Introduction

Gastric cancer (GC) ranks as the fifth most prevalent malignancy globally and the fourth leading cause of cancer-related mortality1. Established risk factors encompass demographic variables (age, gender, race), Helicobacter pylori (Hp) infection, smoking and high-salt dietary intake2. In addition to traditional surgical treatment, endoscopic treatment, radiotherapy, chemotherapy, immunotherapy and biological agent treatment have also made some progress3,4. Nevertheless, patients with advanced-stage disease continue to face poor prognosis due to tumor heterogeneity and therapeutic resistance. This clinical challenge underscores the critical need for identifying novel molecular targets to enhance early diagnosis and therapeutic efficacy, particularly for advanced gastric cancer.

LANCL2, a member of the eukaryotic lanthionine synthetase component C-like protein family, was initially identified in human neural and reproductive tissues5,6. Emerging evidence implicates LANCL2 in diverse biological processes, including Akt-mediated phosphorylation, chemosensitivity modulation (notably to adriamycin), anti-inflammatory responses, glucose metabolism, mitochondrial bioenergetics and cytoskeletal dynamics through abscisic acid signaling pathways7,8,9,10,11. Recent studies further associate LANCL2 dysregulation with tumor progression and prognosis in lung cancer, glioblastoma and EGFR-mutant breast carcinomas12,13, which means that LANCL2 may play an important role in the initiation and development of cancers. However, the expression and function of LANCL2 in GC are still unclear. Notably, we have previously found that LANCL2 is highly expressed in GC and is correlated with the prognosis of patients, but its carcinogenic mechanism needs further study. To further explore the biological role of LANCL2 in GC, we conducted a multi-omics analysis.

This multi-omics study systematically evaluated the oncogenic role of LANCL2 in GC via comprehensive analysis of multiple public databases, encompassing DNA, RNA, and protein levels. We comprehensively evaluated the correlation between LANCL2 and genomic alterations, tumor-immune interactions, DNA repair mechanisms, m6 A modification patterns, ferroptosis regulation, cancer stemness properties, and therapeutic sensitivity in GC using bioinformatics methods, and found that LANCL2 was highly expressed in GC at mRNA and protein levels using clinical tissues and cells. In addition, a clinical prognosis assessment model for GC was established by Cox regression analysis based on LANCL2, and in vitro studies found that LANCL2 could promote the occurrence and development of stomach adenocarcinoma (STAD). These findings identify LANCL2 may serve as a new therapeutic target, and establish a robust foundation for future mechanistic investigations into LANCL2-driven GC.

Materials and methods

Clinical tissue samples

53 paired gastric cancer specimens and adjacent normal tissues were collected from treatment-naïve patients (no prior chemotherapy or radiotherapy) undergoing surgical resection at the First Affiliated Hospital of Lanzhou University from January 2023 to July 2024. Comprehensive clinical records were obtained with informed consent from all participants. This study received ethical approval from the Institutional Review Board of the First Affiliated Hospital of Lanzhou University (LDYYLL2023-26).

Databases

RNA sequencing data and clinical metadata were retrieved from The cancer genome atlas (TCGA) (https://portal.gdc.cancer.gov/), the Genotype-Tissue Expression (GTEx) (https://gtexportal.org/), and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/). Differential LANCL2 expression was analyzed by TIMER2.0 (http://timer.comp-genomics.org/)14. Clinical-pathological correlations (tumor stage, nodal metastasis, histological grade, Hp status and age) were examined using the University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) (http://ualcan.path.uab.edu/). Functional enrichment analyses were performed via STRING (https://string-db.org/), LinkedOmics (http://www.linkedomics.org/), the Gene Expression Profiling Interactive Analysis (GEPIA2) (http://gepia2.cancer-pku.cn/) and the Gene Set Enrichment Analysis (GSEA) (https://www.gsea-msigdb.org/gsea/index.jsp). The genetic alteration analysis was generated through the cbioportal database (http://www.cbioportal.org/) and the Catalogue of Somatic Mutations in Cancer (COSMIC) database (http://cancer.sanger.ac.uk). Furthermore, Immune infiltration patterns of differential expression of LANCL2 in GC were evaluated by TISIDB (http://cis.hku.hk/TISIDB/), the Tumor Immune Syngeneic MOuse (TISMO) (http://tismo.cistrome.org/), and the Tumor Immune Dysfunction and Exclusion (TIDE) (http://tide.dfci.harvard.edu/). The drug treatment response to LANCL2 expression levels were predicted on the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/) and the Gene Set Cancer Analysis (GSCA) database (http://bioinfo.life.hust.edu.cn/GSCA/#/). Detailed analytical protocols are provided in Supplementary Materials.

Survival analysis

The TCGA-STAD patients were stratified into high/low LANCL2 expression groups based on median values. Survival outcomes (overall survival (OS), disease specific survival (DSS) and disease-free survival (DFS)) were compared using Kaplan-Meier analysis. Cox proportional hazards regression assessed clinical subgroup associations and identified independent prognostic factors through univariate/multivariate analyses. A predictive nomogram for 1, 3, and 5-year survival probabilities was developed using R software (v3.6.3) with survival (v3.2-10), survminer (v0.4.9) and rms (v6.2-0) packages. The sample size for each repeated sampling is 150, with a total of 400 sampling occasions. Prognostic data integration followed established methodology15.

Cell lines and cell culture

Human gastric cancer cell lines (HGC-27, MKN-45, AGS, MKN-28, SNU-216, SNU-668, NCI-N87) and normal gastric mucosal epithelial cells (GES-1) were acquired from Yuchi Biotechnology (Shanghai, China). Cell lines were maintained under standard conditions (37 °C, 5% CO₂) in media supplemented with 10% fetal bovine serum. GES-1 in DMEM (high glucose), HGC-27 in MEM, MKN-45/MKN-28/SNU-216/SNU-668/NCI-N87 in RPMI-1640, and AGS in F-12 K.

Quantitative real-time PCR (qRT-PCR)

Total RNA was extracted using the TriQuick Reagent (Solarbio, China), reverse-transcribed with TransScript One-Step gDNA Removal cDNA Synthesis SuperMix (TransScript, China), and amplified with TransStart Top Green qPCR SuperMix (TransScript, China). Primer sequences: β-actin (F:5’- CACGATGGAGGGGCCGGACTCATC-3’; R:5’- TAAAGACACCTCTATGCCAACACAGT-3’), LANCL2 (F:5’- ATGCAGGCGTACAAGGTCTTT-3’; R:5’- GGCATATCCCGTAGCCCTTC-3’).

The internal reference gene was β-Actin, and the primer sequences as follows: forward primer CACGATGGAGGGGCCGGACTCATC and reverse primer TAAAGACACCTCTATGCCAACACAGT. The primer sequences for LANCL2: forward primer ATGCAGGCGTACAAGGTCTTT and reverse primer GGCATATCCCGTAGCCCTTC. Relative expression was calculated by 2 − ΔΔCt method with β-actin normalization.

Western blotting (Wb)

Cells were lysed with RIPA buffer containing protease inhibitors to collect proteins. The protein concentration was determined by BCA assay and then separated by SDS-PAGE gel. The proteins were transferred onto PVDF membranes and blocked. After blocking, the membranes were incubated overnight at 4 °C with primary antibodies against the target proteins. The next day, the membranes were incubated with the corresponding secondary antibodies at room temperature for 1 h. Finally, the blots were imaged using chemiluminescence (Monad, China). The primary antibodies to CDK2 (AF1063), Cyclin A2 (AF2524), p21 (AP021) were from Beyotime (China), LANCL2 (A5-98856) was from Thermo Fisher (America), and those to flag (20543-1-AP) and β-Actin (66009-1-Ig) were from proteintech (China).

Cell transfection for silencing and overexpression of LANCL2

The CDS sequence of LANCL2 was cloned into a CV702 plasmid with flag to achieve transient overexpression of LANCL2 (Genechem, China). The lentivirus containing shRNA targeting LANCL2 (#1 CAGGTTATCTGTATGCCTTACTGTA, #2 GACATCACGGTTTCCAGCATTTGAA) were constructed to silence LANCL2. The two GC cells were inoculated at a density of 4 × 105 cells/well into 6-well plates for 24 h and then transfected with plasmid using Lipofectamine 2000 reagent (Invitrogen, Carlsbad, CA, USA) and the lentivirus. The overexpression and silencing efficiency were confirmed by qRT-PCR and Wb, and the cells were used for the subsequent experiments.

Cell viability assessment

The Cell Counting Kit-8 kit (CCK8) (Solarbio, China) was used to assess cell viability. GC cells were transfected and then 3000 GC cells were seeded into 96-well plates for 0, 24, 48, 72 and 96 h. 10 µl CCK8 was added to each well at each time point and incubated for another 1.5 h at 37℃. The absorbance at 450 nm was determined using a microplate reader.

Colony formation assay

GC cells were seeded into 6-well plates at a density of 700 cells per well, and cultured in 5% CO2 at 37 ℃ for 2 weeks. Colonies were fixed with 4% paraformaldehyde (Solarbio, China) and stained with 0.1% crystal violet (Solarbio, China). The number of clones is counted and photographed with a camera. The number of colony cells was calculated using Image J software. The colony forming efficiency = clone counts/cells (%).

5-Ethynyl-2′-deoxyuridine (EdU) incorporation assay

GC cells were seeded in 24-well plates overnight and incubated with Edu working reagent (Beyotime, China) for 2 h. Cells were fixed with 4% paraformaldehyde for 15 min and then were washed with PBS three times. Cells were permeabilized with 0.3% TritonX-100 and subsequently stained with the reaction solution. 1X Hoechst 33,342 (Beyotime, China) was used for the nuclear staining. Finally, fluorescence detection can be performed.

Cell cycle analysis

GC cells were digested with trypsin and washed with PBS, followed by staining with propidium iodide (PI) (Multi Sciences, China) and incubated for 30 min in the dark. Then, the cell cycle distribution was performed with flow cytometry.

Statistical analysis

Two independent sample t-tests were used for sample comparisons between two groups that met the normal distribution and chi-squareness, and non-parametric Mann -Whitney U-tests were used for those that did not. Paired samples were compared using a paired t-test for normal distribution and a non-parametric Willcoxon rank-sum test for non-conformity. One-way ANOVA was used for qualifying sample comparisons between multiple groups, Kruskal-Wallis H-method was used for non-qualifying ones, and LSD was used for two-way comparisons between groups. Correlation analysis of gene expression was performed using Spearman’s correlation coefficient. P < 0.05 was considered statistically significantly different (*P < 0.05; ** P < 0.01; *** P < 0.001; ns P ≥ 0.05).

Results

Analysis of LANCL2 expression by combining multiple databases

Pan-cancer mRNA expression profiling from TIMER2.0 demonstrated differentially expressed of LANCL2 across multiple malignancies, with pronounced overexpression in GC (Fig. 1A). Then integrative analysis of TCGA-STAD, GTEx, and GEO datasets (GSE29272, GSE66229) confirmed consistent LANCL2 upregulation in both paired and unpaired GC specimens (Fig. 1B-F). Moreover, the elevated LANCL2 expression was also validated in diverse GC cell lines and clinical tissues via qRT-PCR (Fig. 1G-H). Table S1 summarizes the clinicopathological characteristics of the 53 GC patients analysed. Logistic regression analysis showed that LANCL2 expression level was closely correlated with patient age (P < 0.05). In order to further verify the expression of LANCL2, immunohistochemical staining results in HPA database showed that the expression of LANCL2 protein in GC tissues was significantly higher than that in adjacent tissues (Fig. 1I).

Fig. 1
figure 1

The expression of LANCL2 in stomach adenocarcinoma (STAD) and pan-cancer. (A) The TIMER2.0 database showed differential expression of LANCL2 in pan-cancer. (B) The expression of LANCL2 in STAD and normal gastric tissues were analyzed by the TCGA-STAD cohort. (C) The expression of LANCL2 in STAD and normal gastric tissues were analyzed by the TCGA-STAD cohort and GTEx database. (D) The expression of LANCL2 in STAD and normal gastric tissues were analyzed by paired samples from the TCGA-STAD cohort. (E) The expression of LANCL2 in STAD and normal gastric tissues were analyzed by the GSE29272 dataset. (F) The expression of LANCL2 in STAD and normal gastric tissues were analyzed by the GSE66229 dataset. (G) The expression of LANCL2 mRNA in GC cells by qRT-PCR. (H) The expression of LANCL2 mRNA in 53 paired gastric cancer tissues and paraneoplastic tissues by qRT-PCR. (I) The expression of LANCL2 protein in gastric cancer and normal tissues by the HPA database. (*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001).

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Correlation analysis of LANCL2 expression and prognosis of patients in gastric cancer

Given the marked upregulation of LANCL2 in GC, its clinical prognostic value was investigated. Analysis of TCGA-STAD cohort data (patient characteristics in Table 1) revealed significant associations between LANCL2 expression and age, pathological stage, and Hp infection through logistic regression (Fig. S1A). Subsequently, the UALCAN database validation indicated histological grade (G stage) and age as primary correlates, with no significant associations observed for TNM stage or Hp status (Fig. S1B-F). For G stage, the LANCL2 was significantly higher in G2 and G3 stages than in G1 stage, but there was no significant difference between G2 and G3 stages. For age, the LANCL2 was significantly higher in GC patients aged 61–80 years than aged 41–60 years. ROC analysis showed that LANCL2 had a great diagnostic ability for STAD with AUC value of 0.853 (sensitivity: 70.0%; specificity: 93.7%) (Fig. S1G).

Table 1 Baseline clinical characteristics based on stratification of LANCL2 expression in the TCGA-STAD cohort.
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The survival analyses showed that the patients with high expression of LANCL2 predicted poorer OS, PFS and DSS (Fig. S2A-C), and subgroup analyses further demonstrated adverse outcomes in patients with advanced clinicopathological features (Age > 65, T3/T4, N2/N3, Stage III/IV, G2/G3) and Hp negative status in the LANCL2 high expression group (Fig. S2D-I). Given the good prognostic ability of LANCL2, we constructed a clinical prognostic model. Univariate Cox regression identified LANCL2 as a significant prognostic factor (Fig. 2A), and the multifactorial regression analysis showed that LANCL2 was an independent factors for OS in GC patients (Fig. 2B). Integrating these findings, we developed a prognostic nomogram incorporating age, TNM stage, histological grade, and LANCL2 expression. The higher LANCL2 score represented the lower survival probability of gastric cancer patients at 1, 3 and 5 years, and the value of C-index/AUC was 0.635 (0.607–0.663) (Fig. 2C). Its calibration curves suggested satisfactory predictive accuracy for 1, 3, and 5-year survival (Fig. 2D). Taken together, these results collectively suggested LANCL2 may serve as a robust prognostic biomarker in gastric cancer.

Fig. 2
figure 2

Construction and verification of the nomogram prediction model based on LANCL2 expression in TCGA-STAD cohort. (A) Forest plot of univariate Cox regression analysis in STAD. (B) Forest plot of multifactorial Cox regression analysis in STAD. The right side represents the high-risk group and the left side represents the low-risk group. (C) Construction of the nomogram model based on LANCL2 expression in STAD. (D) Evaluation of the predictive performance of the nomogram model by the calibration curve. Ideal line, line of reference; error bars, 95% confidence interval; X-marks, bootstrap resampling correction for each time node (1/3/5 year); Points, the actual value of the survival probability of the original observation; Black tick marks, boundary effect.

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Multi-dimensional analysis of LANCL2 function in GC

To investigate the biological role of LANCL2 in GC, we constructed a protein-protein interaction (PPI) network (Fig. 3A). XRCC6BP1, VOPP1, SAMD5, TMEM100, ECD, PPARG, SEC61G, EPS8, AKT1 and ABCB1 were identified as the top 10 interacting proteins. Co-expression analysis using LinkedOmics revealed genes associated with LANCL2, visualization analysis of volcano plot and heatmaps was performed on the top 50 positively/negatively correlated genes (Fig. 3B-D). The top 50 genes related to LANCL2 in STRING database, the top 50 genes positively associated to LANCL2 in linkedomics database, and the top 50 genes similar to LANCL2 in GEPIA2 database were selected for intersection. The intersected genes of the three sets were VOPP1, CCT6 A, EGFR, and SEC61G (Fig. 3E). The expression of the 4 genes was positively correlated with LANCL2 in STAD, especially VOPP1 (R = 0.73) and EGFR (R = 0.65) (Fig. 3F). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses demonstrated that LANCL2-associated biological processes (BP) primarily involved Notch signalling, vasoconstriction regulation and serine biosynthesis, cellular components (CC) included sodium channel complexes and molecular functions (MF) featured sodium channel activity and WW domain binding. KEGG pathways were enriched for PD-1/PD-L1 signalling, amino acid biosynthesis, and glycine/serine/threonine metabolism (Fig. 4A-B). Furthermore, we explored the top 10 pathways enriched in the GSEA analysis in STAD with G1 to S cell cycle control, sumoylation of DNA replication proteins and DNA strand elongation etc. (Fig. 4C). In addition, based on the ssGSEA algorithm, LANCL2 expression was found to be associated with tumor proliferation, ECM regulation, apoptosis, and G2M checkpoint pathways (Fig. 4D, Fig. S3).

Fig. 3
figure 3

Analysis of LANCL2-related genes in STAD. (A) The protein-protein interaction network of LANCL2 was constructed by the String database (Interactive proteins are indicated by gene name). (B) Volcano map of genes related to LANCL2 were obtained by linkedomics database analysis. (C) Heat map of the top 50 genes positively associated with LANCL2 were obtained by linkedomics database analysis. (D) Heat map of the top 50 genes negatively associated with LANCL2 were obtained by linkedomics database analysis. (E) Venn diagrams of 150 genes associated with LANCL2 were obtained from String, GEPIA2, and Linkedomics databases. (F) The correlation between LANCL2 expression and the expression of overlapping genes in the Venn diagram was analyzed by the GEPIA2 database.

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Fig. 4
figure 4

The functional analysis of LANCL2 in STAD. (A-B) GO and KEGG pathway enrichment analysis of LANCL2-related genes. (C) The signaling pathways correlated with LANCL2 expression were showed by the merged plots of GSEA, according to Reactome, WikiPathways, and PID analyses. (D) Comparison of the scores of 19 signaling pathways with high and low expression of LANCL2. (*P < 0.05, **P < 0.01, ***P < 0.001).

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Genetic analysis revealed the alteration type of LANCL2 in undifferentiated GC was mainly amplification but there were three alteration types in esophagogastric adenocarcinoma, including amplification, mutation, and deep deletion (Fig. S4 A). The total genetic alteration frequency of LANCL2 in GC was 7% (30/434), with the predominant type of genetic alteration being copy number variation in the type of amplification (Fig. S4B). Meanwhile, the somatic mutation frequency of LANCL2 was 2.5% (11/434), including 9 missense mutations, 1 truncating mutation and 1 inframe mutation (Fig. S4 C). COSMIC data confirmed missense substitutions (30.51%) as primary mutations, predominantly C > T (28.81%) and G > A (24.58%) transitions (Fig. S4D-E). Clinically, LANCL2 alterations correlated with higher post-therapy neoplasia, microsatellite instability (MSI), tumor mutational burden (TMB) (Fig. S4 F-H) and worse survival outcomes (Fig. S4I-L).

Correlation analysis of LANCL2 and immune characteristics

Given the critical role of immune status in tumorigenesis and progression, we investigated the role of LANCL2 in tumor immunity using multiple databases and algorithms. TIMER database analysis revealed significant negative correlations between LANCL2 expression and infiltration levels of CD8+ T cells, macrophages, neutrophils and dendritic cells (Fig. 5A). Similarly, the ESTIMATE algorithm showed lower immune, stromal and ESTIMATE scores in the LANCL2-high group (Fig. 5B). It turns out that LANCL2 expression correlated with a variety of immune cell markers, such as CD8B on CD8 + T cells, ICOS and BCL6 on Tfh cells, IL12RB2 and IL27RA on TH1 cells, STAT6 on Th2 cells, IL21R and IL23R on Th17 cells, AHR on Th22 cells, FOXP3 on Treg cells, CCR8, PDCD1 and CTLA4 on exhausted T cells, NOS2 and ROS1 on macrophage M1, CD80 on TAM cells, XCL1 and CD7 on NK cells, and FUT4 on neutrophils (Table 2). TISIDB analysis revealed distinct patterns across immune and molecular subtypes (Fig. 5C-D).

Fig. 5
figure 5

The correlation analysis of LANCL2 expression and immune characteristics in STAD. (A) The correlation between LANCL2 expression and immune cell infiltration was analyzed by the TIMER database. (B) The stromal score, immune score, and estimate score in the different expression group of LANCL2 through the TIMER database. (C) Differential expression of LANCL2 in different immune subtypes by the TISIDB database. C1, wound healing; C2, IFN-γ dominant; C3, inflammatory; C4, lymphocyte depleted; C6, TGF-β dominant. (D) Differential expression of LANCL2 in different molecular subtypes by the TISIDB database. CIN, chromosomal instability; EBV, EBV virus positive; GS, genomically stable; HM-SNV, high mutational single nucleotide variation; HM-indel, high mutational insertions and deletions. (E) Differential expression of different immune checkpoints in LANCL2 high expression group, low expression group, and normal group in TCGA-STAD. (F) TIDE score of the different expression of LANCL2. (G-H) The correlation of microsatellite instability (MSI) and the tumor mutation burden (TMB) with LANCL2 expression by the TIDE database. (I) Comparison of immune cell infiltration levels among different types of copy number variation groups by the TIMER database. (*P < 0.05, **P < 0.01, ***P < 0.001).

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Table 2 The correlation of LANCL2 expression and expression of immune cell markers in STAD. (*P < 0.05, **P < 0.01, ***P < 0.001).
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Building on KEGG pathway analysis (Fig. 4A), LANCL2 is closely related to PD-L1 expression and immune checkpoint inhibitor pathway. Therefore, we further explored the correlation between LANCL2 and immune checkpoints, and the result showed that LANCL2 expression was closely associated with multiple immune checkpoints, such as CD274, CTLA4, HAVCR2, PDCD1LG2 and TIGIT (Fig. 5E, Fig. S5). Tumor immunotherapy has been a major advance in the treatment of human cancers in recent years. TIDE algorithm analysis predicted enhanced response to immune checkpoint inhibitors in the LANCL2-high group, evidenced by lower TIDE scores (Fig. 5F). Notably, LANCL2 was positively correlated with MSI and TMB (Fig. 5G-H), while copy number variations (particularly arm-level gains) showed significant immune infiltration associations (Fig. 5I).

To assess the immunotherapeutic predictive value of LANCL2, we analysed its expression changes under cytokine treatment. The expression of LANCL2 was significantly changed in 4 cell lines, with marked up-regulation in 3 cell lines. Evaluation across mouse immunotherapy models revealed differential LANCL2 expression in responders versus non-responders (Fig. S6B). In human ICB cohorts from TIDE database, LANCL2 demonstrated predictive capacity in 6/25 cohorts (AUC > 0.5) (Fig. S6 C-D).

Correlation analysis of LANCL2 with DNA repair, m6A modification, ferroptosis, and tumor cell stemness

GSEA analysis revealed LANCL2 plays an important role in DNA repair pathways (Fig. 4C), and further investigation demonstrated significant positive correlations between LANCL2 expression and key genes of DNA repair (MLH1, MSH2, MSH6, PMS2, EPCAM) (Fig. 6A-F). Emerging evidence demonstrates the crucial roles of m6 A methylation and ferroptosis in tumorigenesis. Our analysis identified substantial associations between LANCL2 expression and m6 A regulators: METTL3 (writer), YTHDC1 (reader) and FTO (eraser) (Fig. 6G-J, Fig. S7). Similarly, there were strong correlations between LANCL2 and ferroptosis-related markers, such as HSPA5, NFE2L2, FANCD2 and FDFT1 etc. (Fig. 6K, S8). Based on the results of the single-cell sequencing analysis in Fig. 4E, it was noted that LANCL2 was positively correlated with the stemness of a variety of tumor cells. Therefore, the differences between LANCL2 expression in STAD and the degree of stemness has been focused on, and the results showed that the higher expression of LANCL2 was consistent with the higher degree of stemness of the tumor cells (Fig. 6L).

Fig. 6
figure 6

The correlation of LANCL2 expression and DNA repair, m6 A methylation modification, ferroptosis, and tumor cell stemness in TCGA-STAD. (A) Heat map of correlation between LANCL2 expression and DNA repair-related gene expression. (B-F) Scatter plot of correlation between LANCL2 expression and MLH1, MSH2, MSH6, PMS2, and EPCAM expression. (G) Heat map of correlation between LANCL2 expression and m6 A methylation modification-related gene expression. (H-J) Scatter plot of correlation between LANCL2 expression and METTL3, YTHDC1, and FTO expression. (K) Heat map of correlation between LANCL2 expression and ferroptosis-related gene expression. (L) Comparison of the one-class logistic regression machine learning algorithm (OCLR) score of LANCL2 high expression group, low expression group, and normal group. (*P < 0.05, **P < 0.01, ***P < 0.001).

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Correlation analysis of LANCL2 expression and drug sensitivity

The correlation between LANCL2 expression and chemotherapeutic agent sensitivity was investigated by using GDSC and CTRP databases. The LANCL2-low group exhibited significantly higher IC50 values for 5-fluorouracil, doxorubicin, docetaxel, etoposide and mitomycin compared to the LANCL2-high group (Fig. 7A), suggesting enhanced therapeutic efficacy of these agents in GC patients with elevated LANCL2 expression. And the correlation between LANCL2 and drug sensitivity based on the GDSC dataset showed that 5-fluorouracil (nucleoside antimetabolite/analog), CX-5461 (activator of the DNA damage response), CAY10603 (inhibitor of HDAC6), selumetinib (inhibitor of MEK1/2) and cytarabine (inhibitors of DNA synthesis) were the top five drugs positively correlated with LANCL2 expression. CTRP data revealed increased sensitivity to zebularine (DNA methylation inhibitor), Merck60 (HDAC1/2 inhibitor) and tacedinaline (HDAC inhibitor) in tumors with elevated LANCL2 levels (Fig. 7B).

Fig. 7
figure 7

The correlation between LANCL2 expression level and multidrug sensitivity. (A) Comparison of the differences in drug sensitivity between LANCL2 high expression group and low expression group in STAD based on the GDSC database. (B) The correlation between LANCL2 expression and the sensitivity of drugs in pan-cancer through the GSCA database. (*P < 0.05, **P < 0.01, ***P < 0.001).

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LANCL2 promotes the development of GC cells in vitro

Building on functional enrichment analyses linking LANCL2 to GC cell proliferation, we established LANCL2 overexpression and knockdown models in AGS and HGC-27 cells (Fig. 8A-B). The results demonstrated that LANCL2 silencing significantly inhibited cellular proliferation through CCK-8 (Fig. 8C), colony formation (Fig. 8D), and EDU staining assays (Fig. 8E), while overexpression enhanced proliferative capacity. Cell cycle analysis revealed LANCL2 overexpression increased S-phase populations, whereas knockdown induced G2/M-phase arrest in both cell lines, indicating that LANCL2 could regulate the cycle progression of AGS and HGC-27 cells (Fig. 8F). Concurrently, the detection of cycle-related proteins showed that silencing LANCL2 could inhibit the expression of cell cycle proteins CyclinA2 and CDK2, while the expression of cell cycle protein inhibitor P21 increased (Fig. 8G). These findings collectively demonstrate that LANCL2 is critical for promoting GC cell proliferation via regulating cell cycle. The original blots are presented in Supplementary Materials 2.

Fig. 8
figure 8

LANCL2 promoted the proliferation of GC cells in vitro. (A) The expression of LANCL2 using qPCR and Wb. (B) The cell proliferation was measured using CCK8 asssay. (C) The cell proliferation was measured using colony formation assay. (D) The cell proliferation was measured using EDU assay. Red, proliferating cells; Blue, all cell nuclei; Percentage of positive stained cell (%) = Red cells/Blue cells (%); Scale bar, 50 μm. (E) The cell cycle was performed with flow cytometry. (F) Wb of cell cycle-related proteins in AGS and HGC-27. The cell experiments were replicated three times. *P < 0.05; **P < 0.01; ***P < 0.001.

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Discussion

LANCL2 is mainly located in the cell membrane and cytoplasm, and can be expressed in a variety of tissues, such as the heart, brain and testis6. LANCL2 plays an important role in regulating stress response, inflammation and glucose metabolism, and its abnormal expression can lead to chronic inflammation and immune-related diseases, as well as diabetes13. In recent years, LANCL2 has been used as a therapeutic target for chronic inflammatory, metabolic and immune-related diseases, such as the LANCL2 agonist Omilancor (BT-11) for the treatment of inflammatory bowel disease by improving the stability and immunosuppression of Treg cells in the gut16. LANCL2 also plays an important role in the development of tumors. Studies have shown that LANCL2 can enhance the efficacy of doxorubicin by reducing the expression of drug-resistant proteins such as MDR1 in tumor cells, and it can also regulate the Akt and mTOR pathways to affect tumor progression12,16. Zeng et al. found that silencing LANCL2 can induce apoptosis of hepatocellular carcinoma cells7. Moreover, high expression of LANCL2 is closely related to poor prognosis of lung cancer patients12. While these findings collectively suggest LANCL2’s tumorigenic potential, its specific role in GC remained unclear. This study revealed the functional effects and molecular mechanisms of LANCL2 in gastric cancer via bioinformatics and in vitro experiments.

Our findings reveal significant LANCL2 upregulation across multiple tumor types, particularly in digestive system malignancies. Through multi-database analysis (TCGA/GTEx/GEO/HPA), we consistently demonstrated elevated LANCL2 expression in gastric cancer tissues relative to normal controls. This observation was further corroborated by qRT-PCR showing increased LANCL2 levels in GC cell lines versus normal gastric mucosal cells. These consistent results across methodologies position LANCL2 as a promising diagnostic biomarker for GC. Clinico-pathological analysis revealed significant associations between LANCL2 expression and age, pathological stage, histological grade, and H. pylori infection status. H. pylori infection is closely associated with the development of gastric cancer17. Paradoxically, we observed higher LANCL2 levels in H. pylori-negative patients, suggesting a complex interaction requiring further investigation. Importantly, high LANCL2 expression correlated with poor prognosis, particularly in elderly patients with advanced-stage disease, underscoring its potential as a prognostic indicator for STAD.

To delineate LANCL2’s functional landscape, we performed comprehensive pathway analyses through bioinformatics. Through GO and KEGG pathway enrichment analysis, it was found that LANCL2 was mainly associated with PD-L1 expression and PD-1 checkpoint pathway in cancer, Notch signaling pathway, and amino acid metabolism. GSEA identified critical roles in cell cycle regulation and DNA repair mechanisms. These multifaceted associations highlight LANCL2’s potential as a nodal regulator in cancer progression through diverse signaling networks18.

The accumulation of genetic alterations and altered patterns of gene expression promotes the development and progression of cancer, and if such changes can be identified at the pre-cancerous stage, they will aid in the early diagnosis and treatment of cancer19. Through the cBioPortal database, it was noted that the type of LANCL2 gene alteration was mainly amplification, which was consistent with the previous studies12,13. Notably, the amplified copy number variation was mainly found in undifferentiated gastric adenocarcinoma, while the mutation was mainly found in esophageal gastric adenocarcinoma. A previous study has found that p53 mutations can regulate the immune microenvironment of tumors by regulating inflammatory signaling pathways and immune cell recruitment20. The study showed that amplified copy number variation was closely related to immune invasion, and the level of immune invasion was significantly reduced in the group with amplified copy number variation, indicating that LANCL2 amplification inhibited the immune response of the tumor, thus promoting the occurrence and progression of the tumor. The LANCL2 altered group exhibited higher MSI and TMB scores, along with increased recurrence rates, positioning LANCL2 alterations as potential biomarkers for therapeutic resistance and prognosis. Based on the above results, we speculated that the genetic alteration in LANCL2, mainly the gradual accumulation of amplified copy number variation, led to the changes in gene expression and ultimately promoted the progression of STAD. Genetic alterations in LANCL2 may be a promising biomarker for assessing the prognosis of patients with STAD.

Tumorigenesis fundamentally arises from somatic genetic alterations accumulating through clonal evolution19. While neoantigens generated by these alterations typically elicit immune recognition and clearance, tumors develop adaptive mechanisms to evade immune surveillance - an evolutionary balance between host immunity and malignant transformation21. Our findings reveal that LANCL2 expression inversely correlates with immune infiltration levels and associates with multiple immune cell markers, suggesting its involvement in STAD immune evasion. KEGG pathway analysis further implicates LANCL2 in PD-L1/PD-1 checkpoint regulation. Subsequent investigation confirmed significant correlations between LANCL2 expression and key immune checkpoints: CD274 (PD-L1), CTLA4, HAVCR2 (TIM-3), LAG3, PDCD1 (PD-1), PDCD1LG2 (PD-L2), and TIGIT. Notably, high LANCL2 expression correlated with increased microsatellite instability (MSI) and tumor mutational burden (TMB), while predicting lower TIDE scores. These findings suggest dual clinical implications: (1) LANCL2-high tumors may exhibit enhanced response to immune checkpoint blockade (ICB) therapy, and (2) Combinatorial strategies targeting LANCL2 with ICB could synergistically improve gastric cancer treatment outcomes.

DNA repair mechanisms and the DNA damage response constitute critical safeguards for genomic integrity, with their dysregulation being a recognized cancer hallmark22,23. The GSEA analysis revealed LANCL2’s significant involvement in DNA repair pathways specific to STAD, where high LANCL2 expression correlated with elevated DNA repair scores. Notably, this positive association extended to BRCA and LUAD malignancies, suggesting conserved biological functions across tumor types. Focusing on mismatch repair (MMR), a key DNA repair pathway implicated in carcinogenesis, we identified strong positive correlations between LANCL2 expression and MMR-related genes in STAD. We propose that LANCL2 upregulation may induce DNA damage, thereby activating repair mechanisms, particularly MMR through a compensatory mechanism. This potential feedback loop between LANCL2 and genomic instability warrants further mechanistic investigation.

Beyond genomic regulation, our study uncovered LANCL2’s epigenetic interplay with m6 A methylation, M6 A methylation is a common epigenetic modification, which widely exists in mRNA and non-coding RNAs24,25. Previous studies have shown that m6 A methylation is closely related to gastric cancer development26,27,28. LANCL2 expression significantly associated with m6 A regulators across functional categories: writers (methyltransferases), erasers (demethylases), and readers (recognition proteins)29. This tripartite interaction suggests LANCL2 may regulate its own mRNA stability via m6 A-mediated post-transcriptional control, though experimental validation remains essential. Intriguingly, LANCL2 also demonstrated associations with ferroptosis—an iron-dependent cell death mechanism driven by lipid peroxidation30,31. Liu et al. found that Jiyuan oridonin A derivative, induced ferroptosis and thus inhibited the growth of gastric cancer cells32. Zhang et al. found that miR-522 secreted by CAF led to decreased sensitivity to chemotherapy in gastric cancer by inhibiting ferroptosis33. While prior studies link ferroptosis modulation to gastric cancer therapy, our finding that LANCL2 correlates with ferroptosis-related genes introduces novel regulatory possibilities. in addition to this, the tumor cell stemness score was significantly higher in the LANCL2 high expression group than in the low expression group. The result suggested that LANCL2 is essential for maintaining the stemness of gastric cancer cells. Moreover, it was found a negative correlation between LANCL2 expression and the IC50 of several gastric cancer chemotherapeutic agents, suggesting that patients with high LANCL2 expression are more sensitive to gastric cancer chemotherapy. In summary, the above results suggested that LANCL2 may serve as a new target for gastric cancer therapy.

In order to further confirm the role of LANCL2 in gastric cancer, The cell function assays showed that LANCL2 can promote the proliferation of gastric cancer cells in vitro. Further study revealed that LANCL2 primarily promotes gastric cancer cell growth by regulating the S-phase of the cell cycle. However, we observed cell line-specific differences in G2/M phase responses: AGS cells showed a modest (though statistically insignificant) increase in G2/M population, whereas HGC-27 exhibited statistically significant changes. This divergence likely reflects intrinsic differences in differentiation status between the two cell models, but the minor discrepancy does not compromise our overarching conclusion. To further investigate the clinical characteristics affecting the prognosis of gastric cancer patients, the Cox univariate and multifactorial regression analyses were performed, which showed that age, M-stage, and LANCL2 were independent risk factors for the prognosis of gastric cancer patients. In addition, a nomogram model using Age, T stage, N stage, M stage, Pathological stage and LANCL2 was constructed and evaluated with Calibration curve. The results showed that the nomogram had a good prediction effect on the 1, 3, and 5-year OS of gastric cancer patients, and ROC analysis showed that LANCL2 had a great diagnostic ability for STAD with AUC value of 0.853. Currently, the commonly used clinical biomarkers for gastric cancer diagnosis and prognosis monitoring include CEA and CA19-9. However, their diagnostic performance is relatively poor (AUC of CEA: 0.598; AUC of CA19-9: 0.648), and they cannot serve as independent risk factors for prognostic evaluation in gastric cancer patients34. More importantly, the two biomarkers exhibit abnormal expression across multiple cancers, resulting in low specificity. Our study identified LANCL2 as a biomarker with high diagnostic value for gastric cancer (AUC: 0.853) and an independent prognostic risk factor (HR: 1.274). Compared with CEA and CA19-9, LANCL2 indicates superior diagnostic and prognostic performance. However, these findings require further validation through large-scale clinical samples.

Our study still has some limitations. First of all, the diagnosis and prognosis models of LANCL2 for gastric cancer are based on public databases with limited number of gastric cancer patients and small sample size. In future studies, larger data sets and clinical samples are urgently needed to further validate our prediction model. Secondly, the results of this study are obtained by biological algorithm in a public database, which has certain limitations, and biological experiments and clinical trials are needed to verify and support the results of this study. The LANCL2 has been proved indeed to promote the growth of gastric cancer cells, but what role it plays in gastric cancer and the mechanism need to be further explored through biological experiments.

Conclusions

In conclusion, our study provides the first comprehensive multi-dimensional analysis of the relationship between LANCL2 and gastric cancer. Moreover, the results showed that LANCL2 can affect the proliferation of GC cells. The results indicated that LANCL2 may serve as a new prognostic biomarker and be expected to become a potential therapeutic target for gastric cancer. Our study provided an analytical basis and possible research direction for exploring the mechanism of LANCL2 in gastric cancer in the future.