肽组学的进展揭示了许多具有编码潜力的小型开放阅读框，并揭示其中一些微肽与人类癌症密切相关。然而，从序列到结构和功能的系统分析和整合仍然很大程度上尚未发展。作为一种解决方案，我们建立了一个工作流程，使用来自大型队列的公开数据集来收集和分析蛋白质组数据、转录组数据和癌症相关微肽的临床结果。我们最初通过重新分析 8 种癌症类型的 3753 个样本的蛋白质组谱数据，鉴定出了 19,586 种新型微肽。对这些微肽以及相关临床数据的进一步定量分析，确定了 3065 种在癌症中失调的微肽，其中 370 种显示出与预后密切相关。此外，我们采用深度学习框架构建微肽-蛋白质相互作用网络以进行进一步的生物信息学分析，揭示微肽作为生物活性分子参与多种生物过程。总而言之，我们的图谱为微肽的高通量预测和功能探索提供了基准，为它们在癌症中的生物学机制提供了新的见解。 HMPA 可在 http://hmpa.zju.edu.cn 上免费获取。© 作者 2024。由牛津大学出版社出版。

Advancements in peptidomics have revealed numerous small open reading frames with coding potential and revealed that some of these micropeptides are closely related to human cancer. However, the systematic analysis and integration from sequence to structure and function remains largely undeveloped. Here, as a solution, we built a workflow for the collection and analysis of proteomic data, transcriptomic data, and clinical outcomes for cancer-associated micropeptides using publicly available datasets from large cohorts. We initially identified 19 586 novel micropeptides by reanalyzing proteomic profile data from 3753 samples across 8 cancer types. Further quantitative analysis of these micropeptides, along with associated clinical data, identified 3065 that were dysregulated in cancer, with 370 of them showing a strong association with prognosis. Moreover, we employed a deep learning framework to construct a micropeptide-protein interaction network for further bioinformatics analysis, revealing that micropeptides are involved in multiple biological processes as bioactive molecules. Taken together, our atlas provides a benchmark for high-throughput prediction and functional exploration of micropeptides, providing new insights into their biological mechanisms in cancer. The HMPA is freely available at http://hmpa.zju.edu.cn.© The Author(s) 2024. Published by Oxford University Press.