C1QBP Associated With Immune Infiltration Predicts Poor Prognosis in Lung Adenocarcinoma

Abstract

Objectives:

C1QBP is a multi-compartmental protein implicated in diverse cellular processes. However, its clinical predictive value, particularly its association with immune cell infiltration, in lung adenocarcinoma (LUAD) remains unelucidated. Thus, the present study aimed to comprehensively evaluate C1QBP expression patterns, prognostic significance, and its correlation with the tumor immune microenvironment (TIME) in LUAD.

Methods:

We first assessed C1QBP expression levels and prognostic relevance in LUAD using multiple bioinformatics platforms. Subsequently, we analyzed the associations of C1QBP expression with immune cell infiltration and immunotherapeutic response, and identified signaling pathways linked to C1QBP expression via Gene Set Enrichment Analysis (GSEA). Finally, enzyme-linked immunosorbent assay (ELISA) was employed to validate the correlation between serum C1QBP concentration and prognosis in non-small cell lung cancer (NSCLC) patients receiving immunotherapy.

Results:

C1QBP was highly expressed in LUAD tissues, and this high expression was significantly associated with advanced tumor stage. Moreover, high C1QBP expression emerged as an independent risk factor for overall survival (OS) in LUAD patients. Bioinformatics analyses revealed that C1QBP expression was negatively correlated with the infiltration levels of multiple immune cell subsets (including T cells, B cells, and dendritic cells) in LUAD, while patients with low C1QBP expression exhibited higher Immunophenoscore (IPS). GSEA further demonstrated that high C1QBP expression was positively correlated with pathways regulating the tumor cell cycle, but negatively correlated with immune-related signaling pathways. Finally, in NSCLC patients treated with immune checkpoint inhibitors (ICIs), those with higher serum C1QBP concentrations had significantly shorter OS and progression-free survival (PFS).

Conclusions:

Our study identifies C1QBP as a potential oncogene that is closely associated with the TIME in LUAD. Collectively, these findings suggest that C1QBP holds promise as a novel indicator of poor prognosis in LUAD patients.

Keywords

C1QBP LUAD prognosis biomarker immune infiltration

Introduction

Global cancer statistics confirm that lung cancer remains the leading cause of cancer-related morbidity and mortality worldwide.¹ Among its histological subtypes, lung adenocarcinoma (LUAD) is the most prevalent, accounting for over 40% of all lung cancer cases.^2,3 Despite significant advances in the development and clinical application of targeted therapies and immunotherapies, the clinical outcomes of LUAD patients remain suboptimal, for instance, the 5-year overall survival (OS) rate is still below 30%.^4,5 This poor prognosis is closely linked to the unrestrained proliferation of tumor cells and the complexity of the tumor immune microenvironment (TIME), which collectively drive tumor progression, therapeutic resistance, and adverse clinical outcomes.^6,7

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC), yet immunotherapy resistance in LUAD is particularly prominent, partly due to the high heterogeneity of the TIME.⁷ Currently, reliable biomarkers for predicting ICI efficacy and characterizing TIME heterogeneity remain limited; the only widely used ones are programmed death-ligand 1 (PD-L1) expression and tumor mutational burden (TMB).^8,9 Moreover, traditional biomarker detection relies on tumor tissue samples (eg, for PD-L1 testing), which have inherent limitations such as restricted availability and difficulty in repeated sampling, critical drawbacks for dynamic therapeutic monitoring.⁹ In recent years, blood-based liquid biopsy techniques have emerged as a promising alternative, offering noninvasive and real-time detection capabilities that support early cancer diagnosis, efficacy assessment, and drug resistance monitoring.¹⁰

The complement system, a key component of the TIME, is a potent effector of innate immunity that exerts either pro-tumor or anti-tumor effects depending on the cancer type and context.¹¹ Complement C1q, an activator of the classical complement pathway, is expressed in the stroma and vascular endothelium of various human malignancies and has been shown to promote tumor initiation and progression.¹² Complement C1q binding protein (C1QBP), also known as hyaluronan-binding protein 1 (HABP1) or p32, is a multi-compartmental protein primarily localized in mitochondria, with additional expression in other organelles (eg, the endoplasmic reticulum and nucleus) and on the cell membrane.^13,14 Accumulating evidence indicates that the overexpression and secretion of C1QBP on tumor cell surfaces can evade complement-mediated attack while stimulating cytokine production to facilitate cancer growth and metastasis.¹⁵ High C1QBP expression has been associated with poor prognosis in prostate cancer,¹⁶ breast cancer,¹⁷ and gastric cancer,¹⁸ and related to the growth and metastasis of melanoma,¹⁹ pancreatic cancer,²⁰ and liver cancer.²¹ Conversely, C1QBP inhibits the adhesion and metastasis of renal cancer cells.²² These findings suggested that C1QBP may play different roles in different types of cancer.²³

To date, the relationship between C1QBP expression, clinical prognosis, and the TIME in LUAD remains unclear. Therefore, in the present study, we utilized public database resources to systematically evaluate the associations of C1QBP expression with clinical prognosis and immune cell infiltration in LUAD.

Materials and Methods

Bioinformatic Analysis Workflow

Cancer phenotypes and genotypes exhibit substantial complexity, while multi-omics data, encompassing genomics, transcriptomics, and proteomics, offer a holistic view of the molecular landscape underlying cancer. Currently, a suite of bioinformatics tools and resources has been developed to support key research areas, including pan-cancer analysis, the investigation of oncologic therapy and prognosis, immune infiltration assessment, and the analysis of cancer single-cell datasets.²⁴ This study report adheres to the Strengthening the Reporting of Observational Epidemiological Studies (STROBE) statement, and the completed STROBE checklist is provided as Supplemental Material.²⁵

Analysis of C1QBP Expression in LUAD

The cancer transcriptome data utilized in this study were retrieved from the University of California, Santa Cruz (UCSC) Xena database (http://xena.ucsc.edu). Following data standardization via the Sangerbox platform (http://SangerBox.com), we performed differential expression analysis of C1QBP across pan-cancer types.²⁶ For LUAD-specific analyses, C1QBP expression data from the Xiantao platform (https://www.xiantao.love/) were used to conduct differential expression comparisons between paired and unpaired sample groups. Additionally, we analyzed C1QBP protein expression levels in LUAD using the UALCAN database (http://ualcan.path.uab.edu).²⁷ To further validate the differential protein expression of C1QBP between tumor and normal tissues, immunohistochemical staining images of normal lung tissue and LUAD tissue were acquired from the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/).

Analysis of C1QBP in Tumor Microenvironment (TME)

First, based on the SangerBox platform, we used scatter plots to illustrate the correlations between C1QBP expression and 3 key metrics: Immune score, Stromal score, and ESTIMATE score.²⁸ Concurrently, we analyzed the correlation between C1QBP expression and tumor purity using data from the TIMER2.0 database (http://timer.cistrome.org/).²⁹ For LUAD-specific immune-related analyses, we employed the Xiantao platform and the single-sample gene set enrichment analysis (ssGSEA) algorithm to visualize 2 sets of results: first, the differences in enrichment levels of 24 immune cell types between the high and low C1QBP expression groups; second, the correlation between C1QBP expression and these immune cells. To further validate the association between C1QBP expression and immune-infiltrating cells, we additionally utilized 5 algorithms such as CIBERSORT,³⁰ EPIC,³¹ MCPCOUNTER,³² XCELL,³³ and TIMER³⁴ with data from the TIMER2.0 database. Moreover, we assessed the correlation between C1QBP expression and the expression of key immune checkpoint molecules, including Programmed Death-1 (PDCD1), Programmed Death Ligand-1 (CD274), Cytotoxic T-lymphocyte Associated Protein 4 (CTLA4), Indoleamine 2,3-dioxygenase 1 (IDO1), Lymphocyte-activation Gene 3 (LAG3), and T Cell Immunoreceptor with Ig and ITIM Domains (TIGIT). Finally, to explore the predictive value of C1QBP for immunotherapy response in LUAD, we analyzed immunophenotyping score (IPS) data from the TCIA database (https://tcia.at/home). For statistical analyses, multiple testing corrections were performed using the Benjamini-Hochberg method to control the false discovery rate (FDR), with a significance threshold set at FDR < 0.05.

Function Enrichment Analysis

We performed differential expression gene (DEG) analysis between high- and low-C1QBP expression groups in the LUAD dataset using the limma package via the Xiantao platform.³⁵ To investigate the biological functions of these DEGs, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses.^36,37 Results from KEGG and GO analyses were visualized using the Sangerbox platform (http://vip.sangerbox.com/) and Xiantao platform, respectively.

Gene set enrichment analysis (GSEA) is a computational method that determines whether a predefined gene set exhibits statistically significant, concordant differences between 2 biological states.³⁸ In our study, samples were stratified into high- and low-expression groups based on the median C1QBP expression level. DEGs between these 2 groups were identified and categorized into upregulated and downregulated subsets. Subsequent gene set enrichment was performed using the c2.cp.all.v2022.1.Hs.symbols.gmt database. Statistical significance was defined as a normalized enrichment score (|NES|) > 1, FDR < 0.25, and adjusted P-value < .05.

Analysis of Mutation and Methylation in C1QBP

In LUAD, genomic analyses of C1QBP, including investigations of genetic mutations and copy number alterations (CNA), were conducted using the cBioPortal database (http://www.cbioportal.org/). Concurrently, analysis of C1QBP promoter methylation was performed via the UALCAN database.

Patient Enrollment and Sample Collection

Seventy-seven NSCLC patients who received immunotherapy between March 2023 and April 2024 at Beijing Chest Hospital, Capital Medical University were retrospectively included. Inclusion and exclusion criteria included histopathologically confirmed primary NSCLC; stage III/IV according to the eighth edition of TNM staging; receipt of at least 1 cycle of PD-1/PD-L1 inhibitors; Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2; and patients with concurrent active other malignancies, or severe cardiohepatic and renal dysfunction were excluded. Baseline serum samples were collected from patients prior to initiation of immunotherapy, and 10 ml of peripheral blood was drawn into an ethylenediaminetetraacetic acid anticoagulant tube. Samples were centrifuged (3500 rpm, 10 minutes) to separate plasma and serum and stored in a −80°C freezer until C1QBP concentrations were determined. The study conformed to the ethical guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of Beijing Chest Hospital, Capital Medical University (Ethics Approval No.: KY-2018-002).

Enzyme‑Linked Immunosorbent Assay (ELISA)

After samples were thawed, using Shanghai Jianglai Biological (http://www.jonln.com/cus_human_habp1/)’s C1QBP-ELISA kit (cat. no. JL38718, Shanghai Jonln Biological Technology, China) to measure the level of serum C1QBP. Bring all reagents and samples to room temperature before use. Centrifuge the sample again after thawing before the assay. Reconstitute the Standard with 1 ml of sample diluent and produce a 2-fold dilution series. The undiluted standard serves as the high standard (20 ng/ml) and the sample diluent serves as the zero standard (0 ng/ml). Add 100 μl of standard and sample per well, cover with the adhesive strip provided, and incubate for 1 hour at 37°C. Next, remove the liquid of each well, don’t wash. Add 100 μl of Biotin-antibody (1×) to each well, cover with a new adhesive strip, and incubate for 1 hour at 37°C. Aspirate each well and wash by filling each well with wash buffer (300 μl) using a multi-channel pipette and let it stand for 1 minute, repeating the process 2 times for a total of 3 washes. After the last wash, remove any remaining wash Buffer by aspirating or decanting. Add 100 μl of Streptavidin-HRP (1×) to each well and cover the microtiter plate with a new adhesive strip and incubate for 30 minutes at 37°C. Repeat the aspiration/wash process for 5 times as before. Add 90 μl of TMB substrate to each well and incubate for 15 minutes at 37°C (Protect from light). Add 50 μl of Stop solution to each well, gently tap the plate to ensure thorough mixing. Determine the optical density of each well within 5 minutes, using a microplate reader set to 450 nm.

Statistical Analysis

Data processing and statistical analysis were performed using the R software (Version 4.4.1). Differences between qualitative variables were analyzed using Chi-square test. Differences between continuous variables were analyzed using Student’s t-test and analysis of variance (ANOVA). Survival analyses were performed using the Kaplan-Meier method and were compared using the log-rank test. Independent prognostic parameters were identified through the use of stepwise method in univariate and multivariate Cox risk regression analysis, and these parameters are significant. P-values < .05 were considered statistically different.

Result

Expression Levels Analysis of C1QBP in Pan‑Cancer

To investigate tissue-specific differences in C1QBP expression, we integrated tumor tissue data from The Cancer Genome Atlas (TCGA) database with normal tissue data from 2 sources: the Genotype-Tissue Expression (GTEx) database and TCGA itself. As shown in Figure 1A, results revealed that the C1QBP gene was significantly overexpressed in most tumor types, including glioblastoma multiforme (GBM), glioma (GBMLGG), low-grade glioma (LGG), uterine corpus endometrial carcinoma (UCEC), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), LUAD, esophageal carcinoma (ESCA), stomach and esophageal carcinoma (STES), kidney renal papillary cell carcinoma (KIRP), colon adenocarcinoma (COAD), colorectal adenocarcinoma (COADREAD), prostate adenocarcinoma (PRAD), stomach adenocarcinoma (STAD), head and neck squamous cell carcinoma (HNSC), lung squamous cell carcinoma (LUSC), liver hepatocellular carcinoma (LIHC), Wilms tumor (WT), skin cutaneous melanoma (SKCM), bladder urothelial carcinoma (BLCA), thyroid carcinoma (THCA), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), testicular germ cell tumors (TGCT), uterine carcinosarcoma (UCS), acute lymphoblastic leukemia (ALL), acute myeloid leukemia (LAML), and cholangiocarcinoma (CHOL). In contrast, C1QBP exhibited low expression in kidney pan-cancer (KIPAN), kidney renal clear cell carcinoma (KIRC), pheochromocytoma and paraganglioma (PCPG), adrenocortical carcinoma (ACC), and kidney chromophobe (KICH), while no significant expression difference was observed in rectum adenocarcinoma (READ).

Figure 1.

Expression level of C1QBP gene in LUAD. (A) C1QBP expression in pan-cancer was analyzed in TCGA and GTEx. (B) C1QBP mRNA expression in LUAD tissues and paired adjacent normal tissues from TCGA database. (C) Expression level of C1QBP in tumor and unpaired normal tissues in TCGA LUAD data. (D) Protein expression of C1QBP in tumor and normal tissues. (E, F) Representative immunohistochemical staining for C1QBP in LUAD tissue and normal lung tissue, taken from the HPA.

Further analysis focused on LUAD showed that C1QBP mRNA levels were significantly higher in tumor tissues than in either paired or unpaired normal tissues (Figure 1B and C). Consistent with this, data from the UALCAN database demonstrated that the total protein level of C1QBP was elevated in LUAD tissues compared to normal lung tissues (Figure 1D). We also verified C1QBP protein expression using the HPA database, which confirmed that C1QBP was widely distributed in LUAD tumor tissues and exhibited high expression levels (Figure 1E and F).

Correlation Analysis Between Clinical Pathological Characteristics and C1QBP Expression

To evaluate the clinical significance of C1QBP expression, we analyzed TCGA data using the Xiantao online tool. Based on the median C1QBP expression level, 539 LUAD patients were stratified into high- and low-expression groups (Table 1). The clinical parameters analyzed included basic patient information, tumor stage, node stage, metastasis stage, pathological stage, tumor location, and residual tumor status.

Table 1.

Clinicopathological Characteristics of the LUAD Patients Stratified According to C1QBP Level.

Characteristics	Low expression of C1QBP	High expression of C1QBP	P value
n	269	270
Pathologic T stage, n (%)			.103
T1	100 (18.7%)	76 (14.2%)
T2	137 (25.6%)	155 (28.9%)
T3	22 (4.1%)	27 (5%)
T4	7 (1.3%)	12 (2.2%)
Pathologic N stage, n (%)			.000
N0	188 (35.9%)	162 (31%)
N1	45 (8.6%)	52 (9.9%)
N2	21 (4%)	53 (10.1%)
N3	2 (0.4%)	0 (0%)
Pathologic M stage, n (%)			.650
M0	163 (41.8%)	202 (51.8%)
M1	10 (2.6%)	15 (3.8%)
Pathologic stage, n (%)			.006
Stage I	160 (30.1%)	136 (25.6%)
Stage II	67 (12.6%)	58 (10.9%)
Stage III	28 (5.3%)	56 (10.5%)
Stage IV	11 (2.1%)	15 (2.8%)
Gender, n (%)			.011
Female	159 (29.5%)	130 (24.1%)
Male	110 (20.4%)	140 (26%)
Race, n (%)			.336
Asian	5 (1.1%)	3 (0.6%)
Black or African American	33 (7%)	22 (4.7%)
White	206 (43.6%)	203 (43%)
Age, n (%)			.482
⩽65	133 (25.6%)	124 (23.8%)
>65	128 (24.6%)	135 (26%)
Residual tumor, n (%)			.320
R0	170 (45.5%)	187 (50%)
R1&R2	6 (1.6%)	11 (2.9%)
Location, n (%)			.873
Central lung	28 (14.7%)	35 (18.4%)
Peripheral lung	58 (30.5%)	69 (36.3%)
Smoker, n (%)			.974
No	39 (7.4%)	38 (7.2%)
Yes	226 (43%)	222 (42.3%)

Abbreviations: LUAD, lung adenocarcinoma; C1QBP, Complement C1q Binding Protein.

Statistical analysis revealed that C1QBP expression was significantly associated with gender (P = .011), T stage (P = .020), N stage (P = .002), and pathological stage (P = .041), with no significant correlations observed for other clinical features. Specifically, elevated C1QBP expression was detected in patients with advanced pathological stages (II&III&IV), higher T stages (T2&3&4), male gender, and advanced N stages (N2&3&4; Figure S1). Additionally, C1QBP expression was higher in patients with stable disease (SD) or progressive disease (PD) compared to those with complete response (CR) or partial response (PR; Figure S1).

Diagnostic and Prognostic Value of C1QBP

We evaluated the prognostic value of C1QBP in LUAD patients. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.729 (95% confidence interval [CI]: 0.679-0.779), indicating a moderate prognostic predictive ability (Figure 2A). Kaplan-Meier analysis using median-split C1QBP expression revealed that high C1QBP expression in LUAD patients was associated with poor OS (P < .001), progression-free interval (PFI, P = .052), and disease-specific survival (DSS, P = .013; Figure 2B-D). We further validated these findings using alternative cutoffs: Tertile-based OS analysis showed the worst prognosis in the highest expression group, with relatively favorable outcomes and minimal differences between the low and medium expression groups (P = .033), though PFI (P = .12) and DSS (P = .19) did not reach statistical significance. Using the optimal cutoff derived from the Youden index, Kaplan-Meier curves confirmed significant survival differences for OS (P < .001) and DSS (P = .021), while PFI remained non-significant (P = .099; Figure S2). Subgroup analysis demonstrated that patients with low C1QBP expression had significantly longer survival compared to those with high expression in specific subgroups: T2&T3&T4 stages, M0 stage, smokers, age ⩽65 years, and male patients (Figure S3).

Figure 2.

Diagnostic and prognostic value of C1QBP. (A) Diagnostic value of C1QBP in LUAD. (B-D) Kaplan-Meier survival curves for OS, PFI, and DSS in LUAD patients with high and low C1QBP expression. The forest plots showed the C1QBP and clinical characteristics hazard rate in different prognostic indicators: (E, F) Univariate and multivariate Cox regression analyses in OS, (G, H) univariate and multivariate Cox regression analyses in PFI, and (I, J) univariate and multivariate Cox regression analyses in DSS.

To identify potential prognostic factors, we performed univariate and multivariate Cox regression analyses incorporating C1QBP expression and clinicopathological characteristics. Univariate Cox regression identified T stage, N stage, M stage, pathological stage, and C1QBP expression as significant prognostic factors for OS (Figure 2E). Multivariate analysis confirmed high C1QBP expression as an independent prognostic indicator for OS (hazard ratio [HR] = 1.582, 95% CI: 1.103-2.268, P = .013; Figure 2F). For PFI, univariate analysis revealed T stage, N stage, pathological stage, and C1QBP expression as significant prognostic factors (Figure 2G). Multivariate analysis identified advanced T stages (T2&T3&T4, HR = 1.482, 95% CI: 1.068-2.057, P = .019) and advanced pathological stages (II&III&IV, HR = 1.935, 95% CI: 1.273-2.939, P = .002) as independent prognostic factors (Figure 2H). Univariate analysis identified several factors associated with DSS prognosis (Figure 2I), but no independent prognostic factors were confirmed by multivariate analysis (Figure 2J).

Immune Characteristics Analysis of C1QBP

The occurrence and progression of tumors, as well as their response to various therapeutic interventions, are closely intertwined with the TME. Consequently, therapies targeting the TME have emerged as a promising strategy for cancer treatment.³⁹ To explore the role of C1QBP in modulating the LUAD TME, we conducted a comprehensive analysis. Our results first showed that high C1QBP expression was significantly associated with reduced Immune scores, Stromal scores, and ESTIMATE scores, while correlating with increased tumor purity (Figure 3A-D). This finding suggests a relative paucity of immune and stromal components in the TME of LUAD with high C1QBP expression. Importantly, using the ssGSEA algorithm, we further observed that high C1QBP expression was linked to a significantly lower enrichment level of most antitumor immune cell populations, including T cells, B cells, and dendritic cells (Figure 3E).

Figure 3.

Immune characteristics analysis of C1QBP. (A-D) Correlation of C1QBP expression with immune score, stromal score, ESTIMATE score, and tumor purity in LUAD. (E) The ssGSEA algorithm was used to analyze the correlation between the level of immune cell infiltration and C1QBP expression. (F) Correlations of C1QBP expression and immune cell infiltration level based on different algorithms in LUAD. (G) The heatmap shows correlation analyses between C1QBP expression and immune checkpoint genes in LUAD. (H) The IPS in low-C1QBP group and high-C1QBP group.

This immunosuppressive “cold” tumor phenotype was consistently supported by results from the CIBERSORT and TIMER algorithms, which showed that high C1QBP expression correlated with reduced abundances of memory B cells, CD4⁺ T cells, and dendritic cells (Figure 3F). To robustly validate this key finding, we further employed 4 additional algorithms: EPIC, MCPCOUNTER, XCELL, and QUANTISEQ. By conducting a convergent analysis of results across these multiple immune infiltration algorithms, we identified consistent patterns of immune cell alterations associated with high C1QBP expression (Table S1). Among these patterns, B cells, CD4⁺ T cells, and dendritic cells exhibited significant and consistent negative correlations with C1QBP expression across the vast majority of algorithms. Additionally, high C1QBP expression was significantly associated with reduced levels of cancer-associated fibroblasts (CAFs); notably, regulatory T cells (Tregs) also showed a similar trend (Figure S4).

The immunosuppressive role of C1QBP is further underscored by its strong negative correlation with the expression of key immune checkpoint molecules, including programmed death 1 (PD-1), CTLA-4, and LAG-3 (Figure 3G). Most notably, LUAD patients with low C1QBP expression exhibited significantly higher IPS, which indicates a greater potential to benefit from anti-CTLA-4 and/or anti-PD-1 immunotherapy (Figure 3H).

Functional Enrichment Analysis of C1QBP

To date, our findings have demonstrated a significant association between C1QBP expression levels and both prognosis and immune response in LUAD patients. To further elucidate the underlying functional mechanisms of C1QBP, we identified DEGs by comparing the high- and low-C1QBP expression groups. As shown in the volcano plot, a total of 266 DEGs were detected, comprising 111 upregulated genes and 155 downregulated genes (Figure 4A).

Figure 4.

Integrated analysis of DEGs and function enrichment. (A) Volcano plot shows DEGs between low-C1QBP group and high-C1QBP group in LUAD. (B) The circle graph shows the result of KEGG pathway analysis among DEGs. (C) The lollipop plot shows the result of GO functional enrichment analysis among DEGs. (D, E) Result of GSEA between high and low C1QBP expression groups in LUAD.

KEGG pathway enrichment analysis revealed that C1QBP-related DEGs were primarily enriched in the following biological pathways: ribosome, oxidative phosphorylation, Parkinson’s disease, thermogenesis, Huntington’s disease, proteasome, carbon metabolism, RNA transport, Alzheimer’s disease, citric acid cycle (TCA cycle), spliceosome, and metabolic pathways (Figure 4B). GO functional annotation analysis further showed that these DEGs were involved in multiple biological processes and molecular functions associated with C1QBP. At the biological process (BP) level, the main enrichments included ribonucleoprotein complex biogenesis, cytosolic translation, and ribosome biogenesis. At the cellular component (CC) level, significant enrichments were observed in ribosomes, ribosomal subunits, mitochondrial matrix, and mitochondrial inner membrane. At the molecular function (MF) level, the primary associations were with ribosomal structural constituent activity, ribonucleoprotein complex binding, and ribosome binding (Figure 4C).

To further clarify the biological functions of C1QBP in LUAD, we analyzed signaling pathways associated with C1QBP expression levels using GSEA. Our results showed that high C1QBP expression was significantly enriched in the FOXM1 pathway, E2F pathway, and Mcm pathway (Figure 4D), whereas the low C1QBP expression group was significantly enriched in pathways including interferon gamma signaling, PD-1 signaling, NKT pathway, and CTLA4 pathway (Figure 4E). These findings suggest that C1QBP may contribute to LUAD progression by regulating cell cycle-related and immune-associated pathways.

To further distinguish C1QBP-specific functions from the universal molecular features of LUAD, we compared the enriched pathways in the high/low C1QBP expression groups with the differential pathways identified between TCGA LUAD tumor tissues and normal tissues. KEGG pathway enrichment analysis showed that LUAD mainly involved metabolic, digestive and diabetic, and cancer transcriptional dysregulation pathways, while BP pathways involved in LUAD included limb morphogenesis, tissue differentiation, and enzyme activity regulation; CC included nucleosomes and protein complexes; and MF involved enzyme activity and receptor ligand activity. GSEA results further showed significant activation of core cell cycle and proliferation-related pathways in LUAD, such as the E2F pathway, Mcm pathway, and FOXM1 pathway. Notably, the immune-related pathways enriched in the context of C1QBP (interferon gamma signaling, NKT pathway, and CTLA4 pathway) were not detected among the LUAD-specific differential pathways (Figure S5).

Mutation and Methylation Analysis of C1QBP

We analyzed the genetic alteration landscape of C1QBP using data from the cBioPortal database. Results showed that “deep deletion” was the primary type of genetic alteration, followed by “mutation.” Specifically, among 566 LUAD cases, the overall alteration frequency of C1QBP was 1.06%, with deep deletion being the main contributing alteration. Promoter methylation analysis via the UALCAN database revealed that C1QBP promoter methylation levels were significantly lower in LUAD tumor tissues compared to normal tissues. We further investigated the correlation between C1QBP CNA and its mRNA expression in LUAD (n = 501). The findings showed that samples with “gain” of C1QBP exhibited the highest average mRNA expression, followed sequentially by those with “diploid,” “shallow deletion,” and “deep deletion” statuses. Finally, analysis of the correlation between C1QBP methylation and mRNA expression in LUAD using the cBioPortal database demonstrated a significant negative correlation (Spearman’s r = −.12, P < .01; Figure S6).

ELISA Experimental Verification

We used the median serum C1QBP concentration as the cutoff value to stratify 77 NSCLC patients into high- and low-concentration groups. Comparisons of baseline characteristics between the 2 groups are presented in Table S2. Patients in the high serum C1QBP concentration group were more likely to have distant metastases and presented with more advanced pathological stages compared to those in the low-concentration group (P = .014; Table S2). Kaplan-Meier survival analysis showed that patients with higher serum C1QBP concentrations had significantly shorter progression-free survival (PFS, P = .033) and OS (P = .015) than those with lower concentrations (Figure 5A and B). We further included serum C1QBP level, age, smoking status, TNM stage, and type of immunotherapeutic agent in a multivariate Cox regression model. Results indicated that serum C1QBP level was an independent predictor of shorter OS in NSCLC patients receiving immunotherapy (HR = 3.70, 95% CI: 1.01-14.29, P = .048). However, no independent predictive value of serum C1QBP was observed for PFS (HR = 1.64, 95% CI: 0.83-3.23, P = .159; Table S3 and Figure 5C and D).

Figure 5.

ELISA experimental verification. (A) Kaplan-Meier survival curves between the low and high C1QBP concentration groups in OS. (B) Kaplan-Meier survival curves between the low and high C1QBP concentration groups in PFS. (C) Multivariate Cox regression analyses in OS. (D) Multivariate Cox regression analyses in PFS. (E) Kaplan-Meier survival curves for OS between low and high C1QBP concentration groups in LUAD subgroup. (F) Kaplan-Meier survival curves for PFS between low and high C1QBP concentration groups in LUAD subgroup.

Additionally, our cohort included 45 LUAD patients. For this subgroup, the median OS was 17.03 months in the high serum C1QBP group, whereas the median OS in the low serum C1QBP group had not yet been reached (HR = 11.11, 95% CI: 1.41-100, P = .023). This survival trend was highly consistent with that of the overall NSCLC cohort (HR = 4.35, 95% CI: 1.19-14.29, P = .026). Notably, significant differences in PFS based on serum C1QBP levels were also observed: in the LUAD subgroup (HR = 2.63, 95% CI: 1.06-6.67, P = .037) and the overall NSCLC cohort (HR = 2.04, 95% CI: 1.04-4.00, P = .036), respectively (Figure 5E and F).

Discussion

Although advances have been made in LUAD treatment in recent years, the overall survival rate of LUAD patients remains poor, highlighting the urgent need for continued research into innovative therapeutic targets and prognostic biomarkers.^40,41 C1QBP is highly expressed in multiple adenocarcinomas, including LUAD,^42
-44 and its expression level correlates with advanced tumor stage and poor prognosis.^45
-47 In our study, using public databases, we confirmed that C1QBP expression was significantly elevated at both the mRNA and protein levels in LUAD tumor tissues; further, high C1QBP expression was identified as an independent prognostic indicator for OS in LUAD patients. These findings confirm and extend the earlier work of Saha et al.,²³ whose pan-cancer analysis emphasized the broad prognostic value of C1QBP. Additionally, literature has reported that soluble C1QBP is detectable in the serum and pleural effusions of patients with metastatic disease, where it may act as an autocrine growth signal to promote tumor proliferation.⁴⁸ Our study further suggests that high C1QBP expression may contribute to the formation of immunosuppressive microenvironment in LUAD and, for the first time, the present study assesses the utility of serum C1QBP as a candidate biomarker in LUAD.

The interaction between tumors and their microenvironment has emerged as a research focus in tumor biology, as it is critical for elucidating tumor etiology and evaluating immunotherapy efficacy.⁴⁹ Among TME components, stromal and immune cells are recognized as valuable markers for predicting tumor prognosis and treatment responses.^50
-52 The strong negative correlation between C1QBP and key antitumor immune cells suggests that C1QBP may play a role in shaping an immune-excluded phenotype. Notably, B cells, dendritic cells, and CD4⁺ T cells exhibited significant and consistent negative correlations with C1QBP across most analytical algorithms, indicating that the C1QBP-driven immune microenvironment is primarily characterized by the coordinated loss of these key antitumor immune populations. Additionally, high C1QBP expression was significantly associated with reduced CAFs, which aligns with our earlier observations of decreased stromal scores and increased tumor purity. These consistent findings suggest that C1QBP does not shape the immunosuppressive TME by enriching immunosuppressive cells, but rather by actively excluding antitumor immune cells, a mechanism that directly explains its association with immunotherapy resistance. We further found that C1QBP was strongly negatively correlated with multiple immune checkpoint molecules, and patients with high C1QBP expression had lower efficacy scores for anti-CTLA-4 and anti-PD-1 inhibitors. This suggests that immunotherapy may be a more favorable option for patients with low C1QBP expression.⁵³ While our data establish a link between C1QBP and the immunosuppressive TME, the underlying mechanisms remain incompletely understood. Based on our findings, we hypothesize that tumor-cell-derived C1QBP may modulate the secretion of cytokines or chemokines, thereby recruiting immunosuppressive cells, inhibiting the infiltration and function of cytotoxic T cells, downregulating immune checkpoint molecule expression on tumor cells, and ultimately promoting immune escape. Future functional studies, including in vitro and in vivo models of C1QBP knockdown or overexpression, are critical to confirm whether alterations in C1QBP impact the immune landscape and sensitivity to immunotherapy.

The enrichment of C1QBP in oxidative phosphorylation and cell cycle pathways is highly consistent with its known role in mitochondrial function.^54,55 By enhancing mitochondrial metabolism, C1QBP provides the bioenergy and biosynthetic precursors required for rapid tumor proliferation, thereby promoting tumorigenicity.^56,57 Additionally, C1QBP has been reported to regulate tumor invasion and migration by targeting epithelial-mesenchymal transition (EMT) markers, further driving tumor metastasis.^19,58 When comparing C1QBP-associated pathways with differential pathways between LUAD tumors and normal tissues, we found that C1QBP primarily regulates mitochondrial energy metabolism and translational processes, whereas LUAD-specific pathways focus more on development and differentiation. GSEA showed significant activation of cell cycle and proliferation pathways in both C1QBP-high LUAD and LUAD overall, indicating that C1QBP drives classical malignant biological processes in LUAD and enhances tumor proliferative capacity. Notably, immune-related pathways are uniquely associated with C1QBP, conferring tumor-specific functions that enable C1QBP to inhibit tumor immune responses. This explains why high C1QBP expression correlates with worse prognosis and predicts outcomes in patients receiving immunotherapy. Biologically, C1QBP acts as a dual regulator of tumor cell metabolism and immune responses; high levels may equip tumor cells with immune escape capabilities while driving malignant progression. Our functional enrichment analysis supports this notion, and multivariate analysis further validated the prognostic value of serum C1QBP levels for OS in NSCLC patients receiving immunotherapy.

Collectively, our multi-omics analyses and clinical validation demonstrate that C1QBP is an independent poor prognostic factor for OS in LUAD patients, with high expression strongly predicting shorter survival. Although its association with disease progression (PFS/PFI) did not reach statistical significance, its potent impact on OS establishes it as a critical prognostic indicator and potential therapeutic target. As a noninvasive marker, serum C1QBP protein shows promise for clinical application in risk stratification and prognostic management of LUAD patients receiving immunotherapy. Functional enrichment analysis revealed that C1QBP is closely linked to core cancer pathways such as cell cycle regulation and metabolic reprogramming, suggesting that targeting C1QBP may disrupt tumor cell survival and proliferation. Additionally, high C1QBP expression is strongly associated with an immune desert phenotype, implying that targeting C1QBP could reverse the immunosuppressive microenvironment and synergize with existing immunotherapies. The comprehensive evidence from this study identifies C1QBP as a valuable candidate for therapeutic development, warranting further investigation. While our findings lend support to this model, experimental studies are required to verify the underlying mechanisms. Future work will include in vitro functional assays, co-culture models, in vivo animal studies, and the exploration of drug development.

While this study addresses gaps in previous research, it has limitations. First, it is a retrospective study that relied on gene expression data and clinical factors from public databases, which may lack certain detailed clinical information on LUAD patients. Second, although we comprehensively demonstrated an association between C1QBP and the immunosuppressive microenvironment in LUAD, the exact causal relationship remains to be experimentally verified. Third, the validation cohort included a limited number of samples from a single center and included NSCLC patients beyond LUAD. Despite these limitations, high C1QBP levels consistently predicted worse clinical outcomes across the TCGA-LUAD dataset, our center’s independent NSCLC clinical immunotherapy cohort, and the LUAD subgroup, enhancing the robustness and reliability of our conclusions. Moving forward, we plan to conduct a prospective clinical study to validate the value of serum C1QBP as a predictive marker for LUAD-specific immunotherapy in a larger cohort and to explore the molecular mechanisms by which C1QBP influences LUAD immunotherapy responses.

Conclusion

C1QBP emerges as a potential key regulator of immunosuppression in LUAD, with strong associations with poor clinical prognosis and immune evasion. Our findings not only provide compelling rationale for future mechanistic investigations into its functional role but also support exploring C1QBP as a potential therapeutic target, particularly in combination with immunotherapeutic strategies.

Supplemental Material

sj-docx-1-cix-10.1177_11769351261415650 – Supplemental material for C1QBP Associated With Immune Infiltration Predicts Poor Prognosis in Lung Adenocarcinoma

Supplemental material, sj-docx-1-cix-10.1177_11769351261415650 for C1QBP Associated With Immune Infiltration Predicts Poor Prognosis in Lung Adenocarcinoma by Minghang Zhang, Ying Wang, Fei Qi, Xiaomei Yang, Tongmei Zhang and Shaofa Xu in Cancer Informatics

Footnotes

Acknowledgements

We would like to thank the UCSC, Sangerbox, Xiantao, TIMER2.0, TCIA, cBioPortal, HPA, TCGA, UALCAN databases for open access to the LUAD sequencing datasets.

ORCID iDs

Minghang Zhang

Ying Wang

Fei Qi

Xiaomei Yang

Tongmei Zhang

Shaofa Xu

Ethical Considerations

The study was approved by the Ethics Committees of Beijing Chest Hospital, Capital Medical University, Capital Medical University before data collection began (Ethics Approval No.: KY-2018-002).

Consent to Participate

All patients involved in this study had given their informed consent before study.

Author Contributions

MZ, YW, and FQ drafted the manuscript. MZ, YW, and FQ completed the table and figures. TZ and SX managed the article design, reviewed the manuscript, and provided funding support. All authors contributed to the article and approved the submitted version.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was granted by Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes (PWD&RPP-MRI, grant number JYY2023-14); Beijing Municipal Public Welfare Development and Reform Pilot Project for Medical Research Institutes (PWD&RPP-MRI, grant number JYY2023-15). We thank all participants and their families for supporting this study.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories can be found in the article material.

Supplemental Material

Supplemental material for this article is available online.

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