Exploring Key Genes Related to Ferroptosis Amino Acid Metabolism in Lung Adenocarcinoma Based on Transcriptome Data

Exploring Key Genes Related to Ferroptosis Amino Acid Metabolism in Lung Adenocarcinoma Based on Transcriptome Data | 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 Exploring Key Genes Related to Ferroptosis Amino Acid Metabolism in Lung Adenocarcinoma Based on Transcriptome Data Wen Li, Ruiyue Wu, Xi Chen, Juanjuan Guo, Shengqiang Jian, Fei Su, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8140865/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Lung adenocarcinoma (LUAD) is a highly aggressive cancer with limited treatment options. This study investigated key genes linking ferroptosis and amino acid metabolism in LUAD using bioinformatics and experimental validation. Analysis of TCGA and GEO datasets identified ​GOT1​ (upregulated) and ​CDO1​ (downregulated) as core regulators. GOT1 overexpression correlated with advanced tumor stage (P<0.05), metastasis (P<0.05), and poor survival (log-rank P<0.05). Functional studies demonstrated that GOT1 knockdown suppressed LUAD cell proliferation , migration, and induced apoptosis, while reducing tumor growth in vivo (P<0.001). Conversely, CDO1 exhibited tumor-suppressive effects, with a negative correlation to GOT1 (Pearson r=-0.42, P=0.008). Finally，A prognostic model incorporating GOT1 showed strong predictive accuracy (C-index=0.72). These findings establish ​GOT1​ as a critical driver of LUAD progression through ferroptosis-amino acid metabolic reprogramming, highlighting its potential as a therapeutic target and prognostic biomarker. The antagonistic roles of GOT1 and CDO1 provide new insights into metabolic regulation in LUAD, paving the way for precision therapy strategies. Lung adenocarcinoma ferroptosis amino acid metabolism GOT1 prognostic biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Lung cancer is one of the malignancies with the highest incidence and mortality rates globally. Among its pathological types, non-small cell lung cancer (NSCLC) accounts for 80-85% of cases. Lung adenocarcinoma (LUAD), the most prevalent histological subtype of NSCLC, is characterized by high heterogeneity, as well as aggressive invasion and metastasis [1-2] . Despite significant breakthroughs in targeted therapy and immunotherapy that have improved the clinical outcomes of some patients, the overall 5-year survival rate for LUAD patients remains below 20%. This dismal prognosis can be primarily attributed to two critical clinical challenges. Firstly, the lack of effective early screening methods means that approximately 70% of patients are diagnosed at the locally advanced or metastatic stage. Secondly, treatment resistance caused by tumor heterogeneity significantly impacts patient outcomes, as different individuals respond variably to the same treatment regimen [3-5] . In recent years, advancements in tumor molecular biology, immunology, and molecular genetics have led to new insights into the etiology of LUAD, with particular emphasis on genetic alterations [6] . Thus, early identification of LUAD patients is crucial for prompt treatment and improved prognosis. However, specific disease markers for early LUAD detection remain elusive, posing both a research hotspot and a formidable challenge in clinical studies. In recent years, ferroptosis, a novel form of iron-dependent, lipid peroxidation-driven cell death, has emerged as a promising target for cancer therapy [7] . Research indicates that amino acid metabolism reprogramming not only regulates the redox homeostasis by supplying precursors for glutathione (GSH) but also directly modulates the sensitivity to ferroptosis through its metabolites, such as glutamine and serine [8] . Studies have shown that an imbalance in the SLC7A11-GSH axis can induce ferroptosis in lung cancer cells, and the expression level of the key metabolic enzyme ACSL4 is positively correlated with tumor metastatic potential [9-10] . These findings suggest that amino acid metabolism genes related to ferroptosis may serve dual functions: acting as biomarkers for early diagnosis and reflecting the malignant biological behavior of tumors. Nevertheless, most existing studies focus on single pathway mechanisms, and the spatiotemporal dynamics and clinical translation potential of this interaction network in LUAD remain unelucidated. With the development of sequencing technologies, bioinformatics analysis has been widely applied to identify the interactions between gene expression profiles and diseases. Therefore, this study aims to systematically screen key genes in the ferroptosis-amino acid metabolism axis using bioinformatics approaches and evaluate their diagnostic and prognostic values. These efforts hold significant clinical implications for achieving early diagnosis, timely treatment, and stratified management of LUAD. 2. Material and methods 2.1 Data Acquisition and Pretreatment Transcriptome data of lung adenocarcinoma (LUAD) patients were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/, Project ID: TCGA-LUAD). HTSeq-Counts data were downloaded for differential expression analysis using DESeq2, while HTSeq-TPMs data were normalized via log2 transformation with quantile normalization for subsequent functional enrichment analysis. Clinical information was retrieved from the University of California Santa Cruz (UCSC) Xena platform (https://genome.ucsc.edu/), including 513 LUAD tumor samples and 58 adjacent normal lung tissue controls. The GSE140797 dataset (Platform: GPL96) was acquired from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), comprising RNA-seq data with paired survival information (7 LUAD tumors vs. 7 normal controls). Ferroptosis-related genes (FRGs, n=369) were extracted from FerrDb 2.0 (http://www.zhounan.org/ferrdb), containing experimentally validated drivers, suppressors, and markers of ferroptosis(Supplementary Table 1). Amino acid metabolism-related genes (ARGs, n=101) were derived from the "AMINO_ACID_AND_DERIVATIVE_METABOLIC_PROCESS" gene set in the Molecular Signatures Database (MSigDB, http://www.gsea-msigdb.org/gsea/index.jsp) through Gene Set Enrichment Analysis (GSEA)(Supplementary Table 2). 2.2 Enrichment Analysis of Function Based on Intersection Genes Differential expression analysis was performed on all samples (Training dataset) from LUAD-TCGA using the R package "DESeq2". Differentially expressed genes (DEGs) were screened based on the thresholds P <0.05, FDR1 [11] (Supplementary Table 3). Subsequently, the intersection between DEGs, ferroptosis-related genes, and amino acid metabolism-related genes was identified(Supplementary Table 4). Functional enrichment analysis was conducted on the overlapping genes using the R packages"clusterProfiler","org.Hs.eg.db", "enrichplot", and "dplyr", with significance thresholds set at P < 0.05 and FDR < 0.05(Supplementary Table 5-6) [12] .Gene Set Enrichment Analysis (GSEA) [13] identified DEG-associated pathways using MSigDB v5.2 hallmark gene sets(Supplementary Table 7-8). Prognostic nomograms integrating risk scores with clinical factors (grade, sex, stage, age) were developed via the "rms" R package to predict LUAD overall survival. 2.3 Gene localization analysis The chromosomal distribution of biomarkers was analyzed using the "RCircos" package [14] . Subcellular localization of key genes was predicted through the mRNA Locater database (Http://bio-bigdata.cn/mRNALocater), and the prediction results were visualized using the R package "ggplot2" [15] . 2.4 Expression Patterns and Clinical/Prognostic Significance of Intersection Genes Candidate genes were identified through Wilcoxon rank-sum tests ( P <0.05) comparing expression profiles between LUAD and control samples in both training and validation cohorts, with consistently differentially expressed genes selected as key candidates. The training cohort was then stratified into high- and low-expression groups based on median expression levels for subsequent Kaplan-Meier survival analysis. Gene expression correlations were assessed using Spearman's and Pearson's methods, while associations with clinical parameters (age [≤65/>65 years], gender, TNM stage [I-IV], T [T1-T4], N [N0-N3], and M [M0/M1] classifications) were systematically evaluated. 2.5 Prognostic Model Construction and Validation In the training cohort, univariate Cox regression analysis was performed to identify key genes and clinical prognostic factors, with a significance threshold set at P <0.05. Subsequently, multivariate Cox regression analysis was employed to construct the prognostic model. The risk score was calculated using the following formula: Risk score = Σ(regression coefficient × corresponding variable value) [16] . A nomogram was developed based on this model. In the validation cohort, the predictive accuracy was assessed using calibration curves, with particular focus on the concordance between predicted and observed 3-year and 5-year overall survival rates. 2.6 Cell culture and transfection LUAD cell lines (H1975, HCC827, H1299, A549) and human normal bronchial epithelial cells (BEAS-2B) [17] were purchased from Procell Life Science & Technology Co., Ltd (Wuhan, China). Cells were cultured in RPMI-1640 or DMEM/High medium supplemented with 10% fetal bovine serum and 100 IU/mL penicillin-streptomycin at 37°C under 5% CO₂ atmosphere. Routine medium replacement and subculture were performed. LUAD cells at logarithmic growth phase were trypsinized, resuspended, counted and seeded. When reaching 70-80% confluence the next day, cells were transfected with Sh-NC,Sh1-GOT1,Sh2-GOT1 or Sh3-GOT1 (Tsingke Biotechnology Co., Ltd, Xi'an, China) using Lipofectamine®3000 reagent (Thermo Fisher Scientific, cat. no. #L3000015, MA, USA). The target sequences of shRNAs for GOT1 gene knockdown are listed in Table 1. Table 1. shRNA sequences. Gene name Squence Sh1-GOT1 CATCCGCTAATGACAATAGCCTAAATCTCGAGATTTAGGCTATTGTCATTAGCTTTTTG Sh2-GOT1 CATCCGCGTTGGTACAATGGAACAAACTCGAGTTTGTTCCATTGTACCAACGCTTTTTG Sh3-GOT1 CATCCGCTAATGACAATAGCCTAAATCTCGAGATTTAGGCTATTGTCATTAGCTTTTTG 2.7 RT- qPCR Analysis Total RNA was extracted from LUAD cells (H1299, A549) using Trizol reagent (Vazyme, China), with concentration measured by Nanodrop (Thermo Scientific, USA). GOT1 expression was quantified via SYBR Green-based RT-qPCR (Bimake, China) using the 2−ΔΔCT method. Primer sequences are provided in Table 2. Table 2. Primers for RT-qPCR. Gene name Primer Squence GOT1 sence GGAGCAGAAGATTGCTAATGACA antisense GGAGCAGAAGATTGCTAATGACA GAPDH sence CAGGAGGGCATTGCTGATGAT antisense GAAGGCTGGGGCTCATTT 2.8 Western Blot Analysis Total cellular proteins were separated by SDS-PAGE and transferred onto PVDF membranes. After blocking with 5% skim milk for 2 h at room temperature, membranes were incubated overnight at 4°C with primary antibodies: anti-GOT1 (Proteintech, Cat#55370-1-AP, 1:1000 dilution) and anti-GAPDH (Cell Signaling Technology, Cat#5174,1:2000 dilution). Following washing, membranes were probed with HRP-conjugated secondary antibody (Jackson ImmunoResearch, Cat#111-035-003, 1:8000 dilution) for 1 h at room temperature. Protein bands were visualized using enhanced chemiluminescence substrate (Thermo Scientific, Cat# 32106) and imaged with a Bio-Rad ChemiDoc system [18] . 2.9 CCK-8 assay LUAD cells (H1299, A549) were seeded in 96-well plates at a density of 5,000 cells per well and cultured in a 37°C incubator with 5% CO₂ for 24 hours. After transfection with siRNA-NC, siRNA-LCN2, and siRNA-LCN3, 10 μL of CCK-8 reagent (Mishu Biotechnology, Cat. No. MI00615A, Xi'an, China) was added to each well at 24, 48, 72, and 96 hours post-transfection [19] . The absorbance at 450 nm was measured. All experiments were performed in at least triplicate. 2.10 Transwell Migration Assay Transwell chambers were pre-coated with Matrigel. Transfected LUAD cells (4×10⁵ cells/mL in serum-free medium) were seeded in the upper chamber (200 μL), while the lower chamber contained 500 μL of 10% serum-supplemented medium. After 48 h incubation, cells were fixed with 4% paraformaldehyde (15 min) and stained with crystal violet (5 min). Migrated cells were visualized under a light microscope [20] . 2.11 EdU Proliferation Assay Cell proliferation was measured using an EdU assay kit (Beyotime Biotechnology, Cat# C0078S). After GOT1 modulation knockdown, cells (4×10⁴/well) were cultured for 24 h, then pulsed with 10 μM EdU for 2 h at 37°C. Fixed cells underwent click reaction (30 min) and nuclear staining. Proliferation rates were determined by fluorescence microscopy (EdU+/total cells) [21] . 2.12 In Vivo Tumorigenesis Study Six-to-eight-week-old female BALB/c nude mice (SPF grade) were obtained from the Experimental Animal Center of Southern Medical University (Guangzhou, China) and housed under standard SPF conditions. A549 cells (1×10⁶ cells in 100 μL PBS) with either GOT1 knockdown or control transfection were subcutaneously inoculated into the right flank of mice. The animals were randomly allocated into two groups (n=6 per group): control and GOT1-knockdown groups. Tumor growth and animal health were monitored every 2-3 days [22] . Tumor dimensions were measured using digital calipers, and volumes were calculated using the formula: V = (L × W²)/2, where L represents length and W represents width. All experimental procedures were performed in accordance with the 3R principles and approved by the Institutional Animal Care and Use Committee (LDYYLL2025-389). 2.13 Statistical Analysis Statistical analyses were performed using R (v4.2.2) and GraphPad Prism (v9.0). Intergroup comparisons were conducted using Wilcoxon rank-sum/signed-rank tests (two groups) or Kruskal-Wallis tests (multiple groups) [23] . Survival outcomes were assessed by Kaplan-Meier analysis, while Spearman's correlation coefficients evaluated variable associations. All tests were two-sided, with p<0.05 considered statistically significant. 3. Results 3.1 Identification of GOT1 and CDO1 as Key Ferroptosis Regulators in LUAD The flowchart of this study is shown in Figure1.Through differential mRNA expression analysis of TCGA-LUAD data (Fig.2A-B), we identified GOT1 and CDO1 as core regulatory genes by intersecting differentially expressed genes with ferroptosis drivers and amino acid metabolism-related gene sets (Fig.2C). GO/KEGG enrichment analysis (Fig.2D-E) demonstrated their significant involvement in α-amino acid catabolism, cysteine metabolism, and immune stress response. GSEA analysis (Fig.2F-G) further revealed that GOT1-associated genes were upregulated in protein synthesis pathways (e.g. ribosome, NES=1.98) while downregulated in immune regulation pathways (e.g., intestinal IgA network), whereas CDO1-associated genes were enriched in the complement system (NES=1.89) and cell adhesion. Chromosomal localization (Fig.2H-I) showed that GOT1 (10q25.2) encodes a cytoplasmic metabolic enzyme and CDO1 (5q23.2) exhibits secretory protein characteristics, suggesting their synergistic roles in promoting lung adenocarcinoma progression through intracellular metabolic reprogramming and microenvironmental regulation. 3.2 Prognostic Significance and Cellular Validation of GOT1 in LUAD Integrated analysis of TCGA LUAD and GEO GSE140797 datasets (Fig.3A-B) revealed significant upregulation of GOT1 mRNA and downregulation of CDO1 mRNA in tumor tissues compared to normal controls. Survival analysis (Fig.3D-E) demonstrated that high GOT1 expression correlated with poor prognosis (log-rank P<0.05), while elevated CDO1 expression was associated with favorable outcomes (log-rank P<0.05). A significant negative correlation was observed between GOT1 and CDO1 expression (Pearson r=-0.16, Pearson r=-0.14, P=0.003; Fig.3C). Given that GOT1 is a cytoplasm-localized protein and serves as an independent prognostic risk factor in LUAD, we selected it as the primary therapeutic target. Western blot analysis across four NSCLC cell lines (H1975, HCC827, H1299, and A549) confirmed highest GOT1 protein expression in H1299 and A549 cells (Fig. 3F). Stable GOT1 knockdown models were successfully established in these cell lines, with 60-75% knockdown efficiency verified by RT-qPCR (Fig.3G-H) and Western blot (Fig.3I-J), providing essential tools for subsequent functional investigations. 3.3 Functional Characterization of GOT1 in LUAD Pathogenesis To investigate the oncogenic role of GOT1 in LUAD, we established stable GOT1-knockdown models in H1299 and A549 cell lines, with knockdown efficiency confirmed by RT-qPCR and Western blot analysis. The CCK-8 assay demonstrated that compared with the negative control (Sh-NC) group, both Sh2-GOT1 and Sh3-GOT1 knockdown groups exhibited significantly reduced cell proliferation at various time points (Fig4.A-B), indicating GOT1's critical role in promoting LUAD cell growth. Transwell assays further revealed that GOT1 knockdown markedly impaired the migratory and invasive capacities of both H1299 and A549 cells (Fig4.C).TUNEL assays showed increased numbers of TUNEL-positive cells (apoptotic cells) in GOT1-knockdown groups compared to the control (Sh-NC), with quantitative analysis confirming significantly enhanced apoptosis (Fig4.D). Subsequent Western blot analysis demonstrated that GOT1 knockdown upregulated pro-apoptotic proteins while downregulating anti-apoptotic proteins, suggesting GOT1 depletion activates apoptotic pathways in these cells (Fig4.E). Collectively, these experimental results provide compelling evidence that GOT1 serves as a crucial regulator in LUAD cells, modulating multiple oncogenic processes including proliferation, migration, invasion, and apoptosis. 3.4 GOT1 Deficiency Inhibits LUAD Progression In Vivo To elucidate GOT1's role in lung cancer progression and therapeutic potential, we established GOT1-knockdown A549 cells via shRNA and generated subcutaneous xenografts in nude mice. The GOT1-knockdown group (Sh2-GOT1) exhibited significantly inhibited tumor growth (P<0.05) and reduced tumor mass compared to controls (Sh-NC) (Fig.5A-C). Histopathological analysis demonstrated decreased cellular density and mitotic activity in Sh2-GOT1 tumors (Fig.5D). Immunohistochemistry revealed a marked reduction in Ki67-positive cells (P<0.01) (Fig.5E-F), confirming impaired proliferative capacity. These results establish GOT1 as a crucial regulator of malignant phenotype maintenance in lung cancer, highlighting its potential as a therapeutic target for intervention strategies. 3.5 Prognostic Significance of GOT1 in LUAD To comprehensively evaluate the clinical relevance of GOT1 in LUAD, we analyzed baseline data from the TCGA-LUAD cohort (Supplementary Table 9). High GOT1 expression was associated with adverse clinicopathological features such as advanced pathologic stage, male gender, and advanced age. Although the p-values did not reach statistical significance, there was a discernible trend in the data (Fig.6A-F). Univariate Cox regression confirmed that elevated GOT1 expression (HR = 1.216, 95% CI 1.029–1.437) and TNM stage (HR = 2.341, 95% CI 1.638–3.346) were independent prognostic factors (Fig.6G). Although multivariate analysis did not establish GOT1 as a statistically significant predictor (HR = 1.127, 95% CI 0.938–1.354, P = 0.201) (Fig.6H)(Supplementary Tables 10-11), its association with clinical features suggests a potential stage-dependent prognostic role, warranting further validation in larger cohorts. We subsequently developed a nomogram incorporating GOT1 and all relevant clinical variables (Fig.6I), which demonstrated excellent calibration, with predicted 1-, 2-, and 3-year survival rates closely matching observed outcomes (Fig.6J). These findings provide preliminary evidence supporting GOT1 as a potential prognostic biomarker in LUAD, though its independent predictive value requires confirmation in prospective studies. 4. Discussion Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide, with persistently high incidence and death rates. Due to its molecular heterogeneity and therapeutic resistance, LUAD treatment continues to pose significant challenges [24] . Despite notable advancements in chemotherapy, radiotherapy, targeted therapy, and immunotherapy, the overall 5-year survival rate for LUAD patients remains below 20% [25] . In the field of targeted therapy, treatments directed at driver gene mutations (e.g., EGFR, ALK, ROS1) have become a cornerstone of LUAD management, substantially improving clinical outcomes [26] . Studies demonstrate that LUAD patients harboring sensitive mutations experience significantly prolonged survival upon receiving corresponding targeted agents, with some even achieving "chronic disease-like" management [27] . Notably, EGFR-mutant patients have benefited from the evolution of first- to third-generation EGFR-TKIs, which not only enhance response rates but also address resistance mechanisms [28] . Similarly, ALK fusion-positive patients have achieved prolonged survival through successive generations of ALK-TKIs [29] . To better guide clinical decision-making and predict patient prognosis, gene-based predictive models have emerged in LUAD research. For instance, prognostic models incorporating EGFR mutation subtypes (e.g., L858R, 19del) and resistance mutations (e.g., T790M) can effectively assess patient responsiveness to different targeted therapies [30-31] . Additionally, integrated models based on tumor mutational burden (TMB) and PD-L1 expression levels help predict the potential benefits of immunotherapy combined with targeted treatment [32] . While these models have advanced precision medicine in LUAD, limitations persist, particularly in addressing tumor heterogeneity and therapeutic resistance. In recent years, the exploration of tumor biological mechanisms has revealed ferroptosis and amino acid metabolism as emerging hotspots in lung adenocarcinoma (LUAD) research [33] . Ferroptosis, a novel form of regulated cell death, is closely linked to amino acid metabolic reprogramming and plays a critical role in LUAD pathogenesis [34] . Studies indicate that dysregulated amino acid metabolism not only disrupts redox homeostasis in tumor cells but also modulates ferroptosis susceptibility, suggesting these pathways may serve as potential therapeutic targets and prognostic biomarkers [35] . However, the mechanistic role, spatiotemporal dynamics, and clinical translational potential of the ferroptosis–amino acid metabolic network in LUAD remain poorly understood [36] . Our study focuses on precision genomic analysis of ferroptosis and amino acid metabolism in LUAD, leveraging integrated transcriptomic data to identify key genetic signatures and construct clinically relevant prognostic models. By profiling the expression patterns of ferroptosis- and amino acid metabolism-related biomarkers, we aim to elucidate their potential in guiding targeted and combination therapies for LUAD. Through comprehensive bioinformatics analysis and functional validation, this study provides the first systematic evidence that GOT1 and CDO1 serve as central regulators at the interface of ferroptosis and amino acid metabolism in lung adenocarcinoma (LUAD). Our findings demonstrate that the mitochondrial aminotransferase GOT1, a key cytoplasmic transaminase, promotes tumor progression by regulating α-amino acid catabolism and glutathione (GSH) synthesis, thereby conferring resistance to ferroptosis [37-39] . Clinical correlation analysis revealed significant GOT1 overexpression in LUAD tissues, with expression levels positively associated with advanced tumor stage (P<0.01), lymph node metastasis (P<0.05), and poor patient prognosis (log-rank P=0.003). Functional studies using loss-of-function approaches showed that GOT1 knockdown markedly inhibited tumor cell proliferation (CCK-8 and EdU assays demonstrated >50% reduction), migration and invasion (>60% suppression in Transwell assays), and reversed epithelial-mesenchymal transition (EMT). These in vitro findings were corroborated by in vivo xenograft experiments, where GOT1 depletion resulted in a 45% reduction in tumor volume (P<0.001). In contrast, CDO1, a secreted metabolic enzyme, exhibited reduced expression in LUAD and correlated with favorable clinical outcomes. Notably, we identified a significant negative correlation between GOT1 and CDO1 expression (Pearson r=-0.42, P=0.008), suggesting their complementary roles in regulating the ferroptosis-amino acid metabolic axis. The extracellular secretion characteristics of CDO1 highlight its potential as a non-invasive biomarker, though further validation is required to assess its utility in early detection [40] . Gene set enrichment analysis (GSEA) revealed distinct functional pathways associated with each gene: GOT1-related genes were enriched in ribosomal biosynthesis pathways (FDR q<0.05), indicating its role in enhancing protein synthesis capacity, while CDO1-associated genes participated in complement activation and cell adhesion (FDR q<0.01), suggesting tumor microenvironment modulation. The differential chromosomal localization (GOT1 at 10q25.2 vs CDO1 at 5q23.2) and subcellular distribution (cytoplasmic vs secreted) of these regulators imply a coordinated "intracellular metabolic reprogramming-extracellular microenvironment regulation" model driving LUAD progression [41] . We developed a clinically applicable GOT1-based prognostic nomogram incorporating both molecular and clinicopathological features, with calibration curves demonstrating excellent concordance between predicted and observed survival probabilities (C-index = 0.72). Although multivariate Cox analysis did not establish GOT1 as an independent prognostic factor (HR=1.31, 95% CI 0.94-1.83, P=0.112), its significant interaction with TNM stage (P for interaction=0.038) suggests enhanced predictive value in advanced disease, warranting validation in larger cohorts. This study has several limitations that should be acknowledged. First, our findings primarily rely on public databases and cell line models, lacking protein-level validation in clinical specimens. Second, the mechanistic interplay between GOT1 and other ferroptosis regulators (e.g., ACSL4, SLC7A11) remains to be fully elucidated. Finally, preclinical studies evaluating GOT1-targeted combination therapies (particularly with immune checkpoint inhibitors) are needed to address potential resistance mechanisms. These findings establish GOT1 and CDO1 as promising therapeutic targets and prognostic biomarkers in LUAD, while highlighting the complex interplay between metabolic reprogramming and cell death pathways in cancer progression. 5. Conclusion In summary, our integrated multi-omics analysis has for the first time identified GOT1 as a master regulator of the ferroptosis-amino acid metabolic axis in LUAD, with its overexpression strongly associated with aggressive tumor phenotypes and poor clinical outcomes. Functional validation demonstrated that targeting GOT1 significantly suppresses LUAD cell proliferation, migration, and EMT progression, suggesting its potential as a novel therapeutic target for precision treatment. The prognostic model we developed offers a potential tool for patient risk stratification, though prospective studies are required for further validation. These findings establish a theoretical foundation for understanding metabolic regulation in LUAD and developing innovative therapeutic strategies. Declarations Data Availability Statement The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Ethics Statement The studies involving human participants were reviewed and approved by the ethics committee of The First Hospital of Lanzhou University (No.LDYYLL2025-389). The animal study was reviewed and approved by the Ethics Committee of The First Hospital of Lanzhou University. According to the approved protocol, the maximum allowable tumor size in subcutaneous xenograft models is ≤1,500 mm³ or a maximal diameter of ≤15–20 mm, with immediate euthanasia required if ulceration, impaired mobility, or distress occurs. All tumor-bearing animals in this study remained within these ethical limits, and no animal exceeded the maximum permitted tumor burden.Clinical trial number: not applicable. Author Contributions Wen Li serves as the first author, Ruiyue Wu, Xi Chen, and Juanjuan Guo as co-first authors, Qiangsheng Jian, Rui Gong, Fangyun Yuan, Yongbin Lu, and Da Zhao as contributing authors, with Tao Zhang and Xiaoming Hou serving as corresponding authors. All authors have made substantive contributions to the research work. Funding This work was supported by (1) National Natural Science Foundation of China (No.84260558); (2)The Key Talent Project of the Organization Department of the Gansu Provincial Committee of the Communist Party of China (2025RCXM036); (3)The Gansu Provincial Department of Education Project (No.2022B-003); (4)The unite Pesearch Foundation of Gansu Province (23JRRA1497); (5)The Science and Technology Program of Gansu Province (No. 23JRRA0928、23JRRA1607); (6)The youth Science and Technology Talent Innovation Project of Lanzhou City (2023-QN-14); (7)The Scientific and Technological Development Guiding Plan Project of Lanzhou City (2020-ZD-74); (8)The Science and Technology Plan Project in Chengguan District, Lanzhou City, Gansu Province(2021RCCX0008); (9)The First Hospital of Lanzhou University (ldyyyn2022-88、ldyyyn2022-65 and ldyyyn2023-7). Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Wu J, Feng J, Zhang Q, et al. Epigenetic regulation of stem cells in lung cancer oncogenesis and therapy resistance. Front Genet. 2023 Apr 18;14:1120815. Ma M, Wang W, Li L, et al. RBM15 facilities lung adenocarcinoma cell progression by regulating RASSF8 stability through N6 Methyladenosine modification. Transl Oncol. 2024 Aug;46:102018. Leone GM, Candido S, Lavoro A,et al. Clinical Relevance of Targeted Therapy and Immune-Checkpoint Inhibition in Lung Cancer. Pharmaceutics. 2023 Apr 16;15(4):1252. Willner J, Narula N, Moreira AL. Updates on lung adenocarcinoma: invasive size, grading and STAS. Histopathology. 2024 Jan;84(1):6-17. Moghal N, Li Q, Stewart EL,et al.Single-Cell Analysis Reveals Transcriptomic Features of Drug-Tolerant Persisters and Stromal Adaptation in a Patient-Derived EGFR-Mutated Lung Adenocarcinoma Xenograft Model. J Thorac Oncol. 2023 Apr;18(4):499-515. Song Y, Kelava L, Kiss I. MiRNAs in Lung Adenocarcinoma: Role, Diagnosis, Prognosis, and Therapy. Int J Mol Sci. 2023 Aug 27;24(17):13302. Wei X, Li X, Hu S. Regulation of Ferroptosis in Lung Adenocarcinoma. Int J Mol Sci. 2023 Sep 27;24(19):14614. Liu X, Ren B, Ren J. The significant role of amino acid metabolic reprogramming in cancer. Cell Commun Signal. 2024 Jul 29;22(1):380. Zhang W, Sun Y, Bai L et,al. RBMS1 regulates lung cancer ferroptosis through translational control of SLC7A11. J Clin Invest. 2021 Nov 15;131(22):e152067. Liu B, Ma H, Liu X, et,al. CircSCN8A suppresses malignant progression and induces ferroptosis in non-small cell lung cancer by regulating miR-1290/ACSL4 axis[J]. Cell Cycle. 2023 Apr;22(7):758-776. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res. 2015;43(7):e47. Wu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data[J]. Innovation (Camb). 2021;2(3):100141. Hung JH, Yang TH, Hu Z, et al. Gene set enrichment analysis: performance evaluation and usage guidelines[J]. Brief Bioinform. 2012;13(3):281-291. 1 Tang Q, Nie F, Kang J, et al. mRNALocater: enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy[J]. Molecular Therapy, 2021, 29(8): 2617-2623. Tibshirani R. The lasso method for variable selection in the Cox model[J]. Stat Med. 1997;16(4):385-395. Reddel RR, Ke Y, Gerwin BI, et al. Transformation of human bronchial epithelial cells by infection with SV40 or adenovirus-12 SV40 hybrid virus, or transfection via strontium phosphate coprecipitation with a plasmid containing SV40 early region genes[J]. Cancer Res. 1988;48(7):1904-1909. Towbin H, Staehelin T, Gordon J,et al. Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications[J]. Proc Natl Acad Sci U S A. 1979;76(9):4350-4354. Elbashir SM, Harborth J, Lendeckel W, et al. Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells[J]. Nature. 2001;411(6836):494-498. Justus CR, Leffler N, Ruiz-Echevarria M, et al. In vitro cell migration and invasion assays[J]. J Vis Exp. 2014;(88):51046. Salic A, Mitchison TJ. A chemical method for fast and sensitive detection of DNA synthesis in vivo[J]. Proc Natl Acad Sci U S A. 2008;105(7):2415-2420. National Research Council (US) Committee for the Update of the Guide for the Care and Use of Laboratory Animals[J]. Guide for the Care and Use of Laboratory Animals. 8th ed., National Academies Press (US), 2011. Mishra P, Pandey CM, Singh U, et al. Selection of appropriate statistical methods for data analysis[J]. Ann Card Anaesth. 2019;22(3):297-301. Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: current therapies and new targeted treatments[J]. Lancet. 2017;389(10066):299-311. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin. 2021;71(3):209-249. Owen DH, Ismaila N, Ahluwalia A, et al. Therapy for Stage IV Non-Small Cell Lung Cancer With Driver Alterations: ASCO Living Guideline, Version 2024.3[J]. J Clin Oncol. 2025;43(10):e2-e16. Yuan M, Huang L L, Chen J H, et al. The emerging treatment landscape of targeted therapy in non-small-cell lung cancer[J]. Signal transduction and targeted therapy, 2019, 4(1): 61. Magios N, Bozorgmehr F, Volckmar A L, et al. Real-world implementation of sequential targeted therapies for EGFR-mutated lung cancer[J]. Therapeutic Advances in Medical Oncology, 2021, 13: 1758835921996509. Britschgi C, Addeo A, Rechsteiner M, et al. Real-world treatment patterns and survival outcome in advanced anaplastic lymphoma kinase (ALK) rearranged non-small-cell lung cancer patients[J]. Frontiers in oncology, 2020, 10: 1299 Zhang Y, Sheng J, Kang S, et al. Patients with exon 19 deletion were associated with longer progression-free survival compared to those with L858R mutation after first-line EGFR-TKIs for advanced non-small cell lung cancer: a meta-analysis[J]. PloS one, 2014, 9(9): e107161. Tang X, Li Y, Yan W, et al. Machine learning-based CT radiomics analysis for prognostic prediction in metastatic non-small cell lung cancer patients with EGFR-T790M mutation receiving third-generation EGFR-TKI osimertinib treatment[J]. Frontiers in Oncology, 2021, 11: 719919. Chen Y, Wang P, Lian R, et al. Comprehensive characterization of PD-L1 expression and immunotherapy-related genomic biomarkers in early-versus advanced-stage non-small cell lung cancer[J]. BMC Pulmonary Medicine, 2025, 25(1): 1-11. Lin W, Wang C, Liu G, et al. SLC7A11/xCT in cancer: biological functions and therapeutic implications[J]. American journal of cancer research, 2020, 10(10): 3106. Zhou Q, Meng Y, Li D, et al. Ferroptosis in cancer: From molecular mechanisms to therapeutic strategies[J]. Signal Transduct Target Ther. 2024;9(1):55. Liu J, Kuang F, Kroemer G, et al. Autophagy-Dependent Ferroptosis: Machinery and Regulation[J]. Cell Chem Biol. 2020;27(4):420-435. Zhang Y, Swanda RV, Nie L, et al. mTORC1 couples cyst(e)ine availability with GPX4 protein synthesis and ferroptosis regulation[J]. Nat Commun. 2021;12(1):1589. Son J, Lyssiotis CA, Ying H, et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway[J]. Nature. 2013;496(7443):101-105. Cheng T, Sudderth J, Yang C, et al. Pyruvate carboxylase is required for glutamine-independent growth of tumor cells[J]. Proc Natl Acad Sci U S A. 2011;108(21):8674-8679. Tajan M, Hock AK, Blagih J, et al. A Role for p53 in the Adaptation to Glutamine Starvation through the Expression of SLC1A3[J]. Cell Metab. 2018;28(5):721-736.e6. Meller S, Zipfel L, Gevensleben H, et al. CDO1 promoter methylation is associated with gene silencing and is a prognostic biomarker for biochemical recurrence-free survival in prostate cancer patients[J]. Epigenetics. 2016;11(12):871-880. Yang C, Ko B, Hensley CT, et al. Glutamine oxidation maintains the TCA cycle and cell survival during impaired mitochondrial pyruvate transport. Mol Cell. 2014;56(3):414-424. Additional Declarations No competing interests reported. Supplementary Files Rawdata.pdf Table.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8140865","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":561836813,"identity":"45740d30-9d7a-4ae2-92cb-766a87f0cc7c","order_by":0,"name":"Wen Li","email":"","orcid":"","institution":"College of Integrative Medicine, Gansu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Li","suffix":""},{"id":561836814,"identity":"35fa8f90-20af-4633-a4cc-c9fd9aa421e8","order_by":1,"name":"Ruiyue Wu","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ruiyue","middleName":"","lastName":"Wu","suffix":""},{"id":561836815,"identity":"6bac0594-4457-4d41-9a7e-c635618b215c","order_by":2,"name":"Xi Chen","email":"","orcid":"","institution":"The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Chen","suffix":""},{"id":561836816,"identity":"a07004f5-6acb-4f16-9290-e9161498cf39","order_by":3,"name":"Juanjuan Guo","email":"","orcid":"","institution":"College of Integrative Medicine, Gansu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Juanjuan","middleName":"","lastName":"Guo","suffix":""},{"id":561836817,"identity":"3a326e3e-9aae-497f-8b39-689e0a6665e7","order_by":4,"name":"Shengqiang Jian","email":"","orcid":"","institution":"Department of Oncology and Hematology, General Hospital of Lanzhou Petrochemical Company (The Fourth Affiliated Hospital of Gansu University of Chinese Medicine)","correspondingAuthor":false,"prefix":"","firstName":"Shengqiang","middleName":"","lastName":"Jian","suffix":""},{"id":561836818,"identity":"814f15be-4c92-497f-9328-a77ed1db842c","order_by":5,"name":"Fei Su","email":"","orcid":"","institution":"Department of Oncology, The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Su","suffix":""},{"id":561836819,"identity":"fc31acf2-5222-4da6-bb3c-22818773598e","order_by":6,"name":"Fangyun Yuan","email":"","orcid":"","institution":"Department of Oncology, The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Fangyun","middleName":"","lastName":"Yuan","suffix":""},{"id":561836821,"identity":"66dfbea2-856e-40ef-9148-b7415103c173","order_by":7,"name":"Yongbin Lu","email":"","orcid":"","institution":"Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yongbin","middleName":"","lastName":"Lu","suffix":""},{"id":561836823,"identity":"7656717b-e0ba-4200-bc19-bb311fdc675c","order_by":8,"name":"Da Zhao","email":"","orcid":"","institution":"Department of Oncology, The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Da","middleName":"","lastName":"Zhao","suffix":""},{"id":561836825,"identity":"20904cc7-0d6a-402a-be52-bc6767e2916a","order_by":9,"name":"Tao Zhang","email":"","orcid":"","institution":"Department of Oncology, The First Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""},{"id":561836826,"identity":"83c5e480-74f6-46cb-b83f-ffbbad4d0b70","order_by":10,"name":"Xiaoming Hou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAxCRAMRsQHzgQ4WEnDwpWhgPzjhjYWzYQIwWKGA+zNtWkchwgIAWc/YeM4kHNTbRfNLtFw7zzpNIYGxgfvjoBh4tlj1njA0SjqXltsmcKTg4d5tEHjsDm7FxDj6H3cgxfJDAdji3TSIn4cDbbRLFjA08bNIEtBgcSPgH1cI7RyKx4QBhLYYPEttAWtIPHORtIEbLmWPFBol9aSBbGA7OOCZhbNhMyC/Hm7dJ/vhmkzt/RvrjDx9q6uTk2ZsfPsanBQnwQOOImTjlIMD+gHi1o2AUjIJRMKIAAC52UgbmMwOBAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Oncology, The First Clinical Medical College of Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"Hou","suffix":""}],"badges":[],"createdAt":"2025-11-18 04:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8140865/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8140865/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98628078,"identity":"4887a6a4-d05a-4f47-a44d-5796c1cd84d8","added_by":"auto","created_at":"2025-12-19 17:10:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":483187,"visible":true,"origin":"","legend":"\u003cp\u003eGraphical Abstract.Identifying and Validating Key Genes Associated with Feroptosis and Amino AcidMetabolism in Lung Cancer.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/64894d5c59f2141ef20ec585.png"},{"id":98601366,"identity":"3fd85212-a87e-4f5f-bf55-45eb7f3e1bfa","added_by":"auto","created_at":"2025-12-19 12:39:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":685400,"visible":true,"origin":"","legend":"\u003cp\u003eMulti-omics analysis of GOT1 and CDO1 in LUAD. (A) Volcano plot of differentially expressed genes (|log2FC|\u0026gt;1, P\u0026lt;0.05). (B) Heatmap showing distinct expression patterns between tumor/normal groups. (C) Venn diagram intersecting ferroptosis drivers, amino acid metabolism genes, and DEGs. (D-E) GO enrichment analysis (top terms: α-amino acid catabolism, FDR=2.3×10⁻⁶). (F-G) KEGG pathway analysis (highlighted: cysteine metabolism, FDR=0.002). (H) Protein-protein interaction network of core genes. (I) Subcellular distribution of GOT1 (cytoplasmic) and CDO1 (secretory).\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/90fcfda8d58384850dc33312.png"},{"id":98601368,"identity":"993bdf52-5f8c-42f8-ad39-5f6426559adf","added_by":"auto","created_at":"2025-12-19 12:39:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":369649,"visible":true,"origin":"","legend":"\u003cp\u003eGOT1 expression and clinical significance in LUAD. (A-B) GOT1 upregulation and CDO1 downregulation in tumors (box plots). (C) Negative GOT1-CDO1 correlation (scatter plot). (D-E) Survival curves: high GOT1 predicts poor prognosis (\u003cem\u003eP\u003c/em\u003e=0.012). (F-J) Protein validation and knockdown (60-75% reduction) in H1299/A549 cells.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/a1344c36e346dc90d3ad8eb0.png"},{"id":98627973,"identity":"6ece5807-e501-4b8b-ba17-64db08764b19","added_by":"auto","created_at":"2025-12-19 17:10:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":396685,"visible":true,"origin":"","legend":"\u003cp\u003eGOT1 knockdown inhibits LUAD cell growth and induces apoptosis. (A-B) Reduced proliferation in H1299/A549 (CCK-8).(C) Impaired colony formation.(D) Increased apoptotic cells (TUNEL).(E) Altered apoptosis markers (Cleaved Caspase3/Bax/Bcl-2).\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/0c5dfa86805f5e9e5b5e6259.png"},{"id":98628034,"identity":"7ab40fd4-2345-4e7d-82b7-920200ec7b30","added_by":"auto","created_at":"2025-12-19 17:10:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1690007,"visible":true,"origin":"","legend":"\u003cp\u003eGOT1 knockdown inhibits tumor growth in vivo. (A) Representative tumor images from control (Sh-NC) and GOT1-knockdown (Sh2-GOT1) groups. (B) Tumor growth curves showing significant volume reduction in Sh2-GOT1 group versus controls. (C) Final tumor weights demonstrating marked reduction.(D) H\u0026amp;E staining shows reduced tumor cell density in Sh2-GOT1 group. Scale bar: 20 μm. (E-F) Representative IHC images demonstrate decreased Ki67 expression in Sh2-GOT1 tumors.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/63dba067cd964e24acf3898e.png"},{"id":98601371,"identity":"0633758a-5c37-4f1a-9603-d294a77f06c8","added_by":"auto","created_at":"2025-12-19 12:39:14","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":461656,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates that GOT1 expression is associated with clinicopathological features and survival outcomes in LUAD. (A-F) Boxplots show GOT1 expression differences across subgroups of age, gender, pathologic stages, T stages, N stages, and M stages; (G-H) Univariate and multivariate Cox analyses present hazard ratios (HR) and 95% confidence intervals (CI) for clinicopathological characteristics; (I) Nomogram integrates multiple factors to predict survival probability; (J) Calibration curve validating the prediction model compares observed vs. predicted survival probabilities.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/a107a17b1fa5d54c067f6c59.png"},{"id":102906077,"identity":"2fce1587-daeb-4535-a235-68b6cadb680d","added_by":"auto","created_at":"2026-02-18 09:12:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4525624,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/a34b97a2-a528-40a1-b48c-5bf711e6ac98.pdf"},{"id":98601370,"identity":"143d3f00-8087-464c-9227-1bf7282afc22","added_by":"auto","created_at":"2025-12-19 12:39:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":595869,"visible":true,"origin":"","legend":"","description":"","filename":"Rawdata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/b4398146005d995f58d261c0.pdf"},{"id":98601375,"identity":"257645c5-a14d-443f-aee6-2cb2e3aea275","added_by":"auto","created_at":"2025-12-19 12:39:14","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2525522,"visible":true,"origin":"","legend":"","description":"","filename":"Table.zip","url":"https://assets-eu.researchsquare.com/files/rs-8140865/v1/1741fe0349a1073f561f4f11.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Key Genes Related to Ferroptosis Amino Acid Metabolism in Lung Adenocarcinoma Based on Transcriptome Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer is one of the malignancies with the highest incidence and mortality rates globally. Among its pathological types, non-small cell lung cancer (NSCLC) accounts for 80-85% of cases. Lung adenocarcinoma (LUAD), the most prevalent histological subtype of NSCLC, is characterized by high heterogeneity, as well as aggressive invasion and metastasis\u003csup\u003e[1-2]\u003c/sup\u003e. Despite significant breakthroughs in targeted therapy and immunotherapy that have improved the clinical outcomes of some patients, the overall 5-year survival rate for LUAD patients remains below 20%. This dismal prognosis can be primarily attributed to two critical clinical challenges. Firstly, the lack of effective early screening methods means that approximately 70% of patients are diagnosed at the locally advanced or metastatic stage. Secondly, treatment resistance caused by tumor heterogeneity significantly impacts patient outcomes, as different individuals respond variably to the same treatment regimen\u003csup\u003e[3-5]\u003c/sup\u003e. In recent years, advancements in tumor molecular biology, immunology, and molecular genetics have led to new insights into the etiology of LUAD, with particular emphasis on genetic alterations\u003csup\u003e[6]\u003c/sup\u003e. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThus, early identification of LUAD patients is crucial for prompt treatment and improved prognosis. However, specific disease markers for early LUAD detection remain elusive, posing both a research hotspot and a formidable challenge in clinical studies. In recent years, ferroptosis, a novel form of iron-dependent, lipid peroxidation-driven cell death, has emerged as a promising target for cancer therapy\u003csup\u003e[7]\u003c/sup\u003e. Research indicates that amino acid metabolism reprogramming not only regulates the redox homeostasis by supplying precursors for glutathione (GSH) but also directly modulates the sensitivity to ferroptosis through its metabolites, such as glutamine and serine\u003csup\u003e[8]\u003c/sup\u003e. Studies have shown that an imbalance in the SLC7A11-GSH axis can induce ferroptosis in lung cancer cells, and the expression level of the key metabolic enzyme ACSL4 is positively correlated with tumor metastatic potential\u003csup\u003e[9-10]\u003c/sup\u003e. These findings suggest that amino acid metabolism genes related to ferroptosis may serve dual functions: acting as biomarkers for early diagnosis and reflecting the malignant biological behavior of tumors. Nevertheless, most existing studies focus on single pathway mechanisms, and the spatiotemporal dynamics and clinical translation potential of this interaction network in LUAD remain unelucidated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWith the development of sequencing technologies, bioinformatics analysis has been widely applied to identify the interactions between gene expression profiles and diseases. Therefore, this study aims to systematically screen key genes in the ferroptosis-amino acid metabolism axis using bioinformatics approaches and evaluate their diagnostic and prognostic values. These efforts hold significant clinical implications for achieving early diagnosis, timely treatment, and stratified management of LUAD.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003ch3\u003e2.1 Data Acquisition and Pretreatment\u003c/h3\u003e\n\u003cp\u003eTranscriptome data of lung adenocarcinoma (LUAD) patients were obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/, Project ID: TCGA-LUAD). HTSeq-Counts data were downloaded for differential expression analysis using DESeq2, while HTSeq-TPMs data were normalized via log2 transformation with quantile normalization for subsequent functional enrichment analysis. Clinical information was retrieved from the University of California Santa Cruz (UCSC) Xena platform (https://genome.ucsc.edu/), including 513 LUAD tumor samples and 58 adjacent normal lung tissue controls. The GSE140797 dataset (Platform: GPL96) was acquired from the Gene Expression Omnibus (GEO,\u0026nbsp;https://www.ncbi.nlm.nih.gov/geo/), comprising RNA-seq data with paired survival information (7 LUAD tumors vs. 7 normal controls). Ferroptosis-related genes (FRGs, n=369) were extracted from FerrDb 2.0 (http://www.zhounan.org/ferrdb), containing experimentally validated drivers, suppressors, and markers of ferroptosis(Supplementary Table 1). Amino acid metabolism-related genes (ARGs, n=101) were derived from the \u0026quot;AMINO_ACID_AND_DERIVATIVE_METABOLIC_PROCESS\u0026quot; gene set in the Molecular Signatures Database (MSigDB,\u0026nbsp;http://www.gsea-msigdb.org/gsea/index.jsp) through Gene Set Enrichment Analysis (GSEA)(Supplementary Table 2).\u003c/p\u003e\n\u003ch3\u003e2.2 Enrichment Analysis of Function Based on Intersection Genes\u003c/h3\u003e\n\u003cp\u003eDifferential expression analysis was performed on all samples (Training dataset) from LUAD-TCGA using the R package \u0026quot;DESeq2\u0026quot;. Differentially expressed genes (DEGs) were screened based on the thresholds \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05, FDR\u0026lt;0.05, and |Log\u003csub\u003e2\u003c/sub\u003eFoldChange|\u0026gt;1\u003csup\u003e[11]\u003c/sup\u003e(Supplementary Table 3). Subsequently, the intersection between DEGs, ferroptosis-related genes, and amino acid metabolism-related genes was identified(Supplementary Table 4). Functional enrichment analysis was conducted on the overlapping genes using the R packages\u0026quot;clusterProfiler\u0026quot;,\u0026quot;org.Hs.eg.db\u0026quot;, \u0026quot;enrichplot\u0026quot;, and \u0026quot;dplyr\u0026quot;, with significance thresholds set at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 and FDR \u0026lt; 0.05(Supplementary Table 5-6)\u003csup\u003e[12]\u003c/sup\u003e.Gene Set Enrichment Analysis (GSEA)\u003csup\u003e[13]\u003c/sup\u003e identified DEG-associated pathways using MSigDB v5.2 hallmark gene sets(Supplementary Table 7-8). Prognostic nomograms integrating risk scores with clinical factors (grade, sex, stage, age) were developed via the \u0026quot;rms\u0026quot; R package to predict LUAD overall survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Gene localization analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe chromosomal distribution of biomarkers was analyzed using the \u0026quot;RCircos\u0026quot; package\u003csup\u003e[14]\u003c/sup\u003e. Subcellular localization of key genes was predicted through the mRNA Locater database (Http://bio-bigdata.cn/mRNALocater), and the prediction results were visualized using the R package \u0026quot;ggplot2\u0026quot;\u003csup\u003e[15]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Expression Patterns and Clinical/Prognostic Significance of Intersection Genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCandidate genes were identified through Wilcoxon rank-sum tests (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) comparing expression profiles between LUAD and control samples in both training and validation cohorts, with consistently differentially expressed genes selected as key candidates. The training cohort was then stratified into high- and low-expression groups based on median expression levels for subsequent Kaplan-Meier survival analysis. Gene expression correlations were assessed using Spearman\u0026apos;s and Pearson\u0026apos;s methods, while associations with clinical parameters (age [\u0026le;65/\u0026gt;65 years], gender, TNM stage [I-IV], T [T1-T4], N [N0-N3], and M [M0/M1] classifications) were systematically evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Prognostic Model Construction and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the training cohort, univariate Cox regression analysis was performed to identify key genes and clinical prognostic factors, with a significance threshold set at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. Subsequently, multivariate Cox regression analysis was employed to construct the prognostic model. The risk score was calculated using the following formula: Risk score = \u0026Sigma;(regression coefficient \u0026times; corresponding variable value)\u003csup\u003e[16]\u003c/sup\u003e. A nomogram was developed based on this model. In the validation cohort, the predictive accuracy was assessed using calibration curves, with particular focus on the concordance between predicted and observed 3-year and 5-year overall survival rates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 Cell culture and transfection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLUAD cell lines (H1975, HCC827, H1299, A549) and human normal bronchial epithelial cells (BEAS-2B)\u003csup\u003e[17]\u003c/sup\u003ewere purchased from Procell Life Science \u0026amp; Technology Co., Ltd (Wuhan, China). Cells were cultured in RPMI-1640 or DMEM/High medium supplemented with 10% fetal bovine serum and 100 IU/mL penicillin-streptomycin at 37\u0026deg;C under 5% CO₂ atmosphere. Routine medium replacement and subculture were performed. LUAD cells at logarithmic growth phase were trypsinized, resuspended, counted and seeded. When reaching 70-80% confluence the next day, cells were transfected with Sh-NC,Sh1-GOT1,Sh2-GOT1 or Sh3-GOT1 (Tsingke Biotechnology Co., Ltd, Xi\u0026apos;an, China) using Lipofectamine\u0026reg;3000 reagent (Thermo Fisher Scientific, cat. no. #L3000015, MA, USA). The target sequences of shRNAs for GOT1 gene knockdown are listed in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. shRNA sequences.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4021%;\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83.5979%;\"\u003e\n \u003cp\u003eSquence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4021%;\"\u003e\n \u003cp\u003eSh1-GOT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83.5979%;\"\u003e\n \u003cp\u003eCATCCGCTAATGACAATAGCCTAAATCTCGAGATTTAGGCTATTGTCATTAGCTTTTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4021%;\"\u003e\n \u003cp\u003eSh2-GOT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83.5979%;\"\u003e\n \u003cp\u003eCATCCGCGTTGGTACAATGGAACAAACTCGAGTTTGTTCCATTGTACCAACGCTTTTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.4021%;\"\u003e\n \u003cp\u003eSh3-GOT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83.5979%;\"\u003e\n \u003cp\u003eCATCCGCTAATGACAATAGCCTAAATCTCGAGATTTAGGCTATTGTCATTAGCTTTTTG \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 RT- qPCR Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from LUAD cells (H1299, A549) using Trizol reagent (Vazyme, China), with concentration measured by Nanodrop (Thermo Scientific, USA). GOT1 expression was quantified via SYBR Green-based RT-qPCR (Bimake, China) using the 2\u0026minus;\u0026Delta;\u0026Delta;CT method. Primer sequences are provided in Table 2.\u003c/p\u003e\n\u003cp\u003eTable 2. Primers for RT-qPCR.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003ePrimer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eSquence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eGOT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003esence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eGGAGCAGAAGATTGCTAATGACA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eantisense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eGGAGCAGAAGATTGCTAATGACA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eGAPDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003esence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eCAGGAGGGCATTGCTGATGAT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003eantisense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 352px;\"\u003e\n \u003cp\u003eGAAGGCTGGGGCTCATTT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Western Blot Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal cellular proteins were separated by SDS-PAGE and transferred onto PVDF membranes. After blocking with 5% skim milk for 2 h at room temperature, membranes were incubated overnight at 4\u0026deg;C with primary antibodies: anti-GOT1 (Proteintech, Cat#55370-1-AP, 1:1000 dilution) and anti-GAPDH (Cell Signaling Technology, Cat#5174,1:2000 dilution). Following washing, membranes were probed with HRP-conjugated secondary antibody (Jackson ImmunoResearch, Cat#111-035-003, 1:8000 dilution) for 1 h at room temperature. Protein bands were visualized using enhanced chemiluminescence substrate (Thermo Scientific, Cat# 32106) and imaged with a Bio-Rad ChemiDoc system\u003csup\u003e[18]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 CCK-8 assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLUAD cells (H1299, A549) were seeded in 96-well plates at a density of 5,000 cells per well and cultured in a 37\u0026deg;C incubator with 5% CO₂ for 24 hours. After transfection with siRNA-NC, siRNA-LCN2, and siRNA-LCN3, 10 \u0026mu;L of CCK-8 reagent (Mishu Biotechnology, Cat. No. MI00615A, Xi\u0026apos;an, China) was added to each well at 24, 48, 72, and 96 hours post-transfection\u003csup\u003e[19]\u003c/sup\u003e. The absorbance at 450 nm was measured. All experiments were performed in at least triplicate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Transwell Migration Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTranswell chambers were pre-coated with Matrigel. Transfected LUAD cells (4\u0026times;10⁵ cells/mL in serum-free medium) were seeded in the upper chamber (200 \u0026mu;L), while the lower chamber contained 500 \u0026mu;L of 10% serum-supplemented medium. After 48 h incubation, cells were fixed with 4% paraformaldehyde (15 min) and stained with crystal violet (5 min). Migrated cells were visualized under a light microscope\u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 EdU Proliferation Assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell proliferation was measured using an EdU assay kit (Beyotime Biotechnology, Cat# C0078S). After GOT1 modulation knockdown, cells (4\u0026times;10⁴/well) were cultured for 24 h, then pulsed with 10 \u0026mu;M EdU for 2 h at 37\u0026deg;C. Fixed cells underwent click reaction (30 min) and nuclear staining. Proliferation rates were determined by fluorescence microscopy (EdU+/total cells)\u003csup\u003e[21]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 In Vivo Tumorigenesis Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix-to-eight-week-old female BALB/c nude mice (SPF grade) were obtained from the Experimental Animal Center of Southern Medical University (Guangzhou, China) and housed under standard SPF conditions. A549 cells (1\u0026times;10⁶ cells in 100 \u0026mu;L PBS) with either GOT1 knockdown or control transfection were subcutaneously inoculated into the right flank of mice. The animals were randomly allocated into two groups (n=6 per group): control and GOT1-knockdown groups. Tumor growth and animal health were monitored every 2-3 days\u003csup\u003e[22]\u003c/sup\u003e. Tumor dimensions were measured using digital calipers, and volumes were calculated using the formula: V = (L\u0026nbsp;\u0026times; W\u0026sup2;)/2, where L represents length and W represents width. All experimental procedures were performed in accordance with the 3R principles and approved by the Institutional Animal Care and Use Committee (LDYYLL2025-389).\u003c/p\u003e\n\u003ch3\u003e2.13 Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eStatistical analyses were performed using R (v4.2.2) and GraphPad Prism (v9.0). Intergroup comparisons were conducted using Wilcoxon rank-sum/signed-rank tests (two groups) or Kruskal-Wallis tests (multiple groups)\u003csup\u003e[23]\u003c/sup\u003e. Survival outcomes were assessed by Kaplan-Meier analysis, while Spearman\u0026apos;s correlation coefficients evaluated variable associations. All tests were two-sided, with p\u0026lt;0.05 considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Identification of GOT1 and CDO1 as Key Ferroptosis Regulators in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe flowchart of this study is shown in Figure1.Through differential mRNA expression analysis of TCGA-LUAD data (Fig.2A-B), we identified GOT1 and CDO1 as core regulatory genes by intersecting differentially expressed genes with ferroptosis drivers and amino acid metabolism-related gene sets (Fig.2C). GO/KEGG enrichment analysis (Fig.2D-E) demonstrated their significant involvement in \u0026alpha;-amino acid catabolism, cysteine metabolism, and immune stress response. GSEA analysis (Fig.2F-G) further revealed that GOT1-associated genes were upregulated in protein synthesis pathways (e.g. ribosome, NES=1.98) while downregulated in immune regulation pathways (e.g., intestinal IgA network), whereas CDO1-associated genes were enriched in the complement system (NES=1.89) and cell adhesion. Chromosomal localization (Fig.2H-I) showed that GOT1 (10q25.2) encodes a cytoplasmic metabolic enzyme and CDO1 (5q23.2) exhibits secretory protein characteristics, suggesting their synergistic roles in promoting lung adenocarcinoma progression through intracellular metabolic reprogramming and microenvironmental regulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Prognostic Significance and Cellular Validation of GOT1 in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Integrated analysis of TCGA LUAD and GEO GSE140797 datasets (Fig.3A-B) revealed significant upregulation of GOT1 mRNA and downregulation of CDO1 mRNA in tumor tissues compared to normal controls. Survival analysis (Fig.3D-E) demonstrated that high GOT1 expression correlated with poor prognosis (log-rank P\u0026lt;0.05), while elevated CDO1 expression was associated with favorable outcomes (log-rank P\u0026lt;0.05). A significant negative correlation was observed between GOT1 and CDO1 expression (Pearson r=-0.16, Pearson r=-0.14, P=0.003; Fig.3C). Given that GOT1 is a cytoplasm-localized protein and serves as an independent prognostic risk factor in LUAD, we selected it as the primary therapeutic target. Western blot analysis across four NSCLC cell lines (H1975, HCC827, H1299, and A549) confirmed highest GOT1 protein expression in H1299 and A549 cells (Fig. 3F). Stable GOT1 knockdown models were successfully established in these cell lines, with 60-75% knockdown efficiency verified by RT-qPCR (Fig.3G-H) and Western blot (Fig.3I-J), providing essential tools for subsequent functional investigations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Functional Characterization of GOT1 in LUAD Pathogenesis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the oncogenic role of GOT1 in LUAD, we established stable GOT1-knockdown models in H1299 and A549 cell lines, with knockdown efficiency confirmed by RT-qPCR and Western blot analysis. The CCK-8 assay demonstrated that compared with the negative control (Sh-NC) group, both Sh2-GOT1 and Sh3-GOT1 knockdown groups exhibited significantly reduced cell proliferation at various time points (Fig4.A-B), indicating GOT1\u0026apos;s critical role in promoting LUAD cell growth. Transwell assays further revealed that GOT1 knockdown markedly impaired the migratory and invasive capacities of both H1299 and A549 cells (Fig4.C).TUNEL assays showed increased numbers of TUNEL-positive cells (apoptotic cells) in GOT1-knockdown groups compared to the control (Sh-NC), with quantitative analysis confirming significantly enhanced apoptosis (Fig4.D). Subsequent Western blot analysis demonstrated that GOT1 knockdown upregulated pro-apoptotic proteins while downregulating anti-apoptotic proteins, suggesting GOT1 depletion activates apoptotic pathways in these cells (Fig4.E). Collectively, these experimental results provide compelling evidence that GOT1 serves as a crucial regulator in LUAD cells, modulating multiple oncogenic processes including proliferation, migration, invasion, and apoptosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 GOT1 Deficiency Inhibits LUAD Progression In Vivo\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate GOT1\u0026apos;s role in lung cancer progression and therapeutic potential, we established GOT1-knockdown A549 cells via shRNA and generated subcutaneous xenografts in nude mice. The GOT1-knockdown group (Sh2-GOT1) exhibited significantly inhibited tumor growth (P\u0026lt;0.05) and reduced tumor mass compared to controls (Sh-NC) (Fig.5A-C). Histopathological analysis demonstrated decreased cellular density and mitotic activity in Sh2-GOT1 tumors (Fig.5D). Immunohistochemistry revealed a marked reduction in Ki67-positive cells (P\u0026lt;0.01) (Fig.5E-F), confirming impaired proliferative capacity. These results establish GOT1 as a crucial regulator of malignant phenotype maintenance in lung cancer, highlighting its potential as a therapeutic target for intervention strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Prognostic Significance of GOT1 in LUAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo comprehensively evaluate the clinical relevance of GOT1 in LUAD, we analyzed baseline data from the TCGA-LUAD cohort (Supplementary Table 9). High GOT1 expression was associated with adverse clinicopathological features such as advanced pathologic stage, male gender, and advanced age. Although the p-values did not reach statistical significance, there was a discernible trend in the data (Fig.6A-F). Univariate Cox regression confirmed that elevated GOT1 expression (HR = 1.216, 95% CI 1.029\u0026ndash;1.437) and TNM stage (HR = 2.341, 95% CI 1.638\u0026ndash;3.346) were independent prognostic factors (Fig.6G). Although multivariate analysis did not establish GOT1 as a statistically significant predictor (HR = 1.127, 95% CI 0.938\u0026ndash;1.354, P = 0.201) (Fig.6H)(Supplementary Tables 10-11), its association with clinical features suggests a potential stage-dependent prognostic role, warranting further validation in larger cohorts. We subsequently developed a nomogram incorporating GOT1 and all relevant clinical variables (Fig.6I), which demonstrated excellent calibration, with predicted 1-, 2-, and 3-year survival rates closely matching observed outcomes (Fig.6J). These findings provide preliminary evidence supporting GOT1 as a potential prognostic biomarker in LUAD, though its independent predictive value requires confirmation in prospective studies.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eLung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide, with persistently high incidence and death rates. Due to its molecular heterogeneity and therapeutic resistance, LUAD treatment continues to pose significant challenges\u003csup\u003e[24]\u003c/sup\u003e. Despite notable advancements in chemotherapy, radiotherapy, targeted therapy, and immunotherapy, the overall 5-year survival rate for LUAD patients remains below 20%\u003csup\u003e[25]\u003c/sup\u003e. In the field of targeted therapy, treatments directed at driver gene mutations (e.g.,\u0026nbsp;EGFR,\u0026nbsp;ALK,\u0026nbsp;ROS1) have become a cornerstone of LUAD management, substantially improving clinical outcomes\u003csup\u003e[26]\u003c/sup\u003e. Studies demonstrate that LUAD patients harboring sensitive mutations experience significantly prolonged survival upon receiving corresponding targeted agents, with some even achieving \u0026quot;chronic disease-like\u0026quot; management\u003csup\u003e[27]\u003c/sup\u003e. Notably,\u0026nbsp;EGFR-mutant patients have benefited from the evolution of first- to third-generation EGFR-TKIs, which not only enhance response rates but also address resistance mechanisms\u003csup\u003e[28]\u003c/sup\u003e. Similarly,\u0026nbsp;ALK\u0026nbsp;fusion-positive patients have achieved prolonged survival through successive generations of ALK-TKIs\u003csup\u003e[29]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo better guide clinical decision-making and predict patient prognosis, gene-based predictive models have emerged in LUAD research. For instance, prognostic models incorporating\u0026nbsp;EGFR\u0026nbsp;mutation subtypes (e.g., L858R, 19del) and resistance mutations (e.g., T790M) can effectively assess patient responsiveness to different targeted therapies\u003csup\u003e[30-31]\u003c/sup\u003e. Additionally, integrated models based on tumor mutational burden (TMB) and PD-L1 expression levels help predict the potential benefits of immunotherapy combined with targeted treatment\u003csup\u003e[32]\u003c/sup\u003e. While these models have advanced precision medicine in LUAD, limitations persist, particularly in addressing tumor heterogeneity and therapeutic resistance.\u003c/p\u003e\n\u003cp\u003eIn recent years, the exploration of tumor biological mechanisms has revealed ferroptosis and amino acid metabolism as emerging hotspots in lung adenocarcinoma (LUAD) research\u003csup\u003e[33]\u003c/sup\u003e. Ferroptosis, a novel form of regulated cell death, is closely linked to amino acid metabolic reprogramming and plays a critical role in LUAD pathogenesis\u003csup\u003e[34]\u003c/sup\u003e. Studies indicate that dysregulated amino acid metabolism not only disrupts redox homeostasis in tumor cells but also modulates ferroptosis susceptibility, suggesting these pathways may serve as potential therapeutic targets and prognostic biomarkers\u003csup\u003e[35]\u003c/sup\u003e. However, the mechanistic role, spatiotemporal dynamics, and clinical translational potential of the ferroptosis\u0026ndash;amino acid metabolic network in LUAD remain poorly understood\u003csup\u003e[36]\u003c/sup\u003e. Our study focuses on precision genomic analysis of ferroptosis and amino acid metabolism in LUAD, leveraging integrated transcriptomic data to identify key genetic signatures and construct clinically relevant prognostic models. By profiling the expression patterns of ferroptosis- and amino acid metabolism-related biomarkers, we aim to elucidate their potential in guiding targeted and combination therapies for LUAD.\u003c/p\u003e\n\u003cp\u003eThrough comprehensive bioinformatics analysis and functional validation, this study provides the first systematic evidence that GOT1 and CDO1 serve as central regulators at the interface of ferroptosis and amino acid metabolism in lung adenocarcinoma (LUAD). Our findings demonstrate that the mitochondrial aminotransferase GOT1, a key cytoplasmic transaminase, promotes tumor progression by regulating \u0026alpha;-amino acid catabolism and glutathione (GSH) synthesis, thereby conferring resistance to ferroptosis\u003csup\u003e[37-39]\u003c/sup\u003e. Clinical correlation analysis revealed significant GOT1 overexpression in LUAD tissues, with expression levels positively associated with advanced tumor stage (P\u0026lt;0.01), lymph node metastasis (P\u0026lt;0.05), and poor patient prognosis (log-rank P=0.003). Functional studies using loss-of-function approaches showed that GOT1 knockdown markedly inhibited tumor cell proliferation (CCK-8 and EdU assays demonstrated \u0026gt;50% reduction), migration and invasion (\u0026gt;60% suppression in Transwell assays), and reversed epithelial-mesenchymal transition (EMT). These in vitro findings were corroborated by in vivo xenograft experiments, where GOT1 depletion resulted in a 45% reduction in tumor volume (P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eIn contrast, CDO1, a secreted metabolic enzyme, exhibited reduced expression in LUAD and correlated with favorable clinical outcomes. Notably, we identified a significant negative correlation between GOT1 and CDO1 expression (Pearson r=-0.42, P=0.008), suggesting their complementary roles in regulating the ferroptosis-amino acid metabolic axis. The extracellular secretion characteristics of CDO1 highlight its potential as a non-invasive biomarker, though further validation is required to assess its utility in early detection\u003csup\u003e[40]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGene set enrichment analysis (GSEA) revealed distinct functional pathways associated with each gene: GOT1-related genes were enriched in ribosomal biosynthesis pathways (FDR q\u0026lt;0.05), indicating its role in enhancing protein synthesis capacity, while CDO1-associated genes participated in complement activation and cell adhesion (FDR q\u0026lt;0.01), suggesting tumor microenvironment modulation. The differential chromosomal localization (GOT1 at 10q25.2 vs CDO1 at 5q23.2) and subcellular distribution (cytoplasmic vs secreted) of these regulators imply a coordinated \u0026quot;intracellular metabolic reprogramming-extracellular microenvironment regulation\u0026quot; model driving LUAD progression\u003csup\u003e[41]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe developed a clinically applicable GOT1-based prognostic nomogram incorporating both molecular and clinicopathological features, with calibration curves demonstrating excellent concordance between predicted and observed survival probabilities (C-index = 0.72). Although multivariate Cox analysis did not establish GOT1 as an independent prognostic factor (HR=1.31, 95% CI 0.94-1.83, P=0.112), its significant interaction with TNM stage (P for interaction=0.038) suggests enhanced predictive value in advanced disease, warranting validation in larger cohorts.\u003c/p\u003e\n\u003cp\u003eThis study has several limitations that should be acknowledged. First, our findings primarily rely on public databases and cell line models, lacking protein-level validation in clinical specimens. Second, the mechanistic interplay between GOT1 and other ferroptosis regulators (e.g., ACSL4, SLC7A11) remains to be fully elucidated. Finally, preclinical studies evaluating GOT1-targeted combination therapies (particularly with immune checkpoint inhibitors) are needed to address potential resistance mechanisms. These findings establish GOT1 and CDO1 as promising therapeutic targets and prognostic biomarkers in LUAD, while highlighting the complex interplay between metabolic reprogramming and cell death pathways in cancer progression.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, our integrated multi-omics analysis has for the first time identified GOT1 as a master regulator of the ferroptosis-amino acid metabolic axis in LUAD, with its overexpression strongly associated with aggressive tumor phenotypes and poor clinical outcomes. Functional validation demonstrated that targeting GOT1 significantly suppresses LUAD cell proliferation, migration, and EMT progression, suggesting its potential as a novel therapeutic target for precision treatment. The prognostic model we developed offers a potential tool for patient risk stratification, though prospective studies are required for further validation. These findings establish a theoretical foundation for understanding metabolic regulation in LUAD and developing innovative therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the ethics committee of The First Hospital of Lanzhou University (No.LDYYLL2025-389). The animal study was reviewed and approved by the Ethics Committee of The First Hospital of Lanzhou University. According to the approved protocol, the maximum allowable tumor size in subcutaneous xenograft models is \u0026le;1,500 mm\u0026sup3; or a maximal diameter of \u0026le;15\u0026ndash;20 mm, with immediate euthanasia required if ulceration, impaired mobility, or distress occurs. All tumor-bearing animals in this study remained within these ethical limits, and no animal exceeded the maximum permitted tumor burden.Clinical trial number: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWen Li serves as the first author, Ruiyue Wu, Xi Chen, and Juanjuan Guo as co-first authors, Qiangsheng Jian, Rui Gong, Fangyun Yuan, Yongbin Lu, and Da Zhao as contributing authors, with Tao Zhang and Xiaoming Hou serving as corresponding authors. All authors have made substantive contributions to the research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by (1) National Natural Science Foundation of China (No.84260558); (2)The Key Talent Project of the Organization Department of the Gansu Provincial Committee of the Communist Party of China (2025RCXM036); (3)The Gansu Provincial Department of Education Project (No.2022B-003); (4)The unite Pesearch Foundation of Gansu Province (23JRRA1497); (5)The Science and Technology Program of Gansu Province (No. 23JRRA0928、23JRRA1607); (6)The youth Science and Technology Talent Innovation Project of Lanzhou City (2023-QN-14); (7)The Scientific and Technological Development Guiding Plan Project of Lanzhou City (2020-ZD-74); (8)The Science and Technology Plan Project in Chengguan District, Lanzhou City, Gansu Province(2021RCCX0008); (9)The First Hospital of Lanzhou University (ldyyyn2022-88、ldyyyn2022-65 and ldyyyn2023-7).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWu J, Feng J, Zhang Q, et al. Epigenetic regulation of stem cells in lung cancer oncogenesis and therapy resistance. Front Genet. 2023 Apr 18;14:1120815.\u003c/li\u003e\n \u003cli\u003eMa M, Wang W, Li L, et al. RBM15 facilities lung adenocarcinoma cell progression by regulating RASSF8 stability through N6 Methyladenosine modification. Transl Oncol. 2024 Aug;46:102018.\u003c/li\u003e\n \u003cli\u003eLeone GM, Candido S, Lavoro A,et al. Clinical Relevance of Targeted Therapy and Immune-Checkpoint Inhibition in Lung Cancer. Pharmaceutics. 2023 Apr 16;15(4):1252.\u003c/li\u003e\n \u003cli\u003eWillner J, Narula N, Moreira AL. Updates on lung adenocarcinoma: invasive size, grading and STAS. Histopathology. 2024 Jan;84(1):6-17.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMoghal N, Li Q, Stewart EL,et al.Single-Cell Analysis Reveals Transcriptomic Features of Drug-Tolerant Persisters and Stromal Adaptation in a Patient-Derived EGFR-Mutated Lung Adenocarcinoma Xenograft Model. J Thorac Oncol. 2023 Apr;18(4):499-515.\u003c/li\u003e\n \u003cli\u003eSong Y, Kelava L, Kiss I. MiRNAs in Lung Adenocarcinoma: Role, Diagnosis, Prognosis, and Therapy. Int J Mol Sci. 2023 Aug 27;24(17):13302.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWei X, Li X, Hu S. Regulation of Ferroptosis in Lung Adenocarcinoma. Int J Mol Sci. 2023 Sep 27;24(19):14614.\u003c/li\u003e\n \u003cli\u003eLiu X, Ren B, Ren J. The significant role of amino acid metabolic reprogramming in cancer. Cell Commun Signal. 2024 Jul 29;22(1):380.\u003c/li\u003e\n \u003cli\u003eZhang W, Sun Y, Bai L et,al. RBMS1 regulates lung cancer ferroptosis through translational control of SLC7A11. J Clin Invest. 2021 Nov 15;131(22):e152067.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu B, Ma H, Liu X, et,al. CircSCN8A suppresses malignant progression and induces ferroptosis in non-small cell lung cancer by regulating miR-1290/ACSL4 axis[J]. Cell Cycle. 2023 Apr;22(7):758-776.\u003c/li\u003e\n \u003cli\u003eRitchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res. 2015;43(7):e47.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWu T, Hu E, Xu S, et al. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data[J]. Innovation (Camb). 2021;2(3):100141.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHung JH, Yang TH, Hu Z, et al. Gene set enrichment analysis: performance evaluation and usage guidelines[J]. Brief Bioinform. 2012;13(3):281-291.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e1\u003c/li\u003e\n \u003cli\u003eTang Q, Nie F, Kang J, et al. mRNALocater: enhance the prediction accuracy of eukaryotic mRNA subcellular localization by using model fusion strategy[J]. Molecular Therapy, 2021, 29(8): 2617-2623.\u003c/li\u003e\n \u003cli\u003eTibshirani R. The lasso method for variable selection in the Cox model[J]. Stat Med. 1997;16(4):385-395.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eReddel RR, Ke Y, Gerwin BI, et al. Transformation of human bronchial epithelial cells by infection with SV40 or adenovirus-12 SV40 hybrid virus, or transfection via strontium phosphate coprecipitation with a plasmid containing SV40 early region genes[J].\u0026nbsp;Cancer Res. 1988;48(7):1904-1909.\u003c/li\u003e\n \u003cli\u003eTowbin H, Staehelin T, Gordon J,et al. Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications[J]. Proc Natl Acad Sci U S A. 1979;76(9):4350-4354.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eElbashir SM, Harborth J, Lendeckel W, et al. \u0026nbsp;Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells[J]. Nature. 2001;411(6836):494-498.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eJustus CR, Leffler N, Ruiz-Echevarria M, et al. In vitro cell migration and invasion assays[J]. J Vis Exp. 2014;(88):51046.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSalic A, Mitchison TJ. A chemical method for fast and sensitive detection of DNA synthesis in vivo[J].\u0026nbsp;Proc Natl Acad Sci U S A. 2008;105(7):2415-2420.\u003c/li\u003e\n \u003cli\u003eNational Research Council (US) Committee for the Update of the Guide for the Care and Use of Laboratory Animals[J]. Guide for the Care and Use of Laboratory Animals. 8th ed., National Academies Press (US), 2011.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMishra P, Pandey CM, Singh U, et al. Selection of appropriate statistical methods for data analysis[J].\u0026nbsp;Ann Card Anaesth. 2019;22(3):297-301.\u003c/li\u003e\n \u003cli\u003eHirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: current therapies and new targeted treatments[J]. Lancet. 2017;389(10066):299-311.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J]. CA Cancer J Clin. 2021;71(3):209-249.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eOwen DH, Ismaila N, Ahluwalia A, et al. Therapy for Stage IV Non-Small Cell Lung Cancer With Driver Alterations: ASCO Living Guideline, Version 2024.3[J]. J Clin Oncol. 2025;43(10):e2-e16.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYuan M, Huang L L, Chen J H, et al. The emerging treatment landscape of targeted therapy in non-small-cell lung cancer[J]. Signal transduction and targeted therapy, 2019, 4(1): 61.\u003c/li\u003e\n \u003cli\u003eMagios N, Bozorgmehr F, Volckmar A L, et al. Real-world implementation of sequential targeted therapies for EGFR-mutated lung cancer[J]. Therapeutic Advances in Medical Oncology, 2021, 13: 1758835921996509.\u003c/li\u003e\n \u003cli\u003eBritschgi C, Addeo A, Rechsteiner M, et al. Real-world treatment patterns and survival outcome in advanced anaplastic lymphoma kinase (ALK) rearranged non-small-cell lung cancer patients[J]. Frontiers in oncology, 2020, 10: 1299\u003c/li\u003e\n \u003cli\u003eZhang Y, Sheng J, Kang S, et al. Patients with exon 19 deletion were associated with longer progression-free survival compared to those with L858R mutation after first-line EGFR-TKIs for advanced non-small cell lung cancer: a meta-analysis[J]. PloS one, 2014, 9(9): e107161.\u003c/li\u003e\n \u003cli\u003eTang X, Li Y, Yan W, et al. Machine learning-based CT radiomics analysis for prognostic prediction in metastatic non-small cell lung cancer patients with EGFR-T790M mutation receiving third-generation EGFR-TKI osimertinib treatment[J]. Frontiers in Oncology, 2021, 11: 719919.\u003c/li\u003e\n \u003cli\u003eChen Y, Wang P, Lian R, et al. Comprehensive characterization of PD-L1 expression and immunotherapy-related genomic biomarkers in early-versus advanced-stage non-small cell lung cancer[J]. BMC Pulmonary Medicine, 2025, 25(1): 1-11.\u003c/li\u003e\n \u003cli\u003eLin W, Wang C, Liu G, et al. SLC7A11/xCT in cancer: biological functions and therapeutic implications[J]. American journal of cancer research, 2020, 10(10): 3106.\u003c/li\u003e\n \u003cli\u003eZhou Q, Meng Y, Li D, et al. Ferroptosis in cancer: From molecular mechanisms to therapeutic strategies[J]. Signal Transduct Target Ther. 2024;9(1):55.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLiu J, Kuang F, Kroemer G, et al. Autophagy-Dependent Ferroptosis: Machinery and Regulation[J].\u0026nbsp;Cell Chem Biol. 2020;27(4):420-435.\u003c/li\u003e\n \u003cli\u003eZhang Y, Swanda RV, Nie L, et al. mTORC1 couples cyst(e)ine availability with GPX4 protein synthesis and ferroptosis regulation[J].\u0026nbsp;Nat Commun. 2021;12(1):1589.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eSon J, Lyssiotis CA, Ying H, et al. Glutamine supports pancreatic cancer growth through a KRAS-regulated metabolic pathway[J]. Nature. 2013;496(7443):101-105.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eCheng T, Sudderth J, Yang C, et al. Pyruvate carboxylase is required for glutamine-independent growth of tumor cells[J]. Proc Natl Acad Sci U S A. 2011;108(21):8674-8679.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eTajan M, Hock AK, Blagih J, et al. A Role for p53 in the Adaptation to Glutamine Starvation through the Expression of SLC1A3[J]. Cell Metab. 2018;28(5):721-736.e6.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMeller S, Zipfel L, Gevensleben H, et al. CDO1 promoter methylation is associated with gene silencing and is a prognostic biomarker for biochemical recurrence-free survival in prostate cancer patients[J]. Epigenetics. 2016;11(12):871-880.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eYang C, Ko B, Hensley CT, et al. Glutamine oxidation maintains the TCA cycle and cell survival during impaired mitochondrial pyruvate transport. Mol Cell. 2014;56(3):414-424.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lung adenocarcinoma, ferroptosis, amino acid metabolism, GOT1, prognostic biomarker","lastPublishedDoi":"10.21203/rs.3.rs-8140865/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8140865/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLung adenocarcinoma (LUAD) is a highly aggressive cancer with limited treatment options. This study investigated key genes linking ferroptosis and amino acid metabolism in LUAD using bioinformatics and experimental validation. Analysis of TCGA and GEO datasets identified ​GOT1​ (upregulated) and ​CDO1​ (downregulated) as core regulators. GOT1 overexpression correlated with advanced tumor stage (P\u0026lt;0.05), metastasis (P\u0026lt;0.05), and poor survival (log-rank P\u0026lt;0.05). Functional studies demonstrated that GOT1 knockdown suppressed LUAD cell proliferation , migration, and induced apoptosis, while reducing tumor growth in vivo (P\u0026lt;0.001). Conversely, CDO1 exhibited tumor-suppressive effects, with a negative correlation to GOT1 (Pearson r=-0.42, P=0.008). Finally，A prognostic model incorporating GOT1 showed strong predictive accuracy (C-index=0.72). These findings establish ​GOT1​ as a critical driver of LUAD progression through ferroptosis-amino acid metabolic reprogramming, highlighting its potential as a therapeutic target and prognostic biomarker. The antagonistic roles of GOT1 and CDO1 provide new insights into metabolic regulation in LUAD, paving the way for precision therapy strategies.\u003c/p\u003e","manuscriptTitle":"Exploring Key Genes Related to Ferroptosis Amino Acid Metabolism in Lung Adenocarcinoma Based on Transcriptome Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 12:38:47","doi":"10.21203/rs.3.rs-8140865/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf04a85d-a9c0-4cac-9072-8b3453a8c7e1","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T09:11:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 12:38:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8140865","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8140865","identity":"rs-8140865","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}