Certain aspects of diagnosis, prognosis, and treatment of cancer patients are still important challenges to be addressed. Therefore, we propose a pipeline to uncover patterns of alternative polyadenylation (APA), a hidden complexity in cancer transcriptomes, to further accelerate efforts to discover novel cancer genes and pathways. Here, we analyzed expression data for 1045 cancer patients and found a significant shift in usage of poly(A) signals in common tumor types (breast, colon, lung, prostate, gastric, and ovarian) compared to normal tissues. Using machine-learning techniques, we further defined specific subsets of APA events to efficiently classify cancer types. Furthermore, APA patterns were associated with altered protein levels in patients, revealed by antibody-based profiling data, suggesting functional significance. Overall, our study offers a computational approach for use of APA in novel gene discovery and classification in common tumor types, with important implications in basic research, biomarker discovery, and precision medicine approaches.

中文翻译：

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癌症中新基因发现和分类的替代聚腺苷酸模式。

癌症患者的诊断，预后和治疗的某些方面仍然是要解决的重要挑战。因此，我们提出了一条管道，以揭示替代性聚腺苷酸化（APA）模式（癌症转录组中隐藏的复杂性），以进一步加快发现新型癌症基因和途径的努力。在这里，我们分析了1045例癌症患者的表达数据，发现与普通组织相比，常见肿瘤类型（乳腺癌，结肠癌，肺癌，前列腺癌，胃癌和卵巢癌）中poly（A）信号的使用发生了显着变化。使用机器学习技术，我们进一步定义了APA事件的特定子集，以有效地分类癌症类型。此外，基于抗体的分析数据显示，APA模式与患者蛋白质水平的改变有关，提示其功能意义。全面的，