Ma Chuanyu, Li Guandu, Song Xiaohan, Qi Xiaochen, Jiang Tao
Department of Andrology and Sexual Medicine, The Second Hospital of Dalian Medical University, Dalian, China.
Department of Urology, First Affiliated Hospital of Dalian Medical University, Dalian, China.
Front Genet. 2025 May 20;16:1589259. doi: 10.3389/fgene.2025.1589259. eCollection 2025.
Prostate adenocarcinoma (PRAD) is an extremely widespread site of urological malignancy and is the second most common male cancer in the world. Currently, research progress in immunotherapy for prostate treatment is slower compared to other tumours, which is mainly considered to be caused by the low rate of immune response in prostate cancer as a cold tumour. Recent studies have shown that intra-tumour heterogeneity (ITH) is an important impediment to PRAD immunotherapy. Therefore, we set out to investigate the feasibility of judging patients' disease and knowing the clinical treatment based on the level of ITH.
Clinical information and transcriptome expression matrices of PRAD samples were gained from The Cancer Genome Atlas (TCGA) database. The ITH-score of PRAD samples was evaluated using the DEPTH algorithm. The optimal cut-off value of RiskScore was calculated based on the difference in survival curves, and PRAD patients were classified into high ITH and low ITH groups based on the optimal cut-off value. Genes with expression differences were screened by differential expression gene analyses (DEGs), and 103 positively correlated differentially expressed genes were identified based on these genes as well as the ITH-score. We conducted multivariate Cox regression to sift for prognostically relevant genes to structure an ITH-related prognostic signature. GO and KEGG pathway enrichment analyses were performed on these 103 positively correlated differentially expressed genes, and the proportion and type of tumour-infiltrating immune cells were assessed by TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCPCOUNTER, XCELL and EPIC algorithms in patients. In addition, we calculated the relevance of immunotherapy and predicted various drugs that might be used for treatment and evaluated the predictive power of survival models under multiple machine learning algorithms through the training set TCGA-PRAD versus the validation set PRAD-FR cohort. Based on the upregulated differential gene and ITH-score correlation ranking, combined with the prognostic performance of the gene, we chose MYLK2 as an elite gene for ITH, and performed cellular experiments to validate it by PCR and WB, as well as CCK8, scratch experiments, and transwell experiments on si-MYLK2 PRAD. Finally, we constructed cox regression models as well as random forest survival models based on the expression levels of SYNPO2L, MYLK2, CKM and MYL3.
We found that lowering the ITH-score resulted in better survival outcomes. We identified 20 highly correlated differentially expressed genes by calculating the correlation coefficient (cor>0.3) between them by DEGs as well as ITH-score, and selected four genes with p-value less than 0.05 (SYNPO2L, MYLK2, CKM and MYL3) by combining with cox regression. Survival analysis based on the differential expression grouping of SYNPO2L, MYLK2, CKM and MYL3 suggested significant survival differences. The results of biofunctional pathway enrichment analysis suggested that the PRAD-ITH gene set had significant expression in the Mucsle Contraction pathway. Macroscopic differences in the immune landscape and differences in responsiveness to immunotherapy existed between ITH-H and ITH-L. The results of the CMap data suggested that NU.1025 was the most likely drug to treat PRAD. The results of our machine learning model constructed based on ITH-score suggest that the random survival forest (RSF) model performs well in both the training and validation sets and has the potential to be used as a clinical prediction model. experiments verified that MYLK2 plays an important role in the proliferation and migration of PRAD. Our results suggest that the implementation of therapeutic strategies based on key ITH genes may bring new hope for PRAD patients.
Our findings indicate that ITH may be an important biomarker for the prognosis and characterisation of PRAD and that the ITH-related gene MYLK2 may serve as a novel target for the treatment of PRAD patients.
前列腺腺癌(PRAD)是泌尿外科恶性肿瘤中极为常见的发病部位,是全球第二常见的男性癌症。目前,前列腺癌免疫治疗的研究进展相较于其他肿瘤较为缓慢,这主要被认为是由于前列腺癌作为一种“冷肿瘤”,免疫反应率较低所致。最近的研究表明,肿瘤内异质性(ITH)是PRAD免疫治疗的一个重要障碍。因此,我们着手研究基于ITH水平判断患者病情并了解临床治疗的可行性。
从癌症基因组图谱(TCGA)数据库获取PRAD样本的临床信息和转录组表达矩阵。使用DEPTH算法评估PRAD样本的ITH评分。根据生存曲线的差异计算风险评分的最佳临界值,并基于该最佳临界值将PRAD患者分为高ITH组和低ITH组。通过差异表达基因分析(DEG)筛选出表达有差异的基因,并基于这些基因以及ITH评分鉴定出103个正相关的差异表达基因。我们进行多变量Cox回归以筛选出与预后相关的基因,构建ITH相关的预后特征。对这103个正相关的差异表达基因进行基因本体论(GO)和京都基因与基因组百科全书(KEGG)通路富集分析,并通过TIMER、CIBERSORT、CIBERSORT-ABS、QUANTISEQ、MCPCOUNTER、XCELL和EPIC算法评估患者肿瘤浸润免疫细胞的比例和类型。此外,我们计算免疫治疗的相关性,预测可能用于治疗的各种药物,并通过训练集TCGA-PRAD与验证集PRAD-FR队列评估多种机器学习算法下生存模型的预测能力。基于上调的差异基因与ITH评分的相关性排名,结合基因的预后性能,我们选择MYLK2作为ITH的关键基因,并通过PCR和WB进行细胞实验验证,以及对si-MYLK2 PRAD进行CCK8、划痕实验和Transwell实验。最后,我们基于SYNPO2L、MYLK2、CKM和MYL3的表达水平构建Cox回归模型以及随机森林生存模型。
我们发现降低ITH评分会带来更好的生存结果。通过DEG以及ITH评分计算它们之间的相关系数(cor>0.3),我们鉴定出20个高度相关的差异表达基因,并结合Cox回归选择p值小于0.05的四个基因(SYNPO2L、MYLK2、CKM和MYL3)。基于SYNPO2L、MYLK2、CKM和MYL3的差异表达分组进行的生存分析表明存在显著的生存差异。生物功能通路富集分析结果表明,PRAD-ITH基因集在肌肉收缩通路中具有显著表达。ITH-H组和ITH-L组之间存在免疫格局的宏观差异以及对免疫治疗反应性的差异。CMap数据结果表明NU.1025是治疗PRAD最有可能的药物。我们基于ITH评分构建的机器学习模型结果表明,随机生存森林(RSF)模型在训练集和验证集中均表现良好,有潜力用作临床预测模型。实验证实MYLK2在PRAD的增殖和迁移中起重要作用。我们的结果表明,基于关键ITH基因实施治疗策略可能为PRAD患者带来新希望。
我们的研究结果表明,ITH可能是PRAD预后和特征的重要生物标志物,并且ITH相关基因MYLK2可能作为治疗PRAD患者的新靶点。