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The majority of lung cancer patients are diagnosed with metastatic disease. This study identified a set of 73 microRNAs (miRNAs) that classified lung cancer tumors from normal lung tissues with an overall accuracy of 96.3% in the training patient cohort (n = 109) and 91.7% in unsupervised classification and 92.3% in supervised classification in the validation set (n = 375). Based on association with patient survival (n = 1016), 10 miRNAs were identified as potential tumor suppressors (hsa-miR-144, hsa-miR-195, hsa-miR-223, hsa-miR-30a, hsa-miR-30b, hsa-miR-30d, hsa-miR-335, hsa-miR-363, hsa-miR-451, and hsa-miR-99a), and 4 were identified as potential oncogenes (hsa-miR-21, hsa-miR-31, hsa-miR-411, and hsa-miR-494) in lung cancer. Experimentally confirmed target genes were identified for the 73 diagnostic miRNAs, from which proliferation genes were selected from CRISPR-Cas9/RNA interference (RNAi) screening assays. Pansensitive and panresistant genes to 21 NCCN-recommended drugs with concordant mRNA and protein expression were identified. DGKE and WDR47 were found with significant associations with responses to both systemic therapies and radiotherapy in lung cancer. Based on our identified miRNA-regulated molecular machinery, an inhibitor of PDK1/Akt BX-912, an anthracycline antibiotic daunorubicin, and a multi-targeted protein kinase inhibitor midostaurin were discovered as potential repositioning drugs for treating lung cancer. These findings have implications for improving lung cancer diagnosis, optimizing treatment selection, and discovering new drug options for better patient outcomes.
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This content will become publicly available on December 30, 2025
Embedded 3D printing of engineered lung cancer model for assisting fine-needle aspiration biopsy
Lung cancer is a serious global health issue that requires the development of patient-specific, lung cancer model for surgical planning to train interventionalists and improve the accuracy of biopsies. Although the emergence of three-dimensional (3D) printing provides a promising solution to create customized models with complicated architectures, current 3D printing methods cannot accurately duplicate anatomical-level lung constructs with tumor(s) which are applicable for hands-on training and procedure planning. To address this issue, an embedded printing strategy is proposed to create respiratory bronchioles, blood vessels, and tumors in a photocurable yield-stress matrix bath. After crosslinking, a patient-specific lung cancer analogous model is produced, which has tunable transparency and mechanical properties to mimic lung parenchyma. This engineered model not only enables the practical training of fine-needle aspiration biopsy but also provides the necessary information, such as coordinates of aspiration, wound depth, and interference with surrounding tissues, for procedure optimization.
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- Award ID(s):
- 2229004
- PAR ID:
- 10563382
- Publisher / Repository:
- IPO
- Date Published:
- Journal Name:
- Biofabrication
- Volume:
- 17
- Issue:
- 1
- ISSN:
- 1758-5082
- Page Range / eLocation ID:
- 015042
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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