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Abstract Interactions of tumor cells with immune cells in the tumor microenvironment play an important role during malignancy progression. We previously identified that GAS5 inhibited tumor development by suppressing proliferation of tumor cells in non-small cell lung cancer (NSCLC). Herein, we discovered a tumor-suppressing role for tumor cell-derived GAS5 in regulating tumor microenvironment. GAS5 positively coordinated with the infiltration of macrophages and T cells in NSCLC clinically, and overexpression of GAS5 promoted macrophages and T cells recruitment both in vitro and in vivo. Mechanistically, GAS5 stabilized p53 by directly binding to MYBBP1A and facilitating MYBBP1A-p53 interaction, and enhanced p53-mediated transcription of IRF1, which activated type I interferon signaling and increased the production of downstream CXCL10 and CCL5. We also found that activation of type I interferon signaling was associated with better immunotherapy efficacy in NSCLC. Furthermore, the stability of GAS5 was regulated by NAT10, the key enzyme responsible for N4-acetylcytidine (ac4C) modification, which bound to GAS5 and mediated its ac4C modification. Collectively, tumor cell-derived GAS5 could activate type I interferon signaling via the MYBBP1A-p53/IRF1 axis, promoting immune cell infiltration and potentially correlating with immunotherapy efficacy, which suppressed NSCLC progression. Our results suggested GAS5 as a promising predictive marker and potential therapeutic target for combination therapy in NSCLC. A schematic diagram demonstrating the regulatory effect of GAS5 on immune cell infiltration by activating type I interferon signaling via MYBBP1A-p53/IRF1 axis in non-small cell lung cancer. IFN, interferon.

Remote-sensing ship monitoring is a crucial area of research with key applications in military and civilian fields. The ability to extract information, such as ship length, width, and heading from remote-sensing data, particularly from synthetic aperture radar (SAR) images, is of paramount importance. Current state-of-the-art SAR image ship monitoring focuses primarily on ship detection. Assessing the direction of ships usually relies on the observability of wake features. However, the observability of these wake features is often affected by factors, such as the SAR system parameters, ship attributes, and dynamic marine environments. This can make accurate direction assessments a challenging task. In response to these challenges, this study has presented a novel and effective algorithm for ship monitoring from SAR images based on an anchor-free framework and the powerful feature extraction capabilities of convolutional neural networks. The proposed method learned the scattering and morphological information of a ship's bow and stern from high-resolution SAR images to determine the ship's direction with a high level of accuracy using a rotating bounding box. The algorithm was tested on a dataset, achieving an average precision of 90.8% and bow classification accuracy of 92.5%, demonstrating its potential contributing to the advancement of remote sensing.