Background: Current paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma’s functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients.Methods: The GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC.Results: Six CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC.Conclusion: The prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment.

Background: Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs).Methods: We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD.Results: We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our model’s performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells.Conclusion: Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.

Background: Esophageal squamous cell carcinoma (ESCC) is a prevalent and aggressive form of cancer that poses significant challenges in terms of prognosis and treatment. Regulatory T cells (Treg cells) have gained attention due to their influential role in immune modulation within the tumor microenvironment (TME). Understanding the intricate interactions between Treg cells and the tumor microenvironment is essential for unraveling the mechanisms underlying ESCC progression and for developing effective prognostic models and immunotherapeutic strategies.Methods: A combination of single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq analysis was utilized to explore the role of Treg cells within the TME of ESCC. The accuracy and applicability of the prognostic model were assessed through multi-dimensional evaluations, encompassing an examination of the model’s performance across various dimensions, such as the mutation landscape, clinical relevance, enrichment analysis, and potential implications for immunotherapy strategies.Results: The pivotal role of the macrophage migration inhibitory factor (MIF) signaling pathway within the ESCC TME was investigated, with a focus on its impact on Treg cells and other subpopulations. Through comprehensive integration of bulk sequencing data, a Treg-associated signature (TAS) was constructed, revealing that ESCC patients with elevated TAS (referred to as high-TAS individuals) experienced significantly improved prognoses. Heightened immune infiltration and increased expression of immune checkpoint markers were observed in high-TAS specimens. The model’s validity was established through the IMvigor210 dataset, demonstrating its robustness in predicting prognosis and responsiveness to immunotherapy. Heightened therapeutic benefits were observed in immune-based interventions for high-TAS ESCC patients. Noteworthy differences in pathway enrichment patterns emerged between high and low-TAS cohorts, highlighting potential avenues for therapeutic exploration. Furthermore, the clinical relevance of key model genes was substantiated by analyzing clinical samples from ten paired tumor and adjacent tissues, revealing differential expression levels.Conclusion: The study established a TAS that enables accurate prediction of patient prognosis and responsiveness to immunotherapy. This achievement holds significant implications for the clinical management of ESCC, offering valuable insights for informed therapeutic interventions.

Lung cancer remains the leading cause of cancer-related deaths, driven by complex pathogenesis and poor prognosis. Recognizing the pivotal role of mitochondrial RNA modifications (MRM) in cancer progression, this study aims to provide a comprehensive analysis of MRM-related genes and their clinical relevance in lung adenocarcinoma (LUAD). Integrating multi-omic datasets, we systematically explored the molecular features of MRM-related genes across various cancers and identified distinct expression patterns and prognostic associations. Single-cell analysis further reveals MRM-driven cell-cell interactions and pathway activation, particularly in cycling and epithelial cells. Using advanced machine learning techniques, we developed a novel prognostic signature-the Mitochondrial RNA Modification-related Signature (MRMS)-comprising nine genes: TXN, LDHA, HMGA1, SFTPB, KRT8, ALG3, S100A16, HSPD1, and ALDOA. The MRMS demonstrates superior predictive performance for LUAD survival compared to previously reported models. Our findings uniquely link MRMS to increased tumor mutational burden, genetic instability, and an immunosuppressive tumor microenvironment characterized by reduced immune cell infiltration and elevated tumor purity. Additionally, MRMS is associated with immunotherapy-related features, suggesting its potential in predicting treatment response. Experimental validation identified ALG3 as an oncogenic driver in LUAD, influencing tumor cell proliferation, migration, and invasion. In conclusion, this study establishes MRMS as a robust prognostic biomarker and highlights its dual role in shaping the tumor immune microenvironment and guiding therapeutic strategies. These findings provide novel insights into mitochondrial RNA modifications and their potential applications in personalized treatment for LUAD.

Inflammation is a fundamental physiological response of the body to injury and infection, orchestrated by dynamic regulation across multilayered genetic, epigenetic, and cellular signaling networks to restore internal homeostasis. Acute inflammation helps to defend against pathogen invasion and repair tissue damage. However, if the inflammatory response fails to resolve properly or becomes dysregulated, it may develop into chronic inflammation. Persistent or aberrantly activated inflammation has been closely associated with the pathogenesis and progression of numerous major diseases, including cardiovascular diseases, autoimmune diseases, neurodegenerative disorders, cancer, and sepsis(1, 2). In-depth exploration of the molecular mechanisms of inflammation, cellular heterogeneity, and their regulatory networks has thus become a forefront of life sciences and medical research.The inflammatory microenvironment encompasses various immune and non-immune cells, including neutrophils, macrophages, T cells, endothelial cells, and fibroblasts. These cells interact through chemokines, cytokines, and diverse receptor signaling pathways, working together to initiate, amplify, and resolve inflammation(3). During the acute phase, neutrophils directly engage in phagocytosis and elimination of pathogens, while macrophages subsequently mediate the clearance and repair of the microenvironment. In the chronic phase, the accumulation of regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and other immunosuppressive cells can prevent excessive inflammatory reactions but may also result in immune dysfunction, tissue injury, and even fibrosis(4). Molecular biomarkers related to inflammation have become increasingly important in disease classification, prognosis monitoring, and the field of precision medicine. In recent years, emerging technologies that integrate single-cell sequencing and multi-omics analyses have enabled researchers to dissect the inflammatory microenvironment from multiple dimensions, including cellular subpopulations, gene expression, and spatial localization(5, 6).New strategies targeting the inflammatory response are continually being developed, including pro-resolving mediators, cytokine inhibitors, immune cell therapies, and combination multi-target approaches(7). Several key studies in this Research Topic demonstrate significant advances in this field. Feng et al. explored the protective role of muscone in the development of chronic obstructive pulmonary disease (COPD) by establishing a mouse COPD model, finding that muscone significantly improved lung function, upregulated anti-inflammatory cytokines including IL-38, while inhibiting pro-inflammatory factors such as CXCR3, IFN-γ, IL-17A, and RORγt, providing a new therapeutic perspective for inflammatory regulation in COPD(8). Chen et al. investigated the effects of autologous platelet-rich plasma (PRP) treatment in patients with chronic endometritis (CE), demonstrating that PRP significantly reconstructed the uterine local immune microenvironment by reducing the proportions of CD8+ T cells, CD56+ NK cells, Foxp3+ Treg cells, and T-bet+ Th1 cells, while upregulating endometrial receptivity-related genes, thereby improving pregnancy outcomes(9). Guo et al., through a review of skin-homing T cells in recurrent episodes of atopic dermatitis, elucidated the key mechanisms of lymphocyte skin homing in disease recurrence, providing new intervention targets for chronic inflammatory skin diseases(10). With advances in single-cell sequencing and transcriptome analysis, Liu et al. revealed the mechanistic roles of sialylation-related genes CD19 and GPR65 in sepsis-induced acute respiratory distress syndrome, identifying CD14 monocytes as the key cell type(11), while Yang et al. comprehensively analyzed diagnostic biomarkers and immune cell infiltration features in sepsis through machine learning and bioinformatics techniques, identifying CD40LG as a key gene and target of the Chinese medicine Xuebijing(12). Additionally, Hao et al. investigated the role of mitophagy-related genes in acute myocardial infarction and ischemic cardiomyopathy, finding significant differences in the TGFβ pathway between high and low-risk groups, and validating RPS11 as an important diagnostic biomarker(13). Collectively, these studies advance the development of early warning, stratified diagnosis, and personalized treatment strategies for inflammation-related diseases, providing a solid foundation for clinical translation and disease prevention.Research on the molecular mechanisms and therapeutic strategies of inflammation stands at a pivotal stage of rapid transformation. The application of single-cell and multi-omics technologies continues to fuel innovation in understanding disease mechanisms, diagnosis, and intervention strategies. The collection of cutting-edge studies presented in this Research Topic demonstrates significant advances in our understanding of inflammatory processes across different disease contexts. We anticipate that these findings will promote deep integration between basic and clinical research in inflammatory diseases, foster the development of innovative therapeutic approaches, and ultimately achieve precision treatment, benefitting a broader patient population.