An ICD-Associated DAMP Gene signature predicts survival and immunotherapy response of patients with lung adenocarcinoma

Immunostimulatory effects of thermal ablation: Challenges and future prospects

ABSTRACT

This literature explores the immunostimulatory effects of thermal ablation in the tumor microenvironment, elucidating the mechanisms such as immunogenic cell death, tumor-specific antigens, and damage-associated molecular patterns. Furthermore, it outlines critical issues associated with thermal ablation-induced immunostimulatory challenges and offers insights into future research avenues and potential therapeutic strategies.

Bio-responsive Au-miR-183 inhibitor enhances immunotherapy in hepatocellular carcinoma by inducing immunogenic cell death

The treatment of advanced hepatocellular carcinoma (HCC) is limited, and immunotherapy is the current research focus of multi-disciplinary collaborative comprehensive treatment of HCC. Herein, we constructed a bio-responsive Au-miR-183 inhibitor (Au@miR-183i) delivery system targeting liver cancer stem cells (LCSCs), and adopted the strategy of combining αPD-L1 immunotherapy. The multifunctional Au@miR-183i nanocomplexes (NCs), which self-assemble based on the tumor microenvironment, consume NADPH and H2O2, leading to redox homeostasis disturbance, ROS accumulation, regulation of the LCSC niche, and induction of stemness regression. Moreover, self-assembled Au@miR-183i NCs specifically target the delivery of miR-183i to LCSCs, triggering the immunogenic cell death (ICD) effect, promoting the maturation of dendritic cells, inducing infiltration of CD8+ T cells, and facilitating the transformation of 'cold' tumors into 'hot' tumors. More importantly, consistent with the results in vitro, Au@miR-183i NCs demonstrated effective tumor targeting and strong ICD induction in vivo, assisted in enhancing αPD-L1 immunotherapy, and activated a robust systemic anti-tumor immune response in tumor-bearing mouse models. Overall, we provide a simple and universal therapeutic strategy by constructing a multifunctional bio-responsive Au@miR-183i NCs delivery system with LCSC targeting capability. Furthermore, nanocomplex-based ICD inducers have great promise in enhancing anti-tumor immunity and the PD-1/PD-L1 blocking efficacy in HCC, which provides a theoretical basis for effectively eliminating LCSCs and achieving a high-efficiency synergistic treatment strategy for HCC.

Construction of shared gene signature between rheumatoid arthritis and lung adenocarcinoma helps to predict the prognosis and tumor microenvironment of the LUAD patients

Introduction: Rheumatoid arthritis (RA) is a common chronic autoimmune disease with high incidence rate and high disability rate. One of the top complications is cancer, especially lung adenocarcinoma (LUAD). However, the molecular mechanisms linking RA and LUAD are still not clear. Therefore, in this study, we tried to identify the shared genetic signatures and local immune microenvironment between RA and LUAD and construct a clinical model for survival prediction. Methods: We obtained gene expression profiles and clinical information of patients with RA and LUAD from GEO and TCGA datasets. We performed differential analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to discover the shared genes between RA and LUAD. Then, COX regression and LASSO analysis were employed to figure out genes significantly associated with survival. qRT-PCR and Western blot were utilized to validate the expression level of candidate genes. For clinical application, we constructed a nomogram, and also explored the value of RALUADS in characterizing immune infiltration features by CIBERSORT and xCell. Finally, responses to different drug therapy were predicted according to different RALUADS. Results: Our analysis identified two gene sets from differentially expressed genes and WGCNA gene modules of RA and LUAD. Filtered by survival analysis, three most significant shared genes were selected, CCN6, CDCA4 and ERLIN1, which were all upregulated in tumors and associated with poor prognosis. The three genes constituted RA and LUAD score (RALUADS). Our results demonstrated that RALUADS was higher in tumor patients and predicted poor prognosis in LUAD patients. Clinical nomogram combining RALUADS and other clinicopathological parameters had superior performance in survival prediction (AUC = 0.722). We further explored tumor immune microenvironment (TME) affected by RALUADS and observed RALUADS was closely related to the sensitivity of multiple immune blockades, chemotherapy and targeted drugs. Conclusion: Our findings suggest that there are shared physiopathologic processes and molecular profiles between RA and LUAD. RALUADS represents an excellent prognosis predictor and immune-related biomarker, which can be applied to select potential effective drugs and for LUAD patients with RA.

Multi-omics identification of GPCR gene features in lung adenocarcinoma based on multiple machine learning combinations

Background: Lung adenocarcinoma is a common malignant tumor that ranks second in the world and has a high mortality rate. G protein-coupled receptors (GPCRs) have been reported to play an important role in cancer; however, G protein-coupled receptor-associated features have not been adequately investigated. Methods: In this study, GPCR-related genes were screened at single-cell and bulk transcriptome levels based on AUcell, single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network (WGCNA) analysis. And a new machine learning framework containing 10 machine learning algorithms and their multiple combinations was used to construct a consensus G protein-coupled receptor-related signature (GPCRRS). GPCRRS was validated in the training set and external validation set. We constructed GPCRRS-integrated nomogram clinical prognosis prediction tools. Multi-omics analyses included genomics, single-cell transcriptomics, and bulk transcriptomics to gain a more comprehensive understanding of prognostic features. We assessed the response of risk subgroups to immunotherapy and screened for personalized drugs targeting specific risk subgroups. Finally, the expression of key GPCRRS genes was verified by RT-qPCR. Results: In this study, we identified 10 GPCR-associated genes that were significantly associated with the prognosis of lung adenocarcinoma by single-cell transcriptome and bulk transcriptome. Univariate and multivariate showed that the survival rate was higher in low risk than in high risk, which also suggested that the model was an independent prognostic factor for LUAD. In addition, we observed significant differences in biological function, mutational landscape, and immune cell infiltration in the tumor microenvironment between high and low risk groups. Notably, immunotherapy was also relevant in the high and low risk groups. In addition, potential drugs targeting specific risk subgroups were identified. Conclusion: In this study, we constructed and validated a lung adenocarcinoma G protein-coupled receptor-related signature, which has an important role in predicting the prognosis of lung adenocarcinoma and the effect of immunotherapy. It is hypothesized that LDHA, GPX3 and DOCK4 are new potential targets for lung adenocarcinoma, which can achieve breakthroughs in prognosis prediction, targeted prevention and treatment of lung adenocarcinoma and provide important guidance for anti-tumor.