Mou Zhuofan, Spencer Jack, Knight Bridget, John Joseph, McCullagh Paul, McGrath John S, Harries Lorna W
Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Devon, United Kingdom.
Translational Research Exchange at Exeter, Living Systems Institute, University of Exeter, Exeter, United Kingdom.
Front Oncol. 2022 Aug 12;12:914078. doi: 10.3389/fonc.2022.914078. eCollection 2022.
Prostate cancer (PCa) is the second most common male cancer worldwide, but effective biomarkers for the presence or progression risk of disease are currently elusive. In a series of nine matched histologically confirmed PCa and benign samples, we carried out an integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), which identified a set of potential gene markers highly associated with tumour status (malignant . benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (, , , , and ) using least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in TCGA-PRAD dataset, with area under the curve (AUC) of 0.64-0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71-0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. Findings may improve current prognosis tools for PFS and contribute to clinical decision-making in PCa treatment.
前列腺癌（PCa）是全球第二常见的男性癌症，但目前仍难以找到有效的疾病存在或进展风险生物标志物。在一组9对经组织学证实的PCa和良性样本中，我们进行了全转录组范围的基因表达综合分析，包括差异基因表达分析和加权基因共表达网络分析（WGCNA），确定了一组与肿瘤状态（恶性.良性）高度相关的潜在基因标志物。然后，我们使用这些基因，通过来自癌症基因组图谱前列腺腺癌（TCGA-PRAD）数据集的最小绝对收缩和选择算子（LASSO）及逐步多变量Cox回归分析，建立了一个与无进展生存期（PFS）相关的最小基因特征（GS）（、、、和）。我们的特征能够在TCGA-PRAD数据集中预测1年、3年和5年的PFS，曲线下面积（AUC）为0.64 - 0.78，并且我们的特征仍然是一个独立于年龄、Gleason评分以及病理T和N分期的预后因素。一个结合了该特征和Gleason评分的列线图显示出对PFS有更好的预测能力（AUC：0.71 - 0.85），并且在预测PFS方面优于单独的剑桥预后组（CPG）模型以及一些传统使用的临床病理因素。总之，我们已经鉴定并验证了一种新的五基因特征，并建立了一个能有效预测PCa患者PFS的列线图。这些发现可能会改进当前用于PFS的预后工具，并有助于PCa治疗中的临床决策。
