Screening of gastric cancer diagnostic biomarkers in the homologous recombination signaling pathway and assessment of their clinical and radiomic correlations

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-08-29 DOI:10.1002/cam4.70153

Ahao Wu, Tengcheng Hu, Chao Lai, Qingwen Zeng, Lianghua Luo, Xufeng Shu, Pan Huang, Zhonghao Wang, Zongfeng Feng, Yanyan Zhu, Yi Cao, Zhengrong Li

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Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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.

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同源重组信号通路中胃癌诊断生物标记物的筛选及其临床和放射学相关性评估

背景同源重组在胃癌的发生和耐药性中起着至关重要的作用。本研究旨在筛选同源重组通路中新的胃癌诊断生物标志物，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。方法从癌症基因组图谱数据库下载胃癌转录组数据。使用机器学习方法筛选胃癌诊断生物标记物，并通过实验验证。从癌症影像档案库和本院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，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。结果我们通过机器学习筛选出同源重组通路中的RAD51D和XRCC2作为胃癌诊断的生物标记物，RAD51D和XRCC2的表达与病理T分期、N分期和TNM分期显著正相关。同源重组途径阻断可抑制胃癌细胞增殖，促进细胞凋亡，降低胃癌细胞对化疗药物的敏感性。我们利用放射组学特征构建了RAD51D和XRCC2表达预测模型，所有模型都具有很高的准确性。在外部验证队列中，预测模型的准确性仍然不错。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。结论我们筛选出的胃癌诊断生物标记物 RAD51D 和 XRCC2 能在一定程度上通过放射组学特征反映基因的表达状况，这对指导胃癌患者化疗方案的选择具有一定的意义。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.