To investigate the RNA expression profiles and clinical characteristics of patients with lung adenocarcinoma (LUAD), our study utilized data from the TCGA database (https://portal.gdc.cancer.gov/). Utilizing this compre- hensive resource, we obtained RNA-seq data as well as corresponding clinical information for a total of 535 LUAD patients. In order to establish a reliable baseline for comparison, we also collected RNA-seq data for 59 normal lung tissues. By obtaining a substantial dataset comprising both cancerous and normal lung tissue, we were able to perform robust analysis and draw meaningful conclusions. The inclusion of such a diverse sample set provides a comprehensive overview of the RNA expression profiles specific to LUAD, enabling us to make accurate assessments of the disease at the molecular level.

For our experimental analysis, we obtained various LUAD cell lines (H1975, H1299, H23) and a normal lung cell line (16HBE) from Procell. These cell lines were cultured in sterile culture dishes using RPMI-1640 medium supplemented with 10% fetal bovine serum. The cells were incubated at 37°C in a 5% CO₂ environment with saturated humidity, maintaining optimal conditions for their growth and proliferation.

To investigate the RNA expression levels in LUAD cells, we isolated and extracted RNA using the TRIzol reagent (Invitrogen). The concentration of RNA was determined, and subsequently, reverse transcription into cDNA was performed using the Reverse Transcription System kit. Finally, we conducted RT-qPCR reactions to quantify the expression levels of the target genes. The primer sequences used for this analysis were as follows: CLEC4A: Forward 5'-ATGAACAGGTGGTTGGATTGG-3', Reverse 5'-CCAGTACAGGCTCTGGCACAT-3'; ACTIN: Forward 5'-ACTTAGTTG CGTTACACCCTTTC-3', Reverse 5'-TCACCTTCACCGTTCCAGTTT-3'.

To gain insights into the immune infiltrate composition within the tumor microenvironment, we referred to Bindea G's study [10] to obtain marker genes specific to 24 different immune cells. Exploring the infiltration patterns of immune cells in both high and low CLEC4A expression groups, we employed the ssGSEA algorithm and conducted a statistical analysis using the Wilcoxon rank sum test. Furthermore, we investigated the correlation between CLEC4A and the aforementioned 24 immune cell populations using the Spearman correlation method.

The RNAseq data in HTSeq- FPKM format was converted to TPM format, and log2 was transformed. The Wilcoxon rank sum test was used to analyze the expression of CLEC4A in pan-cancer. The results of the differences between paired and unpaired samples for LUAD in TCGA were then presented using grouped comparison plots.

The expression profiles and clinical information of GSE75037 and GSE68465 were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/gds) and used to validate the differential expression and prognostic value of CLEC4A in LUAD.

The TISIDB database (http://cis.hku.hk/TISIDB) integrates multiple data sources in tumor immunology and contains 988 genes associated with anti-tumour immunity to study tumour-immune interactions. Associations between any gene and immunity (e.g., Lymphocytes, Immunomodulators and Chemokines) are pre-calculated for 30 TCGA cancers.

To explore the biological function of CLEC4A and its role in LUAD, an analysis was performed using the LinkedOmics database. LinkedOmic allows the analysis of a wide range of cancers. The LinkFinder module screens for CLEC4A-associated differentially expressed genes in LUAD and presents them through volcanoes and heatmaps. Multi-omics and pan-cancer analysis, Gene Ontology (GO) analysis and Genome Encyclopedia (KEGG) pathway enrichment analysis are performed in the LinInterpreter module. (http://www.linkedomics.org). GO analysis includes biological processes (BP), cellular components (CC) and molecular functions (MF).

Logistic regression and Wilcoxon signed rank test were used to analyse the relationship between CLEC4A expression and clinicopathological characteristics: age, gender, smoking, clinical stage, T stage, M stage, N stage and primary therapy outcome.

To investigate whether prognostic characteristics could be independent of other clinical parameters (including age, gender, smoking, and pathological stage), univariate and multivariate Cox regression models were performed on TCGA-LUAD data. P<0.05 was considered to be statistically significant, and variables with P<0.05 in multivariate Cox analysis were identified as independent prognostic factors for LUAD. In addition, to verify the correlation between CLEC4A expression and survival in LUAD, the effect of CLEC4A on overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS) in LUAD patients was assessed using the Kaplan-Meier method. The relationship between CLEC4A expression and OS in clinical subgroups of LUAD was also analysed. The analysis could not be complete for each clinical parameter as clinical information was not complete for some samples, and there were differences in the total number of samples for some different clinical parameters.

All data in this paper are presented as mean ± standard deviation (SD). The R programming language is used for this study, and all survival analyses are performed using the R package 'survival', with Pearson correlation coefficients used for all correlation analyses. P < 0.05 is considered statistically significant and is expressed as follows:* P < 0.05, ** P < 0.01 and *** P < 0.001.

In order to gain further insights into the relationship between CLEC4A and cancer, we conducted a comprehensive analysis of 33 different tumor types using the TCGA dataset. Our findings, as depicted in Fig. (1A), highlight the distinct expression patterns of CLEC4A in numerous tumor types and their corresponding para- cancerous tissues. We observed significant upregulation of CLEC4A in several cancers, such as Bladder Urothelial Carcinoma (BLCA), Breast invasive carcinoma (BRCA), Cholangio- carcinoma (CHOL), Colon adenocarcinoma (COAD), Esophageal carcinoma (ESCA), Glioblastoma multiforme (GBM), Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Kidney renal papillary cell carcinoma (KIRP), Liver hepatocellular carcinoma (LIHC), Pheochromocytoma and Paraganglioma (PCPG), Stomach adenocarcinoma (STAD), and Thyroid carcinoma (THCA). Conversely, in Cervical squamous cell carcinoma and endocervical adeno- carcinoma (CESC), Kidney Chromophobe (KICH), Lung adenocarcinoma (LUAD), Lung squamous cell carcinoma (LUSC), Pancreatic adenocarcinoma (PAAD), Prostate adenocarcinoma (PRAD), Rectum adenocarcinoma (READ), and Uterine Corpus Endometrial Carcinoma (UCEC), the expression of CLEC4A was found to be downregulated in tumor tissues compared to adjacent normal tissues. To further investigate the role of CLEC4A specifically in LUAD, we analyzed the expression levels of CLEC4A in a substantial sample set comprising 535 LUAD samples and 59 paraneoplastic samples from the TCGA database. Our analysis consistently revealed lower expression of CLEC4A in normal lung tissues compared to LUAD tissues, as shown in Fig. (1A-C). These findings were validated using the independent GSE75037 dataset, which includes 83 matched pairs of lung adenocarcinomas and non-malignant adjacent tissue, as shown in Fig. (1D). To assess the diagnostic potential of CLEC4A in distinguishing LUAD from normal lung tissue, we generated ROC curves using the TCGA internal test set. Fig. (1F) illustrates that CLEC4A exhibits a high discriminatory power, with an area under the curve (AUC) value of 0.788. This indicates that CLEC4A may serve as a reliable diagnostic biomarker for LUAD.

In order to gain mechanistic insights into the role of CLEC4A in LUAD development, we performed qRT-PCR analysis on various lung adenocarcinoma cell lines, including H1975 and H23, as well as the normal lung cell line 16HBE. The results, presented in Fig. (1E), demonstrate significantly lower relative mRNA levels of CLEC4A in H1975 (p-value <0.05) and H23 (p-value <0.05) cell lines, while no significant downregulation was observed in the H1299 cell line (p-value > 0.05) compared to 16HBE. These findings provide additional evidence supporting the potential regulatory role of CLEC4A in LUAD development. In summary, our comprehensive analysis of CLEC4A expression in various tumors, including LUAD, highlights its differential expression patterns and establishes its potential diagnostic value in distinguishing tumor tissues from normal tissues. The downregulation of CLEC4A in certain tumor types, including LUAD, suggests its involvement in tumor development. These findings position CLEC4A as a promising target for further exploration in the field of LUAD research, offering potential diagnostic and therapeutic value.

Fig. (1). (A) Expression levels of CLEC4A in different types of tumor tissues and normal tissues. (B and C) The differences in CLEC4A expression levels between unpaired, paired samples of tumour tissues and normal tissues in the TCGA database LUAD. (D) The results of comparison between LUAD tissues and paracancerous tissues in the GSE75037 dataset. (E) qRT-PCR results of CLEC4A detected from LUAD cells (2-ΔΔCt method). (F) A ROC curve was created to test the value of CLEC4A to identify LUAD tissues.

To conduct our study, we collected data from RNA-seq analysis and clinical records of 533 patients diagnosed with lung adenocarcinoma (LUAD) from the TCGA database (refer to Table 1 for details). The LUAD cohort was divided into two groups, namely the high expression group and the low expression group, based on the median values of CLEC4A expression. We examined the correlation between CLEC4A expression and various clinical features using the Wilcoxon signed rank test (Fig. 2A-I). Through our investigation of the correlation between CLEC4A expression and various pathological cancer stages, we found that the downregulation of CLEC4A is associated with the advanced stages of lung adenocarcinoma. Our analysis revealed significant associations between CLEC4A expression and different clinical parameters. Specifically, low CLEC4A expression was found to be positively correlated with a higher grade of T stage (p=0.01). We can speculate that CLEC4A may achieve its anti-tumor effects by limiting the rate of in situ tumor growth. Furthermore, pathological CLEC4A expression demonstrated significant associations with tumor status (P<0.001), gender (P<0.01), smoking history (P=0.01), age (P=0.05), and response to immunotherapy (P=0.03). These findings highlight the potential role of CLEC4A expression as a prognostic indicator and suggest its relevance in predicting various clinicopathological characteristics of LUAD patients. Moreover, our study utilized univariate logistic regression analysis to further explore the relationship between CLEC4A expression and specific clinical features (Table 2). The results reveal a robust association between CLEC4A and gender, with an odds ratio (OR) of 0.603 (95% confidence interval: 0.428-0.849) and a p-value of 0.004. Additionally, we observed a significant correlation between CLEC4A expression and primary treatment effect, with an OR of 1.967 (95% confidence interval: 1.273-3.097) and a p-value of 0.003. These findings suggest that CLEC4A expression may serve as a predictive biomarker for gender-related differences in LUAD as well as treatment response. These findings contribute to a better understanding of the role of CLEC4A and its potential implications in LUAD management, allowing for the development of targeted therapeutic strategies and personalized treatment approaches.

Fig. (2). Relationship between CLEC4A expression and clinicopathological features. Relationship between CLEC4A expression and T-stage (A), M-stage (B), N-stage (C), tumour status (D), clinical stage (E), immunotherapy response (F), age (G), gender (H), smoker (I).

To verify the prognostic significance of CLEC4A expression in lung adenocarcinoma (LUAD), we conducted an analysis using data from the TCGA and GEO datasets through the Kaplan-Meier Plotter database. High expression levels of CLEC4A were associated with poor prognosis in LUAD patients, as illustrated in Fig. (3A). To account for potential biases arising from non-cancer-related events, we further examined progression-free survival (PFS) and disease-specific survival (DSS) (refer to Fig. 3B-3C). Remarkably, the results for PFS and DSS mirrored those for overall survival (OS), indicating that lower CLEC4A expression was correlated with improved PFS and DSS outcomes. To validate our findings, we employed the GSE68465 dataset as an independent cohort (refer to Fig. 3D). The results were consistent with the TCGA findings, affirming that CLEC4A expression significantly impacts the prognosis of LUAD patients. Furthermore, we utilized Cox proportional hazard regression models to analyze various prognostic factors. Through univariate and multivariate analysis of clinical characteristics (as shown in Fig. 3E-3F), we discovered that CLEC4A expression (HR=1.605, P=0.006), advanced T-stage (HR=1.672, P=0.023), and positive N-stage (HR=2.356, P<0.001) served as independent prognostic factors. These results highlight the potential of CLEC4A expression as a valuable indicator specific to the T-stage and N-stage status, offering significant insights into the prognosis of LUAD patients. Collectively, our comprehensive analysis using the Kaplan-Meier Plotter database, along with validation in an independent dataset, establishes the prognostic value of CLEC4A expression in LUAD. High expression levels of CLEC4A are associated with poor prognosis, while lower expression correlates with improved outcomes in terms of PFS, DSS, and overall survival. Moreover, our Cox regression analysis identifies CLEC4A expression, along with advanced T-stage and positive N-stage, as independent prognostic factors. These findings underscore the significance of CLEC4A as a potential marker for assessing LUAD prognosis and provide valuable insights for personalized treatment strategies and patient management.

Fig. (3). Better prognosis of patients with low CLEC4A expression compared to those with high expression, including overall survival (A), progression-free interval (B), and disease-specific survival (C). The prognosis of patients with low CLEC4A expression in the GSE68465 dataset was similarly better (D). Univariate and multivariate Cox analysis based on pathological parameters of CLEC4A expression (E and F).

In order to further substantiate the predictive significance of CLEC4A in lung adenocarcinoma (LUAD), we sought to explore the relationship between CLEC4A expression and overall survival (OS) among diverse subgroups defined by specific clinical characteristics. Our analysis unveiled compelling findings, indicating that LUAD patients displaying higher levels of CLEC4A expression exhibited improved OS outcomes across several subgroups. Notably, within the Stage I-II subgroup (Fig. 4A), high CLEC4A expression was associated with better OS outcomes. Similarly, in subgroups characterized by T1-T2 stage tumors (Fig. 4B), absence of distant metastasis (M0) (Fig. 4C), male gender (Fig. 4D), age greater than 65 years (Fig. 4E), and White race (Fig. 4F), higher CLEC4A expression levels were consistently correlated with enhanced OS in LUAD patients. These findings signify the potential utility of CLEC4A as a prognostic biomarker in various clinical contexts. The association between elevated CLEC4A expression and improved OS across different subgroups highlights the extensive clinically relevant predictive value of this molecular marker in LUAD. By distinguishing patients with a favorable prognosis, CLEC4A expression assess- ment may aid in treatment planning and therapeutic decision-making, enabling personalized approaches for individuals who may benefit most from specific inter- ventions. Our analysis demonstrates the positive cor- relation between high CLEC4A expression and better OS outcomes not only in the overall LUAD population but also within subgroups stratified by crucial clinical character- istics such as disease stage, tumor size, metastasis status, gender, age, and race. These findings underscore the potential of CLEC4A as a robust predictive indicator, paving the way for its integration into clinical practice to aid in outcome prediction and optimize patient manage- ment strategies in LUAD.

Fig. (4). Association of CLEC4A expression levels in TCGA with OS in different clinical subgroups of LUAD, including stage I-II (A), T1-T2 (B), M0 (C), and age<65.

In order to gain insight into the role of CLEC4A in LUAD, a comprehensive analysis was conducted using the LinkedOmics database. A total of 9,430 genes (depicted as dark green dots) and 10,558 positively associated genes (depicted as dark red dots) that exhibited a negative correlation with CLEC4A expression were examined (Fig. 5A and Table S1). To further investigate the association between CLEC4A expression and specific genes, heatmaps for the top 50 positively and negatively associated genes with CLEC4A expression were generated and presented in Fig. (5B and 5C), respectively. One of the main objectives of this study was to identify the significant genes associated with CLEC4A. This was accomplished by constructing a PPI network, which revealed the top 20 genes that exhibited a significant connection with CLEC4A (Fig. 5D). To gain deeper insights into the biological function of CLEC4A and its associated genes, the LinkInterpreter module in the LinkedOmics portal was utilized for GO-BP, GO-CC, GO-MF, and KEGG analysis (Fig. 5E-H).

Fig. (5). (A) Volcano map of genes significantly associated with CLEC4A in LUAD. (B and C) The top 50 genes positively and negatively associated with CLEC4A in LUAD are shown by heat map, respectively, with red representing positively associated genes and blue representing negatively associated genes. (D) Associations between CLEC4A and PPI. Functional analysis of genes co-expressed by CLEC4A in LUAD from the LinkedOmics database. (E-H) GO annotation and KEGG pathway of CLEC4A in LUAD.

The GO term annotation analysis provides valuable information regarding the biological processes, cellular components, and molecular functions associated with CLEC4A co-expressed genes. The results indicated that these genes primarily contribute to interleukin-4 production, purinergic receptor signaling pathway, leukocyte proliferation, T cell activation, tumor necrosis factor superfamily cytokine production, MHC protein complex, tertiary granule formation, immunological synapse formation, pattern recognition receptor activity, and cytokine receptor activity. Furthermore, the KEGG pathway analysis revealed the significance of CLEC4A co-expressed genes in various pathological conditions. These genes were found to play crucial roles in Staphylococcus aureus infection, autoimmune thyroid disease, Leishmaniasis, Type I rejection, allograft rejection, Type I diabetes mellitus, graft-versus-host disease, and hematopoietic cell lineage regulation. Based on the comprehensive analysis conducted, it can be inferred that the CLEC4A expression network modulates autoimmune responses and hampers tumor proliferation during cancer development, thereby influencing the prognosis of LUAD. These findings shed light on the potential importance of CLEC4A as a therapeutic target in the context of LUAD.

To enhance our comprehension of CLEC4A and its impact on the immune microenvironment, an extensive investigation was conducted using the TISIDB database. This analysis aimed to explore the correlation between CLEC4A and several key immunological features, such as tumor-infiltrating lymphocytes (TIL), immunomodulators, chemokines, and receptors. Immunomodulators were categorized into three groups: immunosuppressive agents, immunostimulants, and major histocompatibility complex (MHC) molecules. The results shown in Fig. (6A), unveiled the six TILs with the highest relevance to CLEC4A, specifically Treg_abundance, MDSC_abundance, Macrophage_abundance, Tfh_abundance, Act_DC_abun dance, and Th1_abundance. Fig. (6B) presents the six immunoinhibitors that exhibited the strongest correlation with CLEC4A, namely HAVCR2_exp, CSF1R_exp, PDCD1LG2_exp, CD244_exp, IL-10_exp, and CD274_exp.

Fig. (6). Association of CLEC4A with different immune features in LUAD of the TISDIB database. (A) Analysis of the relationship between CLEC4A expression and TIL. (B-D) Correlation between CLEC4A and Immunoinhibitor, Immunostimulator and MHC molecule. (E and F) Analysis of the relationship between Chemokine (or receptors) and CLEC4A.

Conversely, Fig. (6C) demonstrates the six immuno- stimulators with the highest CLEC4A correlation, including CD86_exp, CD80_exp, ICOS_exp, IL2RA_exp, CD48_exp, and CD28_exp, indicating that elevated CLEC4A expression was associated with a more robust immune response. Furthermore, our analysis revealed the six MHC molecules (HLA-DRA_exp, HLA-DMB_exp, HLA-DPA1_exp, HLA-DPB1_exp, HLA-DQA1_exp, and HLA-DOA_exp) that exhibited the strongest correlation with CLEC4A, as shown in Fig. (6D). These findings suggest that increased CLEC4A expression is linked to a heightened immune response. Additionally, Fig. (6E) showcases the six chemokines (CCL13_exp, CCL23_exp, CCL17_exp, CCL4_exp, CCL3_exp, and CCL18_exp) that displayed the highest correlation with CLEC4A. Similarly, Fig. (6F) presents the six receptors (CCR2_exp, CCR1_exp, CCR5_exp, CCR6_exp, CCR8_exp, and CCR4_exp) exhibiting the strongest correlation with CLEC4A. Taken together, our study provides compelling evidence that CLEC4A plays an active role in the regulation of various immune molecules in LUAD. Moreover, it influences immune infiltration within the tumor microenvironment. These findings emphasize the significance of CLEC4A in modulating immune responses and may pave the way for targeted therapeutic strategies in the context of LUAD.

In order to gain insights into the connection between CLEC4A expression and immune cell infiltrates, an in-depth analysis was performed to evaluate the correlation between CLEC4A expression and 24 distinct immune cell types in LUAD. The findings of this analysis revealed a robust positive correlation between CLEC4A expression levels and nearly all immune cell infiltrates, including T cells, T helper cells, B cells, NK cells, CD8+ T cells, and various others. Intriguingly, a negative correlation was observed with NK CD56bright cells, as depicted in Fig. (7A). Moreover, the analysis demonstrated significant disparities in immune cell infiltration between the high and low CLEC4A expression groups. Remarkably, the high-expression group exhibited a substantially higher rate of immune cell infiltration compared to the low-expression group, as illustrated in Fig. (7B). These

Fig. (7). Relationship between CLEC4A expression and immune infiltration. (A) Differences in infiltration of different immune cell subpopulations in the CLEC4A high and low expression groups. Non-significant (ns) represents P ≥ 0.05; * represents P < 0.05; ** represents P < 0.01; *** represents P < 0.001. (B) Relationship between CLEC4A expression and immune cells.

findings underscore the direct association between CLEC4A expression and the presence of immune cell infiltrates in LUAD. The strong positive correlation observed between CLEC4A expression and diverse immune cell types suggests a potential role for CLEC4A in regulating immune responses within the tumor microenvironment. Furthermore, the disparities in immune cell infiltration rates between the high and low CLEC4A expression groups indicate that CLEC4A expression levels may impact the immune landscape of LUAD tumors. Understanding the relationship between CLEC4A and immune cell infiltration is crucial in uncovering the underlying mechanisms that contribute to tumor progression and may provide valuable insights for the development of immunotherapeutic strategies targeting CLEC4A in the treatment of LUAD. Further research is warranted to elucidate the precise role of CLEC4A in immune cell modulation and its potential implications for therapeutic interventions.

Fig. (8). The nine genes with the greatest mutational differences between the high and low CLEC4A groups in LUAD. The mutation rate of genes in the low CLEC4A group was large in the high CLEC4A group.

Understanding the role of gene mutations in promoting tumor development and metastasis is crucial for advancing our knowledge of oncology and developing targeted therapies. To investigate the impact of gene mutations on LUAD, particularly in relation to CLEC4A expression, we conducted a comprehensive analysis at the genomic level. Specifically, LUAD patients were divided into two groups based on the median CLEC4A expression values, and a comparison was made regarding the differences in mutations between these two groups. The analysis yielded intriguing results, demonstrating that the frequency of mutations was significantly higher in patients with high CLEC4A expression compared to those with low CLEC4A expression, as indicated in Fig. (8). This finding suggests that high CLEC4A expression may contribute to the improvement of the tumor microenvironment and facilitate disease progression in patients with LUAD.