Radiotranscriptomics signature‐based predictive nomograms for radiotherapy response in patients with nonsmall cell lung cancer: Combination and association of CT features and serum miRNAs levels
Authors: Liyuan Fan
Xiuping Ding
Qiang Cao
Dongni Gao
Baosheng Li
Qiwei Yang
Journal: Cancer Medicine (2020)
Discipline: CLINICAL MEDICINE
Keywords: CT texture features; miRNAs; nomogram; nonsmall cell lung cancer; radiotherapy response.
Affiliations: Cheeloo College of Medicine Shandong University Jinan Shandong China, Department of Radiation Oncology Shandong First Medical University and Shandong Academy of Medical Sciences Shandong Cancer Hospital and InstituteHuaiyin Region Jinan Shandong China, School of Computer Science and Engineering Southeast University Nanjing Jiangsu China

Abstract:  Purpose: We aimed to establish radiotranscriptomics signatures based on serum miRNA levels and computed tomography (CT) texture features and develop nomogram models for predicting radiotherapy response in patients with nonsmall cell lung cancer (NSCLC). Methods: We first used established radioresistant NSCLC cell lines for miRNA selection. At the same time, patients (103 for training set and 71 for validation set) with NSCLC were enrolled. Their pretreatment contrast-enhanced CT texture features were extracted and their serum miRNA levels were obtained. Then, radiotranscriptomics feature selection was implemented with the least absolute shrinkage and selection operator (LASSO), and signatures were generated by logistic or Cox regression for objective response rate (ORR), overall survival (OS), and progression-free survival (PFS). Afterward, radiotranscriptomics signature-based nomograms were constructed and assessed for clinical use. Results: Four miRNAs and 22 reproducible contrast-enhanced CT features were used for radiotranscriptomics feature selection and we generated ORR-, OS-, and PFS- related radiotranscriptomics signatures. In patients with NSCLC who received radiotherapy, the radiotranscriptomics signatures were independently associated with ORR, OS, and PFS in both the training (OR: 2.94, P &lt; .001; HR: 2.90, P &lt; .001; HR: 3.58, P = .001) and validation set (OR: 2.94, P = .026; HR: 2.14, P = .004; HR: 2.64, P = .016). We also obtained a satisfactory nomogram for ORR. The C-index values for the ORR nomogram were 0.86 [95% confidence interval (CI), 0.75 to 0.92] in the training set and 0.81 (95% CI, 0.69 to 0.89) in the validation set. The calibration-in-the-large and calibration slope performed well. Decision curve analysis indicated a satisfactory net benefit. Conclusions: The radiotranscriptomics signature could be an independent biomarker for evaluating radiotherapeutic responses in patients with NSCLC. The radiotranscriptomics signature-based nomogram could be used to predict patients' ORR, which would represent progress in individualized medicine. 

Retracted: No

PubPeer comments:
 In Figure2Buff0c the boxed wells appear to be same. ![file] Could the author provide an explain? , 
 #1 Additional findings. I suspect there are others. ![file] , 
 06 December 2020 The authors have mistakenly put the results of the pre-experiment in Figure 2, and some information were displaced due to the original image coding error. The correct version of Figure 2 is displayed below: While in Table S1, the same sequences are listed for both miR-92a-1-5p-RT and miR-25-5p-R (u201cPrimers for qRT-PCRu201d). The correct sequences for miR-25-5p-R can be found in the corrected version of Table S1. The authors apologize for these errors, and assure the readers that the corrections will not alter the conclusion of the results. ![file] ,