前列腺癌生化复发潜在预测指标的鉴定
Identification of Potential Predictor of Biochemical Recurrence in Prostate Cancer.
作者信息
Wei Jingchao, Wu Xiaohang, Li Yuxiang, Tao Xiaowu, Wang Bo, Yin Guangming
机构信息
Department of Urology, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, People's Republic of China.
Department of Urology, The Third Xiangya Hospital of Central South University, Changsha, People's Republic of China.
出版信息
Int J Gen Med. 2022 May 12;15:4897-4905. doi: 10.2147/IJGM.S355435. eCollection 2022.
BACKGROUND
Prostate cancer is a common malignancy in men. Radical prostatectomy is one of the primary treatment modalities for patients with prostate cancer. However, early identification of biochemical recurrence is a major challenge for post-radical prostatectomy surveillance. There is a lack of reliable predictors of biochemical recurrence. The purpose of this study was to explore potential biochemical recurrence indicators for prostate cancer.
MATERIALS AND METHODS
We analyzed transcriptomic data of cases with biochemical recurrence in The Cancer Genome Atlas (TCGA). Then, we performed integrative bioinformatics analyses to establish a biochemical recurrence predictor model of prostate cancer.
RESULTS
There were 146 differentially expressed genes (DEGs) between prostate cancer and normal prostate, including 12 upregulated and 134 downregulated genes. Comprehensive pathway enrichment analyses revealed that these DEGs were associated with multiple cellular metabolic pathways. Subsequently, according to the random assignment principle, 208 patients were assigned to the training cohort and 205 patients to the validation cohort. Univariate Cox regression analysis showed that 7 genes were significantly associated with the biochemical recurrence of prostate cancer. A model consisting of 5 genes was constructed using LASSO regression and multivariate Cox regression to predict biochemical recurrence of prostate cancer. Expression of PAH and AOC1 decreased with an increasing incidence of prostate cancer, whereas expression of DDC, LINC01436 and ORM1 increased with increasing incidence of prostate cancer. Kaplan-Meier curves and receiver operator characteristic (ROC) curves indicated that the 5-gene model had reliable utility in identifying the risk of biochemical recurrence of prostate cancer.
CONCLUSION
This study provides a model for predicting prostate cancer recurrence after surgery, which may be an optional indicator for postoperative follow-up.
背景
前列腺癌是男性常见的恶性肿瘤。根治性前列腺切除术是前列腺癌患者的主要治疗方式之一。然而，早期识别生化复发是根治性前列腺切除术后监测的一项重大挑战。目前缺乏可靠的生化复发预测指标。本研究的目的是探索前列腺癌潜在的生化复发指标。
材料与方法
我们分析了癌症基因组图谱（TCGA）中生化复发病例的转录组数据。然后，我们进行了综合生物信息学分析，以建立前列腺癌生化复发预测模型。
结果
前列腺癌与正常前列腺之间存在146个差异表达基因（DEG），其中12个上调基因和134个下调基因。综合通路富集分析表明，这些DEG与多种细胞代谢通路相关。随后，根据随机分配原则，将208例患者分配到训练队列，205例患者分配到验证队列。单因素Cox回归分析显示，7个基因与前列腺癌的生化复发显著相关。使用LASSO回归和多因素Cox回归构建了一个由5个基因组成的模型，以预测前列腺癌的生化复发。随着前列腺癌发病率的增加，PAH和AOC1的表达降低，而DDC、LINC01436和ORM1的表达随着前列腺癌发病率的增加而增加。Kaplan-Meier曲线和受试者工作特征（ROC）曲线表明，该5基因模型在识别前列腺癌生化复发风险方面具有可靠的效用。
结论
本研究提供了一个预测前列腺癌术后复发的模型，可能是术后随访的一个可选指标。
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