PTTG1-DLX2 axis drives malignant progression of lung adenocarcinoma by activating the WNT/β-catenin signaling pathway

Introduction

The incidence of lung adenocarcinoma (LUAD), the most common pathologic subtype of lung cancer, continues to increase globally, with a significant increase especially among women and non-smokers (1-3). Although targeted therapies [e.g., tyrosine kinase inhibitors (TKIs) for epidermal growth factor receptor (EGFR) mutations] and immune checkpoint inhibitors have significantly improved the prognosis of some patients (4,5), treatment resistance and distant metastasis accompanying the malignant progression of LUAD are still serious constraints to clinical efficacy (6,7). For example, after treatment with the third-generation EGFR-TKI osimertinib, about 50% of patients become resistant due to the C797S mutation or phenotypic transformation (8,9), and epithelial-mesenchymal transition (EMT)-driven enhancement of tumor cell invasiveness is a key pathological basis for the metastatic dissemination of LUADs (10). The WNT/β-catenin signaling pathway plays a central role in embryonic development and tumorigenesis. Its abnormal activation is closely associated with the proliferation, EMT, and treatment resistance of LUAD cells. However, the specific mechanisms of action of upstream regulatory factors and synergistic molecules in this pathway remain to be further elucidated (11,12).

Abnormally high expression of pituitary tumor transforming gene 1 (PTTG1), a highly conserved oncogene, has been demonstrated in a variety of solid tumors (13-16), which promotes malignant progression of tumors by regulating cell cycle, apoptosis, and extracellular matrix remodeling. In LUAD, our previous study showed that PTTG1 knockdown inhibited LUAD cell proliferation and invasion and affected EMT marker expression (13), but how PTTG1 regulates downstream signaling pathways to mediate the malignant phenotypic transformation is unclear.

Distal-less homeobox 2 (DLX2) is involved in craniofacial and limb formation during embryonic development, and previous studies have revealed that it is abnormally activated in the tumor microenvironment (TME), promoting EMT and tumor metastasis through transcriptional regulation of the WNT and TGF-β pathways (17-20). In lung squamous cell carcinoma, DLX2 may be a potential immune-related prognostic indicator associated with TME remodeling (21). In breast cancer cells, DLX2 overexpression upregulates mesenchymal markers N-cadherin and Vimentin to enhance cell invasion, and its expression level correlates with poor patient prognosis (17,19). However, the regulatory relationship between PTTG1 and DLX2 and whether it drives the malignant progression of LUAD through synergistic activation of the WNT/β-catenin signaling pathway has not been reported.

In this study, we investigated the oncogenic role of PTTG1 in LUAD. Despite growing evidence implicating PTTG1 in tumor progression and DLX2 in regulating EMT and WNT/β-catenin signaling, their mechanistic interplay in LUAD remains largely unexplored. Notably, whether PTTG1 drives LUAD malignancy through a DLX2-dependent pathway has not been previously clarified. Therefore, this study aimed to define the functional role of PTTG1 in LUAD and to determine whether DLX2 mediates its oncogenic effects, with the goal of elucidating a previously unrecognized PTTG1-DLX2 regulatory axis. We present this article in accordance with the ARRIVE and MDAR reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0165/rc).

Methods

Bioinformatics analysis

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Transcriptomic data of LUAD patients were obtained from The Cancer Genome Atlas (TCGA) (https://tcga-data.nci.nih.gov/tcga/), incorporating samples with complete survival information (n=500). In addition, we obtained transcriptome data for GSE50081 (n=127), GSE63459 (n=65), GSE30219 (n=223), and GSE19188 (n=156) from the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). The RNA-seq data type for processing TCGA was log₂[transcript per million (TPM)+1]. Patients were stratified into high- and low-PTTG1 expression groups based on the median PTTG1 expression value within each cohort. Differentially expressed genes (DEGs) between the two groups were identified using the DESeq2 R package (v1.36.0). Genes with |log₂ fold change (FC)| >1 and a false discovery rate (FDR)-adjusted P value <0.05 were considered statistically significant. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and carried out gene set enrichment analysis (GSEA) to explore PTTG1-associated pathways, using the clusterProfiler R package (v4.7.1) (22,23). XCELL, MCPCOUNTER, Cell Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT), Tumor Immune Estimation Resource (TIMER), Estimation of Proportions of Immune and Cancerous cell types (EPIC), QUANTISEQ, and Immunophenoscore (IPS) algorithms were performed using the R package “IOBR” (v0.99.9) for immuno-microenvironment quantification (24). Additionally, single-sample gene set enrichment analysis (ssGSEA) was utilized to quantify the relative infiltration of 24 immune cells, with the set of genes characterizing each immune cell taken from the latest literature (25). In addition, EMT-related genes were obtained from the dbEMT2 database (https://bioinfo-minzhao.org/dbemt/) (26). Representative immunohistochemical (IHC) images of PTTG1 protein expression were downloaded from the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/). The prognostic impact of PTTG1 expression in the GEO cohorts was evaluated using the Kaplan-Meier Plotter online tool (https://kmplot.com/analysis/). Principal component analysis (PCA) was performed to evaluate overall transcriptional variation and to visualize the separation between sample groups. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance of the model, and the area under the curve (AUC) was calculated to quantify predictive accuracy.

Cell culture and transfection

Human LUAD cell lines A549 and H1299 were purchased from the Chinese Academy of Sciences (Shanghai, China) cell bank. We used A549 and H1299 cells for in vitro culture experiments in DMEM medium supplemented with 10% fetal bovine serum, 1% penicillin and streptomycin (Gibco, Waltham, USA), and RPMI 1640 medium (Gibco, ThermoFisher Scientific, Waltham, USA). Small interfering RNA (siRNA) targeting PTTG1/DLX2 was purchased from Gemma Genetics (Shanghai, China), and PTTG1 and DLX2 overexpression plasmids were purchased from Sangon Biotech (Nanjing, China). For transient transfection, A549 and H1299 cells were transfected with siRNA or overexpression plasmids using transfection reagents (Lipofectamine 2000 or Lipo8000™) for 12 hours, followed by subsequent experiments.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR) and western blot

Total RNA was extracted from A549 and H1299 cells using the Rapid Cellular RNA Extraction Kit (K0731, GeneJET RNA, Thermo Fisher Scientific), cDNA was synthesized from total RNA using a dedicated reverse transcription kit. Quantitative real-time PCR was then performed using a SYBR Green qPCR Master Mix (Vazyme, Nanjing, China), with GAPDH used as the internal reference gene. Thermal cycling conditions were: initial denaturation at 95 ℃ for 5 min; 40 cycles of 95 ℃ for 10 s and 60 ℃ for 30 s; followed by a melting-curve analysis (hold at 60 ℃ for 60 s, then increase to 95 ℃ and hold 15 s). Primer sequences: PTTG1, forward 5'-ACCCGTGTGGTTGCTAAGG-3', reverse 5'-ACGTGGTGTTGAAACTTGAGAT-3'; DLX2, forward 5'-ATGCACTCGACCCAGATCG-3', reverse 5'-GGCTTGGTACTGGTAGGAACC-3'; GAPDH, forward 5'-GGAGCGAGATCCCTCCAAAAT-3', reverse 5'-GGCTGTTGTCATACTTCTCATGG-3'. Expression of target genes was calculated using the 2^−ΔΔCT method.

Western blot analysis was performed using RIPA lysis buffer (Servicebio, Wuhan, China) containing PMSF (Servicebio) to collect proteins from A549 and H1299 cells. A total of 20–30 µg protein per lane was loaded onto 10% sodium dodecyl sulfate-polyacrylamide gels (SDS-PAGE). Protein samples were separated using 10% SDS-PAGE, and separated proteins were transferred using a polyvinylidene difluoride (PVDF) membrane (Immobilon-P, Carlsbad, Ireland). The membranes were closed for 15 min using rapid closure solution and then incubated with primary antibody overnight at 4 ℃, followed by incubation with secondary antibody for 2 hours. Signals were detected using an enhanced chemiluminescence (ECL) kit (Bio-sharp, BL520B, Hefei, China), and each experiment was performed in triplicate. Primary antibodies included PTTG1 (18040-1-AP, Proteintech, Wuhan, China), β-catenin (51067-2-AP, Proteintech), p-β-catenin (80084-1-RR, Proteintech), cyclin D1 (60186-1-Ig, Proteintech), DLX-2 (26244-1-AP, Proteintech), β-actin (66009-1-Ig, Proteintech), Snail (ER1706-22, HUABIO), N-cadherin (22018-1-AP, Proteintech), E-cadherin (20874-1-AP, Proteintech), Bcl-2 (ET1702-53, HUABIO), BAX (50599-2-Ig, Proteintech) and c-MYC (10828-1-AP, Proteintech). Secondary antibodies used were anti-rabbit (SA00001-2, Proteintech) and anti-mouse (SA00001-1, Proteintech).

Cell Counting Kit-8 (CCK-8) and clone formation experiments

Twenty-four hours after transfection treatment, A549 and H1299 cells were cultured in 96-well plates (3,000 cells/well). The proliferative capacity of the treated cells was determined at 4, 24, 48, and 72 hours. The 10% CCK-8 reagent (Bio-sharp, BS350E) was added to each plate according to the kit instructions, and the optical density (OD) 450 values were analyzed by a microplate reader (BioTek, Hefei, USA). Clone formation experiments were performed by inoculating 2,000 cells in cell culture plates and allowing them to grow until visible colonies were formed. We then fixed the clones with paraformaldehyde (FPA) for 15 min, stained the clones with 1% crystal violet for 20 min, and counted the number of clones (>50 cells).

Transwell migration and invasion assay

Twenty-four hours after transfection of treated A549 and H1299 cells, they were cultured in 24-well culture plates with 8 mm pore membrane inserts (Corning Incorporated, One Riverfront Plaza, Corning, NY, USA) to measure cell migration ability, and invasion experiments required the addition of matrix gel in the upper chamber. Specifically, 4×10⁴ cells were inoculated in the upper chamber of the Transwell, 200 µL of serum-free medium was added, and 800 µL of medium containing 10% FBS was added to the lower chamber. After 48 h of incubation, transmembrane migrating cells were fixed with FPA, stained with 1% crystal violet, and counted under a light microscope (50×). In addition, A549 and H1299 cells were incubated in 24-well plates and scraped with a 200-µL pipette tip. Cells were cultured in DMEM and RPMI 1640 medium without FBS. Wound images were captured at 0 and 24 h, and the wound area was calculated using ImageJ software (v1.54d).

Immunofluorescence

Cells were seeded on coverslips, followed by fixation with 4% paraformaldehyde and permeabilization with 0.1–0.3% Triton X-100 at room temperature for 20 minutes. Cell samples were blocked with peroxidase blocking buffer at 25 ℃, and then incubated with primary antibodies overnight at 4 ℃. Subsequently, the slides were washed three times with phosphate-buffered saline (PBS), and then incubated with secondary antibodies at 25 ℃ for 30 minutes. An appropriate amount of pre-warmed antibody stripping buffer (37 ℃) was added to cover the coverslips, incubated at 37 ℃ for 5–20 minutes, and this step was repeated twice. Nuclei were stained with 4’,6-diamidino-2-phenylindole (DAPI) for visualization. The sections were scanned using a digital slide scanner (brand: 3DHISTECH; model: Pannoramic MIDI).

Co-immunoprecipitation (Co-IP)

For Co-IP analysis, cells were lysed in IP lysis buffer at 4 ℃ for 30 min, supplemented with a protease and phosphatase inhibitor cocktail (MedChemExpress, Monmouth Junction, USA). Lysates were then centrifuged at 12,000 ×g for 15 min at 4 ℃, and the supernatant containing proteins was incubated with the indicated primary antibody for immunoprecipitation at 4 ℃. The following day, lysates were mixed with magnetic agarose beads (Biolinkedin, Shanghai, China) and incubated for 2–4 h, followed by three washes with IP wash buffer. Proteins were eluted by adding 2× sample buffer and heating at 95 ℃ for 10 min, and finally analyzed by Western blotting.

Cell cycle assay

For cell cycle analysis, the Cell Cycle Analysis Kit (Beyotime, C1052, Shanghai, China) was used. Cells cultured in 6-well plates were harvested after digestion, washed with cold phosphate-buffered saline (PBS), and fixed in 70% ethanol at 4 ℃ for 3 h. After another wash with cold PBS, cells were stained with a propidium iodide (PI)/RNase A solution at 37 ℃ in the dark for 30 min. Cell cycle distribution was then analyzed using a flow cytometry system (FACSCalibur, FACSVerse™, BD Biosciences, San Jose, CA, USA).

Vivo experiments

Four-week-old female BLAB/c nude mice were purchased from GemPharmatech (Nanjing, China). Mice were randomly assigned to cages (five mice per cage) using a computer-generated random number list in Excel (=RAND()), and animals were placed into cages according to the sorted order. Cage positions on the rack were also randomized to minimize environmental bias, following standard ARRIVE/NC3Rs recommendations. To ensure a comfortable environment, five mice were kept in each cage. The nude mice were grown under the same conditions. In this study, nude mice were housed in a specific pathogen-free (SPF) barrier facility under controlled conditions (temperature 20–24 ℃, relative humidity 40–60%) with a 12-h light/dark cycle. All feed and drinking water were sterilized by autoclaving, and cages and bedding were replaced weekly to maintain cleanliness. The initial body weight of the nude mice ranged from 14 to 19 g. A total of 16 mice were used and randomly divided into two groups, with eight mice per group. Tumor growth was monitored for up to four weeks. Mice were euthanized when tumor volume reached approximately 100 mm³ or when predefined humane endpoint criteria were met. To construct a subcutaneous xenograft tumor model, we injected stably transduced A549 cells into the right axillary fossa of mice (5×10⁶ per mouse) and recorded the tumor size weekly. Tumor volume was calculated as follows: volume = (length× width²)/2. Mouse tumor tissues were harvested and rinsed thoroughly with pre-cooled PBS. Pre-cooled RIPA lysis buffer was added at the appropriate ratio, and the tissues were ground into a homogenate on ice. After standing for 30 minutes, the homogenate was centrifuged at 12,000 rpm at 4 ℃ for 15 minutes, and the supernatant was collected for protein quantification. Subsequently, SDS-PAGE electrophoresis was performed, followed by transfer at 220 mA under low temperature for 20 minutes. The membrane was blocked for 1.5–2 hours, then incubated with the primary antibody on a shaker overnight at 4 ℃, and with the fluorescent secondary antibody on a shaker at room temperature for 2 hours. Finally, the Western blot (WB) bands were analyzed after development. All animal experiments were conducted under the project approval (No. [2024] KY031) issued by the Ethics Committee of Bengbu Medical University and in accordance with the university’s guidelines on the care and use of animals. Clinical trial number: not applicable. According to the institutional animal ethics guidelines, the maximal allowable tumor size was 1.5 cm in diameter (approximately 1,500–2,000 mm³), and no animals exceeded this limit during the experiment.

Generation of PTTG1 KO cell line and RNA-seq

In this study, we used a CRISPR ribonucleoprotein (RNP)-based approach to knock down the PTTG1 gene in A549 cells, and designed two sgRNAs (sgRNA1: 5'-UCAGCAAUCAAAGCCUUUAGA-3'; sgRNA2: 5'-AGAAAAAGUCUGUAAAGACCA-3') targeting the early exons encoding the functional proteins and multiple isoforms shared exons, which were validated by genomic low off-target analysis, and formed complexes with Cas9 in a 5:1–10:1 ratio. The RNP was introduced into A549 cells by an electro-transfection system, screened for expansion using a single-cell sorter, and the targeted genomic regions were verified by Sanger sequencing using standard protocols, with PCR amplification of the target locus, purification of PCR products, and sequencing to confirm indels or mutations, and genotypically clear cell lines were frozen and preserved. Transcriptome RNA sequencing (RNA-seq) was performed by Beijing Tsingke Biotechnology Co., Ltd. Changes in the expression levels of messenger RNA (mRNA) transcripts were determined by comparing control cells with A549 cells knocked down for PTTG1. The “DESeq2” R package was used to identify DEGs between the two groups, and a total of 1172 DEGs were identified using |log₂FC| ≥1 and P value <0.05 as the screening criteria.

Statistical analysis

Statistical analysis and plotting were performed using R software (v4.2.1) and GraphPad software (v9.0.0). Kaplan-Meier survival curves were generated and compared using the log-rank test. The association between PTTG1 and DLX2 gene expression was assessed using Spearman correlation analysis. Two pairs of intergroup tests were compared using the Wilcoxon test. Statistical significance of the experiments was evaluated by the T-test in GraphPad Prism version 9 software. All cell experiments were repeated at least three times. Differences were considered statistically significant at P<0.05.

Results

PTTG1 is highly expressed in LUAD and associated with poor prognosis

First, the mRNA expression level of PTTG1 in LUAD was analyzed. The results showed that mRNA expression of PTTG1 was significantly upregulated in LUAD tissues, and the results were consistent in cancer tissues and matched non-cancer tissues (Figure 1A,1B). We next explored the possible relationship between PTTG1 and multiple clinicopathologic factors, and box line plots showed that PTTG1 expression was correlated with N-stage, stage, and survival status (Figure 1C-1F). The ROC analysis showed that PTTG1 achieved an AUC of 0.944, indicating a strong within-cohort discriminative performance between LUAD and control samples (Figure 1G). In addition, the high mRNA expression level of PTTG1 in LUAD was validated in three independent GEO datasets: GSE63459, GSE30219, and GSE19188 (Figure 1H-1J). The IHC staining data from the HPA database showed that the expression of PTTG1 protein in LUAD was higher than that in neighboring tissues (Figure 1K). By survival analysis, we found that the prognosis of the high PTTG1 group was significantly worse than that of the low PTTG1 group, with significantly reduced overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) (Figure 2A-2C). Subgroup analysis showed that patients with high expression of PTTG1 had shorter OS in the subgroups of T2 & T3 & T4 staging, N1 & N2 & N3 staging, M0 staging, age >65 years, smokers, and treatment outcome of progressive disease (PD) & stable disease (SD) (P<0.05, Figure 2D-2K). Time-dependent ROC curves showed that PTTG1 predicted 1-, 3-, and 5-year survival with AUC values greater than 0.6 (Figure 2L). In addition, similar results were shown in the GSE30219, GSE31210, GSE50081, and GSE37745 cohorts (Figure 2M-2P). The above results suggest that PTTG1 is highly expressed in LUAD and may promote the malignant progression of LUAD.

Figure 1 PTTG1 is highly expressed in tumor tissues. (A) Difference in expression of PTTG1 in tumor tissues and normal tissues in LUAD. (B) Difference in PTTG1 expression in LUAD tissues and paired normal samples. (C) Correlation between PTTG1 expression and N stage. (D) Correlation between PTTG1 expression and OS survival status. (E) Correlation of PTTG1 expression with DSS survival status. (F) Correlation of PTTG1 expression with pathological staging. (G) Diagnostic ROC curve of PTTG1 expression. (H-J) PTTG1 expression in cancer versus non-cancer tissues in the GSE63459, GSE30219, and GSE19188 cohorts. (K) Representative IHC images from the Human Protein Atlas database demonstrate PTTG1 protein expression in lung adenocarcinoma (https://www.proteinatlas.org/ENSG00000164611-PTTG1/cancer/lung+cancer#img) and normal tissue (https://www.proteinatlas.org/ENSG00000164611-PTTG1/tissue/lung#img). **, P<0.01; ***, P<0.001. AUC, area under the curve; CI, confidence interval; DSS, disease-specific survival; IHC, immunohistochemical; LUAD, lung adenocarcinoma; N, node; OS, overall survival; PTTG1, pituitary tumor transforming gene 1; ROC, receiver operating characteristic; TPM, transcript per million.

Figure 2 High PTTG1 expression is associated with poor prognosis. (A-C) Survival curves of the effect of PTTG1 expression on overall survival, disease-specific survival, and progression-free survival of LUAD patients. (D-F) Survival curves of patients with pathologic staging T-stage, N-stage, and M-stage LUAD. (G) Survival curves for patients with PD/SD LUAD. (H-K) Survival curves of LUAD patients by gender, age, and whether they smoke. (L) Time-dependent ROC curves at 1, 3, and 5 years in the TCGA-LUAD cohort. (M-P) Survival curves for PTTG1-expressing subgroups in the GSE30219, GSE31210, GSE5008, and GSE37745 cohorts. AUC, area under the curve; CI, confidence interval; DSS, disease specific survival; FPR, false positive rate; HR, hazard ratio; LUAD, lung adenocarcinoma; M, metastasis; N, node; PSI, progress free interval; PTTG1, pituitary tumor transforming gene 1; T, tumor; TCGA, The Cancer Genome Atlas; TPR, true positive rate; os, overall survival.

PTTG1-related pathway enrichment and immune microenvironment analysis

Next, we sought to explore the potential mechanisms by which PTTG1 promotes malignant progression in LUAD. After high and low grouping based on the median number of PTTG1, we identified 1,008 DEGs from the TCGA-LUAD cohort, including 441 up-regulated and 567 down-regulated genes. GO and KEGG enrichment analyses were first performed, which revealed that these PTTG1-associated DEGs were mainly involved in the processes of cell cycle, mitotic regulation, and neuroactive ligand interactions (Figure 3A). Further GSEA enrichment analysis showed that high PTTG1 was positively correlated with the metabolism of amino acids and their derivatives, the CCL18 signaling pathway, and the β-catenin signaling pathway (Figure 3B). Then, to further explore the specific effects of PTTG1 on the LUAD microenvironment, we analyzed the differences in the distribution of immune cells in the TME of LUAD patients by seven immune infiltration algorithms. Heatmap results showed that high expression of PTTG1 had higher infiltration of fibroblasts/CAFs (EPIC and XCELL algorithms); whereas, the low expression group of PTTG1 had higher immune scores and had more infiltration of B cells and CD8⁺ T cells (EPIC and TIMER algorithms) (Figure 3C). Correlation analysis also showed that PTTG1 was significantly positively correlated with Th2 cells and negatively correlated with CD8T cells and Th17 cells as assessed by the ssGSEA algorithm (Figure 3D).

Figure 3 Analysis of PTTG1-related enrichment pathways and immune cell infiltration. (A) Enrichment bar graph showing the results of enrichment analysis of 20 significantly enriched GO and KEGG pathways. (B) GSEA enrichment analysis of the PTTG1 low-expression group versus the high-expression group. (C) Heatmap showing the differences in immune cell infiltration between PTTG1 high and low expression groups assessed by multiple immune infiltration algorithms. (D) Correlation of PTTG1 with immune cells. *, P<0.05; **, P<0.01; ***, P<0.001; ns, not significant. BP, biological process; CC, cellular component; EPIC, Estimation of Proportions of Immune and Cancerous cell types; FDR, false discovery rate; GO, Gene Ontology; IPS, immunophenoscore; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; NES, normalized enrichment score; PTTG1, pituitary tumor transforming gene 1; TIMER, Tumor Immune Estimation Resource.

PTTG1 promotes LUAD progression through activation of the WNT/β-catenin signaling pathway

To further characterize the role of PTTG1 in LUAD cell growth, we generated a PTTG1 knockout (KO) A549 cell line using the CRISPR-Cas9 system. Sequencing results confirmed fragment loss or code-shift mutations in early exons in KO cells compared to wild-type (WT) sequences (Figure S1A). WB assay results showed significant down-regulation of protein expression after PTTG1 knockdown (Figure 4A). CCK-8 assays and colony formation assays were then performed to detect the proliferative capacity of the cells, and we found that the proliferative capacity of PTTG1-KO cells was significantly reduced (Figure 4B,4C). Transwell and wound healing assays further showed that the migratory and invasive capacities of PTTG1-KO cells were significantly down-regulated compared to PTTG1-WT (Figure 4D,4E). In addition, cell cycle analysis showed that knockdown of PTTG1 blocked the cell cycle at the G1 phase (Figure S1B,S1C); we also examined the effect of PTTG1 on the EMT process, and the WB results demonstrated that E-cadherin, N-cadherin, and Snail proteins were significantly down-regulated and overexpression up-regulated after knockdown of PTTG1 (Figure S1D,S1E). Our functional experiments described above suggest that PTTG1 promotes LUAD progression; however, the exact mechanisms involved have not been elucidated. To determine the clear pathway by which PTTG1 affects LUAD progression, we prepared control cells for transcriptome sequencing of A549 cells, and after the knockdown of PTTG1. PCA down-representation showed a significant difference between the WT and KO groups (Figure 4F), and we found that the expression of 1,172 genes was significantly altered, including the down-regulation of 872 genes and the up-regulation of 300 genes (Figure 4G). GO enrichment analysis showed that the relevant DEGs were associated with cell migration regulation, synaptic organization, extracellular matrix, etc. (Figure 4H). KEGG analysis further indicated that PTTG1 was closely associated with oncogenic signaling pathways such as MAPK and WNT (Figure 4I). The next WB results showed that down-regulation of PTTG1 in A549 and H1299 cells significantly decreased the expression of phosphorylated β-catenin (ser675), whereas total β-catenin was unchanged, and the downstream effector molecules, c-myc and cyclinD1, were significantly down-regulated; the expression of pro-apoptotic protein Bax was up-regulated, and that of the inhibitory protein Bcl2 was down-regulated. In contrast, overexpression of PTTG1 had the opposite effect (Figure 5A-5E). Thus, these results suggest that PTTG1 activates the WNT/β-catenin signaling pathway in LUAD cells.

Figure 4 PTTG1 KO functional phenotyping assay and RNA-seq. (A) WB verification of PTTG1 knockdown efficiency. (B) CCK-8 assay demonstrated the effect of PTTG1 knockdown on LUAD cell proliferation. (C) Colony formation assay demonstrated the effect of PTTG1 knockdown on LUAD cell proliferation (crystal violet staining). (D) Transwell assay demonstrated the effect of PTTG1 knockdown on LUAD cell migration (crystal violet staining). (E) Wound healing assay revealed the effect of PTTG1 knockdown on LUAD cell migration. (F) PCA analysis showed significant differences between the two groups. (G) Volcano plots showing the DEGs identified by DESeq2 differential analysis of PTTG1 WT and KO groups. (H,I) GO and KEGG enrichment analysis of PTTG1-associated DEGs. ***, P<0.001. BP, biological process; CC, cellular component; CCK-8, Cell Counting Kit-8; ECM, extracellular matrix; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; KO, knockout; LUAD, lung adenocarcinoma; MF, molecular function; OD, optical density; PCA, principal component analysis; PTTG1, pituitary tumor transforming gene 1; RNA-seq, RNA sequencing; WB, western blot; WT, wild-type.

Figure 5 Western blot detection of PTTG1 regulation of the WNT/β-catenin signaling pathway. (A-C) WB detection of the effects of PTTG1 knockdown or overexpression in A549 cells on the WNT/β-catenin pathway and apoptosis. (D,E) WB detection of the effects of PTTG1 knockdown or overexpression in H1299 cells on the WNT/β-catenin pathway and apoptosis. KO, knockout; NC, negative control; PTTG1, pituitary tumor transforming gene 1; WB, western blot; WT, wild-type.

PTTG1 affects the malignant behavior of LUAD cells through DLX2

To explore the downstream target genes of PTTG1, we first analyzed the gene expression profiles of PTTG1 high and low expression groups in TCGA and detected 1,008 DEGs, including 567 down-regulated genes. Then, with identified 872 down-regulated DEGs were identified by RNA-seq. At the same time, we obtained the EMT core gene set from the EMT-related database dbEMT, and found that there was 1 overlapping gene, DLX2, in the TCGA down-regulated genes and genes down-regulated in the RNA-seq analysis with the EMT-related gene set (Figure 6A). First, mRNA for DLX2 in the TCGA cohort was highly expressed in tumor tissues and was associated with significantly shorter OS, DSS, and PFI in patients with high DLX2 expression (Figure S1F). In both TCGA and GSE50081 cohorts, PTTG1 showed a significant positive correlation (r>0.2, P<0.05) with the mRNA level of DLX2 (Figure 6B,6C). RT-qPCR further confirmed the positive correlation between PTTG1 and DLX2 (Figure 6D,6E). We hypothesize that PTTG1 promotes the proliferation, invasion, and migration of LUAD cells by regulating DLX2 protein expression. To validate this hypothesis, we performed rescue experiments using A549 and H1299 cells with different combinations of PTTG1 and DLX2 expression levels (Figure 6F,6G). Immunofluorescence analysis revealed colocalization of PTTG1 with DLX2 in both A549 and H1299 cells (Figure 6H,6I). Immunoprecipitation assays (endogenous and exogenous levels) confirmed that PTTG1 protein interacts with DLX2 protein (Figure 6J,6K). The effects of these interactions on cell viability, proliferation, invasion, and migration were evaluated using CCK-8, colony formation, scratch healing, and Transwell assays, respectively. Results showed that DLX2 knockdown significantly reversed PTTG1 overexpression-induced increases in cell viability (Figure 6G), proliferation capacity (Figure 7A), and invasive migration ability (Figures 7B,7C,8A,8B). These findings indicate that PTTG1 regulates the biological behavior of LUAD cells by modulating DLX2 protein expression.

Figure 6 Screening of downstream targets for PTTG1. (A) Venn diagram of down-regulated genes in DEGs in TCGA-LUAD, RNA-seq of 872 down-regulated DEGs, and EMT-related genes intersecting genes. (B) Scatterplot of the correlation between PTTG1 and DLX2 expression in the TCGA-LUAD cohort. (C) Correlation scatter plot of PTTG1 with DLX2 expression in the GSE50081 cohort. (D,E) Changes in mRNA expression of PTTG1 and DLX2 in A549 and H1299 cells after knockdown of PTTG1. (F) WB detection of protein levels of p-β-catenin, β-catenin, PTTG1, and DLX2. (G) CCK-8 assay to detect changes in the proliferative capacity of LUAD cells transfected with DLX2 siRNA and overexpressing PTTG1. (H,I) PTTG1 significantly colocalizes with and interacts with DLX2 in lung adenocarcinoma cells. (J,K) The Co-IP experiments suggested that PTTG1 could interact with DLX2. **, P<0.01; ***, P<0.001. CCK-8, Cell Counting Kit-8; Co-IP, co-immunoprecipitation; DAPI, 4’,6-diamidino-2-phenylindole; DEG, differentially expressed gene; DLX2, distal-less homeobox 2; EMT, epithelial-mesenchymal transition; IgG, immunoglobulin G; LUAD, lung adenocarcinoma; mRNA, message RNA; NC, negative control; OD, optical density; OE, overexpression; PTTG1, pituitary tumor transforming gene 1; RNA-seq, RNA sequencing; TCGA, The Cancer Genome Atlas; TPM, transcript per million; WB, western blot.

Figure 7 Functional assays of PTTG1 overexpression and DLX2 knockdown. (A) Colony formation assay of LUAD cells transfected with DLX2 siRNA and overexpressing PTTG1. (B,C) Wound healing assay to assess changes in migration ability of A549 and H1299 cells after interference with DLX2 and PTTG1 overexpression. *, P<0.05; **, P<0.01; ***, P<0.001. DLX2, distal-less homeobox 2; LUAD, lung adenocarcinoma; NC, negative control; OE, overexpression; PTTG1, pituitary tumor transforming gene 1.

Figure 8 PTTG1 promotes the growth of LUAD cells in vivo. (A,B) Transwell migration and invasion assay to assess the changes in migration ability of A549 and H1299 cells after DLX2 interference and PTTG1 overexpression treatment (crystal violet staining). (C) Effect of PTTG1 knockdown on tumor growth in nude mice. (D) Effect of PTTG1 knockdown on tumor volume in nude mice. (E) Effect of PTTG1 knockdown on tumor weight in nude mice. (F) Western blot detection of the effect of PTTG1 knockdown on the protein expression of Ki-67, p-β-catenin, DLX2, and PTTG1 in tumor tissues. **, P<0.01, ***, P<0.001. DLX2, distal-less homeobox 2; KO, knockout; NC, negative control; OE, overexpression; PTTG1, pituitary tumor transforming gene 1; WT, wild-type.

PTTG1 knockdown inhibits tumor growth and WNT pathway activity

To further verify the effect of PTTG1 proliferative ability in vivo, we constructed a subcutaneous xenograft model using BALB/c nude mice. Tumor growth rate, tumor volume, and tumor weight of PTTG1 KO group were significantly lower than those of the control group (Figure 8C-8E). Western blot analysis revealed that the protein expression levels of PTTG1, DLX2, and phosphorylated β-catenin (p-β-catenin) in tumor tissues from the PTTG1 KO group were significantly lower than those in the control group (Figure 8F). These experiments collectively demonstrate that PTTG1 promotes the proliferation of LUAD cells in vivo.

Discussion

LUAD, the most predominant subtype of non-small cell lung cancer, remains the leading cause of malignancy-related death worldwide (27). Existing studies have confirmed that the malignant progression of LUAD is the result of the synergistic regulation of multiple genes and pathways, in which the abnormal activation of signaling pathways is the core mechanism leading to uncontrolled proliferation, invasion, and metastasis of tumor cells (28-31). As a classical oncogenic pathway, the abnormal activation of the WNT/β-catenin pathway is closely related to the EMT process, the maintenance of stem-cell-like properties, and the resistance to chemotherapy in LUAD, and the search for key regulatory molecules upstream of the pathway has become a major challenge for the development of LUAD. The search for key regulatory molecules upstream of this pathway has become a key direction for LUAD mechanism research and target development (32-34).

PTTG1, as a key securin, not only participates in the cell cycle and regulates tumor proliferation, but also may be involved in the EMT process, which in turn promotes tumor metastasis and malignancy (35-37). In addition, PTTG1 is also a functional target of various microRNAs, which can inhibit the expression of PTTG1 by binding to its mRNA, thus affecting tumor cell proliferation, apoptosis, metastasis, and other biological behaviors (38). Recent studies have shown that PTTG1 is highly expressed in a variety of cancers and mediates tumor progression. Zhou et al. revealed that PTTG1 activates the mTOR pathway through the upregulation of asparagine metabolism mediated by asparagine synthetase (ASNS), which promotes the proliferation of hepatocellular carcinoma cells and the progression of hepatocellular carcinoma (14). It was also found that PTTG1 drives colorectal cancer progression through the SKA3/PTTG1/c-MYC signaling loop (39). In addition, PTTG1 promotes aerobic glycolysis and the migration capacity of pancreatic cancer cells by activating the c-MYC signaling pathway (40). Nevertheless, PTTG1 still needs to be studied urgently in the field of LUAD. DLX2, as a key regulator during embryonic development, is abnormally expressed in a variety of cancers in recent years, and is involved in tumorigenesis and development by regulating cell proliferation, apoptosis, invasion, and metastasis (41-43). However, the specific function and regulatory mechanism of DLX2 in LUAD have not been clarified. In bioinformatics analysis, we found that both PTTG1 and DLX2 are highly expressed in LUAD and are highly correlated with poor prognosis, and they may play a synergistic role in the malignant progression of LUAD. In a previous study, we noted a potential link between PTTG1 and amino acid metabolism (13), and GESA enrichment analysis also yielded consistent results: high PTTG1 was significantly enriched for amino acid and its derivatives metabolic pathways, as well as the CCL18 signaling pathway. Whereas CCL18 is produced by tumor-associated macrophages (TAMs), its overexpression has been associated with reduced survival in patients with a variety of cancers and is highly correlated with tumor immunosuppression (44). Immune infiltration analyses using multiple independent computational methods consistently indicated an association between PTTG1 expression and immune cell infiltration patterns, including CD8⁺ T cells. Although these observations support a potential role of PTTG1 in shaping the tumor immune microenvironment, they currently represent correlative evidence, and further validation using tissue-based approaches will be required to establish a causal relationship.

Aberrant activation of the WNT/β-catenin signaling pathway plays a central role in cancer development by regulating cell proliferation, apoptosis, invasion and metastasis, stem cell properties, and angiogenesis (45-47). The molecular mechanisms by which specific molecular targets and WNT pathway activation promote tumor progression have been previously reported. In addition, phosphorylated β-catenin (ser675) is one of the key phosphorylated forms of β-catenin, which usually enhances its nuclear translocation ability and binding efficiency to transcription factors (TCF/LEF), and thus positively regulates WNT/β-catenin pathway activation (48-51). In the present study, modulation of PTTG1 expression led to concordant changes in β-catenin transcriptional output. Specifically, PTTG1 knockdown resulted in reduced phosphorylation of β-catenin at Ser675 and decreased expression of the canonical β-catenin target genes c-MYC and cyclin D1, while total β-catenin protein levels remained unchanged. Conversely, PTTG1 overexpression enhanced p-β-catenin (Ser675) levels and upregulated these downstream targets. Phosphorylation of β-catenin at Ser675 has been shown to promote its transcriptional competence by facilitating co-activator recruitment and target gene expression, independent of changes in total β-catenin abundance. In this context, the coordinated regulation of p-β-catenin (Ser675) and established β-catenin-responsive genes upon PTTG1 modulation is consistent with enhanced WNT/β-catenin signaling activity at the transcriptional level. Although additional direct assessments of β-catenin nuclear localization or TCF/LEF reporter activity were not performed in the present study, the observed changes in downstream transcriptional outputs provide functional evidence supporting a positive regulatory role of PTTG1 in β-catenin-dependent signaling. Further studies using genetic or pharmacological inhibition of β-catenin signaling will help to refine the mechanistic framework of this regulatory interaction. Furthermore, we examined the role of DLX2 in the WNT/β-catenin signaling pathway. Similarly, knockdown of DLX2 decreased the expression of p-β-catenin (ser675), c-myc, and cyclin D1, which are key proteins, whereas overexpression of PTTG1 reversed this result, suggesting that PTTG1 overexpression attenuated the inhibitory effect of DLX2 knockdown on WNT/β-catenin signaling. To clarify the downstream molecular mechanisms by which PTTG1 regulates the malignant progression of LUAD, we confirmed by Co-IP and immunofluorescence colocalization experiments that there is a specific protein interaction between PTTG1 and DLX2 in LUAD cells, and a series of rescue experiments also demonstrated that PTTG1 mediates the role of WNT/β-catenin activation through DLX2 in the development of LUAD. Notably, modulation of PTTG1 expression led to concordant changes in both DLX2 mRNA and protein levels, indicating that PTTG1 positively regulates DLX2 expression. Together with the observed physical interaction and functional rescue, these data suggest that PTTG1 regulates WNT/β-catenin signaling predominantly through controlling DLX2 abundance and activity. PTTG1, as a key molecule in cell cycle regulation, has a close relationship with tumor cell proliferation (52,53). To further confirm the role of PTTG1 in LUAD progression, we performed subcutaneous tumor formation experiments in nude mice. The results showed that knockdown of PTTG1 significantly inhibited tumor growth. Western bolt analysis further confirmed the regulatory role of PTTG1 on DLX2 and WNT signaling pathways.

Although the present study initially elucidated the molecular mechanism of PTTG1-DLX2-WNT/β-catenin, there are still some limitations, and the upstream regulators of PTTG1 self-expression still need to be explored. In terms of research model and clinical relevance, this study mainly relied on A549 cells (KRAS mutant) and H1299 cells (P53-deficient), and needs to be repeated in EGFR mutant (e.g., PC9, H1975) and other cells to validate, and expand the clinical bed sample cohort to test the correlation between PTTG1 and DLX2 protein expression and its impact on patient prognosis. In the in vivo mechanism study, the complexity of the TME was not taken into account in the nude mouse tumorigenic experiments, and a mouse model with a normal immune response or an in situ transplantation model needs to be constructed to explore the regulatory role of PTTG1 on the immune microenvironment and the interaction of the PTTG1-DLX2 axis with other signaling pathways. Of course, due to factors such as transcriptomics methods and cell line selection, PTTG1 may have downstream targets beyond DLX2, which warrants further investigation.

Conclusions

In conclusion, this study demonstrates that PTTG1 promotes the malignant progression of LUAD by interacting with DLX2 and activating the WNT/β-catenin signaling pathway. PTTG1 expression is associated with poor prognosis and may serve as a potential molecular marker and therapeutic target for LUAD. These findings provide a theoretical basis and experimental evidence for the development of targeted therapies and precision medicine strategies in LUAD.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the ARRIVE and MDAR reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0165/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0165/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0165/prf

Funding: This study was supported by the Research Funds of Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), Hefei Comprehensive National Science Center (No. 2023bydjk001); Anhui Provincial Major Science and Technology Project for Key Laboratory (No. S202305a12020038); the Open Research Fund Project of Anhui Province Key Laboratory of Immunology in Chronic Diseases (No. KLICD-2023-Z4); National Innovation Program for College Students (No. 202210367076); Anhui Provincial Undergraduate Innovative Training Program (No. S202310367018); Open Research Fund Project of Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease (Nos. HX2023D01 and HX2023D02); and Anhui Provincial Health Research Project (No. AHWJ2023A20289).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0165/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. All animal experiments were conducted under the project approval (No. [2024] KY031) issued by the Ethics Committee of Bengbu Medical University and in accordance with the university’s guidelines on the care and use of animals.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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