Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived from transcriptome analysis in pancreatic cancer
Machine learning with imaging features to predict the expression of ITGAV, which is a poor prognostic factor derived from transcriptome analysis in pancreatic cancer
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DOI：
10.3892/ijo.2022.5350
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发表时间：
2022-04
影响因子：
5.2
通讯作者：
Yosuke Iwatate;H. Yokota;Isamu Hoshino;F. Ishige;Naoki Kuwayama;M. Itami;Yasukuni Mori;S. Chiba;H. Arimitsu;H. Yanagibashi;W. Takayama;T. Uno;Jason Lin;Yuki Nakamura;Yasutoshi Tatsumi;O. Shimozato;H. Nagase
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文献类型：
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作者：
Yosuke Iwatate;H. Yokota;Isamu Hoshino;F. Ishige;Naoki Kuwayama;M. Itami;Yasukuni Mori;S. Chiba;H. Arimitsu;H. Yanagibashi;W. Takayama;T. Uno;Jason Lin;Yuki Nakamura;Yasutoshi Tatsumi;O. Shimozato;H. Nagase
放射基因组学因从临床图像预测肿瘤的分子生物学特征而引起关注，临床图像最初是数值的集合，例如计算机断层扫描（CT）扫描。使用遗传信息的预测模型是使用从这些数值中提取和计算的数千个图像特征来构建的。在本研究中，对12例患者的胰腺导管腺癌（PDAC）组织进行RNA测序，以确定可用于评估临床病理学的基因，并对107例PDAC样本进行免疫染色，以验证所获得的结果。此外，通过使用CT图像和构建的预测模型的机器学习进行基因表达的放射基因组学分析。RNA测序结果的生物信息学分析表明，整合素αV（ITGAV）与肿瘤转移和预后等临床病理因素密切相关，测序结果与免疫组化结果相关性显著（r= 0.625，P =0.039）。值得注意的是，ITGAV高表达组与低表达组相比，预后（P=0.005）和复发率（P=0.003）显著更差。ITGAV预测模型显示出一定的可检测性（AUC=0.697），预测的ITGAV高表达组也与较差的预后相关（P=0.048）。总之，放射基因组学预测ITGAV在胰腺癌中的表达，以及预后。
Radiogenomics has attracted attention for predicting the molecular biological characteristics of tumors from clinical images, which are originally a collection of numerical values, such as computed tomography (CT) scans. A prediction model using genetic information is constructed using thousands of image features extracted and calculated from these numerical values. In the present study, RNA sequencing of pancreatic ductal adenocarcinoma (PDAC) tissues from 12 patients was performed to identify genes useful in evaluating clinical pathology, and 107 PDAC samples were immunostained to verify the obtained findings. In addition, radiogenomics analysis of gene expression was performed by machine learning using CT images and constructed prediction models. Bioinformatics analysis of RNA sequencing data identified integrin αV (ITGAV) as being important for clinicopathological factors, such as metastasis and prognosis, and the results of sequencing and immunostaining demonstrated a significant correlation (r=0.625, P=0.039). Notably, the ITGAV high-expression group was associated with a significantly worse prognosis (P=0.005) and recurrence rate (P=0.003) compared with the low-expression group. The ITGAV prediction model showed some detectability (AUC=0.697), and the predicted ITGAV high-expression group was also associated with a worse prognosis (P=0.048). In conclusion, radiogenomics predicted the expression of ITGAV in pancreatic cancer, as well as the prognosis.