Key Laboratory of Biomedical Information Engineering of Ministry of Education, Biomedical Informatics and Genomics Center, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, PR China.
National and Local Joint Engineering Research Center of Biodiagnosis and Biotherapy, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi 710004, PR China.
EBioMedicine. 2022 May;79:104014. doi: 10.1016/j.ebiom.2022.104014. Epub 2022 Apr 26.
Accumulative evidences have shown that dysregulation of biological pathways contributed to the initiation and progression of malignant tumours. Several methods for pathway activity measurement have been proposed, but they are restricted to making comparisons between groups or sensitive to experimental batch effects.
We introduced a novel method for individualized pathway activity measurement (IPAM) that is based on the ranking of gene expression levels in individual sample. Taking advantage of IPAM, we calculated the pathway activity of 318 pathways from KEGG database in the 10528 tumour/normal samples of 33 cancer types from TCGA to identify characteristic dysregulated pathways among different cancer types.
IPAM precisely quantified the level of activity of each pathway in pan-cancer analysis and exhibited better performance in cancer classification and prognosis prediction over five widely used tools. The average ROC-AUC of cancer diagnostic model using tumour-educated platelets (TEPs) reached 92.84%, suggesting the potential of our algorithm in early diagnosis of cancer. We identified several pathways significantly deregulated and associated with patient survival in a large fraction of cancer types, such as tyrosine metabolism, fatty acid degradation, cell cycle, p53 signalling pathway and DNA replication. We also confirmed the dominant role of metabolic pathways in cancer pathway dysregulation and identified the driving factors of specific pathway dysregulation, such as PPARA for branched-chain amino acid metabolism and NR1I2, NR1I3 for fatty acid metabolism.
Our study will provide novel clues for understanding the pathological mechanisms of cancer, ultimately paving the way for personalized medicine of cancer.
A full list of funding can be found in the Acknowledgements section.
越来越多的证据表明，生物途径的失调导致了恶性肿瘤的发生和发展。已经提出了几种测量途径活性的方法，但它们仅限于组间比较或对实验批次效应敏感。
我们引入了一种新的个体途径活性测量（IPAM）方法，该方法基于个体样本中基因表达水平的排序。利用 IPAM，我们计算了来自 TCGA 的 33 种癌症类型的 10528 个肿瘤/正常样本中 318 个来自 KEGG 数据库的途径的活性，以鉴定不同癌症类型中特征性失调的途径。
IPAM 在泛癌分析中精确地量化了每条途径的活性水平，并在癌症分类和预后预测方面表现优于五种常用工具。使用肿瘤诱导血小板（TEPs）的癌症诊断模型的平均 ROC-AUC 达到 92.84%，表明我们的算法在癌症早期诊断中的潜力。我们确定了许多在大部分癌症类型中显著失调且与患者生存相关的途径，如酪氨酸代谢、脂肪酸降解、细胞周期、p53 信号通路和 DNA 复制。我们还证实了代谢途径在癌症途径失调中的主导作用，并确定了特定途径失调的驱动因素，如分支氨基酸代谢的 PPARA 和脂肪酸代谢的 NR1I2、NR1I3。
我们的研究将为理解癌症的病理机制提供新的线索，最终为癌症的个性化医学铺平道路。
完整的资助清单可以在致谢部分找到。
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