Differential Transcriptomic Modulation by Histone Deacetylase Inhibitor SAHA in LUAD and LUSC

Differential Transcriptomic Modulation by Histone Deacetylase Inhibitor SAHA in LUAD and LUSC | 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 Research Article Differential Transcriptomic Modulation by Histone Deacetylase Inhibitor SAHA in LUAD and LUSC Fei Wang, Qingjun Yang, Lei Shu, Huiyu Cen, Yating Huang, Xiaojun Ma, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7322428/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Clinical Epigenetics → Version 1 posted 9 You are reading this latest preprint version Abstract Background Histone deacetylases (HDACs) drive non-small cell lung cancer (NSCLC) progression, yet HDAC inhibitor (HDACi) responses vary by tumor subtype. We compared the pan-HDAC inhibitor suberoylanilide hydroxamic acid (SAHA, vorinostat) in two NSCLC models-HER2-mutant lung adenocarcinoma (NCI-H1299; TP53 del , NRAS Q61K ) and EGFR/PI3K-driven squamous carcinoma (NCI-H1703; PDGFRA amp , PIK3CA E542K )-to uncover lineage-specific mechanisms. Methods Cells were treated with SAHA (10 µM, 24 h), a clinically relevant dose yielding ~ 1 µM free drug in plasma. Strand-specific RNA-seq was aligned to GRCh38.p13 and analyzed with edgeR and DESeq2 (FDR < 0.05, |Log₂FC|≥ 1). Pathway, protein-interaction, apoptosis (Annexin V/PI), and migration (scratch, trans-well + mitomycin C) assays complemented transcriptomics. Clinical relevance was assessed by correlating SAHA-regulated genes with disease-free survival in TCGA-LUAD and TCGA-LUSC via GEPIA2. Results SAHA altered 1098 genes in H1299 and 1532 in H1703, with only 437 shared. In H1299, SAHA induced EMT and MAPK-feedback genes while repressing interferon/apoptosis pathways. In H1703, SAHA activated complement/ECM remodeling and suppressed cell-cycle regulators. Class II HDACs ( HDAC4/6 ) were downregulated only in H1703. Functionally, H1703 exhibited greater apoptosis; H1299 showed stronger migration inhibition. SAHA-reversed gene signatures (e.g., HMMR , PLK1 in LUAD; PAPPA , SAMD11 in LUSC) were significantly associated with poor disease-free survival, defining HDAC9-HMMR/PLK1 and HDAC4/6-PAPPA/SAMD11 axes. Conclusions SAHA elicits distinct subtype-specific transcriptional and phenotypic effects in NSCLC. These findings highlight the importance of genetic context in HDACi therapy and support tailored combinations-such as SAHA with MAPK inhibitors in LUAD or HDAC4/6-targeted approaches in LUSC and the use of SAHA-modulated biomarkers for patient stratification. NSCLC HDAC inhibitors SAHA Transcriptomic modulation Personalized therapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Non-small cell lung cancer (NSCLC) constitutes approximately 85% of all lung cancer cases, significantly surpassing the incidence of small cell lung cancer (SCLC) [ 1 ]. Despite advancements in diagnostics and therapeutics, outcomes for NSCLC patients remain poor, primarily due to delayed diagnosis, rapid metastatic spread, and resistance to current treatment modalities [ 2 ]. NSCLC comprises diverse histological subtypes, notably lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), each characterized by unique molecular alterations and clinical behaviors. Correspondingly, LUAD cell lines (e.g., A549, NCI-H1299) are more frequently represented in research repositories compared to LUSC cell lines (e.g., NCI-H1703). Large-cell carcinoma lines (e.g., NCI-H460) and SCLC models (e.g., NCI-H69) constitute smaller proportions, reflecting their relative occurrence in clinical populations [ 3 , 4 ]. These in vitro models collectively provide essential platforms to dissect lung cancer biology and evaluate novel therapies. At the molecular level, aberrations in epigenetic regulation – particularly histone acetylation – play critical roles in NSCLC pathogenesis. The balance of histone acetylation is finely regulated by histone acetyltransferases (HATs) and histone deacetylases (HDACs), with aberrant HDAC activity strongly implicated in lung cancer progression [ 5 , 6 ]. Elevated HDAC levels correlate significantly with aggressive tumor phenotypes and poor clinical outcomes [ 7 ]. HDACs also modulate numerous non-histone substrates involved in critical cellular processes such as cell-cycle control, DNA repair, apoptosis, and signal transduction [ 8 ]. Consequently, heightened HDAC activity fosters tumor growth, angiogenesis, invasion, and metastasis in NSCLC. For instance, HDAC1 overexpression is linked to increased tumor proliferation and poor patient survival [ 10 ], while elevated HDAC3 is associated with enhanced invasiveness [ 11 ]. Given their crucial oncogenic roles, HDACs represent promising therapeutic targets. A variety of HDAC inhibitors (HDACi) have been developed, ranging from hydroxamic acids like suberoylanilide hydroxamic acid (SAHA, vorinostat) to cyclic peptides such as romidepsin [ 12 ]. These inhibitors enhance histone and non-histone protein acetylation, reactivating tumor suppressors and inhibiting cancer progression. SAHA demonstrates considerable anti-cancer potential across multiple lung cancer subtypes: promoting ubiquitin-acetylation network reprogramming in LUAD (A549, H1975) [ 13 ]; inducing G2/M cell-cycle arrest and suppressing HIF-1α/VEGF pathways in large cell lung carcinoma (NCI-H460) [ 14 ]; and enhancing ROS-mediated apoptosis synergistically with chemotherapy in SCLC (H69, H82) [ 15 ]. In NSCLC specifically, SAHA induces apoptosis, cell-cycle arrest, differentiation, and anti-angiogenic effects [ 16 ]. Clinically approved for refractory T-cell lymphoma, SAHA has also shown promising preclinical and early-phase clinical efficacy against NSCLC, especially when combined synergistically with targeted therapies such as EGFR inhibitors [ 17 , 18 ]. Nonetheless, not all NSCLC tumors respond uniformly to HDACi treatment. The genetic makeup of a lung cancer influences drug sensitivity, as mutations in key drivers like KRAS, EGFR, or ALK can modulate therapeutic response [ 19 ]. For example, KRAS mutations – found in a significant subset of LUAD – are known to confer resistance to EGFR-targeted inhibitors [ 20 ]. It is therefore plausible that LUAD and LUSC cells, which harbor distinct mutational landscapes, might exhibit differential responses to HDAC inhibition. However, this possibility has not been directly investigated: whether SAHA elicits different therapeutic effects and transcriptomic changes in LUAD versus LUSC remains unproven. To address this critical gap, we compared SAHA's transcriptomic and phenotypic effects in representative LUAD and LUSC cell lines, NCI-H1299 and NCI-H1703, respectively. NCI-H1299, derived from metastatic LUAD, is characterized by TP53-null status and NRASQ61K mutations, representing a classical RAS/MAPK-driven tumor model [ 21 , 22 ]. Conversely, NCI-H1703, derived from primary LUSC, harbors TP53 inactivation and focal amplifications of receptor tyrosine kinases like PDGFRA and an activating mutation in PIK3CA (E542K), exemplifying an RTK/PI3K-dependent cancer phenotype [ 23 , 24 ]. By analyzing and contrasting the transcriptomic and phenotypic effects of SAHA in these two cell lines, we aimed to delineate subtype-specific mechanisms of HDAC inhibition. This comparative approach provides new insights into how LUAD and LUSC cells differentially respond to epigenetic therapy, paving the way toward more tailored HDACi-based treatment strategies for distinct NSCLC subtypes. Methods Cell Lines and Drug Treatment Human NSCLC cell lines NCI-H1299 (large-cell/undifferentiated adenocarcinoma; TP53 del , NRAS Q61K ) and NCI-H1703 (squamous cell carcinoma; PDGFRA amp , PIK3CA E542K ) were acquired from the American Type Culture Collection (ATCC, Manassas, VA, USA). Authentication was regularly performed every six months using 20-locus STR profiling [25]. Mycoplasma contamination was monitored using the MycoAlert Mycoplasma Detection Kit (Lonza). Cells were cultured in RPMI-1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin in a 5% CO₂ humidified incubator at 37°C. Vorinostat (SAHA; Sigma‑Aldrich #SML0061) was dissolved in dimethyl‑sulfoxide (DMSO) to a 10 mM stock and stored at –20 °C. Dosing regimens were determined in a 0.5–20 µM, 2–48 h pilot screen that quantified global H3K27 acetylation (ELISA/Western) and early apoptosis (Annexin V/PI). A plateau in H3K27ac coupled with <15 % apoptosis was reached at 10 µM for 24 h; this total concentration corresponds to ~1 µM free SAHA in vivo–i.e. the clinical Cmax after a single 400 mg dose [26]. Accordingly, 10 µM × 24 h was chosen for all transcriptomic experiments. For functional assays three exposure tiers were employed: 0.5–2 µM (clinically achievable free drug), 10 µM (transcriptomic peak / total‑plasma Cmax) and 40 µM (mechanistic “saturating” dose commonly used in vitro [27]. Unless otherwise stated, cells were treated for 24 h with 0, 2.5, 10 or 40 µM SAHA; 0.1 % DMSO served as vehicle control. RNA Isolation, Quality Control, Library Preparation, and Sequencing Total RNA was extracted from cells grown in 6-well plates (2 × 10 5 cells per well, ~70 % confluence at harvest) using TRIzol reagent (Invitrogen) and purified with the RNeasy Mini Kit (Qiagen), including an on-column DNase I digestion. RNA purity (A260/280 ratio = 2.05 ± 0.04, A260/230 ratio = 1.98 ± 0.03) was measured using NanoDrop 2000, and RNA integrity (RIN ≥ 9.6) was confirmed by an Agilent 2100 Bioanalyzer. High-quality RNA (500 ng per sample) was used to generate strand-specific poly(A) libraries with the NEBNext Ultra II Directional RNA Library Prep Kit. Libraries were pooled and sequenced on an Illumina NovaSeq 6000 platform (paired-end, 2×151 bp, median depth of 55 million reads per sample, Q30 >93% , ~130× transcriptome breadth). RNA Analyses Read Processing and Alignment Sequencing quality was evaluated using FastQC v0.12.1. Adapters and low-quality bases were removed with fastp v0.23.4 (parameters: -q 20, -l 36). Clean reads were aligned in two-pass mode using STAR v2.7.11a against the human genome GRCh38.91 with Gencode v40 annotations. Alignment metrics were generated using Picard CollectRnaSeqMetrics. Gene-level counts were obtained using featureCounts v2.0.1. Differential expression analysis gene counts were normalized and filtered (CPM > 1 in ≥ 2 samples). Differentially expressed genes (DEGs) were identified using both edgeR v3.44 (quasi-likelihood F-test, FDR < 0.05, |Log₂ fold change| ≥ 1) and DESeq2 v1.42 (Wald test, adjusted p-value < 0.05, |Log₂ fold change| ≥ 1). edgeR results were primarily used for downstream analysis; DESeq2 results were used for validation purposes. Bioinformatics analysis principal component analysis (PCA) and correlation analysis were conducted using R v4.3.1. Gene set enrichment analysis was performed with clusterProfiler v4.10 using MSigDB v2025.1 gene sets. Protein-protein interaction (PPI) networks were constructed using STRING v12 and visualized in Cytoscape v3.10.0. Apoptosis Assays Apoptosis was quantified by Annexin V–FITC/propidium iodide (PI) staining (BD Pharmingen). Cells seeded in 12-well plates (1.5 × 10 5 cells per well) were harvested 24 h after treatment, stained, and analyzed on a BD FACSCanto II flow cytometer (10,000 events per sample). Data were processed in FlowJo v10.9. Results are reported as the mean ± SD of three biological replicates, each performed in technical duplicate [28]. Scratch and Trans-well Migration Assays Cell migration assays were performed in the presence of mitomycin C (10 µg/ml) to inhibit proliferation. Wound healing was monitored at 0 and 24 h, and trans-well migration assays used 8-µm inserts (Corning). Cells on inserts were fixed, stained with crystal violet, and quantified by measuring OD590, normalized to total protein content determined by BCA assay. GEPIA Database Analysis Expression and survival analyses were carried out with GEPIA2 (January 2025 release), which integrates TCGA-LUAD, TCGA-LUSC, and GTEx normal-lung datasets. The downloaded snapshot contained TCGA-LUAD (n = 483), TCGA-LUSC (n = 486), and GTEx lung-normal (n = 288) samples. Transcript-per-million (TPM) expression values were retrieved and re-analysed locally. Kaplan–Meier survival curves were generated from the TPM data in R using the survival and survminer packages. Data Accession Data Availability Raw and processed sequencing data have been deposited in GEO (accession: GSE143423). Statistical Analysis All experiments were independently repeated at least three times. Data are presented as mean ± SD. Statistical significance was assessed using two-tailed Student’s t-tests or one-way ANOVA followed by Tukey's post-hoc tests, with p < 0.05 considered significant. Results Differential Impact of SAHA on the Transcriptome in Different Lung Cancer Cell Lines Upon treatment with the class I/II HDAC inhibitor SAHA, RNA-seq profiling revealed substantial differences in transcriptional responses between LUAD-derived H1299 (NRAS Q61K , TP53 del ) and LUSC-derived H1703 (PDGFRA amp , PIK3CA E542K , TP53 WT ) cell lines. At baseline, 1854 genes were constitutively upregulated in H1299 relative to H1703, whereas 3114 genes showed higher baseline expression in H1703, underscoring significant intrinsic divergence between the cell lines. In H1299 cells treated with SAHA, 1098 genes exhibited significant expression changes (887 upregulated, 211 downregulated). Prominent upregulated genes included ANKRD1 , NGFR , and CPA4 , while key downregulated genes included FOSL1 and PHF19 . In H1703, SAHA induced changes in 1532 genes (889 upregulated, 643 downregulated), prominently increasing TFPI2 , VGF , and SAA1 , and notably decreasing NMMT , AKR1B10 , and TOP2A (Fig. 1A). Intersection analysis across differential expression gene (DEG) sets demonstrated limited overlap: only 334 genes were commonly upregulated and 103 commonly downregulated by SAHA across both lines, reflecting cell-specific responses. Unique SAHA-induced transcripts were abundant, with 316 in H1299 and 322 in H1703, highlighting substantial lineage-specific transcriptional landscapes (Fig. 1B). Heatmap analysis of the top 60 DEGs (30 most up- and 30 most downregulated, ranked by |Log₂FC| × –Log₁₀FDR) identified common SAHA-responsive targets such as CITED2 , ANKRD1 , and MT1X , but with significantly greater induction observed in H1299 (Fig. 1C). Furthermore, lineage-specific gene regulation became evident; H1299 uniquely exhibited downregulation of interferon-related genes IFIT2 and IFIT3 , and epithelial plasticity-related genes NTSR1 and PRRX1 , alongside marked induction of TUBB2B and NOTCH3 . In contrast, H1703 showed preferential repression of mitotic regulators SPC25 and AURKB , and the chemokine CCL2 , coupled with induction of acute-phase and ECM-associated genes LCN2 , S100P , and TYMP (Fig. 1D). This indicates that SAHA amplifies pre-existing lineage-specific transcriptional distinctions. Functional Pathway Enrichment of SAHA-Induced Differentially Expressed Genes Pathway enrichment analysis underscored distinct SAHA-driven signaling alterations in both cell lines (Fig. 2A). In H1299, SAHA treatment strongly activated pathways involved in cellular morphogenesis, cell adhesion, growth-factor responses, and Hippo signaling, with significant induction of NOTCH3 , BMP2 , PDGFB , FGFR3 , and EMT regulators such as SNAI2 . Simultaneously, interferon-alpha response, DNA damage-induced senescence, and pro-apoptotic pathways were markedly downregulated, emphasizing a balance shift toward an EMT-related phenotype (Fig. 2B). Conversely, in H1703, SAHA prominently activated pathways related to extracellular matrix remodeling, complement activation, and chemotaxis, evidenced by robust induction of genes including BIRC3 , TXNIP , LAMB3 , and KRT20 . Meanwhile,pathways strongly downregulated in H1703 primarily involved cell cycle regulation, DNA replication, and DNA repair, indicated by decreased expression of E2F1 , CCNA2 , and CDK1 (Fig. 2B), suggesting a phenotypic shift toward cell cycle arrest and enhanced extracellular matrix remodeling. Gene Set Enrichment Analysis Reveals Lineage-Specific Pathway Modulation by SAHA Gene Set Enrichment Analysis (GSEA) further dissected differential hallmark pathway responses post-SAHA treatment (Fig. 3 A). In H1299, notable positive enrichment was observed for KRAS SIGNALING DN (NES = 1.76) and EPITHELIAL MESENCHYMAL TRANSITION (EMT; NES = 1.60) (Fig. 3 B). These changes were driven by enhanced feedback inhibition ( DUSP / SPRY genes) on ERK–FAK signaling and substantial EMT-related transcriptional activation in the TP53-null context. In contrast, H1703 exhibited pronounced activation of KRAS SIGNALING UP (NES = 1.53) and COMPLEMENT pathways (NES = 1.44) (Fig. 3 C), supported by hyper-acetylation of the PDGFRA super-enhancer and STAT3/NF-κB driven inflammation. Both cell lines exhibited significant downregulation of E2F TARGETS and G2M CHECKPOINT pathways, indicative of impaired cell cycle progression (Fig. 3 D-E). Differential Sensitivity of HDAC Isoforms to SAHA in a Cell Line–Specific Manner HDAC enzyme analysis revealed distinct epigenetic responses underpinning these lineage-specific transcriptional outcomes. SAHA treatment did not alter the transcript levels of nuclear class I HDACs ( HDAC1 , HDAC2 , HDAC8 , HDAC10 ), despite differing baseline expression between the cell lines. However, marked differences emerged in cytoplasmic class II HDAC modulation: H1703 exhibited substantial transcript downregulation of HDAC4 and HDAC6 , disrupting tubulin and HSP90 acetylation balance, thus enhancing p53-mediated checkpoint and complement signaling. Conversely, H1299 displayed minimal modulation of cytoplasmic HDAC7 and HDAC9 , maintaining cytoskeletal dynamics yet intensifying MAPK feedback and modestly promoting apoptosis (Fig. 4 A). PPI network analysis highlighted cell-cycle and histone modification-associated hubs. H1299 hubs primarily encompassed BUB1B , CCNA2 , CCNB1 , MCM7 , MYC , PLK1 , TTK , CCNB2 , and BRCA1 . In contrast, H1703 hubs prominently featured a broader set of cell-cycle regulators including BUB1 , CDK1 , CDC6 , CHEK1 , MAD2L1 , TP53 , CDC45 , and AURKB , reflecting a stronger DNA damage and apoptotic signaling axis post-SAHA treatment (Fig. 4 B-E). Collectively, these data indicate that SAHA induces cell-line-specific transcriptional reprogramming driven by distinct oncogenic mutations and HDAC enzyme profiles. In H1299, RAS-dependency and absence of p53 limit apoptosis but markedly suppress cell migration. In contrast, H1703 cells, enriched in PDGFRA and PI3K signaling and harboring intact p53, exhibit pronounced apoptotic signaling but relatively modest migratory inhibition, highlighting the profound influence of genetic and epigenetic context in shaping HDAC inhibitor responses. Prognostic Significance of SAHA-Regulated Genes in Overall Survival (OS) in LUAD and LUSC To explore the clinical relevance of SAHA-modulated transcriptional changes, we performed Kaplan–Meier survival analyses on independent LUAD and LUSC cohorts. To functionally validate the effect of SAHA on cell fate, we first assessed apoptosis by Annexin V-FITC/PI staining. Flow-cytometric analysis revealed that SAHA induced dose-dependent apoptosis in both H1299 and H1703 cells (Fig. 5 A). In H1299 cells, SAHA treatment led to a progressive increase in late apoptotic/necrotic cells in the upper right (UR) quadrant (from 0.02–61.7%) and early apoptotic cells in the lower right (LR) quadrant (from 0.01–18.2%), accompanied by a marked decrease in viable cells in the lower left (LL) quadrant (from 99.9–17.0%). A similar but more pronounced apoptotic response was observed in H1703 cells, with late apoptotic/necrotic populations (UR quadrant) rising from 0.11–89.5%, and viable cells (LL quadrant) declining sharply from 99.8–0.99% across increasing SAHA concentrations. These data indicate that SAHA triggers apoptosis in a concentration-dependent manner, with H1703 cells exhibiting greater sensitivity. We next examined whether SAHA-induced transcriptional changes were clinically relevant in lung cancer patients. We first identified overlap between DEGs in SAHA-treated H1299 and H1703 cells and genes significantly associated with OS in TCGA-derived datasets. In LUAD patients, high tumor expression of HMMR , S100A16 , GTF3C6 , and PLK1 , as well as low expression of GNG7 , were each associated with significantly reduced OS (p < 0.01). Correspondingly, SAHA treatment robustly suppressed HMMR , S100A16 , GTF3C6 , and PLK1 expression while upregulating GNG7 in H1299 cells. Correlation analysis further revealed that in tumor tissues—but not normal lung— HDAC9 expression correlated positively with HMMR and PLK1 levels (p < 0.05), implicating an HDAC9–HMMR/PLK1 axis in driving proliferation and migration that impacts patient prognosis (Fig. 5 B ) . In LUSC patients, elevated expression of PAPPA and SAMD11 and reduced expression of SLC9A9 predicted poorer OS (p < 0.05). SAHA treatment in H1703 cells significantly decreased PAPPA and SAMD11 transcripts while increasing SLC9A9 levels. Importantly, correlation analysis in LUSC tumor samples—but not in matched normal tissue—demonstrated significant associations between HDAC4 and PAPPA , and between HDAC6 and SAMD11 (p < 0.05), defining a lineage-specific HDAC4/6–PAPPA/SAMD11 regulatory module that modulates anti-apoptotic and dedifferentiation pathways and consequently influences OS (Fig. 5 C ) . Collectively, these data support a model in which SAHA improves clinical prognosis by targeting distinct HDAC–gene axes: in LUAD, SAHA inhibits HDAC9 to downregulate HMMR and PLK1, and in LUSC it targets HDAC4/6 to suppress PAPPA and SAMD11, thereby modulating key survival pathways and extending OS. SAHA Suppresses Cell Migration Through Lineage-Dependent Mechanisms Further, the influence of SAHA on cell migration—an essential step in metastasis—was systematically assessed using cell scratch assays. In this assay, H1299 cells treated with SAHA (0.5 or 1 µg/mL, 48 h) exhibited markedly impaired migratory ability (Fig. 6 A–B), whereas H1703 cells displayed a more modest inhibition under the same conditions (Fig. 6 C–D). Although H1703 cells had a lower baseline migratory capacity compared to H1299, SAHA still significantly suppressed migration in both cell lines. When treatment duration was shortened to 24 hours, SAHA (0.5 or 1 µg/mL) continued to inhibit H1299 cell migration significantly (Fig. 6 A–B), while no substantial effect was observed in H1703 cells under identical conditions (Fig. 6 C–D). Integration of these functional data with transcriptomic analyses revealed lineage-specific molecular correlates underpinning the observed phenotypes: H1299 migration inhibition correlated strongly with upregulation of KRAS_SIGNALING_DN and epithelial-to-mesenchymal transition (EMT)-related pathways, likely driven by enhanced MAPK feedback inhibitors (e.g., DUSP4/6 , SPRY2/4 ). Conversely, modest migration suppression in H1703 correlated with pronounced complement activation and extracellular matrix remodeling pathways, mediated by HDAC4/6 loss and subsequent induction of inflammatory and ECM-associated genes (e.g., BIRC3 , LAMB3 , KRT20 ). Collectively, these results emphasize SAHA’s robust yet divergent modulation of migratory pathways dictated by the distinct genetic (NRAS Q61K , TP53 del in H1299; PDGFRA amp , PIK3CA E542K , TP53 WT in H1703) and epigenetic (differential HDAC enzyme sensitivity) contexts. Our findings underscore the therapeutic potential of SAHA as a targeted epigenetic modulator in lung cancer metastasis management, warranting further mechanistic exploration and clinical translation. Discussion Lung cancer remains one of the most lethal malignancies, and NSCLC – especially LUAD and LUSC – poses a particular challenge due to its marked molecular diversity and propensity for treatment failure. LUAD and LUSC are driven by distinct oncogenic alterations (for example, EGFR or KRAS mutations in LUAD versus PIK3CA or FGFR1 amplifications in LUSC) that critically shape both therapeutic vulnerabilities and resistance profiles [ 29 ]. Among the many factors contributing to NSCLC progression, epigenetic dysregulation—most notably aberrant histone acetylation via HDACs—has emerged as a key driver of tumor cell proliferation, invasion, and metastasis. Elevated HDAC activity not only silences tumor-suppressor genes but also promotes resistance to chemotherapy and targeted agents by altering chromatin accessibility and stabilizing oncogenic proteins [ 30 ]. Accordingly, the pan‐HDAC inhibitor SAHA (vorinostat) has demonstrated robust anticancer activity in preclinical NSCLC models, inducing histone and non‐histone hyperacetylation, triggering apoptosis, arresting the cell cycle, and promoting differentiation [ 31 , 32 ]. SAHA has also synergized with chemotherapy, radiotherapy, and targeted inhibitors in vitro and in vivo. Yet, clinical trials in unselected NSCLC populations have yielded only modest benefit, highlighting the urgent need to understand how genetic and epigenetic contexts modulate HDACi sensitivity. In our work, we compared the transcriptome-wide effects of SAHA in two prototypical NSCLC cell lines: NCI‐H1299 (LUAD, NRAS Q61K , TP53 - ) and NCI‐H1703 (LUSC, PDGFRA amp , PIK3CA E542K , TP53 WT ) – alterations that faithfully recapitulate key subtype‐defining lesions [ 29 ]. Strikingly, SAHA elicited profoundly different gene‐expression programs in these two models. In RAS‐driven, p53‐deficient H1299 cells, SAHA upregulated mesenchymal and developmental pathways (including EMT, Notch, BMP, and PDGF signaling) while sharply repressing interferon and pro‐apoptotic modules. This suggests that, lacking a functional p53 axis, these cells mount a complex differentiation and stress‐response program rather than undergoing robust apoptosis. By contrast, p53‐intact, PDGFRA/PI3K‐amplified H1703 cells responded to SAHA with strong activation of complement and coagulation cascades, extracellular‐matrix remodeling, and inflammatory‐chemotaxis genes, coupled with downregulation of E2F targets and G2–M checkpoint regulators. These data indicate that HDAC inhibition in LUSC predominantly triggers immunomodulatory and cell‐cycle arrest pathways. This dichotomy underscores the pivotal role of p53 status and dominant oncogenic drivers in shaping HDACi sensitivity. Functional p53 cooperates with HDAC inhibitors to amplify cell-cycle arrest and apoptosis – partly by relieving MDM2‐mediated repression and stabilizing p53 when DNA‐repair networks are suppressed [ 33 ]. Indeed, prior studies have shown that HDACi efficacy is substantially blunted in p53‐null settings, whereas p53‐proficient models exhibit dramatic apoptotic responses [ 30 ]. Consistent with this, H1703 but not H1299 underwent pronounced SAHA‐induced apoptosis and proliferative blockade. Importantly, SAHA retains activity in p53‐deficient cells – manifesting more as differentiation or cytostasis than cell death – suggesting that combination regimens (e.g. pairing HDACi with agents that restore p53 function or activate parallel death pathways) may be necessary to overcome resistance in TP53‐mutant NSCLC. We also uncovered an isoform-specific dimension to SAHA’s actions. In H1703 cells, SAHA markedly downregulated class II HDACs (HDAC4 and HDAC6). HDAC6 inhibition hyperacetylates α‐tubulin and HSP90, disrupting cytoskeletal dynamics and chaperone function, which leads to proteotoxic stress and destabilization of key kinases, thereby enhancing p53‐dependent checkpoints [ 34 ]. HDAC4 loss may further de‐repress pro‐apoptotic and differentiation genes by dismantling repressor complexes [ 35 ]. Notably, HDAC4/6 expression correlated with tumor‐promoting genes ( PAPPA , SAMD11 ) in LUSC patient samples, implying a unique dependency that could be therapeutically exploited. These findings advocate for clinical testing of isoform‐selective HDAC4/6 inhibitors – alone or alongside RTK/PI3K antagonists – in molecularly defined squamous NSCLC. An unexpected divergence emerged in migratory behavior. Despite limited apoptosis, H1299 cells displayed strong migration inhibition after SAHA exposure, driven by upregulation of negative RAS–MAPK feedback regulators ( DUSP4/5 , SPRY2/4 ) and EMT modulators. By re-inducing DUSPs, SAHA effectively suppresses KRAS signaling, reducing motility in RAS‐mutant LUAD. In contrast, H1703 cells only modestly curtailed migration, consistent with gene‐expression shifts toward ECM remodeling and complement activation rather than epithelial reprogramming. This context‐dependent anti‐migratory effect suggests that combining SAHA with MAPK inhibitors could further block metastasis in KRAS‐driven adenocarcinomas, whereas in LUSC a different combination (e.g., ECM‐targeting or complement‐modulating agents) may be more effective. Finally, by integrating SAHA-regulated gene signatures with TCGA survival data, we identified prognostic biomarkers whose expression is reversed by HDAC inhibition. LUAD patients with high HMMR , S100A16 , GTF3C6 , PLK1 and low GNG7 – the very profile SAHA normalizes in H1299 – exhibit poorer disease‐free survival. Similarly, LUSC patients with elevated PAPPA / SAMD11 and reduced SLC9A9 – reversed by SAHA in H1703 – have worse outcomes. These biomarkers could guide patient selection and enable precision deployment of HDAC inhibitors, alone or in combination. In summary, our work reveals that SAHA’s antitumor efficacy is intricately shaped by NSCLC subtype–specific genetic and epigenetic contexts (Fig. 7) . Harnessing this knowledge to rationally design combination therapies – for example, pairing SAHA or isoform-selective HDACi with MAPK or RTK/PI3K inhibitors [ 36 ], or integrating immune modulators [ 37 ] – promises to overcome intrinsic and acquired resistance, enhance clinical responses, and ultimately improve outcomes for patients with LUAD and LUSC. Abbreviations HDACs Histone deacetylases NSCLC non-small cell lung cancer HDACi HDAC inhibitor SCLC small cell lung cancer LUAD lung adenocarcinoma LUSC lung squamous cell carcinoma HATs histone acetyltransferases SAHA suberoylanilide hydroxamic acid DEGs differentially expressed genes PCA principal component analysis PPI protein-protein interaction TPM transcript-per-million GSEA Gene Set Enrichment Analysis EMT epithelial-to-mesenchymal transition OS Overall Survival UR upper right LR lower right LL lower left Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declare that they have no competing interests. Authors' contributions FW, XHD, LS, and HYC contributed equally to this work. FW and XHD designed the study and performed the experiments. LS and HYC analyzed the data and prepared the figures. YTH, XJM, and YSL contributed to data collection and interpretation. XLG, CW and GXY assisted in the experimental design and methodology. XHF and XYY provided critical revisions and supervised the project. LMZ and JFS conceptualized the study, and supervised the research. All authors reviewed and approved the final manuscript. Funding We appreciated the National Natural Science Foundation of China (82070406, 82072047, and 82170008), Guangdong Province Postgraduate Education Innovation Plan Project (2022SFKC054), Guangdong Basic and Applied Basic Research Fund Enterprise Joint Fund (2023A1515220024), Guangdong Province Hospital Pharmacy Research Fund (2024A31), Medical Research Foundation of Guangdong Province (2024070), The Open research funds from The Sixth Affiliated Hospital of Guangzhou Medical University (Qingyuan Peoples Hospital, 202201-303) and the research funds from Guangzhou Panyu Central Hospital (PY-2023-007, PY-2023-010, PY-2023-026). Acknowledgements We also thank for the help from BioRender.com for drawing illustration. References Al Bakir M, Spencer S, Kumar P, et al. The evolution of non-small cell lung cancer metastases in TRACERx. Nature . 2023;616(7957):534–542. doi: 10.1038/s41586-023-05729-x Megyesfalvi Z, Pipek O, Kiss A, et al. Clinical insights into small cell lung cancer: tumour heterogeneity, diagnosis, therapy and future directions. CA Cancer J Clin . 2023;73(6):620–652. doi: 10.3322/caac.21785 Gazdar AF, Hirsch FR, Minna JD. From mice to men and back: an assessment of preclinical model systems for the study of lung cancers. J Thorac Oncol . 2016;11(3):287–299. doi: 10.1016/j.jtho.2015.10.009 Gazdar AF, Girard L, Lockwood WW, Lam WL, Minna JD. Lung cancer cell lines as tools for biomedical discovery and research. J Natl Cancer Inst . 2010;102(17):1310–1320. doi: 10.1093/jnci/djq279 Mamdani H, Jalal SI. Histone deacetylase inhibition in non-small cell lung cancer: hype or hope? Front Cell Dev Biol . 2020;8:582370. doi: 10.3389/fcell.2020.582370 Shanmugam G, Rakshit S, Sarkar K. HDAC inhibitors: targets for tumour therapy, immune modulation and lung diseases. Transl Oncol . 2021;14(4):101312. doi: 10.1016/j.tranon.2021.101312 Guo KM, Wang JZ, Wang H, et al. HDAC7 promotes NSCLC proliferation and metastasis via stabilisation by deubiquitinase USP10 and activation of the β-catenin–FGF18 pathway. J Exp Clin Cancer Res . 2022;41:91. doi: 10.1186/s13046-022-02266-9 Zhang Y, Zhao H, Wang X, et al. Mechanisms of HDACs in cancer development. Front Immunol . 2025;16:1529239. doi: 10.3389/fimmu.2025.1529239 Wahi A, Singh R, Gupta P, et al. Progress in discovery and development of natural inhibitors of histone deacetylases as anticancer agents. Naunyn-Schmiedeberg’s Arch Pharmacol . 2024;397(2):675–702. doi: 10.1007/s00210-023-02460-8 Lu R, Jin Y, Zheng M. USP5-dependent HDAC1 promotes cisplatin resistance and malignant progression of non-small cell lung cancer by regulating RILP acetylation. Thorac Cancer . 2024;15(1):e15478. doi: 10.1111/1759-7714.15478 Guerriero JL, Sotayo A, Ponichtera HE, et al. Class IIa HDAC inhibition reduces breast tumours and metastases through anti-tumour macrophages. Nature . 2017;543(7645):428–432. doi: 10.1038/nature21409 Cappellacci L, Perinelli DR, Maggi F, Grifantini M, Petrelli R. Recent progress in histone deacetylase inhibitors as anticancer agents. Curr Med Chem . 2020;27(15):2449–2493. doi: 10.2174/0929867325666181016163110 Wu Q, Chen D, Chen G, Chang J, Zhang W. Suberoylanilide hydroxamic acid treatment reveals crosstalk among proteome, ubiquitylome and acetylome in non-small cell lung cancer A549 cells. Sci Rep . 2015;5:9520. doi: 10.1038/srep09520 Zhao Y, Tan J, Zhang Q, et al. Anticancer activity of SAHA, a potent histone deacetylase inhibitor, in NCI-H460 human large-cell lung carcinoma cells in vitro and in vivo . Int J Oncol . 2014;44(2):451–458. doi: 10.3892/ijo.2013.2193 Bruzzese F, Abbate R, Alexandrova E, et al. Synergistic antitumour effect between vorinostat and topotecan in small cell lung cancer cells is mediated by reactive oxygen species and DNA damage-induced apoptosis. Mol Cancer Ther . 2009;8(11):3075–3087. doi: 10.1158/1535-7163.MCT-09-0254 Li G, Zhu Y, Zhang Y, et al. The roles of histone deacetylases and their inhibitors in cancer therapy. Front Cell Dev Biol . 2020;8:576946. doi: 10.3389/fcell.2020.576946 Iżykowska K, Rassek K, Korsak D, et al. Novel targeted therapies of T-cell lymphomas. J Hematol Oncol . 2020;13:176. doi: 10.1186/s13045-020-01006-w Schelker C, Nowak-Sliwinska P, Borchard G. HDACIs and TKIs combinations and their liposomal delivery for cancer treatment. J Control Release . 2023;358:59–77. doi: 10.1016/j.jconrel.2023.04.006 Tsukumo Y, Naito M, Suzuki T. Influence of EGFR-activating mutations on sensitivity to tyrosine kinase inhibitors in a KRAS-mutant non-small cell lung cancer cell line. PLOS ONE . 2020;15(3):e0229712. doi: 10.1371/journal.pone.0229712 Deng S, Clowers K, Velasco M, et al. Understanding the complexity of the tumour microenvironment in KRAS-mutant lung cancer: finding an alternative path to prevention and treatment. Front Oncol . 2020;9:1556. doi: 10.3389/fonc.2019.01556 Chen J, Yang H, Teo ASM, et al. Genomic landscape of lung adenocarcinoma in East Asians. Nat Genet . 2020;52(2):177–186. doi: 10.1038/s41588-019-0569-6 Ye Y, Luo Y, Sun Y, et al. Single-cell transcriptomic profiling reveals KRAS/TP53-driven neutrophil reprogramming in LUAD: a multi-gene prognostic model and therapeutic targeting of RHOV. Oncol Res . 2025;33(6):1383–1404. doi: 10.32604/or.2025.062584 You L, Yang CT, Jablons DM. ONYX-015 works synergistically with chemotherapy in lung cancer cell lines and primary cultures freshly made from lung cancer patients. Cancer Res . 2000;60(4):1009–1013. [No DOI] Zhang Z, Ren X, Lu X, et al. GZD856, a novel potent PDGFRα/β inhibitor, suppresses the growth and migration of lung cancer cells in vitro and in vivo . Cancer Lett . 2016;375(1):172–178. doi: 10.1016/j.canlet.2016.02.017 Kapeleris J, Müller-Bark J, Ranjit S, et al. Modelling reoxygenation effects in non-small cell lung cancer cell lines and showing epithelial–mesenchymal transition. J Cancer Res Clin Oncol . 2022;148(12):3501–3510. doi: 10.1007/s00432-022-04242-4 Kelly RJ, Rajan A, Force J, et al. Evaluation of KRAS mutations, angiogenic biomarkers and DCE-MRI in patients with advanced non-small-cell lung cancer receiving sorafenib. Clin Cancer Res . 2011;17(5):1190–1199. doi: 10.1158/1078-0432.CCR-10-2331 Gupta M, Concepcion CP, Fahey CG, et al. BRG1 loss predisposes lung cancers to replicative stress and ATR dependency. Cancer Res . 2020;80(18):3841–3854. doi: 10.1158/0008-5472.CAN-20-1744 Salimi V, Ranjbar S, Yousefi B, et al. Sodium butyrate promotes apoptosis in breast cancer cells through reactive oxygen species formation and mitochondrial impairment. Lipids Health Dis . 2017;16:208. doi: 10.1186/s12944-017-0575-y Shen Y, Chen J-Q, Li X-P. Differences between lung adenocarcinoma and lung squamous cell carcinoma: driver genes, therapeutic targets and clinical efficacy. Genes Dis . 2025;12(3):101374. doi: 10.1016/j.gendis.2024.101374 Contreras-Sanzón E, Prado-Garcia H, Romero-Garcia S, et al. Histone deacetylases modulate resistance to therapy in lung cancer. Front Genet . 2022;13:960263. doi: 10.3389/fgene.2022.960263 Traynor AM, Dubey S, Eickhoff JC, et al. Vorinostat in patients with relapsed non-small cell lung cancer: a Wisconsin Oncology Network phase II study. J Thorac Oncol . 2009;4(4):522–526. doi: 10.1097/JTO.0b013e3181952478 Eichner LJ, Curtis SD, Brun SN, et al. HDAC3 is critical in tumour development and therapeutic resistance in KRAS-mutant non-small cell lung cancer. Sci Adv . 2023;9(11):eadd3243. doi: 10.1126/sciadv.add3243 Sonnemann J, Marx C, Becker S, et al. p53-dependent and p53-independent anticancer effects of different histone deacetylase inhibitors. Br J Cancer . 2014;110(3):656–667. doi: 10.1038/bjc.2013.742 Krämer OH, Mahboobi S, Sellmer A. Drugging the HDAC6-HSP90 interplay in malignant cells. Trends Pharmacol Sci . 2014;35(10):501–509. doi: 10.1016/j.tips.2014.08.001 Cuttini E, Goi C, Pellarin E, Vida R, Brancolini C. HDAC4 in cancer: a multitasking platform to drive not only epigenetic modifications. Front Mol Biosci . 2023;10:1116660. doi: 10.3389/fmolb.2023.1116660 Alqosaibi AI, Abdel-Ghany S, Al-Mulhim F, Sabit H. Vorinostat enhances the therapeutic potential of erlotinib via MAPK in lung cancer cells. Cancer Treat Res Commun . 2022;30:100509. doi: 10.1016/j.ctarc.2022.100509 Kurundkar D, Srivastava RK, Chaudhary SC, et al. Vorinostat attenuates epidermoid squamous cell carcinoma growth by dampening mTOR signalling in a human xenograft model. Toxicol Appl Pharmacol . 2012;266(2):233–244. doi: 10.1016/j.taap.2012.11.002 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in Clinical Epigenetics → Version 1 posted Editorial decision: Revision requested 13 Nov, 2025 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Reviews received at journal 01 Oct, 2025 Reviewers agreed at journal 15 Sep, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 10 Aug, 2025 First submitted to journal 07 Aug, 2025 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. 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01:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7322428/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7322428/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13148-026-02051-x","type":"published","date":"2026-01-27T15:59:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89563491,"identity":"46ac5b75-17b4-4ed4-b484-505118b60698","added_by":"auto","created_at":"2025-08-21 10:30:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":609738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential impact of SAHA on the transcriptome in two NSCLC cell lines. \u003c/strong\u003e(A) Volcano plots of differentially expressed genes (DEGs) in H1299 and H1703 treated with SAHA (10 µM, 24 h; n=3). Dashed lines: |Log₂FC| = 1. Red/blue dots: significantly up/downregulated genes. The top ten transcripts for each comparison are annotated. (B) Euler (six-set) diagram summarizing the overlap among the six DEG sets: baseline comparisons (H1299-NC vs H1703-NC) and SAHA-induced changes in each line (SAHA vs NC in H1299 and H1703, both up- and down-regulated). Only intersections containing ≥ 5 genes are shown; numbers indicate gene counts per region. (C) Heatmap of top 60 SAHA-responsive genes across both lines (30 up/30 down), ranked by magnitude of change (|Log₂FC| × –Log₁₀FDR). (D) Lineage-specific heatmaps of 60 exclusive DEGs (30 up / 30 down) for H1299 (left) and H1703 (right) (Z-score normalized).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7322428/v1/7432145158dfb110ded016ac.png"},{"id":89563493,"identity":"3ad8e155-75d2-438b-9ecf-cf4969d3b3b9","added_by":"auto","created_at":"2025-08-21 10:30:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":212718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSAHA triggers divergent pathway signatures in H1299 and H1703 cells. \u003c/strong\u003e(A) Bar plot of Gene Ontology and pathway terms significantly enriched among SAHA-regulated DEGs in H1299 (orange: up-regulated; green: down-regulated) and H1703 (pink: up-regulated; purple: down-regulated) (FDR\u0026lt;0.05). (B) Functional gene-specific bar charts showing FPKM expression (mean ± SD, n = 3) for representative genes in four clusters: (1) EMT/morphogenesis genes (H1299 up), (2) interferon and apoptosis genes (H1299 down), (3) ECM remodeling/chemotaxis genes (H1703 up), and (4) mitotic checkpoint genes (H1703 down).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7322428/v1/6fe805bee0441ab689447d6d.png"},{"id":89563495,"identity":"9317fe7f-1697-45fe-b54a-aab4ee72e916","added_by":"auto","created_at":"2025-08-21 10:30:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":853798,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHallmark pathway modulation by SAHA in NCI-H1299 and NCI-H1703.\u003c/strong\u003e (A) Bubble plot of GSEA for MSigDB Hallmark gene sets in SAHA-treated versus control H1299 (left column) and H1703 (right column) cells. Asterisks denote FDR \u0026lt; 0.05. (B) Enrichment plots for H1299-SAHA: KRAS_SIGNALING_DN (left) and EPITHELIAL_MESENCHYMAL_TRANSITION (right), with nominal p-values. (C) Enrichment plots for H1703-SAHA: KRAS_SIGNALING_UP (left) and COMPLEMENT (right). (D–E) Enrichment plots for down-regulated cell cycle hallmark sets: E2F_TARGETS and G2M_CHECKPOINT in H1299 and H1703, each with nominal p = 0.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7322428/v1/d07449ef59c95c76b115a9f2.png"},{"id":89563496,"identity":"a18f5545-03a2-4de3-af5d-78919e8faa04","added_by":"auto","created_at":"2025-08-21 10:30:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":672991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSAHA treatment induces alterations in HDAC expression and downstream protein–protein interaction (PPI) networks. \u003c/strong\u003e(A) Heatmap of epigenetic regulator expression in H1299 and H1703 cells under control (NC) versus SAHA conditions (Z-score–normalized Log₂CPM). Genes cluster into four modules (Groups 1–4) based on shared expression patterns. (B–C) Bar charts ranking the top 25 hub genes by degree (number of interactions) in the PPI networks constructed from SAHA-upregulated DEGs in H1299 (B) and H1703 (C) cells. Higher bars indicate proteins with more interaction partners. (D–E) STRING-derived PPI networks for SAHA-upregulated proteins in H1299 (D) and H1703 (E) cells. Nodes represent proteins, edge thickness reflects interaction confidence score, and node size denote interaction degree.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7322428/v1/f5831263d5aa00f3231a4a16.png"},{"id":89565586,"identity":"77c9f224-3b83-4971-abdb-e7ab8ebac740","added_by":"auto","created_at":"2025-08-21 10:46:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":446375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSAHA-regulated genes correlate with OS in LUAD and LUSC. \u003c/strong\u003e(A) Representative flow-cytometry plots of Annexin V-FITC versus propidium iodide (PI) staining in H1299 and H1703 cells treated with increasing SAHA concentrations (0, 2.5, 10, 40 µg/mL) for 48 h. Quadrants: viable (LL), early apoptosis (LR), late apoptosis/necrosis (UR), dead/necrosis (UL). (B) Kaplan–Meier OS curves for LUAD patients stratified by high (red) versus low (blue) tumor expression of HMMR, S100A16, GTF3C6, PLK1 (top row) and GNG7 (bottom row). Below each survival plot, scatter plots show Spearman correlation between HDAC9 and the corresponding gene in tumor (left) versus matched normal (right) tissues, with R and p-values. (C) Kaplan–Meier OS curves for LUSC patients stratified by high versus low expression of PAPPA and SAMD11 (top row), and SLC9A9 (bottom row). Scatter plots below depict correlations between HDAC4 and PAPPA, and HDAC6 and SAMD11, in tumor and normal tissues (p \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7322428/v1/1acb0f9f09a715adb4e00849.png"},{"id":89563498,"identity":"3df9da6e-d97b-4370-a0f2-b95ee74a1fa5","added_by":"auto","created_at":"2025-08-21 10:30:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1446586,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSAHA inhibits migration of LUAD and LUSC cells \u003c/strong\u003ein a lineage-specific manner \u003cstrong\u003erespectively. \u003c/strong\u003e(A) Representative phase-contrast images of wound-healing assays in H1299 cells treated with 0, 0.5, or 1.5 µg/mL SAHA at 0, 24, and 48 h. White lines trace wound edges. (B) Quantification of relative wound-closure rates (%) for H1299 at 24 and 48 h (mean ± SD; n = 3). ** p \u0026lt; 0.01; **** p \u0026lt; 0.0001 (two-way ANOVA). (C) Representative wound-healing images for H1703 under the same SAHA concentrations and time points, with wound edges outlined. (D) Semi-quantitative analysis of H1703 migration rates, as in (B). Statistical annotations as above.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7322428/v1/23288334d227371f27b07233.png"},{"id":89564738,"identity":"a452531d-3e65-4664-9001-404838eeac7d","added_by":"auto","created_at":"2025-08-21 10:38:34","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":347730,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic representation of the subtype-specific effects of the histone deacetylase inhibitor SAHA in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cell lines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn summary, our work reveals that SAHA’s antitumor efficacy is intricately shaped by NSCLC subtype–specific genetic and epigenetic contexts \u003cstrong\u003e(Figure 7)\u003c/strong\u003e. Harnessing this knowledge to rationally design combination therapies – for example, pairing SAHA or isoform‐selective HDACi with MAPK or RTK/PI3K inhibitors [36], or integrating immune modulators [37] – promises to overcome intrinsic and acquired resistance, enhance clinical responses, and ultimately improve outcomes for patients with LUAD and LUSC.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7322428/v1/b84e87c011574a1f88589b44.png"},{"id":101691954,"identity":"5b6d24b4-7e15-4fc5-a57b-1406a3568553","added_by":"auto","created_at":"2026-02-02 16:16:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5630901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7322428/v1/de9a6f60-855a-4dd2-82e8-d393de011fe5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differential Transcriptomic Modulation by Histone Deacetylase Inhibitor SAHA in LUAD and LUSC","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer remains one of the leading causes of cancer-related mortality worldwide. Non-small cell lung cancer (NSCLC) constitutes approximately 85% of all lung cancer cases, significantly surpassing the incidence of small cell lung cancer (SCLC) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advancements in diagnostics and therapeutics, outcomes for NSCLC patients remain poor, primarily due to delayed diagnosis, rapid metastatic spread, and resistance to current treatment modalities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. NSCLC comprises diverse histological subtypes, notably lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), each characterized by unique molecular alterations and clinical behaviors. Correspondingly, LUAD cell lines (e.g., A549, NCI-H1299) are more frequently represented in research repositories compared to LUSC cell lines (e.g., NCI-H1703). Large-cell carcinoma lines (e.g., NCI-H460) and SCLC models (e.g., NCI-H69) constitute smaller proportions, reflecting their relative occurrence in clinical populations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These in vitro models collectively provide essential platforms to dissect lung cancer biology and evaluate novel therapies.\u003c/p\u003e\u003cp\u003eAt the molecular level, aberrations in epigenetic regulation \u0026ndash; particularly histone acetylation \u0026ndash; play critical roles in NSCLC pathogenesis. The balance of histone acetylation is finely regulated by histone acetyltransferases (HATs) and histone deacetylases (HDACs), with aberrant HDAC activity strongly implicated in lung cancer progression [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Elevated HDAC levels correlate significantly with aggressive tumor phenotypes and poor clinical outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. HDACs also modulate numerous non-histone substrates involved in critical cellular processes such as cell-cycle control, DNA repair, apoptosis, and signal transduction [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consequently, heightened HDAC activity fosters tumor growth, angiogenesis, invasion, and metastasis in NSCLC. For instance, HDAC1 overexpression is linked to increased tumor proliferation and poor patient survival [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], while elevated HDAC3 is associated with enhanced invasiveness [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven their crucial oncogenic roles, HDACs represent promising therapeutic targets. A variety of HDAC inhibitors (HDACi) have been developed, ranging from hydroxamic acids like suberoylanilide hydroxamic acid (SAHA, vorinostat) to cyclic peptides such as romidepsin [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These inhibitors enhance histone and non-histone protein acetylation, reactivating tumor suppressors and inhibiting cancer progression. SAHA demonstrates considerable anti-cancer potential across multiple lung cancer subtypes: promoting ubiquitin-acetylation network reprogramming in LUAD (A549, H1975) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; inducing G2/M cell-cycle arrest and suppressing HIF-1α/VEGF pathways in large cell lung carcinoma (NCI-H460) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]; and enhancing ROS-mediated apoptosis synergistically with chemotherapy in SCLC (H69, H82) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In NSCLC specifically, SAHA induces apoptosis, cell-cycle arrest, differentiation, and anti-angiogenic effects [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Clinically approved for refractory T-cell lymphoma, SAHA has also shown promising preclinical and early-phase clinical efficacy against NSCLC, especially when combined synergistically with targeted therapies such as EGFR inhibitors [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eNonetheless, not all NSCLC tumors respond uniformly to HDACi treatment. The genetic makeup of a lung cancer influences drug sensitivity, as mutations in key drivers like KRAS, EGFR, or ALK can modulate therapeutic response [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, KRAS mutations \u0026ndash; found in a significant subset of LUAD \u0026ndash; are known to confer resistance to EGFR-targeted inhibitors [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It is therefore plausible that LUAD and LUSC cells, which harbor distinct mutational landscapes, might exhibit differential responses to HDAC inhibition. However, this possibility has not been directly investigated: whether SAHA elicits different therapeutic effects and transcriptomic changes in LUAD versus LUSC remains unproven.\u003c/p\u003e\u003cp\u003eTo address this critical gap, we compared SAHA's transcriptomic and phenotypic effects in representative LUAD and LUSC cell lines, NCI-H1299 and NCI-H1703, respectively. NCI-H1299, derived from metastatic LUAD, is characterized by TP53-null status and NRASQ61K mutations, representing a classical RAS/MAPK-driven tumor model [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Conversely, NCI-H1703, derived from primary LUSC, harbors TP53 inactivation and focal amplifications of receptor tyrosine kinases like PDGFRA and an activating mutation in PIK3CA (E542K), exemplifying an RTK/PI3K-dependent cancer phenotype [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. By analyzing and contrasting the transcriptomic and phenotypic effects of SAHA in these two cell lines, we aimed to delineate subtype-specific mechanisms of HDAC inhibition. This comparative approach provides new insights into how LUAD and LUSC cells differentially respond to epigenetic therapy, paving the way toward more tailored HDACi-based treatment strategies for distinct NSCLC subtypes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eCell Lines and Drug Treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman NSCLC cell lines NCI-H1299 (large-cell/undifferentiated adenocarcinoma; TP53\u003csup\u003edel\u003c/sup\u003e, NRAS\u003csup\u003eQ61K\u003c/sup\u003e) and NCI-H1703 (squamous cell carcinoma; PDGFRA\u003csup\u003eamp\u003c/sup\u003e, PIK3CA\u003csup\u003eE542K\u003c/sup\u003e) were acquired from the American Type Culture Collection (ATCC, Manassas, VA, USA). Authentication was regularly performed every six months using 20-locus STR profiling [25]. Mycoplasma contamination was monitored using the MycoAlert Mycoplasma Detection Kit (Lonza). Cells were cultured in RPMI-1640 medium (Gibco) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin in a 5% CO₂ humidified incubator at 37\u0026deg;C.\u003c/p\u003e\n\u003cp\u003eVorinostat (SAHA; Sigma‑Aldrich #SML0061) was dissolved in dimethyl‑sulfoxide (DMSO) to a 10 mM stock and stored at\u0026nbsp;\u0026ndash;20 \u0026deg;C. Dosing regimens were determined in a 0.5\u0026ndash;20 \u0026micro;M, 2\u0026ndash;48 h pilot screen that quantified global H3K27 acetylation (ELISA/Western) and early apoptosis (Annexin\u0026nbsp;V/PI). A plateau in H3K27ac coupled with \u0026lt;15 % apoptosis was reached at 10 \u0026micro;M for 24 h; this total concentration corresponds to ~1 \u0026micro;M free SAHA in vivo\u0026ndash;i.e. the clinical Cmax after a single 400 mg dose\u0026nbsp;[26]. Accordingly, 10 \u0026micro;M \u0026times; 24 h was chosen for all transcriptomic experiments.\u003c/p\u003e\n\u003cp\u003eFor functional assays three exposure tiers were employed: 0.5\u0026ndash;2 \u0026micro;M (clinically achievable free drug), 10 \u0026micro;M (transcriptomic peak / total‑plasma Cmax) and 40 \u0026micro;M (mechanistic \u0026ldquo;saturating\u0026rdquo; dose commonly used in vitro [27]. Unless otherwise stated, cells were treated for 24 h with 0, 2.5, 10 or 40 \u0026micro;M SAHA; 0.1 % DMSO served as vehicle control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA Isolation, Quality Control, Library Preparation, and Sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from cells grown in 6-well plates (2 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well, ~70 % confluence at harvest) using TRIzol reagent (Invitrogen) and purified with the RNeasy Mini Kit (Qiagen), including an on-column DNase I digestion. RNA purity (A260/280 ratio = 2.05 \u0026plusmn; 0.04, A260/230 ratio = 1.98 \u0026plusmn; 0.03) was measured using NanoDrop 2000, and RNA integrity (RIN \u0026ge; 9.6) was confirmed by an Agilent 2100 Bioanalyzer. High-quality RNA (500 ng per sample) was used to generate strand-specific poly(A) libraries with the NEBNext Ultra II Directional RNA Library Prep Kit. Libraries were pooled and sequenced on an Illumina NovaSeq 6000 platform (paired-end, 2\u0026times;151 bp, median depth of 55 million reads per sample, Q30 \u0026gt;93% , ~130\u0026times; transcriptome breadth).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRead Processing and Alignment Sequencing quality was evaluated using FastQC v0.12.1. Adapters and low-quality bases were removed with fastp v0.23.4 (parameters: -q 20, -l 36). Clean reads were aligned in two-pass mode using STAR v2.7.11a against the human genome GRCh38.91 with Gencode v40 annotations. Alignment metrics were generated using Picard CollectRnaSeqMetrics. Gene-level counts were obtained using featureCounts v2.0.1.\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis gene counts were normalized and filtered (CPM \u0026gt; 1 in \u0026ge; 2 samples). Differentially expressed genes (DEGs) were identified using both edgeR v3.44 (quasi-likelihood F-test, FDR \u0026lt; 0.05, |Log₂ fold change| \u0026ge; 1) and DESeq2 v1.42 (Wald test, adjusted p-value \u0026lt; 0.05, |Log₂ fold change| \u0026ge; 1). edgeR results were primarily used for downstream analysis; DESeq2 results were\u0026nbsp;used for validation purposes.\u003c/p\u003e\n\u003cp\u003eBioinformatics analysis principal component analysis (PCA) and correlation analysis were conducted using R v4.3.1. Gene set enrichment analysis was performed with clusterProfiler v4.10 using MSigDB v2025.1 gene sets. Protein-protein interaction (PPI) networks were constructed using STRING v12 and visualized in Cytoscape v3.10.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApoptosis Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApoptosis was quantified by Annexin V\u0026ndash;FITC/propidium iodide (PI) staining (BD Pharmingen). Cells seeded in 12-well plates (1.5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per well) were harvested 24 h after treatment, stained, and analyzed on a BD FACSCanto II flow cytometer (10,000 events per sample). Data were processed in FlowJo v10.9. Results are reported as the mean \u0026plusmn; SD of three biological replicates, each performed in technical duplicate [28].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScratch and Trans-well Migration Assays\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell migration assays were performed in the presence of mitomycin C (10 \u0026micro;g/ml) to inhibit proliferation. Wound healing was monitored at 0 and 24 h, and trans-well migration assays used 8-\u0026micro;m inserts (Corning). Cells on inserts were fixed, stained with crystal violet, and quantified by measuring OD590, normalized to total protein content determined by BCA assay.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEPIA Database Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExpression and survival analyses were carried out with GEPIA2 (January 2025 release), which integrates TCGA-LUAD, TCGA-LUSC, and GTEx normal-lung datasets. The downloaded snapshot contained TCGA-LUAD (n = 483), TCGA-LUSC (n = 486), and GTEx lung-normal (n = 288) samples. Transcript-per-million (TPM) expression values were retrieved and re-analysed locally. Kaplan\u0026ndash;Meier survival curves were generated from the TPM data in R using the \u003cstrong\u003esurvival\u003c/strong\u003e and \u003cstrong\u003esurvminer\u003c/strong\u003e packages.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Accession\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData Availability Raw and processed sequencing data have been deposited in GEO (accession: GSE143423).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments were independently repeated at least three times. Data are presented as mean \u0026plusmn; SD. Statistical significance was assessed using two-tailed Student\u0026rsquo;s t-tests or one-way ANOVA followed by Tukey\u0026apos;s post-hoc tests, with p \u0026lt; 0.05 considered significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDifferential Impact of SAHA on the Transcriptome in Different Lung Cancer Cell Lines\u003c/h2\u003e\n \u003cp\u003eUpon treatment with the class I/II HDAC inhibitor SAHA, RNA-seq profiling revealed substantial differences in transcriptional responses between LUAD-derived H1299 (NRAS\u003csup\u003eQ61K\u003c/sup\u003e, TP53\u003csup\u003edel\u003c/sup\u003e) and LUSC-derived H1703 (PDGFRA\u003csup\u003eamp\u003c/sup\u003e, PIK3CA\u003csup\u003eE542K\u003c/sup\u003e, TP53\u003csup\u003eWT\u003c/sup\u003e) cell lines. At baseline, 1854 genes were constitutively upregulated in H1299 relative to H1703, whereas 3114 genes showed higher baseline expression in H1703, underscoring significant intrinsic divergence between the cell lines.\u003c/p\u003e\n \u003cp\u003eIn H1299 cells treated with SAHA, 1098 genes exhibited significant expression changes (887 upregulated, 211 downregulated). Prominent upregulated genes included \u003cem\u003eANKRD1\u003c/em\u003e, \u003cem\u003eNGFR\u003c/em\u003e, and \u003cem\u003eCPA4\u003c/em\u003e, while key downregulated genes included \u003cem\u003eFOSL1\u003c/em\u003e and \u003cem\u003ePHF19\u003c/em\u003e. In H1703, SAHA induced changes in 1532 genes (889 upregulated, 643 downregulated), prominently increasing \u003cem\u003eTFPI2\u003c/em\u003e, \u003cem\u003eVGF\u003c/em\u003e, and \u003cem\u003eSAA1\u003c/em\u003e, and notably decreasing \u003cem\u003eNMMT\u003c/em\u003e, \u003cem\u003eAKR1B10\u003c/em\u003e, and \u003cem\u003eTOP2A\u003c/em\u003e (Fig. 1A). Intersection analysis across differential expression gene (DEG) sets demonstrated limited overlap: only 334 genes were commonly upregulated and 103 commonly downregulated by SAHA across both lines, reflecting cell-specific responses. Unique SAHA-induced transcripts were abundant, with 316 in H1299 and 322 in H1703, highlighting substantial lineage-specific transcriptional landscapes (Fig. 1B).\u003c/p\u003e\n \u003cp\u003eHeatmap analysis of the top 60 DEGs (30 most up- and 30 most downregulated, ranked by |Log₂FC| \u0026times; \u0026ndash;Log₁₀FDR) identified common SAHA-responsive targets such as \u003cem\u003eCITED2\u003c/em\u003e, \u003cem\u003eANKRD1\u003c/em\u003e, and \u003cem\u003eMT1X\u003c/em\u003e, but with significantly greater induction observed in H1299 (Fig. 1C). Furthermore, lineage-specific gene regulation became evident; H1299 uniquely exhibited downregulation of interferon-related genes \u003cem\u003eIFIT2\u003c/em\u003e and \u003cem\u003eIFIT3\u003c/em\u003e, and epithelial plasticity-related genes \u003cem\u003eNTSR1\u003c/em\u003e and \u003cem\u003ePRRX1\u003c/em\u003e, alongside marked induction of \u003cem\u003eTUBB2B\u003c/em\u003e and \u003cem\u003eNOTCH3\u003c/em\u003e. In contrast, H1703 showed preferential repression of mitotic regulators \u003cem\u003eSPC25\u003c/em\u003e and \u003cem\u003eAURKB\u003c/em\u003e, and the chemokine \u003cem\u003eCCL2\u003c/em\u003e, coupled with induction of acute-phase and ECM-associated genes \u003cem\u003eLCN2\u003c/em\u003e, \u003cem\u003eS100P\u003c/em\u003e, and \u003cem\u003eTYMP\u003c/em\u003e (Fig. 1D). This indicates that SAHA amplifies pre-existing lineage-specific transcriptional distinctions.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eFunctional Pathway Enrichment of SAHA-Induced Differentially Expressed Genes\u003c/h2\u003e\n \u003cp\u003ePathway enrichment analysis underscored distinct SAHA-driven signaling alterations in both cell lines (Fig. 2A). In H1299, SAHA treatment strongly activated pathways involved in cellular morphogenesis, cell adhesion, growth-factor responses, and Hippo signaling, with significant induction of \u003cem\u003eNOTCH3\u003c/em\u003e, \u003cem\u003eBMP2\u003c/em\u003e, \u003cem\u003ePDGFB\u003c/em\u003e, \u003cem\u003eFGFR3\u003c/em\u003e, and EMT regulators such as \u003cem\u003eSNAI2\u003c/em\u003e. Simultaneously, interferon-alpha response, DNA damage-induced senescence, and pro-apoptotic pathways were markedly downregulated, emphasizing a balance shift toward an EMT-related phenotype (Fig. 2B). Conversely, in H1703, SAHA prominently activated pathways related to extracellular matrix remodeling, complement activation, and chemotaxis, evidenced by robust induction of genes including \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eTXNIP\u003c/em\u003e, \u003cem\u003eLAMB3\u003c/em\u003e, and \u003cem\u003eKRT20\u003c/em\u003e. Meanwhile,pathways strongly downregulated in H1703 primarily involved cell cycle regulation, DNA replication, and DNA repair, indicated by decreased expression of \u003cem\u003eE2F1\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, and \u003cem\u003eCDK1\u003c/em\u003e (Fig. 2B), suggesting a phenotypic shift toward cell cycle arrest and enhanced extracellular matrix remodeling.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eGene Set Enrichment Analysis Reveals Lineage-Specific Pathway Modulation by SAHA\u003c/h2\u003e\n \u003cp\u003eGene Set Enrichment Analysis (GSEA) further dissected differential hallmark pathway responses post-SAHA treatment (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA). In H1299, notable positive enrichment was observed for KRAS SIGNALING DN (NES\u0026thinsp;=\u0026thinsp;1.76) and EPITHELIAL MESENCHYMAL TRANSITION (EMT; NES\u0026thinsp;=\u0026thinsp;1.60) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB). These changes were driven by enhanced feedback inhibition (\u003cem\u003eDUSP\u003c/em\u003e/\u003cem\u003eSPRY\u003c/em\u003e genes) on ERK\u0026ndash;FAK signaling and substantial EMT-related transcriptional activation in the TP53-null context. In contrast, H1703 exhibited pronounced activation of KRAS SIGNALING UP (NES\u0026thinsp;=\u0026thinsp;1.53) and COMPLEMENT pathways (NES\u0026thinsp;=\u0026thinsp;1.44) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC), supported by hyper-acetylation of the \u003cem\u003ePDGFRA\u003c/em\u003e super-enhancer and STAT3/NF-\u0026kappa;B driven inflammation. Both cell lines exhibited significant downregulation of E2F TARGETS and G2M CHECKPOINT pathways, indicative of impaired cell cycle progression (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD-E).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eDifferential Sensitivity of HDAC Isoforms to SAHA in a Cell Line\u0026ndash;Specific Manner\u003c/h2\u003e\n \u003cp\u003eHDAC enzyme analysis revealed distinct epigenetic responses underpinning these lineage-specific transcriptional outcomes. SAHA treatment did not alter the transcript levels of nuclear class I HDACs (\u003cem\u003eHDAC1\u003c/em\u003e, \u003cem\u003eHDAC2\u003c/em\u003e, \u003cem\u003eHDAC8\u003c/em\u003e, \u003cem\u003eHDAC10\u003c/em\u003e), despite differing baseline expression between the cell lines. However, marked differences emerged in cytoplasmic class II HDAC modulation: H1703 exhibited substantial transcript downregulation of \u003cem\u003eHDAC4\u003c/em\u003e and \u003cem\u003eHDAC6\u003c/em\u003e, disrupting tubulin and HSP90 acetylation balance, thus enhancing p53-mediated checkpoint and complement signaling. Conversely, H1299 displayed minimal modulation of cytoplasmic \u003cem\u003eHDAC7\u003c/em\u003e and \u003cem\u003eHDAC9\u003c/em\u003e, maintaining cytoskeletal dynamics yet intensifying MAPK feedback and modestly promoting apoptosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003ePPI network analysis highlighted cell-cycle and histone modification-associated hubs. H1299 hubs primarily encompassed \u003cem\u003eBUB1B\u003c/em\u003e, \u003cem\u003eCCNA2\u003c/em\u003e, \u003cem\u003eCCNB1\u003c/em\u003e, \u003cem\u003eMCM7\u003c/em\u003e, \u003cem\u003eMYC\u003c/em\u003e, \u003cem\u003ePLK1\u003c/em\u003e, \u003cem\u003eTTK\u003c/em\u003e, \u003cem\u003eCCNB2\u003c/em\u003e, and \u003cem\u003eBRCA1\u003c/em\u003e. In contrast, H1703 hubs prominently featured a broader set of cell-cycle regulators including \u003cem\u003eBUB1\u003c/em\u003e, \u003cem\u003eCDK1\u003c/em\u003e, \u003cem\u003eCDC6\u003c/em\u003e, \u003cem\u003eCHEK1\u003c/em\u003e, \u003cem\u003eMAD2L1\u003c/em\u003e, \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eCDC45\u003c/em\u003e, and \u003cem\u003eAURKB\u003c/em\u003e, reflecting a stronger DNA damage and apoptotic signaling axis post-SAHA treatment (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB-E).\u003c/p\u003e\n \u003cp\u003eCollectively, these data indicate that SAHA induces cell-line-specific transcriptional reprogramming driven by distinct oncogenic mutations and HDAC enzyme profiles. In H1299, RAS-dependency and absence of p53 limit apoptosis but markedly suppress cell migration. In contrast, H1703 cells, enriched in PDGFRA and PI3K signaling and harboring intact p53, exhibit pronounced apoptotic signaling but relatively modest migratory inhibition, highlighting the profound influence of genetic and epigenetic context in shaping HDAC inhibitor responses.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003ePrognostic Significance of SAHA-Regulated Genes in Overall Survival (OS) in LUAD and LUSC\u003c/h2\u003e\n \u003cp\u003eTo explore the clinical relevance of SAHA-modulated transcriptional changes, we performed Kaplan\u0026ndash;Meier survival analyses on independent LUAD and LUSC cohorts. To functionally validate the effect of SAHA on cell fate, we first assessed apoptosis by Annexin V-FITC/PI staining. Flow-cytometric analysis revealed that SAHA induced dose-dependent apoptosis in both H1299 and H1703 cells (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eA). In H1299 cells, SAHA treatment led to a progressive increase in late apoptotic/necrotic cells in the upper right (UR) quadrant (from 0.02\u0026ndash;61.7%) and early apoptotic cells in the lower right (LR) quadrant (from 0.01\u0026ndash;18.2%), accompanied by a marked decrease in viable cells in the lower left (LL) quadrant (from 99.9\u0026ndash;17.0%). A similar but more pronounced apoptotic response was observed in H1703 cells, with late apoptotic/necrotic populations (UR quadrant) rising from 0.11\u0026ndash;89.5%, and viable cells (LL quadrant) declining sharply from 99.8\u0026ndash;0.99% across increasing SAHA concentrations. These data indicate that SAHA triggers apoptosis in a concentration-dependent manner, with H1703 cells exhibiting greater sensitivity.\u003c/p\u003e\n \u003cp\u003eWe next examined whether SAHA-induced transcriptional changes were clinically relevant in lung cancer patients. We first identified overlap between DEGs in SAHA-treated H1299 and H1703 cells and genes significantly associated with OS in TCGA-derived datasets. In LUAD patients, high tumor expression of \u003cem\u003eHMMR\u003c/em\u003e, \u003cem\u003eS100A16\u003c/em\u003e, \u003cem\u003eGTF3C6\u003c/em\u003e, and \u003cem\u003ePLK1\u003c/em\u003e, as well as low expression of \u003cem\u003eGNG7\u003c/em\u003e, were each associated with significantly reduced OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Correspondingly, SAHA treatment robustly suppressed \u003cem\u003eHMMR\u003c/em\u003e, \u003cem\u003eS100A16\u003c/em\u003e, \u003cem\u003eGTF3C6\u003c/em\u003e, and \u003cem\u003ePLK1\u003c/em\u003e expression while upregulating \u003cem\u003eGNG7\u003c/em\u003e in H1299 cells. Correlation analysis further revealed that in tumor tissues\u0026mdash;but not normal lung\u0026mdash;\u003cem\u003eHDAC9\u003c/em\u003e expression correlated positively with \u003cem\u003eHMMR\u003c/em\u003e and \u003cem\u003ePLK1\u003c/em\u003e levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), implicating an HDAC9\u0026ndash;HMMR/PLK1 axis in driving proliferation and migration that impacts patient prognosis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eIn LUSC patients, elevated expression of \u003cem\u003ePAPPA\u003c/em\u003e and \u003cem\u003eSAMD11\u003c/em\u003e and reduced expression of \u003cem\u003eSLC9A9\u003c/em\u003e predicted poorer OS (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). SAHA treatment in H1703 cells significantly decreased \u003cem\u003ePAPPA\u003c/em\u003e and \u003cem\u003eSAMD11\u003c/em\u003e transcripts while increasing \u003cem\u003eSLC9A9\u003c/em\u003e levels. Importantly, correlation analysis in LUSC tumor samples\u0026mdash;but not in matched normal tissue\u0026mdash;demonstrated significant associations between \u003cem\u003eHDAC4\u003c/em\u003e and \u003cem\u003ePAPPA\u003c/em\u003e, and between \u003cem\u003eHDAC6\u003c/em\u003e and \u003cem\u003eSAMD11\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), defining a lineage-specific HDAC4/6\u0026ndash;PAPPA/SAMD11 regulatory module that modulates anti-apoptotic and dedifferentiation pathways and consequently influences OS (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eCollectively, these data support a model in which SAHA improves clinical prognosis by targeting distinct HDAC\u0026ndash;gene axes: in LUAD, SAHA inhibits HDAC9 to downregulate HMMR and PLK1, and in LUSC it targets HDAC4/6 to suppress PAPPA and SAMD11, thereby modulating key survival pathways and extending OS.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eSAHA Suppresses Cell Migration Through Lineage-Dependent Mechanisms\u003c/h2\u003e\n \u003cp\u003eFurther, the influence of SAHA on cell migration\u0026mdash;an essential step in metastasis\u0026mdash;was systematically assessed using cell scratch assays. In this assay, H1299 cells treated with SAHA (0.5 or 1 \u0026micro;g/mL, 48 h) exhibited markedly impaired migratory ability (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B), whereas H1703 cells displayed a more modest inhibition under the same conditions (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC\u0026ndash;D). Although H1703 cells had a lower baseline migratory capacity compared to H1299, SAHA still significantly suppressed migration in both cell lines. When treatment duration was shortened to 24 hours, SAHA (0.5 or 1 \u0026micro;g/mL) continued to inhibit H1299 cell migration significantly (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;B), while no substantial effect was observed in H1703 cells under identical conditions (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC\u0026ndash;D).\u003c/p\u003e\n \u003cp\u003eIntegration of these functional data with transcriptomic analyses revealed lineage-specific molecular correlates underpinning the observed phenotypes: H1299 migration inhibition correlated strongly with upregulation of KRAS_SIGNALING_DN and epithelial-to-mesenchymal transition (EMT)-related pathways, likely driven by enhanced MAPK feedback inhibitors (e.g., \u003cem\u003eDUSP4/6\u003c/em\u003e, \u003cem\u003eSPRY2/4\u003c/em\u003e). Conversely, modest migration suppression in H1703 correlated with pronounced complement activation and extracellular matrix remodeling pathways, mediated by HDAC4/6 loss and subsequent induction of inflammatory and ECM-associated genes (e.g., \u003cem\u003eBIRC3\u003c/em\u003e, \u003cem\u003eLAMB3\u003c/em\u003e, \u003cem\u003eKRT20\u003c/em\u003e). Collectively, these results emphasize SAHA\u0026rsquo;s robust yet divergent modulation of migratory pathways dictated by the distinct genetic (NRAS\u003csup\u003eQ61K\u003c/sup\u003e, TP53\u003csup\u003edel\u003c/sup\u003e in H1299; PDGFRA\u003csup\u003eamp\u003c/sup\u003e, PIK3CA\u003csup\u003eE542K\u003c/sup\u003e, TP53\u003csup\u003eWT\u003c/sup\u003e in H1703) and epigenetic (differential HDAC enzyme sensitivity) contexts. Our findings underscore the therapeutic potential of SAHA as a targeted epigenetic modulator in lung cancer metastasis management, warranting further mechanistic exploration and clinical translation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLung cancer remains one of the most lethal malignancies, and NSCLC \u0026ndash; especially LUAD and LUSC \u0026ndash; poses a particular challenge due to its marked molecular diversity and propensity for treatment failure. LUAD and LUSC are driven by distinct oncogenic alterations (for example, EGFR or KRAS mutations in LUAD versus PIK3CA or FGFR1 amplifications in LUSC) that critically shape both therapeutic vulnerabilities and resistance profiles [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Among the many factors contributing to NSCLC progression, epigenetic dysregulation\u0026mdash;most notably aberrant histone acetylation via HDACs\u0026mdash;has emerged as a key driver of tumor cell proliferation, invasion, and metastasis. Elevated HDAC activity not only silences tumor-suppressor genes but also promotes resistance to chemotherapy and targeted agents by altering chromatin accessibility and stabilizing oncogenic proteins [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Accordingly, the pan‐HDAC inhibitor SAHA (vorinostat) has demonstrated robust anticancer activity in preclinical NSCLC models, inducing histone and non‐histone hyperacetylation, triggering apoptosis, arresting the cell cycle, and promoting differentiation [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. SAHA has also synergized with chemotherapy, radiotherapy, and targeted inhibitors in vitro and in vivo. Yet, clinical trials in unselected NSCLC populations have yielded only modest benefit, highlighting the urgent need to understand how genetic and epigenetic contexts modulate HDACi sensitivity.\u003c/p\u003e\u003cp\u003eIn our work, we compared the transcriptome-wide effects of SAHA in two prototypical NSCLC cell lines: NCI‐H1299 (LUAD, NRAS\u003csup\u003eQ61K\u003c/sup\u003e, TP53\u003csup\u003e-\u003c/sup\u003e) and NCI‐H1703 (LUSC, PDGFRA\u003csup\u003eamp\u003c/sup\u003e, PIK3CA\u003csup\u003eE542K\u003c/sup\u003e, TP53\u003csup\u003eWT\u003c/sup\u003e) \u0026ndash; alterations that faithfully recapitulate key subtype‐defining lesions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Strikingly, SAHA elicited profoundly different gene‐expression programs in these two models. In RAS‐driven, p53‐deficient H1299 cells, SAHA upregulated mesenchymal and developmental pathways (including EMT, Notch, BMP, and PDGF signaling) while sharply repressing interferon and pro‐apoptotic modules. This suggests that, lacking a functional p53 axis, these cells mount a complex differentiation and stress‐response program rather than undergoing robust apoptosis. By contrast, p53‐intact, PDGFRA/PI3K‐amplified H1703 cells responded to SAHA with strong activation of complement and coagulation cascades, extracellular‐matrix remodeling, and inflammatory‐chemotaxis genes, coupled with downregulation of E2F targets and G2\u0026ndash;M checkpoint regulators. These data indicate that HDAC inhibition in LUSC predominantly triggers immunomodulatory and cell‐cycle arrest pathways.\u003c/p\u003e\u003cp\u003eThis dichotomy underscores the pivotal role of p53 status and dominant oncogenic drivers in shaping HDACi sensitivity. Functional p53 cooperates with HDAC inhibitors to amplify cell-cycle arrest and apoptosis \u0026ndash; partly by relieving MDM2‐mediated repression and stabilizing p53 when DNA‐repair networks are suppressed [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Indeed, prior studies have shown that HDACi efficacy is substantially blunted in p53‐null settings, whereas p53‐proficient models exhibit dramatic apoptotic responses [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Consistent with this, H1703 but not H1299 underwent pronounced SAHA‐induced apoptosis and proliferative blockade. Importantly, SAHA retains activity in p53‐deficient cells \u0026ndash; manifesting more as differentiation or cytostasis than cell death \u0026ndash; suggesting that combination regimens (e.g. pairing HDACi with agents that restore p53 function or activate parallel death pathways) may be necessary to overcome resistance in TP53‐mutant NSCLC.\u003c/p\u003e\u003cp\u003eWe also uncovered an isoform-specific dimension to SAHA\u0026rsquo;s actions. In H1703 cells, SAHA markedly downregulated class II HDACs (HDAC4 and HDAC6). HDAC6 inhibition hyperacetylates α‐tubulin and HSP90, disrupting cytoskeletal dynamics and chaperone function, which leads to proteotoxic stress and destabilization of key kinases, thereby enhancing p53‐dependent checkpoints [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. HDAC4 loss may further de‐repress pro‐apoptotic and differentiation genes by dismantling repressor complexes [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Notably, HDAC4/6 expression correlated with tumor‐promoting genes (\u003cem\u003ePAPPA\u003c/em\u003e, \u003cem\u003eSAMD11\u003c/em\u003e) in LUSC patient samples, implying a unique dependency that could be therapeutically exploited. These findings advocate for clinical testing of isoform‐selective HDAC4/6 inhibitors \u0026ndash; alone or alongside RTK/PI3K antagonists \u0026ndash; in molecularly defined squamous NSCLC.\u003c/p\u003e\u003cp\u003eAn unexpected divergence emerged in migratory behavior. Despite limited apoptosis, H1299 cells displayed strong migration inhibition after SAHA exposure, driven by upregulation of negative RAS\u0026ndash;MAPK feedback regulators (\u003cem\u003eDUSP4/5\u003c/em\u003e, \u003cem\u003eSPRY2/4\u003c/em\u003e) and EMT modulators. By re-inducing DUSPs, SAHA effectively suppresses KRAS signaling, reducing motility in RAS‐mutant LUAD. In contrast, H1703 cells only modestly curtailed migration, consistent with gene‐expression shifts toward ECM remodeling and complement activation rather than epithelial reprogramming. This context‐dependent anti‐migratory effect suggests that combining SAHA with MAPK inhibitors could further block metastasis in KRAS‐driven adenocarcinomas, whereas in LUSC a different combination (e.g., ECM‐targeting or complement‐modulating agents) may be more effective.\u003c/p\u003e\u003cp\u003eFinally, by integrating SAHA-regulated gene signatures with TCGA survival data, we identified prognostic biomarkers whose expression is reversed by HDAC inhibition. LUAD patients with high \u003cem\u003eHMMR\u003c/em\u003e, \u003cem\u003eS100A16\u003c/em\u003e, \u003cem\u003eGTF3C6\u003c/em\u003e, \u003cem\u003ePLK1\u003c/em\u003e and low \u003cem\u003eGNG7\u003c/em\u003e \u0026ndash; the very profile SAHA normalizes in H1299 \u0026ndash; exhibit poorer disease‐free survival. Similarly, LUSC patients with elevated \u003cem\u003ePAPPA\u003c/em\u003e/\u003cem\u003eSAMD11\u003c/em\u003e and reduced \u003cem\u003eSLC9A9\u003c/em\u003e \u0026ndash; reversed by SAHA in H1703 \u0026ndash; have worse outcomes. These biomarkers could guide patient selection and enable precision deployment of HDAC inhibitors, alone or in combination.\u003c/p\u003e\u003cp\u003eIn summary, our work reveals that SAHA\u0026rsquo;s antitumor efficacy is intricately shaped by NSCLC subtype\u0026ndash;specific genetic and epigenetic contexts \u003cb\u003e(Fig.\u0026nbsp;7)\u003c/b\u003e. Harnessing this knowledge to rationally design combination therapies \u0026ndash; for example, pairing SAHA or isoform-selective HDACi with MAPK or RTK/PI3K inhibitors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], or integrating immune modulators [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] \u0026ndash; promises to overcome intrinsic and acquired resistance, enhance clinical responses, and ultimately improve outcomes for patients with LUAD and LUSC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDACs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHistone deacetylases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003enon-small cell lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDACi\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHDAC inhibitor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSCLC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esmall cell lung cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUAD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elung adenocarcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLUSC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elung squamous cell carcinoma\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHATs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehistone acetyltransferases\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSAHA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003esuberoylanilide hydroxamic acid\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003edifferentially expressed genes\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePCA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprincipal component analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePPI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eprotein-protein interaction\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTPM\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etranscript-per-million\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGSEA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eEMT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eepithelial-to-mesenchymal transition\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall Survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eupper right\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elower right\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eLL\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003elower left\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFW, XHD, LS, and HYC contributed equally to this work. FW and XHD designed the study and performed the experiments. LS and HYC analyzed the data and prepared the figures. YTH, XJM, and YSL contributed to data collection and interpretation. XLG, CW and GXY assisted in the experimental design and methodology. XHF and XYY provided critical revisions and supervised the project. LMZ and JFS conceptualized the study, and supervised the research. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciated the National Natural Science Foundation of China (82070406, 82072047, and 82170008), Guangdong Province Postgraduate Education Innovation Plan Project (2022SFKC054), Guangdong Basic and Applied Basic Research Fund Enterprise Joint Fund (2023A1515220024), Guangdong Province Hospital Pharmacy Research Fund (2024A31), Medical Research Foundation of Guangdong Province (2024070), The Open research funds from The Sixth Affiliated Hospital of Guangzhou Medical University (Qingyuan Peoples Hospital, 202201-303) and the research funds from Guangzhou Panyu Central Hospital (PY-2023-007, PY-2023-010, PY-2023-026).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe also thank for the help from BioRender.com for drawing illustration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAl Bakir M, Spencer S, Kumar P, et al. The evolution of non-small cell lung cancer metastases in TRACERx. \u003cem\u003eNature\u003c/em\u003e. 2023;616(7957):534\u0026ndash;542. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-023-05729-x\u003c/span\u003e\u003cspan address=\"10.1038/s41586-023-05729-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMegyesfalvi Z, Pipek O, Kiss A, et al. Clinical insights into small cell lung cancer: tumour heterogeneity, diagnosis, therapy and future directions. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 2023;73(6):620\u0026ndash;652. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21785\u003c/span\u003e\u003cspan address=\"10.3322/caac.21785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGazdar AF, Hirsch FR, Minna JD. From mice to men and back: an assessment of preclinical model systems for the study of lung cancers. \u003cem\u003eJ Thorac Oncol\u003c/em\u003e. 2016;11(3):287\u0026ndash;299. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jtho.2015.10.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jtho.2015.10.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGazdar AF, Girard L, Lockwood WW, Lam WL, Minna JD. Lung cancer cell lines as tools for biomedical discovery and research. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 2010;102(17):1310\u0026ndash;1320. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jnci/djq279\u003c/span\u003e\u003cspan address=\"10.1093/jnci/djq279\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMamdani H, Jalal SI. Histone deacetylase inhibition in non-small cell lung cancer: hype or hope? \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e. 2020;8:582370. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcell.2020.582370\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2020.582370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShanmugam G, Rakshit S, Sarkar K. HDAC inhibitors: targets for tumour therapy, immune modulation and lung diseases. \u003cem\u003eTransl Oncol\u003c/em\u003e. 2021;14(4):101312. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tranon.2021.101312\u003c/span\u003e\u003cspan address=\"10.1016/j.tranon.2021.101312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo KM, Wang JZ, Wang H, et al. HDAC7 promotes NSCLC proliferation and metastasis via stabilisation by deubiquitinase USP10 and activation of the β-catenin\u0026ndash;FGF18 pathway. \u003cem\u003eJ Exp Clin Cancer Res\u003c/em\u003e. 2022;41:91. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13046-022-02266-9\u003c/span\u003e\u003cspan address=\"10.1186/s13046-022-02266-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Y, Zhao H, Wang X, et al. Mechanisms of HDACs in cancer development. \u003cem\u003eFront Immunol\u003c/em\u003e. 2025;16:1529239. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2025.1529239\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2025.1529239\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWahi A, Singh R, Gupta P, et al. Progress in discovery and development of natural inhibitors of histone deacetylases as anticancer agents. \u003cem\u003eNaunyn-Schmiedeberg\u0026rsquo;s Arch Pharmacol\u003c/em\u003e. 2024;397(2):675\u0026ndash;702. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00210-023-02460-8\u003c/span\u003e\u003cspan address=\"10.1007/s00210-023-02460-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu R, Jin Y, Zheng M. USP5-dependent HDAC1 promotes cisplatin resistance and malignant progression of non-small cell lung cancer by regulating RILP acetylation. \u003cem\u003eThorac Cancer\u003c/em\u003e. 2024;15(1):e15478. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/1759-7714.15478\u003c/span\u003e\u003cspan address=\"10.1111/1759-7714.15478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuerriero JL, Sotayo A, Ponichtera HE, et al. Class IIa HDAC inhibition reduces breast tumours and metastases through anti-tumour macrophages. \u003cem\u003eNature\u003c/em\u003e. 2017;543(7645):428\u0026ndash;432. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature21409\u003c/span\u003e\u003cspan address=\"10.1038/nature21409\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCappellacci L, Perinelli DR, Maggi F, Grifantini M, Petrelli R. Recent progress in histone deacetylase inhibitors as anticancer agents. \u003cem\u003eCurr Med Chem\u003c/em\u003e. 2020;27(15):2449\u0026ndash;2493. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/0929867325666181016163110\u003c/span\u003e\u003cspan address=\"10.2174/0929867325666181016163110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWu Q, Chen D, Chen G, Chang J, Zhang W. Suberoylanilide hydroxamic acid treatment reveals crosstalk among proteome, ubiquitylome and acetylome in non-small cell lung cancer A549 cells. \u003cem\u003eSci Rep\u003c/em\u003e. 2015;5:9520. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep09520\u003c/span\u003e\u003cspan address=\"10.1038/srep09520\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhao Y, Tan J, Zhang Q, et al. Anticancer activity of SAHA, a potent histone deacetylase inhibitor, in NCI-H460 human large-cell lung carcinoma cells \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. \u003cem\u003eInt J Oncol\u003c/em\u003e. 2014;44(2):451\u0026ndash;458. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3892/ijo.2013.2193\u003c/span\u003e\u003cspan address=\"10.3892/ijo.2013.2193\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBruzzese F, Abbate R, Alexandrova E, et al. Synergistic antitumour effect between vorinostat and topotecan in small cell lung cancer cells is mediated by reactive oxygen species and DNA damage-induced apoptosis. \u003cem\u003eMol Cancer Ther\u003c/em\u003e. 2009;8(11):3075\u0026ndash;3087. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1535-7163.MCT-09-0254\u003c/span\u003e\u003cspan address=\"10.1158/1535-7163.MCT-09-0254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi G, Zhu Y, Zhang Y, et al. The roles of histone deacetylases and their inhibitors in cancer therapy. \u003cem\u003eFront Cell Dev Biol\u003c/em\u003e. 2020;8:576946. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcell.2020.576946\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2020.576946\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIżykowska K, Rassek K, Korsak D, et al. Novel targeted therapies of T-cell lymphomas. \u003cem\u003eJ Hematol Oncol\u003c/em\u003e. 2020;13:176. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13045-020-01006-w\u003c/span\u003e\u003cspan address=\"10.1186/s13045-020-01006-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSchelker C, Nowak-Sliwinska P, Borchard G. HDACIs and TKIs combinations and their liposomal delivery for cancer treatment. \u003cem\u003eJ Control Release\u003c/em\u003e. 2023;358:59\u0026ndash;77. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jconrel.2023.04.006\u003c/span\u003e\u003cspan address=\"10.1016/j.jconrel.2023.04.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTsukumo Y, Naito M, Suzuki T. Influence of EGFR-activating mutations on sensitivity to tyrosine kinase inhibitors in a KRAS-mutant non-small cell lung cancer cell line. \u003cem\u003ePLOS ONE\u003c/em\u003e. 2020;15(3):e0229712. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0229712\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0229712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDeng S, Clowers K, Velasco M, et al. Understanding the complexity of the tumour microenvironment in KRAS-mutant lung cancer: finding an alternative path to prevention and treatment. \u003cem\u003eFront Oncol\u003c/em\u003e. 2020;9:1556. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fonc.2019.01556\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2019.01556\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen J, Yang H, Teo ASM, et al. Genomic landscape of lung adenocarcinoma in East Asians. \u003cem\u003eNat Genet\u003c/em\u003e. 2020;52(2):177\u0026ndash;186. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-019-0569-6\u003c/span\u003e\u003cspan address=\"10.1038/s41588-019-0569-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYe Y, Luo Y, Sun Y, et al. Single-cell transcriptomic profiling reveals KRAS/TP53-driven neutrophil reprogramming in LUAD: a multi-gene prognostic model and therapeutic targeting of RHOV. \u003cem\u003eOncol Res\u003c/em\u003e. 2025;33(6):1383\u0026ndash;1404. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.32604/or.2025.062584\u003c/span\u003e\u003cspan address=\"10.32604/or.2025.062584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYou L, Yang CT, Jablons DM. ONYX-015 works synergistically with chemotherapy in lung cancer cell lines and primary cultures freshly made from lung cancer patients. \u003cem\u003eCancer Res\u003c/em\u003e. 2000;60(4):1009\u0026ndash;1013. [No DOI]\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang Z, Ren X, Lu X, et al. GZD856, a novel potent PDGFRα/β inhibitor, suppresses the growth and migration of lung cancer cells \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. \u003cem\u003eCancer Lett\u003c/em\u003e. 2016;375(1):172\u0026ndash;178. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.canlet.2016.02.017\u003c/span\u003e\u003cspan address=\"10.1016/j.canlet.2016.02.017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKapeleris J, M\u0026uuml;ller-Bark J, Ranjit S, et al. Modelling reoxygenation effects in non-small cell lung cancer cell lines and showing epithelial\u0026ndash;mesenchymal transition. \u003cem\u003eJ Cancer Res Clin Oncol\u003c/em\u003e. 2022;148(12):3501\u0026ndash;3510. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00432-022-04242-4\u003c/span\u003e\u003cspan address=\"10.1007/s00432-022-04242-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKelly RJ, Rajan A, Force J, et al. Evaluation of KRAS mutations, angiogenic biomarkers and DCE-MRI in patients with advanced non-small-cell lung cancer receiving sorafenib. \u003cem\u003eClin Cancer Res\u003c/em\u003e. 2011;17(5):1190\u0026ndash;1199. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-10-2331\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-10-2331\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGupta M, Concepcion CP, Fahey CG, et al. BRG1 loss predisposes lung cancers to replicative stress and ATR dependency. \u003cem\u003eCancer Res\u003c/em\u003e. 2020;80(18):3841\u0026ndash;3854. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/0008-5472.CAN-20-1744\u003c/span\u003e\u003cspan address=\"10.1158/0008-5472.CAN-20-1744\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSalimi V, Ranjbar S, Yousefi B, et al. Sodium butyrate promotes apoptosis in breast cancer cells through reactive oxygen species formation and mitochondrial impairment. \u003cem\u003eLipids Health Dis\u003c/em\u003e. 2017;16:208. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12944-017-0575-y\u003c/span\u003e\u003cspan address=\"10.1186/s12944-017-0575-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShen Y, Chen J-Q, Li X-P. Differences between lung adenocarcinoma and lung squamous cell carcinoma: driver genes, therapeutic targets and clinical efficacy. \u003cem\u003eGenes Dis\u003c/em\u003e. 2025;12(3):101374. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.gendis.2024.101374\u003c/span\u003e\u003cspan address=\"10.1016/j.gendis.2024.101374\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eContreras-Sanz\u0026oacute;n E, Prado-Garcia H, Romero-Garcia S, et al. Histone deacetylases modulate resistance to therapy in lung cancer. \u003cem\u003eFront Genet\u003c/em\u003e. 2022;13:960263. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fgene.2022.960263\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2022.960263\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTraynor AM, Dubey S, Eickhoff JC, et al. Vorinostat in patients with relapsed non-small cell lung cancer: a Wisconsin Oncology Network phase II study. \u003cem\u003eJ Thorac Oncol\u003c/em\u003e. 2009;4(4):522\u0026ndash;526. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/JTO.0b013e3181952478\u003c/span\u003e\u003cspan address=\"10.1097/JTO.0b013e3181952478\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEichner LJ, Curtis SD, Brun SN, et al. HDAC3 is critical in tumour development and therapeutic resistance in KRAS-mutant non-small cell lung cancer. \u003cem\u003eSci Adv\u003c/em\u003e. 2023;9(11):eadd3243. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/sciadv.add3243\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.add3243\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSonnemann J, Marx C, Becker S, et al. p53-dependent and p53-independent anticancer effects of different histone deacetylase inhibitors. \u003cem\u003eBr J Cancer\u003c/em\u003e. 2014;110(3):656\u0026ndash;667. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/bjc.2013.742\u003c/span\u003e\u003cspan address=\"10.1038/bjc.2013.742\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKr\u0026auml;mer OH, Mahboobi S, Sellmer A. Drugging the HDAC6-HSP90 interplay in malignant cells. \u003cem\u003eTrends Pharmacol Sci\u003c/em\u003e. 2014;35(10):501\u0026ndash;509. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.tips.2014.08.001\u003c/span\u003e\u003cspan address=\"10.1016/j.tips.2014.08.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCuttini E, Goi C, Pellarin E, Vida R, Brancolini C. HDAC4 in cancer: a multitasking platform to drive not only epigenetic modifications. \u003cem\u003eFront Mol Biosci\u003c/em\u003e. 2023;10:1116660. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmolb.2023.1116660\u003c/span\u003e\u003cspan address=\"10.3389/fmolb.2023.1116660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAlqosaibi AI, Abdel-Ghany S, Al-Mulhim F, Sabit H. Vorinostat enhances the therapeutic potential of erlotinib via MAPK in lung cancer cells. \u003cem\u003eCancer Treat Res Commun\u003c/em\u003e. 2022;30:100509. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ctarc.2022.100509\u003c/span\u003e\u003cspan address=\"10.1016/j.ctarc.2022.100509\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKurundkar D, Srivastava RK, Chaudhary SC, et al. Vorinostat attenuates epidermoid squamous cell carcinoma growth by dampening mTOR signalling in a human xenograft model. \u003cem\u003eToxicol Appl Pharmacol\u003c/em\u003e. 2012;266(2):233\u0026ndash;244. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.taap.2012.11.002\u003c/span\u003e\u003cspan address=\"10.1016/j.taap.2012.11.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NSCLC, HDAC inhibitors, SAHA, Transcriptomic modulation, Personalized therapy","lastPublishedDoi":"10.21203/rs.3.rs-7322428/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7322428/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eHistone deacetylases (HDACs) drive non-small cell lung cancer (NSCLC) progression, yet HDAC inhibitor (HDACi) responses vary by tumor subtype. We compared the pan-HDAC inhibitor suberoylanilide hydroxamic acid (SAHA, vorinostat) in two NSCLC models-HER2-mutant lung adenocarcinoma (NCI-H1299; TP53\u003csup\u003edel\u003c/sup\u003e, NRAS\u003csup\u003eQ61K\u003c/sup\u003e) and EGFR/PI3K-driven squamous carcinoma (NCI-H1703; PDGFRA\u003csup\u003eamp\u003c/sup\u003e, PIK3CA\u003csup\u003eE542K\u003c/sup\u003e)-to uncover lineage-specific mechanisms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eCells were treated with SAHA (10 \u0026micro;M, 24 h), a clinically relevant dose yielding\u0026thinsp;~\u0026thinsp;1 \u0026micro;M free drug in plasma. Strand-specific RNA-seq was aligned to GRCh38.p13 and analyzed with edgeR and DESeq2 (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |Log₂FC|\u0026ge; 1). Pathway, protein-interaction, apoptosis (Annexin V/PI), and migration (scratch, trans-well\u0026thinsp;+\u0026thinsp;mitomycin C) assays complemented transcriptomics. Clinical relevance was assessed by correlating SAHA-regulated genes with disease-free survival in TCGA-LUAD and TCGA-LUSC via GEPIA2.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eSAHA altered 1098 genes in H1299 and 1532 in H1703, with only 437 shared. In H1299, SAHA induced EMT and MAPK-feedback genes while repressing interferon/apoptosis pathways. In H1703, SAHA activated complement/ECM remodeling and suppressed cell-cycle regulators. Class II HDACs (\u003cem\u003eHDAC4/6\u003c/em\u003e) were downregulated only in H1703. Functionally, H1703 exhibited greater apoptosis; H1299 showed stronger migration inhibition. SAHA-reversed gene signatures (e.g., \u003cem\u003eHMMR\u003c/em\u003e, \u003cem\u003ePLK1\u003c/em\u003e in LUAD; \u003cem\u003ePAPPA\u003c/em\u003e, \u003cem\u003eSAMD11\u003c/em\u003e in LUSC) were significantly associated with poor disease-free survival, defining HDAC9-HMMR/PLK1 and HDAC4/6-PAPPA/SAMD11 axes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eSAHA elicits distinct subtype-specific transcriptional and phenotypic effects in NSCLC. These findings highlight the importance of genetic context in HDACi therapy and support tailored combinations-such as SAHA with MAPK inhibitors in LUAD or HDAC4/6-targeted approaches in LUSC and the use of SAHA-modulated biomarkers for patient stratification.\u003c/p\u003e","manuscriptTitle":"Differential Transcriptomic Modulation by Histone Deacetylase Inhibitor SAHA in LUAD and LUSC","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 10:30:29","doi":"10.21203/rs.3.rs-7322428/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-13T06:37:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-07T09:31:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182961147540700820273082412953538831779","date":"2025-10-29T13:07:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-01T14:50:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194157658514107556539910790600597473752","date":"2025-09-15T07:17:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T14:45:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T08:01:51+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T02:20:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Clinical Epigenetics","date":"2025-08-08T01:10:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"clinical-epigenetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clep","sideBox":"Learn more about [Clinical Epigenetics](http://clinicalepigeneticsjournal.biomedcentral.com/)","snPcode":"13148","submissionUrl":"https://submission.nature.com/new-submission/13148/3","title":"Clinical Epigenetics","twitterHandle":"@OAgenetics","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bba0a602-0f3d-46bb-88f5-bc6b70c1f9e6","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:12:44+00:00","versionOfRecord":{"articleIdentity":"rs-7322428","link":"https://doi.org/10.1186/s13148-026-02051-x","journal":{"identity":"clinical-epigenetics","isVorOnly":false,"title":"Clinical Epigenetics"},"publishedOn":"2026-01-27 15:59:16","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-08-21 10:30:29","video":"","vorDoi":"10.1186/s13148-026-02051-x","vorDoiUrl":"https://doi.org/10.1186/s13148-026-02051-x","workflowStages":[]},"version":"v1","identity":"rs-7322428","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7322428","identity":"rs-7322428","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}