Comprehensive pan-cancer analysis of CHRDL1 and experimental validation of its role in lung adenocarcinoma

Abstract

Chordin-like 1 (CHRDL1) is a secreted antagonist of bone morphogenetic proteins, and has been implicated in various biological processes and cancer prognosis. This study offered a detailed examination of CHRDL1 expression across 33 diverse cancer types, leveraging data from The Cancer Genome Atlas (TCGA) and supplementary public datasets. We demonstrated that, for the majority of cancer types, CHRDL1 expression was reduced in tumor tissues compared to normal adjacent tissues. Notably, lower CHRDL1 expression led to negative prognosis in malignancies such as lung adenocarcinoma (LUAD), melanoma (SKCM), and mesothelioma (MESO). Furthermore, CHRDL1 expression was positively correlated with the infiltration of CD4⁺ T cells, CD8⁺ T cells, B cells, neutrophils, macrophages, and dendritic cells in most tumors. Higher CHRDL1 expression correlated with more favorable immune profiles and a reduction in tumor stemness. To assess the effect of CHRDL1 overexpression on LUAD progression, we conducted CCK-8, wound healing, and invasion assays in vitro, along with subcutaneous tumor formation experiments in nude mice. The results showed that the proliferation, migration, and invasion abilities of A549 and H1299 cells with high CHRDL1 expression were reduced, and the growth of A549 cells was also significantly inhibited in nude mice. These findings underscored CHRDL1’s potential as a prognostic biomarker and its influence on tumor immunology and cellular dynamics.

Introduction

Chordin-like 1 (CHRDL1) is a secreted protein from the Chordin family that primarily acts as an antagonist of bone morphogenetic proteins (BMPs), inhibiting their function by binding BMP4 to prevent binding of BMP to their receptors. Prior research has highlighted its involvement in various biological processes, such as neurodevelopment by influencing neural differentiation and growth [1]. Additionally, CHRDL1 plays a role in retinal development [2], kidney injury repair [3], and cardioprotection following myocardial infarction [4] by inhibiting BMP signaling pathways.

Some evidence has shown that CHRDL1 also impacts tumor biology. CHRDL1 affects colorectal cancer (CRC) by downregulating TGF-β/VEGF signaling, reducing the growth, migration, and angiogenesis of CRC cells [5]. In gastric cancer (GC) tissues, CHRDL1 is lowly expressed and associates with poor survival. Knockdown of CHRDL1 activates Akt, ERK, and β-catenin, promoting tumor cell proliferation and migration through BMPR II [6]. While, in oral squamous-cell carcinoma (OSCC), CHRDL1 inhibits OSCC cell invasion and migration by regulating epithelial-mesenchymal transition (EMT) and metastasis through the MAPK pathway [7]. Another study demonstrated that CHRDL1 could inhibit BMP4-induced migration and invasion of breast cancer cells, and that higher CHRDL1 expression was correlated with a more favorable prognosis in breast cancer patients [8].

Meanwhile, several studies have shown that low CHRDL1 expression is generally associated with poorer outcomes in thyroid carcinoma (THCA) [9], LUAD [10], Prostate cancer (PRAD) [11]. These studies have identified CHRDL1 as a promising diagnostic and prognostic biomarker in various cancers, while a comprehensive analysis of its expression across diverse cancers and its prognostic significance remains insufficient.

This study investigates differences in CHRDL1 expression between the tumor and normal tissues through multiple public databases and evaluates its impact on patient prognosis and tumor immune infiltration. Our findings reveal that CHRDL1 is underexpressed in various tumors and shows a significant correlation with immune cell infiltration in tumor tissues. Additionally, in vitro experiments we confirm that overexpression of CHRDL1 reduced the ability of proliferation, migration and invasion of lung adenocarcinoma cells A549 and H1299. The subcutaneous tumor implantation experiment in BALB/c nude mice shows that the proliferation capacity of A549 cells with CHRDL1 overexpression is inhibited.

Overall, our study confirms and supports previous research on CHRDL1 in tumors and reveals a significant correlation between CHRDL1 expression and immune cell infiltration in tumor tissues, which further supports its potential as a biomarker for cancer prognosis.

Materials and Methods

Acquisition and analysis of gene expression data

We obtained the TCGA Pan-Cancer dataset from the UCSC database (https://xenabrowser.net/), which provides uniformly processed data. This dataset includes gene expression profiles and clinical information across various cancer types. Because the TCGA database contains an insufficient number of normal samples for certain cancer types, we conducted a combined analysis using normal human samples from the GTEx database (https://www.gtexportal.org/). Subsequently, the expression data of the CHRDL1 gene (ENSG00000101938) were extracted for each sample, and a log2(x + 0.001) transformation was applied to mitigate potential zero values and stabilize variance. Finally, we compiled transcriptomic and clinical data for 33 distinct cancer types, enabling a comprehensive analysis of CHRDL1 expression patterns [12]. Differential expression analysis of CHRDL1 levels between tumor and normal tissues (including normal adjacent tissues), was conducted by R software (version 4.2.2; https://www.r-project.org/).

Data on protein expression differences of CHRDL1 between tumor and normal tissues were sourced from the cProSite database (https://cprosite.ccr.cancer.gov/). Details on the cellular localization and immunohistochemical staining of CHRDL1 in both normal and cancerous tissues were retrieved from the Human Protein Atlas database (https://www.proteinatlas.org/).

Prognostic value and clinical features associated with CHRDL1

Four key survival outcome metrics—overall survival (OS), disease-free interval (DFI), disease-specific survival (DSS), and progression-free interval (PFS)—were extracted from the UCSC Xena database.

Cancer samples from the TCGA data portal were divided into high-expression and low-expression groups for CHRDL1, using the median expression value as the threshold. Kaplan–Meier survival curves for OS across various cancers were generated using the SANGERBOX tool (http://www.sangerbox.com/) [13]. Analysis of CHRDL1 expression with respect to DSS, DFI and PFI was performed by the ezcox package in R. Hazard ratios (HR) with 95% confidence intervals (95% CI) were determined using log-rank tests and univariate Cox regression analysis. The log-rank test was used for statistical analysis, with p-values < 0.05 indicating statistical significance. In the Cox proportional risk model, we used the low-expression CHRDL1 group as the reference group, and if HR < 1, it indicates that high-expression CHRDL1 has a significantly better prognosis, while HR > 1 indicates that high-expression CHRDL1 is associated with a worse prognosis.

Character analysis of mutations

The mutation rate of CHRDL1 these cancers was evaluated using the ‘Mutations’ section of the TIMER 2.0 website (http://timer.cistrome.org/). Furthermore, the mutational properties of CHRDL1 in different cancer types were investigated via the cBioPortal database (http://www.cbioportal.org/).

Correlation analysis of CHRDL1 expression and immune infiltration

We enhanced our analysis by reassessing immune infiltration scores, including Major Histocompatibility Complex-related molecules (MHC, including HLA, TAP, B2M), Effector Cells (EC, including CD4 + and CD8 + T cells), Suppressive Cells (SC, including Tregs and MDSCs), Checkpoint/Immunomodulators (CP, including IDO1, ICOS, CD24, PD-L2, PD-L1, PD- 1, TIM3, TIGHT, LAG3, CTLA4), Averaging Z Scores (AZ), and Immunophenoscore (IPS), for each patient across various tumors based on gene expression [14]. This evaluation was conducted through the deconvo_ips method through the R package IOBR [15].

Pearson’s correlation coefficients for CHRDL1 expression and the infiltration levels of CD4 +, CD8 +, Neutrophils, Myeloid Dendritic Cells, Macrophages, and B Cells were also derived from TIMER 2.0. Heatmaps illustrating these correlations were generated using the R package ggplot2. Additionally, immune scores for each patient based on CHRDL1 gene expression level were calculated via the R package ESTIMATE [16], and scatter plots depicting these correlations were subsequently generated.

Analysis of CHRDL1 and tumor stemness

Based on the study by Tathiane et al., [17] we utilized the DNA stemness index (DNAsi) and mRNA stemness index (mRNAsi) scores, derived from DNA methylation and mRNA characteristics for each tumor. Then the correlations between CHRDL1 expression with tumor stemness indices were examined and assessed with Pearson’s correlation coefficient. Radar plots were subsequently generated with the ‘ggradar’ package in R.

Reagents

RPMI 1640, Matrigel® Basement Membrane Matrix (Corning Incorporated, New York, USA); Fetal Bovine Serum (FBS, Gibco, Grand Island, USA); Total RNA Extract (Thermo Fisher Scientific, China); Trypsin–EDTA (Invitrogen, Rockville, USA); Puromycin, Phenylmethylsulfonyl fluoride(PMSF) (Sigma-Aldrich, St. Louis, USA); Penicillin–Streptomycin Solution, Cell Counting Kit (CCK- 8), (KeyGEN BioTECH, Beijing, China); RIPA Cell Lysis Buffer, Bicinchoninic Acid (BCA) Protein Assay Kit and Bovine Serum Albumin (BSA) (Beyotime, Shanghai, China); polybrene, Hieff® qPCR SYBR Green Master Mix (High Rox Plus) (Yeasen, Shanghai, China); antibody stripping buffer(NCM Biotech, Jiangsu, China).

Cell culture

A549 and H1299 cells were purchased from KeyGEN BioTECH (Beijing, China) and cultured at 37 °C with 5% CO2 in a controlled-temperature incubator. Both cells were cultured in RPMI 1640 medium supplemented with 10% FBS and 1% penicillin–streptomycin solution.

Construction and grouping of stably transfected cell lines

Lentiviral vectors designed for the overexpression of CHRDL1 (LV-CHRDL1-OE) and corresponding control empty vectors (LV-empty) were developed and synthesized by He Yuan Biologicals (Shanghai, China). When the cells reached 80–90% confluence, they were seeded into 6-well plates at a density of 1 × 10^5 cells per well. Once the cells reached 30–50% confluence, The multiplicity of infection (MOI) was set to 20 and lentiviral viruses were introduced along with 1 µg/µL polybrene and the cells were incubated for 18 h. Then, the culture medium was replaced with fresh complete medium, and the cells were cultured for an additional 24 h. Puromycin was added to the medium at a concentration of 2 μg/mL for 2 weeks, in accordance with the recommendations for lentivirus use, to select stably transfected cells. Untreated cells were designated as the blank control group (BLK), while the cells transfected with the empty vector were designated as the negative control group (NC).

Quantitative real-time PCR

Cells from each group were seeded into 6-well plates at a density of 6 × 10^5 cells per well and cultured for 24 h prior to collection. Total RNA was extracted from these cell lines using a total RNA extraction kit. Quantitative real-time PCR (qRT-PCR) was performed with SYBR Green qPCR Master Mix and the 7300 Plus Real-Time PCR System, following the manufacturer’s protocol. GAPDH was used as an internal reference for normalizing gene expression data. Relative expression levels were then calculated using the 2^−∆∆Ct method to determine the fold changes in gene expression. Each treatment condition was assessed in three independent biological replicates, and each biological sample was tested in triplicate technical replicates. The primers used were as follows: CHRDL1 forward primer: 5'‑GATGGAGAACTGTCATGGGAAC‑3'and reverse primer: 5'‑GGAGGATCATAGTGAGAGCGG‑3'; GAPDH forward primer: 5'‑GGAGCGAGATCCCTCCAAAAT- 3', and reverse primer: 5'‑GGCTGTTGTCATACTTCTCATGG- 3'.

Western blotting

Similarly, after culturing cells in 6-well plates for 24 h, total proteins were extracted. ells were lysed on ice for 30 min with RIPA cell lysis buffer, followed by centrifugation at 15,000 g for 20 min at 4 °C. The protein concentration was subsequently quantified using a Bicinchoninic Acid (BCA) assay to standardize the uptake volume. A total of 10 μg of protein per sample was separated by SDS-PAGE and then transferred onto a PVDF membrane. After blocking with 5% BSA for 1 h at room temperature, the membrane was blotted with primary antibodies, followed by secondary antibodies, and then detected using chemiluminescence detection kit. The images were acquired with the ChemiDoc MP imaging system. After detection, the PVDF membrane was washed with antibody stripping buffer, and the primary antibody was replaced before repeating the antibody blotting steps. Gray scale values were quantified using ImageJ software. The following antibodies were used: CHRDL1 Rabbit pAb, beta-Actin Rabbit pAb (Bioss, Beijing, China), and Goat Anti-Rabbit IgG/HRP (solarbio, Beijing, China).

CCK8 cell proliferative capacity assay

Approximately 5,000 cells per well were seeded into 96-well plates, with 6 replicate wells per group. The CCK8 reagent was diluted in serum-free medium at a 1:10 ratio and 100 µl of the mixture was added to each well. Following a 1-h incubation, the absorbance at 450 nm was measured using a microplate reader. Absorbance measurements were recorded at 0-, 24-, 48-, and 72-h post-incubation [18].

Wound healing assay

Approximately 5 × 10^5 cells were plated into each well of a 6-well plate. After culturing each group of cells until they reached approximately 90% confluence, horizontal scratches were made in the cell monolayer with autoclaved 200 µl pipette tips to ensure consistent depth. Two gentle washes with 1–2 ml of PBS per well were then performed to remove detached cells. Micrographs of wound closure were captured at 0, 24, 48, and 72 h, and the area of cell migration was quantified using ImageJ.

Invasion experiment

For invasion experiments, cells were placed in the upper chamber of a Transwell insert with 100 µL of serum-free RPMI 1640 medium, while the lower chamber was filled with 500 µL of RPMI 1640 containing 10% FBS. Non-migrated cells on the upper surface were carefully removed, after 24 h of incubation. Migrated cells were then fixed with 4% paraformaldehyde for 10 min and stained with 1% crystal violet for 15 min. Finally, images were captured from three randomly selected fields of view under a microscope, and the cells were counted using ImageJ software.

Subcutaneous tumor transplantation experiments

All animal experimental protocols adhered to the Animal Management Rules set by the Chinese Ministry of Health, and the study was approved by the Animal Ethics Committee of Dongfang Hospital, Beijing University of Chinese Medicine. Twelve 5-week-old BALB/c nude mice were purchased from SPF Biotechnology Co., Ltd, Beijing, China. Subsequently, suspension of 1 × 10^6 tumor cells in 100 μL PBS was injected subcutaneously into the flanks of mice. After 3 weeks, mice were anesthetized and euthanized using sodium pentobarbital at a dose of 100 mg/kg by intraperitoneal injection, and subcutaneous tumors were excised, measured, weighed, and imaged.

Results

CHRDL1 expression is commonly downregulated in multiple cancers

We analyzed CHRDL1 mRNA levels in tumor tissues across 33 cancer types from TCGA. CHRDL1 mRNA levels were significantly downregulated in most tumor tissues (Fig. 1A). Additionally, we compared cancer tissues with adjacent non-cancerous tissues of the same patients to identify their differential expression, as shown in Fig. 1B. Paired box-and-whisker plots for additional cancer types are presented in Supplementary Fig. 1. Protein expression analysis of CHRDL1 in cancer and adjacent normal tissues was obtained from the cProSite database. Protein expression results of CHRDL1 for the same four cancer types shown above are presented in Fig. 1C, with additional data provided in Supplementary Fig. 2.

Fig. 1

Expression Levels of CHRDL1 Across Cancers. A Expression levels of CHRDL1 in tumor versus adjacent non-cancerous tissues. B Comparison of CHRDL1 expression levels in tumor and adjacent non-cancerous tissues from the same patient. C Protein expression levels of CHRDL1 in tumor versus adjacent non-cancerous tissues. (*, p < 0.05; **, p < 0.01; ***, p < 0.001)

The protein expression pattern of the CHRDL1 was investigated by utilizing the HPA database, which indicated that the CHRDL1 protein was often secreted to the extracellular level and acted (Fig. 2A). We also obtained IHC images of CHRDL1 from the HPA database and found that the protein of CHRDL1 is localized in the cytoplasm/membrane. And CHRDL1 was lowly expressed in LUAD, UCEC, PRAD, and COAD (Fig. 2B). This was consistent with previous mRNA and protein expression results of CHRDL1 in these cancer types. However, the expression of CHRDL1 was not detected in normal breast tissues, which may be due to the small sample size.

Fig. 2

CHRDL1 Expression in Tissues and Its Subcellular Localization. A Subcellular localization profile of CHRDL1 in human cells from the HPA database. B IHC analysis of CHRDL1 expression in LUAD, UCEC, PRAD, COAD, BRCA of IHC. The blue icon for normal tissue and the red icon for tumor tissue

Analysis of the effect of CHRDL1 on cancer prognosis

To assess the impact of CHRDL1 expression on OS in patients with various cancers, we employed a forest plot (Fig. 3A). The results revealed that low CHRDL1 expression was associated with poor prognosis in MESO, LUAD, and SKCM (HR < 1, p < 0.05), whereas it was associated with improved prognosis in BLCA, LIHC, KIRC, LUSC, and STAD (HR > 1, p < 0.05). We also examined the association between CHRDL1 expression and DSS, PFI, and DFI (Fig. 3B). Kaplan–Meier survival curves for OS in eight cancers significantly associated with CHRDL1 expression are shown in Fig. 3C. Notably, CHRDL1 consistently acted as a protective factor for LUAD patients in OS, DFI, DSS, and PFS, suggesting that CHRDL1 high expression was associated with improved prognosis for LUAD.

Fig. 3

Association of CHRDL1 expression with the prognosis of various cancers. A Cox proportional hazards model for the correlation of CHRDL1 expression with OS. B Correlation Analysis of CHRDL1 Expression with DSS, PFI, and DFI. C Kaplan–Meier analysis of the association between CHRDL1 expression and overall survival

CHRDL1 mutations of the TCGA pan-cancer cohort

We analyzed the mutation landscape of CHRDL1 in the TCGA pan-cancer data, including mutations, structural variations, deep deletions, and amplifications through the cBioPortal database. Mutation data were retrieved for 32 tumor types (Fig. 4A). The several cancers with the highest mutation rates were SKCM (4.95%), UCEC (4.54%), STAD (3.41%), LUAD (3.36%), LUSC (3.08%), and CRC (2.86%). We also found that mutations were the most common type among these cancers. Meanwhile, we visualized specific loci, types, and frequencies of CHRDL1 mutations, revealing that missense mutations were the most common in these cancers (Fig. 4B). Validation analysis through the TIMER2.0 database yielded results largely consistent with the above findings, supplemented with CHRDL1 mutation information for COAD and READ (Fig. 4C).

Fig. 4

Mutational characteristics and genetic alterations of CHRDL1 across various tumor types. A Copy number variations of CHRDL1 across different tumors obtained from the cBioPortal database. Blue indicates a deletion, green indicates a structural variant, red represents amplification, and orange denotes a deep deletion. B Mutations of CHRDL1 of Pan-cancer, with a majority being missense mutations. VWC: von Willebrand factor type C domain (31—93); VWC: von Willebrand factor type C domain (109—172); VWC: von Willebrand factor type C domain (254—316). C Genetic alterations of CHRDL1 as reported in The TIMER2.0 database

Correlation analysis of CHRDL1 expression and immune infiltration

The relationship between immune phenotypes and CHRDL1 expression is illustrated in Fig. 5A. In most tumors, CHRDL1 expression was positively associated with immune EC, while showing a negative correlation with SC and CP. We further examined the relationship between CHRDL1 expression and immune-infiltrating cells in tumor tissues (Fig. 5B). CHRDL1 expression was positively correlated with the infiltration of immune-associated cells in most tumors. Especially, CHRDL1 expression significantly influenced immune cell infiltration in tumor tissues of STAD, PRAD, PAAD, LUSC, LUAD, LIHC, and KIRP. We also calculated the correlation between CHRDL1 expression and immune scores, which aligned with the previously mentioned results. Figure 5C illustrates the correlation between immune scores for five tumors, with details of immune scores for additional tumors presented in Supplementary Fig. 3.

Fig. 5

Pan-cancer analysis of CHRDL1 expression in relation to immune infiltration and tumor stemness. A Analysis of Immunophenoscore versus CHRDL1 expression levels. B Correlation between CHRDL1 expression and six immune cell types. C Evaluation of CHRDL1 expression and its correlation with ImmuneScore in LUAD, COAD, LUSC, LIHC, and PAAD. D Relationships between CHRDL1 expression and DNAsi and mRNAsi, which reflect the stem-like properties of tumor cells. (*, p < 0.05; **, p < 0.01; ***, p < 0.001)

Correlation between CHRDL1 expression and tumor stemness index

To investigate the association between the tumor stemness index and CHRDL1 expression, we analyzed the correlations between value of the CHRDL1 expression and DNAsi and mRNAsi scores for each tumor type using the datasets provided by Tathiane et al. [17] Our analysis indicated that CHRDL1 expression was negatively correlated with both DNAsi and mRNAsi in BLCA, BRCA, COAD, ESCA, HNSC, LUAD, LUSC, PAAD, STAD, and TGCT (Fig. 5D). Specifically, in these tumor, higher CHRDL1 expression was associated with a lower tumor stemness index, suggesting that elevated CHRDL1 expression may reduce stem cell-like properties in these cancers.

CHRDL1 overexpression inhibited the proliferation of NSCLC cells

Lentiviral transfection successfully induced the overexpression of CHRDL1 in the A549 and H1299 cell lines, which was confirmed by qRT-PCR (Fig. 6A) and WB (Fig. 6B) assays. In vitro experiments, including the CCK8 cell proliferation assay (Fig. 6C), invasion assay (Fig. 6D), and wound healing assay (Fig. 6E), demonstrated that CHRDL1 overexpression significantly inhibited cell proliferation, migration, and invasion capabilities of the A549 and H1299 cells. These results suggested that CHRDL1 overexpression negatively affects tumor cell growth and reduces motility. Furthermore, investigate the impact of CHRDL1 on tumorigenicity, we xenografted A549 cells into nude mice. Three weeks after inoculation, the mice were euthanized to evaluate tumor growth (Fig. 7A, B). Tumor volume and weight were significantly lower in mice injected with CHRDL1-overexpressing A549 cells compared to the control group (Fig. 7C, D).

Fig. 6

In vitro experiments to validate the effects of CHRDL1 overexpression in A549 and H1299 cell lines. (A) qRT-PCR, WB (B) Verification of CHRDL1 transfection efficiency of A549 and H1299. The biological effects of CHRDL1 on A549 and H1299 cell lines were verified by CCK- 8 (C), Transwell (D) and wound healing (E) assays. (*, p < 0.05; **, p < 0.01; ***, p < 0.001)

Fig. 7

Experiment of subcutaneous tumors from nude mice (n = 6) xenografted with CHRDL1-overexpression A549 cells. A & B Comparison of normal A549 cells and subcutaneous transplanted tumors overexpressing CHRDL1 cells. The tumor volume (C) and weight (D) were quantified. (**, p < 0.01; ***, p < 0.001)

Discussion

Chordin-like 1 (CHRDL1) is a secreted glycoprotein with a repeating cysteine-rich structural domain, which is involved in various cellular processes and has implications for tumor biology. In this research, we initially evaluated CHRDL1 mRNA expression in cancerous versus normal tissues. Our results demonstrated low CHRDL1 expression in most cancerous tissues. This finding was further validated by protein expression data from the cProSite database and IHC data from the HPA database. These observations suggest a potential role for CHRDL1 as a cancer suppressor gene in these tumors. However, there were relatively few or no data on the protein expression of CHRDL1 in various tumor tissues, which led to certain limitations of this verification method. We obtained Kaplan–Meier survival curves for OS of CHRDL1 in various cancers via the SANGERBOX tool. A similar tool, GEPIA2, can also be used for gene expression analysis, allowing researchers to explore potential cancer biomarkers and therapeutic targets [19]. Kaplan–Meier survival curves and Cox proportional hazards models revealed that low CHRDL1 expression was associated with poor prognosis in LUAD, SKCM, and MESO, whereas it was associated with favorable prognosis in STAD, KIRC, LUSC, BLCA, and LIHC. These findings indicate that, while CHRDL1 may serve as a potential diagnostic marker for certain cancers, its utility as a broad prognostic indicator is limited. Deng et al. found that low expression of CHRDL1 predicted poor prognosis in lung adenocarcinoma, which is consistent with our results, showing a similar prognostic impact [20]. Berglar et al. found that CHRDL1 acts as a potential key regulator of the glioma stem cell-like (GSC) phenotype and reduce tumorigenicity by modulating BMP4 signaling [21]. Sahni et al. observed that pancreatic ductal adenocarcinoma patients with high CHRDL1 expression exhibited better responses to chemotherapy and longer survival after comparing gene expression differences between patients sensitive and insensitive to neoadjuvant chemotherapy [22]. Shen et al. jointly analyzed TCGA and GEO databases jointly and found that CHRDL1 is one of the factors affecting the prognosis of THCA and is a potential therapeutic target and biomarker [23]. However, in contrast to the results of this study, Li et al. identified CHRDL1 as one of the cancer suppressor genes in LUSC through analysis of the Gene Expression Omnibus (GEO) and TCGA databases [24]. We believe these discrepancies may stem from differences in sample size and data processing methods in the comprehensive analysis of LUSC survival data, which could explain the variation in our findings. Overall, these results consistently suggest the potential of CHRDL1 as a promising diagnostic and prognostic biomarker for cancer.

In the last decade, immunotherapy has significantly changed the treatment of advanced tumors, offering varying degrees of survival benefits to patients, and has fewer side effects and adverse reactions compared to chemotherapy [25]. However, the heterogeneity of the immune microenvironment within patients’ tumor tissues results in variable responses to immunotherapy, with only a subset of patients exhibiting high responsiveness [26]. Identifying novel or additional immune biomarkers is crucial for improving patient diagnosis and optimizing treatment strategies. What’s more, a growing body of evidence indicates that the extent and composition of immune infiltration in tumor tissues significantly influence treatment outcomes and patient prognosis [27]. In this study, we investigated the role of CHRDL1 in immune infiltration in various tumor types through public databases, revealing its potential impact on immune microenvironment interactions. IPS is considered a valuable predictor of response to cytotoxic T-cell antigen 4 (CTLA- 4) and apoptosis protein 1 (PD- 1) antibodies, and higher IPS is associated with better response to treatments such as anti-CTLA- 4 and anti-PD- 1 antibodies [28]. Our analysis of IPS suggested that CHRDL1 was generally positively associated with effector cells in many tumors, while it appeared to have a negative correlation with suppressor cells and checkpoint/immunomodulators. The final IPS suggested that high CHRDL1 expression was associated with improved responses to immunotherapy in PRAD, LUAD, BRCA, OV, and LAML, while it had a negative effect on SKCM versus KICH. Subsequently, we further analyzed the Pearson correlation between CHRDL1 and the degree of infiltration of various types of immune cells through TIMER 2.0 website, and the results showed that CHRDL1 was positively correlated with the infiltration of immune cells in many tumors.

While our findings are promising, they are based on bioinformatic analyses and publicly available databases, which have inherent limitations, including the lack of detailed, patient-specific data. Furthermore, the immune microenvironment is multifaceted, and CHRDL1’s role in modulating immune responses may vary depending on the tumor type and stage. These findings should thus be interpreted with caution, and experimental validation is needed to confirm the functional role of CHRDL1 in tumor immune modulation. Additionally, further research should focus on elucidating the mechanistic pathways through which CHRDL1 influences immune cell infiltration and how its expression might interact with other immune checkpoints. Understanding these interactions could open new avenues for targeted therapies aimed at enhancing the efficacy of immunotherapies. Future studies should also consider including a broader range of immune-related factors and explore how CHRDL1 expression may be altered by the tumor microenvironment during treatment.

Subsequently, we performed tumor stemness analysis. It is widely accepted in studies that higher tumor stemness is generally associated with increased cell proliferation, metastasis, and drug resistance [29]. We obtained DNAsi and mRNAsi scores, calculated from DNA methylation and mRNA profiles. We then analyzed the correlation between CHRDL1 expression and tumor stemness indices. The results indicated that higher CHRDL1 expression was associated with a lower tumor stemness index in BLCA, BRCA, COAD, ESCA, HNSC, LUAD, LUSC, PAAD, STAD, and TGCT. It indicated that the low expression of CHRDL1 in these tumors was a factor leading to the increased tumor stemness index.

From the comprehensive bioinformatics analysis, we observed the significant role of CHRDL1 in the diagnosis, immunity, and prognosis of lung adenocarcinoma. Compared to normal lung tissues, CHRDL1 mRNA and protein expression were markedly lower in lung adenocarcinoma tissues. This was accompanied by reduced immune cell infiltration, a lower immunotherapy score, and an increased tumor cell stemness index, ultimately leading to a poorer prognosis. The results are consistent with previous studies of the association between CHRDL1 and lung adenocarcinoma [19].

Finally, we used H1299 and A549 NSCLC cells to investigate the effect of CHRDL1 on lung adenocarcinoma cells. A549 and H1299 cell lines are both derived from lung adenocarcinoma. A549 is characterized by its highly epithelial phenotype, whereas H1299 exhibits more mesenchymal features. Both cell lines display robust proliferation, migration, and invasion capabilities. Together, these two cell lines offer complementary insights into various aspects of lung cancer research, particularly in areas such as tumor immunity, invasion, migration, and therapeutic strategies. Lentiviral transfection was employed to induce high expression of CHRDL1 in the cells, and the successful transfection of CHRDL1 was confirmed through qRT-PCR and WB assays. CCK- 8, wound healing, and invasion assays demonstrated that CHRDL1-overexpressing A549 and H1299 cells exhibited reduced proliferation, migration, and invasion abilities. Furthermore, CHRDL1-overexpressing A549 cells were implanted subcutaneously into nude mice, and the results showed that the growth of subcutaneous tumors was significantly attenuated compared to the control group.

At the same time, this study also has some limitations. First, the sample size used in the subcutaneous tumor transplantation experiments was relatively small, which may reduce the statistical power of the analysis and limit the reliability and generalizability of the results. Therefore, future studies should consider increasing the sample size to improve the statistical significance and reproducibility of the conclusions. Second, while the nude mouse model provides valuable data for studying tumor growth, it cannot fully replicate the complex immune microenvironment of human tumors. Therefore, although we observed the anti-tumor effects of CHRDL1 in the mouse model, further validation of these results is needed in more complex animal models or clinical trials. In addition, although we demonstrated the relationship between the low expression of CHRDL1 in lung adenocarcinoma and immune cell infiltration, the exact mechanisms remain unclear. How CHRDL1 interacts with immune regulatory factors in the tumor microenvironment and its role in different tumor types still require further investigation.

Conclusion

CHRDL1 is significantly downregulated in a variety of tumors compared to normal tissues and influences immune infiltration and tumor stemness, which may contribute to poorer prognosis in many cancers. Experimental studies confirmed that high expression of CHRDL1 inhibited the proliferation and metastasis of lung adenocarcinoma cells. These findings suggested that CHRDL1 is a promising candidate for further investigation as a prognostic biomarker in cancer.