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Pan-cancer analysis identifies CLEC12A as a potential biomarker and therapeutic target for lung adenocarcinoma

Cancer Cell International volume 25, Article number: 128 (2025) Cite this article

Abstract

C-type lectin domain family 12 member A (CLEC12A) is a type II transmembrane glycoprotein widely expressed in innate immune cells, where it plays a crucial role in immune modulation and has been implicated in cancer progression. However, its precise function in oncogenesis and immune infiltration remains incompletely understood. To investigate this, we utilized multiple databases to assess the mRNA and protein expression levels of CLEC12A across normal tissues and a broad spectrum of cancers. We also evaluated its prognostic and diagnostic significance in pan-cancer contexts. Furthermore, the relationship between CLEC12A expression and immune cell infiltration, immune checkpoints, and immune predictors was explored. In addition, Weighted Gene Co-Expression Network Analysis (WGCNA) and differential expression analysis were performed to examine the biological relevance of CLEC12A in lung adenocarcinoma (LUAD). We also leveraged various databases to predict CLEC12A’s response to immunotherapy and drug sensitivity. Finally, in vitro experiments validated the functional role of CLEC12A in LUAD. Our comprehensive pan-cancer analysis revealed that CLEC12A exhibited distinct expression patterns across different cancer types, suggesting its potential as both a diagnostic and prognostic biomarker. Notably, CLEC12A expression was strongly correlated with immune cell infiltration, immune checkpoints, and immune predictors. Functional enrichment analysis highlighted that increased CLEC12A expression in LUAD was associated with a variety of immune-related biological processes and pathways. Moreover, CLEC12A showed significant predictive value for immunotherapy outcomes, and several drugs targeting CLEC12A were identified. In vitro experiments further demonstrated that CLEC12A overexpression inhibited the proliferation, migration, and invasion of LUAD cells. Taken together, our findings position CLEC12A as a promising candidate for cancer detection, prognosis, and as a therapeutic target, particularly in LUAD, where it may serve as a potential target for both immunotherapy and targeted therapy.

Introduction

In recent decades, malignancies have accounted for a substantial proportion of global morbidity and mortality, with approximately 20% of individuals expected to develop cancer and nearly 10% succumbing to the disease [1]. Cancer continues to be one of the most significant threats to human health. Given the marked heterogeneity of malignant tumors, conventional treatment approaches such as surgery and chemoradiotherapy are often unsatisfactory [2, 3]. Although emerging therapeutic modalities, including endocrine therapy, targeted drugs, and immunotherapy, have enhanced clinical outcomes, only a limited fraction of patients experience significant benefits [4, 5]. Consequently, discovering new biomarkers and prospective therapeutic targets is essential for advancing cancer treatment strategies.

C-type lectin domain family 12 member A, also referred to as CLEC12A, is a type II transmembrane glycoprotein predominantly found in innate immune cells like neutrophils, monocytes, macrophages, and dendritic cells [6, 7]. Acting as an inhibitory receptor, CLEC12A is pivotal in modulating immunological reactions [6, 8]. Previous investigations have recognized CLEC12A as a receptor crucial to maintaining homeostasis and modulating inflammation [9, 10]. More recent research has further highlighted its function as a receptor for uric acid crystals (monosodium urate, MSU), which serve as essential danger signals in immunity triggered by cell death [11]. Furthermore, CLEC12A has been acknowledged as a marker for AML blasts, markedly contributing to carcinogenesis [12]. While extensive research has been conducted on CLEC12A within hematologic malignancies, its involvement in solid tumors has yet to be thoroughly investigated. By conducting a thorough pan-cancer analysis, this study seeks to elucidate its prospective features in tumors, along with its diagnostic, prognostic, and therapeutic implications.

In this investigation, CLEC12A was examined by utilizing various publicly available databases, including pan-cancer tissue expression, clinical prognosis, staging, tumor microenvironment (TME), tumor mutation burden (TMB), microsatellite instability (MSI), tumor neoantigens (NEO), and others. Building upon these findings, further investigation was undertaken to examine the molecular pathways, TME, predictive potential of CLEC12A in LUAD, with regard to immunotherapy and pharmacotherapy. Experimental verification was subsequently performed to substantiate the bioinformatics predictions concerning CLEC12A in LUAD (Fig. 1). In conclusion, the findings of this research reveal that CLEC12A serves as both a prognostic and diagnostic biomarker, closely associated with immune infiltration in multiple cancer types, with the most significant therapeutic promise observed in LUAD.

Methodologies and materials

Pan-cancer expression analysis

The Human Protein Atlas (HPA) was utilized to examine CLEC12A mRNA and protein expression levels across normal human tissues and organs. We obtained standardized TPM pan-cancer data, which includes mRNA data from 33 cancer types along with their corresponding normal samples, from both The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, specifically from the UCSC Xena database (https://xenabrowser.net/datapages/). To ensure data consistency and comparability, we excluded samples with expression levels of zero and any cancer type that had fewer than three samples. Additionally, we applied Log2(x + 1) transformations to the transcriptome expression values. The analysis of transcriptomic expression in both pan-cancer and normal tissues, along with the associated clinical characteristics, was conducted using the Xiantao platform (https://www.xiantaozi.com/). We evaluated cohort differences using the Wilcoxon rank-sum test, with a significance threshold of p < 0.05.

Pan-cancer prognostic and diagnostic value assessment

Pan-cancer samples lacking complete transcriptomic expression data and clinical information were excluded from the analysis. The link between CLEC12A expression and overall survival (OS) across diverse cancers was examined through univariate Cox regression and Kaplan-Meier analysis. Prognostic risk indicators were evaluated by utilizing hazard ratios (HRs), 95% confidence intervals, and p-values, with a statistical significance threshold set at p < 0.05. The diagnostic performance of CLEC12A in pan-cancer tissues was evaluated via receiver operating characteristic (ROC) curves.

Pan-cancer genetic mutations and methylation analysis

Cancer genomic data, including somatic mutations and DNA methylation, were retrieved, downloaded, analyzed, and visualized through cBioPortal (http://www.cbioportal.org) [13]. The link between CLEC12A mRNA expression and promoter DNA methylation across various cancer types was ascertained utilizing Spearman correlation analysis. Furthermore, the differential methylation (Delta value) between tumor and normal tissues was assessed.

Association of CLEC12A with TME

The ESTIMATE algorithm [14], designed to assess the fractions of immune and stromal cells within tumor tissues employing gene expression profiles, was applied to ascertain the levels of immune infiltration (Immune score) and stromal content (Stromal score) in TCGA specimens through the “estimate” R package. Data on immune cell infiltration across TCGA cancers were retrieved from the TIMER2 database [15] (http://timer.cistrome.org/). The link between CLEC12A expression and 19 distinct immune cell subsets, such as cancer-associated fibroblasts (CAFs), common lymphoid progenitors, common myeloid progenitors, granulocyte-monocyte progenitors, B cells, neutrophils, CD4+ T cells, endothelial cells (Endo), eosinophils (Eos), regulatory T cells (Treg), T cell follicular helpers (TFH), NK T cells, γ/δ T cells, monocytes, macrophages, dendritic cells, CD8+ T cells, mast cells, and NK cells, was explored using the “ggplot2” R package and analyzed via seven algorithms (CIBERSORT, CIBERSORT-ABS, QUANTISEQ, TIMER, EPIC, MCPCOUNTER, xCELL) [16], with the results presented in a heatmap through Spearman correlation analysis. Additionally, Spearman correlation was employed to examine the link between CLEC12A expression and two immune regulators (60 immune checkpoint genes) [17], and the findings were visualized using heatmaps. Moreover, a comprehensive analysis of CLEC12A expression in relation to TMB [18], MSI [19], NEO [20], non-silent mutation rate [21], silent mutation rate [22], homologous recombination deficiency (HRD) [23], tumor ploidy [24], and aneuploidy score (AS) [25] was executed employing the “maftools” R package, with the outcomes depicted through radar charts.

CLEC12A weighted co-expression network analysis and differential genes functional enrichment analysis

Genes that exhibit similar expression patterns may be co-regulated, functionally related, or part of the same pathway. To investigate these relationships, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) [26], which allows for the classification of genes into distinct modules based on their expression similarity. This approach facilitates the identification of highly co-regulated gene sets and the exploration of their relationship with CLEC12A expression.

Based on the quartiles of CLEC12A expression, the LUAD samples were categorized into four phenotypes: Q1, Q2, Q3, and Q4, where Q1 represents the top 25% of samples with the highest expression and Q4 represents the bottom 25% with the lowest expression. We employed the “WGCNA” R package to construct a weighted gene co-expression network characterized by approximately scale-free properties. The highly cooperative genes were identified by correlating their expression values.

The network modules were generated using the topological overlap measurement (TOM), and co-expressed gene modules were identified by setting a minimum module size of 50 genes and applying the dynamic hybrid cutting method (a bottom-up algorithm) [27]. Finally, key modules were determined through correlation analysis, with significance assigned to those modules containing high-importance genes (defined as genes with gene significance [GS] > 0.5 and module membership degree [MM] > 0.8).

Moreover, differentially expressed genes (DEGs) between high and low CLEC12A expression cohorts (set at a 50% threshold) were ascertained utilizing the “limma” R package [28], with the selection criteria set at log2FC absolute value ≥ 1 and p < 0.05. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) [29, 30] of the DEGs were subsequently executed through the “clusterProfiler” R package.

Construction of CLEC12A nomogram

To evaluate the prognostic value of CLEC12A in LUAD, both univariate and multivariate Cox regression analyses were executed. Furthermore, a nomogram was developed, integrating CLEC12A expression levels alongside clinical factors (such as age, gender, and stage) to estimate 1-, 3-, and 5-year survival probabilities for individuals with LUAD. Calibration curves were applied to confirm the precision of the nomogram’s predictions.

Single-cell sequencing of CLEC12A

The GSE146100 dataset was retrieved from the GEO database. Low-quality cells were filtered out using the “Seurat” R package, after which normalization and variance stabilization were applied. Clustering was then performed, followed by UMAP-based dimensionality reduction. The data were subsequently visualized and annotated with the “Single” R package. Furthermore, the “AUCell” R package was utilized to score immune, metabolic, signaling, proliferative, cell death, and mitochondria-associated biological pathways [31].

Prediction of CLEC12A expression on immunotherapy and drug sensitivity

The predictive value of CLEC12A expression for immunotherapy outcomes was assessed using the EaSIeR model [32], which incorporates features such as CYT, TLS, IFNy, Tcell_inflamed, and Chemokines. Statistical differences in the scores for these features between high and low CLEC12A expression cohorts were evaluated through the Wilcoxon Rank Sum and Signed Rank Tests. Additionally, immunotherapy scores (IPS scores) [33] were computed by analyzing PD-1 or CTLA4 blockade therapy data obtained from the TCIA database (http://tcia.at/). Chemotherapeutic sensitivity related to CLEC12A was predicted using the GDSC database [34], with lollipop plots illustrating the relationship between drug IC50 values and CLEC12A expression. Furthermore, the connectivity map (CMap) database [35] (https://clue.io), which uncovers functional connections among small molecule compounds, genes, and disease conditions based on gene expression perturbations, was queried with the 150 most markedly upregulated and 150 most markedly downregulated DEGs in both high and low CLEC12A expression cohorts to identify the top three small molecule drugs with therapeutic potential. Drugs with negative scores may possess the ability to reverse the targeted biological characteristics.

Cell lines and cell culture

Eight human LUAD cell lines (HCC827, EKVX, H1975, H1299, H3255, A549, NCI-H23, PC9), along with the normal lung epithelial cell line BEAS-2B were utilized. BEAS-2B and A549 cells were kept in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, USA) comprising 10% fetal bovine serum (FBS; Gibco, USA), whereas HCC827, EKVX, H1975, H1299, H3255, NCI-H23, and PC9 cells were grown in RPMI 1640 medium (Gibco, USA) comprising 10% FBS. All cell cultures were cultivated at 37 °C in a humidified atmosphere with 5% CO2.

Transfection

CLEC12A plasmids were introduced into the cells utilizing Lipofectamine 3000 (Invitrogen, USA) per the supplier’s protocol. Post-transfection, the cells were initially kept in an FBS-free 1640/DMEM medium, and after 6 to 8 h, this was substituted with a complete growth medium. Further experimental procedures were carried out 48 h post-transfection.

Western blot (WB) analysis

Total protein was isolated employing RIPA lysis buffer (NCM Biotech, China), which was supplemented with a mixture of phosphatase and protease inhibitors. Following electrophoresis, the denatured proteins were transferred onto a 0.4 μm PVDF membrane at 300 mA for 90 min. The membrane was subsequently incubated with a primary antibody (SAB, USA), succeeded by a secondary antibody (Proteintech, China). CLEC12A protein expression levels were finally detected through chemiluminescence (Bio-Rad, USA).

Cell proliferation assay (CCK-8)

LUAD cells (4.0 × 103) were placed into 96-well plates with 10 µL of CCK-8 solution (Biosharp, China) for a 2-hour incubation, after which cell viability was evaluated by determining the optical density at 450 nm at 18 h, 48 h, 72 h, 96 h, 120 h, and 144 h.

Colony formation assay

A sum of 3,000 LUAD cells per well were plated in 6-well plates and incubated at 37 °C for 14 days. Following the incubation, the colonies underwent fixation in 4% paraformaldehyde for a quarter of an hour. They were then exposed to crystal violet solution staining for half an hour, rinsed with running water, and subsequently counted.

Migration and invasion assays

In wound healing assays, the wound gap was observed at 6, 12, and 24 h post-scratching using a 200 µL pipette tip. Transwell assays were conducted by seeding LUAD cells into a Transwell chamber (Corning, USA) placed on a 24-well plate.

Statistical analysis

Bioinformatics data were analyzed using R software (version 4.1.0). To evaluate differences between two cohorts, we applied either Student’s t-test or the Wilcoxon test, while the Kruskal-Wallis test was employed for comparisons across multiple cohorts. Univariate Cox regression analysis, log-rank tests, and the Kaplan-Meier method were utilized to assess the prognostic significance of CLEC12A in pan-cancer. Additionally, Spearman correlation analysis was conducted to examine the statistical relationships between CLEC12A and various factors, including the abundance of immune cell infiltration, immune regulatory genes, tumor mutational burden (TMB), and microsatellite instability (MSI). P-values were adjusted using the Benjamini-Hochberg method, with statistical significance set at p < 0.05.

Fig. 1
figure 1

Flow chart

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Results

Expression of CLEC12A in normal tissues and tumor

The mRNA and protein expression patterns of CLEC12A in normal human tissues and organs were investigated by utilizing the HPA database (Fig. 2A). In the consensus dataset, CLEC12A mRNA was found to be highly expressed in bone marrow, spleen, lung, and appendix (Fig. S1A), and its protein expression was predominantly observed in the spleen, bone marrow, and lung (Fig. S1B). Furthermore, a significant underexpression of CLEC12A was identified in COAD, KICH, LIHC, LUAD, LUSC, PAAD, READ, and UCEC (p < 0.05), whereas its overexpression was noted in ESCA, GBM, HNSC, KIRC, KIRP and STAD (p < 0.05) (Fig. 2B). Given the limited adjacent normal samples available within the TCGA database, additional analysis was performed using matched normal samples from the GTEx database. Results depicted in Fig. 2C revealed that CLEC12A was markedly underexpressed in ACC, COAD, DLBC, LIHC, LUAD, LUSC, PRAD, READ, THYM, UCEC, and USC (p < 0.05), while overexpression was detected in GBM, HNSC, KIRC, KIRP, LAML, LGG, OV, PAAD, SKCM, STAD, TCGT, and THCA, which is in line with the findings from the TCGA database alone. Additionally, the link between CLEC12A expression and diverse cancer stages was examined, revealing a notable link between CLEC12A expression and the clinical stages of various cancers, such as COAD, HNSC, KIRC, LIHC, LUAD, LUSC, OSCC, STAD, THCA, UCEC, among others (Fig. 2D, Fig. S1C).

Fig. 2
figure 2

Expression profile of CLEC12A in normal organs, tissues and pan-cancer. (A) The summary of CLEC12A mRNA and protein expression in human organs and tissues. (B) The expression of CLEC12A in matched tumor tissues and normal tissues utilizing data from the TCGA database. (C) Matched analysis comparing CLEC12A expression in TCGA database and GTEx database. (D) CLEC12A expression at diverse pathological stages in pan-cancer. ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001

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Prognostic and diagnostic evaluation of CLEC12A in pan-cancer

The prognostic significance of CLEC12A was investigated employing Cox proportional hazard models and Kaplan-Meier analyses. As demonstrated by the univariate Cox regression findings (Fig. 3A), elevated CLEC12A expression was linked to a reduced risk in BLCA, CESC, LUAD, SARC and SKCM (HR < 1), whereas an elevated risk was observed in GBM, LGG and UVM (HR > 1). The Kaplan-Meier survival curves further demonstrated that elevated CLEC12A expression was linked to more favorable prognoses in patients with BLCA, CESC, LUAD, SARC, and SKCM, while higher expression levels were correlated with worse outcomes in GBM, LGG, and UVM (Fig. 3B). Moreover, the diagnostic performance of CLEC12A was assessed in cancer patients utilizing ROC curve AUC values. The findings revealed that analyses from both TCGA alone and the combined TCGA-GTEx datasets indicated strong diagnostic confidence for PAAD, LUSC, LUAD and KIRC patients, with AUC values surpassing 0.8. Additionally, LIHC, KIRP, and HNSC exhibited AUC values exceeding 0.7, indicating solid diagnostic performance (Fig. 3C). These observations suggest that abnormal CLEC12A expression holds significant prognostic relevance in multiple cancer types and could serve as a potential biomarker.

Fig. 3
figure 3

Survival analysis and diagnostic prediction of CLEC12A in pan-cancer. (A) Univariate Cox regression analysis of CLEC12A with OS in pan-cancer. (B) Kaplan–Meier analysis of the link between CLEC12A expression and OS in pan-cancer. (C) AUC of ROC curves examined the diagnosis performance of CLEC12A in pan-cancer

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Genetic mutations and epigenetic modifications of CLEC12A in pan-cancer

Given that gene expression is influenced by epigenetic factors, an investigation into CLEC12A mutation status and DNA methylation levels was carried out across various cancers. As illustrated in Fig. 4A, alterations in CLEC12A were identified in 23 different cancers, with UCS exhibiting the highest mutation rate (7.02%), followed by TGCT and OV, among others. The mutation sites in CLEC12A were emphasized in Fig. 4B, where missense mutations, specifically R201C/H/S, were predominantly located within the Lectin C domain. Single nucleotide variations (SNVs), which involve the alteration of a single nucleotide in the gene sequence, are known to serve a crucial function in carcinogenesis. Upon evaluating CLEC12A SNV diversity, it was observed that UCEC demonstrated high SNV levels (19 cases). Additionally, a certain presence of SNV mutations was noted in LUSC and STAD, though mutation levels in other cancer types were comparatively lower (Fig. 4C). Abnormal DNA methylation is recognized for its potential to elevate cancer risk by modulating gene expression. Methylation levels of CLEC12A across various cancers were investigated, revealing positive correlations between CLEC12A mRNA expression and DNA methylation in DLBC, ESCA, HNSC, LIHC, LUSC, PAAD, PCPG, PRAD, SARC, STAD, UCEC, and UCS. Conversely, negative correlations were observed in KICH, KIRP, LGG, TGCT, THCA, THYM, and UVM (Fig. 4D). Moreover, methylation levels in tumor tissues were generally lower relative to those in normal tissues for most cancers (Fig. 4E). These results underscore the possibility that aberrant CLEC12A expression may be influenced by genetic mutations or dysregulated methylation processes.

Fig. 4
figure 4

Genetic mutations and DNA methylation of CLEC12A in pan-cancer. (A) Mutation frequency of CLEC12A in pan-cancer. (B) Mutational distributions of CLEC12A in pan-cancer. (C) The SNV profile of CLEC12A in pan-cancer. (D) Bubble plot shows the spearman correlation between CLEC12A expression and methylation in pan-cancer. Positive and negative associations are depicted in red and blue, respectively. (E) Bubble plot illustrating differential CLEC12A methylation patterns across cancer types; red bubbles indicate hypermethylation, while blue bubbles represent hypomethylation (the higher the significance, the larger the bubble)

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Immune landscape of CLEC12A in pan-cancer

The TME is markedly shaped by immune infiltration components, rendering it a key predictor of immunotherapy outcomes [36]. Initially, the link between CLEC12A expression and ESTIMATE scores (comprising immune and stromal scores) across various cancers was evaluated, unveiling a notable positive link between CLEC12A expression and immune, stromal, and ESTIMATE scores in the majority of cancers (p < 0.05) (Fig. 5A). Subsequently, seven methods were employed to examine the link between CLEC12A expression and immune infiltration. The outcomes showed varying links between CLEC12A expression and immune cell populations across cancers, with notable positive correlations observed with B cells, CD8+T cells, DC, Monocytic cells, Macrophages, and TFH (Fig. 5B). In addition, immune checkpoint blockade proteins (ICP) are pivotal in modulating the TME and influencing responses to cancer immunotherapy [37]. An examination of the link between CLEC12A expression and immune checkpoint regulators (both stimulatory and inhibitory) showed significant positive correlations in most cancers (Fig. S2A). Given that TMB, MSI, NEO, Non-silent Mutation Rate, Silent Mutation Rate, HRD, Tumor Ploidy, and AS serve as common prognostic indicators for cancer and immunotherapy efficacy [38], the links between these predictors and CLEC12A expression were ascertained. CLEC12A expression demonstrated a significant positive correlation with at least five predictors in BLCA, BRCA, COAD, LGG, and UCEC, while a negative link was noted in LIHC and LUAD (Fig. 5C-E, S2B-F). Furthermore, within clinical cohorts receiving ICB therapy, higher CLEC12A expression was linked to an improved immune response in glioma and gastric cancer (Fig. 5F-G, S2G). These findings indicate that CLEC12A is essential in immune infiltration and holds potential as a novel target for immunotherapy.

Fig. 5
figure 5

Immune landscape of CLEC12A in pan-cancer. (A) The links between the ESTIMATE scores (ESTIMATE score, Immune score, and Stromal score) and CLEC12A expression in pan-cancer. (B) Seven software were employed to analyze the links between CLEC12A expression and immune cell infiltration in pan-cancer. (C-E) The relationships between CLEC12A expression and TMB(C), MSI(D), and NEO(E) were displayed by the radar chart. (F, G) The expression of CLEC12A in valid cohort(R) and invalid cohort(N) and proportion of immunotherapy response between high- and low-CLEC12A cohorts in two Melanoma cohorts receiving ICB therapy

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Functional enrichment analysis of CLEC12A in LUAD

Based on the findings above, CLEC12A exhibits a strong association with prognosis and immune infiltration in pan-cancer, with the most pronounced correlations observed in LUAD. To investigate the biological relevance of CLEC12A in LUAD, an analysis was executed on its co-expressed genes and DEGs for pathway enrichment. WGCNA was utilized for the development of co-expression modules, and Pearson correlation identified the green module, which contained 106 co-expressed genes (Fig. 6A-B). Subsequently, GO enrichment analysis revealed significant immune-related biological processes and molecular functions, encompassing cell activation involved in immune response, leukocyte activation involved in immune response, positive regulation of cytokine production, immune receptor activity and MHC protein complex binding, among others (Fig. 6C). KEGG analysis exhibited enrichment in immune signaling pathways, encompassing Neutrophil extracellular trap formation, Intestinal immune network for IgA production, autoimmune thyroid disease, and Toll-like receptor signaling, among others (Fig. 6D). Further to examine the potential biological functions of CLEC12A expression in LUAD, differential analysis and pathway analysis based on CLEC12A expression levels were conducted to identify enriched pathways and possible underlying mechanisms. Figure 7A presented the DEGs from the CLEC12A high and low expression cohorts following conditional screening. GO analysis showed that DEGs in the high CLEC12A expression cohort were concentrated in immune-related processes and molecular functions, including regulation of immune effector processes, leukocyte-mediated immunity, and immune receptor activity, among others (Fig. 7B). Conversely, the low expression cohort was mainly linked to hormone regulation and vascular regulation (Fig. 7C). KEGG analysis demonstrated that high CLEC12A expression DEGs participated in several immune signaling pathways, encompassing Cytokine-cytokine receptor interaction, Chemokine signaling, Th17 cell differentiation, and NK cell-mediated cytotoxicity, among others (Fig. 7D). In contrast, low CLEC12A expression DEGs were implicated in Neuroactive ligand-receptor interaction, Complement and coagulation cascades, and Platelet activation (Fig. 7E). Furthermore, GSEA analysis revealed that high CLEC12A expression might activate pathways, including the Intestinal Immune Network for IgA Production, Primary Immunodeficiency, Cytokine-Cytokine Receptor Interaction, and NK Cell-Mediated Cytotoxicity (Fig. S3A). These results underscore the crucial role of high CLEC12A expression in immune regulation in LUAD.

Fig. 6
figure 6

Functional analysis of CLEC12A in LUAD. (A) Heatmap depicts the link between modules and the expression of CLEC12A (According to the quartile of CLEC12A expression, the samples were divided into four phenotypes: Q1, Q2, Q3, and Q4(Q1 being the 25% with the highest expression and Q4 being the 25% with the lowest expression)). (B) The link between the green module’s membership and gene significance. (C) Circular histogram for GO analyses of hub genes of the green module. BP, biological processes; CC, cellular components; MF, molecular function. (D) Bar graph for KEGG pathway analyses of hub genes of the green module

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Fig. 7
figure 7

Pathway enrichment analysis of CLEC12A in LUAD. (A) Volcano map shows the DEGs in high- and low-CLEC12A expression cohorts (red: up-regulation; blue: down-regulation). (B, C) GO analyses of up-(B) and low-regulation (C) DEGs. (D, E) KEGG analyses of up-(D) and low-regulation (E) DEGs

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Prognostic value of CLEC12A in LUAD

The analysis of CLEC12A expression and survival outcomes indicated that the survival rate for LUAD was significantly higher in the group with high CLEC12A expression compared to the group with low expression (Fig. 8A). Clinical variables such as age, gender, and tumor stage are known to influence LUAD prognosis. Survival analysis showed that elevated CLEC12A expression aligns with more favorable clinical outcomes (Fig. 3B). Consequently, to ascertain which factors impact the OS rate of LUAD patients, we integrated these clinical variables with CLEC12A expression in the TCGA-LUAD database for univariate and multivariate Cox regression analyses. It was observed that, regardless of whether the univariate or multivariate approach was employed, tumor stage and CLEC12A consistently emerged as independent prognostic factors for LUAD (p < 0.05) (Fig. 8B-C). This finding was subsequently validated across multiple GEO datasets (GSE41271 and GSE72094) (Fig. 8D). Based on these analytical outcomes, a nomogram was developed to estimate the 1, 3, and 5-year survival probabilities for LUAD patients (Fig. 8E). The calibration curve within the nomogram closely mirrors the ideal curve, indicating that the predicted survival probabilities were in strong concordance with the actual observed outcomes (8 F).

Fig. 8
figure 8

Predictive performance of CLEC12A in LUAD. (A) Proportion of patients living and dying in cohorts with CLEC12A different expression levels. Blue represents living patients, and red represents dead patients. (Q1 represents the 25% of the samples with the highest expression, Q4 represents the 25% of the samples with the lowest expression). (B, C) The prognostic significance of CLEC12A and clinicopathologic factors were analyzed by univariate(B) and multivariate(C) Cox analysis in the TCGA-LUAD dataset. (D) Multiple GEO datasets validated the prognostic significance of CLEC12A with clinicopathological factors. (E) Construction of a nomogram for predicting the survival rate of individuals with LUAD. (F) 1-, 3-, and 5-year calibration curves for evaluating the accuracy

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Immune landscape of CLEC12A expression in LUAD

The infiltration of tumor-immune cells has been consistently linked to improved cancer prognosis [39]. In light of this, seven distinct algorithms were employed to analyze the immune infiltration in LUAD related to CLEC12A expression. As depicted in Fig. 9A -B, a negative link was identified between CLEC12A expression and B cell plasma via the CIBERSORT algorithm. However, other immune cell types, encompassing dendritic cell, CD8+T cells, macrophages, and monocytes, exhibited a positive association with CLEC12A expression across multiple algorithms. The high correlation was notably observed in dendritic cells, macrophages and monocytes (Fig. 9A). Furthermore, CLEC12A expression was positively correlated with various scores, including cytotoxicity, immune, stroma, and microenvironment scores (Fig. 9B). Additionally, CLEC12A expression was found to be linked to immune subtypes in LUAD, ranking just below BRCA, LGG, and LIHC (Fig. 9C). Six immune subtypes have been characterized in previous studies, encompassing wound healing (C1), IFN-γ dominant (C2), inflammatory (C3), lymphocyte-depleted (C4), immunologically quiet (C5), and TGF-β dominant (C6), each associated with specific tumor molecular traits and patient outcomes [17]. The findings showed that the C1 subtype was the predominant immune subtype in the low CLEC12A expression cohort, while C2 and C3 subtypes were predominant in the high CLEC12A expression cohort (Fig. 9D). As per relevant studies [17], the C3 subtype has the most favorable prognosis, followed by C2 and C1, whereas the C4 and C6 subtypes are linked to poorer outcomes. This may elucidate why LUAD patients with elevated CLEC12A expression exhibit better survival outcomes compared to those with lower expression.

Fig. 9
figure 9

Analysis of tumor immune infiltration for CLEC12A in LUAD. (A) The lollipop chart shows the correlation between CLEC12A expression and LUAD TME. (B) Seven algorithms evaluate the difference in LUAD TME between high and low CLEC12A expression cohorts. (C) Associations between CLEC12A expression and immune subtypes in 30 cancers. (D) Differences of immune subtypes between high and low expression cohorts of CLEC12A in LUAD

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Single-cell analysis of CLEC12A in LUAD

To gain deeper insights into the connection between CLEC12A and the TME within LUAD, a scRNA-seq dataset of LUAD (GSE146100) from the GEO database was utilized for analysis. Following steps of quality control, normalization, and clustering based on UMAP dimensionality reduction, 11 distinct cellular populations were identified (Fig. 10A). High CLEC12A expression was particularly prominent in DCs as well as Monocytes/Macrophages (Fig. 10B-C). Subsequent pathway enrichment analysis conducted using the “AUCell” R package revealed that in monocytes/macrophages, the group with high CLEC12A expression exhibited higher activity scores in immune, metabolic, and cell death-related biological pathways (Fig. 10D). These observations imply that CLEC12A levels may have a modulatory effect on the TME in LUAD, potentially affecting patient prognosis.

Fig. 10
figure 10

Single-Cell Analysis of CLEC12A in LUAD. (A) UMAP plot visualizes the distribution of cell types in scRNA data (GSE146100). (B) The UMAP plot shows the cell-type expression of CLEC12A in the GSE146100 database. (C) The Kruskal-Wallis rank sum test assessed the difference in expression of CLEC12A in different immune cell types. (D) The bubble map shows the pathway differences in each cell type in the positive and negative CLEC12A expression cohorts. Red represents an increase in the score of the pathway (activation) in the cohort with positive expression of CLEC12A, and blue represents a decrease in the score of the pathway (inhibition) in the cohort with positive expression of CLEC12A. The size of the bubble indicates significance, and larger means more significance

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Predictive assessment of CLEC12A for immunotherapy and drug therapy in LUAD

To thoroughly investigate CLEC12A expression’s capacity as a prognostic indicator for LUAD immunotherapy, multiple methods were employed for evaluation. Initially, the relationship between CLEC12A expression and tumor antigens, including various mutations, NEO, CTA, and TCR/BCR diversity, was examined [17]. Findings showed that the Q1 cohort (those with high CLEC12A expression) exhibited greater TCR/BCR diversity, Lymphocyte Infiltration Signature Scores, TIL Regional Fraction and Leukocyte Fraction, while the Q4 cohort (low CLEC12A expression) presented mutation-associated features such as AS, HRD, Silent Mutation Rate, and Proliferation (Fig. 11A). Additionally, EaSIeR [32], a biomarker-based predictive tool, demonstrated that LUAD patients with elevated CLEC12A expression achieved higher scores in models predicting CYT, TLS, IFNy, T cell inflammation and Chemokines, which suggests a greater potential benefit from immunotherapy for this cohort (Fig. 11B). Furthermore, the IPS (Immunophenoscore), an innovative predictive tool, was utilized to assess the patient’s responses to immunotherapy targeting CTLA-4 and PD-1 [40, 41]. As depicted in Fig. 11C, under conditions of CTLA-4 negative + PD-1 positive, CTLA-4 positive + PD-1 negative, and CTLA-4 positive + PD-1 positive, patients with high CLEC12A expression demonstrated superior immunotherapy benefits in IPS scores, particularly under PD-1 positive conditions (p < 0.05). Moreover, correlation analysis demonstrated a robust positive connection between CLEC12A and PD-1 in LUAD (correlation coefficient = 0.36, p < 0.001) (Fig. 11D), further validated by Fisher’s exact test, which confirmed this significant positive association (P.fisher < 0.001) (Fig. 11E). Additionally, survival analysis revealed that PD-1 expression alone was not markedly linked to prognosis in LUAD patients; however, when co-expressed with CLEC12A, patients exhibiting high levels of both markers experienced markedly better survival outcomes compared to the low expression cohort (Fig. 11F). Alongside immunotherapy, chemotherapy continues to be a vital treatment for LUAD, and several databases were examined to determine the most effective chemotherapeutic agents. GDSC database analysis uncovered a significant inverse link between CLEC12A expression and the IC50 of the top 30 chemotherapeutic agents, implying that patients with high CLEC12A expression could benefit from these drugs (Fig. S4A). To identify potential small-molecule drugs that could counteract CLEC12A-mediated tumor-promoting effects, the CMap database was used, revealing TTNPB, mercaptopurine and STOCK1N-35,696 as promising compounds. TTNPB was identified as having the lowest score, making it the most likely candidate to reverse the molecular alterations caused by dysregulated CLEC12A expression and inhibit tumor-promoting effects (Fig. S4B). Fig. S4C showed the molecular docking results of CLEC12A and TTNPB. Finally, TCGA-LUAD database analysis showed that patients who achieved partial remission (PR) or complete remission (CR) following initial therapy exhibited higher CLEC12A expression, suggesting a better response to treatment in those with elevated CLEC12A expression (Fig. S4D).

Fig. 11
figure 11

Prediction of CLEC12A expression for immunotherapy in LUAD. (A) Heat maps show the associations between CLEC12A expression and multiple immune responses and genomic states. From left to right, the heat map represents the intra-group mean of each immune response score and genome status score for the Q1, Q2, Q3, and Q4 subtypes. (B) The immune prediction in the score of the high- and low-CLEC12A expression cohort was evaluated by the EaSIeR model (including CYT, TLS, IFNy, Tcell_inflamed, and Chemokines features). (C) Immunotherapy IPS scores between high- and low- CLEC12A expression cohorts (pos, positive; neg, negative). (D) The scatter plot shows the Pearson correlation between PDCD1 and CLEC12A. (E) Fischer’s precise test explores the link between PDCD1 and CLEC12A. (F) KM analysis for CLEC12A, PDCD1 and CLEC12A combined with PDCD1 expression and OS in LUAD

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Validation of the role of CLEC12A in LUAD

To further corroborate the biological significance of CLEC12A in LUAD cells, in vitro experiments were conducted. When compared with normal lung epithelial cells (BEAS-2B), CLEC12A protein expression was noted to be diminished in LUAD cell lines (EKVX, H1975, H1299, H3255, A549, H23) (Fig. 12A). Based on the protein expression levels, the H1975 and A549 cell lines, exhibiting relatively lower CLEC12A expression, were chosen for functional validation. A CLEC12A plasmid was constructed and overexpressed in H1975 and A549 cells, with the efficiency of overexpression confirmed through WB (Fig. 12B). The CCK-8 assay demonstrated that CLEC12A overexpression could diminish cell viability in both H1975 and A549 cells (Fig. 12C), a finding further corroborated by a colony formation assay (Fig. 12D), illustrating the suppression of LUAD cell line proliferative capacity. Migration and invasion capabilities were subsequently ascertained through wound healing and transwell assays. The wound healing assay suggested a marked suppression of migration in H1975 and A549 cells with CLEC12A overexpression (Fig. 12E), and the transwell assay demonstrated that CLEC12A overexpression impaired the invasive capacity of LUAD cells (Fig. 12F). These results collectively indicated that CLEC12A overexpression could inhibit LUAD progression, which was in line with the results of the bioinformatics analysis.

Fig. 12
figure 12

Functional effects of CLEC12A on LUAD cells. (A) WB shows the protein expression of CLEC12A in one normal lung cell line and eight lung cancer cell lines. (B) WB shows overexpression of CLEC12A in H1975 and A549 cells. (C, D) The proliferation capacities were measured by CCK8 assay (C) and clone formation assay (D) in H1975 and A549 cells with CLEC12A overexpression. (E, F), The abilities of migration and invasion were measured by wound healing (E) and transwell assay (F) in H1975 and A549 cells with CLEC12A overexpression. *P < 0.05; **P < 0.01; ***P < 0.001

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Discussion

CLEC12A, categorized as a type II transmembrane glycoprotein, is broadly expressed within innate immune cells and has been identified as having a significant function in modulating immune responses and influencing cancer progression [8, 12]. While extensive research has been conducted on CLEC12A in non-cancerous contexts and hematologic malignancies, its specific involvement in solid tumors remains unreported. Consequently, a pan-cancer analysis was undertaken to examine its potential implications for tumor characteristics, its value in prognostic prediction, and its therapeutic relevance.

The expression of CLEC12A was initially compared between normal tissues and a variety of cancers, illustrating that the distinct levels of CLEC12A in diverse tumor types point to its varied potential functions and biological activities. Furthermore, elevated CLEC12A expression was linked to markedly enhanced survival rates in individuals with BLCA, CESC, LUAD, SARC, and SKCM. Additionally, CLEC12A exhibited high predictive reliability for seven types of cancer (AUC > 0.7), with a particularly strong predictive value for PAAD, LUSC, LUAD and KIRC (AUC > 0.8). These findings underscore CLEC12A’s pivotal role in cancer prognosis and suggest it could be a potent prognostic biomarker for cancer patients.

The TME comprises diverse immune cells and stromal components, which are capable of serving pivotal functions in either tumor clearance or immune evasion [39]. Prior research has suggested that the degree of immune cell infiltration within the TME correlates with both tumor development and patient prognosis [42]. Thus, gaining insights into the mechanisms governing immune infiltration in tumors is critical for enhancing treatment outcomes and devising novel therapeutic strategies [43]. In this research, it was observed that CLEC12A expression demonstrated a positive association with immune scores across the TME in the majority of tumors. There are various evaluation algorithms for assessing immune infiltration, each employing unique biomarkers, calculation methods, or evaluation weights. As a result, relying on a single algorithm may introduce selective bias. To improve the reliability of our findings, we have integrated seven algorithms and conducted Spearman correlation analyses between gene expression levels and immune infiltration scores as determined by these algorithms. Consequently, analysis utilizing seven distinct algorithms revealed that in most cancer types, CLEC12A expression was positively linked to the infiltration of B cells, CD8+ T cells, DCs, Monocytic cells, as well as Macrophages M1/M2, indicating that CLEC12A may impact tumor progression and outcomes by influencing the TME. Furthermore, it was discovered that CLEC12A expression markedly positively correlates with a cluster of immune checkpoint genes, encompassing both immunosuppressive and immunostimulatory genes in most cancers. Besides, biomarkers such as TMB, MSI, NEO, Non-silent Mutation Rate, Silent Mutation Rate, HRD, Tumor Ploidy and AS are indicators for forecasting the effectiveness of immune checkpoint inhibitors (ICI) across various cancer types. It was identified in our research that CLEC12A expression showed significant positive correlations with the majority of predictive biomarkers in BLCA, BRCA, COAD, LGG, and UCEC, while demonstrating a significant inverse correlation in LIHC and LUAD. These findings propose CLEC12A as a prospective biomarker for forecasting the success of cancer immunotherapy.

Overall, CLEC12A holds a notably influential role in LUAD. This investigation revealed that CLEC12A mRNA and protein levels were reduced in LUAD, and lower CLEC12A expression was related to more progressive clinical stages and poorer outcomes in individuals with LUAD. Through both univariate and multivariate Cox regression analysis, CLEC12A was identified as an independent prognostic factor, offering protective value for LUAD. In vitro experiments demonstrated that overexpression of CLEC12A markedly suppressed LUAD cell proliferation, invasion, and migration. Regarding the molecular mechanisms activated by CLEC12A dysregulation in LUAD, co-expression network analysis revealed CLEC12A and its co-expressed genes to be implicated in numerous immune-related biological processes and molecular activities. Further analyses indicated that high CLEC12A expression serves a substantial function in immune responses, with KEGG pathway analysis showing that elevated CLEC12A expression can activate various immune signaling pathways, encompassing Cytokine-cytokine receptor interaction, Intestinal immune network for IgA production, and Natural killer cell-mediated cytotoxicity. These pathways have been shown to markedly influence the TME, immune therapy efficacy, and patient prognosis [44]. Order to affirm the predictive potential of CLEC12A in immunotherapy response for LUAD, assessments using the EaSIeR model and IPS scores indicated that LUAD patients with high CLEC12A expression have a better response to immunotherapy and an improved prognosis compared to those with low expression. Additionally, GDSC database analysis uncovered that CLEC12A expression is negatively correlated with the IC50 values of 30 drugs, suggesting that these drugs may be beneficial in preventing cancer progression. Moreover, CMap database analysis pinpointed three compounds—TTNPB, mercaptopurine, and STOCK1N.35,696—as potential inhibitors of CLEC12A-related oncogenesis, with TTNPB being identified as the most potent based on its lowest score. Previous studies have demonstrated TTNPB’s potential therapeutic effects, particularly in inhibiting cancer cell proliferation and inducing cell death [45]. However, additional clinical investigations are required to assess the therapeutic efficacy of these compounds in cancer treatment further. Recent studies have shown that CLEC12A plays a crucial role in maintaining immune homeostasis by recognizing monosodium urate crystals released from dying cells [46, 47]. Its related pathways encompass the innate immune system and IL-1 family signaling pathways [47]. In this study, we found that CLEC12A is strongly associated with immune cell infiltration in LUAD. Intriguingly, CLEC12A has a negative correlation with B - cell plasma cells, while it has a positive correlation with other immune cells (such as CD8+ T cells and macrophages). We conducted a gene correlation analysis in LUAD and found that CLEC12A exhibits a strong correlation with genes associated with immunostimulation, chemokines, immunoinhibition, and human leukocyte antigens (Fig. S5). In the context of chemokines, CLEC12A demonstrates a strong negative correlation with CCL7, CCL15, CCL16, CCL17, CCL23, CCL24, CCL25, XCL1, and XCL2. Notably, CCL25 (also known as TECK) is particularly significant for B cell chemotaxis through its receptor CCR9. Conversely, CLEC12A shows a substantial positive correlation with CCL2, CCL3, CCL4, CCL5, CCL13, CCL14, CCL18, CCL19, CCL21, CCL22, CXCL2, CXCL9, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL16, and CX3CL1. Among these, CCL3, CCL4, CCL5, CCL19, CCL21, CXCL9, CXCL10, CXCL11, CXCL12, and CX3CL1 are particularly crucial for the chemotaxis of CD8 + T cells, especially in the context of antiviral and antitumor responses. Furthermore, CCL2, CCL3, CCL4, CCL5, CCL13, CCL14, CCL18, CCL19, CCL21, CXCL2, CXCL9, CXCL10, CXCL11, CXCL12, CXCL16, and CX3CL1 are key chemokines involved in recruiting macrophages to areas of inflammation, infection, or tissue damage. The observed correlation with these chemokines may help explain CLEC12A’s influence on the chemotaxis of various immune cells.

Despite the comprehensive and systematic analysis of CLEC12A conducted in this study, several limitations must be acknowledged. Firstly, variations in sequencing methods across different databases may introduce potential biases that could impact the reliability and generalizability of the data. Additionally, this study relied solely on publicly available databases, highlighting the need for further validation of these findings in larger clinical cohorts. Secondly, while the study establishes a strong link between CLEC12A expression and immune cell infiltration, as well as its association with cancer prognosis, definitive evidence demonstrating the precise role of CLEC12A in tumor immune infiltration is still lacking. Furthermore, although we confirmed that the overexpression of CLEC12A inhibits the viability, migration, and invasiveness of lung cancer cells in in vivo experiments, further validation in animal models is necessary, and the exact regulatory mechanisms remain unclear. Finally, no clinical trials have yet assessed anti-CLEC12A targeted therapies. Therefore, future efforts should focus on the development and clinical evaluation of anti-tumor immunochemotherapy drugs targeting CLEC12A, alongside additional prospective studies to validate its clinical significance in LUAD.

Conclusion

The identification of CLEC12A’s role in pan-cancer diagnosis and prognosis facilitates the prediction of cancer incidence and development. Furthermore, CLEC12A expression is correlated with various predictive indicators, ICI, and immune cell infiltration in tumors across diverse cancer types, particularly in LUAD. In addition, a deeper investigation into the robust immunobiological functions and anti-cancer therapeutic implications of CLEC12A in LUAD has been conducted, unveiling new strategies for its therapeutic application in LUAD treatment. On the whole, CLEC12A is identified not merely as a prospective diagnostic and prognostic indicator for LUAD but also as a promising therapeutic target in immunotherapy.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63.

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019;69(1):7–34.

    Article  PubMed  Google Scholar 

  3. Yang ZS, Tang XJ, Guo XR, Zou DD, Sun XY, Feng JB, Luo J, Dai LJ, Warnock GL. Cancer cell-oriented migration of mesenchymal stem cells engineered with an anticancer gene (PTEN): an imaging demonstration. Onco Targets Ther. 2014;7:441–6.

    PubMed  PubMed Central  CAS  Google Scholar 

  4. Li K, Zhang A, Li X, Zhang H, Zhao L. Advances in clinical immunotherapy for gastric cancer. Biochim Biophys Acta Rev Cancer. 2021;1876(2):188615.

    Article  PubMed  CAS  Google Scholar 

  5. Sharma P, Hu-Lieskovan S, Wargo JA, Ribas A. Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell. 2017;168(4):707–23.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Marshall AS, Willment JA, Pyz E, Dennehy KM, Reid DM, Dri P, Gordon S, Wong SY, Brown GD. Human MICL (CLEC12A) is differentially glycosylated and is down-regulated following cellular activation. Eur J Immunol. 2006;36(8):2159–69.

    Article  PubMed  CAS  Google Scholar 

  7. Marshall AS, Willment JA, Lin HH, Williams DL, Gordon S, Brown GD. Identification and characterization of a novel human myeloid inhibitory C-type lectin-like receptor (MICL) that is predominantly expressed on granulocytes and monocytes. J Biol Chem. 2004;279(15):14792–802.

    Article  PubMed  CAS  Google Scholar 

  8. Bakker AB, van den Oudenrijn S, Bakker AQ, Feller N, van Meijer M, Bia JA, Jongeneelen MA, Visser TJ, Bijl N, Geuijen CA, et al. C-type lectin-like molecule-1: a novel myeloid cell surface marker associated with acute myeloid leukemia. Cancer Res. 2004;64(22):8443–50.

    Article  PubMed  CAS  Google Scholar 

  9. Redelinghuys P, Whitehead L, Augello A, Drummond RA, Levesque JM, Vautier S, Reid DM, Kerscher B, Taylor JA, Nigrovic PA, et al. MICL controls inflammation in rheumatoid arthritis. Ann Rheum Dis. 2016;75(7):1386–91.

    Article  PubMed  CAS  Google Scholar 

  10. Sagar D, Singh NP, Ginwala R, Huang X, Philip R, Nagarkatti M, Nagarkatti P, Neumann K, Ruland J, Andrews AM, et al. Antibody Blockade of CLEC12A delays EAE onset and attenuates disease severity by impairing myeloid cell CNS infiltration and restoring positive immunity. Sci Rep. 2017;7(1):2707.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Neumann K, Castiñeiras-Vilariño M, Höckendorf U, Hannesschläger N, Lemeer S, Kupka D, Meyermann S, Lech M, Anders HJ, Kuster B, et al. Clec12a is an inhibitory receptor for uric acid crystals that regulates inflammation in response to cell death. Immunity. 2014;40(3):389–99.

    Article  PubMed  CAS  Google Scholar 

  12. Toft-Petersen M, Nederby L, Kjeldsen E, Kerndrup GB, Brown GD, Hokland P, Stidsholt Roug A. Unravelling the relevance of CLEC12A as a cancer stem cell marker in myelodysplastic syndrome. Br J Haematol. 2016;175(3):393–401.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401–4.

    Article  PubMed  Google Scholar 

  14. Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW, Levine DA, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013;4:2612.

    Article  PubMed  Google Scholar 

  15. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509–14.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Yuan Q, Zhang W, Shang W. Identification and validation of a prognostic risk-scoring model based on sphingolipid metabolism-associated cluster in colon adenocarcinoma. Front Endocrinol (Lausanne). 2022;13:1045167.

    Article  PubMed  Google Scholar 

  17. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, et al. The immune landscape of cancer. Immunity. 2018;48(4):812–e830814.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Jardim DL, Goodman A, de Melo Gagliato D, Kurzrock R. The challenges of tumor mutational burden as an immunotherapy biomarker. Cancer Cell. 2021;39(2):154–73.

    Article  PubMed  CAS  Google Scholar 

  19. Hause RJ, Pritchard CC, Shendure J, Salipante SJ. Classification and characterization of microsatellite instability across 18 cancer types. Nat Med. 2016;22(11):1342–50.

    Article  PubMed  CAS  Google Scholar 

  20. Li L, Goedegebuure SP, Gillanders WE. Preclinical and clinical development of neoantigen vaccines. Ann Oncol. 2017;28(suppl12):xii11–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Seiler M, Peng S, Agrawal AA, Palacino J, Teng T, Zhu P, Smith PG, Buonamici S, Yu L. Somatic mutational landscape of splicing factor genes and their functional consequences across 33 cancer types. Cell Rep. 2018;23(1):282–e296284.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Wu R, Oshi M, Asaoka M, Yan L, Benesch MGK, Khoury T, Nagahashi M, Miyoshi Y, Endo I, Ishikawa T, et al. Intratumoral tumor infiltrating lymphocytes (TILs) are associated with cell proliferation and better survival but not always with chemotherapy response in breast cancer. Ann Surg. 2023;278(4):587–97.

    Article  PubMed  Google Scholar 

  23. Hoppe MM, Sundar R, Tan DSP, Jeyasekharan AD. Biomarkers for homologous recombination deficiency in cancer. J Natl Cancer Inst. 2018;110(7):704–13.

    Article  PubMed  Google Scholar 

  24. Zanetti M, Mahadevan NR. Cancer. Immune surveillance from chromosomal chaos? Science. 2012;337(6102):1616–7.

    Article  PubMed  Google Scholar 

  25. Chang TG, Cao Y, Shulman ED, Ben-David U, Schäffer AA, Ruppin E. Optimizing cancer immunotherapy response prediction by tumor aneuploidy score and fraction of copy number alterations. NPJ Precis Oncol. 2023;7(1):54.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics. 2008;24(5):719–20.

    Article  PubMed  CAS  Google Scholar 

  28. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Yu G, Wang LG, Han Y, He QY. ClusterProfiler: an R package for comparing biological themes among gene clusters. Omics. 2012;16(5):284–7.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Han Y, Wang Y, Dong X, Sun D, Liu Z, Yue J, Wang H, Li T, Wang C. TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment. Nucleic Acids Res. 2023;51(D1):D1425–31.

    Article  PubMed  Google Scholar 

  32. Lapuente-Santana Ó, van Genderen M, Hilbers PAJ, Finotello F, Eduati F. Interpretable systems biomarkers predict response to immune-checkpoint inhibitors. Patterns (N Y). 2021;2(8):100293.

    Article  PubMed  CAS  Google Scholar 

  33. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z. Pan-cancer Immunogenomic analyses reveal Genotype-Immunophenotype relationships and predictors of response to checkpoint Blockade. Cell Rep. 2017;18(1):248–62.

    Article  PubMed  CAS  Google Scholar 

  34. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, et al. Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41(Database issue):D955–961.

    PubMed  CAS  Google Scholar 

  35. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet JP, Subramanian A, Ross KN, et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929–35.

    Article  PubMed  CAS  Google Scholar 

  36. Wu T, Dai Y. Tumor microenvironment and therapeutic response. Cancer Lett. 2017;387:61–8.

    Article  PubMed  CAS  Google Scholar 

  37. Robert C. A decade of immune-checkpoint inhibitors in cancer therapy. Nat Commun. 2020;11(1):3801.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Yang K, Ma Y, Chen W, Liu L, Yang Z, He C, Zheng N, Liu X, Cheng X, Song J, et al. CCDC58 is a potential biomarker for diagnosis, prognosis, immunity, and genomic heterogeneity in pan-cancer. Sci Rep. 2024;14(1):8575.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013;39(4):782–95.

    Article  PubMed  CAS  Google Scholar 

  40. Johnson DB, Sullivan RJ, Menzies AM. Immune checkpoint inhibitors in challenging populations. Cancer. 2017;123(11):1904–11.

    Article  PubMed  Google Scholar 

  41. Chen H, Yang M, Wang Q, Song F, Li X, Chen K. The new identified biomarkers determine sensitivity to immune check-point Blockade therapies in melanoma. Oncoimmunology. 2019;8(8):1608132.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Galon J, Angell HK, Bedognetti D, Marincola FM. The continuum of cancer immunosurveillance: prognostic, predictive, and mechanistic signatures. Immunity. 2013;39(1):11–26.

    Article  PubMed  CAS  Google Scholar 

  43. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24(5):541–50.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Cheng Q, Duan W, He S, Li C, Cao H, Liu K, Ye W, Yuan B, Xia Z. Multi-Omics data integration analysis of an Immune-Related gene signature in LGG patients with epilepsy. Front Cell Dev Biol. 2021;9:686909.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Darro F, Cahen P, Vianna A, Decaestecker C, Nogaret JM, Leblond B, Chaboteaux C, Ramos C, Pétein M, Budel V, et al. Growth Inhibition of human in vitro and mouse in vitro and in vivo mammary tumor models by retinoids in comparison with Tamoxifen and the RU-486 anti-progestagen. Breast Cancer Res Treat. 1998;51(1):39–55.

    Article  PubMed  CAS  Google Scholar 

  46. Tang H, Xiao Y, Qian L, Wang Z, Lu M, Yao N, Zhou T, Tian F, Cao L, Zheng P, et al. Mechanistic insights into the C-type lectin receptor CLEC12A-mediated immune recognition of monosodium urate crystal. J Biol Chem. 2024;300(3):105765.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Li K, Neumann K, Duhan V, Namineni S, Hansen AL, Wartewig T, Kurgyis Z, Holm CK, Heikenwalder M, Lang KS, et al. The uric acid crystal receptor Clec12A potentiates type I interferon responses. Proc Natl Acad Sci USA. 2019;116(37):18544–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Funding

This work was supported by Jiangsu medical scientific research project of Jiangsu Health Commission, the National Natural Science Foundation of China (81870409), Sanming Project of Medicine in Shenzhen (SZZYSM202211003), the 789 Outstanding Talent Program of SAHNMU (789ZYRC202070102), and Plan on enhancing scientific research in GMU (2024SRP150).

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Author notes

  1. Desheng Zhou, Yachao Cui and Tianxiang Liang contributed equally to this work.

Authors and Affiliations

  1. Guangzhou Institute of Cancer Research, The Affiliated Cancer Hospital, Guangzhou Medical University, Guangzhou, China

    Desheng Zhou, Zhenpeng Wu, Haiping Yan, Yingchang Li, Yunen Lin & Qiang You

  2. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Yachao Cui & Wenguang Yin

  3. Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China

    Yachao Cui & Wenguang Yin

  4. GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, China

    Yachao Cui & Wenguang Yin

  5. The First Affiliated Hospital of Jinan University, Guangzhou, 510630, China

    Tianxiang Liang

  6. Department of Biotherapy, the Second Affiliated Hospital of Nanjing Medical University, Nanjing, China

    Qiang You

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Contributions

Desheng Zhou: Conceptualization, Data curation, Methodology, Formal analysis, Investigation, Software, Validation, Writing– original draft, Writing– review & editing. Yachao Cui: Formal analysis, Investigation, Software, Visualization, Writing– original draft. Tianxiang Liang: Methodology, Formal analysis, Investigation, Visualization. Zhenpeng Wu: Methodology, Investigation, Visualization. Haiping Yan: Investigation, Visualization. Yingchang Li: Methodology, Investigation, Funding acquisition. Wenguang Yin：Methodology, Supervision. Yunen Lin: Methodology, Supervision. Qiang You: Conceptualization, Methodology, Supervision, Funding acquisition, Writing– review & editing.

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Correspondence to Wenguang Yin, Yunen Lin or Qiang You.

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Zhou, D., Cui, Y., Liang, T. et al. Pan-cancer analysis identifies CLEC12A as a potential biomarker and therapeutic target for lung adenocarcinoma. Cancer Cell Int 25, 128 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03755-5

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12935-025-03755-5

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Cancer Cell International

ISSN: 1475-2867

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