Repurposing Diphenhydramine in Non-Small Cell Lung Cancer: A Mitochondrial Redox Mechanism and a Derived Prognostic Gene Signature

Repurposing Diphenhydramine in Non-Small Cell Lung Cancer: A Mitochondrial Redox Mechanism and a Derived Prognostic Gene Signature | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Repurposing Diphenhydramine in Non-Small Cell Lung Cancer: A Mitochondrial Redox Mechanism and a Derived Prognostic Gene Signature hao Liu, Yue Pan, Meiyu Huang, Weitao Huang, Bingwen Zhang, Chu Sun, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8900171/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Diphenhydramine (DIPH) is a widely used first-generation H1 antihistamine. While multiple antihistamines have recently shown noteworthy anticancer potential, the therapeutic efficacy of DIPH in non-small cell lung cancer (NSCLC) and its underlying mechanisms of action remain unreported. Here, we demonstrate that DIPH potently suppresses the viability, clonogenicity, migration, and invasion of non‑small cell lung cancer (NSCLC) cells. Mechanistically, DIPH induces mitochondrial dysfunction and consequent redox imbalance, culminating in DNA damage and activation of the intrinsic apoptotic pathway—a cascade further validated in patient‑derived lung cancer organoids. To investigate the potential clinical implications of DIPH, we examined DIPH‑related genes (DRGs) in The Cancer Genome Atlas (TCGA) cohort of lung adenocarcinoma (LUAD, the predominant NSCLC subtype). This analysis revealed two DRG‑based molecular subtypes with significantly distinct survival outcomes and enabled the construction of a robust DRG‑derived prognostic signature. The signature served as an independent predictor of survival and was associated with specific alterations in the tumor immune microenvironment. Collectively, our findings not only report the first evidence of DIPH’s antitumor efficacy in NSCLC but also delineate its underlying mitochondria–redox–DNA damage axis, thereby establishing a translational framework that links this drug‑induced pathway to a clinically actionable molecular signature in LUAD. Biological sciences/Cancer Health sciences/Oncology Diphenhydramine Non-small cell lung cancer Mitochondrial apoptosis DNA damage response Redox homeostasis Prognostic signature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Lung cancer remains the leading cause of cancer-related death worldwide, and non–small cell lung cancer (NSCLC) accounts for roughly 85% of all cases. Within NSCLC, lung adenocarcinoma (LUAD) is the most common histological subtype and contributes to nearly 40% of lung cancer mortality ( 1 , 2 ). Despite significant advances in targeted therapies and immunotherapy, outcomes for patients with advanced or metastatic LUAD are still far from satisfactory, underscoring an urgent need for novel and accessible treatment strategies ( 3 , 4 ). In this context, drug repurposing—the application of approved drugs to new disease indications—has gained considerable traction as a viable approach to accelerate anticancer drug development ( 5 ). Since these repurposed agents already have well-established pharmacokinetic profiles and safety records, this approach can cut down on both time and costs when compared to developing entirely new drugs from scratch( 6 ). Several widely used medications, including statins ( 7 ), metformin ( 8 ), and certain antidepressants ( 9 ) for their potential anticancer effects across various malignancies. Intriguingly, antihistamines, a class of drugs with extensive clinical use for allergic conditions, have recently attracted attention for their off-target antitumor properties ( 10 ), positioning them as a promising yet underexplored resource for oncology repurposing. We focused on diphenhydramine (DIPH), an FDA-approved, first-generation H1 antagonist that has been used for decades to treat allergies and insomnia and is also familiar to oncology practice through its use in chemotherapy-induced nausea/vomiting and supportive care settings ( 11 – 14 ). Its longstanding approval, broad utility in clinical oncology supportive care, and well-characterized safety profile render it a strategically advantageous candidate for direct anticancer drug repurposing. While preliminary evidence, including a study in melanoma ( 15 ), suggests DIPH possesses off‑target antitumor properties, its efficacy in lung cancer remains unverified, and the molecular basis for any potential activity in this context is entirely unknown. To address this, we explored a mechanistic hypothesis centered on the recognized vulnerability of cancer cells to mitochondrial and redox stress( 16 – 18 ). Cancer cells, including NSCLC, frequently operate under constitutive metabolic and oxidative stress, relying on adaptive antioxidant systems to maintain viability—a therapeutic opportunity termed “redox vulnerability”( 19 , 20 ). For this reason, we hypothesized that DIPH could exert antitumor effects in NSCLC by inducing mitochondrial dysfunction and disrupting redox homeostasis, thereby leveraging this vulnerability to trigger intrinsic apoptosis. To explore the clinical translation potential of DIPH, we performed a study on a panel of diphenhydramine-related genes (DRGs) from PubChem in the lung adenocarcinoma (LUAD) cohort of The Cancer Genome Atlas (TCGA).By integrating computationally predicted drug-related genes with large-scale clinical transcriptome datasets, ( 21 – 23 ) this approach facilitates the identification of LUAD prognostic biomarkers, reveals the heterogeneity of drug sensitivity in LUAD, guides rational patient selection for drug repurposing, and infers actionable biological insights ( 21 – 23 ). Our analysis delineated two clinically distinct molecular subtypes based on DRG expression patterns, which were associated with markedly different survival probabilities. Leveraging this molecular stratification, we further derived a parsimonious DRG‑based prognostic signature. This signature not only independently predicted patient survival but also delineated a specific immunosuppressive microenvironment in high‑risk subgroups, thereby linking DIPH‑associated molecular features to both clinical outcome and tumor immune context. Collectively, these results extend DIPH’s therapeutic scope beyond cellular models and offer a clinically informed, gene-expression-driven framework to identify the LUAD patients who are most suitable for future studies into antihistamine repurposing. Materials and Methods Cell lines and chemicals Human non‑small cell lung cancer (NSCLC) cell lines A549, H1299, H1975, HCC827, and PC9 were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cells were maintained in RPMI‑1640 medium (Gibco, Inchinnan, UK) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin‑streptomycin (Gibco) at 37°C in a humidified atmosphere with 5% CO₂. All cell lines were authenticated and routinely tested to confirm the absence of mycoplasma contamination. Diphenhydramine (DIPH) and N‑acetylcysteine (NAC) were purchased from MedChemExpress (MCE, Shanghai, China). Stock solutions (10 mM) were prepared in dimethylsulfoxide (DMSO), aliquoted, and stored at -20°C. Working concentrations were freshly diluted in complete culture medium prior to each experiment. Cell viability and half‑maximal inhibitory concentration (IC₅₀) determination Cells were seeded into 96‑well plates (1 × 10⁴ cells/well) and allowed to adhere overnight. After treatment with DIPH (0–1000 µM) for 48 h, cell viability was assessed using the Cell Counting Kit‑8 (CCK‑8; MedChemExpress) according to the manufacturer’s protocol. Absorbance at 450 nm was measured using a BioTek microplate reader (Winooski, VT, USA). IC₂₅, IC₅₀, and IC₇₅ values were calculated via nonlinear regression using GraphPad Prism software (version 10.6.1). Clonogenic assay Cells were seeded into 6‑well plates at a low density (200 cells/well) and allowed to attach overnight. Subsequently, cells were treated with the indicated concentrations of DIPH for 48 h. After treatment, the medium was replaced with fresh drug‑free complete medium, and cells were cultured undisturbed for an additional 12 days. Colonies were then fixed with 4% paraformaldehyde, stained with 0.5% crystal violet, and counted (a colony defined as > 50 cells). The surviving fraction was calculated as: (number of colonies / number of cells seeded) × 100%. Wound-healing assay Cells were seeded into 96-well plates and grown to confluence. A uniform scratch was generated using a WoundMaker™ scratch tool (Essen BioScience, Ann Arbor, MI, USA). Wound images were acquired at 0, 24, 48, and 72 h using an Incucyte live-cell imaging system (Essen BioScience, Ann Arbor, MI, USA), and wound closure was quantified by measuring changes in scratch width over time. Transwell invasion assay Cell invasion was evaluated using Matrigel‑coated Transwell chambers (Corning, Corning, NY, USA). Cells suspended in serum‑free medium were seeded into the upper chamber, while medium containing 10% FBS was added to the lower chamber as a chemoattractant. After 24 h, non‑invading cells were removed, and cells that had invaded the lower membrane were fixed, stained with crystal violet, and imaged using an EVOS M7000 microscope (Thermo Fisher Scientific, Waltham, MA, USA). Invaded cells were quantified using ImageJ software (version 1.53t, NIH). Cell-based caspase-3/7 activity and ATP assays Caspase‑3/7 activity and ATP content were measured using the Caspase‑Glo® 3/7 Assay and CellTiter‑Glo® 2.0 Assay (Promega, Madison, WI, USA), respectively, following the manufacturer’s instructions. Luminescence was recorded using a microplate reader (BioTek). patient-derived lung cancer organoids Fresh tumor tissues from surgical resections (pathologically confirmed by H&E staining) were obtained with ethical approval from the Institutional Review Board of The First Affiliated Hospital of Guangzhou Medical University (No. 2021-95). Written informed consent was obtained from all patients and/or their legal guardians prior to sample collection. Tissues were minced, enzymatically digested, filtered, and centrifuged to obtain single-cell suspensions. Cells were resuspended in cold Matrigel and seeded as domes on pre-warmed plates. After polymerization, domes were overlaid with lung cancer organoid culture medium and maintained at 37°C with 5% CO₂. For drug response assessment, established organoids were treated with DIPH at concentrations equivalent to the IC₅₀ (M-DIPH) or ten times the IC₅₀ (10×M-DIPH) determined in A549 cells. After 72 hours of treatment, intracellular ATP levels were quantified using the CellTiter-Glo® 2.0 Assay (Promega) following the manufacturer's protocol. Western blot analysis Western blot analysis Cells were lysed in RIPA buffer (Beyotime, Shanghai, China) containing protease and phosphatase inhibitors. Protein concentration was determined using a BCA assay kit (Thermo Fisher Scientific). Equal amounts of protein were separated by SDS‑PAGE and transferred to PVDF membranes (Millipore, Billerica, MA, USA). After blocking with 5% non‑fat milk, membranes were incubated overnight at 4°C with primary antibodies against γ‑H2AX, p‑ATM, p‑CHK1, p‑CHK2, c-Casp3, c-Casp9, Cyt‑c, BAX, CAT, SOD2, CD31, Ki67 (Cell Signaling Technology, Danvers, MA, USA, or Abmart, Shanghai, China; all 1:1000), and GAPDH (1:5000). After incubation with HRP‑conjugated secondary antibodies (Abmart; 1:5000), signals were developed with ECL substrate and visualized using a ChemiDoc™ XRS+ system (Bio‑Rad, Hercules, CA, USA). ROS detection Intracellular ROS levels were assessed using the fluorescent probe 2′,7′‑dichlorodihydrofluorescein diacetate (DCFH‑DA; Beyotime). After treatment, cells were incubated with 10 µM DCFH‑DA in serum‑free medium at 37°C for 20 min in the dark, washed with PBS, and immediately imaged using a fluorescence microscope (Zeiss Axiovert 25, Germany). TUNEL assay Cell apoptosis was evaluated using a TUNEL assay kit (YEASEN, Shanghai, China). After treatment, cells were fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton X-100, and incubated with the TUNEL reaction mixture according to the manufacturer’s protocol. Nuclei were counterstained with DAPI (Beyotime, Shanghai, China). Fluorescence images were captured using a fluorescent microscope (Zeiss). Syngeneic tumor model and imaging procedures All animal procedures were approved by the Laboratory Animal Use Management Committee of the Experimental Animal Institute of Guangzhou Medical University (approval numbers: 2021-042, 2021 − 167, and 2022 − 223) and were performed in accordance with the relevant guidelines and regulations. All methods are reported in accordance with ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) ( 24 ). Male C57BL/6 mice were used to establish a syngeneic lung cancer model. Lewis lung carcinoma (LLC) cells (1 × 10⁶ cells per mouse) were suspended in phosphate-buffered saline (PBS) and subcutaneously inoculated into the right axilla. Once tumors became palpable, mice were randomly assigned to either a vehicle control group (PBS) or a diphenhydramine (DIPH) treatment group. DIPH was administered intraperitoneally at 20 mg/kg once daily for 14 consecutive days. All mice were housed under specific pathogen-free conditions. At the study endpoint, mice were anesthetized with isoflurane (induction at 3–5% and maintenance at 1–2% in oxygen), followed by intraperitoneal injection of D-luciferin (Yeasen Biotechnology, Shanghai, China; 150 mg/kg). Ten minutes after luciferin injection, tumor growth was assessed by in vivo imaging using the IVIS Spectrum small-animal imaging system (PerkinElmer, Waltham, MA, USA) under standardized exposure settings. Following imaging, mice were euthanized by cervical dislocation while still under deep anesthesia, and tumors were excised and photographed to compare gross morphology between groups. Immunofluorescence staining Excised tumor tissues were fixed in 4% paraformaldehyde, paraffin-embedded, and sectioned. After deparaffinization and antigen retrieval, sections were blocked and incubated overnight at 4°C with primary antibodies against CD31, Ki67, and c-Casp3 (all from Cell Signaling Technology; 1:200). The next day, sections were washed and incubated with the corresponding fluorescent secondary antibodies (Beyotime; 1:1000), followed by nuclear counterstaining with DAPI. Fluorescence images were acquired using a fluorescence microscope under standardized settings (Zeiss). Hematoxylin and eosin (H&E) staining At the end of treatment, major organs (heart, liver, spleen, lung, and kidney) were harvested, fixed in 4% paraformaldehyde, paraffin‑embedded, and sectioned. Sections were deparaffinized, rehydrated, and stained with hematoxylin and eosin according to standard histological procedures. Stained sections were examined under a Nikon Eclipse E200 light microscope (Nikon, Tokyo, Japan) to evaluate potential DIPH‑associated tissue toxicity. Data acquisition Candidate targets potentially modulated by diphenhydramine were systematically extracted from the PubChem database PubChem( https://pubchem.ncbi.nlm.nih.gov ). LUAD bulk RNA-sequencing transcriptomic datasets, genomic mutation profiles, and the matched clinical annotations were obtained from The Cancer Genome Atlas (TCGA; https://cancergenome.nih.gov/ ) and GSE72094 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72094 ) were obtained to gain gene expression and clinical data of validation cohorts from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). All downstream bioinformatic analyses and data visualizations were implemented using R software version 4.5.2. Consensus Clustering of Diphenhydramine-Related Genes (DRGs) Diphenhydramine-related genes (DRGs) were first intersected with the LUAD transcriptome, and differentially expressed candidates between tumor and adjacent normal tissues were identified using the limma package, with genes meeting a significance threshold of P < 0.05 retained for subsequent analyses. Cox regression (coxph) was applied to each gene, and genes meeting P < 0.05 were retained. Hazard ratios with 95% CIs were calculated. The expression matrix of these DRGs was then used as the input for unsupervised consensus clustering to define DRG-associated molecular subtypes. Consensus clustering was performed with the ConsensusClusterPlus package in R, applying a k-means clustering algorithm with Euclidean distance. The optimal k was determined by visual inspection of the cumulative distribution function (CDF) curves, delta area plots, and consensus heatmaps, and two robust clusters were ultimately selected for downstream analyses. Qualification and validation of the DRGs prognostic signature Univariate Cox proportional hazards modeling was applied to screen diphenhydramine-associated candidate genes with prognostic relevance. Genes passing P < 0.05 were advanced to LASSO–Cox regularized regression for multigene risk-model derivation. The penalty parameter λ was optimized using the minimum cross-validated error criterion, and gene-specific coefficients were incorporated into the linear risk score: Risk score = Σ (Gene expression × LASSO coefficient). TCGA and GEO LUAD patients were stratified into high- and low-risk subsets using the median score as the cut-point. Overall-survival differences were evaluated via Kaplan–Meier estimation within the survival and survminer environments. Model discrimination was quantified through time-dependent ROC analysis, while multivariate Cox adjustment tested independence across age, sex, tumor stage, and risk score. Analysis of immune cell infiltration The ESTIMATE computational framework was employed to deconvolute the non-malignant cellular components of the tumor microenvironment, generating Stromal score, Immune score, and an aggregated ESTIMATE score, along with inferred tumor purity. Microenvironmental cell abundance was interpreted as positively concordant with score magnitude. To further resolve immune-cell composition, CIBERSORT was applied using a 22-immune-subset reference signature to quantify relative infiltration levels within LUAD specimens. Statistical analysis All statistical analyses were performed using GraphPad Prism (version 10.6.1) and R (version 4.5.2). Data are presented as mean ± SEM of independent biological replicates (where n represents the total number of samples pooled from at least three independent experiments). Differences between two groups were assessed with an unpaired two‑tailed Student t test. For comparisons among more than two groups, one‑way ANOVA with Tukey’s post hoc test was applied. Statistical significance is denoted as * P < 0.05, ** P < 0.01. Results Figure 1 | DIPH inhibits proliferation, clonogenicity, migration, and invasion of NSCLC cells. To evaluate the antitumor potential of diphenhydramine (DIPH), we first determined its half-maximal inhibitory concentration (IC₅₀) in a panel of five human NSCLC cell lines (A549, PC9, H1299, H1975, and HCC827). Based on their relatively higher sensitivity (lower IC₅₀ values), A549, PC9, and HCC827 cells were selected for subsequent functional analyses (Fig. 1 A). Clonogenic assays demonstrated that DIPH potently and dose-dependently suppressed the long-term proliferative capacity of all three cell lines, using concentrations corresponding to IC₂₅ (designated as low‑dose DIPH, L‑DIPH), IC₅₀ (medium‑dose, M‑DIPH), and IC₇₅ (high‑dose, H‑DIPH) (Fig. 1 B). Beyond proliferation, DIPH significantly impaired the malignant invasive and migratory phenotypes critical for metastasis. Transwell invasion assays revealed a marked reduction in the invasive capacity of treated cells (Fig. 1 C). Consistently, wound-healing assays showed that DIPH substantially impeded cell migration, delaying wound closure at 48 hours post-treatment (Fig. 1 D- 1 E). These results establish that DIPH broadly suppresses core oncogenic behaviors of NSCLC cells in vitro, including proliferation, clonogenic survival, invasion, and migration. Figure 2 | DIPH induces mitochondrial dysfunction, DNA damage, and mitochondrial apoptosis in NSCLC cells and patient-derived organoids. Having established the broad anti-tumor phenotypic effects of DIPH, we next investigated whether these effects were mediated through the induction of programmed cell death. Caspase-3/7 activity assays revealed a concentration-dependent increase in apoptotic execution in sensitive A549 and PC9 cells following DIPH treatment (Fig. 2 A). To delineate the pathway involved, we assessed key mitochondrial apoptosis markers. Western blotting showed increased levels of cleaved caspase-3 (c-Casp3) and cleaved caspase-9 (c-Casp9), cytochrome c (Cyt c) release, and upregulation of the pro-apoptotic protein BAX (Fig. 2 B). Consistently, DIPH treatment significantly reduced intracellular ATP levels (Fig. 2 C), indicating mitochondrial functional impairment. Collectively, these molecular alterations, together with the ATP depletion phenotype, provide convergent evidence that DIPH triggers mitochondrial dysfunction and activates the intrinsic (mitochondria-dependent) apoptotic pathway in NSCLC cells. Concurrently, key phosphorylation markers of the DNA damage response ( 25 , 26 )—including γ-H2AX, phosphorylated ATM (Ser1981; p-ATM), CHK1 (Ser345; p-CHK1), and CHK2 (Thr68; p-CHK2)—were markedly increased (Fig. 2 D), indicating significant genotoxic stress upon DIPH exposure. To validate the translational relevance of these findings, we employed patient-derived lung cancer organoids. DIPH treatment induced profound morphological disruption within 24 hours and significantly reduced ATP levels by 72 hours (Fig. 2 E), mirroring the mitochondrial dysfunction and cytotoxic effects observed in monolayer cultures. Taken together, these data demonstrate that DIPH are mediated through the induction of mitochondrial dysfunction and DNA damage, which converge to activate the mitochondrial apoptotic pathway in NSCLC models, including patient-relevant systems. Figure 3 | DIPH disrupts redox homeostasis via mitochondrial dysfunction to drive apoptosis in NSCLC cells. Given that mitochondrial dysfunction is a key inducer of redox imbalance, and that sustained redox imbalance can further damage mitochondrial membranes and DNA—thereby triggering mitochondrial-dependent apoptosis—we further explored the role of redox homeostasis disruption in DIPH-mediated apoptosis( 27 , 28 ). DCFH-DA fluorescence assays showed that DIPH treatment increased intracellular ROS levels in A549 cells in both low- and medium-dose groups (Fig. 3 A). Western blot analysis further showed decreased levels of the antioxidant enzymes CAT and SOD2 in A549 and PC9 cells after DIPH exposure, suggesting impaired cellular antioxidant defenses (Fig. 3 B). These results indicate that DIPH disrupts the cellular redox balance. To functionally assess whether this redox disruption is central to DIPH-induced apoptosis, we co-treated cells with the ROS scavenger N-acetylcysteine (NAC). NAC co-treatment partially rescued DIPH-induced suppression of colony formation (Fig. 3 C). TUNEL staining showed that NAC reduced DIPH-induced apoptotic signals in A549 cells (Fig. 3 D). Furthermore, co-treatment with NAC significantly attenuated the DIPH-induced increase in caspase‑3/7 activity and partially restored the DIPH-suppressed cellular ATP levels in A549 cells (Fig. 3 E). Taken together, these findings demonstrate that DIPH induces mitochondrial dysfunction, which leads to a critical disruption of redox homeostasis. This redox imbalance is functionally required for the subsequent mitochondrial apoptotic pathway in NSCLC cells. Figure 4 | DIPH suppresses tumor growth and induces apoptosis in vivo with a favorable therapeutic window. To translate our in vitro findings into a physiologically relevant context, we evaluated the antitumor efficacy of DIPH using a syngeneic lung cancer model. Lewis lung carcinoma (LLC) cells were implanted subcutaneously into immunocompetent C57BL/6 mice. Bioluminescence imaging (Fig. 4 A) and direct examination of excised tumors (Fig. 4 B) consistently showed that tumor growth was significantly suppressed in DIPH-treated mice compared to the vehicle control group. Immunofluorescence analysis of tumor sections provided mechanistic insight into this growth inhibition. Tumors from DIPH-treated mice exhibited a marked increase in c-Casp3 alongside a pronounced decrease in the proliferation marker Ki67 (Fig. 4 C), indicating that DIPH promotes apoptosis while simultaneously inhibiting proliferative activity in vivo. To investigate whether anti-angiogenic effects contributed to tumor suppression, we performed CD31 staining. No significant difference in microvessel density was observed between the treatment and control groups (Fig. 4 D), suggesting that the antitumor effect of DIPH is not primarily mediated by the inhibition of tumor vascularization. Notably, despite its potent pro-apoptotic effect within tumors, DIPH treatment showed no evidence of systemic toxicity. Comprehensive histological assessment of major organs—including the heart, liver, spleen, lungs, and kidneys—revealed normal architecture without signs of inflammation, necrosis, or other pathological alterations (Fig. 4 E). This dissociation between tumor cytotoxicity and systemic safety aligns with our mechanistic model, wherein DIPH selectively exploits the inherent redox vulnerability of cancer cells. We propose that the constitutively stressed redox state of NSCLC cells lowers their threshold for DIPH-induced mitochondrial dysfunction and redox imbalance, leading to apoptotic death. In contrast, normal tissues with robust redox homeostasis and reserve capacity are likely more resilient to such perturbation, thereby establishing a favorable therapeutic window for DIPH. Figure 5 | Identification and clinical relevance of diphenhydramine-related molecular subtypes in LUAD To explore the potential clinical implications of DIPH, we performed integrative bioinformatic analyses to define the molecular landscape associated with diphenhydramine (DIPH) in lung adenocarcinoma (LUAD). A total of 183 DRGs were curated from PubChem. Integrative transcriptomic profiling of LUAD in TCGA revealed 52 genes with significant tumor-versus-normal differential expression (Fig. 5 A). 23 DRGs exhibited prognostic significance on univariate Cox screening, including 6 risk-associated (HR > 1) and 17 survival-favorable genes (HR < 1) (Fig. 5 B). Notably, functional enrichment of these prognostic DRGs highlighted pathways implicated in cell survival, stress response, apoptosis, and motility—processes directly modulated by DIPH in our experimental models, thereby establishing a molecular link between the drug’s action and patient tumor biology (Figure S1 ). We next investigated whether these DRGs could define biologically coherent patient subsets. Correlation analysis revealed predominantly positive co-expression among the prognostic DRGs (Fig. 5 C). Consensus clustering based on their expression profiles robustly identified two distinct molecular subtypes, designated Cluster A (n = 397) and Cluster B (n = 558) (Figs. 5 D-E), which were clearly separated in principal component analysis (Fig. 5 F). Critically, patients in Cluster B had a significantly poorer overall survival compared to those in Cluster A (Fig. 5 G), establishing the clinical prognostic value of this DRG-based classification. The distribution of key clinical features across these subtypes is summarized in Fig. 5 H- 5 I. Figure 6 | Construction and validation of a DRG-based prognostic risk signature in LUAD To develop a quantitative tool for prognosis, we constructed a parsimonious risk signature from the prognostic DRGs. Using LASSO-Cox regression, we refined the 23 survival-associated DRGs into a 7-gene signature comprising CHRM2, ADRA1A, ADRB1, CHRNA4, NTSR1, BCHE, and SLC7A11 (Figs. 6 A-B). A risk score was calculated for each patient in the TCGA and an independent GEO cohort. Patients stratified into high- and low-risk groups based on the median score exhibited significantly divergent survival, with the high-risk group demonstrating markedly poorer OS (Fig. 6 C). The signature’s predictive accuracy was supported by time-dependent ROC analysis (Fig. 6 D). Multivariate Cox regression confirmed that the risk score was an independent prognostic factor, even after adjusting for other clinical variables (Fig. 6 E). This molecular risk framework showed strong internal consistency. The high-risk group was enriched for the expression of specific signature genes (CHRNA4, NTSR1, SLC7A11) and was predominantly composed of patients from the poor-prognosis Cluster B (Figs. 6 F-G). Sankey diagram visualization confirmed this alignment between cluster membership, risk stratification, and survival outcome (Fig. 6 H). Furthermore, a nomogram integrating the risk score with clinical factors showed good calibration (Fig. 6 I-J), and decision curve analysis demonstrated its superior net clinical benefit over traditional clinical parameters alone (Fig. 6 K-L), underscoring the signature’s potential clinical utility. Figure 7 | The DRG-based risk signature delineates distinct tumor immune microenvironment landscapes Given the interplay between tumor cell biology and host immunity, we investigated whether the DRG-based risk stratification corresponded to differences in the tumor immune microenvironment (TIME). CIBERSORT deconvolution revealed distinct immune cell infiltration patterns between the high- and low-risk groups (Fig. 7 A). Consistently, ESTIMATE algorithm scores (StromalScore, ImmuneScore, ESTIMATEScore) were significantly lower in the high-risk group (Fig. 7 B), indicating an overall “immune-cold” phenotype characterized by reduced stromal and immune cell presence and higher tumor purity. Further analysis delineated the specific immune context. Correlation analysis uncovered structured relationships among immune subsets (Fig. 7 C). Importantly, significant associations were observed between the seven core DRG signature genes, the risk score, and the abundance of specific immune populations (Fig. 7 D). Direct comparison confirmed significant depletion of cytotoxic CD8⁺ T cells and enrichment of immunosuppressive M2 macrophages in high-risk tumors (Figs. 7 E-L). These results establish that the DRG-based prognostic stratification is underpinned by a profoundly altered and potentially less immunogenic tumor immune microenvironment. In conclusion, This study demonstrates that diphenhydramine (DIPH) suppresses lung cancer by inducing mitochondrial redox collapse and apoptosis, which is further validated in a syngeneic mouse model. We derived a DIPH-related gene signature that stratifies patients and correlates with an immunosuppressive microenvironment, offering a mechanistic and translational foundation for drug repurposing in precision oncology (Fig. 8 ). Discussion As an important bioactive amine, histamine plays a complex and crucial role in the occurrence, development, and tumor microenvironment regulation of cancer ( 29 – 31 ). Studies have shown that various tumor cells themselves or immune cells in the tumor microenvironment (especially mast cells) can synthesize and release histamine in large quantities ( 32 ). Histamine mainly exerts its effects by binding to its four G protein-coupled receptors (H1R–H4R), regulating tumor cell proliferation, survival, migration, angiogenesis, and immune responses ( 33 , 34 ). Among these receptors, H1R is highly expressed in many cancer cells and is considered to have a pro-tumorigenic effect; its activation can promote tumor growth and immune suppression ( 35 , 36 ). In addition, histamine can help tumor cells evade immune surveillance by regulating the polarization of tumor-associated macrophages, inhibiting T cell function, and promoting the expression of immune checkpoint molecules (such as VISTA) ( 37 – 39 ). Therefore, histamine and its signaling pathways have become a highly concerned biological target in cancer therapy. Based on the important role of histamine in cancer, histamine receptor antagonists (actually inverse agonists), represented by H1 antihistamines, are being re-evaluated for their potential in tumor therapy ( 40 – 42 ). Studies have shown that certain H1 antihistamines, especially newer-generation agents such as fexofenadine, loratadine, and desloratadine, can not only inhibit tumor growth by blocking H1R but also exert anticancer effects through multiple receptor-independent mechanisms. These mechanisms include: Enhancing antitumor immunity: Reversing histamine-mediated T cell exhaustion, promoting M1 macrophage polarization, and producing synergistic effects with immune checkpoint inhibitors (e.g., anti-PD-1/CTLA-4 therapy) ( 39 , 43 ); Inducing tumor cell death: Particularly, cationic amphiphilic antihistamines can disrupt lysosomal stability, induce lysosomal membrane permeabilization (LMP), and trigger lysosome-dependent cell death (LCD) ( 44 , 45 ); Overcoming drug resistance: They can sensitize tumor cells to conventional chemotherapeutic agents (e.g., vinorelbine, docetaxel) ( 46 , 47 ). Directly inhibiting proliferation and inducing apoptosis: Through pathways such as affecting the cell cycle, inducing mitochondrial dysfunction, and causing DNA damage ( 48 , 49 ). Therefore, the "repurposing" of existing antihistamines for cancer therapy represents a highly promising strategy for rapid translational research. Building on our previous work, we focused on elucidating the distinctive anticancer mechanism of a class of H1 antihistamines—diphenhydramine (DIPH)—in non–small cell lung cancer (NSCLC), with the aim of further expanding the drug-repurposing potential of this class. We found that the antitumor effects of DIPH differ from those reported for other antihistamines, which often act via immunomodulation or lysosome-dependent pathways. Instead, DIPH disrupts tumor-cell metabolic homeostasis and triggers a lethal “mitochondria–redox–DNA damage” cascade. Specifically, DIPH induces rapid mitochondrial dysfunction, leading to ATP depletion and subsequent catastrophic imbalance of redox homeostasis, which is characterized by a sharp accumulation of reactive oxygen species (ROS) and the exhaustion of key antioxidant enzymes (e.g., CAT, SOD2). This redox crisis further triggers DNA damage and forms a self-amplifying positive feedback loop therewith, ultimately and irreversibly driving cells into the intrinsic apoptotic pathway. This core mechanism—the "mitochondrial dysfunction–redox imbalance–DNA damage" axis—has been validated in patient-derived organoid models. Functionally, the antioxidant NAC can significantly reverse the DIPH-induced cytotoxic effect, confirming that redox dysregulation is the central hub of its action. Notably, the pro-oxidative effect exhibited by DIPH here corroborates the therapeutic concept of "redox vulnerability": due to persistent metabolic stress, tumor cells maintain a fragile redox balance, rendering them selectively sensitive to further oxidative insults (such as those induced by DIPH). This finding provides a novel perspective for the anticancer mechanism of antihistamines. To further explore the clinical application potential of diphenhydramine, we employed a bioinformatic strategy that links DIPH-associated molecular signatures to patient prognostic outcomes. Analysis of diphenhydramine-related genes (DRGs) in the TCGA-LUAD cohort identified two prognostic molecular subtypes and enabled the construction of a robust 7-gene prognostic signature panel. This signature panel independently predicted survival outcomes and was concordant with a high-risk subgroup characterized by an immunosuppressive ("immune-cold") tumor microenvironment, which featured reduced CD8⁺ T-cell infiltration and increased M2 macrophage abundance. This association between the mechanistic footprint of DIPH and specific immune landscapes provides a translational framework for patient stratification. This indicates that future clinical evaluations of DIPH could be conducted by selecting patients whose tumors harbor this high-risk, immunosuppressive molecular signature, thereby increasing the likelihood of detecting clinical benefits. Future research should focus on the following key directions:(i) Identifying the precise molecular targets of DIPH on mitochondria;(ii) Validating its therapeutic efficacy in patient-derived xenograft models representative of the identified high-risk subtype;(iii) Exploring potential synergistic combinations, particularly with immunotherapies, given the essential role of the immune microenvironment associated with the DRG signature. In addition, systematic comparative studies are warranted to investigate the performance of different antihistamines across various cancer models. Such studies will help clarify their mechanisms of action and identify the most promising candidates for specific indications. Declarations Data availability statement The datasets generated and/or analysed during the current study are available in the following repositories: The Cancer Genome Atlas (TCGA) LUAD dataset: https://cancergenome.nih.gov/. Gene Expression Omnibus (GEO) dataset GSE72094: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72094. All data generated or analysed during this study are included in this published article and its supplementary information files. The diphenhydramine-related genes (DRGs) analysed in this study were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). Further inquiries can be directed to the corresponding author. Author contributions Conceptualization: HJX, LH, PY; Methodology: LH, PY; Investigation: LH, PY, HMY, HWT, ZBW, SC, ZX, ZBH, CJN, DHS, CQ, LHT, FXZ; Visualization: LH, PY, ZX, ZBH, CJN, DHS, CQ, LHT, FXZ; Funding acquisition: HJX; Project administration: HJX, LH, PY; Supervision: HJX; original draft: HJX, LH, PY, HMY, HWT, ZBW, SC; Writing – review & editing: HJX, LH, PY. Declaration of competing interests The authors have no conflicts of interest to declare. Funding This work was Supported by R&D Program of Guangzhou National Laboratory, Grant No. SRPG22-017, 2023ZD0519700. References Siegel, R. L., Miller, K. D., Fuchs, H. E. & Jemal, A. Cancer statistics, 2022. CA Cancer J. Clin. 72 , 7–33 (2022). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71 , 209–249 (2021). Arbour, K. C. & Riely, G. J. Systemic Therapy for Locally Advanced and Metastatic Non-Small Cell Lung Cancer: A Review. Jama 322 , 764–774 (2019). Xia, Y., Sun, M., Huang, H. & Jin, W. L. Drug repurposing for cancer therapy. Signal. Transduct. Target. Ther. 9 , 92 (2024). Weth, F. R. et al. Unlocking hidden potential: advancements, approaches, and obstacles in repurposing drugs for cancer therapy. Br. J. Cancer . 130 , 703–715 (2024). Cheng, F., Hong, H., Yang, S. & Wei, Y. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. Brief. Bioinform . 18 , 682–697 (2017). Jiang, W., Hu, J. W., He, X. R., Jin, W. L. & He, X. Y. Statins: a repurposed drug to fight cancer. J. Exp. Clin. Cancer Res. 40 , 241 (2021). Hua, Y. et al. Metformin and cancer hallmarks: shedding new lights on therapeutic repurposing. J. Transl Med. 21 , 403 (2023). Dong, F. et al. SSRI antidepressant citalopram reverses the Warburg effect to inhibit hepatocellular carcinoma by directly targeting GLUT1. Cell. Rep. 43 , 114818 (2024). Liu, X. et al. Loratidine is associated with improved prognosis and exerts antineoplastic effects via apoptotic and pyroptotic crosstalk in lung cancer. J. Exp. Clin. Cancer Res. 43 , 5 (2024). F, A. L., De Las Vecillas Sanchez, L., Gilbert, L., Copaescu, A. M. & Isabwe, G. A. C. Premedication Protocols to Prevent Hypersensitivity Reactions to Chemotherapy: a Literature Review. Clin. Rev. Allergy Immunol. 62 , 534–547 (2022). Ariza-Salamanca, D. F. et al. Expert Consensus on the Use of Diphenhydramine for Short-Term Insomnia: Efficacy, Safety, and Clinical Applications. J Clin. Med 14 (2025). Hamano, H. et al. Diphenhydramine may be a preventive medicine against cisplatin-induced kidney toxicity. Kidney Int. 99 , 885–899 (2021). Kris, M. G., Gralla, R. J., Clark, R. A., Tyson, L. B. & Groshen, S. Antiemetic control and prevention of side effects of anti-cancer therapy with lorazepam or diphenhydramine when used in combination with metoclopramide plus dexamethasone. A double-blind, randomized trial. Cancer 60 , 2816–2822 (1987). Or, C. R. et al. Diphenhydramine induces melanoma cell apoptosis by suppressing STAT3/MCL-1 survival signaling and retards B16-F10 melanoma growth in vivo. Oncol. Rep. 36 , 3465–3471 (2016). Iqbal, M. J. et al. Interplay of oxidative stress, cellular communication and signaling pathways in cancer. Cell. Commun. Signal. 22 , 7 (2024). Trachootham, D., Alexandre, J. & Huang, P. Targeting cancer cells by ROS-mediated mechanisms: a radical therapeutic approach? Nat. Rev. Drug Discov . 8 , 579–591 (2009). Helfinger, V. & Schröder, K. Redox control in cancer development and progression. Mol. Aspects Med. 63 , 88–98 (2018). Weng, M. S., Chang, J. H., Hung, W. Y., Yang, Y. C. & Chien, M. H. The interplay of reactive oxygen species and the epidermal growth factor receptor in tumor progression and drug resistance. J. Exp. Clin. Cancer Res. 37 , 61 (2018). Wu, X. et al. Discovery of Nonquinone Substrates for NAD(P)H: Quinone Oxidoreductase 1 (NQO1) as Effective Intracellular ROS Generators for the Treatment of Drug-Resistant Non-Small-Cell Lung Cancer. J. Med. Chem. 61 , 11280–11297 (2018). Graves, O. K. et al. Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma. Biomed. Pharmacother . 161 , 114486 (2023). Jassim, A. et al. Gene context drift identifies drug targets to mitigate cancer treatment resistance. Cancer Cell. 43 , 1608–1621e1609 (2025). Zheng, J. et al. Integrating Single-Cell Transcriptome-Wide Mendelian Randomization and Differentially Expressed Gene Analyses to Prioritize Dynamic Immune-Related Drug Targets for Cancers. Adv. Sci. (Weinh) . 12 , e07451 (2025). Percie du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. J. Cereb. Blood Flow. Metab. 40 , 1769–1777 (2020). Bonner, W. M. et al. GammaH2AX and cancer. Nat. Rev. Cancer . 8 , 957–967 (2008). Perona, R., Moncho-Amor, V., Machado-Pinilla, R., Belda-Iniesta, C. & Sánchez Pérez, I. Role of CHK2 in cancer development. Clin. Transl Oncol. 10 , 538–542 (2008). Xu, X., Pang, Y. & Fan, X. Mitochondria in oxidative stress, inflammation and aging: from mechanisms to therapeutic advances. Signal. Transduct. Target. Ther. 10 , 190 (2025). Wu, H., Wei, H., Sehgal, S. A., Liu, L. & Chen, Q. Mitophagy receptors sense stress signals and couple mitochondrial dynamic machinery for mitochondrial quality control. Free Radic Biol. Med. 100 , 199–209 (2016). Massari, N. A., Nicoud, M. B. & Medina, V. A. Histamine receptors and cancer pharmacology: an update. Br. J. Pharmacol. 177 , 516–538 (2020). Falus, A. et al. Paracrine and autocrine interactions in melanoma: histamine is a relevant player in local regulation. Trends Immunol. 22 , 648–652 (2001). Rivera, E. S. et al. Histamine as an autocrine growth factor: an unusual role for a widespread mediator. Semin Cancer Biol. 10 , 15–23 (2000). Castells, M. et al. Mast cell activation syndrome: Current understanding and research needs. J. Allergy Clin. Immunol. 154 , 255–263 (2024). Blaya, B. et al. Histamine and histamine receptor antagonists in cancer biology. Inflamm. Allergy Drug Targets . 9 , 146–157 (2010). Zhao, H. et al. Inflammation and tumor progression: signaling pathways and targeted intervention. Signal. Transduct. Target. Ther. 6 , 263 (2021). Shi, Z. et al. Distinct roles of histamine H1- and H2-receptor signaling pathways in inflammation-associated colonic tumorigenesis. Am. J. Physiol. Gastrointest. Liver Physiol. 316 , G205–g216 (2019). Ma, J. et al. Desloratadine, a Novel Antigrowth Reagent for Bladder Cancer. Technol. Cancer Res. Treat. 19 , 1533033820926591 (2020). Li, H. et al. The allergy mediator histamine confers resistance to immunotherapy in cancer patients via activation of the macrophage histamine receptor H1. Cancer Cell. 40 , 36–52e39 (2022). Nguyen, P. L. & Cho, J. Pathophysiological Roles of Histamine Receptors in Cancer Progression: Implications and Perspectives as Potential Molecular Targets. Biomolecules 11 (2021). Fritz, I., Wagner, P. & Olsson, H. Improved survival in several cancers with use of H(1)-antihistamines desloratadine and loratadine. Transl Oncol. 14 , 101029 (2021). Ellegaard, A. M. et al. Repurposing Cationic Amphiphilic Antihistamines for Cancer Treatment. EBioMedicine 9, 130–139 (2016). Verdoodt, F. et al. Antihistamine use and risk of ovarian cancer: A population-based case-control study. Maturitas 120 , 47–52 (2019). Laverdière, I. et al. Leukemic stem cell signatures identify novel therapeutics targeting acute myeloid leukemia. Blood Cancer J. 8 , 52 (2018). Chiang, C. H. et al. Efficacy of cationic amphiphilic antihistamines on outcomes of patients treated with immune checkpoint inhibitors. Eur. J. Cancer . 174 , 1–9 (2022). Liu, B. et al. Cationic amphiphilic antihistamines inhibit STAT3 via Ca(2+)-dependent lysosomal H(+) efflux. Cell. Rep. 42 , 112137 (2023). Fehrenbacher, N. et al. Sensitization to the lysosomal cell death pathway by oncogene-induced down-regulation of lysosome-associated membrane proteins 1 and 2. Cancer Res. 68 , 6623–6633 (2008). Fernández-Nogueira, P. et al. Histamine receptor 1 inhibition enhances antitumor therapeutic responses through extracellular signal-regulated kinase (ERK) activation in breast cancer. Cancer Lett. 424 , 70–83 (2018). Trybus, E., Król, T. & Trybus, W. The Multidirectional Effect of Azelastine Hydrochloride on Cervical Cancer Cells. Int J. Mol. Sci 23 (2022). de Guadalupe Chávez-López, M., Hernández-Gallegos, E., Vázquez-Sánchez, A. Y., Gariglio, P. & Camacho, J. Antiproliferative and proapoptotic effects of astemizole on cervical cancer cells. Int. J. Gynecol. Cancer . 24 , 824–828 (2014). Matsumoto, N. et al. Histamine H1 receptor antagonists selectively kill cisplatin-resistant human cancer cells. Sci. Rep. 11 , 1492 (2021). Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Editor invited by journal 25 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 23 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8900171","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":602415298,"identity":"6caa81a1-4027-4754-b493-f9d3bd378d2d","order_by":0,"name":"hao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACPgmGxIcfKv7LMc5///BBQkUNYS1sEgyPjSXOMBszN+QwGzw4c4wYLYzPBHjbmBPZG3LYJB+2MBOhRbo5jUGyjS2Bt+HssYrEBjYG/vbuBPxaZI6lPSg4x5Mn2diXdiNxhwyDxJmzGwg4LCfdQKJMotiwmcHsRuIZNgYDiVxCWvK/SfCwGSTuP8ZgVpDYxkyMloQ0CZ62hMTGHh4zBmK1JAMD+YAx4wy2ZImEM8d4CPqFXyIBFJUH5BhnMB/8+KOiRo6/vRe/FgzAQ5ryUTAKRsEoGAVYAQCzCUnOoKkVEgAAAABJRU5ErkJggg==","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"hao","middleName":"","lastName":"Liu","suffix":""},{"id":602415299,"identity":"feedb254-7789-4cd2-b2e9-950cdd8e74ab","order_by":1,"name":"Yue Pan","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Pan","suffix":""},{"id":602415300,"identity":"a6d9a4c8-e8ca-4683-a5bb-c10fb74a4cf5","order_by":2,"name":"Meiyu Huang","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meiyu","middleName":"","lastName":"Huang","suffix":""},{"id":602415301,"identity":"1a5b268c-12e5-4b00-a0c3-bc95a4ba84c9","order_by":3,"name":"Weitao Huang","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weitao","middleName":"","lastName":"Huang","suffix":""},{"id":602415302,"identity":"ed9dde16-1ff0-4c58-89ac-730530562c6c","order_by":4,"name":"Bingwen Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bingwen","middleName":"","lastName":"Zhang","suffix":""},{"id":602415303,"identity":"cb13cade-018f-4dec-8b63-a23ed4a0258c","order_by":5,"name":"Chu Sun","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chu","middleName":"","lastName":"Sun","suffix":""},{"id":602415304,"identity":"c61ad804-6cdb-404b-9758-adf916f5f515","order_by":6,"name":"Xuan Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Zhang","suffix":""},{"id":602415305,"identity":"0b29fec6-717b-47f3-90e3-77571028f525","order_by":7,"name":"Binghao Zhang","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Binghao","middleName":"","lastName":"Zhang","suffix":""},{"id":602415306,"identity":"b75bff9a-cce0-4d67-99c7-71557aa7b621","order_by":8,"name":"Jiani Chen","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiani","middleName":"","lastName":"Chen","suffix":""},{"id":602415307,"identity":"91a8d32b-96b2-44a1-b76f-a03a1c538aed","order_by":9,"name":"Hongsheng Deng","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongsheng","middleName":"","lastName":"Deng","suffix":""},{"id":602415308,"identity":"e695e402-8027-44d6-9683-d0a2aca75aa4","order_by":10,"name":"Qi Cai","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Cai","suffix":""},{"id":602415309,"identity":"1f868eb1-7074-4af9-9dfb-c413bf51d616","order_by":11,"name":"Huiting Liu","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huiting","middleName":"","lastName":"Liu","suffix":""},{"id":602415310,"identity":"207a4939-faf4-4a09-be81-9e958f43424b","order_by":12,"name":"Xianzhe Fan","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xianzhe","middleName":"","lastName":"Fan","suffix":""},{"id":602415311,"identity":"3fb59c4a-7b5f-4b24-92bc-c7f91e6677e6","order_by":13,"name":"Jianxing He","email":"","orcid":"","institution":"First Affiliated Hospital of Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jianxing","middleName":"","lastName":"He","suffix":""}],"badges":[],"createdAt":"2026-02-17 10:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8900171/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8900171/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104780078,"identity":"ffe4e32d-1dfb-48e6-bcd0-bb619389072b","added_by":"auto","created_at":"2026-03-17 07:50:11","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1101033,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDIPH inhibits proliferation, clonogenicity, migration, and invasion of NSCLC cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Measurement of IC₅₀ values of DIPH in five NSCLC cell lines (A549, PC9, H1299, H1975, HCC827). (B) Clonogenic assays performed in A549, PC9, and HCC827 cells treated with DIPH at IC₂₅, IC₅₀, and IC₇₅ concentrations. (C) Transwell invasion assays conducted in A549, PC9 cells after DIPH treatment. (D) Wound-healing assays performed for 72 hours in A549, PC9, and HCC827 cells treated with DIPH. Scale bars : 400 μm.\u003csup\u003e *\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/a79c28f545279c0c2c6e22c0.jpeg"},{"id":104373835,"identity":"62ad3668-c2ba-4175-9f75-8ae9ee70fb67","added_by":"auto","created_at":"2026-03-11 06:03:58","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1023396,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDIPH induces mitochondrial dysfunction, DNA damage, and mitochondrial apoptosis in NSCLC cells and patient-derived organoids.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Caspase-3/7 activity assays in A549, PC9, and H1299. (B) ATP content measurement in A549, PC9, and H1299. (C) Western blot analysis of mitochondrial apoptosis-related proteins (c-Casp3, c-Casp9, Cyt-c, BAX) in A549 and PC9 cells. (D) Western blot analysis of DNA damage-related proteins (γ-H2AX, p-ATM, p-CHK1, p-CHK2) in A549 and PC9 cells after DIPH treatment. (E) Morphology and ATP assessment in patient-derived lung cancer organoids. Scale bars: 200 µm. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/22aeed6e8fddbc733cbecda1.jpeg"},{"id":104406210,"identity":"86f0f35d-23b2-45c3-9993-142f710f9073","added_by":"auto","created_at":"2026-03-11 12:25:03","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":790376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDIPH disrupts redox homeostasis via mitochondrial dysfunction to drive apoptosis in NSCLC cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Intracellular ROS detection using DCFH-DA. Scale bars : 50 μm. (B) Western blot analysis of CAT and SOD2. (C) Clonogenic assays conducted with NAC co-treatment to assess ROS involvement. (D) TUNEL assays performed with NAC co-treatment to examine apoptosis. Scale bars : 50 μm. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/bfd9f4192047f4d635957082.jpeg"},{"id":104373839,"identity":"c8b8c265-d58d-433a-a75a-73c3e0f8f996","added_by":"auto","created_at":"2026-03-11 06:03:58","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1131873,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDIPH suppresses tumor growth and induces apoptosis in vivo with a favorable therapeutic window.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Bioluminescence imaging of LLC tumors in C57BL/6 mice. (B) Ex vivo imaging of harvested tumors. (C) Immunofluorescence staining of c-Casp3 and Ki67. Scale bars: 50 µm (20X), 25 µm (40X). (D) Immunofluorescence staining of CD31. Scale bars: 50 µm (20X), 25 µm (40X). (E) H\u0026amp;E staining of major organs. Scale bars: 50 µm. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/ee43f1771e60898616d2b468.jpeg"},{"id":104406069,"identity":"497a3ca3-6685-42c4-90a3-f2db2da0fd1f","added_by":"auto","created_at":"2026-03-11 12:24:46","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1162016,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and clinical relevance of diphenhydramine-related molecular subtypes in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Differential expression screening of DRGs in TCGA; (B) Univariate Cox analysis of prognostic DRGs; (C) Correlation network among prognostic DRGs; (D) Consensus clustering CDF curve for determining optimal k=2 to 9; (E) Consensus heatmap at k=2; (F) PCA validation of DRG-driven clusters; (G) Kaplan–Meier survival comparison of the two clusters; (H)Differential expression of key prognostic DRGs across subtypes; (I)Distribution of major clinical characteristics between subtypes. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/bde37f34172895745679524c.jpeg"},{"id":104406026,"identity":"1564c955-29fe-4f32-9f0b-15f7bba231e7","added_by":"auto","created_at":"2026-03-11 12:24:35","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":907802,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of a DRG-based prognostic risk signature in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)LASSO coefficient profiles of candidate DRGs; (B)Cross-validation plot for penalty parameter tuning; (C)Kaplan–Meier overall survival comparison by risk groups; (D)Time-dependent ROC curves for model discrimination; (E)Multivariate Cox forest plot for prognostic independence; (F) Heatmap of the 7-gene risk signature across high- vs. low-risk samples; (G)Risk score comparison between consensus subtypes (Cluster A vs. Cluster B); (H)Integrated Sankey diagram; (I)Nomogram calibration curves at 1-, 3- and 5-years; (J)1-year decision curve analysis; (K)3-year decision curve analysis; (L)Composite Cox-nomogram for individualized survival estimation. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/06e864be8b569e30178f7832.jpeg"},{"id":104373842,"identity":"1918695f-9e39-4965-b4c7-b320d41b4bdb","added_by":"auto","created_at":"2026-03-11 06:03:59","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1498311,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe DRG-based risk signature delineates distinct tumor immune microenvironment landscapes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)Immune infiltration landscape by CIBERSORT; (B)Stromal and immune microenvironmental scores; (C)Inter-immune cell correlation matrix; (D)Correlation of immune subsets with 7-gene risk score; (E-L)Immune subset correlations with the continuous DRG risk score. \u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/8f0ba7263aabd2f38734fd44.jpeg"},{"id":104780025,"identity":"fdce992a-e222-4b1e-a6ab-93d04c969272","added_by":"auto","created_at":"2026-03-17 07:49:22","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":503997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic model illustrating the antitumor mechanism and translational framework of diphenhydramine (DIPH) in non‑small cell lung cancer (NSCLC).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe model summarizes the key findings: DIPH induces mitochondrial dysfunction and redox imbalance in NSCLC cells, triggering DNA damage and intrinsic apoptosis (left panel). This antitumor efficacy is confirmed in a syngeneic mouse model without systemic toxicity (middle panel). Translational analysis of DIPH‑related genes (DRGs) in patient cohorts defines prognostic molecular subtypes and a gene signature associated with an immunosuppressive tumor microenvironment, linking the drug’s mechanism to a clinically relevant stratification framework (right panel). Arrows indicate the directional flow from mechanistic discovery to clinical implication.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/ddc2bc880ba8a436db79a394.jpeg"},{"id":104784285,"identity":"af7747f2-e01f-4de8-8c8e-ad4fa570837e","added_by":"auto","created_at":"2026-03-17 08:06:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9637694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/f1ecf9c3-dc5d-410a-a01e-5808127d43ef.pdf"},{"id":104373837,"identity":"1abf5d04-133f-4862-8220-792b39b9e2fe","added_by":"auto","created_at":"2026-03-11 06:03:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":823164,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8900171/v1/f9fa5cc9dc4814cf33c7016d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Repurposing Diphenhydramine in Non-Small Cell Lung Cancer: A Mitochondrial Redox Mechanism and a Derived Prognostic Gene Signature","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains the leading cause of cancer-related death worldwide, and non\u0026ndash;small cell lung cancer (NSCLC) accounts for roughly 85% of all cases. Within NSCLC, lung adenocarcinoma (LUAD) is the most common histological subtype and contributes to nearly 40% of lung cancer mortality (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Despite significant advances in targeted therapies and immunotherapy, outcomes for patients with advanced or metastatic LUAD are still far from satisfactory, underscoring an urgent need for novel and accessible treatment strategies (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In this context, drug repurposing\u0026mdash;the application of approved drugs to new disease indications\u0026mdash;has gained considerable traction as a viable approach to accelerate anticancer drug development (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Since these repurposed agents already have well-established pharmacokinetic profiles and safety records, this approach can cut down on both time and costs when compared to developing entirely new drugs from scratch(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Several widely used medications, including statins (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), metformin (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), and certain antidepressants (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) for their potential anticancer effects across various malignancies. Intriguingly, antihistamines, a class of drugs with extensive clinical use for allergic conditions, have recently attracted attention for their off-target antitumor properties (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e), positioning them as a promising yet underexplored resource for oncology repurposing.\u003c/p\u003e \u003cp\u003eWe focused on diphenhydramine (DIPH), an FDA-approved, first-generation H1 antagonist that has been used for decades to treat allergies and insomnia and is also familiar to oncology practice through its use in chemotherapy-induced nausea/vomiting and supportive care settings (\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Its longstanding approval, broad utility in clinical oncology supportive care, and well-characterized safety profile render it a strategically advantageous candidate for direct anticancer drug repurposing. While preliminary evidence, including a study in melanoma (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), suggests DIPH possesses off‑target antitumor properties, its efficacy in lung cancer remains unverified, and the molecular basis for any potential activity in this context is entirely unknown. To address this, we explored a mechanistic hypothesis centered on the recognized vulnerability of cancer cells to mitochondrial and redox stress(\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Cancer cells, including NSCLC, frequently operate under constitutive metabolic and oxidative stress, relying on adaptive antioxidant systems to maintain viability\u0026mdash;a therapeutic opportunity termed \u0026ldquo;redox vulnerability\u0026rdquo;(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). For this reason, we hypothesized that DIPH could exert antitumor effects in NSCLC by inducing mitochondrial dysfunction and disrupting redox homeostasis, thereby leveraging this vulnerability to trigger intrinsic apoptosis.\u003c/p\u003e \u003cp\u003eTo explore the clinical translation potential of DIPH, we performed a study on a panel of diphenhydramine-related genes (DRGs) from PubChem in the lung adenocarcinoma (LUAD) cohort of The Cancer Genome Atlas (TCGA).By integrating computationally predicted drug-related genes with large-scale clinical transcriptome datasets, (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) this approach facilitates the identification of LUAD prognostic biomarkers, reveals the heterogeneity of drug sensitivity in LUAD, guides rational patient selection for drug repurposing, and infers actionable biological insights (\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Our analysis delineated two clinically distinct molecular subtypes based on DRG expression patterns, which were associated with markedly different survival probabilities. Leveraging this molecular stratification, we further derived a parsimonious DRG‑based prognostic signature. This signature not only independently predicted patient survival but also delineated a specific immunosuppressive microenvironment in high‑risk subgroups, thereby linking DIPH‑associated molecular features to both clinical outcome and tumor immune context. Collectively, these results extend DIPH\u0026rsquo;s therapeutic scope beyond cellular models and offer a clinically informed, gene-expression-driven framework to identify the LUAD patients who are most suitable for future studies into antihistamine repurposing.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCell lines and chemicals\u003c/h2\u003e \u003cp\u003eHuman non‑small cell lung cancer (NSCLC) cell lines A549, H1299, H1975, HCC827, and PC9 were obtained from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cells were maintained in RPMI‑1640 medium (Gibco, Inchinnan, UK) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin‑streptomycin (Gibco) at 37\u0026deg;C in a humidified atmosphere with 5% CO₂. All cell lines were authenticated and routinely tested to confirm the absence of mycoplasma contamination.\u003c/p\u003e \u003cp\u003eDiphenhydramine (DIPH) and N‑acetylcysteine (NAC) were purchased from MedChemExpress (MCE, Shanghai, China). Stock solutions (10 mM) were prepared in dimethylsulfoxide (DMSO), aliquoted, and stored at -20\u0026deg;C. Working concentrations were freshly diluted in complete culture medium prior to each experiment.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell viability and half‑maximal inhibitory concentration (IC₅₀) determination\u003c/h3\u003e\n\u003cp\u003eCells were seeded into 96‑well plates (1 \u0026times; 10⁴ cells/well) and allowed to adhere overnight. After treatment with DIPH (0\u0026ndash;1000 \u0026micro;M) for 48 h, cell viability was assessed using the Cell Counting Kit‑8 (CCK‑8; MedChemExpress) according to the manufacturer\u0026rsquo;s protocol. Absorbance at 450 nm was measured using a BioTek microplate reader (Winooski, VT, USA). IC₂₅, IC₅₀, and IC₇₅ values were calculated via nonlinear regression using GraphPad Prism software (version 10.6.1).\u003c/p\u003e\n\u003ch3\u003eClonogenic assay\u003c/h3\u003e\n\u003cp\u003eCells were seeded into 6‑well plates at a low density (200 cells/well) and allowed to attach overnight. Subsequently, cells were treated with the indicated concentrations of DIPH for 48 h. After treatment, the medium was replaced with fresh drug‑free complete medium, and cells were cultured undisturbed for an additional 12 days. Colonies were then fixed with 4% paraformaldehyde, stained with 0.5% crystal violet, and counted (a colony defined as \u0026gt;\u0026thinsp;50 cells). The surviving fraction was calculated as: (number of colonies / number of cells seeded) \u0026times; 100%.\u003c/p\u003e\n\u003ch3\u003eWound-healing assay\u003c/h3\u003e\n\u003cp\u003eCells were seeded into 96-well plates and grown to confluence. A uniform scratch was generated using a WoundMaker\u0026trade; scratch tool (Essen BioScience, Ann Arbor, MI, USA). Wound images were acquired at 0, 24, 48, and 72 h using an Incucyte live-cell imaging system (Essen BioScience, Ann Arbor, MI, USA), and wound closure was quantified by measuring changes in scratch width over time.\u003c/p\u003e\n\u003ch3\u003eTranswell invasion assay\u003c/h3\u003e\n\u003cp\u003eCell invasion was evaluated using Matrigel‑coated Transwell chambers (Corning, Corning, NY, USA). Cells suspended in serum‑free medium were seeded into the upper chamber, while medium containing 10% FBS was added to the lower chamber as a chemoattractant. After 24 h, non‑invading cells were removed, and cells that had invaded the lower membrane were fixed, stained with crystal violet, and imaged using an EVOS M7000 microscope (Thermo Fisher Scientific, Waltham, MA, USA). Invaded cells were quantified using ImageJ software (version 1.53t, NIH).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCell-based caspase-3/7 activity and ATP assays\u003c/h2\u003e \u003cp\u003eCaspase‑3/7 activity and ATP content were measured using the Caspase‑Glo\u0026reg; 3/7 Assay and CellTiter‑Glo\u0026reg; 2.0 Assay (Promega, Madison, WI, USA), respectively, following the manufacturer\u0026rsquo;s instructions. Luminescence was recorded using a microplate reader (BioTek).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003epatient-derived lung cancer organoids\u003c/h3\u003e\n\u003cp\u003eFresh tumor tissues from surgical resections (pathologically confirmed by H\u0026amp;E staining) were obtained with ethical approval from the Institutional Review Board of The First Affiliated Hospital of Guangzhou Medical University (No. 2021-95). Written informed consent was obtained from all patients and/or their legal guardians prior to sample collection. Tissues were minced, enzymatically digested, filtered, and centrifuged to obtain single-cell suspensions. Cells were resuspended in cold Matrigel and seeded as domes on pre-warmed plates. After polymerization, domes were overlaid with lung cancer organoid culture medium and maintained at 37\u0026deg;C with 5% CO₂. For drug response assessment, established organoids were treated with DIPH at concentrations equivalent to the IC₅₀ (M-DIPH) or ten times the IC₅₀ (10\u0026times;M-DIPH) determined in A549 cells. After 72 hours of treatment, intracellular ATP levels were quantified using the CellTiter-Glo\u0026reg; 2.0 Assay (Promega) following the manufacturer's protocol.\u003c/p\u003e\n\u003ch3\u003eWestern blot analysis\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eWestern blot analysis\u003c/div\u003e \u003cp\u003eCells were lysed in RIPA buffer (Beyotime, Shanghai, China) containing protease and phosphatase inhibitors. Protein concentration was determined using a BCA assay kit (Thermo Fisher Scientific). Equal amounts of protein were separated by SDS‑PAGE and transferred to PVDF membranes (Millipore, Billerica, MA, USA). After blocking with 5% non‑fat milk, membranes were incubated overnight at 4\u0026deg;C with primary antibodies against γ‑H2AX, p‑ATM, p‑CHK1, p‑CHK2, c-Casp3, c-Casp9, Cyt‑c, BAX, CAT, SOD2, CD31, Ki67 (Cell Signaling Technology, Danvers, MA, USA, or Abmart, Shanghai, China; all 1:1000), and GAPDH (1:5000). After incubation with HRP‑conjugated secondary antibodies (Abmart; 1:5000), signals were developed with ECL substrate and visualized using a ChemiDoc\u0026trade; XRS+ system (Bio‑Rad, Hercules, CA, USA).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eROS detection\u003c/h2\u003e \u003cp\u003eIntracellular ROS levels were assessed using the fluorescent probe 2\u0026prime;,7\u0026prime;‑dichlorodihydrofluorescein diacetate (DCFH‑DA; Beyotime). After treatment, cells were incubated with 10 \u0026micro;M DCFH‑DA in serum‑free medium at 37\u0026deg;C for 20 min in the dark, washed with PBS, and immediately imaged using a fluorescence microscope (Zeiss Axiovert 25, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTUNEL assay\u003c/h2\u003e \u003cp\u003eCell apoptosis was evaluated using a TUNEL assay kit (YEASEN, Shanghai, China). After treatment, cells were fixed with 4% paraformaldehyde, permeabilized with 0.1% Triton X-100, and incubated with the TUNEL reaction mixture according to the manufacturer\u0026rsquo;s protocol. Nuclei were counterstained with DAPI (Beyotime, Shanghai, China). Fluorescence images were captured using a fluorescent microscope (Zeiss).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSyngeneic tumor model and imaging procedures\u003c/h2\u003e \u003cp\u003e All animal procedures were approved by the Laboratory Animal Use Management Committee of the Experimental Animal Institute of Guangzhou Medical University (approval numbers: 2021-042, 2021\u0026thinsp;\u0026minus;\u0026thinsp;167, and 2022\u0026thinsp;\u0026minus;\u0026thinsp;223) and were performed in accordance with the relevant guidelines and regulations. All methods are reported in accordance with ARRIVE guidelines (Animal Research: Reporting of In Vivo Experiments) (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Male C57BL/6 mice were used to establish a syngeneic lung cancer model. Lewis lung carcinoma (LLC) cells (1 \u0026times; 10⁶ cells per mouse) were suspended in phosphate-buffered saline (PBS) and subcutaneously inoculated into the right axilla. Once tumors became palpable, mice were randomly assigned to either a vehicle control group (PBS) or a diphenhydramine (DIPH) treatment group. DIPH was administered intraperitoneally at 20 mg/kg once daily for 14 consecutive days. All mice were housed under specific pathogen-free conditions. At the study endpoint, mice were anesthetized with isoflurane (induction at 3\u0026ndash;5% and maintenance at 1\u0026ndash;2% in oxygen), followed by intraperitoneal injection of D-luciferin (Yeasen Biotechnology, Shanghai, China; 150 mg/kg). Ten minutes after luciferin injection, tumor growth was assessed by in vivo imaging using the IVIS Spectrum small-animal imaging system (PerkinElmer, Waltham, MA, USA) under standardized exposure settings. Following imaging, mice were euthanized by cervical dislocation while still under deep anesthesia, and tumors were excised and photographed to compare gross morphology between groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence staining\u003c/h2\u003e \u003cp\u003eExcised tumor tissues were fixed in 4% paraformaldehyde, paraffin-embedded, and sectioned. After deparaffinization and antigen retrieval, sections were blocked and incubated overnight at 4\u0026deg;C with primary antibodies against CD31, Ki67, and c-Casp3 (all from Cell Signaling Technology; 1:200). The next day, sections were washed and incubated with the corresponding fluorescent secondary antibodies (Beyotime; 1:1000), followed by nuclear counterstaining with DAPI. Fluorescence images were acquired using a fluorescence microscope under standardized settings (Zeiss).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHematoxylin and eosin (H\u0026amp;E) staining\u003c/h2\u003e \u003cp\u003eAt the end of treatment, major organs (heart, liver, spleen, lung, and kidney) were harvested, fixed in 4% paraformaldehyde, paraffin‑embedded, and sectioned. Sections were deparaffinized, rehydrated, and stained with hematoxylin and eosin according to standard histological procedures. Stained sections were examined under a Nikon Eclipse E200 light microscope (Nikon, Tokyo, Japan) to evaluate potential DIPH‑associated tissue toxicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition\u003c/h2\u003e \u003cp\u003eCandidate targets potentially modulated by diphenhydramine were systematically extracted from the PubChem database PubChem(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). LUAD bulk RNA-sequencing transcriptomic datasets, genomic mutation profiles, and the matched clinical annotations were obtained from The Cancer Genome Atlas (TCGA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov/\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and GSE72094 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72094\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72094\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were obtained to gain gene expression and clinical data of validation cohorts from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All downstream bioinformatic analyses and data visualizations were implemented using R software version 4.5.2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eConsensus Clustering of Diphenhydramine-Related Genes (DRGs)\u003c/h2\u003e \u003cp\u003eDiphenhydramine-related genes (DRGs) were first intersected with the LUAD transcriptome, and differentially expressed candidates between tumor and adjacent normal tissues were identified using the limma package, with genes meeting a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 retained for subsequent analyses. Cox regression (coxph) was applied to each gene, and genes meeting P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were retained. Hazard ratios with 95% CIs were calculated. The expression matrix of these DRGs was then used as the input for unsupervised consensus clustering to define DRG-associated molecular subtypes. Consensus clustering was performed with the ConsensusClusterPlus package in R, applying a k-means clustering algorithm with Euclidean distance. The optimal k was determined by visual inspection of the cumulative distribution function (CDF) curves, delta area plots, and consensus heatmaps, and two robust clusters were ultimately selected for downstream analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eQualification and validation of the DRGs prognostic signature\u003c/h2\u003e \u003cp\u003eUnivariate Cox proportional hazards modeling was applied to screen diphenhydramine-associated candidate genes with prognostic relevance. Genes passing P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were advanced to LASSO\u0026ndash;Cox regularized regression for multigene risk-model derivation. The penalty parameter λ was optimized using the minimum cross-validated error criterion, and gene-specific coefficients were incorporated into the linear risk score: Risk score\u0026thinsp;=\u0026thinsp;Σ (Gene expression \u0026times; LASSO coefficient). TCGA and GEO LUAD patients were stratified into high- and low-risk subsets using the median score as the cut-point. Overall-survival differences were evaluated via Kaplan\u0026ndash;Meier estimation within the survival and survminer environments. Model discrimination was quantified through time-dependent ROC analysis, while multivariate Cox adjustment tested independence across age, sex, tumor stage, and risk score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of immune cell infiltration\u003c/h2\u003e \u003cp\u003eThe ESTIMATE computational framework was employed to deconvolute the non-malignant cellular components of the tumor microenvironment, generating Stromal score, Immune score, and an aggregated ESTIMATE score, along with inferred tumor purity. Microenvironmental cell abundance was interpreted as positively concordant with score magnitude. To further resolve immune-cell composition, CIBERSORT was applied using a 22-immune-subset reference signature to quantify relative infiltration levels within LUAD specimens.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed using GraphPad Prism (version 10.6.1) and R (version 4.5.2). Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of independent biological replicates (where n represents the total number of samples pooled from at least three independent experiments). Differences between two groups were assessed with an unpaired two‑tailed Student t test. For comparisons among more than two groups, one‑way ANOVA with Tukey\u0026rsquo;s post hoc test was applied. Statistical significance is denoted as *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003e| DIPH inhibits proliferation, clonogenicity, migration, and invasion of NSCLC cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo evaluate the antitumor potential of diphenhydramine (DIPH), we first determined its half-maximal inhibitory concentration (IC₅₀) in a panel of five human NSCLC cell lines (A549, PC9, H1299, H1975, and HCC827). Based on their relatively higher sensitivity (lower IC₅₀ values), A549, PC9, and HCC827 cells were selected for subsequent functional analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Clonogenic assays demonstrated that DIPH potently and dose-dependently suppressed the long-term proliferative capacity of all three cell lines, using concentrations corresponding to IC₂₅ (designated as low‑dose DIPH, L‑DIPH), IC₅₀ (medium‑dose, M‑DIPH), and IC₇₅ (high‑dose, H‑DIPH) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Beyond proliferation, DIPH significantly impaired the malignant invasive and migratory phenotypes critical for metastasis. Transwell invasion assays revealed a marked reduction in the invasive capacity of treated cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Consistently, wound-healing assays showed that DIPH substantially impeded cell migration, delaying wound closure at 48 hours post-treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). These results establish that DIPH broadly suppresses core oncogenic behaviors of NSCLC cells in vitro, including proliferation, clonogenic survival, invasion, and migration.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u003cb\u003e| DIPH induces mitochondrial dysfunction, DNA damage, and mitochondrial apoptosis in NSCLC cells and patient-derived organoids.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHaving established the broad anti-tumor phenotypic effects of DIPH, we next investigated whether these effects were mediated through the induction of programmed cell death. Caspase-3/7 activity assays revealed a concentration-dependent increase in apoptotic execution in sensitive A549 and PC9 cells following DIPH treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). To delineate the pathway involved, we assessed key mitochondrial apoptosis markers. Western blotting showed increased levels of cleaved caspase-3 (c-Casp3) and cleaved caspase-9 (c-Casp9), cytochrome c (Cyt c) release, and upregulation of the pro-apoptotic protein BAX (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Consistently, DIPH treatment significantly reduced intracellular ATP levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), indicating mitochondrial functional impairment. Collectively, these molecular alterations, together with the ATP depletion phenotype, provide convergent evidence that DIPH triggers mitochondrial dysfunction and activates the intrinsic (mitochondria-dependent) apoptotic pathway in NSCLC cells. Concurrently, key phosphorylation markers of the DNA damage response (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u0026mdash;including γ-H2AX, phosphorylated ATM (Ser1981; p-ATM), CHK1 (Ser345; p-CHK1), and CHK2 (Thr68; p-CHK2)\u0026mdash;were markedly increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), indicating significant genotoxic stress upon DIPH exposure. To validate the translational relevance of these findings, we employed patient-derived lung cancer organoids. DIPH treatment induced profound morphological disruption within 24 hours and significantly reduced ATP levels by 72 hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE), mirroring the mitochondrial dysfunction and cytotoxic effects observed in monolayer cultures. Taken together, these data demonstrate that DIPH are mediated through the induction of mitochondrial dysfunction and DNA damage, which converge to activate the mitochondrial apoptotic pathway in NSCLC models, including patient-relevant systems.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u003cb\u003e| DIPH disrupts redox homeostasis via mitochondrial dysfunction to drive apoptosis in NSCLC cells.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven that mitochondrial dysfunction is a key inducer of redox imbalance, and that sustained redox imbalance can further damage mitochondrial membranes and DNA\u0026mdash;thereby triggering mitochondrial-dependent apoptosis\u0026mdash;we further explored the role of redox homeostasis disruption in DIPH-mediated apoptosis(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDCFH-DA fluorescence assays showed that DIPH treatment increased intracellular ROS levels in A549 cells in both low- and medium-dose groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Western blot analysis further showed decreased levels of the antioxidant enzymes CAT and SOD2 in A549 and PC9 cells after DIPH exposure, suggesting impaired cellular antioxidant defenses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). These results indicate that DIPH disrupts the cellular redox balance. To functionally assess whether this redox disruption is central to DIPH-induced apoptosis, we co-treated cells with the ROS scavenger N-acetylcysteine (NAC). NAC co-treatment partially rescued DIPH-induced suppression of colony formation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). TUNEL staining showed that NAC reduced DIPH-induced apoptotic signals in A549 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Furthermore, co-treatment with NAC significantly attenuated the DIPH-induced increase in caspase‑3/7 activity and partially restored the DIPH-suppressed cellular ATP levels in A549 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Taken together, these findings demonstrate that DIPH induces mitochondrial dysfunction, which leads to a critical disruption of redox homeostasis. This redox imbalance is functionally required for the subsequent mitochondrial apoptotic pathway in NSCLC cells.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u003cb\u003e| DIPH suppresses tumor growth and induces apoptosis in vivo with a favorable therapeutic window.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo translate our in vitro findings into a physiologically relevant context, we evaluated the antitumor efficacy of DIPH using a syngeneic lung cancer model. Lewis lung carcinoma (LLC) cells were implanted subcutaneously into immunocompetent C57BL/6 mice. Bioluminescence imaging (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and direct examination of excised tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) consistently showed that tumor growth was significantly suppressed in DIPH-treated mice compared to the vehicle control group. Immunofluorescence analysis of tumor sections provided mechanistic insight into this growth inhibition. Tumors from DIPH-treated mice exhibited a marked increase in c-Casp3 alongside a pronounced decrease in the proliferation marker Ki67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), indicating that DIPH promotes apoptosis while simultaneously inhibiting proliferative activity in vivo. To investigate whether anti-angiogenic effects contributed to tumor suppression, we performed CD31 staining. No significant difference in microvessel density was observed between the treatment and control groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), suggesting that the antitumor effect of DIPH is not primarily mediated by the inhibition of tumor vascularization.\u003c/p\u003e \u003cp\u003eNotably, despite its potent pro-apoptotic effect within tumors, DIPH treatment showed no evidence of systemic toxicity. Comprehensive histological assessment of major organs\u0026mdash;including the heart, liver, spleen, lungs, and kidneys\u0026mdash;revealed normal architecture without signs of inflammation, necrosis, or other pathological alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). This dissociation between tumor cytotoxicity and systemic safety aligns with our mechanistic model, wherein DIPH selectively exploits the inherent redox vulnerability of cancer cells. We propose that the constitutively stressed redox state of NSCLC cells lowers their threshold for DIPH-induced mitochondrial dysfunction and redox imbalance, leading to apoptotic death. In contrast, normal tissues with robust redox homeostasis and reserve capacity are likely more resilient to such perturbation, thereby establishing a favorable therapeutic window for DIPH.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u003cb\u003e| Identification and clinical relevance of diphenhydramine-related molecular subtypes in LUAD\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo explore the potential clinical implications of DIPH, we performed integrative bioinformatic analyses to define the molecular landscape associated with diphenhydramine (DIPH) in lung adenocarcinoma (LUAD). A total of 183 DRGs were curated from PubChem. Integrative transcriptomic profiling of LUAD in TCGA revealed 52 genes with significant tumor-versus-normal differential expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). 23 DRGs exhibited prognostic significance on univariate Cox screening, including 6 risk-associated (HR\u0026thinsp;\u0026gt;\u0026thinsp;1) and 17 survival-favorable genes (HR\u0026thinsp;\u0026lt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Notably, functional enrichment of these prognostic DRGs highlighted pathways implicated in cell survival, stress response, apoptosis, and motility\u0026mdash;processes directly modulated by DIPH in our experimental models, thereby establishing a molecular link between the drug\u0026rsquo;s action and patient tumor biology (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe next investigated whether these DRGs could define biologically coherent patient subsets. Correlation analysis revealed predominantly positive co-expression among the prognostic DRGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Consensus clustering based on their expression profiles robustly identified two distinct molecular subtypes, designated Cluster A (n\u0026thinsp;=\u0026thinsp;397) and Cluster B (n\u0026thinsp;=\u0026thinsp;558) (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD-E), which were clearly separated in principal component analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). Critically, patients in Cluster B had a significantly poorer overall survival compared to those in Cluster A (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG), establishing the clinical prognostic value of this DRG-based classification. The distribution of key clinical features across these subtypes is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u003cb\u003e| Construction and validation of a DRG-based prognostic risk signature in LUAD\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo develop a quantitative tool for prognosis, we constructed a parsimonious risk signature from the prognostic DRGs. Using LASSO-Cox regression, we refined the 23 survival-associated DRGs into a 7-gene signature comprising CHRM2, ADRA1A, ADRB1, CHRNA4, NTSR1, BCHE, and SLC7A11 (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). A risk score was calculated for each patient in the TCGA and an independent GEO cohort. Patients stratified into high- and low-risk groups based on the median score exhibited significantly divergent survival, with the high-risk group demonstrating markedly poorer OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The signature\u0026rsquo;s predictive accuracy was supported by time-dependent ROC analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Multivariate Cox regression confirmed that the risk score was an independent prognostic factor, even after adjusting for other clinical variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eThis molecular risk framework showed strong internal consistency. The high-risk group was enriched for the expression of specific signature genes (CHRNA4, NTSR1, SLC7A11) and was predominantly composed of patients from the poor-prognosis Cluster B (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G). Sankey diagram visualization confirmed this alignment between cluster membership, risk stratification, and survival outcome (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Furthermore, a nomogram integrating the risk score with clinical factors showed good calibration (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI-J), and decision curve analysis demonstrated its superior net clinical benefit over traditional clinical parameters alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eK-L), underscoring the signature\u0026rsquo;s potential clinical utility.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003e| The DRG-based risk signature delineates distinct tumor immune microenvironment landscapes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGiven the interplay between tumor cell biology and host immunity, we investigated whether the DRG-based risk stratification corresponded to differences in the tumor immune microenvironment (TIME). CIBERSORT deconvolution revealed distinct immune cell infiltration patterns between the high- and low-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Consistently, ESTIMATE algorithm scores (StromalScore, ImmuneScore, ESTIMATEScore) were significantly lower in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB), indicating an overall \u0026ldquo;immune-cold\u0026rdquo; phenotype characterized by reduced stromal and immune cell presence and higher tumor purity. Further analysis delineated the specific immune context. Correlation analysis uncovered structured relationships among immune subsets (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Importantly, significant associations were observed between the seven core DRG signature genes, the risk score, and the abundance of specific immune populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Direct comparison confirmed significant depletion of cytotoxic CD8⁺ T cells and enrichment of immunosuppressive M2 macrophages in high-risk tumors (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-L). These results establish that the DRG-based prognostic stratification is underpinned by a profoundly altered and potentially less immunogenic tumor immune microenvironment.\u003c/p\u003e \u003cp\u003eIn conclusion, This study demonstrates that diphenhydramine (DIPH) suppresses lung cancer by inducing mitochondrial redox collapse and apoptosis, which is further validated in a syngeneic mouse model. We derived a DIPH-related gene signature that stratifies patients and correlates with an immunosuppressive microenvironment, offering a mechanistic and translational foundation for drug repurposing in precision oncology (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs an important bioactive amine, histamine plays a complex and crucial role in the occurrence, development, and tumor microenvironment regulation of cancer (\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Studies have shown that various tumor cells themselves or immune cells in the tumor microenvironment (especially mast cells) can synthesize and release histamine in large quantities (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Histamine mainly exerts its effects by binding to its four G protein-coupled receptors (H1R\u0026ndash;H4R), regulating tumor cell proliferation, survival, migration, angiogenesis, and immune responses (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Among these receptors, H1R is highly expressed in many cancer cells and is considered to have a pro-tumorigenic effect; its activation can promote tumor growth and immune suppression (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In addition, histamine can help tumor cells evade immune surveillance by regulating the polarization of tumor-associated macrophages, inhibiting T cell function, and promoting the expression of immune checkpoint molecules (such as VISTA) (\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Therefore, histamine and its signaling pathways have become a highly concerned biological target in cancer therapy.\u003c/p\u003e \u003cp\u003eBased on the important role of histamine in cancer, histamine receptor antagonists (actually inverse agonists), represented by H1 antihistamines, are being re-evaluated for their potential in tumor therapy (\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Studies have shown that certain H1 antihistamines, especially newer-generation agents such as fexofenadine, loratadine, and desloratadine, can not only inhibit tumor growth by blocking H1R but also exert anticancer effects through multiple receptor-independent mechanisms. These mechanisms include: Enhancing antitumor immunity: Reversing histamine-mediated T cell exhaustion, promoting M1 macrophage polarization, and producing synergistic effects with immune checkpoint inhibitors (e.g., anti-PD-1/CTLA-4 therapy) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e); Inducing tumor cell death: Particularly, cationic amphiphilic antihistamines can disrupt lysosomal stability, induce lysosomal membrane permeabilization (LMP), and trigger lysosome-dependent cell death (LCD) (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e); Overcoming drug resistance: They can sensitize tumor cells to conventional chemotherapeutic agents (e.g., vinorelbine, docetaxel) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Directly inhibiting proliferation and inducing apoptosis: Through pathways such as affecting the cell cycle, inducing mitochondrial dysfunction, and causing DNA damage (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Therefore, the \"repurposing\" of existing antihistamines for cancer therapy represents a highly promising strategy for rapid translational research.\u003c/p\u003e \u003cp\u003eBuilding on our previous work, we focused on elucidating the distinctive anticancer mechanism of a class of H1 antihistamines\u0026mdash;diphenhydramine (DIPH)\u0026mdash;in non\u0026ndash;small cell lung cancer (NSCLC), with the aim of further expanding the drug-repurposing potential of this class. We found that the antitumor effects of DIPH differ from those reported for other antihistamines, which often act via immunomodulation or lysosome-dependent pathways. Instead, DIPH disrupts tumor-cell metabolic homeostasis and triggers a lethal \u0026ldquo;mitochondria\u0026ndash;redox\u0026ndash;DNA damage\u0026rdquo; cascade. Specifically, DIPH induces rapid mitochondrial dysfunction, leading to ATP depletion and subsequent catastrophic imbalance of redox homeostasis, which is characterized by a sharp accumulation of reactive oxygen species (ROS) and the exhaustion of key antioxidant enzymes (e.g., CAT, SOD2). This redox crisis further triggers DNA damage and forms a self-amplifying positive feedback loop therewith, ultimately and irreversibly driving cells into the intrinsic apoptotic pathway. This core mechanism\u0026mdash;the \"mitochondrial dysfunction\u0026ndash;redox imbalance\u0026ndash;DNA damage\" axis\u0026mdash;has been validated in patient-derived organoid models. Functionally, the antioxidant NAC can significantly reverse the DIPH-induced cytotoxic effect, confirming that redox dysregulation is the central hub of its action. Notably, the pro-oxidative effect exhibited by DIPH here corroborates the therapeutic concept of \"redox vulnerability\": due to persistent metabolic stress, tumor cells maintain a fragile redox balance, rendering them selectively sensitive to further oxidative insults (such as those induced by DIPH). This finding provides a novel perspective for the anticancer mechanism of antihistamines.\u003c/p\u003e \u003cp\u003eTo further explore the clinical application potential of diphenhydramine, we employed a bioinformatic strategy that links DIPH-associated molecular signatures to patient prognostic outcomes. Analysis of diphenhydramine-related genes (DRGs) in the TCGA-LUAD cohort identified two prognostic molecular subtypes and enabled the construction of a robust 7-gene prognostic signature panel. This signature panel independently predicted survival outcomes and was concordant with a high-risk subgroup characterized by an immunosuppressive (\"immune-cold\") tumor microenvironment, which featured reduced CD8⁺ T-cell infiltration and increased M2 macrophage abundance. This association between the mechanistic footprint of DIPH and specific immune landscapes provides a translational framework for patient stratification. This indicates that future clinical evaluations of DIPH could be conducted by selecting patients whose tumors harbor this high-risk, immunosuppressive molecular signature, thereby increasing the likelihood of detecting clinical benefits.\u003c/p\u003e \u003cp\u003eFuture research should focus on the following key directions:(i) Identifying the precise molecular targets of DIPH on mitochondria;(ii) Validating its therapeutic efficacy in patient-derived xenograft models representative of the identified high-risk subtype;(iii) Exploring potential synergistic combinations, particularly with immunotherapies, given the essential role of the immune microenvironment associated with the DRG signature. In addition, systematic comparative studies are warranted to investigate the performance of different antihistamines across various cancer models. Such studies will help clarify their mechanisms of action and identify the most promising candidates for specific indications.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the following repositories: The Cancer Genome Atlas (TCGA) LUAD dataset: https://cancergenome.nih.gov/. Gene Expression Omnibus (GEO) dataset GSE72094: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE72094. All data generated or analysed during this study are included in this published article and its supplementary information files. The diphenhydramine-related genes (DRGs) analysed in this study were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov). Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: HJX, LH, PY; Methodology: LH, PY; Investigation: LH, PY, HMY, HWT, ZBW, SC, ZX, ZBH, CJN, DHS, CQ, LHT, FXZ; Visualization: LH, PY, ZX, ZBH, CJN, DHS, CQ, LHT, FXZ; Funding acquisition: HJX; Project administration: HJX, LH, PY; Supervision: HJX; original draft: HJX, LH, PY, HMY, HWT, ZBW, SC; Writing – review \u0026amp; editing: HJX, LH, PY.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was Supported by R\u0026amp;D Program of Guangzhou National Laboratory, Grant No. SRPG22-017, 2023ZD0519700.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L., Miller, K. D., Fuchs, H. E. \u0026amp; Jemal, A. Cancer statistics, 2022. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 7\u0026ndash;33 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 209\u0026ndash;249 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArbour, K. C. \u0026amp; Riely, G. J. Systemic Therapy for Locally Advanced and Metastatic Non-Small Cell Lung Cancer: A Review. \u003cem\u003eJama\u003c/em\u003e \u003cb\u003e322\u003c/b\u003e, 764\u0026ndash;774 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia, Y., Sun, M., Huang, H. \u0026amp; Jin, W. L. Drug repurposing for cancer therapy. \u003cem\u003eSignal. Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 92 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeth, F. R. et al. Unlocking hidden potential: advancements, approaches, and obstacles in repurposing drugs for cancer therapy. \u003cem\u003eBr. J. Cancer\u003c/em\u003e. \u003cb\u003e130\u003c/b\u003e, 703\u0026ndash;715 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, F., Hong, H., Yang, S. \u0026amp; Wei, Y. Individualized network-based drug repositioning infrastructure for precision oncology in the panomics era. \u003cem\u003eBrief. Bioinform\u003c/em\u003e. \u003cb\u003e18\u003c/b\u003e, 682\u0026ndash;697 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, W., Hu, J. W., He, X. R., Jin, W. L. \u0026amp; He, X. Y. Statins: a repurposed drug to fight cancer. \u003cem\u003eJ. Exp. Clin. Cancer Res.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 241 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHua, Y. et al. Metformin and cancer hallmarks: shedding new lights on therapeutic repurposing. \u003cem\u003eJ. Transl Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 403 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, F. et al. SSRI antidepressant citalopram reverses the Warburg effect to inhibit hepatocellular carcinoma by directly targeting GLUT1. \u003cem\u003eCell. Rep.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 114818 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, X. et al. Loratidine is associated with improved prognosis and exerts antineoplastic effects via apoptotic and pyroptotic crosstalk in lung cancer. \u003cem\u003eJ. Exp. Clin. Cancer Res.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 5 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF, A. L., De Las Vecillas Sanchez, L., Gilbert, L., Copaescu, A. M. \u0026amp; Isabwe, G. A. C. Premedication Protocols to Prevent Hypersensitivity Reactions to Chemotherapy: a Literature Review. \u003cem\u003eClin. Rev. Allergy Immunol.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e, 534\u0026ndash;547 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAriza-Salamanca, D. F. et al. Expert Consensus on the Use of Diphenhydramine for Short-Term Insomnia: Efficacy, Safety, and Clinical Applications. \u003cem\u003eJ Clin. Med\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHamano, H. et al. Diphenhydramine may be a preventive medicine against cisplatin-induced kidney toxicity. \u003cem\u003eKidney Int.\u003c/em\u003e \u003cb\u003e99\u003c/b\u003e, 885\u0026ndash;899 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKris, M. G., Gralla, R. J., Clark, R. A., Tyson, L. B. \u0026amp; Groshen, S. Antiemetic control and prevention of side effects of anti-cancer therapy with lorazepam or diphenhydramine when used in combination with metoclopramide plus dexamethasone. A double-blind, randomized trial. \u003cem\u003eCancer\u003c/em\u003e \u003cb\u003e60\u003c/b\u003e, 2816\u0026ndash;2822 (1987).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOr, C. R. et al. Diphenhydramine induces melanoma cell apoptosis by suppressing STAT3/MCL-1 survival signaling and retards B16-F10 melanoma growth in vivo. \u003cem\u003eOncol. Rep.\u003c/em\u003e \u003cb\u003e36\u003c/b\u003e, 3465\u0026ndash;3471 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIqbal, M. J. et al. Interplay of oxidative stress, cellular communication and signaling pathways in cancer. \u003cem\u003eCell. Commun. Signal.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 7 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrachootham, D., Alexandre, J. \u0026amp; Huang, P. Targeting cancer cells by ROS-mediated mechanisms: a radical therapeutic approach? \u003cem\u003eNat. Rev. Drug Discov\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 579\u0026ndash;591 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHelfinger, V. \u0026amp; Schr\u0026ouml;der, K. Redox control in cancer development and progression. \u003cem\u003eMol. Aspects Med.\u003c/em\u003e \u003cb\u003e63\u003c/b\u003e, 88\u0026ndash;98 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeng, M. S., Chang, J. H., Hung, W. Y., Yang, Y. C. \u0026amp; Chien, M. H. The interplay of reactive oxygen species and the epidermal growth factor receptor in tumor progression and drug resistance. \u003cem\u003eJ. Exp. Clin. Cancer Res.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e, 61 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, X. et al. Discovery of Nonquinone Substrates for NAD(P)H: Quinone Oxidoreductase 1 (NQO1) as Effective Intracellular ROS Generators for the Treatment of Drug-Resistant Non-Small-Cell Lung Cancer. \u003cem\u003eJ. Med. Chem.\u003c/em\u003e \u003cb\u003e61\u003c/b\u003e, 11280\u0026ndash;11297 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGraves, O. K. et al. Discovery of drug targets and therapeutic agents based on drug repositioning to treat lung adenocarcinoma. \u003cem\u003eBiomed. Pharmacother\u003c/em\u003e. \u003cb\u003e161\u003c/b\u003e, 114486 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJassim, A. et al. Gene context drift identifies drug targets to mitigate cancer treatment resistance. \u003cem\u003eCancer Cell.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e, 1608\u0026ndash;1621e1609 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, J. et al. Integrating Single-Cell Transcriptome-Wide Mendelian Randomization and Differentially Expressed Gene Analyses to Prioritize Dynamic Immune-Related Drug Targets for Cancers. \u003cem\u003eAdv. Sci. (Weinh)\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, e07451 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePercie du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. \u003cem\u003eJ. Cereb. Blood Flow. Metab.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 1769\u0026ndash;1777 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBonner, W. M. et al. GammaH2AX and cancer. \u003cem\u003eNat. Rev. Cancer\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 957\u0026ndash;967 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerona, R., Moncho-Amor, V., Machado-Pinilla, R., Belda-Iniesta, C. \u0026amp; S\u0026aacute;nchez P\u0026eacute;rez, I. Role of CHK2 in cancer development. \u003cem\u003eClin. Transl Oncol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 538\u0026ndash;542 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu, X., Pang, Y. \u0026amp; Fan, X. Mitochondria in oxidative stress, inflammation and aging: from mechanisms to therapeutic advances. \u003cem\u003eSignal. Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 190 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, H., Wei, H., Sehgal, S. A., Liu, L. \u0026amp; Chen, Q. Mitophagy receptors sense stress signals and couple mitochondrial dynamic machinery for mitochondrial quality control. \u003cem\u003eFree Radic Biol. Med.\u003c/em\u003e \u003cb\u003e100\u003c/b\u003e, 199\u0026ndash;209 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMassari, N. A., Nicoud, M. B. \u0026amp; Medina, V. A. Histamine receptors and cancer pharmacology: an update. \u003cem\u003eBr. J. Pharmacol.\u003c/em\u003e \u003cb\u003e177\u003c/b\u003e, 516\u0026ndash;538 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFalus, A. et al. Paracrine and autocrine interactions in melanoma: histamine is a relevant player in local regulation. \u003cem\u003eTrends Immunol.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 648\u0026ndash;652 (2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRivera, E. S. et al. Histamine as an autocrine growth factor: an unusual role for a widespread mediator. \u003cem\u003eSemin Cancer Biol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 15\u0026ndash;23 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastells, M. et al. Mast cell activation syndrome: Current understanding and research needs. \u003cem\u003eJ. Allergy Clin. Immunol.\u003c/em\u003e \u003cb\u003e154\u003c/b\u003e, 255\u0026ndash;263 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlaya, B. et al. Histamine and histamine receptor antagonists in cancer biology. \u003cem\u003eInflamm. Allergy Drug Targets\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e, 146\u0026ndash;157 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, H. et al. Inflammation and tumor progression: signaling pathways and targeted intervention. \u003cem\u003eSignal. Transduct. Target. Ther.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 263 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi, Z. et al. Distinct roles of histamine H1- and H2-receptor signaling pathways in inflammation-associated colonic tumorigenesis. \u003cem\u003eAm. J. Physiol. Gastrointest. Liver Physiol.\u003c/em\u003e \u003cb\u003e316\u003c/b\u003e, G205\u0026ndash;g216 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, J. et al. Desloratadine, a Novel Antigrowth Reagent for Bladder Cancer. \u003cem\u003eTechnol. Cancer Res. Treat.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e, 1533033820926591 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. et al. The allergy mediator histamine confers resistance to immunotherapy in cancer patients via activation of the macrophage histamine receptor H1. \u003cem\u003eCancer Cell.\u003c/em\u003e \u003cb\u003e40\u003c/b\u003e, 36\u0026ndash;52e39 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen, P. L. \u0026amp; Cho, J. Pathophysiological Roles of Histamine Receptors in Cancer Progression: Implications and Perspectives as Potential Molecular Targets. \u003cem\u003eBiomolecules\u003c/em\u003e 11 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFritz, I., Wagner, P. \u0026amp; Olsson, H. Improved survival in several cancers with use of H(1)-antihistamines desloratadine and loratadine. \u003cem\u003eTransl Oncol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 101029 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllegaard, A. M. et al. Repurposing Cationic Amphiphilic Antihistamines for Cancer Treatment. \u003cem\u003eEBioMedicine\u003c/em\u003e 9, 130\u0026ndash;139 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerdoodt, F. et al. Antihistamine use and risk of ovarian cancer: A population-based case-control study. \u003cem\u003eMaturitas\u003c/em\u003e \u003cb\u003e120\u003c/b\u003e, 47\u0026ndash;52 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaverdi\u0026egrave;re, I. et al. Leukemic stem cell signatures identify novel therapeutics targeting acute myeloid leukemia. \u003cem\u003eBlood Cancer J.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 52 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiang, C. H. et al. Efficacy of cationic amphiphilic antihistamines on outcomes of patients treated with immune checkpoint inhibitors. \u003cem\u003eEur. J. Cancer\u003c/em\u003e. \u003cb\u003e174\u003c/b\u003e, 1\u0026ndash;9 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, B. et al. Cationic amphiphilic antihistamines inhibit STAT3 via Ca(2+)-dependent lysosomal H(+) efflux. \u003cem\u003eCell. Rep.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 112137 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFehrenbacher, N. et al. Sensitization to the lysosomal cell death pathway by oncogene-induced down-regulation of lysosome-associated membrane proteins 1 and 2. \u003cem\u003eCancer Res.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e, 6623\u0026ndash;6633 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFern\u0026aacute;ndez-Nogueira, P. et al. Histamine receptor 1 inhibition enhances antitumor therapeutic responses through extracellular signal-regulated kinase (ERK) activation in breast cancer. \u003cem\u003eCancer Lett.\u003c/em\u003e \u003cb\u003e424\u003c/b\u003e, 70\u0026ndash;83 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrybus, E., Kr\u0026oacute;l, T. \u0026amp; Trybus, W. The Multidirectional Effect of Azelastine Hydrochloride on Cervical Cancer Cells. \u003cem\u003eInt J. Mol. Sci\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Guadalupe Ch\u0026aacute;vez-L\u0026oacute;pez, M., Hern\u0026aacute;ndez-Gallegos, E., V\u0026aacute;zquez-S\u0026aacute;nchez, A. Y., Gariglio, P. \u0026amp; Camacho, J. Antiproliferative and proapoptotic effects of astemizole on cervical cancer cells. \u003cem\u003eInt. J. Gynecol. Cancer\u003c/em\u003e. \u003cb\u003e24\u003c/b\u003e, 824\u0026ndash;828 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatsumoto, N. et al. Histamine H1 receptor antagonists selectively kill cisplatin-resistant human cancer cells. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1492 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Diphenhydramine, Non-small cell lung cancer, Mitochondrial apoptosis, DNA damage response, Redox homeostasis, Prognostic signature","lastPublishedDoi":"10.21203/rs.3.rs-8900171/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8900171/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDiphenhydramine (DIPH) is a widely used first-generation H1 antihistamine. While multiple antihistamines have recently shown noteworthy anticancer potential, the therapeutic efficacy of DIPH in non-small cell lung cancer (NSCLC) and its underlying mechanisms of action remain unreported. Here, we demonstrate that DIPH potently suppresses the viability, clonogenicity, migration, and invasion of non‑small cell lung cancer (NSCLC) cells. Mechanistically, DIPH induces mitochondrial dysfunction and consequent redox imbalance, culminating in DNA damage and activation of the intrinsic apoptotic pathway\u0026mdash;a cascade further validated in patient‑derived lung cancer organoids. To investigate the potential clinical implications of DIPH, we examined DIPH‑related genes (DRGs) in The Cancer Genome Atlas (TCGA) cohort of lung adenocarcinoma (LUAD, the predominant NSCLC subtype). This analysis revealed two DRG‑based molecular subtypes with significantly distinct survival outcomes and enabled the construction of a robust DRG‑derived prognostic signature. The signature served as an independent predictor of survival and was associated with specific alterations in the tumor immune microenvironment. Collectively, our findings not only report the first evidence of DIPH\u0026rsquo;s antitumor efficacy in NSCLC but also delineate its underlying mitochondria\u0026ndash;redox\u0026ndash;DNA damage axis, thereby establishing a translational framework that links this drug‑induced pathway to a clinically actionable molecular signature in LUAD.\u003c/p\u003e","manuscriptTitle":"Repurposing Diphenhydramine in Non-Small Cell Lung Cancer: A Mitochondrial Redox Mechanism and a Derived Prognostic Gene Signature","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-11 06:03:51","doi":"10.21203/rs.3.rs-8900171/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-15T13:44:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201648097671621697083205748018585492519","date":"2026-03-06T21:41:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-04T20:18:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T20:11:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-25T14:34:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-23T06:49:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-23T06:45:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"33e4c071-22f2-4627-ad97-175315194c44","owner":[],"postedDate":"March 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64108839,"name":"Biological sciences/Cancer"},{"id":64108840,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-03-11T06:03:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-11 06:03:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8900171","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8900171","identity":"rs-8900171","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}