Lactobacillus plantarum-Derived Extracellular Vesicles Modulate Macrophage Polarization and Gut Homeostasis for Alleviating Ulcerative Colitis.

Extracellular vesicles released by probiotics have been demonstrated to effectively alleviate intestinal inflammation, yet the precise underlying mechanisms remain unclear. In this research, for the first time, Lactobacillus plantarum UJS001 (LP-UJS) was isolated from fermented sauerkraut in Zhenjiang, China. Thereafter, the therapeutic effect of LP-UJS-derived extracellular vesicles (LP-UJS-EVs) on dextran sulfate sodium-induced ulcerative colitis (UC) in mice was analyzed to elucidate the immune mechanisms. According to our findings, LP-UJS-EVs played a pivotal role in restoring the intestinal barrier and alleviating intestinal inflammation. Notably, LP-UJS-EVs induced M2 polarization of macrophages, promoted the release of IL-10 and TGF-ß, inhibited the release of histamine, IL-6, and TNF-α, and exerted regulatory effects on intestinal microflora, as evidenced by the reduced abundances of Coprococcus, Parabacteroides, Staphylococcus, and Allobaculum, alongside the enhanced abundance of Prevotella. Furthermore, both LP-UJS and LP-UJS-EVs affected the lysine degradation pathway and significantly increased the abundance of related metabolites, especially oxoadipic acid. In summary, our results underscore the substantial therapeutic potential of LP-UJS and its secreted EVs in the treatment of UC.

Polydopamine nanoparticles cross-linked hyaluronic acid photothermal hydrogel with cascading immunoinducible effects for in situ antitumor vaccination.

Tumor vaccine, which can effectively prevent tumor recurrence and metastasis, is a promising tool in tumor immunotherapy. However, heterogeneity of tumors and the inability to achieve a cascade effect limit the therapeutic effects of most developing tumor vaccine. We have developed a cascading immunoinducible in-situ mannose-functionalized polydopamine loaded with imiquimod phenylboronic hyaluronic acid nanocomposite gel vaccine (M/P-PDA@IQ PHA) through a boronic ester-based reaction. This reaction utilizes mannose-functionalized polydopamine loaded with imiquimod (M/P-PDA@IQ NAs) as a cross-linking agent to react with phenylboronic-grafted hyaluronic acid. Under near-infrared light irradiation, the M/P-PDA@IQ PHA caused local hyperthermia to trigger immunogenic cell death of tumor cells and tumor-associated antigens (TAAs) releasing. Subsequently, the M/P-PDA@IQ NAs which were gradually released by the pH/ROS/GSH-triggered degradation of M/P-PDA@IQ PHA, could capture and deliver these TAAs to lymph nodes. Finally, the M/P-PDA@IQ NAs facilitated maturation and cross-presentation of dendritic cells, as well as activation of cytotoxic T lymphocytes. Overall, the M/P-PDA@IQ PHA could serve as a novel in situ vaccine to stimulate several key nodes including TAAs release and capture, targeting lymph nodes and enhanced dendritic cells uptake and maturation as well as T cells activation. This cascading immune activation strategy can effectively elicit antitumor immune response.

Interplay of Sphingolipid Metabolism in Predicting Prognosis of GBM Patients: Towards Precision Immunotherapy.

Background: In spite of numerous existing bio-surveillance systems for predicting glioma (GBM) prognosis, enhancing the efficacy of immunotherapy remains an ongoing conundrum. The continual scrutiny of the dynamic interplay between the sphingolipid metabolic pathway and tumor immunophenotypes has unveiled potential implications. However, the intricate orchestration of functional and regulatory mechanisms by long non-coding RNAs (lncRNAs) in GBM, particularly in the context of sphingolipid metabolism, remains cryptic. Methods: We harnessed established R packages to intersect gene expression profiles of GBM patients within the The Cancer Genome Atlas (TCGA) database with the compilation of sphingolipid metabolism genes from GeneCards. This enabled us to discern markedly distinct lncRNAs, which were subsequently deployed to construct a robust prognostic model utilizing Lasso-Cox regression analysis. We then scrutinized the immune microenvironment across various risk strata using the ssGSEA and CIBERSORT algorithms. To evaluate mutation patterns and drug resistance profiles within patient subgroups, we devised the "Prophytic" and "Maftools" packages, respectively. Results: Our investigation scrutinized lncRNAs linked to sphingolipid metabolism, utilizing glioma specimens from TCGA. We meticulously curated 1224 sphingolipid-associated genes gleaned from GeneCards and pinpointed 272 differentially expressed mRNAs via transcriptomic analysis. Enrichment analyses underscored their significance in sphingolipid processes. A prognostic model founded on 17 meticulously selected lncRNAs was systematically constructed and validated. This model adeptly stratified GBM patients into high- and low-risk categories, yielding highly precise prognostic insights. We also discerned correlations between immune cell infiltration and genetic mutation discrepancies, along with distinct therapeutic responses through drug sensitivity analysis. Notably, computational findings were corroborated through experimental validation by RT-PCR. Conclusion: In summation, our exhaustive inquiry underscores the multifaceted utility of the sphingolipid metabolic pathway as an autonomous diagnostic and prognostic indicator for glioma patients. Furthermore, we amalgamate a profusion of substantiated evidence concerning immune infiltration and gene mutations, thereby reinforcing the proposition that sphingolipid metabolism may function as a pivotal determinant in the panorama of immunotherapeutic interventions.

Pan-Cancer Analysis Reveals SOX2 as a Promising Prognostic and Immunotherapeutic Biomarker Across Various Cancer Types, Including Pancreatic Cancer.

SOX2 is associated with the initiation, growth, and progression of various tumors and is related to stem cells. However, further studies of SOX2 in a pan-cancer context are warranted. In this study, we obtained pan-cancer and clinical data from TCGA, GTEx, STRING, and TISIDB databases and we analyzed the relationship between SOX2 expression levels and changes in gene diagnostics and survival prognosis. Additionally, we compared the expression levels of SOX2 in pancreatic cancer and healthy pancreatic tissues using Wilcoxon's rank-sum test. Functional enrichment analysis was conducted to identify potential signaling pathways and biological functions. To determine the prognostic value, we used the area under the curve (AUC) and Cox regression analysis. We further developed nomograms to predict overall survival at 1, 6, and 12 months after cancer diagnosis. Moreover, we assessed immune cell infiltration using single-sample gene set enrichment analysis. The methylation status of SOX2 was analyzed using the UALCAN and MethSurv databases. Furthermore, we verified the differential expression of SOX2 in pancreatic cancer cell lines by western blotting and quantitative polymerase chain reaction. We also confirmed the effect of SOX2 on the invasion and migration of pancreatic cancer cells using transwell and scratch assays. The biological effects were confirmed using a clone-formation assay. Our findings suggest that SOX2 is highly expressed in various tumor tissues and has potential clinical significance. It can be used as a new biomarker for pancreatic adenocarcinoma and plays a crucial role in immune infiltration.

CTSK and PLAU as Prognostic Biomarker and Related to Immune Infiltration in Pancreatic Cancer: Evidence from Bioinformatics Analysis and qPCR.

Pancreatic adenocarcinoma (PAAD) is a malignancy with the highest mortality rate worldwide. There is a pressing need for novel biomarkers that can facilitate early detection and serve as targets for therapeutic interventions beyond the commonly utilized CA199 marker. This study utilized microarray datasets (GSE15471, GSE62165, and GSE28735) from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) and construct a protein-protein interaction network using STRING and Cytoscape. Hub genes were selected using BiNGO. Expression profiles and clinical data from the Cancer Genome Atlas (TCGA) were then used to compare the expression levels of CTSK and PLAU in pancreatic cancer and healthy pancreatic tissues via the Wilcoxon rank-sum test, with further validation using qPCR. Functional enrichment analysis was conducted to explore potential signaling pathways and biological functions. Prognostic values were assessed by the Kaplan-Meier and Cox regression analyses, and an overall survival (OS) nomogram was created to predict 1-, 2-, and 3-year survival after cancer diagnosis. The infiltration of immune cells was evaluated by single-sample gene set enrichment analysis. The methylation status of both genes was analyzed using the UALCAN and MethSurv databases. The results demonstrated that CTSK and PLAU were overexpressed in pancreatic cancer and that the hypomethylation status of both genes was associated with a poor prognosis. The overexpression of both genes was positively correlated with various immune cells, and functional enrichment analysis revealed that they were associated with immune cell infiltration. Besides, the effects of PLAU on the migration and invasion of pancreatic cancer cells were also verified by scratch and transwell experiments. Consequently, CTSK and PLAU have potential as prognostic biomarkers for pancreatic cancer.

ALKBH5-PYCR2 Positive Feedback Loop Promotes Proneural-Mesenchymal Transition Via Proline Synthesis In GBM.

AlkB homolog 5, RNA demethylase (ALKBH5) is abnormally highly expressed in glioblastoma multiforme (GBM) and is negatively correlated with overall survival in GBM patients. In this study, we found a new mechanism that ALKBH5 and pyrroline-5-carboxylate reductase 2 (PYCR2) formed a positive feedback loop involved in proline synthesis in GBM. ALKBH5 promoted PYCR2 expression and PYCR2-mediated proline synthesis; while PYCR2 promoted ALKBH5 expression through the AMPK/mTOR pathway in GBM cells. In addition, ALKBH5 and PYCR2 promoted GBM cell proliferation, migration, and invasion, as well as proneural-mesenchymal transition (PMT). Furthermore, proline rescued AMPK/mTOR activation and PMT after silencing PYCR2 expression. Our findings reveal an ALKBH5-PYCR2 axis linked to proline metabolism, which plays an important role in promoting PMT in GBM cells and may be a promising therapeutic pathway for GBM.

Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.

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.

Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation.

Background: Gastric cancer (GC) represents a malignancy with a multi-factorial combination of genetic, environmental, and microbial factors. Targeting lysosomes presents significant potential in the treatment of numerous diseases, while lysosome-related genetic markers for early GC detection have not yet been established, despite implementing this process by assembling artificial intelligence algorithms would greatly break through its value in translational medicine, particularly for immunotherapy. Methods: To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. Then, the reliability of the model was initially confirmed by the results of enrichment analyses currently in use. And the immunological implications of the genes comprising the predictor was explored and response of GC patients were evaluated to immunotherapy and chemotherapy. Further, we performed systematic laboratory work to evaluate the build-up of the central genes, both at the expression stage and at the functional aspect, by which we could also demonstrate the reliability of the model to guide cancer immunotherapy. Results: Eight lysosomal-related genes were selected for predictive model construction based on the inclusion of RMSE as a reference standard and RF algorithm for ranking, namely ADRB2, KCNE2, MYO7A, IFI30, LAMP3, TPP1, HPS4, and NEU4. Taking into account accuracy, precision, recall, and F1 measurements, a preliminary determination of our study was carried out by means of applying the extra tree and random forest algorithms, incorporating the ROC-AUC value as a consideration, the Extra Tree model seems to be the optimal option with the AUC value of 0.92. The superiority of diagnostic signature is also reflected in the analysis of immune features. Conclusion: In summary, this study is the first to integrate around 20 mainstream ML algorithms to construct an AI-based diagnostic predictor for gastric cancer based on lysosomal-related genes. This model will facilitate the accurate prediction of early gastric cancer incidence and the subsequent risk assessment or precise individualized immunotherapy, thus improving the survival prognosis of GC patients.

Lactate in the tumor microenvironment: A rising star for targeted tumor therapy.

Metabolic reprogramming is one of fourteen hallmarks of tumor cells, among which aerobic glycolysis, often known as the "Warburg effect," is essential to the fast proliferation and aggressive metastasis of tumor cells. Lactate, on the other hand, as a ubiquitous molecule in the tumor microenvironment (TME), is generated primarily by tumor cells undergoing glycolysis. To prevent intracellular acidification, malignant cells often remove lactate along with H+, yet the acidification of TME is inevitable. Not only does the highly concentrated lactate within the TME serve as a substrate to supply energy to the malignant cells, but it also works as a signal to activate multiple pathways that enhance tumor metastasis and invasion, intratumoral angiogenesis, as well as immune escape. In this review, we aim to discuss the latest findings on lactate metabolism in tumor cells, particularly the capacity of extracellular lactate to influence cells in the tumor microenvironment. In addition, we examine current treatment techniques employing existing medications that target and interfere with lactate generation and transport in cancer therapy. New research shows that targeting lactate metabolism, lactate-regulated cells, and lactate action pathways are viable cancer therapy strategies.

Cascade biomimetic intelligent nanotheranostic agents for imaging-guided tumor synergistic therapy.

Aim: Achieving drug-targeting delivery and environment-responsive releasing to realize imaging-guided precise tumor therapy. Materials & methods: Graphene oxide (GO) was used as the drug-delivery system to load indocyanine green (ICG) and doxorubicin (DOX) to form a GO/ICG&DOX nanoplatform, in which GO can quench the fluorescence of ICG and DOX. MnO2 and folate acid-functionalized erythrocyte membrane were further coated into the surface of GO/ICG&DOX to obtain an FA-EM@MnO2-GO/ICG&DOX nanoplatform. Results: The FA-EM@MnO2-GO/ICG&DOX nanoplatform has longer blood circulation time, precise targeting delivery to tumor tissues and catalase-like activity. Both in vitro and in vivo results demonstrated that the FA-EM@MnO2-GO/ICG&DOX nanoplatform has better therapeutic efficacy. Conclusion: The authors successfully fabricated a glutathione-responsive FA-EM@MnO2-GO/ICG&DOX nanoplatform, which can achieve drug-targeting delivery and precise drug release.

NCAPG2 could be an immunological and prognostic biomarker: From pan-cancer analysis to pancreatic cancer validation.

More recently, NCAPG2 has emerged as an intrinsically essential participant of the condensin II complex involved in the process of chromosome cohesion and stabilization in mitosis, and its position in particular tumours is now being highlighted. Simultaneously, the genetic properties of NCAPG2 hint that it might have enormous potential to interpret the malignant progression of tumors in a broader perspective, that is, in pan-cancer. Yet, at present, this recognition remains merely superficial and there is a lack of more detailed studies to explore the underlying pathogenesis. To meet this need, the current study was undertaken to comprehensively elucidate the potential functions of NCAPG2 in pan-cancer, based on a combination of existing databases like TCGA and GTEx. NCAPG2 was identified to be overexpressed in almost every tumor and to exhibit significant prognostic and diagnostic efficacy. Furthermore, the correlation between NCAPG2 and selected immune features, namely immune cell infiltration, immune checkpoint genes, TMB, MSI, etc. also indicates that NCAPG2 could potentially be applied in guidance of immunotherapy. Subsequently, in pancreatic cancer, this study further clarified the utility of NCAPG2 that downregulation of its expression could result in reduced proliferation, invasion and metastasis of pancreatic cancer cells, among such phenotypical changes, the epithelial-mesenchymal transition disruption could be at least one of the possible mechanisms raising or enhancing tumorigenesis. Taken above, NCAPG2, as a member of pan-oncogenes, would serve as a biomarker and potential therapeutic target for a range of malignancies, sharing new insights into precision medicine.