挖掘评估目前用于治疗恶病质的药物。
属性加权算法将 26 个基因评为癌症恶病质小鼠肌肉的转录组特征。基于肌肉转录组数据，深度学习和随机森林模型在区分癌症恶病质病例方面表现更好。文献挖掘显示，褪黑素和英夫利昔单抗的组合与肌肉转录组特征中上调的 2 个关键基因（Rorc 和 Fbxo32）有负面相互作用。
机器学习、荟萃分析和文献挖掘的整合被发现是识别癌症恶病质稳健转录组特征的有效方法，这对改善该疾病的临床诊断和管理具有重要意义。
