RTKN2 is Associated with Unfavorable Prognosis and Promotes Progression in Non-Small-Cell Lung Cancer.

BACKGROUND: Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related mortality worldwide. However, the molecular mechanism of NSCLC remains unknown. Accumulating data show that Rhotekin 2 (RTKN2) functions as a novel crucial regulator of diverse biological processes; however, its pathological role in NSCLC remains unclear. METHODS: In this study, we investigated the function of RTKN2 in NSCLC. The expression of RTKN2 mRNA was analyzed in tumor tissues and paired adjacent tissues from patients by qRT-PCR. The role of RTKN2 in cell proliferation, apoptosis, migration, and invasion was investigated. The potential mechanisms were explored. RESULTS: We found that the level of RTKN2 mRNA was up-regulated in NSCLC tissues and cell lines. RTKN2 knockout inhibited the proliferation of human NSCLC cell lines A549 via inducing apoptosis by increasing the level of Bax and decreasing the level of Bcl-2. Furthermore, silencing of RTKN2 reduced the migration and invasion of A549 cells through up-regulated matrix metalloproteinase-9 (MMP9) and MMP2 expression. CONCLUSION: These data suggest that RTKN2 may not only be a prognostic biomarker candidate but also provide a potential therapeutic target for NSCLC diagnosis and treatment.

A CT-based radiomics nomogram for prediction of human epidermal growth factor receptor 2 status in patients with gastric cancer.

OBJECTIVE: To develop and validate a computed tomography (CT)-based radiomics nomogram for predicting human epidermal growth factor receptor 2 (HER2) status in patients with gastric cancer. METHODS: This retrospective study included 134 patients with gastric cancer (HER2-negative: n=87; HER2-positive: n=47) from April 2013 to March 2018, who were then randomly divided into training (n=94) and validation (n=40) cohorts. Radiomics features were obtained from the CT images showing gastric cancer. Least absolute shrinkage and selection operator (LASSO) regression analysis was utilized for building the radiomics signature. A multivariable logistic regression method was applied to develop a prediction model incorporating the radiomics signature and independent clinicopathologic risk predictors, which were then visualized as a radiomics nomogram. The predictive performance of the nomogram was assessed in the training and validation cohorts. RESULTS: The radiomics signature was significantly associated with HER2 status in both training (P<0.001) and validation (P=0.023) cohorts. The prediction model that incorporated the radiomics signature and carcinoembryonic antigen (CEA) level demonstrated good discriminative performance for HER2 status prediction, with an area under the curve (AUC) of 0.799 [95% confidence interval (95% CI): 0.704-0.894] in the training cohort and 0.771 (95% CI: 0.607-0.934) in the validation cohort. The calibration curve of the radiomics nomogram also showed good calibration. Decision curve analysis showed that the radiomics nomogram was useful. CONCLUSIONS: We built and validated a radiomics nomogram with good performance for HER2 status prediction in gastric cancer. This radiomics nomogram could serve as a non-invasive tool to predict HER2 status and guide clinical treatment.

Deep Learning Features Improve the Performance of a Radiomics Signature for Predicting KRAS Status in Patients with Colorectal Cancer.

RATIONALE AND OBJECTIVES: We assess the performance of a model combining a deep convolutional neural network and a hand-crafted radiomics signature for predicting KRAS status in patients with colorectal cancer (CRC). MATERIALS AND METHODS: The primary cohort consisted of 279 patients with clinicopathologically confirmed CRC between April 2011 and April 2015. Portal venous phase computed tomographic images were analyzed to extract traditional hand-crafted radiomics features as well as deep learning features. A Wilcoxon rank sum test, the minimum redundancy maximum relevance algorithm, and multivariable logistic regression analysis were used to select features and build a radiomics signature. A combined model was then developed using multivariable logistic regression analysis. An independent validation cohort of 119 patients from May 2015 to April 2016 was used to confirm the combined model's predictive performance. RESULTS: The C-index of hand-crafted radiomics signature's discriminative ability was 0.719 (95% confidence interval, CI: 0.658-0.776) for the primary cohort and 0.720 (95% CI: 0.625-0.813) for the validation cohort. The C-index of the deep radiomics signature's discriminative ability was 0.754 (95% CI: 0.696-0.813) for the primary cohort and 0.786 (95% CI: 0.702-0.863) for the validation cohort. The combined model, which merged the hand-crafted radiomics features and deep radiomics features, achieve a C-index of 0.815 (95% CI: 0.766-0.868) for the primary cohort and 0.832 (95% CI: 0.762-0.905) for the validation cohort. CONCLUSION: This study presents a model that incorporates the hand-crafted and deep radiomics signature, which can be used for individualized preoperative prediction of KRAS mutations in patients with CRC.

[Preoperative evaluation of histologic grade in invasive breast cancer with T2W-MRI based radiomics signature].

OBJECTIVE: To develop and validate a fat-suppressed (T2 weighted-magnetic resonance imaging, T2W-MRI) based radiomics signature to preoperatively evaluate the histologic grade (grade I/II VS. grade III) of invasive breast cancer.
 Methods: A total of 202 patients with MRI examination and pathologically confirmed invasive breast cancer from June 2011 to February 2017 were retrospectively enrolled. After retrieving fat-suppressed T2W images and tumor segmentation, radiomics features were extracted and valuable features were selected to build a radiomic signature with the least absolute shrinkage and selection operator (LASSO) method. Mann-Whitney U test was used to explore the correlation between radiomics signature and histologic grade. Receiver operating characteristics (ROC) curve was applied to determine the discriminative performance of the radiomics signature [area under curre (AUC), sensitivity, specificity, and accuracy]. An independent validation dataset was used to confirm the discriminatory power of radiomics signature. 
 Results: Eight radiomics features were selected to build a radiomics signature, which showed good performance for preoperatively evaluating histologic grade of invasive breast cancer, with an AUC of 0.802 (95% CI 0.729 to 0.875), sensitivity of 78.7%, specificity of 70.3% and accuracy of 73.7% in training dataset and AUC of 0.812 (95% CI 0.686 to 0.938), sensitivity of 80.0%, specificity of 73.3% and accuracy of 76.0% in the validation dataset.
 Conclusion: The fat-suppressed T2W-MRI based radiomics signature can be used to preoperatively evaluate the histologic grade of invasive breast cancer, which may assist clinical decision-maker.