Identification of STMN1 as a lactylation‑related driver of lung cancer progression using Mendelian randomization

Introduction

The incidence of lung cancer is increasing worldwide (1). Notably, lung cancer is strongly associated with tobacco exposure, and it is characterized by a high tumor mutational burden, rapid disease progression, high propensity for recurrence and metastasis, and poor overall survival (OS) (2,3). Immune checkpoint inhibitors (ICIs) target signaling pathways such as programmed cell death protein 1/programmed cell death ligand 1 and cytotoxic T-lymphocyte-associated protein 4, and offer renewed optimism for lung cancer treatment (4); however, their immunotherapeutic efficacy remains suboptimal. This is primarily due to the complex tumor microenvironment (TME) of lung cancer, which is characterized by low immunogenicity and prominent immune evasion (5). Therefore, to overcome the current challenges in lung cancer treatment, in-depth investigations are required into the molecular pathogenesis of lung cancer and the identification of target genes with clinical translational potential.

Lactate is a crucial metabolite within the TME (6) and lactate metabolism, driven by the Warburg effect, is a major pathway that undergoes alterations in cancer (7). This metabolic alteration leads to the overproduction of lactate, even in the presence of sufficient oxygen; this boosts tumor cell proliferation and creates an immunosuppressive TME (8). Increased lactate levels hinder immune function by altering the pH of the TME, thereby impairing the activity of cytotoxic T lymphocytes and dendritic cells (9). Additionally, lactate acts as the substrate for histone lactylation, a novel post-translational modification that has far-reaching effects on gene regulation. Histone lactylation participates in immune evasion by altering gene expression in the immune cells within the TME (10). This leads to increased expression of genes that inhibit immune activity, thereby assisting tumors in evading immune surveillance (11,12). For example, LDH-mediated histone H3K18 lactylation upregulates Nur77 to drive immune escape in small cell lung cancer (13,14).

Traditional observational studies are susceptible to confounding factors and reverse causality, hindering the establishment of conclusive gene-disease connections (15). Mendelian randomization (MR) overcomes this issue by employing genetic variants as instrumental variables (IVs). This method takes advantage of the random inheritance of these variants at birth to enhance causal inference. It allows for efficient deduction of causal links between exposures (such as gene expression and protein levels) and disease outcomes (such as lung cancer incidence) at the population level, while markedly reducing biases (16,17). Recently, MR analyses that integrate data from protein quantitative trait loci and expression quantitative trait loci (eQTL) have successfully identified numerous potential therapeutic targets in prostate cancer (18) and breast cancer (19), providing a novel paradigm for precision oncology investigations.

Given the critical roles of lactate metabolism and histone lactylation in tumor progression and immune regulation, the current study employed the MR approach to screen for lactylation-related genes, which may have a causal relationship with lung cancer. The present study aimed to offer novel perspectives for the mechanistic research and therapeutic target exploration of lung cancer.

Materials and methods

Study design and data sources

The present study employed a two-sample MR method. The eQTL datasets of lactylation-related genes were used as exposure variables, and the genome-wide association study (GWAS) summary statistics of lung cancer were used as outcome variables. As described previously (20–23), a catalog of 46 lactylation-related genes was compiled.

eQTL exposure data

The eQTL exposure data were acquired from the IEU OpenGWAS Project (https://opengwas.io/), a global public database. Single nucleotide polymorphisms (SNPs) exhibiting a strong association with the expression of the 46 lactylation-related genes (P<5×10−8) were selected as IVs. To avoid weak instrument bias, only SNPs with an F-statistic >10 were included as IVs, ensuring a strong association between the IVs and gene expression.

Lung cancer outcome data

The lung cancer outcome data were sourced from the Finnish database (finngen_R12_C3_lung cancer_EXALLC; http://r12.finngen.fi/pheno/C3_lung_cancer_EXALLC), which included 909 lung cancer cases and 3,788,794 healthy controls. The GWAS summary statistics in this database contained ~21,325,071 SNPs.

MR analysis

The MR analyses were performed using R software (version 4.4.2; http://cran.r-project.org/bin/windows/base/old/4.4.2/) packages ‘TwoSampleMR’ (version 0.5.6; http://CRAN.R-project.org/package=TwoSampleMR),‘ieugwasr’ (version 0.1.7; http://CRAN.R-project.org/package=ieugwasr) and ‘MRInstruments’ (version 0.4.0; http://CRAN.R-project.org/package=MRInstruments). IV selection was conducted by retaining SNPs strongly associated with lactylation-related gene expression (P<5×10-8) and with an F-statistic>10 to avoid weak instrument bias; SNPs were then clumped to remove linkage disequilibrium (LD) using a threshold of r<0.001 and a window size of 1Mb to ensure independence. The processed eQTL data were used as the exposure dataset, and the lung cancer GWAS summary statistics were used as the outcome dataset. Prior to analysis, allele harmonization was performed to ensure consistency of allele orientation and effect direction between exposure and outcome datasets. Strand-ambiguous SNPs and those with conflicting effect directions were excluded. The specific methods employed in the present study included inverse variance weighting (IVW), MR Egger, weighted median, simple mode and weighted mode. For each gene, the causal impact size (β), P-value, standard error, 95% confidence interval (95% CI) and odds ratio were recorded. Subsequently, The heterogeneity and pleiotropy of the odds ratio were calculated using Cochran's Q test (for heterogeneity) and the MR Egger intercept test (for horizontal pleiotropy), followed by multivariate sensitivity analyses including leave-one-out validation and MR-PRESSO outlier detection. The reliability of the MR findings was confirmed using scatter diagrams, forest plots and leave-one-out sensitivity analysis. Funnel plots were generated to assess potential publication bias and the presence of outliers. The screening criteria for core causal genes were set as P<0.05 in the IVW method and uniform OR direction trends across all five analytical approaches.

Multiple testing correction

The multiple testing correction protocol was performed as follows: First, the IVW P-values of all lactylation-related genes were extracted from the MR analysis results to construct a raw P-value dataset. Second, the ‘p.adjust’ function in R software (version 4.4.2; r-project.org/) was used to implement false discovery rate (FDR) and Bonferroni corrections, with the correction base set as the number of effectively analyzed candidate genes; the FDR correction threshold was defined as 0.05 to balance false-positive and false-negative risks, whereas the Bonferroni correction threshold was set to 0.05 divided by the number of effectively analyzed genes as a strict verification standard. Finally, genes with corrected P-values below the corresponding thresholds were identified as significantly associated genes, and the screening criteria for core causal genes were updated to: IVW method P<0.05, consistent OR directions across all five MR analytical methods, and statistical significance after at least one multiple testing correction.

Transcriptomic analysis

The transcriptomic data, including five Gene Expression Omnibus (GEO) datasets, among which GSE6044 included nine lung cancer tissues and 5 normal lung tissues, GSE11969 included 9 lung cancer tissues and 5 normal lung tissues, GSE40275 included 15 lung cancer tissues and 41 normal lung tissues, and GSE43346 included 23 lung cancer tissues and 42 normal lung tissues. (ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6044; ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE11969; ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40275; ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE43346). Expression data from three independent studies: George et al (24), Cai et al (25) and Liu et al (26) were also included.

Analysis of expression and survival

RNA sequencing data from the GEO datasets underwent differential expression profiling using the R software package ‘limma’ (version 3.58.1; http://www.bioconductor.org/packages/release/bioc/html/limma.html). Log2 fold change (log2FC) and adjusted P-value (P.adj) for Stathmin1 (STMN1) were calculated in tumor vs. normal tissues derived from healthy individuals. The differential expression profiles were visualized using box plots. Kaplan-Meier survival analysis, paired with the log-rank test, was used to explore the relationship between STMN1 expression and OS, with all computations conducted in the ‘survival’ R package (version 3.5–8; http://CRAN.R-project.org/package=survival) and survival curves visualized for publication using ‘survminer’ (version 0.4.9; http://CRAN.R-project.org/package=survminer). The findings from multivariate Cox proportional hazards regression analysis were illustrated using a forest plot, which was generated with the ‘forestplot’ R package (version 2.0.5; CRAN.R-project.org/package=forestplot).

Immune correlations

The ‘MCPcounter’ (Microenvironment Cell Populations counter) method was used to measure the infiltration abundance of 10 immune cell subsets (version 1.0.0; http://github.com/ebecht/MCPcounter). All analyses were conducted using the George et al (24) dataset. Samples were dichotomized into the low and high STMN1 expression group by taking the median STMN1 expression level as the cut-off value. The abundances of major immune cell infiltration were further characterized using the Cell-type Identification By Estimating Relative Subsets of RNA Transcripts (‘CIBERSORT’ R package, version 1.0; cibersort.stanford.edu/) (27), MCPcounter (28), Estimation of Proportions of Immune and Cancer cells (EPIC) (‘EPIC’ R package, version 1.1.0; http://github.com/GfellerLab/EPIC) (29), Quantitative Transcriptomics for Immune cell Quantification (quanTIseq) (‘quanTIseq’ R package, version 2.1.0; http://icbi-lab.github.io/quanTIseq/) (30) and xCell algorithms (‘xCell’ R package, version 1.1; http://xcell.ucsf.edu/) (31). Moreover, the expression patterns of immune checkpoint molecules were compared between the high and low STMN1 expression groups. The ESTIMATE algorithm (‘ESTIMATE’ R package, version 1.0.13; http://CRAN.R-project.org/package=ESTIMATE) and Spearman's rank correlation analysis (‘stats’ R package, version 4.4.0; http://CRAN.R-project.org/package=stats) were applied to calculate the correlation coefficient (R) and significance (P-value) between STMN1 expression and stromal score, immune score, estimate score and tumor purity.

Mutation data processing

The association between STMN1 expression and lung cancer-related genomic alterations, including copy number variations (CNVs) and somatic single-nucleotide variants/indels, was evaluated using Spearman's rank correlation analysis and OR analysis. CNVs were obtained from cBioPortal (cbioportal.org/) and classified into amplification and homozygous deletion types. Somatic mutation data, including SNPs, and insertions and deletions, were obtained from the UCSC genome browser (https://genome.ucsc.edu/). The R software package ‘maftools’ was used for the data integration and analysis (version 2.14.0; bioconductor.org/packages/release/bioc/html/maftools.html), and the R software packages ‘read.maf’ and ‘oncoplot’ (integrated in maftools version 2.14.0) were used for file import facilitation and generating waterfall plot visualizations. The enrichment of mutated genes in patients with lung cancer and high or low STMN1 expression in oncogenic signaling pathways was also analyzed. All analyses were conducted using the George et al (24) dataset. Samples were dichotomized into the low STMN1 expression group and high STMN1 expression group by taking the median STMN1 expression level (9.477354) as the cut-off value. The Drug-Gene Interaction database (DGIdb) (32) was used to generate a hypothesis on mutant gene druggability and match the gene lists to drug-gene interactions.

Functional enrichment and pathway analysis

The STMN1-associated proteins with differential expression were identified, and their expression patterns were visualized using volcano plots and heatmaps. A protein-protein interaction (PPI) network was built using Cytoscape (version 3.10.4; cytoscape.org/Cytoscape) and the hub proteins within this network were identified using the cytoHubba plugin. All analyses were conducted using the Liu et al (26) dataset. Additionally, the top 20 proteins co-expressed with STMN1 were selected for in-depth investigation. Enrichment analysis was conducted on these proteins to clarify the potential mechanisms underlying the role of STMN1 in lung cancer. Prior to this analysis, Pearson correlation analysis was used to filter STMN1-associated genes based on the criteria of a correlation coefficient >0.3 and P<0.05. Subsequently, over-representation analysis (ORA) was performed by leveraging datasets from Kyoto Encyclopedia of Genes and Genomes (KEGG; genome.jp/kegg/), Reactome (reactome.org/), WikiPathways (https://www.wikipathways.org/) and Gene Ontology (GO; geneontology.org/Gene Ontology), with support from the R packages including ‘clusterProfiler’ (version 4.10.0; http://bioconductor.org/packages/clusterProfiler/), ‘org.Hs.eg.db’ (version 3.18.0; http://bioconductor.org/packages/org.Hs.eg.db/) and ‘ReactomePA’ (version 1.46.0; http://bioconductor.org/packages/ReactomePA/).

Cell lines and culture conditions

The RP1 cell line is a well-characterized immortalized lung cancer cell line established by immortalizing the primary lung cells isolated from RbL/L/Trp53L/L (Rb1 and Trp53 conditional double knockout) mice. Specifically, the cell line was derived from spontaneous lung cancer tumors induced by intranasal inhalation of adenovirus-mediated Cre recombinase in these knockout mice, which triggers specific deletion of Rb1 and Trp53 genes in lung tissues. Primary tumor cells were subsequently isolated from the induced lung cancer lesions and immortalized to generate the RP1 cell line, which has been verified to maintain stable proliferation and malignant phenotypes over continuous passages in vitro. This cell line was generously donated by the research group of Professor Hongbin Ji (Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, China) (33). The human lung cancer cell line DMS114, human lung sarcomatoid carcinoma cell line H196 and 293T cell line were purchased from The Cell Bank of Type Culture Collection of The Chinese Academy of Sciences. DMS114 and H196 were cultured in RPMI-1640 medium (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% fetal bovine serum (FBS; Gibco; Thermo Fisher Scientific, Inc.), 100 IU/ml penicillin and 100 µg/ml streptomycin. 293T cells were cultured in Dulbecco's Modified Eagle Medium (Gibco; Thermo Fisher Scientific, Inc.) supplemented with 10% FBS, 100 IU/ml penicillin, and 100 µg/ml streptomycin. All of the cells were maintained in a humidified incubator at 37°C with 5% CO2 and were passaged every 2–3 days once they reached 80–90% confluence.

The cell lines were authenticated by short tandem repeat (STR) profiling, and were confirmed to match reference STR profiles in the American Type Culture Collection (atcc.org/), DSMZ (dsmz.de/), Japanese Collection of Research Bioresources (cellbank.nibio.go.jp/) and ExPASY/Cellosaurus databases (cellosaurus.org/), verifying their identity and ruling out cross-contamination.

Construction of stable cell lines

Briefly, 293T cells were seeded in 10-cm culture dishes at a density of 5×106 cells/dish 24 h prior to transfection, ensuring cells reached 70–80% confluence. All lentiviral vectors (pLKO.1, pCDH/hygro, psPAX2, pMD2.G) were obtained from Addgene, Inc. Lentiviral packaging was performed using a third-generation packaging system.

For knockdown, short hairpin RNA (shRNA) sequences were inserted into the pLKO.1 lentiviral vector, and an empty vector was used as a negative control. Lentiviral packaging and infection were performed identically for both knockdown and overexpression constructs. For transfection, a total of 6 µg plasmid DNA was used at a ratio of lentiviral vector: psPAX2: pMD2.G=2:1:1. Transfection was performed using polyethylenimine (cat. no. 40820ES, Yeasen Biotech Co., Ltd.) at 37°C in a 5% CO2 incubator for 6 h. Lentiviral supernatants were collected at 48 h post-transfection and filtered through a 0.45 µm filter. RP1, DMS114 and H196 cells were infected with lentivirus at a multiplicity of infection of 10 for 24 h. The culture medium was replaced with fresh complete medium. Cells were cultured at 37°C in a 5% CO2 incubator for an additional 48 h before antibiotic selection. Stable knockdown cell lines were established by selection with 1 µg/ml puromycin for 2 weeks, and maintained in medium containing 0.5 µg/ml puromycin thereafter. The shRNA sequences are listed in Table I.

Table I. shRNA sequences.

Table I.

shRNA sequences.

shRNA	Targeting portion, 5′→3′
shNC	CCTAAGGTTAAGTCGCCCTCG
Mouse
STMN1-sh1	GCAGAAGAAAGACGCAAGTCT
STMN1-sh2	AGAAGGACAAGCACGTGGAAG
Human
STMN1-sh1	CTGGAGGAAATTCAGAAGAAA
STMN1-sh2	GAGCACGAGAAAGAAGTGCTT

[i] NC, negative control; sh, short hairpin; STMN1, stathmin 1.

For overexpression, the human STMN1 gene was amplified using PCR and then ligated with the digested pCDH/hygro lentiviral vector to construct a recombinant plasmid (STMN1-OE). For the negative control, an empty pCDH/hygro vector without the STMN1 insert was used to infect cells in parallel. The successful construction of the recombinant plasmid was then verified through transformation, single colony screening and Sanger sequencing. Subsequently, the recombinant plasmid was co-transfected with packaging plasmids into 293T cells for viral packaging, followed by the collection of lentivirus-containing supernatant. Ultimately, DMS114 and H196 cells were infected with the viral supernatant. Stable STMN1 overexpression cell lines were established by selection with 200 µg/ml hygromycin B for 10–14 days, and maintained in complete medium supplemented with 100 µg/ml hygromycin B.

Western blotting

Total proteins were isolated from cultured lung cancer cells (DMS114, H196, RP1) and mouse subcutaneous tumor tissue using radioimmunoprecipitation assay (RIPA) lysis buffer (supplemented with protease and phosphatase inhibitors). Histones were extracted employing the 0.2 mol/l H2SO4 method: Cell pellets were resuspended in histone extraction buffer [10 mmol/l HEPES (pH 7.9), 1.5 mmol/l MgCl2, 10 mmol/l KCl, 0.5 mmol/l DTT] and incubated on ice for 30 min. 5 mol/l H2SO4 to achieve a final concentration of 0.2 mol/l, and the mixture was incubated at 4°C overnight. Following centrifugation at 13,700 × g at 4°C for 10 min, 100% trichloroacetic acid was added to the supernatant to achieve a final concentration of 20%, followed by precipitation at 4°C for 2 h. The precipitate was collected and washed three times with cold acetone, lyophilized under vacuum and dissolved in histone lysis buffer [20 mmol/l Tris-HCl (pH 7.5), 7 mol/l urea, 2 mol/l thiourea, 4% CHAPS]. A total of 20 µg of total protein or 1 µg of histone protein were loaded per lane and separated by 12% SDS-PAGE gels. The proteins were then transferred to PVDF membranes, which were blocked with 5% skimmed milk for 60 min at room temperature to minimize non-specific antibody adsorption. For immunodetection, the blocked membranes were incubated with primary antibodies at 4°C for 16 h, followed by incubation with HRP-linked secondary antibodies for 1 h at room temperature. The protein bands were visualized using the Pierce ECL Western blotting Substrate (Thermo Fisher Scientific, Inc.), and the band optical density was analyzed using ImageJ software (version 1.53t; National Institutes of Health). The primary antibodies used in the present study were as follows: Anti-STMN1 (1:1,000; cat. no. TB2777;), anti-Bcl-2 (1:1,000; cat. no. T40056), anti-Vimentin (1:1,000; cat. no. T55134), anti-GAPDH (1:10,000; cat. no. P60037) (all from Abmart Pharmaceutical Technology Co., Ltd.), global histone lysine lactylation (Pan Kla) (1:1,000; cat. no. 1401), histone H3 lysine 18 lactylation (H3K18la) (1:1,000; cat. no. 1427RM) (both from PTM BIO LLC) and Histone H3 (1:1,000; cat. no. 17168-1-AP; Proteintech Group, Inc.). HRP-linked secondary antibodies used were horseradish peroxidase-conjugated goat anti-rabbit IgG (1:5,000; cat. no. SA00001-2; Proteintech Group, Inc.) and horseradish peroxidase-conjugated goat anti-mouse IgG (1:5,000; cat. no. SA00001-1; Proteintech Group, Inc.).

Co-immunoprecipitation (co-IP)

For IP, 500 µg total protein (200 µl of cell lysate) extracted from human lung cancer DMS114 and H196 was diluted to 500 µl with IP lysis buffer [20 mmol/l Tris-HCl (pH 7.5), 150 mmol/l NaCl, 1% Nonidet P-40 (NP-40), 1 mmol/l EDTA, supplemented with protease and phosphatase inhibitors]. The protein solution was pre-cleared with 20 µl protein A/G agarose beads (Santa Cruz Biotechnology, Inc.) at 4°C for 1 h to reduce non-specific binding. After centrifugation at 118,000 × g at 4°C for 10 min, the supernatant was incubated with 2 µg anti-STMN1 antibody (cat. no. TB2777; Abmart Pharmaceutical Technology Co., Ltd.) or 2 µg normal IgG (cat. no. 30000-0-AP; Proteintech Group, Inc.) as a negative control at 4°C overnight with gentle rotation. Subsequently, 30 µl protein A/G agarose beads were added and incubated for another 4 h at 4°C. The beads were washed four times with IP wash buffer [20 mmol/l Tris-HCl (pH 7.5), 300 mmol/l NaCl, 1% NP-40, 1 mmol/l EDTA]. After a final centrifugation at 78,400 × g at 4°C for 3 min, immune complexes were eluted by boiling with 2X SDS loading buffer for 10 min. The eluted proteins were then subjected to SDS-PAGE and western blotting as aforementioned.

Cell viability and colony formation assays

Cell viability was evaluated using the Cell Counting Kit-8 (CCK-8; cat. no. B34302; Selleck Chemicals). Briefly, DMS114 and H196 cells were seeded onto 96-well culture plates at a density of ~3,000 cells/well. After adhering to the plate, the cells were subjected to the required treatment: Continuous culture for an additional 48 h in a standard 37°C, 5% CO2 incubator under normal growth conditions. The CCK-8 reagent was then added according to the manufacturer's instructions at 37°C with 5% CO2 for 2 h. To assess cell viability, the absorbance value was measured at 450 nm wavelength using a microplate reader.

For the colony formation assay, DMS114 and H196 cells were seeded onto 6-well plates at a density of ~500 cells/well and maintained in culture for 14 days to allow colony formation. After 14 days, the cells were then washed with phosphate-buffered saline (PBS) to eliminate leftover medium. They were subsequently fixed in 4% paraformaldehyde (PFA) at room temperature for 15 min and stained with 0.1% crystal violet solution at room temperature for 20 min to make colonies visible. The cells were then re-rinsed with PBS to eliminate excess stain and finally air-dried at room temperature for subsequent colony counting and analysis. Colonies were defined as cell clusters containing >50 cells, and counting was performed using ImageJ software (version 1.53t; National Institutes of Health) in a blinded manner.

Wound-healing assay

DMS114 and H196 cells with stable STMN1-knockdown (STMN1-sh) or overexpression (STMN1-OE) were seeded onto 6-well plates. Subsequently, 2 ml culture medium (supplemented with 10% FBS) was added to each well, and the cells were cultured for 24 h to allow adherence and initial growth. Upon reaching 95–100% cell confluence, vertical scratches were made on the confluent cell monolayer using a sterile 200-µl pipette tip. The scratched cells and cell debris were gently rinsed off with PBS to ensure a clear scratch area. The plates were then refreshed with 2 ml serum-free media to minimize the interference of cell proliferation, and the cells were incubated for another 24 h. Cell migration was monitored by capturing images of the scratch areas at 0, 12 and 24 h under a light microscope (×4 magnification). For each experimental group, three random fields were imaged to ensure data reproducibility. The migration rate was calculated using the following formula: Migration rate (%)=[(Scratch width at 0 h-Scratch width at each time point)/Scratch width at 0 h] ×100.

Flow cytometric analysis

DMS114 and H196 cells were collected by centrifugation at 300 × g at 4°C for 5 min, after which, the supernatant was discarded and the pellet was washed with 1 ml PBS. The cells were then centrifuged again at 300 × g at 4°C for 5 min and the supernatant was discarded. The cell pellet was resuspended in 300 µl Stain Buffer (cat. no. 420201; Biolegend, Inc.), and PI-PE (cat. no. HY-D0815; MedChemExpress) and Annexin V-FITC (cat. no. 640949; Biolegend, Inc.) were added, at 4°C in the dark for 15 min. Cell apoptosis was detected using a FACSCalibur flow cytometer (BD Biosciences). Data were analyzed using FlowJo software (version 10.8.1, BD Biosciences). Early apoptosis and late apoptosis were combined, and the total apoptosis rate was calculated using the following formula: Total apoptosis rate (%)=early apoptotic cells (%) + late apoptotic cells (%).

Transwell migration and invasion cell assays

Cell migration and invasion were evaluated using the Transwell assays (0.4-µm pore-size inserts in 12-well plates). For the cell migration assay, ~3×104 DMS114 or H196 cells were suspended in 200 µl serum-free medium. This cell suspension was seeded into the upper chambers of Transwell inserts, whereas 600 µl medium supplemented with 10% FBS was added to the lower chambers as a chemoattractant. After incubating the cells at 37°C for 48 h, the non-migrated cells were removed from the upper surface of the inserts using cotton swabs. The migrated cells adhering to the lower surface were fixed in 4% PFA for 20 min and then stained with 0.1% crystal violet for 20 min at room temperature, followed by rinsing with PBS. Images of the cells were captured under a light microscope at ×10 magnification across three random fields, followed by cell counting.

For the cell invasion assay, the insert chambers were pre-coated with 50 µl Matrigel (diluted to 1:8 in ice-cold serum-free culture medium) at 37°C for 60 min to construct a matrix barrier layer. The subsequent protocol was identical to that described for the migration assay.

Subcutaneous tumor model

6-8-week-old male C57BL/6 mice (weight, 18~22 g) were purchased from the Laboratory Animal Center of Huazhong University of Science and Technology (Wuhan, China). A total of 15 mice were included All mice were housed at a temperature of 22±2°C, with a relative humidity of 50–60%, a 12 h light/12 h dark cycle, and free access to standard chow and water. In order to establish subcutaneous tumors, the stably transduced RP1 cells (transduced with NC, STMN1-sh1 and STMN1-sh2) were respectively inoculated into the right axilla (~1×106 cells in 100 µl PBS/mouse, n=5/group). Tumor dimensions were recorded every 4 days for a total of 24 days. Tumor volume was calculated using the following formula: Tumor volume=1/2 × length × width2. Humane endpoints were set as follows: i) tumor volume exceeding 2,000 mm3; ii) >20% body weight loss; iii) severe signs of distress or impaired mobility. No mice met these endpoints On day 24, the mice were euthanized by cervical dislocation. Death was confirmed by two indicators: i) Complete cessation of thoracic movement and breathing for ≥1 min; and ii) absence of hindlimb withdrawal reflex when gently pinched with forceps. After confirming death, the tumor tissues were dissected, weighed and processed: Tissues were fixed in 4% PFA) at room temperature for 24 h, followed by paraffin embedding and sectioning at a thickness of 4 µm. Some tissue were lysed with RIPA lysis buffer (supplemented with protease and phosphatase inhibitors) for protein extraction and western blotting.

Hematoxylin and eosin (H&E) staining

The paraffin-embedded sections of subcutaneous tumor tissues were prepared. After gradient deparaffinization with xylene and rehydration with ethanol at room temperature, the sections were stained with hematoxylin for 10 sec at room temperature. Subsequently, the tissue sections were differentiated in 1% hydrochloric acid-ethanol at room temperature for 20 sec, blued in 0.5% ammonia water for 2 min and counterstained with eosin for 3 min at room temperature. The tissue sections were then dehydrated with gradient ethanol, cleared with xylene and mounted using neutral balsam at room temperature. Images were captured using a light microscope (Olympus BX53, Olympus Corporation).

Immunofluorescence staining

The paraffin-embedded subcutaneous tumor tissue sections were deparaffinized with xylene, rehydrated with a graded series of ethanol and subjected to antigen retrieval using citrate buffer (pH 6.0) at 95°C for 15 min. The sections were then permeabilized with 0.2% Triton X-100 at room temperature for 10 min and blocked with 5% bovine serum albumin (BSA, cat. no. A1933, Sigma-Aldrich; Merck KGaA) at room temperature for 1 h. Subsequently, the sections were incubated with a primary antibody against Vimentin (cat. no. T55134; Abmart Pharmaceutical Technology Co., Ltd.) at a dilution of 1:200 at 4°C overnight, followed by incubation with Cy3-conjugated goat anti-rabbit IgG (cat. no. GB21303; Wuhan Servicebio Technology Co., Ltd.) at a dilution of 1:400 for 1 h at room temperature. The cell nuclei were counterstained with DAPI at room temperature for 5 min. Images of the cells were captured under a fluorescence microscope, and the mean fluorescence intensity was analyzed and semi-quantified using ImageJ software (version 1.53t; National Institutes of Health).

Immunohistochemistry

The paraffin-embedded subcutaneous tumor tissue sections were deparaffinized with xylene, rehydrated with a graded series of ethanol. Antigen retrieval was performed using citrate buffer (pH 6.0) under high-pressure at 121°C for 15 min to restore antigen epitopes. To eliminate endogenous peroxidase interference, the prepared sections were incubated with 3% hydrogen peroxide (H2O2) at room temperature for 10 min, and to block the non-specific antibody binding, they were treated with 5% BSA at room temperature for 60 min. The sections were then incubated with primary antibodies, including anti-Ki67 (cat. no. TW0001; Abmart Pharmaceutical Technology Co., Ltd.), anti-Caspase-3 (cat. no. 19677-1-AP; Proteintech Group, Inc.) and anti-Cleaved-Caspase-3 (C-Caspase-3; cat. no. AF7022; Affinity Biosciences) overnight at 4°C. The next day, the sections were incubated with HRP-conjugated secondary antibodies (cat. no. SA00001-2; Proteintech Group, Inc.) at room temperature for 1 h. DAB chromogen (cat. no. DA1010; Beijing Solarbio Science & Technology Co., Ltd.) was used for visualization of protein expression with subsequent counterstaining with hematoxylin at room temperature for 10 sec. Under an optical microscope, three random high-power fields (×40 magnification) were assessed for each section. Positive cells were semi-quantified using Image-Pro Plus 6.0 software (Media Cybernetics, Inc.). The percentage of positive cells was calculated using the following formula: Positive cells (%)=[(Number of positive cells in each field/Total number of cells in each field)] ×100, to assess protein expression levels. Integrated optical density (IOD) of positive staining was quantified using Image-Pro Plus 6.0 software (Media Cybernetics, Inc.). IOD represents the total optical density of positively stained areas, reflecting the overall expression level of the target protein. For C-Caspase-3, the IOD value was normalized to the IOD of total Caspase-3 to indicate the level of apoptotic activation.

TUNEL staining

Paraffin-embedded tumor tissue sections were deparaffinized in xylene and rehydrated through a graded ethanol series. Following antigen retrieval using proteinase K (20 µg/ml) for 20 min at 37°C, the sections were incubated with the TUNEL reaction mixture at 37°C for 60 min in the dark, according to the manufacturer's instructions (cat. no. C1088; Beyotime Biotechnology). Subsequently, the sections were mounted with an anti-fade mounting medium containing DAPI to visualize cell nuclei. Images were captured using a fluorescence microscope equipped with appropriate filter sets for FITC and DAPI detection (Olympus BX53, Olympus Corporation, Tokyo, Japan). To ensure representative quantification, at least 3 randomly selected non-overlapping fields of view per section were observed. The apoptotic index was calculated as the ratio of TUNEL-positive cells (green fluorescence) to the total number of cells (blue fluorescence).

Cell lactate concentration measurement

Cellular lactate concentrations were measured using Lactate Colorimetric Assay Kit (cat. no. K607-100, BioVision, Inc., according to the manufacturer's instructions. DMS114 and H196 cells with stable STMN1 knockdown or overexpression were seeded in 6-well plates at a density of 1×106 cells/well and cultured for 48 h. After washing twice with ice-cold PBS, cells were lysed in lactate assay buffer and centrifuged at 12,000 × g for 10 min at 4°C to remove cell debris. The supernatant was collected and incubated with the lactate detection reagent mix at 37°C for 30 min in the dark. The absorbance was measured at 570 nm using a microplate reader, and lactate concentrations were calculated based on a standard curve generated with known lactate concentrations.

Statistical analysis

Pearson correlation coefficient was used to assess linear associations between STMN1 expression and continuous variables with approximately normal distributions, such as the expression levels of co-expressed genes. Spearman's rank correlation coefficient was applied to evaluate non-linear or non-normally distributed relationships, including those between STMN1 expression and immune cell infiltration scores, stromal/immune/ESTIMATE scores, tumor purity and genomic alteration frequencies. ORA was utilized for functional enrichment assessments. For the in vitro and in vivo experimental data, the results are presented as the mean ± standard deviation. Comparisons between two groups were performed using unpaired Student's t-test, whereas those among multiple groups were performed using one-way analysis of variance (ANOVA). When ANOVA results were significant, Bonferroni's multiple comparisons test was used for post-hoc analysis, comparing each experimental group with the control group. All in vitro experiments were performed with three biological replicates. P<0.05 was considered to indicate a statistically significant difference. For MR analyses, additional multiple testing corrections were performed using the FDR and Bonferroni methods, with FDR-corrected P<0.05 regarded as statistically significant.

Results

MR analysis of lactylation-associated genes and lung cancer

From the eQTL data of 46 lactylation-related genes, the IVs meeting strict quality control criteria for MR analysis were identified: 63 SNPs for the platelet-type phosphofructokinase (PFKP) gene (F-statistic=315.6), 8 SNPs for the SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 (SMARCA4) gene (F-statistic=92.7) and 7 SNPs for the STMN1 gene (F-statistic=109.9). All of the IVs satisfied the strong instrument threshold (F>10). Following allele harmonization, no strand-ambiguous SNPs or SNPs with conflicting effect directions were detected. The detailed information on the finally included IVs is provided in Table II.

Table II. Basic information of instrumental variables selected from core lactylation-related genes.

Table II.

Basic information of instrumental variables selected from core lactylation-related genes.

Gene	No. of SNPs	P-value	Average F-statistic
PFKP	63	1.00×10−200−7.14×10−138	315.6
SMARCA4	8	2.04×10−82−8.55×10−13	92.7
STMN1	7	7.51×10−77−1.63×10−10	109.9

[i] PFKP, platelet-type phosphofructokinase; SMARCA4, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; SNPs, single nucleotide polymorphisms; STMN1, Stathmin 1.

To investigate the causal relationships between these three core lactylation-related genes and lung cancer, MR analyses were conducted using multiple statistical approaches, including IVW, MR Egger, weighted median, simple mode and weighted mode. Among these methods, IVW served as the main model for causal inference ('I), and the detailed quantitative results of all five MR analytical methods for each gene are summarized in Table III. IVW analysis revealed that PFKP (OR=0.909, 95% CI: 0.845–0.979) and SMARCA4 (OR=0.633, 95% CI: 0.409–0.979) were associated with a lower risk of lung cancer, whereas STMN1 (OR=1.741, 95% CI: 1.182–2.564) was associated with an elevated risk.

Table III. Results of MR analyses for core lactylation-related genes and small-cell lung cancer risk.

Table III.

Results of MR analyses for core lactylation-related genes and small-cell lung cancer risk.

A, PFKP
Analysis	No. of SNPs	β	SE	P-value	OR (95%CI)
MR Egger	63	−0.135	0.067	0.048	0.874 (0.766–0.996)
Weighted median	63	−0.071	0.057	0.209	0.931 (0.833–1.041)
IVW	63	−0.095	0.038	0.012	0.909 (0.845–0.979)
Simple mode	63	−0.110	0.083	0.191	0.896 (0.761–1.055)
Weighted mode	63	−0.087	0.061	0.159	0.916 (0.813–1.033)
B, SMARCA4
Analysis	No. of SNPs	β	SE	P-value	OR (95%CI)
MR Egger	8	−0.518	0.551	0.384	0.596 (0.202–1.754)
Weighted median	8	−0.514	0.265	0.053	0.598 (0.355–1.006)
IVW	8	−0.458	0.223	0.040	0.633 (0.409–0.979)
Simple mode	8	−0.711	0.442	0.152	0.491 (0.207–1.169)
Weighted mode	8	−0.551	0.288	0.098	0.576 (0.328–1.014)
C, STMN1
Analysis	No. of SNPs	β	SE	P-value	OR (95%CI)
MR Egger	7	0.414	0.453	0.402	1.513 (0.623–3.674)
Weighted median	7	0.419	0.242	0.084	1.520 (0.946–2.442)
IVW	7	0.555	0.198	0.005	1.741 (1.182–2.564)
Simple mode	7	0.782	0.370	0.079	2.185 (1.058–4.514)
Weighted mode	7	0.321	0.301	0.328	1.379 (0.764–2.489)

[i] CI, confidence interval; IVW, inverse variance weighting; MR, Mendelian randomization; OR, odds ratio; PFKP, platelet-type phosphofructokinase; SE, standard error; SMARCA4, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; SNPs, single nucleotide polymorphisms; STMN1, Stathmin 1.

Scatter diagrams were drawn to illustrate the detailed MR analyses results. The calculation of individual causal impacts demonstrated that increased SNP-mediated effects on STMN1 expression were associated with elevated lung cancer risk (Fig. 1A). Conversely, enhanced SNP effects on PFKP and SMARCA4 expression were linked to more pronounced protective effects against lung cancer (Fig. 1B and C). The effects of SNPs corresponding to each of the three core exposure factors (STMN1, PFKP and SMARCA4) on lung cancer risk were assessed by generating forest plots. Examining the effects of SNPs linked to each of the three key genes revealed consistent patterns across all analytical frameworks (Fig. 1D-F).

Figure 1. MR analysis and pleiotropy, heterogeneity and sensitivity analyses of lactylation-related genes and lung cancer. (A) Impact of SNPs on STMN1, (B) PFKP and (C) SMARCA4 expression and lung cancer risk. (D) Effect sizes of SNPs related to STMN1 on lung cancer risk. (E) Forest plot showing the effect sizes of SNPs related to PFKP and (F) SMARCA4 on lung cancer risk. (G) Leave-one-out sensitivity analysis plot for STMN1. (H) Leave-one-out sensitivity analysis plot for PFKP. (I) Leave-one-out sensitivity analysis plot for SMARCA4. (J) Funnel plot for assessing publication bias and outliers related to STMN1, (K) PFKP. (L) Funnel plot for assessing publication bias and outliers related to SMARCA4. CI, confidence interval; IVW, inverse variance weighting; MR, Mendelian randomization; OR, odds ratio; PFKP, platelet-type phosphofructokinase; SE, standard error; SMARCA4, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; SNPs, single nucleotide polymorphisms; STMN1, Stathmin 1.

Multiple testing correction results

A total of three genes with valid MR results underwent multiple testing correction. STMN1 was the only gene significantly associated with lung cancer risk in both correction methods. PFKP and SMARCA4 were only significant after FDR correction, but not after Bonferroni correction (Table IV).

Table IV. Multiple testing correction summary.

Table IV.

Multiple testing correction summary.

Gene	P-value	P-FDR	P-bonferroni	Significance-FDR	Significance-bonferroni
PFKP	0.0118	0.0178	0.0355	TRUE	FALSE
SMARCA4	0.0397	0.0397	0.1190	TRUE	FALSE
STMN1	0.0050	0.0150	0.0150	TRUE	TRUE

[i] FDR, false discovery rate; PFKP, platelet-type phosphofructokinase; SMARCA4, SWI/SNF-related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; STMN1, Stathmin 1.

Sensitivity, pleiotropy and heterogeneity evaluations for MR outcomes

To enhance the reliability of the MR analyses findings, the IVs underwent rigorous screening standards, and all the samples were confined to a European ancestry cohort to reduce the risk of false-negative results and population stratification bias. The heterogeneity across SNPs linked to the three exposure factors (STMN1, PFKP and SMARCA4) was evaluated using Cochran's Q test and the MR Egger approach. The results showed no notable heterogeneity among the SNPs corresponding to the three genes, thus verifying the stability of the selected IVs (Table V).

Table V. Heterogeneity and pleiotropy tests for core lactylation-related genes.

Table V.

Heterogeneity and pleiotropy tests for core lactylation-related genes.

	Heterogeneity test (IVW method)			Pleiotropy test (MR Egger intercept)
Gene	Q value	df	P-value	Intercept	SE	P-value
PFKP	59.83	62	0.554	0.012	0.017	0.471
SMARCA4	7.49	7	0.380	0.007	0.062	0.908
STMN1	1.65	6	0.949	0.016	0.048	0.744

[i] IVW, inverse variance weighting; MR, Mendelian randomization; PFKP, platelet-type phosphofructokinase; SE, standard error; SMARCA4, SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4; STMN1, Stathmin 1.

Moreover, the MR Egger intercept test was conducted to detect the occurrence of horizontal pleiotropy. The respective intercept values were 0.012, 0.007 and 0.016 for PFKP, SMARCA4 and STMN1 genes, respectively, with no statistical significance. This result indicated that MR estimates were not biased by directional pleiotropy. The MR-PRESSO test did not identify any outliers (Table V).

A leave-one-out sensitivity assessment was additionally performed to evaluate the impact of individual SNPs on the overall IVW estimate. Excluding a single SNP had a minimal effect on the overall causal estimate (Fig. 1G-I). In addition, funnel plots were symmetric (Fig. 1J-L), indicating no significant publication bias or outlier interference. This observation confirmed that no individual SNP exerted a disproportionate influence on the outcomes, thus enhancing the credibility and stability of the MR results.

Among the three core lactylation-related genes identified by MR analysis, STMN1 was the only gene that exhibited a significant causal association with increased lung cancer risk after both FDR and Bonferroni corrections, and its upregulation in lung cancer tissues was correlated with poor patient prognosis.

Expression, survival and functional enrichment analyses of STMN1

Expression analyses of the GEO datasets (GSE6044, GSE43346, GSE11969 and GSE40275) revealed that STMN1 was significantly upregulated in lung cancer tissues compared with in normal tissues (Fig. 2A). Furthermore, the patients exhibiting high STMN1 expression had a shorter OS time (Fig. 2B), and high STMN1 expression was associated with a higher risk of poor prognosis (Fig. 2C).

Figure 2. Prognostic and functional enrichment analyses of STMN1 expression in lung cancer. (A) Box plots showing STMN1 expression levels in lung cancer tissues and normal lung tissues across four Gene Expression Omnibus datasets (GSE6044, GSE43346, GSE11969 and GSE40275). (B) Kaplan-Meier survival curve analysis for the overall survival of patients with lung cancer based on STMN1 expression (high vs. low). (C) Forest plot of multivariate Cox proportional hazards regression analysis, depicting the association between STMN1 expression and the prognosis of patients with lung cancer. Bar plots of functional enrichment analyses. *P<0.05. (D) KEGG pathway enrichment, (E) Reactome pathway enrichment and (F) WikiPathways enrichment. The horizontal axis depicts the enriched gene count and the vertical axis depicts pathway names; the color gradient indicates-log10(P-value). KEGG, Kyoto Encyclopedia of Genes and Genomes; P.adj, adjusted P-value; STMN1, Stathmin 1.

Functional enrichment analyses were performed to investigate the biological roles of STMN1. KEGG pathway enrichment analysis (Fig. 2D) demonstrated significant enrichment in pathways, including ‘spliceosome’ and ‘ATP-dependent chromatin remodeling’. Reactome enrichment analysis (Fig. 2E) highlighted its enrichment in processes, including ‘processing of capped intron-containing pre-mRNA’, ‘mRNA splicing major pathway’ and ‘mRNA splicing’. WikiPathways enrichment analysis (Fig. 2F) revealed its role in the ‘mRNA processing’ pathway.

Immune correlations

Immune cell infiltration profiling revealed that the infiltration scores of B lineage cells, CD4+ T cells, fibroblasts, monocytic lineage cells and neutrophils were significantly lower in the high STMN1 expression group as compared with those in the low STMN1 expression group (Fig. 3A). The correlation analysis of STMN1 expression and immune checkpoint molecules further revealed an inverse association between STMN1 and most immune checkpoint molecules (Fig. 3B), suggesting a potential role of STMN1 in modulating checkpoint-mediated immune suppression. In order to validate the association between STMN1 and key antitumor immune cells, the infiltration abundances of CD4+ and CD8+ T cells and M1 macrophages were quantified using five independent algorithms (CIBERSORT, MCPcounter, EPIC, quanTIseq and xCell). All of the algorithms demonstrated a weak inverse correlation between STMN1 expression and the infiltration of these three cell types (Fig. 3C-E), highlighting the association between STMN1 and reduced antitumor immune cell infiltration. ESTIMATE algorithm-derived immune scores combined with Spearman's rank correlation analysis confirmed that high STMN1 expression was weakly negatively correlated with the overall immune score (r=−0.276; Fig. 3F). These results indicated that STMN1 high expression may be associated with the characteristics of an immunosuppressive TME in lung cancer, suggesting a potential role of STMN1 in regulating TME immune status.

Figure 3. Correlation between STMN1 expression and immune characteristics in lung cancer. (A) Bar plot comparing immune cell infiltration scores (quantified using the MCPcounter algorithm) between high and low STMN1 expression groups. (B) Heatmap of Spearman correlation coefficients linking STMN1 expression to immune checkpoint genes. The color gradient (blue to red) indicates the strength of correlation (negative to positive). (C) Association between STMN1 expression level and infiltration abundance of CD4+ T cells, quantified using five algorithms (CIBERSORT, MCPcounter, EPIC, quanTIseq and xCell). (D) Scatter diagram showing the association between STMN1 expression level and infiltration abundance of CD8+ T cells, quantified using five algorithms (CIBERSORT, MCPcounter, EPIC, quanTIseq and xCell). (E) Scatter diagram showing the association between STMN1 expression level and infiltration abundance of M1 macrophages, quantified using five algorithms (CIBERSORT, MCPcounter, EPIC, quanTIseq and xCell). (F) Scatter diagrams of Spearman correlation between STMN1 expression and Stromal Score, Immune Score, ESTIMATE Score and Tumor Purity (calculated using the ESTIMATE algorithm). Each plot includes the r- and P-values. *P<0.05, **P<0.01. CIBERSORT, Cell-type Identification By Estimating Relative Subsets of RNA Transcripts; EPIC, Estimation of Proportions of Immune and Cancer cells; MCPcounter, Microenvironment Cell Populations counter; quanTIseq, Quantitative Transcriptomics for Immune cell Quantification; STMN1, Stathmin 1.

Genomic alteration analyses of STMN1

Waterfall plots visualized the genetic mutation patterns of lung cancer samples across STMN1 expression subgroups. In the high STMN1 expression group (Fig. 4A), all 35 lung cancer samples (100%) harbored genetic mutations. Moreover, 34 of 36 samples (94.44%) in the low STMN1 expression group (Fig. 4B) exhibited genetic alterations, with mutation types comparable to those in the high STMN1 expression subgroup. OR analysis was performed to quantify the association between STMN1 expression and gene mutation status. Numerous genes, including TRIM58, GCN1L1, KIAA1239, LRP1, SLITRK3, APOB, GPRC6A and TP53, showed an OR of >1, indicating a higher mutation probability in the high STMN1 expression group. On the other hand, genes such as MYPN, COBL and LRFN5, had an OR of <1, suggesting a lower mutation probability with high STMN1 expression (Fig. 4C).

Figure 4. Genomic alterations and druggable target analysis based on STMN1 expression in lung cancer. Waterfall plots showing genetic mutation patterns in (A) high and (B) low STMN1 expression lung cancer subgroups. Distinct colors indicate various mutation categories. (C) Forest plot of OR analysis for gene mutation probability between STMN1-high and STMN1-low groups. Horizontal lines indicate 95% CIs and squares represent OR values. Treemaps showing the enrichment of key oncogenic signaling pathways in (D) high and (E) low STMN1 expression subgroups. Each rectangle represents a pathway, with its area proportional to the enrichment percentage; the sample number (N) and enrichment percentage of each pathway are annotated. Druggable target categories (analyzed using DGIdb) in (F) high and (G) low STMN1 expression groups. *P<0.05, **P<0.01. CI, confidence interval; DGIdb, Drug-Gene Interaction Database; OR, odds ratio; STMN1, Stathmin 1.

As compared with in the low STMN1 expression group (Fig. 4E), the high STMN1 expression group (Fig. 4D) was relatively highly enriched in the TP53 signaling pathway (94.12% vs. 83.78%), Hippo signaling pathway (79.41% vs. 72.97%), cell cycle (79.41% vs. 70.27%) and WNT signaling pathway (64.71% vs. 56.76%). By contrast, the enrichment levels of the RTK-RAS (76.47% vs. 78.38%) and NOTCH (76.47% vs. 75.68%) signaling pathways demonstrated no statistically significant difference between the two groups. The druggable categories in the lung cancer subgroups (high and low STMN1 expression groups) were analyzed using the DGIdb. In the high STMN1 subgroup, druggable categories included ‘serine threonine kinase’ (such as TTN), ‘transporter’ (such as RYR2) and TP53-related histone modification pathways, with ‘druggable genome’ involving COL11A1 and RB1 (Fig. 4F). The druggable categories in the low STMN1 expression subgroup included ‘serine threonine kinase’ (such as TTN), TP53-related RNA-directed DNA polymerases, ‘fibrinogen’ (TNR) and ‘drug resistance’ (TP53), with ‘druggable genome’ involving ABCA13 and COL11A1 (Fig. 4G). Despite the overlapping core elements, gene composition in druggable categories was different, suggesting that STMN1 expression may shape druggable target landscapes in lung cancer and guide subgroup-specific therapies.

Identification and functional analysis of proteins associated with STMN1 expression

To identify the proteins associated with varying STMN1 expression levels, the protein expression patterns were visualized using volcano plots and heatmaps. These analyses revealed 452 differentially expressed proteins, including 72 upregulated and 380 downregulated proteins (Fig. 5A and B). Next, a PPI network was drawn, and Cytoscape software was used to select hub proteins from the top 20 upregulated and top 20 downregulated proteins (Fig. 5C-F).

Figure 5. Identification and functional analysis of differentially expressed proteins in association with STMN1. (A) Volcano plot displaying proteins with differential expression between high and low STMN1 expression groups. (B) Heatmap illustrating the expression profiles of 452 expression-divergent proteins. (C) Top 20 upregulated hub proteins, ranked by MCC scores as calculated in Cytoscape. (D) PPI network of the top upregulated hub proteins identified by MCC scoring, with nodes colored to represent functional modules. (E) Bar plot of the top 20 downregulated hub proteins, ranked by MCC scores calculated in Cytoscape. (F) PPI network of the top downregulated hub proteins identified by MCC scoring, with nodes colored to represent functional modules. (G) Dot plot of GO enrichment analysis for STMN1-associated proteins. Bubble size indicates the gene counts, and the color gradient denotes-log10(P-value). FC, fold change; GO, Gene Ontology; MCC, maximal clique centrality; PPI, protein-protein interaction; STMN1, Stathmin 1.

Subsequent GO enrichment analysis showed that STMN1-associated proteins were involved in the ‘regulation of G0 to G1 cell cycle transition’ and ‘negative regulation of messenger RNA catabolic process’ (Fig. 5G). These results indicated that STMN1 could regulate cell cycle progression and maintain mRNA stability in lung cancer.

Effects of STMN1 on cell proliferation and migration in DMS114 and H196 cells

The effects of STMN1 expression were further assessed using cells transduced with NC, STMN1-sh1, STMN1-sh2, vector and STMN1-OE. Western blotting was performed to verify the efficiency of STMN1 knockdown and overexpression in DMS114 and H196 cells (Fig. 6A-D). In both cell lines, STMN1-sh1 and STMN1-sh2 significantly reduced STMN1 protein expression compared with the negative control group (Fig. 6A and C). Conversely, OE-STMN1 markedly increased STMN1 protein levels relative to the vector control (Fig. 6B and D). These results confirmed the successful construction of stable STMN1-knockdown and -overexpression cell lines for subsequent functional assays. Subsequently, CCK-8 assays demonstrated that STMN1 knockdown significantly suppressed cell proliferation in both DMS114 and H196 cells, whereas STMN1-OE transduction significantly accelerated cell proliferation (Fig. 6E-H). Moreover, wound healing assays showed that STMN1 knockdown markedly impaired cell migration in both DMS114 and H196 cells, whereas its overexpression significantly accelerated cell migration (Fig. 6I-L).

Figure 6. Effects of STMN1 on the proliferation and migration of DMS114 and H196 cells. (A) Western blot analysis was performed to assess STMN1 knockdown efficiency in DMS114 and H196 cells. (B) Western blot analysis was performed to assess STMN1 overexpression efficiency in DMS114 and H196 cells, with quantification of relative protein expression normalized to GAPDH. (C) Representative western blot images showing STMN1 protein expression in DMS114 and H196 cells following STMN1 knockdown, with GAPDH used as a loading control. (D) Representative Western blot images showing STMN1 protein expression in DMS114 and H196 cells following STMN1 overexpression, with GAPDH used as a loading control. (E) Proliferation of DMS114 and (F) H196 cells following STMN1 knockdown. (G) Proliferation of DMS114 and (H) H196 cells following STMN1 overexpression. (I) Migration of DMS114 cells following STMN1 knockdown and (J) overexpression. (K) Representative wound healing assays showing the migration of H196 cells following STMN1 knockdown. (L) Representative images and bar graph of wound healing assays showing the migration of H196 cells following STMN1 overexpression. Scale bar, 300 µm. *P<0.05, **P<0.01, ***P<0.001. NC, negative control; OE, overexpression; sh, short hairpin; STMN1, Stathmin 1.

Functional characterization of the effects of STMN1 on apoptosis, colony formation and cell invasion/migration

To characterize the functional role of STMN1 in lung cancer, its effects on apoptosis, colony formation and invasion/migration were assessed in DMS114 and H196 cells. Flow cytometric analysis showed that STMN1 knockdown significantly enhanced apoptosis in both cell lines. Total apoptosis rate was significantly higher in the STMN1-sh groups compared with the control group. By contrast, STMN1 overexpression modestly decreased apoptosis, indicating that the pro-apoptotic effect of STMN1 seems to be mainly achieved through its knockdown rather than overexpression (Fig. 7A-D). Colony formation assays revealed that STMN1 knockdown markedly diminished colony-forming ability, which was evidenced by fewer and smaller colonies compared with that in the NC group (Fig. 7E and F). By contrast, STMN1-OE transduction significantly enhanced this capacity. Additionally, Transwell assays demonstrated that STMN1 knockdown impaired invasive and migratory capacities (with fewer membrane-penetrating cells), whereas its overexpression significantly elevated these abilities (Fig. 7G-J).

Figure 7. Effects of STMN1 on apoptosis, colony formation, invasion and migration of DMS114 (human lung cancer cell line) and H196 (human lung sarcomatoid carcinoma cell line) cells. (A) Representative flow cytometry of Annexin V/PI staining for total apoptosis in DMS114 cells after STMN1 knockdown. (B) Representative flow cytometric dot plots and bar graphs of Annexin V/PI staining for early and late apoptosis in DMS114 cells after STMN1 overexpression (OE-STMN1) compared with empty vector control (Vector). (C) Representative flow cytometric dot plots and bar graphs of Annexin V/PI staining for total apoptosis in H196 cells after STMN1 knockdown (STMN1-sh1, STMN1-sh2) compared with negative control (NC). (D) Representative flow cytometric dot plots and bar graphs of Annexin V/PI staining for early and late apoptosis in H196 cells after STMN1 overexpression (OE-STMN1) compared with empty vector control (Vector). (E and F) Representative images and bar graphs of colony formation assays: Colony formation of DMS114 cells after STMN1 knockdown and OE. (G-J) Representative images and bar graphs of Transwell assays: Invasion and migration of DMS114 and H196 cells after STMN1 knockdown and OE. Scale bar, 120 µm. *P<0.05, **P<0.01, ***P<0.001. NC, negative control; OE, overexpression; sh, short hairpin; STMN1, Stathmin 1.

STMN1 knockdown inhibits tumor growth in vivo

A murine shRNA model targeting STMN1 was established to assess the in vivo effects of STMN1. As compared with in the NC group, both STMN1-sh1 and -sh2 groups exhibited reduced tumor volumes (Fig. 8A), decreased tumor weights (Fig. 8B) and slower tumor growth kinetics (Fig. 8C). The maximum tumor volume was 1,654 mm3, corresponding to a maximum diameter of 15 mm. H&E staining and immunohistochemical analysis revealed that STMN1 knockdown was associated with reduced expression of the proliferation marker Ki67, and elevated levels of the apoptosis marker C-Caspase-3/Caspase-3 in tumor tissues (Fig. 8D-F). Immunofluorescence staining demonstrated that Vimentin expression was reduced in the STMN1-sh1 and -sh2 groups, indicating impaired epithelial-mesenchymal transition (EMT) progression (Fig. 8G and H). The TUNEL assay further confirmed a significant increase in apoptotic cells in STMN1-knockdown tumors (Fig. 8I and J). Western blotting validated these findings, showing downregulated Vimentin and Bcl-2 (anti-apoptotic protein) expression levels in the STMN1-sh1 and -sh2 groups (Fig. 8K). These results were consistent with the enhanced apoptosis and suppressed EMT. STMN1 knockdown efficiency was also confirmed in tumor tissues (Fig. 8K). Collectively, these results indicated that STMN1 knockdown could induce tumor regression by suppressing cell proliferation, enhancing apoptosis and impairing EMT in vivo.

Figure 8. STMN1 knockdown inhibits tumor growth in vivo. Tumor growth characteristics of the STMN1-sh1/sh2 and NC group: (A) Representative images of subcutaneous tumors are shown (n=5/group). (B) Bar graph of tumor weights. (C) Line graph of tumor volume growth. (D) Representative H&E staining and immunohistochemical staining for Ki67, C-Caspase-3, and Caspase-3 in NC, STMN1-sh1, and STMN1-sh2 groups. Scale bar, 50 µm. (E) Bar graph showing the relative IOD of Ki67 staining in NC, STMN1-sh1, and STMN1-sh2 groups. (F) Bar graph showing the relative IOD of C-Caspase-3 normalized to total Caspase-3 in NC, STMN1-sh1, and STMN1-sh2 groups. (G) Representative immunofluorescence staining of vimentin in tumor tissues from the NC, STMN1-sh1 and STMN1-sh2 groups. Scale bar, 50 µm. (H) Bar graph of the relative integrated optical density of Vimentin immunofluorescence staining. (I) Representative images of TUNEL staining in tumor tissues from the NC, STMN1-sh1 and STMN1-sh2 groups. Scale bar, 50 µm. (J) Proportion of TUNEL-positive cells relative to total cells in tumor tissues. (K) Western blot analysis verifying the expression of STMN1, Vimentin and Bcl-2 (anti-apoptotic protein) in tumor tissues. GAPDH served as a loading control. **P<0.01, ***P<0.001. C-Caspase-3, cleaved-Caspase-3; H&E, hematoxylin and eosin; MFI, mean fluorescence intensity; NC, negative control; sh, short hairpin; STMN1, Stathmin 1; IOD, Integrated Optical Density.

STMN1 promotes histone lactylation

To elucidate the effect of STMN1 on histone lactylation, histones were extracted from DMS114 and H196 cells following STMN1 knockdown or overexpression. The levels of Pan Kla and H3K18la were decreased upon STMN1 knockdown and increased upon STMN1 overexpression, demonstrating that STMN1 promotes histone lactylation (Fig. 9A-D).

Figure 9. STMN1 promotes histone lactylation and undergoes lactylation modification itself. (A) Western blotting of Pan Kla and H3K18la in DMS114 cells with STMN1 knockdown (sh1, sh2) or NC, using Histone H3 as loading control. (B) Western blotting of Pan Kla and H3K18la in H196 cells with STMN1 knockdown (sh1, sh2) or NC, using Histone H3 as loading control. (C) Western blotting of Pan Kla and H3K18la in DMS114 cells with STMN1 OE) or vector control, using Histone H3 as loading control. (D) Western blotting of Pan Kla and H3K18la in H196 cells with STMN1 overexpression (OE) or vector control, using Histone H3 as loading control. (E-H) Cellular lactate concentrations in DMS114 and H196 cells with STMN1 knockdown or STMN1-OE was measured using a lactate assay kit, verifying the regulatory effect of STMN1 on cellular lactate production. (I-L) Co-IP combined with western blotting was performed to validate the lactylation of STMN1 itself in DMS114 and H196 cells, and the effect of STMN1 knockdown/overexpression on its own lactylation level was quantitatively analyzed, with IgG as the negative control and GAPDH as the input internal reference. **P<0.01, ***P<0.001. H3K18la, histone H3 lysine 18 lactylation; IP, immunoprecipitation; NC, negative control; OE, overexpression; Pan Kla, global histone lactylation; sh, short hairpin; STMN1, Stathmin 1.

To explore the underlying mechanism, cellular lactate concentrations were measured and it was revealed that STMN1 knockdown significantly reduced lactate levels, whereas STMN1 overexpression increased lactate production (Fig. 9E-H), indicating that STMN1 may regulate histone lactylation by modulating cell lactate generation.

To determine whether STMN1 itself undergoes lactylation, co-IP and western blotting was performed. The results confirmed that STMN1 was lactylated in both DMS114 and H196 cells, and its lactylation level was positively associated with its expression: STMN1 knockdown decreased its lactylation, whereas STMN1 overexpression increased it (Fig. 9I-L). Collectively, these findings indicated that STMN1 may not only promote cellular lactate production and histone lactylation, but also undergoes lactylation itself.

Discussion

The present study investigated the association between lactate metabolism and lung cancer pathogenesis. The oncogenic role and regulatory mechanisms of STMN1 in lung cancer were investigated by integrating MR analysis with multi-dimensional functional validation.

Traditional observational studies are limited by their inability to fully eliminate confounding factors and reverse causality. By contrast, the MR approach capitalizes on the random inheritance of genetic variants, offering robust evidence for causal inference between exposures and outcomes (34). Recently, the mechanisms underlying lactate and lactylation in cancer, along with their potential as therapeutic targets and clinical applications in cancer, have been increasingly elucidated (35–37). In this context, the current study sought to identify lactylation-related genes with a direct causal link to lung cancer. In the parallel analysis of multiple genes, multiple testing can increase the risk of false positives. Therefore, both FDR and Bonferroni multiple testing correction methods were implemented. Three core candidates were screened from 46 lactylation-related genes. Ultimately, only STMN1 was confirmed to exhibit a significant association with lung cancer susceptibility, with an OR of 1.741. This finding suggested that elevated STMN1 may serve as a key pathogenic driver of lung cancer.

As a microtubule-binding protein, STMN1 is involved in microtubule polymerization and depolymerization, and participates in critical cellular processes, such as mitosis and apoptosis. Its upregulation in numerous tumor cells contributes to malignant phenotypes, including proliferation, migration and invasion (38,39). Numerous studies have indicated that increased STMN1 expression is associated with unfavorable clinical outcomes in patients with prostate cancer (40), gallbladder carcinoma (41), colorectal carcinoma (42), pancreatic cancer (43) and breast carcinoma (44). By contrast, STMN1 can exert tumor-suppressive effects on bladder cancer (45), highlighting its context-dependent role in carcinogenesis. To date, to the best of our knowledge, the role of STMN1 in lung cancer has not been explored. A previous study on hepatocellular carcinoma identified 16 lactylation-related prognosis-associated differentially expressed genes, including STMN1, by analyzing the gene expression profiles from The Cancer Genome Atlas database (46). Additionally, STMN1 has been recognized as a marker of glycolysis (47), further linking its role to metabolic reprogramming in cancer. Elevated expression of immune checkpoint molecules can weaken antitumor immune reactions, thereby allowing the tumor to evade immune surveillance and promote disease advancement (48). The current study demonstrated that STMN1 was significantly linked to an unfavorable prognosis in patients with lung cancer. Furthermore, high STMN1 expression was correlated with reduced immune cell infiltration and lower immune scores in lung cancer tissues. These observations support the dual potential of STMN1 as both a prognostic marker and a therapeutic target in lung cancer. The combination of STMN1 inhibitors with ICIs may enhance the anti-lung cancer efficacy of ICIs, which could be related to the potential improvement of the TME immune status associated with STMN1 downregulation. This hypothesis warrants further validation in future studies.

Using functional enrichment analysis, and in vitro and in vivo experiments, the present study initially elucidated the multi-dimensional mechanisms by which STMN1 could promote lung cancer progression. GO enrichment analysis showed that proteins related to STMN1 participated in G0-G1 cell cycle transition and negative regulation of mRNA catabolism. This suggested that STMN1 may support the rapid proliferation of tumor cells through two parallel pathways: Accelerating cell cycle progression and ii) maintaining the stability of carcinogenesis-related mRNA. The former can shorten the time for lung cancer cells to switch from the quiescent phase to the proliferative phase, whereas the latter can sustain the continuous activation of oncogenic signaling pathways by inhibiting the degradation of tumor suppressor gene mRNAs (49).

Both cell-based and animal studies further validated the regulatory function of STMN1 in cell functions and the TME. STMN1 knockdown notably suppressed the proliferation, migration and invasion capabilities of DMS114 human lung cancer cells and H196 human lung sarcomatoid carcinoma cells, induced cell apoptosis, and downregulated the expression levels of the anti-apoptotic protein Bcl-2 and the EMT marker Vimentin. This result partially overlaps with the reported mechanism of STMN1 in other tumors. For example, STMN1 can promote cell migration by regulating microtubule dynamic stability in breast cancer (50). Consistent with these findings, the present study demonstrated that STMN1 promoted EMT by upregulating vimentin expression in DMS114 and H196 cells. These results indicated that the pro-tumor and pro-metastatic function of STMN1 is conserved across different cancer types, supporting the existence of universal oncogenic pathways regulated by STMN1.

In the present study, the enrichment ratio of oncogenic pathways, such as TP53 and Hippo signaling pathways, was notably greater in the high STMN1 expression group compared with that in the low STMN1 expression group. Additionally, DGIdb analysis revealed differences in the categories of druggable targets between the two groups, providing further insights into understanding the interaction between STMN1 and other oncogenic pathways. For example, STMN1 may enhance the resistance of lung cancer cells to apoptotic signals by activating TP53 mutation-related pathways. Meanwhile, the high enrichment of the Hippo signaling pathway could further amplify STMN1-mediated cell proliferation, forming an ‘STMN1-TP53-Hippo’ synergistic oncogenic network. Thus, it may be hypothesized that the combined targeted intervention against this network (such as by using an STMN1 inhibitor and a TP53 repair agent) might be more effective in inhibiting lung cancer progression compared with a single-target intervention, offering a molecular foundation for developing precise therapeutic regimens.

To further assess the functional link between STMN1 and lactate metabolism, a series of mechanistic experiments were performed. First, intracellular lactate levels were measured in lung cancer cells with STMN1 knockdown or overexpression, and it was revealed that STMN1 positively regulated lactate production: Depletion of STMN1 reduced lactate concentration, whereas its overexpression increased it. This result directly connects STMN1 to the upstream metabolic process of lactate generation, which is the precursor for protein lactylation. Subsequently, it was confirmed that STMN1 could promote Pan Kla and H3K18la in lung cancer cells, as evidenced by reduced lactylation levels after STMN1 knockdown and enhanced lactylation after overexpression. Notably, the current study further verified that STMN1 itself undergoes lactylation modification. These findings not only validate the role of STMN1 as a lactylation-related gene, but also reveal a novel post-translational modification of STMN1, which may be involved in the feedback regulation of its oncogenic function. The newly discovered link between STMN1 and lactate metabolism/lactylation further expands the mechanistic understanding. STMN1 may promote lung cancer progression not only by regulating microtubule dynamics and cell cycle, but also by enhancing lactate production and subsequent protein lactylation, including its own self-lactylation. This dual regulatory mechanism suggests that STMN1 acts as a key node connecting metabolic reprogramming and oncogenic signaling in lung cancer, which may explain its strong pathogenic effect in the current MR analysis.

Despite the rigorous design and reliable results of the present study, there are certain limitations. First, the exposure data for MR analysis were derived from eQTL databases, which may have constraints regarding sample scale and ethnic background. In the current study, the lung cancer GWAS datasets were derived from the Finnish population, whereas the eQTL effect of STMN1 might vary across different ethnic groups. As no publicly available Asian or multi-ethnic lung cancer GWAS datasets with sufficient sample size and comprehensive genotype-phenotype annotations currently exist, the generalizability of the MR findings to non-European populations remains to be verified. In the future, we aim to validate the MR results in Asian or multi-ethnic cohorts once relevant datasets are released. Second, the study did not investigate the direct association between STMN1, lactate metabolism and histone lactylation in depth. As a lactylation-related gene, it remains unclear whether lactate metabolites regulate STMN1 expression. Subsequent verification using techniques such as metabolomics and chromatin immunoprecipitation-sequencing is required.

In conclusion, the present study clarified the causal association between STMN1 and lung cancer using the MR method, and revealed its mechanism in regulating tumor cell functions and TME. The study provided a novel theoretical foundation for prognostic evaluation and targeted treatment of lung cancer. In the future, the clinical value of STMN1 should be further verified to promote the development of related targeted drugs.

Acknowledgements

Not applicable.

Funding

This study was supported by the National Natural Science Foundation of China (grant no. 82472879).

Availability of data and materials

The data generated in the present study may be requested from the corresponding author.

Authors' contributions

YC, YZ and HW jointly conceptualized the study framework and designed the research protocols. HW and YZ performed in vitro and in vivo experiments, with HW assisting in data collection and preliminary sorting. SZ and SH led the bioinformatics analyses and interpreted the key results. YC drafted the initial manuscript, whereas SH and QZ conceived and designed the study, interpreted data, and revised the manuscript. FH and SZ supplemented experimental data and verified result reproducibility. All authors reviewed the manuscript and agreed to the submission, and all authors read and approved the final manuscript. YC and SH confirm the authenticity of all the raw data.

Ethics approval and consent to participate

The present study was approved by the Animal Experimentation Ethics Committee of the Huazhong University of Science and Technology (IACUC no. 4847; Wuhan, China).

Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

References