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. 2025 Aug 21;22:28. doi: 10.1186/s12014-025-09553-5

Revelation of prognosis and tumor microenvironment of colorectal cancer based on genes related to antibody-dependent cellular phagocytosis and single-cell landscape

Leilei Yang 1,2,#, Jiaju Han 1,#, Weiwei Ma 3, Ruili Zhang 1,2,✉, Shenkang Zhou 1,2,✉
PMCID: PMC12372309  PMID: 40841999

Abstract

Background

Increasing evidence highlights the crucial role of antibody-dependent cellular phagocytosis (ADCP) in colorectal cancer (CRC). However, how to use ADCP-related genes to predict prognosis in CRC and guide treatment remains unelucidated.

Methods

Gene expression profiles and clinical data information on CRC were sourced from the Cancer Genome Atlas (TCGA) database. We obtained the validation set GSE29621 and CRC single-cell dataset GSE178341 from the Gene Expression Omnibus (GEO) database and the ADCP-related gene set from the literature. Based on the TCGA-CRC cohort, univariate Cox and LASSO Cox regression analyses were employed to screen for ADCP-related genes linked with prognosis. Then a prognostic model was set up through multivariate Cox regression analysis. We further graphed a nomogram based on clinical information and risk scoring and evaluated its prognostic value using Kaplan-Meier (K-M) survival curves and receiver operation characteristic (ROC) curves. Based on the single-cell data analysis model, the expression levels of genes in different cell clusters were evaluated by scoring individual cells using the AUCell R package. Finally, functional enrichment, immune infiltration, and somatic mutation analyses were performed on the high- and low-ADCP-related risk score (ADCPRS) groups clustered by the median value of the ADCPRS. In addition, small molecular drugs for the treatment of CRC patients were analyzed using drug sensitivity analysis of IC50 and molecular docking.

Results

This project created a prognostic model based on 7 feature genes using the TCGA training set. The K-M survival curves and ROC curves indicated that the model, as well as the nomogram, was capable of accurately predicting prognosis for CRC patients. Based on scRNA-seq data analysis, the 7 feature genes were examined to be expressed across 8 cell clusters (Monocytes, CD8 + T cells, Epithelial cells, B cells, Macrophages, HSC, Endothelial cells, and Fibroblasts). We scored individual cells and revealed that cells with higher scores were mainly concentrated in B cells and macrophages. Functional enrichment analysis manifested that the upregulated differentially expressed genes (DEGs) in the high-ADCPRS group were mainly enriched in signaling pathways such as the Drug metabolism cytochrome P450, Neuroactive ligand-receptor interaction, and Calcium signaling pathway. Immune infiltration analysis manifested that Th1 cells, iDCs, and Th2 cells had higher abundance in the low-ADCPRS group. Gene mutation analysis uncovered that both high- and low-ADCPRS groups had high mutation rates, with APC and TP53 being the top two genes with the highest mutation rates. Moreover, the drug sensitivity analysis and molecular docking uncovered that Dasatinib, Benzaldehyde, and Tegafur may aid in treating CRC patients.

Conclusion

The prognostic model developed in this project functioned as a potential tool for risk assessment. The 7 model genes may serve as prognostic biomarkers for CRC, which can guide treatment decisions for CRC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-025-09553-5.

Keywords: Colorectal cancer, Antibody-dependent cellular phagocytosis, Single cell, Prognosis, Tumor microenvironment

Introduction

Colorectal cancer (CRC), including colon and rectal tumors, is a common cancer affecting the digestive system, contributing to the majority of cancer-related mortality and morbidity worldwide [1]. Statistically, approximately 20% of patients with CRC have already developed distant metastasis upon diagnosis, and up to 50% of CRC patients with local diseases will eventually develop metastasis [2]. Despite significant advances in regular screening, adjuvant chemotherapy, molecular targeted therapy, and other treatment strategies, the prognosis and survival rates of metastatic CRC patients remain poor, highlighting the significance of distinguishing CRC risk and identifying specific therapeutic targets [3–6]. Therefore, innovating and identifying biomarkers that can predict prognosis is urgent to help design treatment strategies for CRC patients.

Antibody-dependent cellular phagocytosis (ADCP) of tumors is implicated in the binding of target cells (such as tumor cells) and effector cells (such as macrophages) induced by antibodies, leading to the phagocytosis of target cells by effector cells. After phagocytosis, the target cells inside the effector cells are acidified, digested, and degraded [7]. Macrophage-mediated ADCP appears to be a major mechanism for many therapeutic anti-cancer antibodies, where the Fc region of the anti-tumor antibody can bind to Fcγ receptors (FcγR) on macrophages, triggering ADCP via the phosphorylation of immunoreceptor tyrosine-based activation motif (ITAM) of FcγR, and subsequently inducing phagocytosis through activation of downstream signaling via guanine nucleotide exchange factors (Rac-GEF) [7]. Overdijk et al. [8]. put forward that ADCP contributes to the anti-tumor activity of therapeutic antibodies in lymphoma and multiple myeloma. The dual checkpoint blockade of CD47 and LILRB1 may enhance antibody therapy for chronic lymphocytic leukemia (CLL) and lymphoma by boosting macrophage-mediated ADCP [9]. Furthermore, Guo et al. [10]. identified differences between left-sided and right-sided CRC using single-cell sequencing data and unraveled that M2-like macrophage-induced ADCP is more pronounced in left-sided CRC and is connected with the favorable prognosis in CRC. Therefore, we attempted to set up an ADCP-related prognostic model to predict CRC prognosis.

This study identified 7 characteristic genes through bioinformatics analysis, and constructed a prognostic model. Then, the predictive performance of the model was verified in the independent validation set GSE29621. The predictive performance of the model was evaluated and validated using the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. The expression levels of model genes in various cell types were examined based on scRNA-seq data. Based on the ADCP-related risk score (ADCPRS), TCGA-CRC cohorts were further split into high- and low-ADCPRS groups. Subsequently, the differences in enrichment pathways, immune microenvironment, and somatic mutations between the two groups were assessed. Additionally, the small molecular drugs for the treatment of CRC patients were screened out by analyzing drug sensitivity and molecular docking. Taken together, the present work proffered scientific support for prognosis prediction and treatment of CRC.

Materials and methods

Data source

From the TCGA database [11]we obtained the gene expression profile and clinical data information of CRC (cancer samples: n = 650; normal samples: n = 51). The GSE29621 validation set and the CRC single-cell dataset (GSE178341) were sourced from the GEO [12] database.

We acquired a gene set related to ADCP from the literature, which consisted of 3405 genes [13].

Establishment and verification of the prognostic model

Based on the TCGA training set, univariate Cox regression analysis was carried out to screen for genes related to survival (P < 0.05). Further use of the glmnet R package and LASSO regression reduced multicollinearity for screening survival-related ADCP-related genes. Finally, multivariate Cox regression was utilized to generate the final prognostic model. We calculated the risk score for each patient based on the following formula:

graphic file with name d33e288.gif

Based on the ADCPRS of each CRC patient, we divided the patients into the high ADCPRS group and the low ADCPRS group using the median as the threshold. To evaluate the predictive performance of the model, we evaluated the model in the independent dataset GSE29621. Firstly, through the Kaplan-Meier (K-M) survival curve, we compared the differences in overall survival (OS) between the high and low ADCPRS groups. Subsequently, we plotted the receiver operating characteristic (ROC) curve and calculated the AUC values for 1-year, 3-year, and 5-year OS to further evaluate the predictive performance of the model.

Identification of model gene expression and aucell score in single cells

The Seurat R package was applied in quality control (QC) to remove low-quality cells from the GSE178341 dataset based on QC criteria (RNA count greater than 50, mitochondrial gene expression percentages below 5%). The data standardization was fulfilled by utilizing the “SCTransform” function of the Seurat R package. We then employed the analysis of variance to screen out the top 1500 genes with highly variable features. The principal component analysis (PCA) on single-cell samples was launched, followed by dimension reduction and cluster recognition by utilizing unified manifold approximation and projection (UMAP), with clustering results visualized by UMAP. Combined with two reference annotation sets HumanPrimaryCellAtlas and BlueprintEncode, we annotated single cells by utilizing the automatic annotation tool in SingleR. In addition, based on the prognostic model genes, we used the AUCell R package to score all cells and crafted a box plot of the scoring results sorted by the mean value.

Establishment and evaluation of the nomogram

To quantitatively predict the OS rate of CRC patients, we integrated the patients’ risk scores and clinical characteristics (including age, gender, TNM stage, and tumor stage) based on the TCGA-CRC dataset, and used the “rms” package in R language to construct a prognostic nomogram to predict the survival probabilities of patients at 1 year, 3 years, and 5 years. Subsequently, calibration curves and ROC curves were drawn to evaluate the predictive performance of the nomogram. Additionally, to assess the potential application value of the nomogram in actual clinical decision-making, we used decision curve analysis (DCA) to evaluate its clinical benefit. To verify the generalization ability of the nomogram, we selected the independent validation set GSE29621 as an external validation dataset, and used the same variable combination (risk score + clinical characteristics) to construct the nomogram in the validation set, and conducted calibration curve, ROC curve, and DCA analyses to systematically evaluate the stability and applicability of the model.

Enrichment analyses of differentially expressed genes (DEGs) in high/low-ADCPRS groups

The identification of DEGs between the two ADCPRS groups was achieved by using the edgeR package (FDR < 0.05,|log (FC)|>1). The upregulated DEGs in the high-ADCPRS group were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses by using the clusterProfiler R package, with results visualized using the enrichplot R package.

Analysis of tumor-infiltrating immune cells and prediction of response to immunotherapy

In this work, we used the TCGA-CRC queue and applied the single-sample gene set enrichment analysis (ssGSEA) algorithm from the GSVA R package to evaluate the differences in immune-related cells and functional scores between high- and low-ADCPRS groups of patients. The xcell algorithm in the IOBR package was employed to test the tumor microenvironment (TME) of each sample in the two different groups. Subsequently, we conducted the Wilcoxon test and plotted box plots of the two groups to visualize the difference between the two groups.

By using the tumor immune dysfunction and exclusion (TIDE) score [14]we predicted the response of the two groups to immunotherapy, with the Wilcoxon test processed and the violin plot graphed. Based on The Cancer Immunome Atlas database [15]we illustrated the differences in immunophenoscore (IPS) between high- and low-ADCPRS groups.

Somatic mutation analysis

When analyzing the SNV mutation data of TCGA-CRC, we used the maftools R package to probe into the similarities and differences in mutation types and mutation rates between the high- and low-ADCPRS groups. In addition, a waterfall plot showing the mutation status of the top 20 genes with the highest mutation frequency in the two different groups was crafted.

Drug sensitivity and molecular docking

To dig out potential drugs for the model genes, we utilized the CellMiner database [16] to identify drugs linked with these genes. After completing the drug screening, we employed the ggplot2 R package to visually display the top 9 drugs with the highest correlation. The method not only helped us understand the link between drugs and genes but also provided data supporting further drug research and clinical applications.

To further dissect the interaction between these drugs and genetic targets, we launched molecular docking on genes with significantly different expression and their related drugs in drug sensitivity analysis. The 3D structures of potential active ingredients and key gene targets were sourced from the PDB database [17] and PubChem [18] database. AutoDOCK software was then employed in the docking analysis of small molecular drug targets. Moreover, PyMOL software was applied to display the molecular docking between the small molecular drug and the protein macromolecule, thereby validating potential effective interaction sites between them.

Results

Building and validation of the prognostic model

Transcriptome data and clinical characteristics data of CRC patients were obtained from the TCGA database (n = 701). In univariate Cox regression analysis, we first selected 212 prognostic genes from the ADCP-related gene data (P < 0.01) (Table S1). Subsequently, further analysis was carried out on these prognostic genes using LASSO regression, with 7 genes selected (Fig. 1A-B). Finally, the multivariate Cox regression model was created based on 7 genes (Fig. 1C).

Fig. 1.

Fig. 1

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Construction of the prognostic model A: Cross-validation for parameter selection in LASSO Cox. B: LASSO coefficient spectrum of ADCP-related genes. C: Forest plot of 7 model genes based on multivariate Cox regression in the TCGA-CRC queue

graphic file with name d33e409.gif

We got the risk score for each patient by using the above formula and sorted patients into a high-ADCPRS group and a low-ADCPRS group based on the median ADCPRS. To verify the predictive ability of this model, we conducted an external validation on the independent dataset GSE29621 in the GEO database. The K-M survival curve analysis showed that the OS period of patients in the low ADCPRS group was significantly better than that of the high ADCPRS group (p < 0.01) (Fig. 2A). Additionally, we further evaluated the predictive accuracy of the model in this validation set through the ROC curve, and found that the AUC values at 1 year, 3 years, and 5 years were 0.73, 0.76, and 0.67 respectively (Fig. 2B), suggesting that the model has good predictive performance and potential clinical application value.

Fig. 2.

Fig. 2

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Validation of the prognostic model A: The K-M survival curves of patients in the high ADCPRS group and the low ADCPRS group in the GEO cohort. B: The ROC curves for 1/3/5 years in the GEO cohort

Establishment and validation of the prognostic nomogram

To quantitatively predict prognosis in CRC patients, we integrated the clinical features and risk scores of patients in the TCGA-CRC cohort, constructed a prognostic nomogram, and predicted OS rates at 1, 3, and 5 years for patients (Fig. 3A). By comparing the observed OS with the predicted OS by prognostic nomogram for 1, 3, and 5 years, we found good consistency between them (Fig. 3B). Further analysis uncovered that compared to using risk score and clinical features alone, the AUC value of the prognostic nomogram in ROC curve analysis was the highest, reaching 0.82 (Fig. 3C). According to DCA, the prognostic nomogram exhibited favorable benefits (Fig. 3D). In summary, the nomogram has good predictive performance in the TCGA-CRC cohort.

Fig. 3.

Fig. 3

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Construction and evaluation of the nomogram A: In the TCGA-CRC cohort, a nomogram was constructed based on risk score and clinical features (age, gender, TNM, tumor stage) to predict 1-year, 3-year and 5-year OS. B: Calibration curve of the nomogram in the TCGA-CRC cohort. C: ROC curve of the nomogram and clinical features in the TCGA-CRC cohort. D: DCA of the nomogram in the TCGA-CRC cohort. E: Nomogram for predicting 1-year, 3-year and 5-year OS based on risk score and clinical features in the validation set GSE29621. F: Calibration curve of the nomogram in the validation set GSE29621. G: ROC curve of the nomogram and clinical features in the validation set GSE29621. H: Decision curve of the nomogram in the validation set GSE29621

To further verify the generalization ability of the nomogram, we conducted an external validation in the independent validation set GSE29621. In this validation set, we also established the nomogram based on the patients’ clinical information and risk scores (Fig. 3E). The calibration curve showed that the nomogram also demonstrated a good calibration effect (Fig. 3F), a higher prediction accuracy (with the highest AUC reaching 0.82) (Fig. 3G), and significantly better clinical benefits than the individual model (Fig. 3H). These results indicated that the nomogram constructed in this study not only showed good predictive ability in the training set, but also had good applicability in the independent data set, and had the potential to become an auxiliary tool for prognosis assessment of CRC patients.

Expression of model genes and aucell scores in single cells

Based on the scRNA-seq data from GSE178341, we utilized the top 1,500 variable genes for PCA to reduce dimensionality. Subsequently, UMAP was employed to identify and visualize 38 cell clusters from the cell clustering results after dimensionality reduction (Fig. 4A). Then, based on the SingleR method, 8 cell types were identified and annotated, including CD8 + T cells, B cells, Epithelial cells, Monocytes, Macrophages, Endothelial cells, HSCs, and Fibroblasts (Fig. 4B). Further, we found that seven model genes were expressed at varying levels across eight cell types (Fig. 4C). All cells were scored by using the AUCell R package, and these high-scored cells were mainly concentrated on B cells and macrophages (Fig. 4D-E).

Fig. 4.

Fig. 4

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Expression patterns and AUCell scores of model genes in different cell clusters A: The UMAP algorithm clustered cells and colored the UMAP plot according to different cell clusters. B: The SingleR package for cluster annotation, with cell types visualized by using UMAP. C: Expression of model genes in different cell types D: AUCell scoring on individual cells based on model genes. E: Box plot of AUCell scores for different cell types (sorted by mean, highest on the left)

Enrichment analysis of DEGs between high and Low-ADCPRS groups

To evaluate the biological differences between different ADCPRS groups, we screened DEGs and identified 867 upregulated DEGs in the high-ADCPRS group (Table S2). GO and KEGG enrichment analyses were performed on these upregulated genes. GO enrichment analysis demonstrated that these genes were mainly linked to humoral immune response, channel activity, receptor-ligand activity, and collagen-containing extracellular matrix (Fig. 5A). KEGG analysis demonstrated that these genes were mainly gathered in signaling pathways such as Drug Metabolism Cytochrome P450, Neuroactive ligand-receptor interaction, Calcium signaling pathway, and Complementary and Cogeneration cascades (Fig. 5B).

Fig. 5.

Fig. 5

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Screening and enrichment analysis of upregulated DEGs in the high-ADCPRS group. A: GO enrichment analysis. B: KEGG enrichment analysis

Immune-Related analysis and prediction of response to immunotherapy

Then, we evaluated the TME of the high- and low-ADCPRS groups and dissected the differences in tumor immune cell infiltration and immune function. The results based on the xcell algorithm uncovered that immune cells such as CD4 + Memory T cells, CLP, MEP, pDC, and Plasma cells had higher abundance in the low-ADCPRS group, while Astrocytes, HSCs, Myocytes, and Endothelial cells were highly infiltrated in the high-ADCPRS group (p < 0.05) (Fig. 6A). Further analysis of the abundance of immune cells using the ssGSEA method revealed high infiltration of iDCs, Th1 cells, and Th2 cells in the low-ADCPRS group(p < 0.05) (Fig. 6B), indicating that the low ADCPRS group had stronger anti-tumor immune activity, while the high ADCPRS group may be more immunosuppressive. Immune function analysis demonstrated that Check point, MHC class I, Parainflammation, Inflammation promoting, and T cell co-stimulation were significantly elevated in the low-ADCPRS group (p < 0.05), indicating that the immune system was activated in patients in the low ADCPRS group (Fig. 6C) The analysis of HLA genes manifested that the expression of HLA-A, HLA-DQB1, and HLA-G, genes was significantly upregulated (p < 0.05) in the low-ADCPRS group (Fig. 6D), indicating that the low ADCPRS group had more antigen presentation. Analysis of immune checkpoint genes revealed that ICOS and TMIGD2 had higher gene expression levels in the low-ADCPRS group (p < 0.05), suggesting that the low ADCPRS group may be more sensitive to the immune checkpoint inhibitors (Fig. 6E). The immunotherapy response of CRC patients was predicted by using the TIDE score and IPS score, which showed that the low-ADCPRS group had a lower TIDE score and higher IPS score (p < 0.01) (Fig. 6F, G). This suggests that patients in the low ADCPRS group may be beneficiaries of immunotherapy, while the high ADCPRS group may be more in need of other treatment strategies.

Fig. 6.

Fig. 6

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Analysis of immune-related features and response to immunotherapy in high- and low-CORS groups A: The evaluation of the infiltration of immune cells by the xcell algorithm. B: Levels of immune cell infiltration between high- and low-ADCPRS groups. C: Analysis of immune-related functions in the high- and low-ADCPRS groups. D: Expression of HLA genes. E: Immune checkpoint gene expression. F: Plot of TIDE. G: Plot of IPS * represents p < 0.05. ** represents p < 0.01. *** represents p < 0.001

Somatic mutation analysis of high- and low-ADCPRS groups

Somatic gene mutations can serve as a major event facilitating tumorigenesis. We compared the types of gene mutations between the two groups by analyzing the mutation data of TCGA-CRC. The results indicated that the two genes with the highest mutation rates, whether in the high-ADCPRS group or the low-ADCPRS group, were APC and TP53, suggesting their key role in the occurrence and development of CRC and may serve as potential diagnostic markers or therapeutic targets. In the two ADCPRS groups, the most common type of mutation was missense mutation, and the predominant variant type was single nucleotide polymorphism (SNP). Additionally, single nucleotide variations (SNVs) were mainly manifested as C to T transitions (Fig. 7A-B). Then, we displayed the top 20 mutated genes in the high-ADCPRS group and the low-ADCPRS group, respectively. In the high-ADCPRS group, the top five mutated genes were APC (70%), TP53 (60%), TTN (60%), SYNE1 (40%), and KRAS (35%) (Fig. 7C). In the low-ADCPRS group, the top five mutated genes were APC (65%), TP53 (61%), PIK3CA (48%), KRAS (42%), and TTN (39%) (Fig. 7D). Further analysis exhibited high mutation rates of genes in both groups. In the 20 CRC samples from the high-ADCPRS group, the mutation rate of genes reached 100%. Similarly, in the 31 CRC samples from the low-ADCPRS group, the gene mutation rate also reached 100% (Fig. 7C-D). In conclusion, the high mutation rates in both groups indicated that genetic mutations were ubiquitous in CRC and were important drivers of tumorigenesis and development. Moreover, the high mutation rate also suggested that CRC patients may benefit from therapy targeting mutated genes, while also emphasizing the importance of genomic analysis in personalized therapy.

Fig. 7.

Fig. 7

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Overview of somatic mutations in the high- and low-ADCPRS groups A-B: Summary of mutation types for the high-ADCPRS group and low-ADCPRS group. C-D: Waterfall plot of the top 20 mutated genes in the high-ADCPRS group (20 samples) and low-ADCPRS group (31 samples)

Analysis of potential small molecular drugs

In this project, we screened potential anti-tumor drugs and found significant links between drugs and specific genes. Benzaldehyde (Cor = 0.564) and Tegafur (Cor = 0.496) were significantly positively connected with the JPH3 gene. AT-13,148 (Cor=−0.444) exhibited a significant negative linkage with CRYBA4. NVP-BGJ398 (Cor = 0.481) and KU-55,933 (Cor = 0.446) had a significant positive linkage with RYR2. JNJ-47,117,096 (Cor = 0.435) had a significant positive linkage with CRLF1 (p < 0.001); Dasatinib (Cor=−0.472) and Neratinib (Cor=−0.439) demonstrated a negative correlation with DPP7 (Fig. 8). Therefore, we speculated that positively correlated drugs (such as Benzaldehyde, Tegafur, NTVP-BGJ398, KU-55933, JNJ-47117096) may be more effective in CRC patients with low expression of the corresponding genes. Negative correlation drugs (such as AT-13148, Dasatinib, Neratinib) may be more effective in CRC patients with high expression of corresponding genes, because these drugs may exert anti-tumor effects by targeting these gene-related signaling pathways or biological characteristics. However, further experimental and clinical validation is needed to determine the exact efficacy of these drugs and the appropriate population.

Fig. 8.

Fig. 8

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Analysis of the correlation between model genes and small molecular drugs based on IC50 values

We further analyzed the impact of single-gene expression levels on drug sensitivity, finding that CRC patients with low expression of the JPH3 gene possessed considerably higher sensitivity to Benzaldehyde and Tegafur compared to those with high expression of JPH3. CRC patients with low expression of the DPP7 gene also showed significantly higher sensitivity to Dasatinib and Neratinib compared to the DPP7 high-expression group (p < 0.05) (Fig. 9). Therefore, Benzaldehyde, Tegafur, Dasatinib, and Neratinib may have a positive impact on the treatment of CRC patients, especially in those with lower levels of specific gene expression. However, the IC50 values of AT-13,148, NVP-BGJ398, and KU-55,933 were not significantly different between the two groups. This may be due to limitations in the sample size of the database. Overall, our drug prediction results provided potential therapeutic strategies for future personalized treatments.

Fig. 9.

Fig. 9

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Sensitivity analysis of the expression levels of model genes to relevant drugs based on the IC50 values. * represents p < 0.05. ** represents p < 0.01. *** represents p < 0.001

Molecular Docking

Based on the results of the drug sensitivity analysis mentioned above, we selected three sets of drug-gene pairs (DPP7/Dasatinib, JPH3/Benzaldehyde, and JPH3/Tegafur) for molecular docking analysis. The results demonstrated an interaction between Dasatinib and DPP7, which formed hydrogen bonds through the Leu281 (A) and Asn264 (A) sites and strengthened their binding through hydrophobic interactions involving V313, Y329, H330, N264, V268, M271, and L281 (Fig. 10A). Benzaldehyde interacted with JPH3, forming hydrogen bonds through the Lys78 (A) site, and hydrophobic interactions through W81, E76, K92, K78, E605, and E606, thus enhancing the binding between the two (Fig. 10B). Tegafur interacted with JPH3 through hydrogen bonding at the Asn318 (A) site, which was strengthened by the hydrophobic interactions involving G195, N318, R319, V199, and N341 (Fig. 10C). Given the above results, good drug-binding activity may have significant therapeutic potential in the treatment of CRC.

Fig. 10.

Fig. 10

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Molecular docking between key pharmacological substances and model genes. A: 2D and 3D views of the optimal conformations of Dasatinib and DPP7. B: 2D and 3D views of the optimal conformation of Benzaldehyde and JPH3. C: 2D and 3D views of the optimal conformations of Tegafur and JPH3

Discussion

With further research on ADCP, mounting studies indicated that ADCP exerts an influence on tumor progression. For example, Resiquimod (R848), an immune system activator that targets tumor-associated macrophages (TAMs) to convert TAMs into M1 type, enhances the ADCP function of antibodies and thus reinforces the anti-tumor effect of therapeutic antibodies [19]. However, there is limited research on the functions of ADCP in CRC currently, especially on the impact of ADCP-related gene features on CRC prognosis. Therefore, this project employed bioinformatics methods to identify 7 ADCP-related genes in CRC, developed a prognostic evaluation model based on these genes, and then dissected the expression of model genes in different cell clusters based on single-cell sequencing data. Further analyses of the immune differences and small molecular drugs based on high- and low-ADCPRS groups were launched. This work may provide a basis for identifying promising prognostic biomarkers in the treatment of CRC patients.

The 7 identified candidate genes (CRLF1, CRYBA4, DPP7, DRD4, JPH3, KCNQ2, and RYR2) in CRC were crucial for constructing the prognostic model. CRLF1 is a member of the cytokine receptor family, involved in immune function, cell growth, hematopoiesis, inflammation, and differentiation [20]. Li et al. [21]. pointed out that CRLF1 plays a tumor-suppressive role in the progression of CRC, serving as a downstream target of miR-3065-3p to hinder CRC stemness and liver metastasis. CRYBA4, as a protein-encoding gene, is implicated in various eye diseases [22, 23]. A positive linkage between CRYBA4 and the adverse prognosis of CRC patients has been revealed [24]. However, there is no research illuminating the potential mechanism of this gene’s relation with CRC. DPP7 is implicated in regulating lymphocyte quiescence and immunity, with increasing evidence suggesting its connections with cancer and diseases [25, 26]. In CRC, Zhang et al. [27]. put forward that elevated DPP7 levels were significantly linked with lymphatic invasion and shorter OS rate, and the expression of DPP7 in CRC samples was considerably higher than that in non-tumor tissues, implying DPP7 is a potential biomarker for CRC. DRD4 belongs to the dopamine receptor family and has a bearing on cancer progression, such as tumor cell proliferation, death, invasion, and migration [28]. A study identified DRD4 as a candidate gene linked with survival in CRC patients [29]. Wang et al. [30]. also put forward that DRD4 correlates with the progression of CRC and can function as a molecular biomarker for CRC. JPH3 is reported to be a tumor suppressor in various gastrointestinal tumors [31, 32]. Hu et al. [33]. discovered that DNA methylation modulates the expression of JPH3 in CRC and leads to its downregulation in tumors. It increases mitochondria-mediated apoptosis through the Unfolded Protein Response (UPR) and disrupts calcium homeostasis, suggesting that JPH3 may be a biomarker for CRC. KCNQ2 is a potassium channel that plays an instrumental role in controlling membrane potential and intestinal ion homeostasis, having bearings on gastrointestinal cancer and often undergoing genetic changes in cancer [34]. KCNQ2 has been proven to be a potential biomarker to predict the prognosis and survival of CRC patients [35]. The Ryanodine receptor (RYR) is a calcium release channel located in the endoplasmic reticulum or sarcoplasmic reticulum (ER/SR), which can rapidly release Ca2+ from the ER/SR, playing a series of physiological functions including cell growth, differentiation, metabolism, and apoptosis [36]. Chen et al. [37]. unraveled that RYR2 is a potential target for treating CRC metastasis, with high expression of RYR2 being negatively linked with the prognosis of CRC patients. Overexpression of RYR2 can increase superoxide production in CRC cells, thereby elevating the levels of reactive oxygen species (ROS) in the cytoplasm. Therefore, these model genes may play a certain role in the occurrence and development of CRC, which could become new targets for future CRC treatment.

Based on single-cell RNA sequencing, we successfully clustered samples into 8 cell clusters: CD8 + T cells, Epithelial cells, B cells, Monocytes, Macrophages, Endothelial cells, HSCs, and Fibroblasts. Furthermore, based on 7 model genes, all individual cells were scored by AUCell, with cells with higher scores mainly concentrated in B cells and macrophages. A large body of evidence supported that tumor-infiltrating B lymphocytes (TIL-Bs) and the antibodies they produce are a significant feature of the human immune response to cancer, with B cells being an essential component of the CRC infiltrating immune cells [38, 39]. ADCP and antibody-dependent cellular cytotoxicity (ADCC) are mechanisms of B cell response that can facilitate the activation of CD4 + and CD8 + T cells, maintaining anti-tumor immune memory [40]. Macrophages are a necessary condition for inducing ADCP in cancer cells. Studies suggested that ADCP macrophages play an anti-tumor role in antibody therapy because macrophages can engulf tumor cells bound to antibodies [7, 41]. B cells and macrophages can provide references for patients’ responses to adjuvant therapy and prognosis.

When analyzing the infiltration characteristics of immune cells in the TME, we observed that iDCs, Th1 cells, and Th2 cells showed higher levels of infiltration in the low-ADCPRS group. iDCs are sentinels of the immune system, capable of capturing and processing foreign antigens and then presenting this information to T cells to activate specific immune responses [42]. Th1 cells are mainly responsible for cell-mediated immune responses. They can secrete cytokines such as interferon-γ (IFN-γ) to enhance anti-tumor immunity [43]. Th2 cells mainly participate in humoral immune responses by secreting cytokines such as IL-4, facilitating B cells to produce antibodies [44]. Although the infiltration of multiple immune cells was increased in the low ADCPRS group, the enhancement of certain immune functions (such as T cell co-stimulation) may be more correlated with the function of specific cell types (such as Th1 cells), while the role of other cells (such as pDC) may not be fully revealed. In the low-ADCPRS group, the higher abundance of immune cells may indicate an active but potentially dysregulated immune response. This may be because tumor cells can express specific antigens that activate immune cells. Moreover, TME may repress their normal function, leading to immune escape [45, 46]. In addition, the infiltration of Astrocytes and Endothelial cells increased in the high ADCPRS group. However, the specific effects of these lymphocytes on immune function remain unclear, and further studies are needed. Further, we uncovered that CRC patients in the low-ADCPRS group not only exhibited fewer immune escape features (low TIDE score) but also showed higher immunogenicity (high IPS). The finding coincides with the highly infiltrated immune cells, suggesting that CRC cells in the low-ADCPRS group may have a more active immune response and lower potential for immune escape. Therefore, patients in the low-ADCPRS group may respond better to immunotherapy. In conclusion, the high infiltration of immune cells is a complex phenomenon. Future research is required to further elucidate how to utilize this knowledge to innovate new cancer treatment methods to refine patient outcomes and survival rates.

Functional enrichment analysis revealed that the upregulated DEGs in the high-ADCPRS group were mainly enriched in the Calcium signaling pathway. Calcium signaling is implicated with various cellular activities and processes such as cell proliferation and death [47]. The regular intake of calcium supplements may help reduce the risk of developing CRC [48]. Wu et al. [49]. pointed out that the genetic polymorphisms in the calcium signaling pathway genes may be involved in the potential mechanism of CRC pathogenesis through TME. Another important pathway is Neuroactive ligand-receptor interaction. A study found that repressing the Neuroactive ligand-receptor interaction pathway can boost the response of colon cancer to immunotherapy [50]. This project also predicted small molecular drugs for CRC patients, such as Tegafur, Benzaldehyde, and Dasatinib. Dasatinib is a promising combination drug that boosts the sensitivity of metastatic CRC to immunotherapy. Studies have revealed that the combination therapy of Dasatinib and anti-PD-1 antibodies can not only repress stromal reactions in CRC and induce immune cell infiltration but also lead to an increase in CD62L-, CD44 + effector memory cells [51]. Benzaldehyde is an organic compound composed of a benzene ring and a formyl group, which has anti-inflammatory and antioxidant properties [52]but there is currently no research on the pharmacological effects of Benzaldehyde in CRC. Tegafur is a frequently applied antitumor agent that hinders the growth of tumor cells by suppressing DNA synthesis [53]. Yang et al. [54]. asserted that the efficacy of Tegafur combined with Oxaliplatin in the treatment of advanced CRC is superior to that of Capecitabine combined with Oxaliplatin. Therefore, these drugs may be promising small molecular drugs for CRC treatment, awaiting further research to validate their actual effects in the development of CRC.

In summary, we constructed a 7-gene prognostic model based on the TCGA-CRC training set and investigated the expression levels of the 7 model genes in different cell clusters using scRNA-seq data. It was further found that there were significant differences in immune-related features between high and low ADCPRS groups. The differences in gene mutation and functional enrichment further provide an important basis for understanding the molecular mechanism of CRC and developing personalized treatment strategies. In addition, drug sensitivity and molecular docking were used to predict potential drugs that may be effective in CRC treatment, providing a new direction for the clinical use of drugs. However, some limitations persist in this work. Currently, the prognostic model we constructed was only validated on the GSE29621 dataset, necessitating further validation in a broader population of CRC patients to comprehensively evaluate the survival prediction ability of the model. Secondly, this project did not consider clinical information related to CRC surgery and tumor markers. Therefore, more clinical cases are necessary to confirm our conclusions. The work, albeit with shortcomings, shed new insights into the search for potential prognostic biomarkers.

Supplementary Information

Supplementary Material 1 (24.6KB, xlsx)
Supplementary Material 2 (74.6KB, xlsx)

Author contributions

(I) Conception and design: Leilei Yang, Jiaju Han

(II) Administrative support: Shenkang Zhou, Ruili Zhang

(III) Provision of study materials or patients: Shenkang Zhou

(IV) Collection and assembly of data: Leilei Yang

(V) Data analysis and interpretation: Jiaju Han, Weiwei Ma

(VI) Manuscript writing: Leilei Yang, Jiaju Han

(VII) Final approval of manuscript: All authors.

Funding

This work was supported by Zhejiang Medical and Health Science and Technology Plan Project (2022PY028).

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Leilei Yang and Jiaju Han contributed equally to this work.

Contributor Information

Ruili Zhang, Email: zhangrl@enzemed.com.

Shenkang Zhou, Email: tzyywcwk@163.com.

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Associated Data

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Supplementary Materials

Supplementary Material 1 (24.6KB, xlsx)
Supplementary Material 2 (74.6KB, xlsx)

Data Availability Statement

No datasets were generated or analysed during the current study.

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