Terrematte Patrick, Andrade Dhiego Souto, Justino Josivan, Stransky Beatriz, de Araújo Daniel Sabino A, Dória Neto Adrião D
Bioinformatics Multidisciplinary Environment (BioME), Metropole Digital Institute (IMD), Federal University of Rio Grande do Norte (UFRN), Natal 59078-400, Brazil.
Department of Engineering and Technology (DETEC), Pau dos Ferros Multidisciplinary Center, Federal Rural University of Semi-arid (UFERSA), Pau dos Ferros 59900-000, Brazil.
Cancers (Basel). 2022 Apr 24;14(9):2111. doi: 10.3390/cancers14092111.
Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.
透明细胞肾细胞癌（ccRCC）患者的生存结局较差，尤其是发生转移时。在基因组数据中识别能够帮助预测ccRCC侵袭性及其耐药性的生物标志物至关重要。因此，我们开展了一项研究，旨在评估基因特征，并提出一种与先前特征相比具有更高预测能力和泛化性的新特征。利用癌症基因组图谱（TCGA-KIRC）和国际癌症基因组联盟（ICGC-RECA）的ccRCC队列，我们评估了14种特征和六种特征选择方法的Cox回归线性生存模型，并进行了功能分析和差异基因表达分析。在本研究中，我们建立了一个由13个基因组成的特征（AR、AL353637.1、DPP6、FOXJ1、GNB3、HHLA2、IL4、LIMCH1、LINC01732、OTX1、SAA1、SEMA3G、ZIC2），其表达水平能够预测ccRCC患者的不同结局。此外，我们将我们的特征与文献中的其他特征进行了比较。使用最小冗余最大相关（mRMR）集成方法获得了性能最佳的基因特征。与其他特征相比，该特征具有独特之处，例如在不同队列中的泛化性以及在重要通路（尿路上皮癌、慢性肾病和移行细胞癌、肾结石病）中的功能富集。在我们的特征中的13个基因中，有8个已知与ccRCC患者生存相关，4个与免疫相关。我们的模型使用受试者工作特征（ROC）曲线下面积（AUC）指标的性能为0.82，并且在队列之间具有良好的泛化性。我们的研究结果揭示了两个高表达基因簇（SAA1、OTX1、ZIC2、LINC01732、GNB3和IL4）和低表达基因簇（AL353637.1、AR、HHLA2、LIMCH1、SEMA3G、DPP6和FOXJ1），它们均与不良预后相关。该特征有可能在临床实践中用于支持患者的治疗护理和随访。
