Given the vast range of molecular mechanisms giving rise to breast cancer, it is unlikely universal cures exist. However, by providing a more precise prognosis for breast cancer patients through integrative models, treatments can become more individualized, resulting in more successful outcomes. Specifically, we combine gene expression, pseudogene expression, miRNA expression, clinical factors, and pseudogene-gene functional networks to generate these models for breast cancer prognostics. Establishing a LASSO-generated molecular gene signature revealed that the increased expression of genes STXBP5, GALP and LOC387646 indicate a poor prognosis for a breast cancer patient. We also found that increased CTSLP8 and RPS10P20 and decreased HLA-K pseudogene expression indicate poor prognosis for a patient. Perhaps most importantly we identified a pseudogene-gene interaction, GPS2-GPS2P1 (improved prognosis) that is prognostic where neither the gene nor pseudogene alone is prognostic of survival. Besides, miR-3923 was predicted to target GPS2 using miRanda, PicTar, and TargetScan, which imply modules of gene-pseudogene-miRNAs that are potentially functionally related to patient survival. In our LASSO-based model, we take into account features including pseudogenes, genes and candidate pseudogene-gene interactions. Key biomarkers were identified from the features. The identification of key biomarkers in combination with significant clinical factors (such as stage and radiation therapy status) should be considered as well, enabling a specific prognostic prediction and future treatment plan for an individual patient. Here we used our PseudoFuN web application to identify the candidate pseudogene-gene interactions as candidate features in our integrative models. We further identified potential miRNAs targeting those features in our models using PseudoFuN as well. From this study, we present an interpretable survival model based on LASSO and decision trees, we also provide a novel feature set which includes pseudogene-gene interaction terms that have been ignored by previous prognostic models. We find that some interaction terms for pseudogenes and genes are significantly prognostic of survival. These interactions are cross-over interactions, where the impact of the gene expression on survival changes with pseudogene expression and vice versa. These may imply more complicated regulation mechanisms than previously understood. We recommend these novel feature sets be considered when training other types of prognostic models as well, which may provide more comprehensive insights into personalized treatment decisions.

中文翻译：

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假基因功能网络是乳腺癌患者生存的预后


鉴于导致乳腺癌的分子机制多种多样，不太可能存在通用的治疗方法。然而，通过综合模型为乳腺癌患者提供更精确的预后，治疗可以变得更加个体化，从而获得更成功的结果。具体来说，我们结合基因表达、假基因表达、miRNA 表达、临床因素和假基因-基因功能网络来生成这些乳腺癌预后模型。建立 LASSO 生成的分子基因特征表明，基因 STXBP5、GALP 和 LOC387646 表达增加表明乳腺癌患者预后不良。我们还发现，CTSLP8 和 RPS10P20 增加以及 HLA-K 假基因表达减少表明患者预后不良。也许最重要的是，我们发现了一种假基因-基因相互作用，GPS2-GPS2P1（改善预后），它具有预后作用，而基因或假基因单独都不能预测生存。此外，使用 miRanda、PicTar 和 TargetScan 预测 miR-3923 会靶向 GPS2，这意味着基因-伪基因-miRNA 的模块可能与患者生存在功能上相关。在我们基于 LASSO 的模型中，我们考虑了包括假基因、基因和候选假基因-基因相互作用在内的特征。从这些特征中识别出了关键的生物标志物。还应考虑关键生物标志物的识别与重要的临床因素（例如分期和放射治疗状态）的结合，从而为个体患者提供特定的预后预测和未来的治疗计划。在这里，我们使用 PseudoFuN Web 应用程序将候选伪基因-基因相互作用识别为我们的综合模型中的候选特征。 我们还使用 PseudoFuN 进一步鉴定了针对模型中这些特征的潜在 miRNA。在这项研究中，我们提出了一个基于 LASSO 和决策树的可解释的生存模型，我们还提供了一个新颖的特征集，其中包括以前的预后模型忽略的假基因-基因相互作用项。我们发现假基因和基因的一些相互作用项对生存有显着的预测作用。这些相互作用是交叉相互作用，其中基因表达对生存的影响随着假基因表达而变化，反之亦然。这些可能意味着比以前理解的更复杂的监管机制。我们建议在训练其他类型的预后模型时也考虑这些新颖的特征集，这可能为个性化治疗决策提供更全面的见解。