Immunotherapies including adaptive immune checkpoint inhibitors (ICIs) and chimeric antigen receptor (CAR) T cells, have developed the treatment of cancer in clinic, and most of them focus on activating T cell immunity. Although these strategies have obtained unprecedented clinical responses, only limited subsets of cancer patients could receive long-term benefits, highlighting the demand for identifying novel targets for the new era of tumor immunotherapy. Innate immunity has been demonstrated to play a determinative role in the tumor microenvironment (TME) and influence the clinical outcomes of tumor patients. A thorough comprehension of the innate immune cells that infiltrate tumors would allow for the development of new therapeutics. In this review, we outline the role and mechanism of innate immunity in TME. Moreover, we discuss innate immunity-based cancer immunotherapy in basic and clinical studies. Finally, we summarize the challenges in sufficiently motivating innate immune responses and the corresponding strategies and measures to improve anti-tumor efficacy. This review could aid the comprehension of innate immunity and inspire the creation of brand-new immunotherapies for the treatment of cancer.

Collagen contributes to extracellular matrix formation and stiffness, providing a three-dimensional framework that supports the development and growth of solid tumors. By interacting with specific tumor cell receptors, collagen influences tumor cell signaling pathways, promoting cancer progression and drug resistance. Recent advancements in understanding the tumor extracellular matrix have underscored collagen's role in fostering an immunosuppressive tumor microenvironment (TME) and acting as a barrier to immunotherapy. Understanding the immunosuppressive mechanisms of collagen in the TME has revealed novel therapeutic targets and opportunities. This review highlights the immunoregulatory functions of collagen in the TME and provides a comprehensive overview of integrating collagen scores with traditional immunoscore-based immunotyping methods to enhance response prediction. Additionally, we discuss recent therapeutic developments in collagen targeting and their clinical potential for enhancing anti-cancer immunity.

KRAS mutation is one of the most prevalent oncogenic driver mutations in NSCLC. UNC5C, as a member of dependence receptors of netrin-1, is a conditional tumor suppressor in cancer progression and metastasis via inducing apoptosis. Despite UNC5C is intensively reported to be downregulated in multiple types of malignancies, the mechanism of UNC5C loss or downregulation in cancer remains unclear. In this study, we identified UNC5C as a downstream effector of oncogenic KRAS signaling pathway. We found that oncogenic KRAS suppressed UNC5C and inhibition of oncogenic KRAS upregulated UNC5C in KRAS-mutant NSCLC. Mechanically, oncogenic KRAS-mediated downregulation of UNC5C was dependent on activation of RAF/MEK/ERK rather than PI3K/AKT/mTOR pathway. More specifically, ERK2, but not ERK1, was involved in the control of UNC5C expression. Critically, FOS, a downstream transcription factor of ERK pathway, was responsible for the transcriptional repression of UNC5C in KRAS-mutant NSCLC. In addition, UNC5C, rather than other dependence receptors of netrin-1, was most strongly downregulated in NSCLC and functioned as a tumor suppressor. In conclusion, we reported oncogenic KRAS-mediated transcriptional suppression of UNC5C and deciphered the exact underlying molecular mechanism in NSCLC, thus providing novel insights into the interplay between oncogenes and tumor suppressor genes in KRAS-driven NSCLC.

The determination of RNA secondary structure (RSS) could help understand RNA’s functional mechanisms, guiding the design of RNA-based therapeutics, and advancing synthetic biology applications. However, traditional methods such as NMR for determining RSS are typically time-consuming and labor-intensive. As a result, the accurate prediction of RSS remains a fundamental yet unmet need in RNA research. Various deep learning (DL)-based methods achieved improved accuracy over thermodynamic-based methods. However, the over-parameterization nature of DL makes these methods prone to overfitting and thus limits their generalizability. Meanwhile, the inconsistency of RSS predictions between these methods further aggravated the crisis of generalizability. Here, we propose TrioFold to achieve enhanced generalizability of RSS prediction by integrating base-pairing clues learned from both thermodynamic- and DL-based methods by ensemble learning and convolutional block attention mechanism. TrioFold achieves higher accuracy in intra-family predictions and enhanced generalizability in inter-family and cross-RNA-types predictions. Additionally, we have developed an online webserver equipped with widely used RSS prediction algorithms and analysis tools, providing an accessible platform for the RNA research community. This study demonstrated new opportunities to improve generalizability for RSS predictions by efficient ensemble learning of base-pairing clues learned from both thermodynamic- and DL-based algorithms.