Zinc Finger Protein-Based Prognostic Model in Lung Adenocarcinoma Highlights FGD3 as a Potential Marker for Lorlatinib Resistance

Zinc Finger Protein-Based Prognostic Model in Lung Adenocarcinoma Highlights FGD3 as a Potential Marker for Lorlatinib Resistance | 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 Zinc Finger Protein-Based Prognostic Model in Lung Adenocarcinoma Highlights FGD3 as a Potential Marker for Lorlatinib Resistance Jiayue Sun, Xiaoyi Huang, Dinglong Xue, Jiazhuang Li, Yaru Huang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7976542/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 Background Lung adenocarcinoma is the most common type of lung cancer and a major cause of cancer death. Zinc finger proteins are involved in tumor progression and may serve as diagnostic and therapeutic targets. Methods RNA-seq and clinical data were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognosis-associated ZNF genes were identified through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. An eight-gene prognostic model was established and evaluated using Kaplan–Meier survival curves and time-dependent receiver operating characteristic (ROC) analyses, with external validation in the GSE50081 and GSE26939 datasets. A nomogram integrating independent prognostic factors was constructed and assessed by calibration plots. Immune infiltration, mutation profiles, and drug response were analyzed. Finally, we focused on a key molecule FGD3, examining its expression in LUAD cells and tissues, including lorlatinib-resistant models. Results The signature consisted of TRIM6, TRIM29, CTCFL, FGD3, GATA4, CASZ1, TRAF2 , and ZNF322 . T stage, N stage, and the risk score emerged as independent predictors of overall survival. The nomogram showed robust predictive value. High-risk patients exhibited an immune “desert” phenotype, higher somatic mutation rates, and elevated tumor mutational burden. Immunotherapy prediction and drug sensitivity analysis suggested that the model could potentially predict responses to immunotherapy, chemotherapy, and targeted therapy. FGD3 expression was downregulated in LUAD tissues and lorlatinib-resistant cells, decreasing with prolonged lorlatinib exposure. Restoration of FGD3 suppressed H3122LR proliferation and partially reversed resistance. Conclusion We developed and validated a novel ZNF-based prognostic model for LUAD that may serve as a valuable tool for risk stratification and guiding personalized treatment strategies. Zinc finger protein family Lung adenocarcinoma Prognostic risk model FGD3 Lorlatinib resistance Immune infiltration Drug sensitivity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1 Introduction Lung cancer was the most commonly diagnosed cancer in 2022, with approximately 2.5 million new cases, accounting for 12.4% of all cancer cases worldwide. Similarly, it ranks first in both incidence and mortality among cancers in China [1]. Non-small cell lung cancer (NSCLC) accounts for 80–85% of newly diagnosed cases, with lung adenocarcinoma (LUAD) being the most prevalent histological subtype [2]. Although most patients initially respond to standard chemotherapy, relapse often occurs rapidly [3]. LUAD exhibits high heterogeneity driven by complex genetic alterations, such as EGFR , KRAS , and ALK mutations, which serve as major targets for molecular therapy [4]. For patients without detectable driver mutations, immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic option. Despite these therapeutic advances, the prognosis for advanced LUAD remains poor, partly due to the lack of reliable prognostic markers to guide personalized treatment strategies. Currently, various biological markers have been used to predict the survival outcomes and treatment responses of patients with LUAD. For example, TP53 mutations, PD-L1 expression levels, immune microenvironment characteristics, and tumor metabolism-related molecules all play important roles in prognostic assessment [5]. However, the predictive capability of these individual markers is limited by the intrinsic heterogeneity of the disease [6], highlighting the urgent need for more accurate prognostic models. Zinc finger proteins (ZNFs) represent the largest transcription factor family in the human genome [7]. These proteins contain one or more zinc ions coordinated with specific amino acids, forming diverse zinc-finger domains [8]. This structural diversity enables ZNFs to bind DNA, RNA, and proteins, thereby participating in numerous biological processes, including transcriptional regulation, DNA repair, protein degradation, signal transduction, cell proliferation, adhesion, migration, and cytoskeletal organization [9]. Emerging evidence suggests that ZNFs can function as either oncogenes or tumor suppressors, depending on the cellular context [7]. For instance, ZNF131 has been reported to promote non-small cell lung cancer (NSCLC) cell proliferation, invasion, and stemness [10]. Similarly, the zinc finger protein ZNF687 accelerates LUAD cell proliferation and tumor progression [11]. Conversely, ZNF652 acts as a tumor suppressor by downregulating cyclin D3 in lung cancer cells [12]. Additionally, CBX4 , another zinc finger protein, promotes LUAD cell proliferation while concurrently inhibiting metastasis [13]. Despite these findings, the comprehensive role of ZNFs in LUAD remains incompletely understood, and their utility as prognostic biomarkers or therapeutic targets has yet to be fully elucidated. It is hypothesized that ZNFs may regulate LUAD progression through distinct oncogenic or tumor-suppressive mechanisms, and that modulating their expression could provide novel therapeutic opportunities. Therefore, an in-depth investigation of ZNFs in LUAD is urgently needed. In this pilot study, transcriptomic and clinical data for LUAD were obtained from the TCGA database. ZNFs were retrieved from the UniProt database, and differentially expressed ZNFs (DE-ZNFs) were identified. TCGA data were used as the training set to construct a prognostic model, which was subsequently validated using an independent GEO test cohort. Finally, the model’s prognostic performance and its potential to identify candidate anti-tumor agents were evaluated, offering new insights into individualized treatment strategies for LUAD patients. 2 Methods and materials 2.1 Data acquisition RNA sequencing data and corresponding clinical information for LUAD were obtained from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ) using the TCGAbiolinks package [14], comprising 513 LUAD and 58 normal lung tissue samples. External validation cohorts were derived from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ), specifically from GSE50081 (127 LUAD samples) [15] and GSE26939 (116 LUAD samples)[16]. Additionally, two GEO datasets containing paired LUAD tumor and adjacent normal tissues, GSE32863 (58 paired samples)[17] and GSE75037(83 paired samples)[18], were used for external validation of FGD3 expression differences. The GEO datasets were normalized using the limma package [19]. Annotation GPL570, GPL9053 and GPL6884 were used for ID conversion. Genes with missing values were excluded, and for those with one-to-many mapping relationships, the mean expression value was retained. Genes belonging to the zinc finger protein family (Supplementary Table S1 ) were retrieved from the Universal Protein Resource (UniProt) database ( https://www.uniprot.org/ ). 2.2 Identification of differentially expressed ZNFs genes in LUAD Differential expression analysis of the TCGA count data from LUAD tumors and adjacent normal tissue samples was performed using the limma package in R software (version 4.4.0) [19]. Genes met the criteria of log2|fold change| values > 1 and adjusted p-values < 0.05 were selected. By intersecting these genes with the previously obtained ZNF gene set, differentially expressed ZNFs (DE- ZNFs ) were identified. Heatmaps were generated using the pheatmap package, and volcano plots were constructed with the ggplot2 package. 2.3 Functional enrichment analysis Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed using the clusterProfiler package[20]. A p-value < 0.05 was set as the threshold for statistical significance. The results of the enrichment analyses were visualized using the ggplot2 and ggnewscale packages. 2.4 Risk prognostic model based on DE-ZNFs Using TCGA as the training set, univariate Cox regression analysis was performed on DE-ZNFs with the survival package, using overall survival (OS) as the evaluation criterion. Genes significantly associated with OS ( p < 0.05) were retained. Subsequently, least absolute shrinkage and selection operator (LASSO) regression was conducted using the glmnet and survival packages, and the results were visualized with the ggplot2 and forestplot packages. To avoid overfitting, multivariate Cox regression analysis was then applied to identify prognostic genes. The risk score for the model was calculated as follows \(\::Risk\:score=\beta\:1E1+\beta\:2E2+\cdots\:+\beta\:iEi\) , where β represents the regression coefficient, E denotes the expression level of each prognostic gene, and i is the number of genes. LUAD patients were stratified into high- and low-risk groups based on the median risk score. Kaplan–Meier survival curves were generated, and log-rank tests were conducted to evaluate survival differences between groups. The model’s predictive performance was assessed using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) representing its discriminatory ability. To further validate the predictive power of the model, its performance was evaluated in two independent datasets, GSE50081 [15] and GSE26939 [16]. 2.5 Immune cell infiltration analysis CIBERSORT analysis was performed using the IOBR package to compare the relative abundances of 22 immune cell types between the high- and low-risk groups[21]. Single-sample gene set enrichment analysis (ssGSEA) was performed with the GSVA package to evaluate differences in 28 immune cell types[22]. Additionally, xCell analysis was conducted using the xCell package to quantify the abundances of 64 immune cell types[23]. Correlation heatmaps between the eight model genes and the 22 CIBERSORT-derived immune cell types were generated using the corrplot and ggcorrplot packages. The estimate package was applied to calculate stromal scores, immune scores, estimate scores (sum of stromal and immune scores), and tumor purity. Pearson’s product–moment correlation was used to assess associations between the risk score and stromal score, immune score, estimate score, and tumor purity. 2.6 Prediction of immunotherapy response LUAD data were downloaded from the TIDE database ( http://tide.dfci.harvard.edu/ ) for tumor immune dysfunction and exclusion (TIDE) analysis[24]. A higher TIDE score indicates a poorer response to immunotherapy. The immunophenoscore (IPS) for each patient was obtained from the Cancer Immunome Atlas ( https://tcia.at/ ) and compared between different risk groups. Unlike the TIDE score, a higher IPS is positively associated with a better predicted response to immunotherapies targeting PD-L1 and CTLA-4[25]. Additionally, immunotherapy data from the urothelial carcinoma IMvigor210 dataset were used to predict responses to immunotherapy in each risk group[26]. 2.7 Tumor mutation burden and chromosomal location of model genes The tumor mutation burden (TMB) data for the TCGA cohort were downloaded from the cBioPortal database ( https://www.cbioportal.org/ ) and calculated using the Maftools package[27]. Waterfall plots were generated to visualize the top 15 genes with the highest mutation frequency in the high- and low-risk groups. The ggpubr package was used to create violin and bar plots, and chi-square tests were applied for group comparisons. The rtracklayer, RCircos, and magrittr packages were employed to plot copy number variation (CNV) landscapes, enabling visualization of changes in DE-ZNFs model genes across human chromosomes. 2.8 Differential gene enrichment analysis between high- and low-risk groups Differentially expressed genes (DEGs) between the two groups were identified using the DESeq2 package with the Wald test. Genes with an absolute log₂ (fold change) > 1 and p < 0.05 were selected. GO, KEGG, and GSEA of the DEGs were performed using the clusterProfiler package. Gene set variation analysis (GSVA) was conducted using the GSVA package[22]. 2.9 Prediction of antitumor drug sensitivity Drug sensitivity data were obtained from two major pharmacogenomic databases: the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Therapeutics Response Portal (CTRP), which contain large-scale screening results of numerous anti-cancer drugs across hundreds of cell lines. GDSC1, GDSC2 and CTRP2 drug response data were obtained from the GDSC website ( https://www.cancerrxgene.org/ ) and the CTRP website ( http://portals.broadinstitute.org/ctrp.v2.1/ ). The oncoPredict package was applied to predict the sensitivity of high- and low-risk LUAD patients, as defined by the prognostic model, to commonly used chemotherapeutic agents and molecularly targeted drugs[28]. The half-maximal inhibitory concentration (IC 50 ) represents the drug concentration required to inhibit 50% of tumor cell viability. A lower IC 50 value indicates greater drug sensitivity. IC 50 values were expressed in micromolar (µM) units, and the natural logarithm of the IC 50 was used for downstream drug response analyses. 2.10 Nomogram construction To evaluate the association between clinical characteristics and risk grouping, univariate and multivariate Cox regression analyses were performed to determine whether the clinical variables and the risk score were independent prognostic factors for LUAD patients. Clinical data for LUAD patients in the TCGA cohort were obtained from the UCSC Xena platform ( https://xenabrowser.net/datapages/ ). Fisher’s exact test was applied to compare clinical characteristics between high- and low-risk groups using the tableone package. A nomogram was then constructed to enhance the prognostic accuracy and predictive power of the model[29]. The risk score, T stage, and N stage were identified as independent prognostic variables to estimate the probability of overall survival (OS) at 1, 2, and 3 years. Calibration curves were generated to assess the agreement between predicted and observed outcomes, thereby evaluating the nomogram’s predictive performance. 2.11 Validation of prognostic gene expression The expression levels of the prognostic genes were validated using the TCGA LUAD dataset. Bar plots were generated with the ggpubr and ggsci packages, and group comparisons were conducted using the Wilcoxon test. In addition, immunohistochemical (IHC) data for the prognostic genes in LUAD tissues were obtained from the Human Protein Atlas (HPA) ( https://www.proteinatlas.org/ ). Receiver operating characteristic (ROC) curves for the prognostic model genes were constructed using the pROC package to evaluate their discriminatory performance. 2.12 Cell culture and clinical specimen collection LUAD cell lines (H3122, H2228, H1650, H1993, and PC9) were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Human bronchial epithelial (HBE) cells were kindly provided by the Heilongjiang Cancer Institute (Harbin, China). All cell lines were authenticated by short tandem repeat (STR) analysis prior to experimentation. H3122, H1650, H2228, and PC9 cells were cultured in RPMI 1640 medium (HyClone, USA) supplemented with 10% fetal bovine serum (Pan-Biotech, Germany) and 1% penicillin–streptomycin (HyClone, USA). H1993 cells were maintained in DMEM medium (HyClone, USA). All cells were incubated at 37°C in a humidified atmosphere containing 5% CO₂. A total of 12 paired fresh-frozen LUAD tumor tissues and adjacent normal tissues were collected from patients at Harbin Medical University Cancer Hospital between 2024 and 2025 for protein extraction. Additionally, 14 paired paraffin-embedded LUAD and adjacent normal tissue sections were obtained from the same institution between 2020 and 2022 for immunohistochemistry. None of the patients had received preoperative therapy. 2.13 Western blot analysis Cells and tissues were harvested and lysed on ice for 30 min using lysis buffer (Boster, Wuhan, China; #AR0102-100) supplemented with 1% protease inhibitor cocktail (Boster, Wuhan, China #AR1192). The lysates were centrifuged at 12,000 rpm for 15 min at 4°C, and the supernatants were collected. Protein concentration was determined using a protein quantification kit (Beyotime, Shanghai, China; #P0012S). Equal amounts of protein (25 µg) were loaded per lane, separated by SDS–PAGE, and transferred to PVDF membranes. The membranes were blocked with 5% nonfat milk overnight at 4°C and incubated with primary antibodies for 24 h at 4°C. The following day, membranes were washed and incubated with HRP-conjugated secondary antibodies for 50 min at room temperature. Immunoreactivity was detected using an ECL kit (Thermo Fisher Scientific, Waltham, MA, USA). The primary antibodies were as follows: rabbit anti-FGD3 (Proteintech, Wuhan, China; #20347-1-AP; 1:1000) and mouse anti-GAPDH (Abcam, Cambridge, UK; #ab8245; 1:50,000). 2.14 IC 50 assay Lorlatinib was purchased from MedChemExpress (Monmouth Junction, NJ, USA; #HY-15728). A total of 5,000 cells were seeded into each well of 96-well plates and cultured in 100 µl RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) for 12 hours. After cell adhesion, the culture medium was replaced with 100 µl of complete medium containing varying concentrations of lorlatinib. Following 48 hours of drug treatment, the medium was removed, and 100 µl of fresh medium containing 10 µl of CCK-8 reagent (Seven, Shanghai, China; #SC11901) was added to each well. The plate was incubated for 1 hour at 37°C, and absorbance at 450 nm was measured using a multifunctional microplate reader. Dose-response curves were generated based on the absorbance values at each drug concentration, and the IC 50 values for each cell group were calculated. 2.15 Lorlatinib-resistant cells Parental H3122 cells were initially exposed to a low concentration of lorlatinib (0.04 µM) until they achieved full proliferation. The cells were then passaged and treated with a doubled concentration of lorlatinib. This process was repeated incrementally until the lorlatinib concentration reached 1 µM. The resulting cells exhibited an IC₅₀ value more than 50 times higher than that of the parental cells. This resistant cell line was designated as H3122 lorlatinib-resistant cells (H3122LR). H3122LR cells were subsequently expanded and continuously maintained in medium containing the same lorlatinib concentration during each passage. 2.16 Immunohistochemistry Immunohistochemistry was performed using the high sensitive and rapid immunohistochemical kit (pH 9.0) (Elabscience, Wuhan, China; #E-IR-R220). Briefly, paraffin-embedded sections were baked at 65°C for 60 minutes, then placed in antigen retrieval and deparaffinization solution and microwaved at low power for 30 minutes, followed by natural cooling to room temperature. After washing, sections were incubated with peroxidase blocking buffer for 15 minutes, washed again, and incubated overnight at 4°C with primary antibody against FGD3 (Proteintech, Wuhan, China; #20347-1-AP; 1:100). On the following day, sections were incubated with secondary antibody at room temperature for 30 minutes, followed by DAB staining for 2 minutes and hematoxylin counterstaining for 30 seconds. The sections were then dehydrated through graded ethanol series, cleared with xylene, mounted with neutral resin, and air-dried. FGD3 immunostaining was independently evaluated by two investigators blinded to patient information. The percentage of positive cells was scored as 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%), and 4 (76–100%). Staining intensity was scored as 0 (negative), 1 (light yellow), 2 (dark yellow), and 3 (brown). The final immunoreactive score (IRS) was calculated by summing the percentage and intensity scores, ranging from 0 to 7. 2.17 Plasmid transfection and FGD3 overexpression FGD3 was overexpressed in H3122LR cells either transiently or stably. For transient overexpression, cells at ~ 70% confluence were transfected with an FGD3 plasmid or empty vector using Lipofectamine 3000 (Invitrogen, USA). After 6 h, the medium was replaced, and cells were cultured for 24–48 h before downstream assays. For stable overexpression, cells were transfected as above and selected with puromycin until resistant colonies were established. Transfection efficiency and FGD3 expression were confirmed by Western blot. Plasmid sequences are provided in Supplementary Table S2 . 2.18 Colony formation assay H3122LR cells transfected with an FGD3 overexpression plasmid or empty vector were seeded into 6-well plates and cultured in complete medium. After 10 days, colonies were fixed with 4% paraformaldehyde, stained with crystal violet, and counted. Experiments were performed in triplicate. 2.19 CCK-8 assay Cell viability was assessed using the Cell Counting Kit-8 (CCK-8, Sevenbio, Beijing, China) according to the manufacturer’s instructions. Cells were seeded in 96-well plates and cultured for 1–4 days. CCK-8 solution was then added to each well at a 1:10 dilution and incubated at 37°C for 1 h. Absorbance at 450 nm was measured using a microplate reader (Multiskan FC, Thermo, China). 2.20 Wound-healing assay H3122LR cells transfected with an FGD3 overexpression plasmid or empty vector were seeded in 6-well plates and cultured for 24 h. A scratch was made through the center of each well using a 10 µL pipette tip, and serum-free medium was added. Wound closure was quantified as the percentage of wound area reduction over the healing period. All experiments were performed in triplicate. 2.21 Cell migration and invasion assays For the transwell assay, 105 cells were seeded in the upper chamber (8-µm pore size, Corning, USA) in serum-free medium, either uncoated (migration) or coated with Matrigel (BD Biosciences, invasion). The lower chamber contained 600 µL of medium with 10% FBS. After 24 h (migration) or 48 h (invasion), cells on the lower surface of the membrane were fixed with 4% paraformaldehyde and stained with crystal violet. Cell numbers were quantified as the average number of cells per field of view. 2.22 Statistical analysis For variables with normal distribution between two groups, unpaired Student’s t-tests were used to assess statistical significance. When data were not normally distributed, Mann–Whitney U tests were applied. For categorical clinical variables with small sample sizes, Fisher’s exact test was used to evaluate group differences. Pearson’s or Spearman’s correlation analyses were performed to examine linear relationships between variables. Kaplan–Meier survival analysis was conducted to compare overall survival between high- and low-risk groups, with differences assessed by the log-rank test. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were used to evaluate the predictive performance of the risk score. All analyses were performed using R software (version 4.4.0) and GraphPad Prism (version 9.5). Data are presented as means, with statistical significance defined as p < 0.05. 3 Results 3.1 Acquisition of DE-ZNFs in LUAD and functional enrichment analysis The workflow of this study was illustrated in Fig. 1 . A total of 19,934 RNA-seq genes and clinical information for 513 LUAD patients and 58 control samples were downloaded. Among them, 1,834 ZNFs genes were obtained. After differential analysis, 270 DE-ZNFs were identified, including 83 downregulated and 187 upregulated genes (Fig. 2 A, B). To evaluate the potential functions of DE-ZNFs in LUAD, GO enrichment analysis was performed. Sixteen biological process (BP) pathways, 5 cellular component (CC) pathways, and 23 molecular function (MF) pathways were identified, with the top five displayed. DE-ZNFs were mainly involved in the protein polyubiquitination process (Fig. 2 C, D). KEGG enrichment analysis revealed a total of 111 enriched pathways, including key biological processes such as ubiquitin-mediated proteolysis, lysine degradation, endocytosis, and chromatin remodeling (Fig. 2 E). Notable signaling pathways identified by GSEA and KEGG enrichment included TNFA signaling via NF-kappa B, KRAS signaling, and the E2F transcription factor pathway (Fig. 2 E, F). 3.2 Development and validation of the prognostic model for LUAD Univariate Cox regression analysis identified 63 DE-ZNFs significantly associated with OS in the TCGA training set (Fig. 3 A). After excluding samples with missing values and OS = 0, 500 cancer samples remained. LASSO regression was then applied to further select 19 DE-ZNFs (Fig. 3 B, C). Finally, multivariate Cox regression was used to establish the predictive model and identify prognostic-related genes: TRIM6, TRIM29, CTCFL, FGD3, GATA4, CASZ1, TRAF2, and ZNF322 (Fig. 3 D). The risk score was calculated as follows \(\::Risk\:score=(0.21\times\:ExpTRIM6)+(0.079\times\:ExpTRIM29)+(0.259\times\:ExpCTCFL)+(-0.377\times\:ExpFGD3)+(0.239\times\:ExpGATA4)+(-0.344\times\:ExpCASZ1)+(0.427\:\times\:\:ExpTRAF2)\:+\:(-0.256\:\times\:\:ExpZNF322).\) LUAD samples were then divided into high- and low-risk groups based on the median risk score. Risk curves and scatter plots illustrated that the high-risk group exhibited higher risk scores and mortality rates compared to the low-risk group (Fig. 3 E, F). The heatmap showed that TRIM29, GATA4, TRIM6, CTCFL, and TRAF2 were highly expressed in the high-risk group, whereas FGD3, CASZ1, and ZNF322 were highly expressed in the low-risk group (Fig. 3 G). Kaplan–Meier analysis demonstrated that patients in the high-risk group had significantly poorer OS than those in the low-risk group (Fig. 4 A). Time-dependent ROC analysis yielded AUC values of 0.756, 0.676, and 0.683 for 1-, 2-, and 3-year survival, respectively (Fig. 4 B). Two independent GEO validation datasets also confirmed shorter OS in the high-risk groups (Fig. 4 C, D). For the GSE50081 validation dataset, 1-, 2-, and 3-year AUC values were 0.714, 0.735, and 0.758, respectively; for GSE26939, the corresponding AUCs were 0.721, 0.792, and 0.792 (Fig. 4 E, F). 3.3 The prognostic model serves as a dependable indicator for tumor immunity. According to the results from CIBERSORT, ssGSEA, and xCell analyses, the low-risk group exhibited a higher proportion of immune cell infiltration (Fig. 5 ), suggesting stronger tumor immunity compared with the high-risk LUAD patients. The prognostic-related gene FGD3 showed positive correlations with the infiltration of memory B cells, CD8 + T cells, and M1 macrophages, while ZNF322 and CASZ1 were associated with increased resting CD4 memory T cells (Fig. 6 A). Patients in the high-risk group had lower immune scores, lower stromal scores, and higher tumor purity based on the ESTIMATE algorithm (Fig. 6 B). The heatmap demonstrated that FGD3 was significantly positively correlated with immune and stromal scores, but negatively correlated with tumor purity. In contrast, CASZ1 and TRAF2 exhibited opposite correlation patterns compared to FGD3 (Fig. 6 C). This prognostic model represents an innovative and effective biomarker for selecting anti-tumor immunotherapies. The TIDE score indicated that the high-risk group exhibited poorer immunotherapy efficacy (Fig. 6 D). Validation using the IMvigor210 dataset confirmed that patients in the high-risk group had worse survival outcomes (Fig. 6 E). To explore the relationship between risk score and immune checkpoint blockade (ICB) biomarkers, we found that expression levels of PD-L1, PD-L2, and CTLA4 were lower in the high-risk group, suggesting a reduced response to therapies targeting these molecules (Fig. 6 F). CD80 and CD86, key ligands for T cell activation, were also downregulated in tumor cells from the high-risk group, which may contribute to immune escape (Fig. 6 F). Furthermore, based on the IPS score, the low-risk group might derive greater benefit from anti-CTLA4 therapy (Fig. 6 G). This comprehensive analysis elucidates the complex interactions between zinc finger proteins and immune therapy, confirming the prognostic model related to ZNFs as a valuable tool for guiding immunotherapy in LUAD. 3.4 Risk score performance in TMB, enrichment and drug selection To investigate the molecular consequences of genetic differences between the high- and low-risk groups and to better understand the biological processes associated with poor survival in the high-risk group, we analyzed the genomic variability of the ZNFs model within the TCGA LUAD cohort. Oncoprint visualization revealed that somatic mutation frequency was higher in high-risk patients (95.92%) compared to the low-risk group (88.21%). The top 15 mutated genes were identified, with missense mutations and multi-hit mutations being the most common types. Notably, mutation rates of TP53, TTN, MUC16, and KRAS exceeded expectations and were significantly elevated in the high-risk group relative to the low-risk group (Fig. 7 A, B). Consistently, tumor mutation burden (TMB) was significantly higher in the high-risk group (Fig. 7 C, D). However, stratification of patients based on the median TMB showed that those with higher TMB had longer overall survival (Figure S1 ). The chromosomal locations of the 8 prognosis-related ZNFs genes are displayed (Fig. 7 E). GO functional enrichment analysis of DE-ZNFs between risk groups indicated predominant localization in the collagen-containing extracellular matrix, cornified envelope, and axon terminus. Molecular functions enriched in these genes included hormone activity, bile acid binding, and heparin binding (Fig. 8 A). After ID conversion, 311 downregulated and 554 upregulated genes were retained for further analysis. KEGG pathway analysis revealed significant differences between risk groups in pathways such as chemical carcinogenesis–DNA adducts, neuroactive ligand–receptor interaction, steroid hormone biosynthesis, and drug metabolism via cytochrome P450 (Fig. 8 B). GSEA identified 229 significantly enriched pathways ( p < 0.05), with the top 10 pathways predominantly enriched in the high-risk group. These pathways were associated with cell division, cell cycle regulation, DNA replication, transcription and translation, chromosome maintenance, DNA methylation, and keratinization (Fig. 8 C), potentially explaining the shorter overall survival observed in the high-risk group. GSVA enrichment analysis further demonstrated that poor prognosis in the high-risk group was associated with activation of the MYC signaling pathway, glycolysis, mTOR signaling, DNA repair, E2F transcription factors, unfolded protein response, and oxidative phosphorylation (Fig. 8 D). These findings suggest potential therapeutic targets for LUAD patients involving ZNFs. To identify potential chemotherapeutic and targeted agents for high-risk LUAD patients, we performed drug sensitivity prediction using available pharmacogenomic datasets. The high-risk group exhibited potential resistance to carboplatin (Fig. 9 A), gefitinib (Fig. 9 B), and crizotinib (Fig. 9 C), whereas increased sensitivity was predicted for paclitaxel (Fig. 9 D), docetaxel (Fig. 9 E), erlotinib (Fig. 9 F), vinorelbine (Fig. 9 G), and trametinib (Fig. 9 H). 3.5 Nomogram construction Clinical information for the high- and low-risk groups was downloaded, and inter-group comparisons were performed using Fisher’s exact test, as summarized in Table 1 . The high-risk group exhibited a higher proportion of male patients, advanced T stage, increased lymph node metastasis, and higher overall stage. Univariate and multivariate Cox regression analyses were conducted on clinicopathological factors—including gender, age, stage, TNM classification, and risk score—to identify independent prognostic factors for LUAD. Variables with p-values less than 0.05 in both analyses were deemed independent risk factors; accordingly, T stage, N stage, and risk score were selected (Fig. 10 A, B). These independent prognostic factors were then integrated to construct a nomogram, facilitating the prediction of 1-, 2-, and 3-year overall survival (OS) probabilities in the TCGA-LUAD cohort. Due to the limited sample size in the T4 and N3 categories, singular fit issues arose during Cox regression modeling; hence, T3 and T4 were combined, as were N2 and N3 groups (Fig. 10 C). Calibration plots for 1-, 2-, and 3-year OS demonstrated good agreement between predicted and observed survival outcomes (Fig. 10 D). Table 1 Baseline clinical information table for high- and low-risk groups Overall (n = 500) High (n = 250) Low (n = 250) P value Gender (%) Female 271 (54.2) 114 (45.6) 157 (62.8) < 0.001 Male 229 (45.8) 136 (54.4) 93 (37.2) Age(mean) 0.016 65.24 (10.07) 64.14 (10.87) 66.33 (9.10) T (%) < 0.001 T1 167 (33.6) 65 (26.2) 102 (41.0) T2 269 (54.1) 135 (54.4) 134 (53.8) T3 43 (8.7) 34 (13.7) 9 (3.6) T4 18 (3.6) 14 (5.6) 4 (1.6) N (%) 0.007 N0 323 (66.1) 147 (59.3) 176 (73.0) N1 95 (19.4) 55 (22.2) 40 (16.6) N2 69 (14.1) 44 (17.7) 25 (10.4) N3 2 (0.4) 2 (0.8) 0 (0.0) M (%) 0.375 M0 334 (93.3) 170 (91.9) 164 (94.8) M1 24 (6.7) 15 (8.1) 9 (5.2) Stage (%) < 0.001 Stage I 269 (54.6) 107 (43.5) 162 (65.6) Stage II 119 (24.1) 69 (28.0) 50 (20.2) Stage III 80 (16.2) 54 (22.0) 26 (10.5) Stage IV 25 (5.1) 16 (6.5) 9 (3.6) 3.6 Expression and survival analysis of eight prognostic genes Our study validated the expression differences of the eight ZNF prognostic model genes between patients and normal samples in the TCGA-LUAD database. We validated the differential expression of the eight ZNF prognostic model genes between LUAD patients and normal samples in the TCGA-LUAD dataset. The results showed that TRIM6, GATA4, TRAF2, and ZNF322 were significantly upregulated in LUAD patients, whereas TRIM29, FGD3, and CASZ1 were downregulated (Fig. 11 ). Protein expression levels of these eight prognostic genes in LUAD and normal tissues were further analyzed using data from the Human Protein Atlas (HPA) database. The protein expression patterns of GATA4, TRAF2, and ZNF322 were consistent with their RNA expression trends, showing elevated levels in LUAD patients. Similarly, FGD3 and CASZ1 exhibited lower protein expression in patients, consistent with RNA data. No significant differences were observed between tumor and normal tissues for CTCFL at either the protein or RNA level. Notably, TRIM6 protein was expressed at low levels in both tumor and normal tissues, and TRIM29 protein expression showed an inverse pattern relative to its RNA expression (Fig. 12 ). Kaplan–Meier survival analysis demonstrated that higher expression of TRIM6, TRIM29, and TRAF2, along with lower expression of FGD3, CASZ1, and ZNF322, were associated with poorer overall survival in LUAD patients. Expression levels of the remaining two genes did not show significant prognostic relevance (Fig. S2 ). 3.7 Loss of FGD3 Contributes to LUAD Cell Proliferation and Drug Resistance Among the eight prognostic-related genes, FGD3 exhibited the strongest correlation with ESTIMATE scores and a relatively high correlation coefficient within the prognostic model. Consequently, we prioritized FGD3 for downstream validation. Paired RNA-seq data from TCGA showed significantly lower FGD3 expression in LUAD tissues compared to normal samples (Fig. S3 A), and consistent results were observed in two paired GEO datasets (Fig. S5 ). Protein expression of FGD3 was further validated in LUAD cell lines and normal bronchial epithelial (HBE) cells. Except for PC9, FGD3 protein levels in H3122, H2228, H1650, and H1993 cells were significantly lower than those in HBE cells (Fig. 13 A). A similar expression pattern was observed in matched tumor and adjacent normal tissues from LUAD patients (Fig. 13 B). The immunohistochemistry results showed that compared to LUAD tissues, FGD3 expression was significantly higher in normal tissues. Cellular localization revealed that FGD3 was expressed in both the cytoplasm and nucleus, with a greater expression in the cytoplasm. FGD3 was more highly expressed in the epithelial cells of the trachea and bronchi (Fig. 14 ). GO, KEGG, and GSEA enrichment analyses revealed that genes differentially expressed with FGD3 were mainly enriched in cilium movement and cell adhesion pathways in LUAD (Fig. S3 B–D). Drug sensitivity prediction indicated that the low FGD3 expression group was less sensitive to the first-generation ALK inhibitor crizotinib (Fig. 13 C). In lorlatinib-resistant H3122 cells (H3122LR) (Fig. S4 ), FGD3 expression was decreased compared to parental cells (Fig. 13 A). Moreover, FGD3 expression showed a dose- and time-dependent decline in ALK-positive cell lines H3122 and H2228 with prolonged lorlatinib treatment and increasing drug concentration (Fig. 13 D). The FGD3 plasmid was transfected into H3122LR cells, and transfection efficiency was confirmed by Western blot (Fig. 15 A). Colony formation assays showed that FGD3 overexpression markedly reduced the number of colonies formed by H3122LR cells (Fig. 15 B). Consistently, cell viability was significantly decreased upon FGD3 overexpression (Fig. 15 C). Furthermore, transwell assays demonstrated that upregulation of FGD3 substantially impaired both migration and invasion capabilities (Fig. 15 D). Wound-healing assays further indicated that FGD3 overexpression attenuated the cells’ wound closure ability (Fig. 15 E). FGD3 overexpression can partially reverse lorlatinib resistance in H3122LR cells (Fig. 15 F). 4 Discussion ZNFs constitute the largest family of transcription factors in mammalian cells. They are involved in the initiation, progression, and metastasis of malignant tumors, as well as in drug resistance, by regulating transcriptional and translational processes [30], highlighting their potential as novel therapeutic targets. ZNFs have been described as a double-edged sword, exhibiting both oncogenic and tumor-suppressive functions, and serving as reliable prognostic biomarkers [31]. Numerous clinicopathological datasets, prognostic data, and ZNF-based cancer prognostic models have been developed for various cancers, including pancreatic cancer, soft tissue sarcoma, colon adenocarcinoma, esophageal cancer, breast cancer, and osteosarcoma [32–38]. Many prognostic models are currently utilized in clinical practice; for example, the Sokal, Euro, EUTOS, and ELTS scoring systems are employed to evaluate prognosis in chronic myeloid leukemia (CML) [39]. However, despite LUAD being the most prevalent malignant tumor in China [40], no prognostic model based on zinc finger proteins has yet been established for this disease. In this study, we identified 270 DE-ZNFs between LUAD and normal tissues. Through univariate Cox regression analysis, LASSO and Multiple Cox regression analysis we developed a prognostic model containing eight core genes for OS, which demonstrated the ability to predict patient prognosis effectively. This model was rigorously validated by GEO data through K-M survival plots and time ROC curves. Subsequently, we extended our investigation to assess immunotherapy responses between the high- and low-risk groups. Immune infiltration analysis revealed that the high-risk group exhibited an immune “desert” phenotype, characterized by relatively low levels of immune cells such as activated B cells and natural killer cells within the tumor macroenvironment. Dendritic cells, key players in antigen presentation and immune activation [41, 42], were also relatively scarce. Furthermore, CD4 + memory T cells, which play a critical role in tumor immune responses by maintaining long-term immune memory and enhancing anti-tumor activity [43], were diminished in the high-risk group. The deficiency of CD4 + memory T cells may reflect tumor immune escape mechanisms that suppress immune surveillance. Consistently, ESTIMATE analysis, TIDE scores, and IMvigor210 data confirmed that the high-risk group had lower immune scores, higher tumor purity, and poorer responses to immunotherapy. Moreover, expression levels of PD-L1, PD-L2, and CTLA-4 were reduced in the high-risk group, potentially impairing T cell recognition and indicating a poor response to immune checkpoint inhibitors [44–46]. IPS score predictions aligned with these findings, suggesting that the high-risk group may be less responsive to anti-CTLA-4 therapy. Additionally, CD80 and CD86, essential co-stimulatory molecules on dendritic cells and B cells that facilitate T cell activation [47], were expressed at lower levels in the high-risk group. This suggests impaired antigen-presenting cell function and insufficient immune activation, which may contribute to immune escape. To better understand the molecular characteristics between different groups, TMB analysis was carried out. The progressive accumulation of somatic mutations throughout life may contribute to carcinogenesis [48]. The overall mutation rate was higher in the high-risk group compared to the low-risk group (95.92% vs. 88.21%), suggesting greater tumor heterogeneity in the high-risk group. TP53, a classic tumor suppressor which is crucial for a variety of cellular processes, including DNA repair, cell cycle arrest, and apoptosis [49], is the most commonly mutated in human cancers, including NSCLC. Co-mutations of TP53 and other driver genes further contribute to the poor prognosis of lung cancer [50]. Besides, KRAS’s a proto-oncogene, KRAS mutations are particularly prevalent in 30% LUAD [51]. TTN is also frequently mutated in a variety of cancers, overexpression of TTN-AS1 correlates with poor prognosis in lung cancer [52]. MUC16 mutation is reported to decrease the immune response in LUAD [53]. Our results showed higher mutation frequencies of TP53 (55% vs. 48%), TTN (56% vs. 41%), KRAS (33% vs. 28%), and MUC16 (46% vs. 38%) in the high-risk group compared to the low-risk group, which may partly explain the poorer prognosis of the high-risk patients. Previous studies reported higher TMB results in more neo-antigens, increasing chances for T-cell recognition, and clinically correlates with better immune checkpoint inhibitors outcomes [27]. However, our findings showed that the high-risk group had higher TMB but exhibited poorer response to immunotherapy. Additionally, there was no significant difference in overall survival between patients with high and low TMB. This aligns with literature suggesting that TMB alone is an imperfect biomarker for immunotherapy response, and composite predictors incorporating other factors such as MHC and T-cell receptor repertoire are needed [6]. We observed that differentially expressed genes in LUAD were enriched in pathways related to cellular metabolism, proliferation, genomic stability, and regulation of gene expression. These aberrant molecular processes likely cooperate to accelerate tumorigenesis and progression in the high-risk group. GSVA analysis revealed enrichment of MYC and mTOR signaling pathways in high-risk LUAD, suggesting their potential as therapeutic targets. Furthermore, enrichment of DNA damage repair, E2F transcription factor, and unfolded protein response pathways indicated that tumor cells in the high-risk group may exhibit enhanced DNA repair capacity, sustained proliferative signaling [54] and disrupted endoplasmic reticulum proteostasis [55], thereby contributing to increased treatment resistance and tumor survival. Additionally, the risk assessment model could serve as a reliable tool to guide clinical treatment selection. For instance, patients in the high-risk group may be resistant to carboplatin, gefitinib, and crizotinib but more sensitive to paclitaxel, docetaxel, erlotinib, vinorelbine, and trametinib. Moreover, we found that the risk score significantly correlated with clinicopathological parameters such as T stage and N stage, which supported the development of a predictive nomogram with high accuracy for overall survival. Of the eight genes comprising our prognostic signature, TRIM6, GATA4, TRAF2, and ZNF322 were highly expressed in LUAD samples, whereas TRIM29, FGD3, and CASZ1 showed lower expression. Similar protein-level expression patterns were confirmed using the HPA database. Kaplan–Meier survival analysis suggested that TRAF2, TRIM29, and TRIM6 may promote carcinogenesis in LUAD, while CASZ1, FGD3, and ZNF322 likely act as tumor suppressors. As reported in previous literature, TRAF2 is an oncogenic protein involved in TNF-mediated apoptosis in NSCLC via NFκB [56]. Accumulating evidence suggests that TRIM29 is also a transcription factor involved in NSCLC carcinogenesisvia and confirmed to be highly expressed in anlotinib-resistant NSCLC cells [57]. Previous studies have proposed that TRIM6 acts as an E3-ubiquitin ligase educes ferroptosis and chemosensitivity in lung cancer [58]. Our data on ZNF322 contrast with previous reports: while prior studies describe ZNF322 as an oncogenic transcription factor promoting neo-angiogenesis and tumorigenesis in lung cancer [59–62], our findings suggest a different role. The role of CASZ1 varies across different types of tumors, and its role in lung cancer is also controversial. According to TCGA data, CASZ1 is expressed at low levels in LUAD samples. However, a study has reported that CASZ1 promotes migration, invasion, and metastasis in lung cancer [63]. GATA4 has been shown to function as an essential tumor suppressor in lung cancer both in vitro and in vivo [64]. FGD3 regulates the actin cytoskeleton and cell morphology and inhibits cancer cell migration, correlating with favorable prognosis in breast and pancreatic cancers [65, 66]. Here, we validated for the first time that FGD3 protein expression is significantly reduced in LUAD tissues and cell lines compared to normal controls. Functional assays further demonstrated that FGD3 overexpression can partially reverse lorlatinib resistance in H3122LR cells, indicating a potential role of FGD3 in modulating drug sensitivity. GO enrichment analysis suggested that FGD3 is involved in cilium movement, which may explain its higher expression in normal epithelial cells of the trachea and bronchi. KEGG pathway analysis revealed that FGD3 is associated with cell adhesion-related pathways in LUAD, consistent with the phenotypic changes observed during lorlatinib resistance, such as increased membrane protrusions and cytoskeletal remodeling. Previous studies have also shown that FGD3 facilitates actin cytoskeleton remodeling and filopodia formation. Collectively, these findings suggest that the downregulation of FGD3 contributes to the acquisition of lorlatinib resistance, and restoring FGD3 expression may partially counteract this resistant phenotype. In summary, we constructed a robust prognostic model for LUAD based on eight ZNF genes, which not only provides a novel survival predictor but also offers insights into the potential biological roles of ZNF family members in tumor progression and therapy response. The signature may serve as a clinically useful biomarker for guiding individualized treatment decisions. Moreover, we are the first to characterize FGD3 in LUAD, revealing its downregulation in tumor tissues and lorlatinib-resistant cells. Functional assays indicate that FGD3 overexpression can partially reverse drug resistance, suggesting a potential role in modulating cytoskeletal dynamics and cell adhesion, consistent with our enrichment analyses. These findings highlight FGD3 as a potential predictive biomarker and therapeutic target in LUAD. Nevertheless, our study has limitations. The prognostic signature was validated only in public cohorts, and its generalizability should be confirmed in prospective clinical samples. In addition, further investigation is required to clarify whether FGD3 can serve as an independent prognostic predictor and to elucidate its precise molecular mechanisms in LUAD progression. 5 Conclusion This study presents a novel ZNF-based prognostic model for LUAD and underscores FGD3 as a promising biomarker and therapeutic target to counteract drug resistance. Abbreviations The following abbreviations are used in this manuscript: NSCLC Non-small cell lung cancer SCLC Small cell lung cancer LUAD Lung adenocarcinoma ICI Immune checkpoint inhibitor ZNF Zinc finger protein DE-ZNF Differentially expressed ZNF TCGA The cancer genome atlas GEO Gene expression omnibus GO Gene ontology KEGG Kyoto encyclopedia of genes and genomes GSEA Gene set enrichment analysis OS Overall survival LASSO Least absolute shrinkage and selection operator ssGSEA Single-sample gene set enrichment analysis TIDE Tumor immune dysfunction and exclusion IPS Immunophenoscore TMB Tumor mutation burden CNV Copy number variation GSVA Gene set variation analysis GDSC Genomics of drug sensitivity in cancer CTRP Cancer therapeutics response portal IC50 Half maximal inhibitory concentration HPA Human protein atlas STR Short-tandem repeat H3122LR H3122 Lorlatinib-resistant cells BP Biological process CC Cellular component MF Molecular function Declarations Acknowledgements We gratefully acknowledge the Cancer Genome Atlas (TCGA) Database and the Gene Expression Omnibus (GEO) for providing extensive data. The flowchart graphic was generated using smart.servier.com. Author contributions Conceptualization, Jiayue Sun, Xiaoyi Huang, Dinglong Xue, Jiazhuang Li, Yaru Huang, Yue Yang and Qingwei Meng; Data curation, Jiayue Sun, Xiaoyi Huang; Formal analysis, Jiayue Sun and Yue Yang; Funding acquisition, Qingwei Meng; Methodology, Jiayue Sun, Dinglong Xue, and Jiazhuang Li; Project administration, Jiayue Sun and Yaru Huang; Resources, Jiayue Sun; Software, Jiayue Sun; Supervision, Xiaoyi Huang and Qingwei Meng; Validation, Yue Yang; Writing – original draft,Jiayue Sun, Xiaoyi Huang, Dinglong Xue, Jiazhuang Li, Yaru Huang, Yue Yang; Writing – review & editing, Jiayue Sun, Xiaoyi Huang, Dinglong Xue, Jiazhuang Li, Yaru Huang, Yue Yang and Qingwei Meng. All authors have read and agreed to the published version of the manuscript. Funding This research was supported by the National Natural Science Foundation of China (Grant No. 81672931), the Climbing Program of Harbin Medical University Cancer Hospital (PDYS2024-01), and the Key Research and Development Program of Heilongjiang Province (2022ZX06C11). Availability of data and materials All datasets analyzed in this study are publicly available in online repositories. RNA sequencing data and corresponding clinical information for LUAD were obtained from TCGA (https://portal.gdc.cancer.gov/) and the UCSC Xena platform (https://xenabrowser.net/datapages/). External validation cohorts were retrieved from GEO (https://www.ncbi.nlm.nih.gov/geo) under accession numbers GSE50081 , GSE26939 , GSE32863 , and GSE75037 . Zinc finger protein family genes were obtained from UniProt (https://www.uniprot.org/), and the complete list is provided in Supplementary Table S1 . For immune-related analyses, LUAD data were downloaded from the TIDE database (http://tide.dfci.harvard.edu/) for TIDE analysis, and IPS for each patient were obtained from TCIA (https://tcia.at/). TMB data for the TCGA cohort were acquired from cBioPortal (https://www.cbioportal.org/). Immunohistochemical data for prognostic genes in LUAD tissues were retrieved from the HPA (https://www.proteinatlas.org/). All other data generated or analyzed during this study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate All clinical tissue and paraffin-embedded specimens were obtained from Harbin Medical University Cancer Hospital. The pathological type of LUAD was confirmed by both intraoperative frozen-section analysis and postoperative histopathological examination. For each patient, paired tumor and adjacent normal tissues were collected. 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Am J Cancer Res 13:176–189 Gao L, Hu Y, Tian Y, Fan Z, Wang K, Li H, et al 2019; Lung cancer deficient in the tumor suppressor GATA4 is sensitive to TGFBR1 inhibition. Nat Commun 10:1665 Susini T, Renda I 2020; FGD3 gene as a new prognostic factor in breast cancer. Anticancer Res 40:3645–3649 Guo F. 2022; FGD3 binds with HSF4 to suppress p65 expression and inhibit pancreatic cancer progression. Oncogene 41(5):838–851. Additional Declarations No competing interests reported. Supplementary Files FigureS1.jpg Supplementary Information Figure S1: OS survival curve for low- and high-TMB subgroups in LUAD-TCGA cohort; FigureS2.jpg Figure S2: Stratified survival analysis of risk related model genes in TCGA dataset. FigureS3.jpg Figure S3: Paired expression plot of FGD3 in LUAD from TCGA, and enrichment analysis of differentially expressed genes associated with FGD3. FigureS4.jpg Figure S4: The IC50 of lorlatinib in H3122 and H3122LR cell lines. FigureS5.jpg Figure S5: Paired expression plot of FGD3 in LUAD from two GEO datasets. TableS1ZNFs.xlsx Table S1: Supplementary Table S1: Zinc finger protein family genes from the Universal Protein Resource database. TableS2FGD3.xls Table S2: Plasmid sequences for FGD3 overexpression. invation.pdf migration.pdf woundhealing0h.pdf woundhealing24h.pdf Originalgelimagewithmolecularweightmarkersandthetargetproteinindicated.pdf IHC.pdf 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. 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2","display":"","copyAsset":false,"role":"figure","size":240964,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed ZNFs and functional analysis. \u003cstrong\u003eA, B\u003c/strong\u003e Heat map and volcano plot of ZNF related differentially expressed genes, showing 187 upregulated and 83 downregulated ZNF genes. \u003cstrong\u003eC\u003c/strong\u003e Top 5 GO terms, BP, CC, and MF enrichment results of DE-ZNFs. \u003cstrong\u003eD\u003c/strong\u003e The cnetplot showed the BP enrichment results. \u003cstrong\u003eE\u003c/strong\u003e Top 10 enriched pathways involving DE-ZNFs based on KEGG enrichment. \u003cstrong\u003eF\u003c/strong\u003e Pathways involving DE-ZNFs according to GSEA analysis\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/88bde89a701cf7d1921e0ba4.jpeg"},{"id":97141041,"identity":"5b0465d7-8fbc-44e5-aa27-aaed2dde5169","added_by":"auto","created_at":"2025-12-01 10:06:09","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":245525,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment of the prognostic model based on OS-relevant DE-ZNFs. \u003cstrong\u003eA\u003c/strong\u003e Selection of 63 ZNFs genes by univariate Cox regression analysis. \u003cstrong\u003eB, C\u003c/strong\u003e 19 ZNFs genes underwent parameter selection via LASSO regression. \u003cstrong\u003eD\u003c/strong\u003e Multivariate Cox regression analysis derived forest plot showing that 8 ZNFs genes were as the most prominent parameters to construct the best prognostic model. Forest plot and hazard ratio of each of the genes in the model. \u003cstrong\u003eE\u003c/strong\u003e Distribution of the patients’ risk score. \u003cstrong\u003eF\u003c/strong\u003ePatients’ survival time along with their risk scores. \u003cstrong\u003eG\u003c/strong\u003e The heatplot showing expressions of the 8 model genes in the high and low-risk groups\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/60c10b457e1d38cf7d57411a.jpeg"},{"id":97129000,"identity":"debc3ac9-8f4c-4183-84f2-5e066d402347","added_by":"auto","created_at":"2025-12-01 08:37:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1204578,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the prognostic model on internal and external LUAD cohorts. \u003cstrong\u003eA, B\u003c/strong\u003e OS survival curves and ROC curves for low- and high-risk subgroups in LUAD-TCGA cohort. \u003cstrong\u003eC-F\u003c/strong\u003e OS survival curves and ROC curves in GSE50081 and GSE26939 cohort\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/e315b6f2e016361408e24585.png"},{"id":97129037,"identity":"29fed785-eb5d-4846-8786-e9cb05e5509d","added_by":"auto","created_at":"2025-12-01 08:37:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2875461,"visible":true,"origin":"","legend":"\u003cp\u003eTumor-immune microenvironment analysis of the high- and low-risk groups. \u003cstrong\u003eA \u003c/strong\u003eDifference between 22 tumor-infiltrating immune cells by cibersort. The blue bar indicates the low-risk group and the red bar the high-risk group. \u003cstrong\u003eB\u003c/strong\u003e Relative infiltration levels of 28 immune cell subsets between the low- and high-risk groups in the TCGA-LUAD cohort. \u003cstrong\u003eC\u003c/strong\u003eXCell Boxplot illustrating the relative abundance of infiltrating immune cell types in patients belonging to high and low-risk groups. Statistical significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns: not significant\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/823c3eaa279e8ead94e95613.png"},{"id":97141433,"identity":"ca997171-14d2-4bb6-ba16-81ebdd8a3f74","added_by":"auto","created_at":"2025-12-01 10:06:42","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2917868,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive value of ZNF prognostic model for immunotherapy. \u003cstrong\u003eA\u003c/strong\u003e Difference between 22 tumor-infiltrating immune cells and 8 model genes. \u003cstrong\u003eB \u003c/strong\u003eCorrelation analysis between risk score and estimate results based on the estimate algorithm. \u003cstrong\u003eC\u003c/strong\u003e Correlation heatmap between estimate results and 8 model genes. \u003cstrong\u003eD\u003c/strong\u003e TIDE score between the high- and low-risk group. \u003cstrong\u003eE\u003c/strong\u003e Immunotherapy response between high- risk and low-risk group in IMvigor210 dataset. \u003cstrong\u003eF \u003c/strong\u003eExpression of ICB genes in the high- and low-risk group. \u003cstrong\u003eG\u003c/strong\u003e Comparison of IPS between different risk groups in LUAD\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/d48569f97dcb0c661e19e5f3.png"},{"id":97142194,"identity":"ec2a8a31-a6ec-4f37-868c-e0050f5af183","added_by":"auto","created_at":"2025-12-01 10:07:23","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2747529,"visible":true,"origin":"","legend":"\u003cp\u003eMutation frequency measures of prognostic groups. \u003cstrong\u003eA\u003c/strong\u003eOncoprint visualization of the mutation atlas in high-risk groups across LUAD samples from TCGA cohort. \u003cstrong\u003eB\u003c/strong\u003e Oncoprint visualization of the mutation atlas in low-risk groups across LUAD samples from TCGA cohort. \u003cstrong\u003eC, D\u003c/strong\u003e The violin plot and the proportional bar chart illustrated that the TMB was higher in the high-risk group. \u003cstrong\u003eE\u003c/strong\u003e The location of CNV alterations of DE-ZNFs model genes on chromosomes\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/1d906ab4c9ebd65d4b0f795b.png"},{"id":97129053,"identity":"49d3055c-ed3a-4e17-8e45-d15f62741f6e","added_by":"auto","created_at":"2025-12-01 08:37:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2235940,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional analysis of DE-ZNFs genes between high- and low-risk groups. \u003cstrong\u003eA \u003c/strong\u003eTop 5 GO terms, BP, CC, and MF enrichment results of DE-ZNFs between high- and low-risk groups. \u003cstrong\u003eB\u003c/strong\u003e Top 10 enriched KEGG pathways of DE-ZNFs genes between high- and low-risk groups. \u003cstrong\u003eC\u003c/strong\u003e GSEA pathways of DE-ZNFs genes between high- and low-risk groups. \u003cstrong\u003eD\u003c/strong\u003e GSVA enrichment of DE-ZNFs genes between high- and low-risk groups\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/06d55279cd0ccc311d343af0.png"},{"id":97129009,"identity":"150d1c75-7d91-494b-9099-fa8014864466","added_by":"auto","created_at":"2025-12-01 08:37:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":2918062,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction of anti-tumor drugs sensitivity using the ZNF predictive model. \u003cstrong\u003eA-H\u003c/strong\u003e The patients in high-risk group were less sensitive to Carboplatin (\u003cstrong\u003eA\u003c/strong\u003e), Gefitinib (\u003cstrong\u003eB\u003c/strong\u003e), and Crizotinib (\u003cstrong\u003eC\u003c/strong\u003e), while the low-risk patients probably benefitted more from Paclitaxel (\u003cstrong\u003eD\u003c/strong\u003e), Docetaxel (\u003cstrong\u003eE\u003c/strong\u003e), Erlotinib (\u003cstrong\u003eF\u003c/strong\u003e), Vinorelbine (\u003cstrong\u003eG\u003c/strong\u003e), and Trametinib (\u003cstrong\u003eH\u003c/strong\u003e). Statistical significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns: not significant\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/7b21f95ed2393f9662273b67.png"},{"id":97142036,"identity":"25516ea2-b8c9-47e1-b7d1-d3c8db97acb3","added_by":"auto","created_at":"2025-12-01 10:07:17","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":2230630,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of prognostic signature on predicting the OS in LUAD patients. \u003cstrong\u003eA\u003c/strong\u003e Univariate Cox independent prognostic analysis of stage, age, gender, grade, race, T stage, M stage, N stage, and risk score. \u003cstrong\u003eB\u003c/strong\u003eMultivariate Cox independent prognostic analysis of stage, age, gender, grade, race, T stage, M stage, N stage, and risk score. \u003cstrong\u003eC \u003c/strong\u003eThe nomogram based on T stage, N stage and risk score. \u003cstrong\u003eD\u003c/strong\u003e Calibration curves of the nomogram and observed 1-, 2-, and 3-year survival of LUAD patients. The dashed line represents the ideal performance, and the actual performance of the signature is represented by the black lines\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/e7e2d6f2432a80670e3b81df.png"},{"id":97129003,"identity":"2178543c-d5ba-4479-8fdd-cf1d59cadb19","added_by":"auto","created_at":"2025-12-01 08:37:12","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1205166,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of expressions of risk model genes in TCGA dataset. Statistical significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns: not significant\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/3b0c24fd5dd39b7e0e47f22a.png"},{"id":97129024,"identity":"f4f96688-3413-4cb5-bd58-e996860971d8","added_by":"auto","created_at":"2025-12-01 08:37:12","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":1519721,"visible":true,"origin":"","legend":"\u003cp\u003eExpression levels of the risk related model genes were evaluated in HPA database\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/7c949a49b36e7af96bcebd3c.png"},{"id":97141601,"identity":"cbc05f7d-60b8-4691-9a88-bf9fd409a301","added_by":"auto","created_at":"2025-12-01 10:06:50","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":2690582,"visible":true,"origin":"","legend":"\u003cp\u003eFGD3 is down-regulated in LUAD and possibly involved in drug sensitivity. \u003cstrong\u003eA\u003c/strong\u003e The expression of FGD3 in LUAD cell lines and normal bronchial epithelial cells, revealed by western blot. \u003cstrong\u003eB\u003c/strong\u003e The protein level of FGD3 in both cancerous and adjacent tissues of LUAD patients. T, tumor; N, paired para-cancerous normal tissues. \u003cstrong\u003eC\u003c/strong\u003e Drug sensitivity prediction suggested that the low FGD3 expression group was less sensitive to crizotinib. Significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns: not significant. \u003cstrong\u003eD\u003c/strong\u003e The changes of FGD3 under different treatment time and concentration of lorlatinib in ALK-positive cell lines H3122 and H2228\u003c/p\u003e","description":"","filename":"image13.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/18c91aad96ff76588dcf93fa.png"},{"id":97142882,"identity":"3aaf16d4-bcef-4781-8b79-16374f817520","added_by":"auto","created_at":"2025-12-01 10:08:02","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":2798587,"visible":true,"origin":"","legend":"\u003cp\u003eThe immunohistochemistry results showed that compared to LUAD tissues, FGD3 expression was significantly higher in normal tissues. Cellular localization revealed that FGD3 was expressed in both the cytoplasm and nucleus, with a greater expression in the cytoplasm. FGD3 was more highly expressed in the epithelial cells of the trachea and bronchi. \u003cstrong\u003eA\u003c/strong\u003e IHC images of FGD3 in LUAD and adjacent tissues (showing 4 cases). \u003cstrong\u003eB\u003c/strong\u003e The IRS of FGD3 in both cancerous and adjacent tissues of LUAD patients (n = 14). Statistical significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns: not significant\u003c/p\u003e","description":"","filename":"image14.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/92e380604edc969c8f16221a.png"},{"id":97129006,"identity":"60eafa66-55e2-4eb3-9e11-f1bdf48938d4","added_by":"auto","created_at":"2025-12-01 08:37:12","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":2909186,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional validation of FGD3 in H3122LR cells. \u003cstrong\u003eA\u003c/strong\u003e Expression of FGD3 protein in H3122LR cells after transfection with p-FGD3. \u003cstrong\u003eB\u003c/strong\u003e Colony formation of H3122LR cells after p-FGD3 transfection. \u003cstrong\u003eC\u003c/strong\u003e Cell viability of H3122LR cells after p-FGD3 transfection. \u003cstrong\u003eD\u003c/strong\u003e Migration and invasion of H3122LR cells after p-FGD3 transfection. \u003cstrong\u003eE\u003c/strong\u003e Wound-healing assay of H3122LR cells after p-FGD3 transfection. \u003cstrong\u003eF\u003c/strong\u003e IC\u003csub\u003e50 \u003c/sub\u003eof lorlatinib in H3122LR cells after p-FGD3 transfection. Statistical significance: *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, ns: not significant\u003c/p\u003e","description":"","filename":"image15.png","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/5276cc8187a8bd81b76c1cdc.png"},{"id":101303810,"identity":"2f536758-eb24-40a1-8e5d-b20d7f178192","added_by":"auto","created_at":"2026-01-28 10:00:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6367928,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/493ee2a4-234d-47bd-a67a-3ca23badfa3d.pdf"},{"id":97128994,"identity":"c61cdfad-f800-4cf5-b9d0-5f0d1af61c4e","added_by":"auto","created_at":"2025-12-01 08:37:11","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":140267,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure S1: OS survival curve for low- and high-TMB subgroups in LUAD-TCGA cohort;\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/50a6b2975bfec3fe774df6cb.jpg"},{"id":97142342,"identity":"d3b474d7-7e7d-4e61-9669-6036aac1ea25","added_by":"auto","created_at":"2025-12-01 10:07:32","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":979868,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2: Stratified survival analysis of risk related model genes in TCGA dataset.\u003c/p\u003e","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/308569ccd327c152935f1c6a.jpg"},{"id":97129012,"identity":"a69a9c67-03a6-42eb-abae-7141b490ddf1","added_by":"auto","created_at":"2025-12-01 08:37:12","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":861442,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3: Paired expression plot of FGD3 in LUAD from TCGA, and enrichment analysis of differentially expressed genes associated with FGD3.\u003c/p\u003e","description":"","filename":"FigureS3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/b9e1cb21f934c068788ab8fd.jpg"},{"id":97128998,"identity":"b3d46f33-c869-402e-a163-7e5e8dfce25a","added_by":"auto","created_at":"2025-12-01 08:37:11","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":154489,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4: The IC50 of lorlatinib in H3122 and H3122LR cell lines.\u0026nbsp;\u003c/p\u003e","description":"","filename":"FigureS4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/b78cd23175b18f88cf057a6c.jpg"},{"id":97128996,"identity":"406713dc-341e-4de0-9afb-8688a85a5080","added_by":"auto","created_at":"2025-12-01 08:37:11","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":112277,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S5: Paired expression plot of FGD3 in LUAD from two GEO datasets.\u003c/p\u003e","description":"","filename":"FigureS5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/054ab5f284faeaa6566d9bb7.jpg"},{"id":97129010,"identity":"671625b9-05f1-41a5-9333-58b5efdecfb1","added_by":"auto","created_at":"2025-12-01 08:37:12","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":32185,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1: Supplementary Table S1: Zinc finger protein family genes from the Universal Protein Resource database.\u0026nbsp;\u003c/p\u003e","description":"","filename":"TableS1ZNFs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/3201a196defb901c3a47a0be.xlsx"},{"id":97141544,"identity":"fd9787c6-6d06-4b49-bec5-3423d101c265","added_by":"auto","created_at":"2025-12-01 10:06:47","extension":"xls","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":23552,"visible":true,"origin":"","legend":"\u003cp\u003eTable S2: Plasmid sequences for FGD3 overexpression.\u003c/p\u003e","description":"","filename":"TableS2FGD3.xls","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/9c766b48d8db8f9e9183511f.xls"},{"id":97142124,"identity":"29beef22-83e8-4d83-83ff-d70a0fe16918","added_by":"auto","created_at":"2025-12-01 10:07:22","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":593125,"visible":true,"origin":"","legend":"","description":"","filename":"invation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/1084a7d75d5af435b506207e.pdf"},{"id":97129041,"identity":"9aa78ac2-5642-4aa0-b4d9-8b5c9eaf15ef","added_by":"auto","created_at":"2025-12-01 08:37:13","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":318006,"visible":true,"origin":"","legend":"","description":"","filename":"migration.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/1ce4021ed488f73cdb04f7f6.pdf"},{"id":97142147,"identity":"25de4271-11c6-47bd-aba7-a473b2647593","added_by":"auto","created_at":"2025-12-01 10:07:22","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1692114,"visible":true,"origin":"","legend":"","description":"","filename":"woundhealing0h.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/2a906296deeaa9f4780e0928.pdf"},{"id":97129049,"identity":"496bd3d4-0b73-4969-aa3f-3d0d5a87bb42","added_by":"auto","created_at":"2025-12-01 08:37:13","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":2132268,"visible":true,"origin":"","legend":"","description":"","filename":"woundhealing24h.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/2c0bdc5ea5f28f6f59cf97ae.pdf"},{"id":97142104,"identity":"462d861c-194c-46ad-b052-aea7dad5b634","added_by":"auto","created_at":"2025-12-01 10:07:21","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":2056797,"visible":true,"origin":"","legend":"","description":"","filename":"Originalgelimagewithmolecularweightmarkersandthetargetproteinindicated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/79cbc6df08c88a4f2b2b4148.pdf"},{"id":97142929,"identity":"83a14eb9-5507-4720-9e77-7654d997f8da","added_by":"auto","created_at":"2025-12-01 10:08:08","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":15096925,"visible":true,"origin":"","legend":"","description":"","filename":"IHC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7976542/v1/8e2ce5cfcca6477f51aeab2e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Zinc Finger Protein-Based Prognostic Model in Lung Adenocarcinoma Highlights FGD3 as a Potential Marker for Lorlatinib Resistance","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eLung cancer was the most commonly diagnosed cancer in 2022, with approximately 2.5\u0026nbsp;million new cases, accounting for 12.4% of all cancer cases worldwide. Similarly, it ranks first in both incidence and mortality among cancers in China [1]. Non-small cell lung cancer (NSCLC) accounts for 80\u0026ndash;85% of newly diagnosed cases, with lung adenocarcinoma (LUAD) being the most prevalent histological subtype [2]. Although most patients initially respond to standard chemotherapy, relapse often occurs rapidly [3]. LUAD exhibits high heterogeneity driven by complex genetic alterations, such as \u003cem\u003eEGFR\u003c/em\u003e, \u003cem\u003eKRAS\u003c/em\u003e, and \u003cem\u003eALK\u003c/em\u003e mutations, which serve as major targets for molecular therapy [4]. For patients without detectable driver mutations, immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic option. Despite these therapeutic advances, the prognosis for advanced LUAD remains poor, partly due to the lack of reliable prognostic markers to guide personalized treatment strategies. Currently, various biological markers have been used to predict the survival outcomes and treatment responses of patients with LUAD. For example, TP53 mutations, PD-L1 expression levels, immune microenvironment characteristics, and tumor metabolism-related molecules all play important roles in prognostic assessment [5]. However, the predictive capability of these individual markers is limited by the intrinsic heterogeneity of the disease [6], highlighting the urgent need for more accurate prognostic models.\u003c/p\u003e\u003cp\u003eZinc finger proteins (ZNFs) represent the largest transcription factor family in the human genome [7]. These proteins contain one or more zinc ions coordinated with specific amino acids, forming diverse zinc-finger domains [8]. This structural diversity enables ZNFs to bind DNA, RNA, and proteins, thereby participating in numerous biological processes, including transcriptional regulation, DNA repair, protein degradation, signal transduction, cell proliferation, adhesion, migration, and cytoskeletal organization [9]. Emerging evidence suggests that ZNFs can function as either oncogenes or tumor suppressors, depending on the cellular context [7]. For instance, \u003cem\u003eZNF131\u003c/em\u003e has been reported to promote non-small cell lung cancer (NSCLC) cell proliferation, invasion, and stemness [10]. Similarly, the zinc finger protein \u003cem\u003eZNF687\u003c/em\u003e accelerates LUAD cell proliferation and tumor progression [11]. Conversely, \u003cem\u003eZNF652\u003c/em\u003e acts as a tumor suppressor by downregulating cyclin D3 in lung cancer cells [12]. Additionally, \u003cem\u003eCBX4\u003c/em\u003e, another zinc finger protein, promotes LUAD cell proliferation while concurrently inhibiting metastasis [13].\u003c/p\u003e\u003cp\u003eDespite these findings, the comprehensive role of ZNFs in LUAD remains incompletely understood, and their utility as prognostic biomarkers or therapeutic targets has yet to be fully elucidated. It is hypothesized that ZNFs may regulate LUAD progression through distinct oncogenic or tumor-suppressive mechanisms, and that modulating their expression could provide novel therapeutic opportunities. Therefore, an in-depth investigation of ZNFs in LUAD is urgently needed. In this pilot study, transcriptomic and clinical data for LUAD were obtained from the TCGA database. ZNFs were retrieved from the UniProt database, and differentially expressed ZNFs (DE-ZNFs) were identified. TCGA data were used as the training set to construct a prognostic model, which was subsequently validated using an independent GEO test cohort. Finally, the model\u0026rsquo;s prognostic performance and its potential to identify candidate anti-tumor agents were evaluated, offering new insights into individualized treatment strategies for LUAD patients.\u003c/p\u003e"},{"header":"2 Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data acquisition\u003c/h2\u003e\u003cp\u003eRNA sequencing data and corresponding clinical information for LUAD were obtained from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the TCGAbiolinks package [14], comprising 513 LUAD and 58 normal lung tissue samples. External validation cohorts were derived from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), specifically from GSE50081 (127 LUAD samples) [15] and GSE26939 (116 LUAD samples)[16]. Additionally, two GEO datasets containing paired LUAD tumor and adjacent normal tissues, GSE32863 (58 paired samples)[17] and GSE75037(83 paired samples)[18], were used for external validation of FGD3 expression differences. The GEO datasets were normalized using the limma package [19]. Annotation GPL570, GPL9053 and GPL6884 were used for ID conversion. Genes with missing values were excluded, and for those with one-to-many mapping relationships, the mean expression value was retained. Genes belonging to the zinc finger protein family (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) were retrieved from the Universal Protein Resource (UniProt) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Identification of differentially expressed ZNFs genes in LUAD\u003c/h2\u003e\u003cp\u003eDifferential expression analysis of the TCGA count data from LUAD tumors and adjacent normal tissue samples was performed using the limma package in R software (version 4.4.0) [19]. Genes met the criteria of log2|fold change| values\u0026thinsp;\u0026gt;\u0026thinsp;1 and adjusted p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected. By intersecting these genes with the previously obtained \u003cem\u003eZNF\u003c/em\u003e gene set, differentially expressed \u003cem\u003eZNFs\u003c/em\u003e (DE-\u003cem\u003eZNFs\u003c/em\u003e) were identified. Heatmaps were generated using the pheatmap package, and volcano plots were constructed with the ggplot2 package.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Functional enrichment analysis\u003c/h2\u003e\u003cp\u003eGene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed using the clusterProfiler package[20]. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set as the threshold for statistical significance. The results of the enrichment analyses were visualized using the ggplot2 and ggnewscale packages.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Risk prognostic model based on DE-ZNFs\u003c/h2\u003e\u003cp\u003eUsing TCGA as the training set, univariate Cox regression analysis was performed on DE-ZNFs with the survival package, using overall survival (OS) as the evaluation criterion. Genes significantly associated with OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were retained. Subsequently, least absolute shrinkage and selection operator (LASSO) regression was conducted using the glmnet and survival packages, and the results were visualized with the ggplot2 and forestplot packages. To avoid overfitting, multivariate Cox regression analysis was then applied to identify prognostic genes. The risk score for the model was calculated as follows\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\::Risk\\:score=\\beta\\:1E1+\\beta\\:2E2+\\cdots\\:+\\beta\\:iEi\\)\u003c/span\u003e\u003c/span\u003e, where β represents the regression coefficient, \u003cem\u003eE\u003c/em\u003e denotes the expression level of each prognostic gene, and \u003cem\u003ei\u003c/em\u003e is the number of genes. LUAD patients were stratified into high- and low-risk groups based on the median risk score. Kaplan\u0026ndash;Meier survival curves were generated, and log-rank tests were conducted to evaluate survival differences between groups. The model\u0026rsquo;s predictive performance was assessed using receiver operating characteristic (ROC) curve analysis, with the area under the curve (AUC) representing its discriminatory ability. To further validate the predictive power of the model, its performance was evaluated in two independent datasets, GSE50081 [15] and GSE26939 [16].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Immune cell infiltration analysis\u003c/h2\u003e\u003cp\u003eCIBERSORT analysis was performed using the IOBR package to compare the relative abundances of 22 immune cell types between the high- and low-risk groups[21]. Single-sample gene set enrichment analysis (ssGSEA) was performed with the GSVA package to evaluate differences in 28 immune cell types[22]. Additionally, xCell analysis was conducted using the xCell package to quantify the abundances of 64 immune cell types[23]. Correlation heatmaps between the eight model genes and the 22 CIBERSORT-derived immune cell types were generated using the corrplot and ggcorrplot packages. The estimate package was applied to calculate stromal scores, immune scores, estimate scores (sum of stromal and immune scores), and tumor purity. Pearson\u0026rsquo;s product\u0026ndash;moment correlation was used to assess associations between the risk score and stromal score, immune score, estimate score, and tumor purity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Prediction of immunotherapy response\u003c/h2\u003e\u003cp\u003eLUAD data were downloaded from the TIDE database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for tumor immune dysfunction and exclusion (TIDE) analysis[24]. A higher TIDE score indicates a poorer response to immunotherapy. The immunophenoscore (IPS) for each patient was obtained from the Cancer Immunome Atlas (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcia.at/\u003c/span\u003e\u003cspan address=\"https://tcia.at/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and compared between different risk groups. Unlike the TIDE score, a higher IPS is positively associated with a better predicted response to immunotherapies targeting PD-L1 and CTLA-4[25]. Additionally, immunotherapy data from the urothelial carcinoma IMvigor210 dataset were used to predict responses to immunotherapy in each risk group[26].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Tumor mutation burden and chromosomal location of model genes\u003c/h2\u003e\u003cp\u003eThe tumor mutation burden (TMB) data for the TCGA cohort were downloaded from the cBioPortal database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and calculated using the Maftools package[27]. Waterfall plots were generated to visualize the top 15 genes with the highest mutation frequency in the high- and low-risk groups. The ggpubr package was used to create violin and bar plots, and chi-square tests were applied for group comparisons. The rtracklayer, RCircos, and magrittr packages were employed to plot copy number variation (CNV) landscapes, enabling visualization of changes in DE-ZNFs model genes across human chromosomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Differential gene enrichment analysis between high- and low-risk groups\u003c/h2\u003e\u003cp\u003eDifferentially expressed genes (DEGs) between the two groups were identified using the DESeq2 package with the Wald test. Genes with an absolute log₂ (fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected. GO, KEGG, and GSEA of the DEGs were performed using the clusterProfiler package. Gene set variation analysis (GSVA) was conducted using the GSVA package[22].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 Prediction of antitumor drug sensitivity\u003c/h2\u003e\u003cp\u003eDrug sensitivity data were obtained from two major pharmacogenomic databases: the Genomics of Drug Sensitivity in Cancer (GDSC) and the Cancer Therapeutics Response Portal (CTRP), which contain large-scale screening results of numerous anti-cancer drugs across hundreds of cell lines. GDSC1, GDSC2 and CTRP2 drug response data were obtained from the GDSC website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the CTRP website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://portals.broadinstitute.org/ctrp.v2.1/\u003c/span\u003e\u003cspan address=\"http://portals.broadinstitute.org/ctrp.v2.1/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The oncoPredict package was applied to predict the sensitivity of high- and low-risk LUAD patients, as defined by the prognostic model, to commonly used chemotherapeutic agents and molecularly targeted drugs[28]. The half-maximal inhibitory concentration (IC\u003csub\u003e50\u003c/sub\u003e) represents the drug concentration required to inhibit 50% of tumor cell viability. A lower IC\u003csub\u003e50\u003c/sub\u003e value indicates greater drug sensitivity. IC\u003csub\u003e50\u003c/sub\u003e values were expressed in micromolar (\u0026micro;M) units, and the natural logarithm of the IC\u003csub\u003e50\u003c/sub\u003e was used for downstream drug response analyses.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.10 Nomogram construction\u003c/h2\u003e\u003cp\u003eTo evaluate the association between clinical characteristics and risk grouping, univariate and multivariate Cox regression analyses were performed to determine whether the clinical variables and the risk score were independent prognostic factors for LUAD patients. Clinical data for LUAD patients in the TCGA cohort were obtained from the UCSC Xena platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Fisher\u0026rsquo;s exact test was applied to compare clinical characteristics between high- and low-risk groups using the tableone package. A nomogram was then constructed to enhance the prognostic accuracy and predictive power of the model[29]. The risk score, T stage, and N stage were identified as independent prognostic variables to estimate the probability of overall survival (OS) at 1, 2, and 3 years. Calibration curves were generated to assess the agreement between predicted and observed outcomes, thereby evaluating the nomogram\u0026rsquo;s predictive performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e2.11 Validation of prognostic gene expression\u003c/h2\u003e\u003cp\u003eThe expression levels of the prognostic genes were validated using the TCGA LUAD dataset. Bar plots were generated with the ggpubr and ggsci packages, and group comparisons were conducted using the Wilcoxon test. In addition, immunohistochemical (IHC) data for the prognostic genes in LUAD tissues were obtained from the Human Protein Atlas (HPA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Receiver operating characteristic (ROC) curves for the prognostic model genes were constructed using the pROC package to evaluate their discriminatory performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e2.12 Cell culture and clinical specimen collection\u003c/h2\u003e\u003cp\u003eLUAD cell lines (H3122, H2228, H1650, H1993, and PC9) were purchased from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Human bronchial epithelial (HBE) cells were kindly provided by the Heilongjiang Cancer Institute (Harbin, China). All cell lines were authenticated by short tandem repeat (STR) analysis prior to experimentation. H3122, H1650, H2228, and PC9 cells were cultured in RPMI 1640 medium (HyClone, USA) supplemented with 10% fetal bovine serum (Pan-Biotech, Germany) and 1% penicillin\u0026ndash;streptomycin (HyClone, USA). H1993 cells were maintained in DMEM medium (HyClone, USA). All cells were incubated at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂. A total of 12 paired fresh-frozen LUAD tumor tissues and adjacent normal tissues were collected from patients at Harbin Medical University Cancer Hospital between 2024 and 2025 for protein extraction. Additionally, 14 paired paraffin-embedded LUAD and adjacent normal tissue sections were obtained from the same institution between 2020 and 2022 for immunohistochemistry. None of the patients had received preoperative therapy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e2.13 Western blot analysis\u003c/h2\u003e\u003cp\u003eCells and tissues were harvested and lysed on ice for 30 min using lysis buffer (Boster, Wuhan, China; #AR0102-100) supplemented with 1% protease inhibitor cocktail (Boster, Wuhan, China #AR1192). The lysates were centrifuged at 12,000 rpm for 15 min at 4\u0026deg;C, and the supernatants were collected. Protein concentration was determined using a protein quantification kit (Beyotime, Shanghai, China; #P0012S). Equal amounts of protein (25 \u0026micro;g) were loaded per lane, separated by SDS\u0026ndash;PAGE, and transferred to PVDF membranes. The membranes were blocked with 5% nonfat milk overnight at 4\u0026deg;C and incubated with primary antibodies for 24 h at 4\u0026deg;C. The following day, membranes were washed and incubated with HRP-conjugated secondary antibodies for 50 min at room temperature. Immunoreactivity was detected using an ECL kit (Thermo Fisher Scientific, Waltham, MA, USA). The primary antibodies were as follows: rabbit anti-FGD3 (Proteintech, Wuhan, China; #20347-1-AP; 1:1000) and mouse anti-GAPDH (Abcam, Cambridge, UK; #ab8245; 1:50,000).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e2.14 IC\u003csub\u003e50\u003c/sub\u003e assay\u003c/h2\u003e\u003cp\u003eLorlatinib was purchased from MedChemExpress (Monmouth Junction, NJ, USA; #HY-15728). A total of 5,000 cells were seeded into each well of 96-well plates and cultured in 100 \u0026micro;l RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) for 12 hours. After cell adhesion, the culture medium was replaced with 100 \u0026micro;l of complete medium containing varying concentrations of lorlatinib. Following 48 hours of drug treatment, the medium was removed, and 100 \u0026micro;l of fresh medium containing 10 \u0026micro;l of CCK-8 reagent (Seven, Shanghai, China; #SC11901) was added to each well. The plate was incubated for 1 hour at 37\u0026deg;C, and absorbance at 450 nm was measured using a multifunctional microplate reader. Dose-response curves were generated based on the absorbance values at each drug concentration, and the IC\u003csub\u003e50\u003c/sub\u003e values for each cell group were calculated.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e2.15 Lorlatinib-resistant cells\u003c/h2\u003e\u003cp\u003eParental H3122 cells were initially exposed to a low concentration of lorlatinib (0.04 \u0026micro;M) until they achieved full proliferation. The cells were then passaged and treated with a doubled concentration of lorlatinib. This process was repeated incrementally until the lorlatinib concentration reached 1 \u0026micro;M. The resulting cells exhibited an IC₅₀ value more than 50 times higher than that of the parental cells. This resistant cell line was designated as H3122 lorlatinib-resistant cells (H3122LR). H3122LR cells were subsequently expanded and continuously maintained in medium containing the same lorlatinib concentration during each passage.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e2.16 Immunohistochemistry\u003c/h2\u003e\u003cp\u003eImmunohistochemistry was performed using the high sensitive and rapid immunohistochemical kit (pH 9.0) (Elabscience, Wuhan, China; #E-IR-R220). Briefly, paraffin-embedded sections were baked at 65\u0026deg;C for 60 minutes, then placed in antigen retrieval and deparaffinization solution and microwaved at low power for 30 minutes, followed by natural cooling to room temperature. After washing, sections were incubated with peroxidase blocking buffer for 15 minutes, washed again, and incubated overnight at 4\u0026deg;C with primary antibody against FGD3 (Proteintech, Wuhan, China; #20347-1-AP; 1:100). On the following day, sections were incubated with secondary antibody at room temperature for 30 minutes, followed by DAB staining for 2 minutes and hematoxylin counterstaining for 30 seconds. The sections were then dehydrated through graded ethanol series, cleared with xylene, mounted with neutral resin, and air-dried. FGD3 immunostaining was independently evaluated by two investigators blinded to patient information. The percentage of positive cells was scored as 0 (0%), 1 (1\u0026ndash;25%), 2 (26\u0026ndash;50%), 3 (51\u0026ndash;75%), and 4 (76\u0026ndash;100%). Staining intensity was scored as 0 (negative), 1 (light yellow), 2 (dark yellow), and 3 (brown). The final immunoreactive score (IRS) was calculated by summing the percentage and intensity scores, ranging from 0 to 7.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e2.17 Plasmid transfection and FGD3 overexpression\u003c/h2\u003e\u003cp\u003eFGD3 was overexpressed in H3122LR cells either transiently or stably. For transient overexpression, cells at ~\u0026thinsp;70% confluence were transfected with an FGD3 plasmid or empty vector using Lipofectamine 3000 (Invitrogen, USA). After 6 h, the medium was replaced, and cells were cultured for 24\u0026ndash;48 h before downstream assays. For stable overexpression, cells were transfected as above and selected with puromycin until resistant colonies were established. Transfection efficiency and FGD3 expression were confirmed by Western blot. Plasmid sequences are provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e2.18 Colony formation assay\u003c/h2\u003e\u003cp\u003eH3122LR cells transfected with an FGD3 overexpression plasmid or empty vector were seeded into 6-well plates and cultured in complete medium. After 10 days, colonies were fixed with 4% paraformaldehyde, stained with crystal violet, and counted. Experiments were performed in triplicate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e2.19 CCK-8 assay\u003c/h2\u003e\u003cp\u003eCell viability was assessed using the Cell Counting Kit-8 (CCK-8, Sevenbio, Beijing, China) according to the manufacturer\u0026rsquo;s instructions. Cells were seeded in 96-well plates and cultured for 1\u0026ndash;4 days. CCK-8 solution was then added to each well at a 1:10 dilution and incubated at 37\u0026deg;C for 1 h. Absorbance at 450 nm was measured using a microplate reader (Multiskan FC, Thermo, China).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e2.20 Wound-healing assay\u003c/h2\u003e\u003cp\u003eH3122LR cells transfected with an FGD3 overexpression plasmid or empty vector were seeded in 6-well plates and cultured for 24 h. A scratch was made through the center of each well using a 10 \u0026micro;L pipette tip, and serum-free medium was added. Wound closure was quantified as the percentage of wound area reduction over the healing period. All experiments were performed in triplicate.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e2.21 Cell migration and invasion assays\u003c/h2\u003e\u003cp\u003eFor the transwell assay, 105 cells were seeded in the upper chamber (8-\u0026micro;m pore size, Corning, USA) in serum-free medium, either uncoated (migration) or coated with Matrigel (BD Biosciences, invasion). The lower chamber contained 600 \u0026micro;L of medium with 10% FBS. After 24 h (migration) or 48 h (invasion), cells on the lower surface of the membrane were fixed with 4% paraformaldehyde and stained with crystal violet. Cell numbers were quantified as the average number of cells per field of view.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e2.22 Statistical analysis\u003c/h2\u003e\u003cp\u003eFor variables with normal distribution between two groups, unpaired Student\u0026rsquo;s t-tests were used to assess statistical significance. When data were not normally distributed, Mann\u0026ndash;Whitney U tests were applied. For categorical clinical variables with small sample sizes, Fisher\u0026rsquo;s exact test was used to evaluate group differences. Pearson\u0026rsquo;s or Spearman\u0026rsquo;s correlation analyses were performed to examine linear relationships between variables. Kaplan\u0026ndash;Meier survival analysis was conducted to compare overall survival between high- and low-risk groups, with differences assessed by the log-rank test. Receiver operating characteristic (ROC) curves and area under the curve (AUC) values were used to evaluate the predictive performance of the risk score. All analyses were performed using R software (version 4.4.0) and GraphPad Prism (version 9.5). Data are presented as means, with statistical significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Acquisition of DE-ZNFs in LUAD and functional enrichment analysis\u003c/h2\u003e\u003cp\u003eThe workflow of this study was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A total of 19,934 RNA-seq genes and clinical information for 513 LUAD patients and 58 control samples were downloaded. Among them, 1,834 ZNFs genes were obtained. After differential analysis, 270 DE-ZNFs were identified, including 83 downregulated and 187 upregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B). To evaluate the potential functions of DE-ZNFs in LUAD, GO enrichment analysis was performed. Sixteen biological process (BP) pathways, 5 cellular component (CC) pathways, and 23 molecular function (MF) pathways were identified, with the top five displayed. DE-ZNFs were mainly involved in the protein polyubiquitination process (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D). KEGG enrichment analysis revealed a total of 111 enriched pathways, including key biological processes such as ubiquitin-mediated proteolysis, lysine degradation, endocytosis, and chromatin remodeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Notable signaling pathways identified by GSEA and KEGG enrichment included TNFA signaling via NF-kappa B, KRAS signaling, and the E2F transcription factor pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Development and validation of the prognostic model for LUAD\u003c/h2\u003e\u003cp\u003eUnivariate Cox regression analysis identified 63 DE-ZNFs significantly associated with OS in the TCGA training set (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). After excluding samples with missing values and OS\u0026thinsp;=\u0026thinsp;0, 500 cancer samples remained. LASSO regression was then applied to further select 19 DE-ZNFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, C). Finally, multivariate Cox regression was used to establish the predictive model and identify prognostic-related genes: TRIM6, TRIM29, CTCFL, FGD3, GATA4, CASZ1, TRAF2, and ZNF322 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The risk score was calculated as follows\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\::Risk\\:score=(0.21\\times\\:ExpTRIM6)+(0.079\\times\\:ExpTRIM29)+(0.259\\times\\:ExpCTCFL)+(-0.377\\times\\:ExpFGD3)+(0.239\\times\\:ExpGATA4)+(-0.344\\times\\:ExpCASZ1)+(0.427\\:\\times\\:\\:ExpTRAF2)\\:+\\:(-0.256\\:\\times\\:\\:ExpZNF322).\\)\u003c/span\u003e\u003c/span\u003eLUAD samples were then divided into high- and low-risk groups based on the median risk score. Risk curves and scatter plots illustrated that the high-risk group exhibited higher risk scores and mortality rates compared to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, F). The heatmap showed that TRIM29, GATA4, TRIM6, CTCFL, and TRAF2 were highly expressed in the high-risk group, whereas FGD3, CASZ1, and ZNF322 were highly expressed in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). Kaplan\u0026ndash;Meier analysis demonstrated that patients in the high-risk group had significantly poorer OS than those in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Time-dependent ROC analysis yielded AUC values of 0.756, 0.676, and 0.683 for 1-, 2-, and 3-year survival, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Two independent GEO validation datasets also confirmed shorter OS in the high-risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D). For the GSE50081 validation dataset, 1-, 2-, and 3-year AUC values were 0.714, 0.735, and 0.758, respectively; for GSE26939, the corresponding AUCs were 0.721, 0.792, and 0.792 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003e3.3 The prognostic model serves as a dependable indicator for tumor immunity.\u003c/h2\u003e\u003cp\u003eAccording to the results from CIBERSORT, ssGSEA, and xCell analyses, the low-risk group exhibited a higher proportion of immune cell infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), suggesting stronger tumor immunity compared with the high-risk LUAD patients. The prognostic-related gene FGD3 showed positive correlations with the infiltration of memory B cells, CD8\u0026thinsp;+\u0026thinsp;T cells, and M1 macrophages, while ZNF322 and CASZ1 were associated with increased resting CD4 memory T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Patients in the high-risk group had lower immune scores, lower stromal scores, and higher tumor purity based on the ESTIMATE algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). The heatmap demonstrated that FGD3 was significantly positively correlated with immune and stromal scores, but negatively correlated with tumor purity. In contrast, CASZ1 and TRAF2 exhibited opposite correlation patterns compared to FGD3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). This prognostic model represents an innovative and effective biomarker for selecting anti-tumor immunotherapies. The TIDE score indicated that the high-risk group exhibited poorer immunotherapy efficacy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Validation using the IMvigor210 dataset confirmed that patients in the high-risk group had worse survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). To explore the relationship between risk score and immune checkpoint blockade (ICB) biomarkers, we found that expression levels of PD-L1, PD-L2, and CTLA4 were lower in the high-risk group, suggesting a reduced response to therapies targeting these molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). CD80 and CD86, key ligands for T cell activation, were also downregulated in tumor cells from the high-risk group, which may contribute to immune escape (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Furthermore, based on the IPS score, the low-risk group might derive greater benefit from anti-CTLA4 therapy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG). This comprehensive analysis elucidates the complex interactions between zinc finger proteins and immune therapy, confirming the prognostic model related to ZNFs as a valuable tool for guiding immunotherapy in LUAD.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec29\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Risk score performance in TMB, enrichment and drug selection\u003c/h2\u003e\u003cp\u003eTo investigate the molecular consequences of genetic differences between the high- and low-risk groups and to better understand the biological processes associated with poor survival in the high-risk group, we analyzed the genomic variability of the ZNFs model within the TCGA LUAD cohort. Oncoprint visualization revealed that somatic mutation frequency was higher in high-risk patients (95.92%) compared to the low-risk group (88.21%). The top 15 mutated genes were identified, with missense mutations and multi-hit mutations being the most common types. Notably, mutation rates of TP53, TTN, MUC16, and KRAS exceeded expectations and were significantly elevated in the high-risk group relative to the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Consistently, tumor mutation burden (TMB) was significantly higher in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D). However, stratification of patients based on the median TMB showed that those with higher TMB had longer overall survival (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The chromosomal locations of the 8 prognosis-related ZNFs genes are displayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). GO functional enrichment analysis of DE-ZNFs between risk groups indicated predominant localization in the collagen-containing extracellular matrix, cornified envelope, and axon terminus. Molecular functions enriched in these genes included hormone activity, bile acid binding, and heparin binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). After ID conversion, 311 downregulated and 554 upregulated genes were retained for further analysis. KEGG pathway analysis revealed significant differences between risk groups in pathways such as chemical carcinogenesis\u0026ndash;DNA adducts, neuroactive ligand\u0026ndash;receptor interaction, steroid hormone biosynthesis, and drug metabolism via cytochrome P450 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). GSEA identified 229 significantly enriched pathways (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the top 10 pathways predominantly enriched in the high-risk group. These pathways were associated with cell division, cell cycle regulation, DNA replication, transcription and translation, chromosome maintenance, DNA methylation, and keratinization (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC), potentially explaining the shorter overall survival observed in the high-risk group. GSVA enrichment analysis further demonstrated that poor prognosis in the high-risk group was associated with activation of the MYC signaling pathway, glycolysis, mTOR signaling, DNA repair, E2F transcription factors, unfolded protein response, and oxidative phosphorylation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). These findings suggest potential therapeutic targets for LUAD patients involving ZNFs. To identify potential chemotherapeutic and targeted agents for high-risk LUAD patients, we performed drug sensitivity prediction using available pharmacogenomic datasets. The high-risk group exhibited potential resistance to carboplatin (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA), gefitinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB), and crizotinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC), whereas increased sensitivity was predicted for paclitaxel (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD), docetaxel (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE), erlotinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF), vinorelbine (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG), and trametinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eH).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec30\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Nomogram construction\u003c/h2\u003e\u003cp\u003eClinical information for the high- and low-risk groups was downloaded, and inter-group comparisons were performed using Fisher\u0026rsquo;s exact test, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The high-risk group exhibited a higher proportion of male patients, advanced T stage, increased lymph node metastasis, and higher overall stage. Univariate and multivariate Cox regression analyses were conducted on clinicopathological factors\u0026mdash;including gender, age, stage, TNM classification, and risk score\u0026mdash;to identify independent prognostic factors for LUAD. Variables with p-values less than 0.05 in both analyses were deemed independent risk factors; accordingly, T stage, N stage, and risk score were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eA, B). These independent prognostic factors were then integrated to construct a nomogram, facilitating the prediction of 1-, 2-, and 3-year overall survival (OS) probabilities in the TCGA-LUAD cohort. Due to the limited sample size in the T4 and N3 categories, singular fit issues arose during Cox regression modeling; hence, T3 and T4 were combined, as were N2 and N3 groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). Calibration plots for 1-, 2-, and 3-year OS demonstrated good agreement between predicted and observed survival outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e10\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline clinical information table for high- and low-risk groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;500)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e271 (54.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e114 (45.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e157 (62.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e229 (45.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136 (54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e93 (37.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(mean)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.24 (10.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.14 (10.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.33 (9.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e167 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65 (26.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e102 (41.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e269 (54.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e135 (54.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e134 (53.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43 (8.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34 (13.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4 (1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e323 (66.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e147 (59.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e176 (73.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e95 (19.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e40 (16.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69 (14.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44 (17.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25 (10.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2 (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0 (0.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eM (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.375\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e334 (93.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e170 (91.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e164 (94.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15 (8.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (5.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eStage (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e269 (54.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e107 (43.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e162 (65.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e119 (24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69 (28.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80 (16.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54 (22.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26 (10.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e16 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9 (3.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec31\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Expression and survival analysis of eight prognostic genes\u003c/h2\u003e\u003cp\u003eOur study validated the expression differences of the eight ZNF prognostic model genes between patients and normal samples in the TCGA-LUAD database. We validated the differential expression of the eight ZNF prognostic model genes between LUAD patients and normal samples in the TCGA-LUAD dataset. The results showed that TRIM6, GATA4, TRAF2, and ZNF322 were significantly upregulated in LUAD patients, whereas TRIM29, FGD3, and CASZ1 were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Protein expression levels of these eight prognostic genes in LUAD and normal tissues were further analyzed using data from the Human Protein Atlas (HPA) database. The protein expression patterns of GATA4, TRAF2, and ZNF322 were consistent with their RNA expression trends, showing elevated levels in LUAD patients. Similarly, FGD3 and CASZ1 exhibited lower protein expression in patients, consistent with RNA data. No significant differences were observed between tumor and normal tissues for CTCFL at either the protein or RNA level. Notably, TRIM6 protein was expressed at low levels in both tumor and normal tissues, and TRIM29 protein expression showed an inverse pattern relative to its RNA expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Kaplan\u0026ndash;Meier survival analysis demonstrated that higher expression of TRIM6, TRIM29, and TRAF2, along with lower expression of FGD3, CASZ1, and ZNF322, were associated with poorer overall survival in LUAD patients. Expression levels of the remaining two genes did not show significant prognostic relevance (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec32\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Loss of FGD3 Contributes to LUAD Cell Proliferation and Drug Resistance\u003c/h2\u003e\u003cp\u003eAmong the eight prognostic-related genes, FGD3 exhibited the strongest correlation with ESTIMATE scores and a relatively high correlation coefficient within the prognostic model. Consequently, we prioritized FGD3 for downstream validation. Paired RNA-seq data from TCGA showed significantly lower FGD3 expression in LUAD tissues compared to normal samples (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA), and consistent results were observed in two paired GEO datasets (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). Protein expression of FGD3 was further validated in LUAD cell lines and normal bronchial epithelial (HBE) cells. Except for PC9, FGD3 protein levels in H3122, H2228, H1650, and H1993 cells were significantly lower than those in HBE cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). A similar expression pattern was observed in matched tumor and adjacent normal tissues from LUAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003eB). The immunohistochemistry results showed that compared to LUAD tissues, FGD3 expression was significantly higher in normal tissues. Cellular localization revealed that FGD3 was expressed in both the cytoplasm and nucleus, with a greater expression in the cytoplasm. FGD3 was more highly expressed in the epithelial cells of the trachea and bronchi (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e14\u003c/span\u003e). GO, KEGG, and GSEA enrichment analyses revealed that genes differentially expressed with FGD3 were mainly enriched in cilium movement and cell adhesion pathways in LUAD (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB\u0026ndash;D). Drug sensitivity prediction indicated that the low FGD3 expression group was less sensitive to the first-generation ALK inhibitor crizotinib (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003eC). In lorlatinib-resistant H3122 cells (H3122LR) (Fig. \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e), FGD3 expression was decreased compared to parental cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003eA). Moreover, FGD3 expression showed a dose- and time-dependent decline in ALK-positive cell lines H3122 and H2228 with prolonged lorlatinib treatment and increasing drug concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e13\u003c/span\u003eD). The FGD3 plasmid was transfected into H3122LR cells, and transfection efficiency was confirmed by Western blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eA). Colony formation assays showed that FGD3 overexpression markedly reduced the number of colonies formed by H3122LR cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eB). Consistently, cell viability was significantly decreased upon FGD3 overexpression (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eC). Furthermore, transwell assays demonstrated that upregulation of FGD3 substantially impaired both migration and invasion capabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eD). Wound-healing assays further indicated that FGD3 overexpression attenuated the cells\u0026rsquo; wound closure ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eE). FGD3 overexpression can partially reverse lorlatinib resistance in H3122LR cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e15\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eZNFs constitute the largest family of transcription factors in mammalian cells. They are involved in the initiation, progression, and metastasis of malignant tumors, as well as in drug resistance, by regulating transcriptional and translational processes [30], highlighting their potential as novel therapeutic targets. ZNFs have been described as a double-edged sword, exhibiting both oncogenic and tumor-suppressive functions, and serving as reliable prognostic biomarkers [31]. Numerous clinicopathological datasets, prognostic data, and ZNF-based cancer prognostic models have been developed for various cancers, including pancreatic cancer, soft tissue sarcoma, colon adenocarcinoma, esophageal cancer, breast cancer, and osteosarcoma [32\u0026ndash;38]. Many prognostic models are currently utilized in clinical practice; for example, the Sokal, Euro, EUTOS, and ELTS scoring systems are employed to evaluate prognosis in chronic myeloid leukemia (CML) [39]. However, despite LUAD being the most prevalent malignant tumor in China [40], no prognostic model based on zinc finger proteins has yet been established for this disease.\u003c/p\u003e\u003cp\u003eIn this study, we identified 270 DE-ZNFs between LUAD and normal tissues. Through univariate Cox regression analysis, LASSO and Multiple Cox regression analysis we developed a prognostic model containing eight core genes for OS, which demonstrated the ability to predict patient prognosis effectively. This model was rigorously validated by GEO data through K-M survival plots and time ROC curves. Subsequently, we extended our investigation to assess immunotherapy responses between the high- and low-risk groups. Immune infiltration analysis revealed that the high-risk group exhibited an immune \u0026ldquo;desert\u0026rdquo; phenotype, characterized by relatively low levels of immune cells such as activated B cells and natural killer cells within the tumor macroenvironment. Dendritic cells, key players in antigen presentation and immune activation [41, 42], were also relatively scarce. Furthermore, CD4\u0026thinsp;+\u0026thinsp;memory T cells, which play a critical role in tumor immune responses by maintaining long-term immune memory and enhancing anti-tumor activity [43], were diminished in the high-risk group. The deficiency of CD4\u0026thinsp;+\u0026thinsp;memory T cells may reflect tumor immune escape mechanisms that suppress immune surveillance. Consistently, ESTIMATE analysis, TIDE scores, and IMvigor210 data confirmed that the high-risk group had lower immune scores, higher tumor purity, and poorer responses to immunotherapy. Moreover, expression levels of PD-L1, PD-L2, and CTLA-4 were reduced in the high-risk group, potentially impairing T cell recognition and indicating a poor response to immune checkpoint inhibitors [44\u0026ndash;46]. IPS score predictions aligned with these findings, suggesting that the high-risk group may be less responsive to anti-CTLA-4 therapy. Additionally, CD80 and CD86, essential co-stimulatory molecules on dendritic cells and B cells that facilitate T cell activation [47], were expressed at lower levels in the high-risk group. This suggests impaired antigen-presenting cell function and insufficient immune activation, which may contribute to immune escape.\u003c/p\u003e\u003cp\u003eTo better understand the molecular characteristics between different groups, TMB analysis was carried out. The progressive accumulation of somatic mutations throughout life may contribute to carcinogenesis [48]. The overall mutation rate was higher in the high-risk group compared to the low-risk group (95.92% vs. 88.21%), suggesting greater tumor heterogeneity in the high-risk group.\u003c/p\u003e\u003cp\u003eTP53, a classic tumor suppressor which is crucial for a variety of cellular processes, including DNA repair, cell cycle arrest, and apoptosis [49], is the most commonly mutated in human cancers, including NSCLC. Co-mutations of TP53 and other driver genes further contribute to the poor prognosis of lung cancer [50]. Besides, KRAS\u0026rsquo;s a proto-oncogene, KRAS mutations are particularly prevalent in 30% LUAD [51]. TTN is also frequently mutated in a variety of cancers, overexpression of TTN-AS1 correlates with poor prognosis in lung cancer [52]. MUC16 mutation is reported to decrease the immune response in LUAD [53]. Our results showed higher mutation frequencies of TP53 (55% vs. 48%), TTN (56% vs. 41%), KRAS (33% vs. 28%), and MUC16 (46% vs. 38%) in the high-risk group compared to the low-risk group, which may partly explain the poorer prognosis of the high-risk patients. Previous studies reported higher TMB results in more neo-antigens, increasing chances for T-cell recognition, and clinically correlates with better immune checkpoint inhibitors outcomes [27]. However, our findings showed that the high-risk group had higher TMB but exhibited poorer response to immunotherapy. Additionally, there was no significant difference in overall survival between patients with high and low TMB. This aligns with literature suggesting that TMB alone is an imperfect biomarker for immunotherapy response, and composite predictors incorporating other factors such as MHC and T-cell receptor repertoire are needed [6].\u003c/p\u003e\u003cp\u003eWe observed that differentially expressed genes in LUAD were enriched in pathways related to cellular metabolism, proliferation, genomic stability, and regulation of gene expression. These aberrant molecular processes likely cooperate to accelerate tumorigenesis and progression in the high-risk group. GSVA analysis revealed enrichment of MYC and mTOR signaling pathways in high-risk LUAD, suggesting their potential as therapeutic targets. Furthermore, enrichment of DNA damage repair, E2F transcription factor, and unfolded protein response pathways indicated that tumor cells in the high-risk group may exhibit enhanced DNA repair capacity, sustained proliferative signaling [54] and disrupted endoplasmic reticulum proteostasis [55], thereby contributing to increased treatment resistance and tumor survival. Additionally, the risk assessment model could serve as a reliable tool to guide clinical treatment selection. For instance, patients in the high-risk group may be resistant to carboplatin, gefitinib, and crizotinib but more sensitive to paclitaxel, docetaxel, erlotinib, vinorelbine, and trametinib. Moreover, we found that the risk score significantly correlated with clinicopathological parameters such as T stage and N stage, which supported the development of a predictive nomogram with high accuracy for overall survival.\u003c/p\u003e\u003cp\u003eOf the eight genes comprising our prognostic signature, TRIM6, GATA4, TRAF2, and ZNF322 were highly expressed in LUAD samples, whereas TRIM29, FGD3, and CASZ1 showed lower expression. Similar protein-level expression patterns were confirmed using the HPA database. Kaplan\u0026ndash;Meier survival analysis suggested that TRAF2, TRIM29, and TRIM6 may promote carcinogenesis in LUAD, while CASZ1, FGD3, and ZNF322 likely act as tumor suppressors. As reported in previous literature, TRAF2 is an oncogenic protein involved in TNF-mediated apoptosis in NSCLC via NFκB [56]. Accumulating evidence suggests that TRIM29 is also a transcription factor involved in NSCLC carcinogenesisvia and confirmed to be highly expressed in anlotinib-resistant NSCLC cells [57]. Previous studies have proposed that TRIM6 acts as an E3-ubiquitin ligase educes ferroptosis and chemosensitivity in lung cancer [58]. Our data on ZNF322 contrast with previous reports: while prior studies describe ZNF322 as an oncogenic transcription factor promoting neo-angiogenesis and tumorigenesis in lung cancer [59\u0026ndash;62], our findings suggest a different role. The role of CASZ1 varies across different types of tumors, and its role in lung cancer is also controversial. According to TCGA data, CASZ1 is expressed at low levels in LUAD samples. However, a study has reported that CASZ1 promotes migration, invasion, and metastasis in lung cancer [63]. GATA4 has been shown to function as an essential tumor suppressor in lung cancer both in vitro and in vivo [64]. FGD3 regulates the actin cytoskeleton and cell morphology and inhibits cancer cell migration, correlating with favorable prognosis in breast and pancreatic cancers [65, 66]. Here, we validated for the first time that FGD3 protein expression is significantly reduced in LUAD tissues and cell lines compared to normal controls. Functional assays further demonstrated that FGD3 overexpression can partially reverse lorlatinib resistance in H3122LR cells, indicating a potential role of FGD3 in modulating drug sensitivity. GO enrichment analysis suggested that FGD3 is involved in cilium movement, which may explain its higher expression in normal epithelial cells of the trachea and bronchi. KEGG pathway analysis revealed that FGD3 is associated with cell adhesion-related pathways in LUAD, consistent with the phenotypic changes observed during lorlatinib resistance, such as increased membrane protrusions and cytoskeletal remodeling. Previous studies have also shown that FGD3 facilitates actin cytoskeleton remodeling and filopodia formation. Collectively, these findings suggest that the downregulation of FGD3 contributes to the acquisition of lorlatinib resistance, and restoring FGD3 expression may partially counteract this resistant phenotype.\u003c/p\u003e\u003cp\u003eIn summary, we constructed a robust prognostic model for LUAD based on eight ZNF genes, which not only provides a novel survival predictor but also offers insights into the potential biological roles of ZNF family members in tumor progression and therapy response. The signature may serve as a clinically useful biomarker for guiding individualized treatment decisions. Moreover, we are the first to characterize FGD3 in LUAD, revealing its downregulation in tumor tissues and lorlatinib-resistant cells. Functional assays indicate that FGD3 overexpression can partially reverse drug resistance, suggesting a potential role in modulating cytoskeletal dynamics and cell adhesion, consistent with our enrichment analyses. These findings highlight FGD3 as a potential predictive biomarker and therapeutic target in LUAD. Nevertheless, our study has limitations. The prognostic signature was validated only in public cohorts, and its generalizability should be confirmed in prospective clinical samples. In addition, further investigation is required to clarify whether FGD3 can serve as an independent prognostic predictor and to elucidate its precise molecular mechanisms in LUAD progression.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study presents a novel ZNF-based prognostic model for LUAD and underscores FGD3 as a promising biomarker and therapeutic target to counteract drug resistance.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"705\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eNon-small cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eSmall cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eLUAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eLung adenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eICI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eImmune checkpoint inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eZNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eZinc finger protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eDE-ZNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eDifferentially expressed ZNF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eThe cancer genome atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eGene expression omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eGene ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eKyoto encyclopedia of genes and genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eGene set enrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eLASSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eLeast absolute shrinkage and selection operator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003essGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 621px;\"\u003e\n \u003cp\u003eSingle-sample gene set enrichment analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTIDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eTumor immune dysfunction and exclusion\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eIPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eImmunophenoscore\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eTumor mutation burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eCNV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eCopy number variation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eGene set variation analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eGDSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eGenomics of drug sensitivity in cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eCTRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eCancer therapeutics response portal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eIC50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eHalf maximal inhibitory concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eHPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eHuman protein atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eShort-tandem repeat\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eH3122LR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eH3122 Lorlatinib-resistant cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eBiological process\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eCellular component\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 621px;\"\u003e\n \u003cp\u003eMolecular function\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the Cancer Genome Atlas (TCGA) Database and the Gene Expression Omnibus (GEO) for providing extensive data. The flowchart graphic was generated using smart.servier.com.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Jiayue Sun, Xiaoyi Huang, Dinglong Xue, Jiazhuang Li, Yaru Huang, Yue Yang and Qingwei Meng; Data curation, Jiayue Sun, Xiaoyi Huang; Formal analysis, Jiayue Sun and Yue Yang; Funding acquisition, Qingwei Meng; Methodology, Jiayue Sun, Dinglong Xue, and Jiazhuang Li; Project administration, Jiayue Sun and Yaru Huang; Resources, Jiayue Sun; Software, Jiayue Sun; Supervision, Xiaoyi Huang and Qingwei Meng; Validation, Yue Yang; Writing \u0026ndash; original draft,Jiayue Sun, Xiaoyi Huang, Dinglong Xue, Jiazhuang Li, Yaru Huang, Yue Yang; Writing \u0026ndash; review \u0026amp; editing, Jiayue Sun, Xiaoyi Huang, Dinglong Xue, Jiazhuang Li, Yaru Huang, Yue Yang and Qingwei Meng. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (Grant No. 81672931), the Climbing Program of Harbin Medical University Cancer Hospital (PDYS2024-01), and the Key Research and Development Program of Heilongjiang Province (2022ZX06C11).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets analyzed in this study are publicly available in online repositories. RNA sequencing data and corresponding clinical information for LUAD were obtained from \u003cstrong\u003eTCGA\u003c/strong\u003e (https://portal.gdc.cancer.gov/) and the \u003cstrong\u003eUCSC Xena\u003c/strong\u003e platform (https://xenabrowser.net/datapages/). External validation cohorts were retrieved from \u003cstrong\u003eGEO\u003c/strong\u003e (https://www.ncbi.nlm.nih.gov/geo) under accession numbers \u003cstrong\u003eGSE50081\u003c/strong\u003e, \u003cstrong\u003eGSE26939\u003c/strong\u003e, \u003cstrong\u003eGSE32863\u003c/strong\u003e, and \u003cstrong\u003eGSE75037\u003c/strong\u003e. Zinc finger protein family genes were obtained from \u003cstrong\u003eUniProt\u003c/strong\u003e (https://www.uniprot.org/), and the complete list is provided in \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eFor immune-related analyses, LUAD data were downloaded from the \u003cstrong\u003eTIDE\u003c/strong\u003e database (http://tide.dfci.harvard.edu/) for TIDE analysis, and \u003cstrong\u003eIPS\u003c/strong\u003e for each patient were obtained from \u003cstrong\u003eTCIA\u003c/strong\u003e (https://tcia.at/). \u003cstrong\u003eTMB\u003c/strong\u003e data for the TCGA cohort were acquired from \u003cstrong\u003ecBioPortal\u003c/strong\u003e (https://www.cbioportal.org/). Immunohistochemical data for prognostic genes in LUAD tissues were retrieved from the \u003cstrong\u003eHPA\u003c/strong\u003e (https://www.proteinatlas.org/).\u003c/p\u003e\n\u003cp\u003eAll other data generated or analyzed during this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll clinical tissue and paraffin-embedded specimens were obtained from Harbin Medical University Cancer Hospital. The pathological type of LUAD was confirmed by both intraoperative frozen-section analysis and postoperative histopathological examination. For each patient, paired tumor and adjacent normal tissues were collected. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Harbin Medical University Cancer Hospital, which granted a waiver of informed consent (approval No. KY2021-15; April 23, 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al 2024; Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Oncogene 41(5):838\u0026ndash;851.\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":"Zinc finger protein family, Lung adenocarcinoma, Prognostic risk model, FGD3, Lorlatinib resistance, Immune infiltration, Drug sensitivity","lastPublishedDoi":"10.21203/rs.3.rs-7976542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7976542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eLung adenocarcinoma is the most common type of lung cancer and a major cause of cancer death. Zinc finger proteins are involved in tumor progression and may serve as diagnostic and therapeutic targets.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eRNA-seq and clinical data were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognosis-associated ZNF genes were identified through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. An eight-gene prognostic model was established and evaluated using Kaplan\u0026ndash;Meier survival curves and time-dependent receiver operating characteristic (ROC) analyses, with external validation in the GSE50081 and GSE26939 datasets. A nomogram integrating independent prognostic factors was constructed and assessed by calibration plots. Immune infiltration, mutation profiles, and drug response were analyzed. Finally, we focused on a key molecule FGD3, examining its expression in LUAD cells and tissues, including lorlatinib-resistant models.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe signature consisted of \u003cem\u003eTRIM6, TRIM29, CTCFL, FGD3, GATA4, CASZ1, TRAF2\u003c/em\u003e, and \u003cem\u003eZNF322\u003c/em\u003e. T stage, N stage, and the risk score emerged as independent predictors of overall survival. The nomogram showed robust predictive value. High-risk patients exhibited an immune \u0026ldquo;desert\u0026rdquo; phenotype, higher somatic mutation rates, and elevated tumor mutational burden. Immunotherapy prediction and drug sensitivity analysis suggested that the model could potentially predict responses to immunotherapy, chemotherapy, and targeted therapy. FGD3 expression was downregulated in LUAD tissues and lorlatinib-resistant cells, decreasing with prolonged lorlatinib exposure. Restoration of FGD3 suppressed H3122LR proliferation and partially reversed resistance.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eWe developed and validated a novel ZNF-based prognostic model for LUAD that may serve as a valuable tool for risk stratification and guiding personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Zinc Finger Protein-Based Prognostic Model in Lung Adenocarcinoma Highlights FGD3 as a Potential Marker for Lorlatinib Resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:37:06","doi":"10.21203/rs.3.rs-7976542/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":"d6edb025-8a21-487c-bc3e-350ece5fc9e0","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-28T09:51:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:37:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7976542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7976542","identity":"rs-7976542","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}