Cord Blood Bank, Guangzhou Institute of Eugenics and Perinatology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, 510000, China.
Division of Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Yuexiu District, Guangzhou, Guangdong, 510000, P. R. China.
BMC Genom Data. 2022 Jul 6;23(1):51. doi: 10.1186/s12863-022-01063-9.
Colon adenocarcinoma (COAD) is one of the leading causes of death worldwide. Cancer stem cells (CSCs) are vital for COAD chemoresistance and recurrence, however little is known about stem cell-related biomarkers in drug resistance and COAD prognosis prediction.
To uncover the roles of CSC in COAD tumorigenesis, chemoresistance, and prognosis, we retrieved COAD patients' RNAseq data from TCGA (The Cancer Genome Atlas). We further performed analysis of differentially expressed genes (DEGs) and mRNA expression-based stemness index (mRNAsi) to identify stemness-related COAD biomarkers. We then evaluated the roles of mRNAsi in tumorigenesis, clinical-stage, overall survival (OS), and chemoresistance. Afterward, we used identified prognostic stemness-related genes (PSRGs) to construct a prediction model. After constructing the prediction model, we used elastic Net regression and area under the curve (AUC) to explore the prediction value of PSRGs based on risk scores and the receiver operator characteristic (ROC) curve. To elucidate the underlying interconnected systems, we examined relationships between the levels of TFs, PSRGs, and 50 cancer hallmarks by a Pearson correlation analysis.
Twelve thousand one hundred eight DEGs were identified by comparing 456 primary COADs and 41 normal solid tissue samples. Furthermore, we identified 4351 clinical stage-related DEGs, 16,516 stemness-associated DEGs, and 54 chemoresistance-related DEGs from cancer stages: mRNAsi, and COAD chemoresistance. Compared to normal tissue samples, mRNAsi in COAD patients were marked on an elevation and involved in prognosis (p = 0.027), stemness-related DEGs based on chemoresistance (OR = 3.28, p ≤ 0.001) and AJCC clinical stage relating (OR = 4.02, p ≤ 0.001) to COAD patients. The prediction model of prognosis were constructed using the 6 PSRGs with high accuracy (AUC: 0.659). The model identified universal correlation between NRIP2 and FDFT1 (key PRSGs), and some cancer related transcription factors (TFs) and trademarks of cancer gene were in the regulatory network.
We found that mRNAsi is a reliable predictive biomarker of tumorigenesis and COAD prognosis. Our established prediction model of COAD chemoresistance, which includes the six PSRGs, is effective, as the model provides promising therapeutic targets in the COAD.
结肠腺癌（COAD）是全球主要的死亡原因之一。癌症干细胞（CSCs）对 COAD 的化疗耐药性和复发至关重要，但对于药物耐药性和 COAD 预后预测中与干细胞相关的生物标志物知之甚少。
为了揭示 CSC 在 COAD 肿瘤发生、化疗耐药和预后中的作用，我们从 TCGA（癌症基因组图谱）中检索了 COAD 患者的 RNAseq 数据。我们进一步进行了差异表达基因（DEGs）和基于 mRNA 表达的干细胞指数（mRNAsi）分析，以鉴定与干细胞相关的 COAD 生物标志物。然后，我们评估了 mRNAsi 在肿瘤发生、临床分期、总生存期（OS）和化疗耐药中的作用。此后，我们使用鉴定的预后相关干细胞基因（PSRGs）构建预测模型。构建预测模型后，我们使用弹性网络回归和曲线下面积（AUC）基于风险评分和接收器操作特征（ROC）曲线来探索 PSRGs 的预测价值。为了阐明潜在的相互关联的系统，我们通过 Pearson 相关分析检查了 TF、PSRGs 和 50 个癌症标志之间的水平关系。
通过比较 456 例原发性 COAD 和 41 例正常实体组织样本，鉴定出 12108 个 DEGs。此外，我们从癌症阶段鉴定出 4351 个与临床阶段相关的 DEGs、16516 个与干细胞相关的 DEGs 和 54 个与化疗耐药相关的 DEGs，mRNAsi、COAD 化疗耐药和干细胞相关 DEGs。与正常组织样本相比，COAD 患者的 mRNAsi 升高，与预后相关（p=0.027），基于化疗耐药的干细胞相关 DEGs（OR=3.28，p≤0.001）和 AJCC 临床分期相关（OR=4.02，p≤0.001）。使用 6 个具有高准确性的 PSRGs（AUC：0.659）构建了预后预测模型。该模型确定了 NRIP2 和 FDFT1（关键 PRSGs）之间的普遍相关性，以及一些癌症相关转录因子（TFs）和癌症基因的标志在调控网络中。
我们发现 mRNAsi 是肿瘤发生和 COAD 预后的可靠预测生物标志物。我们建立的 COAD 化疗耐药预测模型，包含 6 个 PSRGs，是有效的，因为该模型为 COAD 提供了有希望的治疗靶点。
