Department of Digestive Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Medical Innovation Centre, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Cancer Med. 2024 Aug;13(16):e70153. doi: 10.1002/cam4.70153.
Homologous recombination plays a vital role in the occurrence and drug resistance of gastric cancer. This study aimed to screen new gastric cancer diagnostic biomarkers in the homologous recombination pathway and then used radiomic features to construct a prediction model of biomarker expression to guide the selection of chemotherapy regimens.
Gastric cancer transcriptome data were downloaded from The Cancer Genome Atlas database. Machine learning methods were used to screen for diagnostic biomarkers of gastric cancer and validate them experimentally. Computed Tomography image data of gastric cancer patients and corresponding clinical data were downloaded from The Cancer Imaging Archive and our imaging centre, and then the Computed Tomography images were subjected to feature extraction, and biomarker expression prediction models were constructed to analyze the correlation between the biomarker radiomics scores and clinicopathological features.
We screened RAD51D and XRCC2 in the homologous recombination pathway as biomarkers for gastric cancer diagnosis by machine learning, and the expression of RAD51D and XRCC2 was significantly positively correlated with pathological T stage, N stage, and TNM stage. Homologous recombination pathway blockade inhibits gastric cancer cell proliferation, promotes apoptosis, and reduces the sensitivity of gastric cancer cells to chemotherapeutic drugs. Our predictive RAD51D and XRCC2 expression models were constructed using radiomics features, and all the models had high accuracy. In the external validation cohort, the predictive models still had decent accuracy. Moreover, the radiomics scores of RAD51D and XRCC2 were also significantly positively correlated with the pathologic T, N, and TNM stages.
The gastric cancer diagnostic biomarkers RAD51D and XRCC2 that we screened can, to a certain extent, reflect the expression status of genes through radiomic characteristics, which is of certain significance in guiding the selection of chemotherapy regimens for gastric cancer patients.
同源重组在胃癌的发生和耐药中起着至关重要的作用。本研究旨在筛选同源重组途径中胃癌的新诊断生物标志物，然后利用放射组学特征构建生物标志物表达预测模型，以指导化疗方案的选择。
从癌症基因组图谱数据库中下载胃癌转录组数据。使用机器学习方法筛选胃癌诊断的生物标志物，并通过实验进行验证。从癌症成像档案和我们的影像中心下载胃癌患者的计算机断层扫描图像数据和相应的临床数据，然后对计算机断层扫描图像进行特征提取，并构建生物标志物表达预测模型，分析生物标志物放射组学评分与临床病理特征之间的相关性。
我们通过机器学习筛选出同源重组途径中的 RAD51D 和 XRCC2 作为胃癌诊断的生物标志物，RAD51D 和 XRCC2 的表达与病理 T 分期、N 分期和 TNM 分期呈显著正相关。同源重组途径阻断抑制胃癌细胞增殖，促进凋亡，降低胃癌细胞对化疗药物的敏感性。我们使用放射组学特征构建了预测 RAD51D 和 XRCC2 表达的模型，所有模型都具有很高的准确性。在外部验证队列中，预测模型仍然具有较高的准确性。此外，RAD51D 和 XRCC2 的放射组学评分与病理 T、N 和 TNM 分期也呈显著正相关。
我们筛选出的胃癌诊断生物标志物 RAD51D 和 XRCC2 可以在一定程度上通过放射组学特征反映基因的表达状态，对指导胃癌患者化疗方案的选择具有一定意义。
