Based on machine learning, CDC20 has been identified as a biomarker for postoperative recurrence and progression in stage I & II lung adenocarcinoma patients.

Objective: By utilizing machine learning, we can identify genes that are associated with recurrence, invasion, and tumor stemness, thus uncovering new therapeutic targets. Methods: To begin, we obtained a gene set related to recurrence and invasion from the GEO database, a comprehensive gene expression database. We then employed the Weighted Gene Co-expression Network Analysis (WGCNA) to identify core gene modules and perform functional enrichment analysis on them. Next, we utilized the random forest and random survival forest algorithms to calculate the genes within the key modules, resulting in the identification of three crucial genes. Subsequently, one of these key genes was selected for prognosis analysis and potential drug screening using the Kaplan-Meier tool. Finally, in order to examine the role of CDC20 in lung adenocarcinoma (LUAD), we conducted a variety of in vitro and in vivo experiments, including wound healing assay, colony formation assays, Transwell migration assays, flow cytometric cell cycle analysis, western blotting, and a mouse tumor model experiment. Results: First, we collected a total of 279 samples from two datasets, GSE166722 and GSE31210, to identify 91 differentially expressed genes associated with recurrence, invasion, and stemness in lung adenocarcinoma. Functional enrichment analysis revealed that these key gene clusters were primarily involved in microtubule binding, spindle, chromosomal region, organelle fission, and nuclear division. Next, using machine learning, we identified and validated three hub genes (CDC45, CDC20, TPX2), with CDC20 showing the highest correlation with tumor stemness and limited previous research. Furthermore, we found a close association between CDC20 and clinical pathological features, poor overall survival (OS), progression-free interval (PFI), progression-free survival (PFS), and adverse prognosis in lung adenocarcinoma patients. Lastly, our functional research demonstrated that knocking down CDC20 could inhibit cancer cell migration, invasion, proliferation, cell cycle progression, and tumor growth possibly through the MAPK signaling pathway. Conclusion: CDC20 has emerged as a novel biomarker for monitoring treatment response, recurrence, and disease progression in patients with lung adenocarcinoma. Due to its significance, further research studying CDC20 as a potential therapeutic target is warranted. Investigating the role of CDC20 could lead to valuable insights for developing new treatments and improving patient outcomes.

Characterizing Biphoton Spatial Wave Function Dynamics with Quantum Wavefront Sensing.

With an extremely high dimensionality, the spatial degree of freedom of entangled photons is a key tool for quantum foundation and applied quantum techniques. To fully utilize the feature, the essential task is to experimentally characterize the multiphoton spatial wave function including the entangled amplitude and phase information at different evolutionary stages. However, there is no effective method to measure it. Quantum state tomography is costly, and quantum holography requires additional references. Here, we introduce quantum Shack-Hartmann wavefront sensing to perform efficient and reference-free measurement of the biphoton spatial wave function. The joint probability distribution of photon pairs at the back focal plane of a microlens array is measured and used for amplitude extraction and phase reconstruction. In the experiment, we observe that the biphoton amplitude correlation becomes weak while phase correlation shows up during free-space propagation. Our work is a crucial step in quantum physical and adaptive optics and paves the way for characterizing quantum optical fields with high-order correlations or topological patterns.

Mechanisms of denitrifying granular sludge disintegration and calcium ion-enhanced re-granulation in acidic wastewater treatment.

Granular sludge is an alternative technology for the direct treatment of acidic nitrate-containing wastewater. Rapid remediation of disintegrated granules is essential to achieve efficient nitrogen removal. In this study, denitrifying granules were inactivated and disintegrated when the influent nitrate-nitrogen concentration was elevated from 240 to 360 mg L-1 in acidic wastewater (pH = 4.1) in a sequencing batch reactor. Tightly bound extracellular polymeric substances (TB-EPS) decreased by 60%, and extracellular protein (PN) was the main component of the reduced EPS. The three-dimensional excitation emission matrices (3D-EEM) results confirmed that the PNs that decreased were mainly tryptophan-like, tyrosine-like, and aromatic. This study further confirmed that the decrease in PN was mainly from the destruction of C=O (amide I) and N-H functional groups. Overloading of nitrogen-inhibited denitrifying activity and the destruction and dissolution of TB-EPS by acidic pH were responsible for granule disintegration, with PNs playing a major role in maintaining granule stability. Based on this, new granules with an average particle size of 454.4 µm were formed after calcium chloride addition; EPS nearly doubled during granule formation with PN as the dominant component, accounting for 64.7-78.4% of the EPS. Atomic force microscopy (AFM) revealed that PN-PN adhesion increased by 1.6-4.9 times in the presence of calcium ions, accelerating the re-granulation of disintegrated particles. This study provides new insights into the disintegration and remediation of granular sludge under acidic conditions.

Lactylated Apolipoprotein C-II Induces Immunotherapy Resistance by Promoting Extracellular Lipolysis.

Mortality rates due to lung cancer are high worldwide. Although PD-1 and PD-L1 immune checkpoint inhibitors boost the survival of patients with non-small-cell lung cancer (NSCLC), resistance often arises. The Warburg Effect, which causes lactate build-up and potential lysine-lactylation (Kla), links immune dysfunction to tumor metabolism. The role of non-histone Kla in tumor immune microenvironment and immunotherapy remains to be clarified. Here, global lactylome profiling and metabolomic analyses of samples from patients with NSCLC is conducted. By combining multi-omics analysis with in vitro and in vivo validation, that intracellular lactate promotes extracellular lipolysis through lactyl-APOC2 is revealed. Mechanistically, lactate enhances APOC2 lactylation at K70, stabilizing it and resulting in FFA release, regulatory T cell accumulation, immunotherapy resistance, and metastasis. Moreover, the anti-APOC2K70-lac antibody that sensitized anti-PD-1 therapy in vivo is developed. This findings highlight the potential of anti lactyl-APOC2-K70 approach as a new combination therapy for sensitizing immunotherapeutic responses.

An Integrated Multi-omics prediction model for stroke recurrence based on Lnet transformer layer and dynamic weighting mechanism.

BACKGROUND AND OBJECTIVE: Stroke is a disease with high mortality and disability. Importantly, the fatality rate demonstrates a significant increase among patients afflicted by recurrent strokes compared to those experiencing their initial stroke episode. Currently, the existing research encounters three primary challenges. The first is the lack of a reliable, multi-omics image dataset related to stroke recurrence. The second is how to establish a high-performance feature extraction model and eliminate noise from continuous magnetic resonance imaging (MRI) data. The third is how to integration multi-omics data and dynamically weighted for different omics data. METHODS: We systematically compiled MRI and conventional detection data from a cohort comprising 737 stroke patients and established PSTSZC, a multi-omics dataset for predicting stroke recurrence. We introduced the first-ever Integrated Multi-omics Prediction Model for Stroke Recurrence, MPSR, which is based on ResNet, Lnet-transformer, LSTM and dynamically weighted DNN. The MPSR model comprises two principal modules, the Feature Extraction Module, and the Integrated Multi-Omics Prediction Module. In the Feature Extraction module, we proposed a novel Lnet regularization layer, which effectively addresses noise issues in MRI data. In the Integrated Multi-omics Prediction Module, we propose a dynamic weighted mechanism based on evaluators, which mitigates the noise impact brought about by low-performance omics. RESULTS: We compared seven single-omics models and six state-of-the-art multi-omics stroke recurrence models. The experimental results demonstrate that the MPSR model exhibited superior performance. The accuracy, AUROC, specificity, and sensitivity of the MPSR model can reach 0.96, 0.97, 1, and 0.94, respectively, which is higher than the results of contrast model. CONCLUSION: MPSR is the first available high-performance multi-omics prediction model for stroke recurrence. We assert that the MPSR model holds the potential to function as a valuable tool in assisting clinicians in accurately diagnosing individuals with a predisposition to stroke recurrence.

Panoramic analysis of cell death patterns reveals prognostic and immune profiles of head and neck squamous cell carcinoma.

Head and neck squamous cell carcinoma (HNSCC) has been characterized by a low therapeutic response and poor prognosis. Currently, there are no reliable predictive models for HNSCC progression and therapeutic efficacy. This study explores the role of diverse patterns of cell death in tumor development, positing them as predictive factors of HNSCC prognosis. We utilized bulk transcriptome and single-cell transcriptome, align with clinical information from TCGA and GEO database, to analyze genes associated with 15 types of cell death and construct a cell death index (CDI) signature. The associations of CDI with tumor-infiltrating immune cells and immunotherapy-related biomarkers were also evaluated using various algorithms. The CDI signature emerged as a robust prognosis biomarker that could identify patients who can benefit potentially from immunotherapy, thus improving diagnostic accuracy and optimizing clinical decisions in HNSCC management. Notably, we discovered that CAAP1 deficiency not only induced apoptosis but also enhanced anti-tumor immunity, suggesting its potential as a target for clinical drug development.

KIF2C as a potential therapeutic target: insights from lung adenocarcinoma subtype classification and functional experiments.

Objective: this study evaluates the prognostic relevance of gene subtypes and the role of kinesin family member 2C (KIF2C) in lung cancer progression. Methods: high-expression genes linked to overall survival (OS) and progression-free interval (PFI) were selected from the TCGA-LUAD dataset. Consensus clustering analysis categorized lung adenocarcinoma (LUAD) patients into two subtypes, C1 and C2, which were compared using clinical, drug sensitivity, and immunotherapy analyses. A random forest algorithm pinpointed KIF2C as a prognostic hub gene, and its functional impact was assessed through various assays and in vivo experiments. Results: The study identified 163 key genes and distinguished two LUAD subtypes with differing OS, PFI, pathological stages, drug sensitivity, and immunotherapy response. KIF2C, highly expressed in the C2 subtype, was associated with poor prognosis, promoting cancer cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT), with knockdown reducing tumor growth in mice. Conclusion: The research delineates distinct LUAD subtypes with significant clinical implications and highlights KIF2C as a potential therapeutic target for personalized treatment in LUAD.

Absence of alka(e)nes triggers profound remodeling of glycerolipid and carotenoid composition in cyanobacteria membrane.

Alka(e)nes are produced by many living organisms and exhibit diverse physiological roles, reflecting a high functional versatility. Alka(e)nes serve as waterproof wax in plants, communicating pheromones for insects, and microbial signaling molecules in some bacteria. Although alka(e)nes have been found in cyanobacteria and algal chloroplasts, their importance for photosynthetic membranes has remained elusive. In this study, we investigated the consequences of the absence of alka(e)nes on membrane lipid composition and photosynthesis using the cyanobacterium Synechocystis PCC6803 as a model organism. By following the dynamics of membrane lipids and the photosynthetic performance in strains defected and altered in alka(e)ne biosynthesis, we show that drastic changes in the glycerolipid contents occur in the absence of alka(e)nes, including a decrease in the membrane carotenoid content, a decrease in some digalactosyldiacylglycerol (DGDG) species and a parallel increase in monogalactosyldiacylglycerol (MGDG) species. These changes are associated with a higher susceptibility of photosynthesis and growth to high light in alka(e)ne-deficient strains. All these phenotypes are reversed by expressing an algal photoenzyme producing alka(e)nes from fatty acids. Therefore, alkenes, despite their low abundance, are an essential component of the lipid composition of membranes. The profound remodeling of lipid composition that results from their absence suggests that they play an important role in one or more membrane properties in cyanobacteria. Moreover, the lipid compensatory mechanism observed is not sufficient to restore normal functioning of the photosynthetic membranes, particularly under high light intensity. We conclude that alka(e)nes play a crucial role in maintaining lipid homeostasis of thylakoid membranes, thereby contributing to the proper functioning of photosynthesis, particularly under elevated light intensities.

In situ acid production by organic matter induced with trace homogeneous Fenton reagent for membrane fouling control.

The homogeneous Fenton process involves both coagulation and oxidation, but it requires added acidity, so it is rarely used to control membrane fouling. This work found that the pH of neutral simulated wastewater sharply declined to 4.1 after pre-treatment with 0.1 mM Fenton reagent (Fe2+:H2O2=1:1) without added acidity. This occurred mainly because the trace homogeneous Fenton reagent induced in situ acid production by organic matter in the wastewater, which supplied the acidic conditions required for the Fenton reaction and ensured that the reaction could proceed continuously. Then, oxidation during the pre-Fenton process enhanced the electrostatic repulsion forces and effectively weakened the hydrogen bonds of organic matter at the membrane surface by altering the net charge and hydroxyl content of organic matter, while coagulation caused the foulants to gather and form large aggregates. These changes diminished the deposition of foulants onto the membrane surface and resulted in a looser fouling layer, which eventually caused the membrane fouling rate to decline from 83 % to 24 % and the flux recovery rate to increase from 44 % to 98 % during 2 h of filtration. This membrane fouling mitigation ability is much superior to that of pre-H2O2, pre-Fe2+ or pre-Fe3+ processes with equivalent doses.

Overcoming noise in quantum teleportation with multipartite hybrid entanglement.

Quantum entanglement and decoherence are the two counterforces of many quantum technologies and protocols. For example, while quantum teleportation is fueled by a pair of maximally entangled resource qubits, it is vulnerable to decoherence. Here, we propose an efficient quantum teleportation protocol in the presence of pure decoherence and without entangled resource qubits entering the Bell-state measurement. Instead, we use multipartite hybrid entanglement between the auxiliary qubits and their local environments within the open-quantum system context. With a hybrid-entangled initial state, it is the decoherence that allows us to achieve high fidelities. We demonstrate our protocol in an all-optical experiment.

CDC45 promotes the stemness and metastasis in lung adenocarcinoma by affecting the cell cycle.

OBJECTIVE: This study aimed to assess the functions of cell division cycle protein 45 (CDC45) in Non-small cell lung cancer (NSCLC) cancer and its effects on stemness and metastasis. METHODS: Firstly, differentially expressed genes related to lung cancer metastasis and stemness were screened by differential analysis and lasso regression. Then, in vitro, experiments such as colony formation assay, scratch assay, and transwell assay were conducted to evaluate the impact of CDC45 knockdown on the proliferation and migration abilities of lung cancer cells. Western blotting was used to measure the expression levels of related proteins and investigate the regulation of CDC45 on the cell cycle. Finally, in vivo model with subcutaneous injection of lung cancer cells was performed to verify the effect of CDC45 on tumor growth. RESULTS: This study identified CDC45 as a key gene potentially influencing tumor stemness and lymph node metastasis. Knockdown of CDC45 not only suppressed the proliferation and migration abilities of lung cancer cells but also caused cell cycle arrest at the G2/M phase. Further analysis revealed a negative correlation between CDC45 and cell cycle-related proteins, stemness-related markers, and tumor mutations. Mouse experiments confirmed that CDC45 knockdown inhibited tumor growth. CONCLUSION: As a novel regulator of stemness, CDC45 plays a role in regulating lung cancer cell proliferation, migration, and cell cycle. Therefore, CDC45 may serve as a potential target for lung cancer treatment and provide a reference for further mechanistic research and therapeutic development.

ROS-dependent S-palmitoylation activates cleaved and intact gasdermin D.

Gasdermin D (GSDMD) is the common effector for cytokine secretion and pyroptosis downstream of inflammasome activation and was previously shown to form large transmembrane pores after cleavage by inflammatory caspases to generate the GSDMD N-terminal domain (GSDMD-NT)1-10. Here we report that GSDMD Cys191 is S-palmitoylated and that palmitoylation is required for pore formation. S-palmitoylation, which does not affect GSDMD cleavage, is augmented by mitochondria-generated reactive oxygen species (ROS). Cleavage-deficient GSDMD (D275A) is also palmitoylated after inflammasome stimulation or treatment with ROS activators and causes pyroptosis, although less efficiently than palmitoylated GSDMD-NT. Palmitoylated, but not unpalmitoylated, full-length GSDMD induces liposome leakage and forms a pore similar in structure to GSDMD-NT pores shown by cryogenic electron microscopy. ZDHHC5 and ZDHHC9 are the major palmitoyltransferases that mediate GSDMD palmitoylation, and their expression is upregulated by inflammasome activation and ROS. The other human gasdermins are also palmitoylated at their N termini. These data challenge the concept that cleavage is the only trigger for GSDMD activation. They suggest that reversible palmitoylation is a checkpoint for pore formation by both GSDMD-NT and intact GSDMD that functions as a general switch for the activation of this pore-forming family.

R-loop and diseases: the cell cycle matters.

The cell cycle is a crucial biological process that is involved in cell growth, development, and reproduction. It can be divided into G1, S, G2, and M phases, and each period is closely regulated to ensure the production of two similar daughter cells with the same genetic material. However, many obstacles influence the cell cycle, including the R-loop that is formed throughout this process. R-loop is a triple-stranded structure, composed of an RNA: DNA hybrid and a single DNA strand, which is ubiquitous in organisms from bacteria to mammals. The existence of the R-loop has important significance for the regulation of various physiological processes. However, aberrant accumulation of R-loop due to its limited resolving ability will be detrimental for cells. For example, DNA damage and genomic instability, caused by the R-loop, can activate checkpoints in the cell cycle, which in turn induce cell cycle arrest and cell death. At present, a growing number of factors have been proven to prevent or eliminate the accumulation of R-loop thereby avoiding DNA damage and mutations. Therefore, we need to gain detailed insight into the R-loop resolution factors at different stages of the cell cycle. In this review, we review the current knowledge of factors that play a role in resolving the R-loop at different stages of the cell cycle, as well as how mutations of these factors lead to the onset and progression of diseases.

CO2 Electrolyzers.

CO2 electrolyzers have progressed rapidly in energy efficiency and catalyst selectivity toward valuable chemical feedstocks and fuels, such as syngas, ethylene, ethanol, and methane. However, each component within these complex systems influences the overall performance, and the further advances needed to realize commercialization will require an approach that considers the whole process, with the electrochemical cell at the center. Beyond the cell boundaries, the electrolyzer must integrate with upstream CO2 feeds and downstream separation processes in a way that minimizes overall product energy intensity and presents viable use cases. Here we begin by describing upstream CO2 sources, their energy intensities, and impurities. We then focus on the cell, the most common CO2 electrolyzer system architectures, and each component within these systems. We evaluate the energy savings and the feasibility of alternative approaches including integration with CO2 capture, direct conversion of flue gas and two-step conversion via carbon monoxide. We evaluate pathways that minimize downstream separations and produce concentrated streams compatible with existing sectors. Applying this comprehensive upstream-to-downstream approach, we highlight the most promising routes, and outlook, for electrochemical CO2 reduction.

Selective Electrified Propylene-to-Propylene Glycol Oxidation on Activated Rh-Doped Pd.

Renewable-energy-powered electrosynthesis has the potential to contribute to decarbonizing the production of propylene glycol, a chemical that is used currently in the manufacture of polyesters and antifreeze and has a high carbon intensity. Unfortunately, to date, the electrooxidation of propylene under ambient conditions has suffered from a wide product distribution, leading to a low faradic efficiency toward the desired propylene glycol. We undertook mechanistic investigations and found that the reconstruction of Pd to PdO occurs, followed by hydroxide formation under anodic bias. The formation of this metastable hydroxide layer arrests the progressive dissolution of Pd in a locally acidic environment, increases the activity, and steers the reaction pathway toward propylene glycol. Rh-doped Pd further improves propylene glycol selectivity. Density functional theory (DFT) suggests that the Rh dopant lowers the energy associated with the production of the final intermediate in propylene glycol formation and renders the desorption step spontaneous, a concept consistent with experimental studies. We report a 75% faradic efficiency toward propylene glycol maintained over 100 h of operation.

DeepION: A Deep Learning-Based Low-Dimensional Representation Model of Ion Images for Mass Spectrometry Imaging.

Mass spectrometry imaging (MSI) is a high-throughput imaging technique capable of the qualitative and quantitative in situ detection of thousands of ions in biological samples. Ion image representation is a technique that produces a low-dimensional vector embedded with significant spectral and spatial information on an ion image, which further facilitates the distance-based similarity measurement for the identification of colocalized ions. However, given the low signal-to-noise ratios inherent in MSI data coupled with the scarcity of annotated data sets, achieving an effective ion image representation for each ion image remains a challenge. In this study, we propose DeepION, a novel deep learning-based method designed specifically for ion image representation, which is applied to the identification of colocalized ions and isotope ions. In DeepION, contrastive learning is introduced to ensure that the model can generate the ion image representation in a self-supervised manner without manual annotation. Since data augmentation is a crucial step in contrastive learning, a unique data augmentation strategy is designed by considering the characteristics of MSI data, such as the Poisson distribution of ion abundance and a random pattern of missing values, to generate plentiful ion image pairs for DeepION model training. Experimental results of rat brain tissue MSI show that DeepION outperforms other methods for both colocalized ion and isotope ion identification, demonstrating the effectiveness of ion image representation. The proposed model could serve as a crucial tool in the biomarker discovery and drug development of the MSI technique.

Recent advances in understanding the regulatory mechanism of plasma membrane H+-ATPase through the brassinosteroid signaling pathway.

The polyhydroxylated steroid phytohormone brassinosteroids (BRs) control many aspects of plant growth, development and responses to environmental changes. Plasma membrane (PM) H+-ATPase, the well-known PM proton pump, is a central regulator in plant physiology, which mediates not only plant growth and development, but also adaptation to stresses. Recent studies highlight that PM H+-ATPase is at least partly regulated via the BR signaling. Firstly, the BR cell surface receptor BRASSINOSTEROID-INSENSITIVE 1 (BRI1) and multiple key components of BR signaling directly or indirectly influence PM H+-ATPase activity. Secondly, the SMALL AUXIN UP RNA (SAUR) gene family physically interacts with BRI1 to enhance organ development of Arabidopsis by activating PM H+-ATPase. Thirdly, RNA-sequencing (RNA-seq) assays showed that the expression of some SAUR genes is upregulated under the light or sucrose conditions, which is related to the phosphorylation state of the penultimate residue of PM H+-ATPase in a time-course manner. In this review, we describe the structural and functional features of PM H+-ATPase, and summarize recent progress toward understanding the regulatory mechanism of PM H+-ATPase by BRs, and briefly introduce how PM H+-ATPase activity is modulated by its own biterminal regions and the post-translational modifications.

RACGAP1 promotes the progression and poor prognosis of lung adenocarcinoma through its effects on the cell cycle and tumor stemness.

OBJECTION: Investigating the key genes and mechanisms that influence stemness in lung adenocarcinoma. METHODS: First, consistent clustering analysis was performed on lung adenocarcinoma patients using stemness scoring to classify them. Subsequently, WGCNA was utilized to identify key modules and hub genes. Then, machine learning methods were employed to screen and identify the key genes within these modules. Lastly, functional analysis of the key genes was conducted through cell scratch assays, colony formation assays, transwell migration assays, flow cytometry cell cycle analysis, and xenograft tumor models. RESULTS: First, two groups of patients with different stemness scores were obtained, where the high stemness score group exhibited poor prognosis and immunotherapy efficacy. Next, LASSO regression analysis and random forest regression were employed to identify genes (PBK, RACGAP1) associated with high stemness scores. RACGAP1 was significantly upregulated in the high stemness score group of lung adenocarcinoma and closely correlated with clinical pathological features, poor overall survival (OS), recurrence-free survival (RFS), and unfavorable prognosis in lung adenocarcinoma patients. Knockdown of RACGAP1 suppressed the migration, proliferation, and tumor growth of cancer cells. CONCLUSION: RACGAP1 not only indicates poor prognosis and limited immunotherapy benefits but also serves as a potential targeted biomarker influencing tumor stemness.

Site-selective protonation enables efficient carbon monoxide electroreduction to acetate.

Electrosynthesis of acetate from CO offers the prospect of a low-carbon-intensity route to this valuable chemical--but only once sufficient selectivity, reaction rate and stability are realized. It is a high priority to achieve the protonation of the relevant intermediates in a controlled fashion, and to achieve this while suppressing the competing hydrogen evolution reaction (HER) and while steering multicarbon (C2+) products to a single valuable product--an example of which is acetate. Here we report interface engineering to achieve solid/liquid/gas triple-phase interface regulation, and we find that it leads to site-selective protonation of intermediates and the preferential stabilization of the ketene intermediates: this, we find, leads to improved selectivity and energy efficiency toward acetate. Once we further tune the catalyst composition and also optimize for interfacial water management, we achieve a cadmium-copper catalyst that shows an acetate Faradaic efficiency (FE) of 75% with ultralow HER (<0.2% H2 FE) at 150 mA cm-2. We develop a high-pressure membrane electrode assembly system to increase CO coverage by controlling gas reactant distribution and achieve 86% acetate FE simultaneous with an acetate full-cell energy efficiency (EE) of 32%, the highest energy efficiency reported in direct acetate electrosynthesis.

Integrating bulk-RNA sequencing and single-cell sequencing analyses to characterize adenosine-enriched tumor microenvironment landscape and develop an adenosine-related prognostic signature predicting immunotherapy in lung adenocarcinoma.

The adenosine-signaling axis has been recognized as an important immunomodulatory pathway in tumor immunity. However, the biological role of the adenosine-signaling axis in the remodeling of the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unclear. Here, we quantified adenosine signaling (ado_sig) in LUAD samples using the GSVA method and assessed the prognostic value of adenosine in LUAD. Afterward, we explored the heterogeneity of the tumor-immune microenvironment at different adenosine levels. In addition, we analyzed the potential biological pathways engaged by adenosine. Next, we established single-cell transcriptional profiles of LUAD and analyzed cellular composition and cell-cell communication analysis under different adenosine microenvironments. Moreover, we established adenosine-related prognostic signatures (ARS) based on comprehensive bioinformatics analysis and evaluated the efficacy of ARS in predicting immunotherapy. The results demonstrated that adenosine signaling adversely impacted the survival of immune-enriched LUAD. The high-adenosine microenvironment exhibited elevated pro-tumor-immune infiltration, including M2 macrophages and displayed notably increased epithelial-mesenchymal transition (EMT) transformation. Furthermore, adenosine signaling displayed significant associations with the expression patterns and prognostic value of immunomodulators within the TME. Single-cell sequencing data revealed increased fibroblast occupancy and a prominent activation of the SPP1 signaling pathway in the high adenosine-signaling microenvironment. The ARS exhibited promising effectiveness in prognostication and predicting immunotherapy response in LUAD. In summary, overexpression of adenosine can cause a worsened prognosis in the LUAD with abundant immune infiltration. Moreover, increased adenosine levels are associated with pro-tumor-immune infiltration, active EMT transformation, pro-tumor angiogenesis, and other factors promoting cancer progression, which collectively contribute to the formation of an immunosuppressive microenvironment. Importantly, the ARS developed in this study demonstrate high efficacy in evaluating the response to immunotherapy.