Cancer research is pivotal for understanding cancer biology, discovering new therapeutic targets, and advancing precision medicine. However, it faces challenges such as data complexity, dispersed analytical tools, and the lack of a unified platform. To address these issues, we developed the GEO Cancer Analysis Suite (GCAS), an R package and visualization interface via shinyApp. GCAS includes four main modules: differential gene expression analysis, correlation studies, pan-cancer analysis, and immune infiltration and drug sensitivity analysis. These modules facilitate the identification of potential cancer biomarkers, elucidation of gene regulatory networks, comprehensive multi-cancer analysis, and assessment of gene expression in relation to immune cell infiltration and drug sensitivity. Using GCAS, GAPDH was found to be upregulated in multiple lung cancer and breast datasets and positively correlated with the m6A regulatory gene IGF2BP3. Further in vitro assays suggested that IGF2BP3 regulates GAPDH mRNA stability. Immune infiltration analysis indicated a negative correlation between GAPDH expression and CD4 T cell infiltration scores. Drug sensitivity analysis revealed a significant negative correlation between GAPDH expression and sensitivity to EGFR-targeting drugs, particularly Erlotinib. GCAS is a crucial tool in cancer research, simplifying data analysis and enhancing the discovery of novel biomarkers, immune landscape profiles, and drug sensitivity predictions, significantly contributing to cancer research and precision medicine.

Digital literacy (DL) is increasingly recognized as a valuable competency for future healthcare professionals, yet its integration in preventive medicine curricula remains inconsistent. This study examined students’ DL familiarity and competencies to inform curriculum development. A cross-sectional anonymous survey was conducted among fourth- and fifth-year preventive medicine undergraduates at a single site (one institution) in China, Soochow University. A validated 10-item instrument assessed four dimensions: Digital Tool Proficiency, Information Search Evaluation, Information Security Privacy, and Digital Health Ethics Application using a 5-point Likert scale. Confirmatory Factor Analysis validated the structure, and gender comparisons were performed using Fisher’s exact tests and t-tests. Among 188 valid respondents (62.3

ABSTRACT Smoking injury extends beyond the epithelium and endothelium; we show that alveolar macrophage senescence is a central driver. Using single‑cell RNA sequencing of human bronchoalveolar lavage fluid integrated with macrophage models exposed to cigarette smoke extract (CSE), we profiled senescence at the cell‑type level. The results of functional assays (mitochondrial reactive oxygen species (mitoROS), DNA damage, Senescence‐associated β‐galactosidase, p16/p21, apoptosis, phagocytosis, senescence‐associated secretory phenotype) confirmed the biology of these effects. Smokers’ macrophages were enriched for senescence and the SASP. In vitro, CSE increased mitoROS and DNA damage signaling, impaired phagocytosis, and induced apoptosis. Multiple cohort analyses revealed the GTP Binding Protein Overexpressed In Skeletal Muscle (GEM) as a causal driver: GEM was elevated in smokers and correlated with CDKN1A, and its perturbation altered the phenotype. GEM increased mitoROS, suppressed SIRT3/SOD2, lowered adenosine triphosphate (ATP), and amplified p16/p21 and SASP. Pharmacologic SIRT3 activation reversed these defects. In addition, the results from the mouse smoking model strongly support the role of the GEM/SIRT3 pathway in mediating the effects of cigarette smoke on macrophage senescence. Upstream, CSE induced ATF3 to transactivate GEM, while IGF2BP2 stabilized GEM mRNA via m6A. These data suggest that GEM–SIRT3 is an actionable biomarker and target for precision intervention in smoking‑related lung aging.

Algae, especially microalgae, are versatile in terms of nutrition, feed, biofertilizers, biofuel, and so on. In the realm of oncotherapy, algal extracts have been extensively used as anti-cancer active ingredients; however, what has been ignored is the anti-cancer value induced by themselves. Thanks to their unparalleled advantages, for example, intrinsic tumor homing, immunogenicity, and in situ production of anti-cancer agents, algae pave a new way in anti-cancer research. Algae have been reported about selective cytotoxicity to cancer cells and could work for oxygen-dependent strategies such as photodynamic therapy, owing to their natural photosynthetic abilities. Interestingly, integrating with customized nanomaterials (NMs), algae have been demonstrated to have unprecedented potential in overcoming barriers to existing treatment methods. Thus, in this review, starting from the classification of algae, the diverse effects of algae are thoroughly introduced, followed by the current engineering strategies of algae; lastly, the emerging development of algae-based therapeutics is timely summarized with an emphasis on the intelligent creation of biohybrid systems by choosing algae and tailored NMs. This review presents a comprehensive exploration of engineered algae-involved innovative cancer therapy, with a discussion of the future challenges and outlook, which will help design creative therapy paradigms and facilitate their clinical applications.

Lung adenocarcinoma (LUAD), the predominant subtype of non-small cell lung cancer (NSCLC), presents significant challenges in early diagnosis and personalized treatment. Recent research has focused on the role of the tumor microenvironment, particularly tumor-associated fibroblasts (CAFs), in tumor progression. This study systematically analyzed CAF immune infiltration-related genes to construct a prognostic model for LUAD, confirming its predictive value for patient outcomes. The risk score derived from CAF-related genes (CAFRGs) was negatively correlated with immune microenvironment scores and linked to the expression of immune checkpoint genes, indicating that high-risk patients may exhibit immune escape characteristics. Analysis via the TIDE tool revealed that low-risk patients had more active T-cell immune responses. The risk score also correlated with anti-tumor drug sensitivity, particularly to doramapimod. Notably, COX6A1 emerged as a key gene in the model, with its upregulation associated with immune cell infiltration and immune escape. Further in vitro experiments demonstrated that COX6A1 regulates LUAD cell migration, proliferation, and senescence, suggesting its role in tumor immune evasion. Additionally, further co-culture studies of lung cancer cells and fibroblasts revealed that COX6A1 knockdown promotes the expression of CAF-related cytokines, enhancing CAF infiltration. Overall, this study provides a foundation for personalized treatment of LUAD and highlights COX6A1 as a promising therapeutic target within the tumor immune microenvironment, guiding future clinical research.

Early diagnosis of lung cancer is crucial for improving patient prognosis. In this study, we developed a diagnostic model for lung cancer based on serum proteomic data from the GSE168198 dataset using four machine learning algorithms (nnet, glmnet, svm, and XGBoost). The model’s performance was validated on datasets that included normal controls, disease controls, and lung cancer data containing both. Furthermore, the model’s diagnostic capability was further validated on an independent external dataset. Our analysis identified SLC16A4 as a key protein in the model, which was significantly downregulated in lung cancer serum samples compared to normal controls. The expression of SLC16A4 was closely associated with clinical pathological features such as gender, tumor stage, lymph node metastasis, and smoking history. Functional assays revealed that overexpression of SLC16A4 significantly inhibited lung cancer cell proliferation and induced cellular senescence, suggesting its potential role in lung cancer development. Additionally, correlation analyses showed that SLC16A4 expression was linked to immune cell infiltration and the expression of immune checkpoint genes, indicating its potential involvement in immune escape mechanisms. Based on multi-omics data from the TCGA database, we further discovered that the low expression of SLC16A4 in lung cancer may be regulated by DNA copy number variations and DNA methylation. In conclusion, this study not only established an efficient diagnostic model for lung cancer but also identified SLC16A4 as a promising biomarker with potential applications in early diagnosis and immunotherapy.

Mitophagy, a conserved cellular mechanism, is crucial for cellular homeostasis through the selective clearance of impaired mitochondria. Its emerging role in cancer development has sparked interest, particularly in lung adenocarcinoma (LUAD). Our study aimed to construct a risk model based on mitophagy-related genes (MRGs) to predict survival outcomes, immune response, and chemotherapy sensitivity in LUAD patients. We mined the GeneCards database to identify MRGs and applied LASSO/Cox regression to formulate a prognostic model. Validation was performed using two independent Gene Expression Omnibus (GEO) cohorts. Patients were divided into high- and low-risk categories according to the median risk score. The high-risk group demonstrated significantly reduced survival. Multivariate Cox analysis confirmed the risk score as an independent predictor of prognosis, and a corresponding nomogram was developed to facilitate clinical assessments. Intriguingly, the risk score correlated with immune infiltration levels, oncogenic expression profiles, and sensitivity to anticancer agents. Enrichment analyses linked the risk score with key oncological pathways and biological processes. Within the model, MTERF3 emerged as a critical regulator of lung cancer progression. Functional studies indicated that the MTERF3 knockdown suppressed the lung cancer cell proliferation and migration, enhanced mitophagy, and increased the mitochondrial superoxide production. Our novel prognostic model, grounded in MRGs, promises to refine therapeutic strategies and prognostication in lung cancer management.

IntroductionLung cancer, with the highest global mortality rate among cancers, presents a grim prognosis, often diagnosed at an advanced stage in nearly 70% of cases. Recent research has unveiled a novel mechanism of cell death termed disulfidptosis, which is facilitated by glucose scarcity and the protein SLC7A11.MethodsUtilizing the least absolute shrinkage and selection operator (LASSO) regression analysis combined with Cox regression analysis, we constructed a prognostic model focusing on disulfidptosis-related genes. Nomograms, correlation analyses, and enrichment analyses were employed to assess the significance of this model. Among the genes incorporated into the model, CHRNA5 was selected for further investigation regarding its role in LUAD cells. Biological functions of CHRNA5 were assessed using EdU, transwell, and CCK-8 assays.ResultsThe efficacy of the model was validated through internal testing and an external validation set, with further evaluation of its robustness and clinical applicability using a nomogram. Subsequent correlation analyses revealed associations between the risk score and infiltration of various cancer types, as well as oncogene expression. Enrichment analysis also identified associations between the risk score and pivotal biological processes and KEGG pathways. Our findings underscore the significant impact of CHRNA5 on LUAD cell proliferation, migration, and disulfidptosis.ConclusionThis study successfully developed and validated a robust prognostic model centered on disulfidptosis-related genes, providing a foundation for predicting prognosis in LUAD patients.

Trends in sonodynamic therapy (SDT) have stimulated greater research efforts toward the development of novel sonosensitizers due to their crucial roles in increasing reactive oxygen species (ROS). Herein, a new scintillator, NaCe0.7FX:Gd0.1,Tb0.2, was explored as an ultrasmall inorganic sonosensitizer for glutathione (GSH) depletion-enhanced SDT of cancer. Specifically, fluorine vacancy-donated defective NaCe0.7FX:Gd0.1,Tb0.2 scintillating nanodots (ScNDs) were prepared via a high-temperature reaction method. PEGylated ScNDs, namely ScND-PEG, exhausted GSH at first, which further augmented the sonodynamic performance of ROS generation. In vitro studies demonstrated that US-irradiated ScND-PEG exerted significant cytotoxicity on CT26 tumor cells by triggering immunogenic cell death (ICD). In vivo studies revealed that ScND-PEG-mediated enhanced SDT substantially inhibited tumor development by activating cytotoxic T lymphocytes and secreting antitumor proinflammatory cytokines. With the assistance of immune checkpoint blockade (ICB), improved antitumor outcomes were further obtained with prolonged survival time. Notably, no significant acute or chronic toxicity occurred during our treatment schedule based on ScND-PEG. Overall, our findings paved the way for identifying a new kind of ScND-based inorganic sonosensitizer as a reserve, which might open up new opportunities for advancing sonodynamic-immune oncotherapy.

Lung cancer treatment is evolving, and the role of senescent macrophages in tumor immune evasion has become a key focus. This study explores how senescent macrophages interact with lung cancer cells, contributing to tumor progression and immune dysfunction. As aging impairs macrophage functions, including phagocytosis and metabolic signaling, it promotes chronic inflammation and cancer development. p16INK4a-positive macrophages are common in aged mice, and their clearance slows tumor growth, suggesting these cells support tumor proliferation and immune evasion. Targeting the senescence-associated secretory phenotype (SASP) and reprogramming senescent macrophages offers potential therapeutic benefits, including reversing immune aging and boosting anti-tumor immunity. However, translating these findings into clinical practice requires further molecular understanding and rigorous clinical trials.

Lung cancer remains the leading cause of cancer-related mortality globally, with a poor prognosis primarily due to late diagnosis and limited treatment options. This research highlights the critical demand for advanced prognostic tools by creating a model centered on aging-related genes (ARGs) to improve prediction and treatment strategies for lung adenocarcinoma (LUAD). By leveraging datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we developed a prognostic model that integrates 14 ARGs using the least absolute shrinkage and selection operator (LASSO) alongside Cox regression analyses. The model exhibited strong predictive performance, achieving area under the curve (AUC) values greater than 0.8 for one-year survival in both internal and external validation cohorts. The risk scores generated by our model were significantly correlated with critical features of the tumor microenvironment, including the presence of cancer-associated fibroblasts (CAFs) and markers of immune evasion, such as T-cell dysfunction and exclusion. Higher risk scores correlated with a more tumor-promoting microenvironment and increased immune suppression, highlighting the model’s relevance in understanding LUAD progression. Additionally, XRCC6, a protein involved in DNA repair and cellular senescence, was found to be upregulated in LUAD. Functional assays demonstrated that the knockdown of XRCC6 led to decreased cell proliferation, whereas its overexpression alleviated DNA damage, highlighting its significance in tumor biology and its potential therapeutic applications. This study provides a novel ARG-based prognostic model for LUAD, offering valuable insights into tumor dynamics and the tumor microenvironment, which may guide the development of targeted therapies and improve patient outcomes.

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Artificial Intelligence (AI) is revolutionizing public health education through its capacity for intricate analysis of large-scale health datasets and the tailored dissemination of health-related information and interventions. This article conducts a profound exploration into the integration of AI within public health, accentuating its scientific foundations, prospective progress, and practical application scenarios. It underscores the transformative potential of AI in crafting individualized educational programs, developing sophisticated behavioral models, and informing the creation of health policies. The manuscript strives to thoroughly evaluate the extant landscape of AI applications in public health, scrutinizing critical challenges such as the propensity for data bias and the imperative of safeguarding privacy. By dissecting these issues, the article contributes to the conversation on how AI can be harnessed responsibly and effectively, ensuring that its application in public health education is both ethically grounded and equitable. The paper’s significance is multifold: it aims to provide a blueprint for policy formulation, offer actionable insights for public health authorities, and catalyze the progression of health interventions toward increasingly sophisticated and precise approaches. Ultimately, this research anticipates fostering an environment where AI not only augments public health education but also does so with a steadfast commitment to the principles of justice and inclusivity, thereby elevating the standard and reach of health education initiatives globally.

IntroductionCancer’s inherent heterogeneity, marked by diverse genetic and molecular alterations, presents significant challenges for developing effective treatments. One such alteration is the regulation of disulfidoptosis, a recently discovered programmed cell death pathway. RPN1, a key regulator associated with disulfidoptosis, may influence various aspects of tumor biology, including immune evasion and cellular senescence. This study aims to dissect the role of RPN1 in pan-cancer and its potential as a therapeutic target.MethodsWe employed a pan-cancer analysis to explore RPN1 expression and its association with clinical outcomes across multiple tumor types. Immune cell infiltration and expression of immune checkpoint genes were analyzed in relation to RPN1. Additionally, cellular senescence markers were assessed in RPN1 knockdown tumor cells. Gene regulatory mechanisms were studied through gene copy number variations, DNA methylation analysis, and transcriptional regulation by SP1.ResultsRPN1 is overexpressed in a wide range of tumor types and correlates with poor clinical outcomes, including overall survival, disease-specific survival, and progression-free intervals. Our analysis shows that RPN1 is involved in immune evasion, correlating with the presence of myeloid dendritic cells, macrophages, and tumor-associated fibroblasts, and influencing T-cell activity. RPN1 knockdown led to reduced tumor cell proliferation and induced cellular senescence, marked by increased senescence-associated biomarkers and β-galactosidase activity. RPN1 expression was found to be regulated by gene copy number variations, reduced DNA methylation, and transcriptional control via SP1.DiscussionThese findings highlight RPN1 as a key pan-cancer regulator, influencing immune microenvironment interactions and cellular senescence. The regulation of disulfidoptosis by RPN1 presents a promising avenue for therapeutic intervention. Targeting RPN1 could enhance immunotherapy efficacy and help mitigate tumor progression, offering a potential strategy for cancer treatment.

Lung cancer is a multi-etiological disease and radon is known as the second most common risk factor for the disease. The tumor suppressor gene TP53 is commonly mutated in human lung cancer, thus contributing to the alteration of mitochondrial outer membrane permeability (MOMP). Thus, this study aimed to investigate the role of the P53-mediated mitochondrial pathway in radon-induced lung cancer. Long-term radon exposure downregulated P53, enhanced mitochondrial membrane potential (MMP), and increased mitochondrial DNA copy number in BEAS-2B cells, as well as in the lung tissues of mice exposed to radon. The above effects significantly contributed to the radon-induced malignant transformation of BEAS-2B cells. In addition, high-throughput sequencing analysis revealed that BTG2 downregulation was involved in the P53 mitochondrial pathway-mediated radon-induced malignant transformation. BTG2 overexpression significantly restored the TP53 -mediated alterations in MOMP, apoptosis, and MMP. Mechanically, TP53 downregulation was regulated by m 6 A demethylase ALKBH5, which was validated by treatment of ALKBH5 inhibitor IOX1 and mRNA stability assay in BEAS-2B and H1299 cells. Our research shows that the ALKBH5-mediated downregulation of TP53 affects the carcinogenesis of radon, and the BTG2 suppression in the P53-mediated mitochondrial pathway plays an important role in radon-induced lung cancer.

The educational landscape of toxicology is increasingly integrating computational methodologies due to ethical concerns about animal testing and advancements in biotechnological and data analysis tools. This paper examines the evolution and significance of the Toxicology in the 21st century (Tox21) initiative and its impact on computational toxicology education. It contrasts computational toxicology with traditional methods, highlighting the limitations of conventional approaches and the new perspectives offered by computational techniques. The study emphasizes the importance of incorporating computational toxicology into curricula, including case studies that demonstrate how this integration enhances students’ problem-solving abilities, real-time data analysis skills, and innovation capabilities. Furthermore, it outlines effective teaching content and methods, including software tools, online resources, and academic literature. The paper also addresses the challenges and limitations faced in this educational shift and explores prospects for advancing computational toxicology education. By documenting these developments, the study aims to clarify the current advancements in toxicology education and the preparedness of students to address global chemical safety challenges with innovative solutions.

Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is one of the most prominent housekeeping proteins and is widely used as an internal control in some semi-quantitative assays. In addition to glycolysis, GAPDH is involved in several cancer-related biological processes and has been reported to be commonly dysregulated in multiple cancer types. Therefore, its role in the physiological process of cancer needs to be urgently elucidated. Pan-cancer analysis indicated that GAPDH is ubiquitously highly expressed in most cancer types, and that patients with a high GAPDH expression of in tumor tissues have a poor prognosis. The concordance of GAPDH expression in tumors with the infiltration of immune cells and immune checkpoints implies a certain association between GAPDH and the tumor microenvironment as well as tumor development. Gene Set Enrichment Analysis revealed that GAPDH may contribute to multiple important cancer-related pathways and biological processes. Multi-omics analysis and in vitro cell experiments revealed that GAPDH overexpression is regulated by DNA copy number amplification and promoter methylation modification. Importantly, a transcription factor, forkhead box M1 (FOXM1), which is capable of regulating GAPDH expression, was also identified and was confirmed to be an oncogene and ubiquitously highly expressed in multiple cancer types. Semi-quantitative chromatin immunoprecipitation, quantitative PCR, and dual-luciferase assays showed that FOXM1 mainly binds to the promoter region of GAPDH in two cancer cell lines. The present findings revealed the implication of GAPDH in tumor development, thus bringing attention to this important molecule and casting doubts on its role as an internal reference gene in cancer studies.

Abstract Age at exposure is a major modifier of radiation-induced carcinogenesis. We used mouse models to elucidate the mechanism underlying age-related susceptibility to radiation-induced tumorigenesis. Radiation exposure in infants was effective at inducing tumors in B6/B6-Chr18MSM-F1 ApcMin/+ mice. Loss of heterozygosity analysis revealed that interstitial deletion may be considered a radiation signature in this model and tumor number containing a deletion correlated with the susceptibility to radiation-induced tumorigenesis as a function of age. Furthermore, in Lgr5-eGFP-ires-CreERT2; Apcflox/flox mice, deletions of both floxed Apc alleles in Lgr5-positive stem cells in infants resulted in the formation of more tumors than in adults. These results suggest that tumorigenicity of Apc-deficient stem cells varies with age and is higher in infant mice. Three-dimensional immunostaining analyses indicated that the crypt architecture in the intestine of infants was immature and different from that in adults concerning crypt size and the number of stem cells and Paneth cells per crypt. Interestingly, the frequency of crypt fission correlated with the susceptibility to radiation-induced tumorigenesis as a function of age. During crypt fission, the percentage of crypts with lysozyme-positive mature Paneth cells was lower in infants than that in adults, whereas no difference in the behavior of stem cells or Paneth cells was observed regardless of age. These data suggest that morphological dynamics in intestinal crypts affect age-dependent susceptibility to radiation-induced tumorigenesis; oncogenic mutations in infant stem cells resulting from radiation exposure may acquire an increased proliferative potential for tumor induction compared with that in adults.