Background Anti-angiogenic therapy represents a promising strategy for non-small-cell lung cancer (NSCLC) but its application in lung squamous cell carcinoma (SQC) is limited due to the high-risk adverse effects. Accumulating evidence indicates that long noncoding RNAs (lncRNAs) mediate in tumor progression by participating in the regulation of VEGF in NSCLC, which might guide the development of new antiangiogenic strategies. Methods Differential lncRNA expression in SQC was analyzed in AE-meta and TCGA datasets, and further confirmed in lung cancer tissues and adjacent normal tissues with RT-qPCR and in-situ hybridization. Statistical analysis was performed to evaluate the clinical correlation between LINC00173.v1 expression and survival characteristics. A tube formation assay, chick embryo chorioallantoic membrane assay and animal experiments were conducted to detect the effect of LINC00173.v1 on the proliferation and migration of vascular endothelial cells and tumorigenesis of SQC in vivo. Bioinformatics analysis, RNA immunoprecipitation and luciferase reporter assays were performed to elucidate the downstream target of LINC00173.v1. The therapeutic efficacy of antisense oligonucleotide (ASO) against LINC00173.v1 was further investigated in vivo. Chromatin immunoprecipitation and high throughput data processing and visualization were performed to identify the cause of LINC00173.v1 overexpression in SQC. Results LINC00173.v1 was specifically upregulated in SQC tissues, which predicted poorer overall and progression-free survival in SQC patients. Overexpression of LINC00173.v1 promoted, while silencing LINC00173.v1 inhibited the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC cells in vitro and in vivo. Our results further revealed that LINC00173.v1 promoted the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC cells by upregulating VEGFA expression by sponging miR-511-5p. Importantly, inhibition of LINC00173.v1 via the ASO strategy reduced the tumor growth of SQC cells, and enhanced the therapeutic sensitivity of SQC cells to cisplatin in vivo. Moreover, our results showed that squamous cell carcinoma-specific factor ΔNp63α contributed to LINC00173.v1 overexpression in SQC. Conclusion Our findings clarify the underlying mechanism by which LINC00173.v1 promotes the proliferation and migration of vascular endothelial cells and the tumorigenesis of SQC, demonstrating that LINC00173.v1-targeted drug in combination with cisplatin may serve as a rational regimen against SQC.

Background Lymphovascular invasion (LVI) status facilitates the selection of optimal therapeutic strategy for breast cancer patients, but in clinical practice LVI status is determined in pathological specimens after resection. Purpose To explore the use of dynamic contrast‐enhanced (DCE)‐magnetic resonance imaging (MRI)‐based radiomics for preoperative prediction of LVI in invasive breast cancer. Study Type Prospective. Population Ninety training cohort patients (22 LVI‐positive and 68 LVI‐negative) and 59 validation cohort patients (22 LVI‐positive and 37 LVI‐negative) were enrolled. Field Strength/Sequence 1.5 T and 3.0 T, T1‐weighted DCE‐MRI. Assessment Axillary lymph node (ALN) status for each patient was evaluated based on MR images (defined as MRI ALN status), and DCE semiquantitative parameters of lesions were calculated. Radiomic features were extracted from the first postcontrast DCE‐MRI. A radiomics signature was constructed in the training cohort with 10‐fold cross‐validation. The independent risk factors for LVI were identified and prediction models for LVI were developed. Their prediction performances and clinical usefulness were evaluated in the validation cohort. Statistical Tests Mann–Whitney U‐test, chi‐square test, kappa statistics, least absolute shrinkage and selection operator (LASSO) regression, logistic regression, receiver operating characteristic (ROC) analysis, DeLong test, and decision curve analysis (DCA). Results Two radiomic features were selected to construct the radiomics signature. MRI ALN status (odds ratio, 10.452; P < 0.001) and the radiomics signature (odds ratio, 2.895; P = 0.031) were identified as independent risk factors for LVI. The value of the area under the curve (AUC) for a model combining both (0.763) was higher than that for MRI ALN status alone (0.665; P = 0.029) and similar to that for the radiomics signature (0.752; P = 0.857). DCA showed that the combined model added more net benefit than either feature alone. Data Conclusion The DCE‐MRI‐based radiomics signature in combination with MRI ALN status was effective in predicting the LVI status of patients with invasive breast cancer before surgery. Level of Evidence: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019;50:847–857.

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