Integration of Clinical Trial Spatial Multi-omics Analysis and Virtual Clinical Trials Enables Immunotherapy Response Prediction and Biomarker Discovery.

Computational methods that simulate tumors mathematically to describe cellular and molecular interactions are emerging as promising tools to simulate the impact of therapy entirely in silico, potentially greatly accelerating the delivery of new therapeutics to patients. To facilitate the design of dosing regimens and identification of potential biomarkers for immunotherapy, we developed a new computational model to track tumor progression at the organ scale while capturing the spatial heterogeneity of the tumor in HCC. This computational model of spatial quantitative systems pharmacology (spQSP) was designed to simulate the effects of combination immunotherapy. The model was initiated using literature-derived parameter values and fitted to the specifics of HCC. Model validation was done through comparison to spatial multi-omics data from a neoadjuvant HCC clinical trial combining anti-PD-1 immunotherapy and a multitargeted tyrosine kinase inhibitor (TKI) cabozantinib. Validation using spatial proteomics data from Imaging Mass Cytometry (IMC) demonstrated that closer proximity between CD8 T cells and macrophages correlated with non-response. We also compared the model output with Visium spatial transcriptomics (ST) profiling of samples from post-treatment tumor resections in the clinical trial and from another independent study of anti-PD1 monotherapy. ST data confirmed simulation results, suggesting the importance of spatial patterns of tumor vasculature and TGFß in tumor and immune cell interactions. Our findings demonstrate that incorporating mathematical modeling and computer simulations with high-throughput spatial multi-omics data provides a novel approach for patient outcome prediction and biomarker discovery.

Virtual patient analysis identifies strategies to improve the performance of predictive biomarkers for PD-1 blockade.

Patients with metastatic triple-negative breast cancer (TNBC) show variable responses to PD-1 inhibition. Efficient patient selection by predictive biomarkers would be desirable, but is hindered by the limited performance of existing biomarkers. Here, we leveraged in-silico patient cohorts generated using a quantitative systems pharmacology model of metastatic TNBC, informed by transcriptomic and clinical data, to explore potential ways to improve patient selection. We tested 90 biomarker candidates, including various cellular and molecular species, by a cutoff-based biomarker testing algorithm combined with machine learning-based feature selection. Combinations of pre-treatment biomarkers improved the specificity compared to single biomarkers at the cost of reduced sensitivity. On the other hand, early on-treatment biomarkers, such as the relative change in tumor diameter from baseline measured at two weeks after treatment initiation, achieved remarkably higher sensitivity and specificity. Further, blood-based biomarkers had a comparable ability to tumor- or lymph node-based biomarkers in identifying a subset of responders, potentially suggesting a less invasive way for patient selection.

Virtual clinical trials via a QSP immuno-oncology model to simulate the response to a conditionally activated PD-L1 targeting antibody in NSCLC.

Recently, immunotherapies for antitumoral response have adopted conditionally activated molecules with the objective of reducing systemic toxicity. Amongst these are conditionally activated antibodies, such as PROBODY® activatable therapeutics (Pb-Tx), engineered to be proteolytically activated by proteases found locally in the tumor microenvironment (TME). These PROBODY® therapeutics molecules have shown potential as PD-L1 checkpoint inhibitors in several cancer types, including both effectiveness and locality of action of the molecule as shown by several clinical trials and imaging studies. Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for triple-negative breast cancer (TNBC), to computationally predict the effectiveness and targeting specificity of a PROBODY® therapeutics drug compared to the non-modified antibody. We begin with the analysis of anti-PD-L1 immunotherapy in non-small cell lung cancer (NSCLC). As a first contribution, we have improved previous virtual patient selection methods using the omics data provided by the iAtlas database portal compared to methods previously published in literature. Furthermore, our results suggest that masking an antibody maintains its efficacy while improving the localization of active therapeutic in the TME. Additionally, we generalize the model by evaluating the dependence of the response to the tumor mutational burden, independently of cancer type, as well as to other key biomarkers, such as CD8/Treg Tcell and M1/M2 macrophage ratio. While our results are obtained from simulations on NSCLC, our findings are generalizable to other cancer types and suggest that an effective and highly selective conditionally activated PROBODY® therapeutics molecule is a feasible option.

Van der Waals polarity-engineered 3D integration of 2D complementary logic.

Vertical three-dimensional integration of two-dimensional (2D) semiconductors holds great promise, as it offers the possibility to scale up logic layers in the z axis1-3. Indeed, vertical complementary field-effect transistors (CFETs) built with such mixed-dimensional heterostructures4,5, as well as hetero-2D layers with different carrier types6-8, have been demonstrated recently. However, so far, the lack of a controllable doping scheme (especially p-doped WSe2 (refs. 9-17) and MoS2 (refs. 11,18-28)) in 2D semiconductors, preferably in a stable and non-destructive manner, has greatly impeded the bottom-up scaling of complementary logic circuitries. Here we show that, by bringing transition metal dichalcogenides, such as MoS2, atop a van der Waals (vdW) antiferromagnetic insulator chromium oxychloride (CrOCl), the carrier polarity in MoS2 can be readily reconfigured from n- to p-type via strong vdW interfacial coupling. The consequential band alignment yields transistors with room-temperature hole mobilities up to approximately 425 cm2 V-1 s-1, on/off ratios reaching 106 and air-stable performance for over one year. Based on this approach, vertically constructed complementary logic, including inverters with 6 vdW layers, NANDs with 14 vdW layers and SRAMs with 14 vdW layers, are further demonstrated. Our findings of polarity-engineered p- and n-type 2D semiconductor channels with and without vdW intercalation are robust and universal to various materials and thus may throw light on future three-dimensional vertically integrated circuits based on 2D logic gates.

Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC.

Immune checkpoint inhibitors remained the standard-of-care treatment for advanced non-small cell lung cancer (NSCLC) for the past decade. In unselected patients, anti-PD-(L)1 monotherapy achieved an overall response rate of about 20%. In this analysis, we developed a pharmacokinetic and pharmacodynamic module for our previously calibrated quantitative systems pharmacology model (QSP) to simulate the effectiveness of macrophage-targeted therapies in combination with PD-L1 inhibition in advanced NSCLC. By conducting in silico clinical trials, the model confirmed that anti-CD47 treatment is not an optimal option of second- and later-line treatment for advanced NSCLC resistant to PD-(L)1 blockade. Furthermore, the model predicted that inhibition of macrophage recruitment, such as using CCR2 inhibitors, can potentially improve tumor size reduction when combined with anti-PD-(L)1 therapy, especially in patients who are likely to respond to anti-PD-(L)1 monotherapy and those with a high level of tumor-associated macrophages. Here, we demonstrate the application of the QSP platform on predicting the effectiveness of novel drug combinations involving immune checkpoint inhibitors based on preclinical or early-stage clinical trial data.

Fully bio-based intumescent flame retardant hybrid: A green strategy towards reducing fire hazard and improving degradation of polylactic acid.

Polylactic acid (PLA) is a promising renewable polymer material with excellent biodegradability and good mechanical properties. However, the easy flammability and slow natural degradation limited its further applications, especially in high-security fields. In this work, a fully bio-based intumescent flame-retardant system was designed to reduce the fire hazard of PLA. Firstly, arginine (Arg) and phytic acid (PA) were combined through electrostatic ionic interaction, followed by the introduction of starch as a carbon source, namely APS. The UL-94 grade of PLA/APS composites reached V-0 grade by adding 3 wt% of APS and exhibited excellent anti-dripping performance. With APS addition increasing to 7 wt%, LOI value increased to 26 % and total heat release decreased from 58.4 (neat PLA) to 51.1 MJ/m2. Moreover, the addition of APS increased its crystallinity up to 83.5 % and maintained the mechanical strength of pristine PLA. Noteworthy, APS accelerated the degradation rate of PLA under submerged conditions. Compared with pristine PLA, PLA/APS showed more apparent destructive network morphology and higher mass and Mn loss, suggesting effective degradation promotion. This work provides a full biomass modification strategy to construct renewable plastic with both good flame retardancy and high degradation efficiency.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

Markerless gait analysis through a single camera and computer vision.

The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.

From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling.

Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.

ALKBH5 facilitates the progression of skin cutaneous melanoma via mediating ABCA1 demethylation and modulating autophagy in an m6A-dependent manner.

Background: N6-methyladenosine (m6A) is the most common and abundant mRNA modification, playing an essential role in biological processes and tumor development. However, the role of m6A methylation in skin cutaneous melanoma (SKCM) is not yet clear. This study analyzed the expression of m6A-related functional genes in SKCM and aimed to explore the key demethylase ALKBH5 mediated m6A modification and its potential mechanism in human SKCM. Methods: Based on public databases, the m6A-related gene expression landscape in SKCM was portrayed. MeRIP-Seq and RNA-Seq were used to recognize the downstream target of ALKBH5. In vivo and in vitro functional phenotype and rescue functional experiments were performed to explore the mechanism of the ALKBH5-m6A-ABCA1 axis in SKCM. Results: We found ALKBH5 upregulated in SKCM, associated with poor prognosis. ALKBH5 can promote melanoma cell proliferation, colony formation, migration, and invasion and inhibit autophagy in vitro, facilitating tumor growth and metastasis in vivo. We identified ABCA1, a membrane protein that assists cholesterol efflux, as a downstream target of ALKBH5-mediated m6A demethylation. Finally, our data demonstrated that ALKBH5 promoted SKCM via mediating ABCA1 downregulation by reducing ABCA1 mRNA stability in an m6A-dependent manner. Conclusion: Our findings exhibited the functional value of the key demethylase ALKBH5 mediated m6A modification in the progression of SKCM, suggesting the ALKBH5-m6A-ABCA1 axis as a potential therapeutic target in SKCM.

Factors Affecting Workers' Mental Stress in Handover Activities During Human-Robot Collaboration.

OBJECTIVE: This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot's end-effector on workers' mental stress during handover tasks. BACKGROUND: Human-robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers' stress. METHODS: Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed. RESULTS: Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for "surprise" and "predictability" subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front. CONCLUSION: The mental stress of workers during HRC is lower when the robot's end effector (1) approaches a worker within the worker's field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory. APPLICATION: The outcome of this study can serve as a guide to design HRC tasks with a low level of workers' mental stress.

Inhibition of phosphatidylinositol 3-kinase catalytic subunit alpha by miR-203a-3p reduces hypertrophic scar formation via phosphatidylinositol 3-kinase/AKT/mTOR signaling pathway.

Background: Hypertrophic scar (HS) is a common fibroproliferative skin disease that currently has no truly effective therapy. Given the importance of phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA) in hypertrophic scar formation, the development of therapeutic strategies for endogenous inhibitors against PIK3CA is of great interest. Here, we explored the molecular mechanisms underlying the protective effects of miR-203a-3p (PIK3CA inhibitor) against excessive scar. Methods: Bioinformatic analysis, immunohistochemistry, immunofluorescence, miRNA screening and fluorescence in situ hybridization assays were used to identify the possible pathways and target molecules mediating HS formation. A series of in vitro and in vivo experiments were used to clarify the role of PIK3CA and miR-203a-3p in HS. Mechanistically, transcriptomic sequencing, immunoblotting, dual-luciferase assay and rescue experiments were executed. Results: Herein, we found that PIK3CA and the phosphatidylinositol 3-kinase (PI3K)/AKT/mTOR pathway were upregulated in scar tissues and positively correlated with fibrosis. We then identified miR-203a-3p as the most suitable endogenous inhibitor of PIK3CA. miR-203a-3p suppressed the proliferation, migration, collagen synthesis and contractility as well as the transdifferentiation of fibroblasts into myofibroblasts in vitro, and improved the morphology and histology of scars in vivo. Mechanistically, miR-203a-3p attenuated fibrosis by inactivating the PI3K/AKT/mTOR pathway by directly targeting PIK3CA. Conclusions: PIK3CA and the PI3K/AKT/mTOR pathway are actively involved in scar fibrosis and miR-203a-3p might serve as a potential strategy for hypertrophic scar therapy through targeting PIK3CA and inactivating the PI3K/AKT/mTOR pathway.

Lithiophilic Covalent Organic Framework as Anode Coating for High-Performance Lithium Metal Batteries.

The growth of disorganized lithium dendrites and weak solid electrolyte interphase greatly impede the practical application of lithium metal batteries. Herein, we designed and synthesized a new kind of stable polyimide covalent organic frameworks (COFs), which have a high density of well-aligned lithiophilic quinoxaline and phthalimide units anchored within the uniform one-dimensional channels. The COFs can serve as an artificial solid electrolyte interphase on lithium metal anode, effectively guiding the uniform deposition of lithium ions and inhibiting the growth of lithium dendrites. The unsymmetrical Li||COF-Cu battery exhibits a Coulombic efficiency of 99 % at a current density of 0.5 mA cm-2 , which can be well retained up to 400 cycles. Meanwhile, the Li-COF||LFP full cell shows a Coulombic efficiency over 99 % at a charge of 0.3 C. And its capacity can be well maintained up to 91 % even after 150 cycles. Therefore, the significant electrochemical cycling stability illustrates the feasibility of employing COFs in solving the disordered deposition of lithium ions in lithium metal batteries.

Exploring the impact of human-robot interaction on workers' mental stress in collaborative assembly tasks.

Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). However, working and interacting with collaborative robots in close proximity can be psychologically stressful. Therefore, understanding the impacts of human-robot interaction (HRI) on mental stress is crucial for enhancing workplace well-being. To this end, this study investigated how the HRI factors - presence, complexity, and modality - affect the psychological stress of workers. We employed both the NASA-Task Load Index for subjective assessment and physiological metrics including galvanic skin responses, electromyography, and heart rate for objective evaluation. An experimental setup was implemented in which human operators worked together with a collaborative robot on Lego assembly tasks, using different interaction paradigms including pressing buttons, showing hand gestures, and giving verbal commands. The results revealed that the introduction of interactions during HRC helped reduce mental stress and that complex interactions resulted in higher mental stress than simple interactions. Meanwhile, using hand gestures led to significantly higher mental stress than pressing buttons and verbal commands. The findings provided practical insights for mitigating mental stress in the workplace and promoting wellness in the era of HRC.

Eliciting the antitumor immune response with a conditionally activated PD-L1 targeting antibody analyzed with a quantitative systems pharmacology model.

Conditionally activated molecules, such as Probody therapeutics (PbTx), have recently been investigated to improve antitumoral response while reducing systemic toxicity. PbTx are engineered to be proteolytically activated by proteases that are preferentially active locally in the tumor microenvironment (TME). Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for other drugs, to evaluate the effectiveness and targeting specificity of an anti-PD-L1 PbTx compared to the non-modified antibody. We have informed the model using the PbTx dynamics and pharmacokinetics published in the literature for anti-PD-L1 in patients with triple-negative breast cancer (TNBC). Our results suggest masking of the antibody slightly decreases its efficacy, while increasing the localization of active therapeutic component in the TME. We also perform a parameter optimization for the PbTx design and drug dosing regimens to maximize the response rate. Although our results are specific to the case of TNBC, our findings are generalizable to any conditionally activated PbTx molecule in solid tumors and suggest that design of a highly effective and selective PbTx is feasible.

Cost-effectiveness analysis of insecticide ban aimed at preventing Parkinson's disease in Central California.

BACKGROUND: Our study assessed whether banning specific insecticides to reduce the PD burden in three Central California (CA) counties is cost-effective. METHOD: We applied a cost-effectiveness analysis using a cohort-based Markov model to estimate the impact and costs of banning seven insecticides that were previously associated with PD in these counties as well as mixture exposures to some of these pesticides. We relied for our estimations on the cohort of 65- and 66-year-olds living in these counties who were unaffected by PD at baseline in 2020 and projected their incidence, costs, and reduction in quality-adjusted-life-years (QALY) loss due to developing PD over a 20-year period. We included a shiny app for modeling different scenarios (https://sherlockli.shinyapps.io/pesticide_pd_economics_part_2/). RESULTS: According to our scenarios, banning insecticides to reduce the occurrence of PD in three Central CA counties was cost-effective relative to not banning insecticides. In the worst-case scenario of exposure to a single pesticide, methomyl, versus none would result in an estimated 205 (95 % CI: 75, 348) additional PD cases or 12 % (95 % CI: 4 %, 20 %) increase in PD cases over a 20-year period based on residential proximity to pesticide applications. The increase in PD cases due to methomyl would increase health-related costs by $72.0 million (95 % CI: $5.5 million, $187.4 million). Each additional PD patient due to methomyl exposure would incur $109,327 (95 % CI, $5554, $347,757) in costs per QALY loss due to PD. Exposure to methomyl based on workplace proximity to pesticide applications generated similar estimates. The highest PD burden and associated costs would be incurred from exposure to multiple pesticides simultaneously. CONCLUSION: Our study provides an assessment of the cost-effectiveness of banning specific insecticides to reduce PD burden in terms of health-related QALYs and related costs. This information may help policymakers and stakeholders to make decisions concerning the regulation of pesticides.

Novel approach for enhancing skin allograft survival by bioadhesive nanoparticles loaded with rapamycin.

Skin graft rejection is a significant challenge in skin allografts for skin defects, particularly in extensive burn injury patients when autografts are insufficient. Enhancing the survival duration of allogeneic skin grafts can improve the success rate of subsequent autologous skin grafting, thereby promoting the therapeutic efficacy for wound healing. Rapamycin (Rapa), a potent immunosuppressant with favorable efficacy in organ transplantation, is limited by its systemic administration-associated toxicity and side effects. Therefore, addressing the short survival time of allogeneic skin grafts and minimizing the toxicity related to systemic application of immunosuppressive agents is an urgent requirement. Here, we present a topical formulation based on bioadhesive poly (lactic acid)-hyperbranched polyglycerol nanoparticles (BNPs) with surface-modified encapsulation of Rapamycin (Rapa/BNPs), applied for local immunosuppression in a murine model of allogeneic skin grafts. Our Rapa/BNPs significantly prolong nanoparticle retention, reduce infiltration of T lymphocytes and macrophages, decrease the level of pro-inflammatory cytokines and ultimately extend skin allograft survival with little systemic toxicity compared to free Rapa or Rapamycin-loaded non-bioadhesive nanoparticles (Rapa/NNPs) administration. In conclusion, Rapa/BNPs effectively deliver local immunosuppression and demonstrate potential for enhancing skin allograft survival while minimizing localized inflammation, thus potentially increasing patient survival rates for various types of skin defects.

Verteporfin-Loaded Bioadhesive Nanoparticles for the Prevention of Hypertrophic Scar.

Hypertrophic scarring (HS) is a common skin injury complication with unmet needs. Verteporfin (VP) should be an ideal HS-targeted therapeutic drug due to its efficient fibrosis and angiogenesis inhibitory abilities. However, its application is restricted by its side effects such as dose-dependent cytotoxicity on normal cells. Herein, the bioadhesive nanoparticles encapsulated VP (VP/BNPs) are successfully developed to attenuate the side effects of VP and enhance its HS inhibition effects by limiting VP releasing slowly and stably in the lesion site but not diffusing easily to normal tissues. VP/BNPs displayed significant inhibition on the proliferation, migration, collagen deposition, and vessel formation of human hypertrophic scar fibroblasts (HSFBs) and dermal vascular endothelial cells (HDVECs). In a rat tail HS model, VP/BNPs treated HS exhibits dramatic scar repression with almost no side effects compared with free VP or VP-loaded non-bioadhesive nanoparticles (VP/NNPs) administration. Further immunofluorescence analysis on scar tissue serial sections validated VP/BNPs effectively inhibited the collagen deposition and angiogenesis by firmly confined in the scar tissue and persistently releasing VP targeted to nucleus Yes-associated protein (nYAP) of HSFBs and HDVECs. These findings collectively suggest that VP/BNPs can be a promising and technically advantageous agent for HS therapies.

Pickering Emulsions and Viscoelastic Solutions Constructed by a Rosin-Based CO2-Responsive Surfactant.

Designing stimulus-switch viscoelastic solutions and Pickering emulsions with reversible CO2-responsive behavior remains a challenge. A rosin-based CO2-responsive surfactant, N-cetyl-maleimidepimaric acid N,N-dimethylenediamide (C16MPAN), was synthesized and used to prepare CO2-triggered viscoelastic solutions and Pickering emulsions. This surfactant exhibited excellent CO2-responsive performance in water and formed a viscoelastic solution. This viscoelastic system was investigated by dynamic light scattering (DLS), rheology, and cryogenic transmission electron microscopy (Cory-TEM). The shear viscosity of the system increased by 3-4 orders of magnitude after bubbling with CO2 and a large number of elongated, flexible, tubular wormlike micelles were observed. Further, Pickering emulsions were prepared by C16MPAN+ synergistically with cellulose nanocrystals (CNCs), whose stability and switchability were investigated via adsorption isotherm, droplet size, contact angle, and macroscopic photographs. C16MPAN+ was adsorbed with CNCs to form mechanical barriers at the oil-water interface, making the emulsion stable for at least three months, and desorption from CNCs enabled emulsion breaking. The cycle could be switched reversibly multiple times and the particle size distribution of emulsion was basically the same. This work enriches the application of biomass resources in intelligent responsive materials.

Identification of molecular subgroups and establishment of risk model based on the response to oxidative stress to predict overall survival of patients with lung adenocarcinoma.

OBJECTIVE: Oxidative stress is associated with the occurrence and development of lung cancer. However, the specific association between lung cancer and oxidative stress is unclear. This study aimed to investigate the role of oxidative stress in the progression and prognosis of lung adenocarcinoma (LUAD). METHODS: The gene expression profiles and corresponding clinical information were collected from GEO and TCGA databases. Differentially expressed oxidative stress-related genes (OSRGs) were identified between normal and tumor samples. Consensus clustering was applied to identify oxidative stress-related molecular subgroups. Functional enrichment analysis, GSEA, and GSVA were performed to investigate the potential mechanisms. xCell was used to assess the immune status of the subgroups. A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets. RESULTS: A total of 40 differentially expressed OSRGs and two oxidative stress-associated subgroups were identified. Enrichment analysis revealed that cell cycle-, inflammation- and oxidative stress-related pathways varied significantly in the two subgroups. Furthermore, a risk model was developed and validated based on the OSRGs, and findings indicated that the risk model exhibits good prediction and diagnosis values for LUAD patients. CONCLUSION: The risk model based on the oxidative stress could act as an effective prognostic tool for LUAD patients. Our findings provided novel genetic biomarkers for prognosis prediction and personalized clinical treatment for LUAD patients.