Increased glucose influx and glycogenesis in lung cancer cells surviving after irradiation

Identification of a Biomarker Panel in Extracellular Vesicles Derived From Non-Small Cell Lung Cancer (NSCLC) Through Proteomic Analysis and Machine Learning

Antigen fingerprint profiling of tumour-derived extracellular vesicles (TDEVs) in the body fluids is a promising strategy for identifying tumour biomarkers. In this study, proteomic and immunological assays reveal significantly higher CD155 levels in plasma extracellular vesicles (EVs) from patients with non-small cell lung cancer (NSCLC) than from healthy individuals. Utilizing CD155 as a bait protein on the EV membrane, CD155+ TDEVs are enriched from NSCLC patient plasma EVs. In the discovery cohort, 281 differentially expressed proteins are identified in TDEVs of the NSCLC group compared with the healthy control group. In the verification cohort, 49 candidate biomarkers are detected using targeted proteomic analysis. Of these, a biomarker panel of seven frequently and stably detected proteins-MVP, GYS1, SERPINA3, HECTD3, SERPING1, TPM4, and APOD-demonstrates good diagnostic performance, achieving an area under the curve (AUC) of 1.0 with 100% sensitivity and specificity in receiver operating characteristic (ROC) curve analysis, and 92.3% sensitivity and 88.9% specificity in confusion matrix analysis. Western blotting results confirm upregulation trends for MVP, GYS1, SERPINA3, HECTD3, SERPING1 and APOD, and TPM4 is downregulated in EVs of NSCLC patients compared with healthy individuals. These findings highlight the potential of this biomarker panel for the clinical diagnosis of NSCLC.

Unveiling the crucial role of glycosylation modification in lung adenocarcinoma metastasis through artificial neural network-based spatial multi-omics single-cell analysis and Mendelian randomization

BACKGROUND

Investigations into the intricacies of glycosylation modifications, a prevalent post-translational alteration observed in neoplasms, especially remain elusive in the context of lung adenocarcinoma. Through the integration of multiple omics approaches, the investigation aimed to delineate the significance of glycosylation in lung adenocarcinoma, with an objective to pinpoint viable biological targets.

METHODS

Initial steps involved the identification of genes differentially expressed in relation to glycosylation at the aggregate transcriptome level within lung adenocarcinoma tissues. This was followed by analyses of localization and function employing both single-cell and spatial transcriptomics to provide a more nuanced understanding. In pursuit of elucidating functional disparities in glycosylation patterns, a predictive framework employing artificial neural networks was constructed. To ascertain causal relationships between specific genes and lung adenocarcinoma, Mendelian randomization was applied, culminating in the experimental validation of these genes' roles.

RESULTS

Analysis at the single-cell level uncovered marked glycosylation modification expressions in metastatic tissues of lung adenocarcinoma. Moreover, tissues of lung adenocarcinoma with elevated expression of genes associated with glycosylation displayed enhanced differentiation and activation across signaling pathways including TGF-β, oxidative stress, and WNT. Through spatial transcriptomics, zones of intense glycosylation modification were pinpointed within tumor nests and proximate to tumor-associated blood vessels. An artificial neural network-derived prognostic model demonstrated outstanding predictive capability, with AUC scores achieving 0.84, 0.83, and 0.89 for 1, 3, and 5-year forecasts, respectively. The group identified as high-risk was characterized by pronounced immunosuppression and diminished responsiveness to immunotherapy. Mendelian randomization analysis pinpointed GLANT2 (OR = 1.3654, p < 0.05) and GYS1 (OR = 1.2668, p < 0.05) as genes contributing to the pathogenesis of lung adenocarcinoma. Cell assays have reaffirmed that the inhibition of GYS1 significantly reduces proliferation and invasion in lung adenocarcinoma cell lines, while also decreasing glycogen storage and the formation of glycosylation end products, indicating suppression of glycosylation processes. These findings identify GYS1 as a prospective glycosylation-linked biological target for lung adenocarcinoma therapy.

Blocking ACSL6 Compromises Autophagy via FLI1-Mediated Downregulation of COLs to Radiosensitize Lung Cancer

Lung cancer (LC) is the leading cause of cancer-related mortality worldwide. Radiotherapy is the main component of LC treatment; however, its efficacy is often limited by radioresistance development, resulting in unsatisfactory clinical outcomes. Here, we found that LC radiosensitivity is up-regulated by decreased expression of long-chain acyl-CoA synthase 6 (ACSL6) after irradiation. Deletion of ACSL6 results in significant elevation of Friend leukemia integration 1 transcription factor (FLI1) and a marked decline of collagens (COLs). Blocking of ACSL6 impairs the tumor growth and upregulates FLI1, which reduces the levels of COLs and compromises irradiation-induced autophagy, leading to considerable therapeutic benefits during radiotherapy. Moreover, the direct interaction between ACSL6 and FLI1 and engagement between FLI1 and COLs indicates the involvement of the ACSL6-FLI1-COL axis. Finally, the potently adjusted autophagy flux reduces its otherwise contributive capability in surviving irradiation stress and leads to satisfactory radiosensitization for LC radiotherapy. These results demonstrate that enhanced ACSL6 expression promotes the aggressive performance of irradiated LC through increased FLI1-COL-mediated autophagy flux. Thus, the ACSL6-FLI1-Col-autophagy axis may be targeted to enhance the radiosensitivity of LC and improve the management of LC in radiotherapy.

Increased [18F]FDG uptake of radiation-induced giant cells: a single-cell study in lung cancer models

Positron emission tomography (PET), a cornerstone in cancer diagnosis and treatment monitoring, relies on the enhanced uptake of fluorodeoxyglucose ([18F]FDG) by cancer cells to highlight tumors and other malignancies. While instrumental in the clinical setting, the accuracy of [18F]FDG-PET is susceptible to metabolic changes introduced by radiation therapy. Specifically, radiation induces the formation of giant cells, whose metabolic characteristics and [18F]FDG uptake patterns are not fully understood. Through a novel single-cell gamma counting methodology, we characterized the [18F]FDG uptake of giant A549 and H1299 lung cancer cells that were induced by radiation, and found it to be considerably higher than that of their non-giant counterparts. This observation was further validated in tumor-bearing mice, which similarly demonstrated increased [18F]FDG uptake in radiation-induced giant cells. These findings underscore the metabolic implications of radiation-induced giant cells, as their enhanced [18F]FDG uptake could potentially obfuscate the interpretation of [18F]FDG-PET scans in patients who have recently undergone radiation therapy.

Identification and validation of a disulfidptosis-related genes prognostic signature in lung adenocarcinoma

Disulfidptosis, a newly revealed form of cell death, regulated by numerous genes that has been recently identified. The exact role of disulfidptosis in lung adenocarcinoma (LUAD) still uncertain. Objective of this study was to explore potential prognostic markers among disulfidptosis genes in LUAD. By combining transcriptomic information from Gene Expression Omnibus databases and The Cancer Genome Atlas, we identified differentially expressed and prognostic disulfidptosis genes. By conducting least absolute shrinkage and selection operator with multivariate Cox regression, four disulfidptosis genes were selected to create the prognostic signature. The implementation of the signature separated the training and validation cohorts into groups with high- and low-risk. Subsequently, the model was verified by conducting an independent analysis of receiver operating characteristic (ROC) curves. Further comparisons were made between the two risk-divided groups with regards the tumor microenvironment, immune cell infiltration, immunotherapy response, and drug sensitivity. The signature was constructed using four disulfidptosis-related genes: SLC7A11, SLC3A2, NCKAP1, and GYS1. According to ROC curves, the signature was effective for predicting LUAD prognosis. In addition, the prognostic signature correlated with sensitivity to chemotherapeutic agents and the efficacy of immunotherapy in LUAD. Finally, through external validation, we showed that NCKAP1 are correlated with tumor migration, proliferation, and invasion of LUAD cells. GYS1 affects immune cell, especially M2 macrophage infiltration in the tumor microenvironment. The disulfidptosis four-gene model can reliably predict the prognosis of patients diagnosed with LUAD, thereby providing valuable information for clinical applications and immunotherapy.

Applications and Advances of Multicellular Tumor Spheroids: Challenges in Their Development and Analysis

Biomedical research requires both in vitro and in vivo studies in order to explore disease processes or drug interactions. Foundational investigations have been performed at the cellular level using two-dimensional cultures as the gold-standard method since the early 20th century. However, three-dimensional (3D) cultures have emerged as a new tool for tissue modeling over the last few years, bridging the gap between in vitro and animal model studies. Cancer has been a worldwide challenge for the biomedical community due to its high morbidity and mortality rates. Various methods have been developed to produce multicellular tumor spheroids (MCTSs), including scaffold-free and scaffold-based structures, which usually depend on the demands of the cells used and the related biological question. MCTSs are increasingly utilized in studies involving cancer cell metabolism and cell cycle defects. These studies produce massive amounts of data, which demand elaborate and complex tools for thorough analysis. In this review, we discuss the advantages and disadvantages of several up-to-date methods used to construct MCTSs. In addition, we also present advanced methods for analyzing MCTS features. As MCTSs more closely mimic the in vivo tumor environment, compared to 2D monolayers, they can evolve to be an appealing model for in vitro tumor biology studies.

Review: Mechanisms and perspective treatment of radioresistance in non-small cell lung cancer

Radiotherapy is the major treatment of non-small cell lung cancer (NSCLC). The radioresistance and toxicity are the main obstacles that leading to therapeutic failure and poor prognosis. Oncogenic mutation, cancer stem cells (CSCs), tumor hypoxia, DNA damage repair, epithelial-mesenchymal transition (EMT), and tumor microenvironment (TME) may dominate the occurrence of radioresistance at different stages of radiotherapy. Chemotherapy drugs, targeted drugs, and immune checkpoint inhibitors are combined with radiotherapy to treat NSCLC to improve the efficacy. This article reviews the potential mechanism of radioresistance in NSCLC, and discusses the current drug research to overcome radioresistance and the advantages of Traditional Chinese medicine (TCM) in improving the efficacy and reducing the toxicity of radiotherapy.