
==== Front
Transl Oncol
Transl Oncol
Translational Oncology
1936-5233
Neoplasia Press

S1936-5233(24)00220-1
10.1016/j.tranon.2024.102093
102093
Original Research
Bulk and single-cell RNA sequencing analyses coupled with multiple machine learning to develop a glycosyltransferase associated signature in colorectal cancer
Chen Xin abc1
Zhang Dan abc1
Ou Haibin abc
Su Jing abc
Wang You youwang@whu.edu.cn
abc⁎
Zhou Fuxiang happyzhoufx@sina.com
abc⁎
a Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, PR China
b Hubei Key Laboratory of Tumor Biological Behaviors, Zhongnan Hospital, Wuhan University, Wuhan, PR China
c Hubei Clinical Cancer Study Center, Zhongnan Hospital, Wuhan University, PR China
⁎ Corresponding authors. youwang@whu.edu.cnhappyzhoufx@sina.com
1 Equal contribution.

31 8 2024
11 2024
31 8 2024
49 10209321 1 2024
10 7 2024
11 8 2024
© 2024 Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Highlights

• Glycosyltransferases associated with CRC were redefined at the single-cell RNA level using five algorithms.

• Seven machine learning algorithms were rigorously assessed for CRC prognosis prediction using nested cross-validation.

• The model was validated on four cohorts, analyzing TME, immunotherapy responses, mutations, and risk-specific pathways.

• Identifying therapies for high-GARS patients marks a key step towards personalized CRC treatment.

Background

This study aims to identify key glycosyltransferases (GTs) in colorectal cancer (CRC) and establish a robust prognostic signature derived from GTs.

Methods

Utilizing the AUCell, UCell, singscore, ssgsea, and AddModuleScore algorithms, along with correlation analysis, we redefined genes related to GTs in CRC at the single-cell RNA level. To improve risk model accuracy, univariate Cox and lasso regression were employed to discover a more clinically subset of GTs in CRC. Subsequently, the efficacy of seven machine learning algorithms for CRC prognosis was assessed, focusing on survival outcomes through nested cross-validation. The model was then validated across four independent external cohorts, exploring variations in the tumor microenvironment (TME), response to immunotherapy, mutational profiles, and pathways of each risk group. Importantly, we identified potential therapeutic agents targeting patients categorized into the high-GARS group.

Results

In our research, we classified CRC patients into distinct subgroups, each exhibiting variations in prognosis, clinical characteristics, pathway enrichments, immune infiltration, and immune checkpoint genes expression. Additionally, we established a Glycosyltransferase-Associated Risk Signature (GARS) based on machine learning. GARS surpasses traditional clinicopathological features in both prognostic power and survival prediction accuracy, and it correlates with higher malignancy levels, providing valuable insights into CRC patients. Furthermore, we explored the association between the risk score and the efficacy of immunotherapy.

Conclusion

A prognostic model based on GTs was developed to forecast the response to immunotherapy, offering a novel approach to CRC management.

Keywords

Glycosyltransferase
Glycosylation
Colorectal cancer
Single-cell sequencing
Machine learning
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pmcIntroduction

Colorectal cancer (CRC) stands as one of the most common cancers and occupies the second leading cause of cancer-related mortality [1]. If detected early, CRC is curable. Statistically, 20 % of patients newly diagnosed with CRC have already reached the metastatic stage, and an additional 25 % with initially localized disease eventually experience metastasis [2]. Notably, the advent of immune checkpoint inhibitors (ICI) has marked a therapeutic revolution over the past decade and has achieved unprecedented outcomes across multiple tumors [3]. However, recent research findings indicate that both ICI monotherapy and immune-based combination therapies are related to the risk of hypertransaminasemia [4]. Furthermore, a meta-analysis has also demonstrated that a specific set of immune-related adverse events (irAEs) associated with ICI therapy significantly impacts the outcome [5]. In some cases of advanced colorectal cancer with liver metastases, the addition of ICI appears to have no impact on chemotherapy compliance [6]. Therefore, there is an urgent need for biomarkers to assist in the decision-making process for ICI therapy [7], facilitating treatment escalation in colorectal cancer patients with poor prognosis and reducing toxicity in those with low-risk disease.

Glycosylation is a prevalent modification of lipids and proteins that influences various facets of cancer, including cell adhesion, interaction with the extracellular matrix, signaling pathways, proliferation, and both proximal and distal communication [8]. Glycosyltransferases (GTs) represent a substantial enzyme family that plays a vital role in the glycosylation process. Lately, an increasing number of studies works have been linking GTs with the advancement and spread of cancer. For instance, in prostate cancer, the overexpression of GALNT7 altered O-glycosylation, thereby promoting tumor growth [9]. Similarly, the suppression of GALNT1 in gastric cancer resulted in reduced proliferation, migration, and invasion, with GALNT1 affecting the glycosylation of CD44, a promoter of gastric cancer malignancy [10]. ST6GAL1 was proven to foster the growth and invasion of prostate tumor in vitro and in vivo analyses [11]. Another notable glycosyltransferase, FUT8, is frequently overexpressed in various cancer types and exerts a pivotal role in immune escape, antibody-dependent cellular cytotoxicity, and the modulation of TGF-β, EGF, and E-cadherin [[12], [13], [14], [15]]. While previous research has established a strong correlation between GTs and malignant tumor behavior, their specific molecular regulatory roles in CRC are not fully revealed and warrant further investigation. Consequently, our study incorporates GTs into the development of risk maps to evaluate innovative approaches in forecasting CRC patient prognosis.

In our study, the most critical 636 GTs in CRC were identified by single-cell RNA sequencing (scRNA-seq) and correlation analyze for following study. We clarified the role of GTs within the TME and their influence on the prognosis of CRC. To identify a more clinically relevant subset of GTs, we employed both univariate Cox regression and lasso regression methods. Subsequently, seven machine learning algorithms were benchmarked from a survival perspective using a nested cross-validation approach in order to establish a Glycosyltransferase-Associated Risk Signature (GARS). This signature was rigorously validated across four independent external cohorts, demonstrating its effectiveness in prognosticating CRC outcomes based on GTs profiles. Furthermore, we investigated the capacity of this risk signature to predict various critical aspects, including tumor mutation burden, characteristics of the immune microenvironment, the status of immune checkpoints, and responsiveness to immunotherapy. We also screened potential therapeutic agents for high-risk groups. Moreover, we identified four genes significantly associated with the immune microenvironment and immune function in CRC for more focused research. Our findings could offer a novel perspective on the prognosis and therapeutic approaches of CRC.

Materials and methods

Collection of primary data

The CRC scRNA-seq data were acquired from the Gene Expression Omnibus (GEO) database (n = 61), including GSE132257, GSE132465 and GSE144735 (https://www.ncbi.nlm.nih.gov/geo/).

The training set, which includes colorectal adenocarcinoma (COAD) and rectum adenocarcinoma (READ), was obtained from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). This collection encompasses RNA expression profiles, gene mutations, and pertinent clinical data. Validation set was obtained from the GEO expression profiles GSE17536, GSE17537, GSE29621, and GSE39582. To ensure consistency in the data, the expression data were converted to transcripts per million (TPM) format. The "sva" package was employed to eliminate batch effects, and a log2 transformation was applied to all data. A total of 185 GTs were extracted from previously published studies (Supplementary Table S1) [16].

Processing of scRNA-seq data and the acquisition and processing of GTs

We performed validation employing the "Seurat" package. The selection criteria included the expression of genes expressed in a minimum of three cells, with each cell expressing between 200 and 7000 genes, and a maximum of 20 % mitochondrial gene expression. Ultimately, this process yielded 109,452 suitable cells. The "FindVariableFeatures" package was employed to screen the top 3000 genes exhibiting high variability. To mitigate batch effects, we applied canonical correlation analysis (CCA) using the "findIntegrationAnchors" function, and integrated and scaled the data using "IntegrateData" and "ScaleData". For identification of relevant cell clusters, the t-distribution stochastic neighbor embedding (t-SNE) technique was employed initially, focusing on the analysis of the first 20 principal components. This approach led to 20 distinct cell subgroups by "FindNeighbors" and "FindClusters" packages with a resolution of 0.8. In order to identify gene markers, a revised threshold was set (p 〈 0.01 and log2 〉 0.25). The cell clusters were carefully identified by utilizing well-known marker genes. We evaluated the activity of GTs in each cell using a range of algorithms, including "AUCell", "UCell", "singscore", "ssgsea", and "AddModuleScore". Cells were divided into categorized into distinct GT groups based on the median area under the curve (AUC) score. The categorization was visually represented by the R package "ggplot2".

We conducted various methods to pinpoint differentially expressed genes (DEGs) between high and low GTs AUC subgroups., ultimately identifying 536 DEGs. The top 100 genes, identified through correlation analysis, will be preserved for subsequent research. After integration, a total of 573 genes were identified as having the most significant impact on the activity of GTs in CRC.

Tissue distribution of cell subsets

We computed the observed to expected ratio (Ro/e) in different tissues to evaluate the tissue specificity. Using the Chi-squared test, we determined the expected number of cells for each cell subset and tissue combination. The Ro/e index was then used to classify subset preferences in a particular organization (3 < Ro/ e ≤ 5; ++, 1.5≤ Ro/e ≤ 3, +,1 < Ro/e < 1.5, ±; and −, Ro/e = 0) [17].

Machine learning benchmark for risk signature construction

We conducted a univariate analysis of the 636 genes to pinpoint those significantly correlated with patient survival (p < 0.01). Subsequently, we employed Lasso regression to refine the selection of prognostic genes.

We then assessed seven machine learning methods including lasso, elastic network (Enet), Ridge, partial least squares regression for Cox (plsRcox), CoxBoost, eXtreme Gradient Boosting survival (XGBoost), and supervised principal components (SuperPC) via nested cross-validation (CV) in the TCGA-COAD and TCGA-READ (TCGA-CRC) cohorts. This step was crucial to develop a precise and reliable risk signature. Inner five-fold cross-validation was used for fine-tuning the hyperparameters of each algorithm, while outer ten-fold cross-validation was utilized for the assessment of model performance [18]. A risk score was calculated for each CRC patient using the coefficients obtained from ridge analysis, patients in the TCGA-CRC cohort were then divided into groups based on the median score.

Nomogram construction and verification

For the development of clinical nomogram models, uni- and multivariable Cox regression were performed on the clinicopathological and risk signatures. Multivariable Cox was utilized to identify significant variables (p < 0.05). We then constructed a CRC prognostic nomogram using the rms package, incorporating the risk score, patient age, and cancer stage as independent prognostic criteria to estimate the overall survival (OS) at one year [19]. The nomogram's reliability was evaluated using receiver operating curves (ROC), calibration, and concordance index curves. Futhermore, we performed stratified analysis based on age, pathological T, N stage, and clinical stage to determine the risk score's prognostic value.

Evaluation of immune infiltration

Next, we utilized the TIMER 2.0 database, which compiles 7 assessment methods, to analyze immune infiltration of CRC patients. The data were utilized to generate a heatmap to quantify immune cell infiltration in the TME. We also employed Bioconductor packages to assess gene expression differences, correlations between DNA methylation and gene expression, and the types of immunomodulator alterations (amplification or deletion) [20].

We analyzed the composition and permeability of immune cells using CIBERSORT. We used ESTIMATE to evaluate immune, stromal, and ESTIMATE scores, across different risk groups. The higher these scores, the greater the proportion of components in the TME.

Immunotherapy comparisons

TIDE (http://tide.dfci.harvard.edu/), a method to explore the ability of tumors to evade the immune system. We utilized data from the TIDE database to compare various immune-related scores between different risk groups, including TIDE, Interferon gamma (IFNG), Dysfunction, Exclusion, Myeloid-derived Suppressor Cells (MDSC), and Tumor-Associated Macrophages (TAM, specifically M2 type) [21].

Responsiveness to immune checkpoint blocks

Immune checkpoints, vital molecules present on immune cells, modulate immune activation. We evaluated the expression of immune checkpoint genes (ICGs) of each group. We also explored the associations among ICG expression, model genes, and risk scores. Transcriptomic data and corresponding clinical information from the IMvigor210 cohort, which underwent treatment with the anti-PD-L1 medication (http://researchpub.gene.com/IMvigor210CoreBiologies) [22]. Furthermore, the GSE78220 cohort will be acquired to evaluate the predictive capacity of the GARS in response to immune checkpoint blockade (ICB) therapy.

Enrichment analysis

The Gene Set Variation Analysis (GSVA) used the MSigDB signature gene sets "h.all.v7.5.1.symbols.gmt" (https://www.gseamsigdb.org/gsea/msigdb/index.jsp). To assess activity of each gene set within individual samples, we utilized the GSEABase package for analysis and evaluation. Gene Set Enrichment Analysis (GSEA) was employed to explore pathways and biological processes. A total of ten signatures representing gut biology in normal and tumor states were subjected to analysis. We compared these phenotypic features between high- and low-GARS groups, offering insights into their distinct biological characteristics [23].

GeneMANIA

GeneMANIA (http://www.genemania.org) serves as a comprehensive database for gene-related information that facilitates the identification of interconnections among genes [24]. The GeneMANIA website was employed to identify proteins that exhibited a robust association with 22 model genes.

Potential therapeutic drug development and validation

Using data from the Cancer Therapeutics Response Portal (CTRP) and profiling relative inhibition simultaneously in mixtures (PRISM) datasets, we developed potential compounds for high-GARS CRC patients [25]. To assess drug sensitivity, both datasets provide a AUC of dose-reponse. Meanwhile, from the Cancer Cell Line Encyclopedia (CCLE) database, we obtained the expression data of CCLs. The Wilcoxon rank sum test was performed to analyze the differences in drug response between the different risk groups (log2FC>0.2), and then the Spearman correlation was used to screen the compounds (R < 0.4). Consequently, drugs that satisfied both of the above two conditions were identified as potential candidates for the high-GARS group.

The NCI-60 database contains 60 different tumor cells from 9 cancer types that were evaluated using the CellMiner interface (https://discover.nci.nih.gov/cellminer). Pearson correlation analysis was performed to assess the correlation between 22 model genes and drug sensitivity.

Statistical analysis

R 4.1.2 was employed for data cleaning, analysis, and visualization of results. Univariate, multivariate Cox, and Kaplan-Meier survival analyses were performed by the Survival package. The ROC and calibration curves were plotted by the timeROC and rms packages. A p-value <0.05 was considered to indicate statistical significance.

Results

Analysis of single-cell sequencing data in CRC

Fig. 1 shows the flowchart of the research. Initially, we performed quality control on the single-cell dataset. Some cells were excluded, and the proportion of mitochondrial, ribosomal, and erythroid genes was restricted (Supplementary Figure S1A). A marked positive correlation (R = 0.87) was observed between sequencing depth and total intracellular sequences, as illustrated in Supplementary Figure S1B. The study incorporated 61 samples, with cell distribution analysis in each indicating consistency and the absence of batch effects, thereby validating their suitability (Fig. 2A).Fig. 1 The flowchart of the study.

Fig 1

Fig. 2 Insights into single-cell data and GTs activity. (A) Composition of cells in 61 CRC samples. (B) Cell populations categorized into 26 clusters. (C) Expression patterns of cell typing marker genes. (D) Annotation of CRC samples into 12 unique cell types in the TME using t-SNE. (E) Tissue preference estimation for each cluster (Ro/e). (F) Average expression of GTs across 12 cell types determined by five methods. (G) Differential expression patterns of 12 cell types in normal and tumor tissues.

Fig 2

Cells were then categorized into 26 distinct subsets through dimensionality reduction, employing t-SNE (Fig. 2B). Fig. 2C illustrates the expression of cell type marker genes, while Fig. 2D depicts the distribution of each cell proportion as a t-SNE plot. A total of 12 cell types were identified, including Plasma cells, Mast cells, Fibroblasts, Macrophages, and Tumor cells. Further, we assessed cell distribution specificity in normal and tumor tissues by calculating Ro/e (Fig. 2E). As expected, the normal tissue exhibited higher counts of plasma cells, mast cells, and fibroblasts, while the tumor tissue demonstrated a greater prevalence of epithelioid cells, macrophages, and T cells.

In order to ascertain the distinctions in the activities exhibited by GT in different cells, we employed five methods: AUCell, UCell, singscore, ssgsea, and AddModuleScore (Fig. 2F and Supplementary Figure S1C). GTs scores were elevated in plasma cells, epithelial cells, and fibroblasts, but lower in T cells and B cells. Furthermore, notable differences in GT scores were observed between normal and tumor tissues (Fig. 2G).

Identifying the most important genes for GTs activity

To begin with, the batch effect of the TCGA and GEO cohorts was removed, and we then obtained better results (Supplementary Figure S2A and S2B). We then utilized the "AUCell" R package to investigate GTs activity across various cell types, aiming to understand GTs expression characteristics (Fig. 3A). Notably, cells expressing more GTs activity, especially epithelial cells, showed higher AUC value (Fig. 3B). Subsequently, each cell was assigned an AUC score for its relevant GTs and classified into either a high or low GTs group based on the median AUC score. The high GTs group exhibited a greater prevalence in tumor tissues (Fig. 3C). We then investigated potential mechanisms between GTs groups, the result indicated associations mainly with protein secretion, glycolysis, apoptosis, and DNA repair (Supplementary Figure S2C). The volcano map shows significantly up-regulated GTs (Supplementary Figure S2D).Fig. 3 Identification of key genes linked to GTs activity. (A, B) AUCell scores and categorization of GTs activity within each cell. (C) The estimation of tissue preference for risk groups (Ro/e). (D) Top 100 genes associated with GTs scores. (E, F) GO and DO enrichment of 573 genes associated with GTs activity.

Fig 3

Differential analyses led to the identification of 536 DEGs related to GTs between the low- and high-GTs groups. Furthermore, correlation analysis was employed to identify the genes most closely associated with the activity of GTs. The top 100 were selected for in-depth analysis (Fig. 3D). After integrating these genes, we finally obtained a total of 573 genes. Disease Ontology (DO) enrichment results showed that these 573 genes were significantly associated with CRC (Fig. 3E). Meanwhile, Gene Ontology (GO) enrichment showed the predominant gene set signatures were related to protein localization to the endoplasmic reticulum, cell-substrate junction, and cell adhesion (Fig. 3F). Overall, we yielded the 573 genes most relevant to GTs activity in CRC.

Construction and validation of GARS

Univariate Cox identified 41 prognostic genes as per the expression of 573 GTs genes. (Fig. 4A and B). We used TCGA-CRC as a training set for model construction and LASSO regression to further investigate prognostic genes (Fig. 4C and D). This progress culminated in the selection of 22 key genes (LITAF, TERF2IP, FNBP1, LEPROTL1, RGCC, IDS, CDC42SE2, S100P, CXCL1, SPINK1, CDH1, NCOA7, NPDC1, EMP2, TIMP1, TKT, TMEM98, MANF, ETS2, TUBA1C, MUC12, SPINK4) under the most suitable regularization parameters. Through evaluating 7 machine learning methods, we constructed the model with optimal hyperparameters that offers the greatest accuracy and minimal risk of overfitting. Nested cross-validation (CV) was executed in TCGA-CRC. As shown in the picture (Fig. 4E, F and G), the Ridge survival model performed best. The ridge algorithm with adjusted hyperparameters was applied to the TCGA-CRC cohort. The three most significant contributing genes to GARS, TERF2IP, NPDC1, TIMP1, was demonstrated in Supplementary Figure S2E.Fig. 4 Construction and validation of GARS. (A) Forest map shows the GTs linked to prognosis. (B) Correlation between prognostically relevant GTs. (C, D) LASSO regression analysis with 22 model genes. (E, F) Evaluation of C-index and IBS across 7 machine learning algorithms (G) Comparison of AUC values at 1 year between 7 machine learning algorithms. (H) Survival analysis and corresponding AUC values. (I) Assessment across TCGA, GSE17536, GSE17537, GSE29621, GSE39582, and the entire Cohort.

Fig 4

Patients were divided into two groups based on their GARS score. As shown in Fig. 4H-I, within the TCGA-CRC training dataset and four validation cohorts, patients in the high-GARS group consistently showed notably poorer overall survival (OS) compared to low-risk group. The trend was also seen in a combined meta-cohort (p < 0.0001).

Nomogram development and identification of independent risk factors

We estimated the predictive accuracy of a risk signature for CRC by integrating clinicopathological features and risk scores (Supplementary Figure S3A and S3B). Age, M stage, stage and risk score were considered as significant independent prognostic factors (p < 0.01). Therefore, as shown in Fig. 5A, a nomogram was build combining stage and risk score. The nomogram was validated effectively in predicting actual survival outcomes by the calibration plot (Fig. 5B). Furthermore, DCA revealed that, in terms of effectiveness and stage, the nomogram exhibited superior performance in identifying high-GARS patients (Fig. 5C). TimeROC analysis also showed that the nomogram was more predictive than clinicopathologic features in the TCGA CRC cohort (Fig. 5D).Fig. 5 Construction of a more accurate nomogram. (A) CRC patient risk assessment nomogram. (B) Calibration curves for 1, 3, and 5 years. (C) Decision curve. (D) The predictive performance of different clinical features, nomogram scores and risk scores. (E) Comparison of clinical characteristics in GARS groups. (F) Variation in risk scores across clinical characteristics. (G) The 22 model gene mutations in a waterfall plot.

Fig 5

We explored the distribution of clinical features across GARS groups using a percentage bar chart (Supplementary Figure S3C). Moreover, the predictive ability of risk categories was evaluated based on the clinical characteristics of CRC patients. We found that among the different clinical categories, the prognosis of the high-GARS category was worse, indicating a broad applicability of the prognostic model (Fig. 5E). Additionally, we observed a significant correlation between risk score and CRC stage (Fig. 5F). As a powerful molecular marker, TMB was used to quantitatively assess the mutations carried by tumor cells. The waterfall map showed that the top three genes with the highest mutation frequency were CDH1, FNBP1, NCOA7 (Fig. 5G).

Model gene mutation and pathway analysis

We analyzed the co-occurrence probability of the top 20 genes with the highest mutation rates (Supplementary Figure S4A and S4B). The high-GARS group exhibited a higher propensity for gene co-occurrence, except for APC, TP53 and KRAS (Supplementary Figure S4C and S4D). Regarding copy number variations (CNVs), we observed that gains or losses were present in only a small subset of samples across 22 genes (Supplementary Figure S4E). LEPROTL1, CDC42SE2, MANF and SPINK4 showed highly negative associations with Aneuploidy Score, Homologous Recombination Defects, Fraction Altered, Number of Segments, and Nonsilent Mutation Rate, whereas RGCC and TMEM98 exhibited highly positive associations with Aneuploidy Score, Homologous Recombination Defects and Fraction Altered (Supplementary Figure S4F).

In our previous study, we developed a metabolism-based molecular subtyping guideline to categorize CRC into ketolytic and glycolytic subtypes, according to the expression of OXCT1, ACAT1, GLUT1and PFKPF3 [26]. Intriguingly, the correlation between the ACAT1 and OXCT1 and the risk score was negative, which encode the key enzymes in ketone body metabolism (Supplementary Figure S4G). This indicated that the high-GARS group had a propensity to the glycolytic subtype, which may provide an explanation for their poorer prognosis.

Immune microenvironments and responsiveness to immunotherapy

Immunomodulators are crucial in cancer immunotherapy, some of which are currently under clinical evaluation and have shown clinical benefit [27]. We started by examining gene expression of immunomodulators and somatic copy number alterations (SCNAs) across different GARS groups. Gene expression of immunomodulators (Fig. 6A) varied between risk subtypes, among which we observed a significantly higher amplification of TNF, VEGFA, CD40, MICA and MICB upregulation in the low-risk group, whereas ICOSLGI, IL4, IL13 and ITGB2 were deleted in the high-GARS group.Fig. 6 Analysis of immune infiltration. (A) Regulation of immunomodulators, including mRNA expression, methylation, amplification and deletion frequency for 75 Immunomodulator genes across risk subtypes. (B) Differences between GARS groups in immune infiltrate status (C) TIDE scores between CRC patients in GARS groups. ns, not significant, * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. (D) Correlation between TIDE and riskscore.

Fig 6

We then turned our attention to the immune microenvironment, seven separate algorithms indicated that high-GARS group had more immune cell infiltration, consisting of Fibroblast, Endothelial cells and T cells as illustrated in Fig. 6B and Supplementary Figure S5A. Furthermore, a positive correlation was found between high-GARS and immune scores such as immune, stromal, and ESTIMATE scores (Supplementary Figure S5B). We then investigated the association between the risk scores and commonly identified immunotherapy biomarkers. The histogram showed that CD44, HHLA2 and IDO1 were more abundant in the low-GARS group (Supplementary Figure S5C). The TIDE score, which reflects tumor immune dysfunction and rejection, provides a framework for evaluating the probability of tumor immune evasion. The results showed that the high-GARS group had increased dysfunction scores and increased exclusion scores, while the low-GARS group had increased IFNG scores. TIDE scores differed significantly between the two groups (Fig. 6C). Furthermore, a positive correlation was observed between the TIDE score and the risk score, as illustrated in Fig. 6D.

Risk signature response to PD-L1 blockade immunotherapy

T-cell immunotherapy has recently gained recognition as an effective cancer therapy, notably enhancing patient outcomes [28]. In the IMvigor210 and GSE78220 cohorts, we evaluated the capacity of the GARS to predict the response to immune checkpoint therapy. We observed that the low-risk group had a significantly longer OS in IMvigor210 cohort (Fig. 7A). The 298 patients showed varied responses to anti-PD-L1 receptor blockers, categorized as complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Compared with CR/PR patients, the risk scores were higher in SD/PD patients (Fig. 7B). The proportion of SD/PD was higher in the high-risk group (Fig. 7C). There were significant differences in survival between stage I+II patients and stage III+IV patients (Fig. 7D and E). This trend continued in the GSE78220 cohort, we also found that the low-risk group had significantly longer OS in the GSE78220 cohort (Fig. 7F). Similarly, SD/PD patients gained higher risk scores, and there were more SD/PD patients in the high risk group (Fig. 7G-H).Fig. 7 Evaluating risk score in PD-L1 blockade immunotherapy. (A) Variations in risk scores across immunotherapy responses in the IMvigor210 cohort. (B) Immunotherapy response distribution. (C) Assessment of prognostic differences between GARS groups. (D) Differences in prognosis between GARS groups in early stage patients. (E) Differences in prognosis between GARS groups in advanced stage patients. (F) Variations in prognosis among GARS groups in the GSE78220 cohort. (G) Patterns of immunotherapy responses among GARS groups in the GSE78220 cohort

Fig 7

Pathway enrichment analysis

KEGG enrichment was employed to investigate the underlying mechanism linked to poor prognosis in CRC patients. The high GARS group had lower activity in the cell cycle, P53 signaling, and N-glycan biosynthesis pathways, but higher activity in glycosaminoglycan biosynthesis. Comparatively, the glycosaminoglycan biosynthesis scores were higher in the high GARS group (Fig. 8A). GeneMANIA revealed that TERF21P strongly interacts with other key genes in our model (Fig. 8B). We then selected ten signatures representing gut biology to explore transcriptional patterns specifically relevant to risk groups (Fig. 8C). This revealed that high GARS group was more associated with WNT and STEM pathways, while the low GARS group was more correlated with WNT and CRYPT pathways. HALLMARK enrichment also exhibited that the risk score significantly related to KRAS and P53 signaling pathways. In contrast, the correlation with pathways such as MTORC1 signaling, glycolysis, IL-6 signaling, and the PI3K/AKT signaling pathway were negative (Fig. 8D). These results might provide insights into the prognostic differences between the GARS groups.Fig. 8 Analysis of enrichment and functional categorization. (A) Analysis of pathway enrichment. (B) Visualizing the gene-protein interaction network involving the 22 model genes. (C) Phenotypic and functional characteristics of GARS groups. (D) Evaluation of the correlation between the risk score and the HALLMARK pathways.

Fig 8

Model gene and immunity

We examined the correlation across the 22 model genes and the immune microenvironment, aiming to better understand the interplay between GARS and immune factors (Fig. 9A). We found that several genes, including LITAF, FNBP1, RGCC, IDS, NCOA7, and TIMP1, showed a strong positive correlation with stromal, immune, and ESTIMATE scores. Genes such as SPINK1, CDH1, TKT, and ETS2 showed a significant negative correlation. Furthermore, our analysis extended to the correlation with different immune cells (Fig. 9B and C), the results indicated that TIMP1, FNBP1 and LITAF were positively correlated with almost all types of immune cell. We then examined the difference of GARS expression in all kinds of immune cells, TKT, TIMP1 and LITAF exhibited highly expressed in multiple cell types (Fig. 9D). In addition, there was a prominent difference in TKT, TIMP1 and LITAF in almost all immune cells types when comparing normal tissue and the tumor tissues (Fig. 9E). Collectively, the findings suggest that TKT, TIMP1, and LITAF may be viable targets for clinical immunotherapy.Fig. 9 Model genes and the immune microenvironment. (A) Associations between model genes and immune microenvironment. (B, C) Correlations between model genes and immune cells. (D) Differential expression of model genes in various immune cells. (E) Differences in model gene expression in various immune cells between risk groups.

Fig 9

Identifying potential therapeutic agents for high GARS patients

We identified potential therapeutic agents for high GARS group using sensitivity data from CTRP and PRISM datasets (Fig. 10A). Patients in the low GARS group showed more sensitivities to Cisplatin and Gemcitabine, while patients in the high GARS group were more suitable for Gefitinib and Imatinib (Fig. 10B-E). We identified potential agents and generated four CTRP-derived agents for the high GARS group (NSC23766, PLX-4720, niclosamide, AT7867) and three PRISM- derived agents (tedizolid phosphate, YM-155, MK-2461). The AUC values not only showed a negative correlation with GARS but were also significantly lower in the high GARS group (Fig. 10F). Some model genes were associated with the sensitivity to specific chemotherapeutic drugs (p < 0.001; Fig. 10G). For example, increased SPINK4 expression correlated with increased resistance to LOXO-101 in CRC patients, while the elevated FNBP1 was led to enhanced tumor sensitivity to nelarabine, fludarabine and chlorambucil. These findings may be vital for designing more effective treatment strategies for CRC patients, especially in high GARS groups.Fig. 10 Identifying potential therapeutic agents for high-GARS group. (A) Strategic blueprint for pinpointing potential therapeutic compounds. (B-E) Assessment of response to gefitinib, cisplatin, gemcitabine, and imatinib between the GARS groups. (F) Evaluation of the differential drug response of CTRP-derived compounds between the GARS groups. (G) The correlation between key genes and chemotherapy drugs.

Fig 10

Discussion

The immune system plays a significant role in the development of cancer and the efficacy of immunotherapy [29]. Notely, glycosylation mechanisms play a significant role in CRC, including cell growth, apoptosis inhibition, increased cell migration, cell adhesion, tumor cell invasion and metastasis [30]. However, the diagnostic and predictive capabilities of glycosylated biomarkers in immunotherapy need to be further investigated and confirmed [31]. It has been shown that aberrant glycosylation signaling, tumorigenesis, metastasis and even chemoresistance are linked to altered expression and genetic mutations of GTs [32,33]. Mutations in the GTs that deviate from normal are partly responsible for irregular glycosylation patterns, playing a role in the development of molecular subtypes of CRC [34]. The discovery and development of immune checkpoints has increased the potential for overcoming cancer. However, the majority of patients exhibit innate or acquired resistance to immunotherapy [35]. Therefore, it is critical to discover a reliable risk profile to distinguish patients who are more likely to benefit from immunotherapy.

In our study, we investigated a new biomarker and developed a scoring system of prognostic based on GTs, which shows promise in overcoming immune resistance and enhancing patient prognosis. Additionally, we have identified 22 potential therapeutic targets, including TIMP1, FNBP1, and LITAF, along with 2 therapeutic agents for high-GARS, PLX-4720 and YM-155. Overall, our study constructs a robust GARS framework, providing valuable predictions about prognosis and therapeutic response of CRC, especially immunotherapy.

We performed single-cell sequencing on 61 CRC samples, identifying 12 different cell types. We then applied five independent scores and averaged them, considering that colorectal cancer is a disease with inter-tumor heterogeneity and the unreliability of a single scoring method. Among them, epithelial cells and fibroblasts had the highest activity levels of GTs and showed significant differences between normal and tumor tissues. Machine learning approaches are often used to predict the patients survival and help build a robust and reliable prognostic model [36]. Subsequently, the key genes regulating GTs activity were studied and prognostic models were created using machine learning. The high-GARS group exhibited a less favorable outcome, and the prognostic model in four GEO databases showed stable performance and good accuracy. In addition, according to the nomogram, riskscores had superior performance over other clinical characteristics.

Enrichment analyses showed significant positive associations of risk scores with glycolysis, N-glycan biosynthesis, and the P53 signaling pathway. Furthermore, risk scores showed a significant negative correlation with OXCT1, ACAT1, while encoding key enzymes in ketone body metabolism. This suggests that high-risk group may be associated with defective ketone body metabolism and may led to a poorer prognosis. Based on our previous study [26], the combination of targeted P53 and ketogenic therapy may be a potentially effective treatment option in the high risk group. However, further clinical studies are needed to see if this works.

TME consists of various immune cell types, cancer-associated fibroblasts (CAFs), endothelial cells, and other constituents [37]. To further understand the relationship between immunity and tumor prognosis, we employed 7 methods to quantify immune cell infiltration. Our findings unveiled that high-risk group exhibited more infiltrated CAFs and epithelial cells, and higher stromal and estimation scores. The combination of ICB with other therapies may enhance the immune response within the TME [38]. We observed a significantly higher amplification of TNF, VEGFA, CD40, MICA and MICB upregulation of low-risk group. ICOSLGI, IL4, IL13 and ITGB2 were significantly deleted in the high-GARS. Notably, an elevated TIDE score suggested an increased probability of evading immune surveillance, thereby rendering immunotherapy less appropriate. This study unveiled a positive relation between TIDE and GARS, suggesting that patients with lower GARS might be better candidates for immunotherapy. Additionally, within the IMvigor210 cohort, we noticed that the low-GARS group experienced substantial clinical benefits and significantly prolonged OS. Taken together, these results highlight the potential of GARS as a useful tool for identifying CRC patients who may benefit from immunotherapy.

We explored the correlation of model genes and the TME, while TIMP1, FNBP1, LITAF were the most significant genes related to immune cells. TIMP 1 was reported to promote cell proliferation and invasiveness through FAK/Akt signaling pathway in CRC and TIMP 1 inhibited sorafenib-induced iron apoptosis in CRC cells through PI3K/Akt signaling pathway [39,40]. LITAF exerts its inhibitory effects on CRC stemness and metastasis by modulating FOXO1-mediated SIRT1 expression [41]. However, the precise functions of these genes in the TME remain uncharted territory, and further research is needed to unravel their contributions. In conclusion, our research identifies potential therapeutic candidates for the treatment of CRC.

The landscape of CRC treatment has transitioned into the era of precision medicine, demanding early identification of treatment-sensitive patients to enable personalized therapeutic strategies [42] . For this reason, we explored the CTRP, PRISM databases, aiming to tailor agents for the high-GARS group. Beyond its prognostic value, GARS holds potential for precision oncology. Our investigation identified two promising drugs, PLX-4720 and YM-155, for the high GARS group. PLX-4720, a BRAF inhibitor, has demonstrated prolonged MAPK pathway inhibition and enhanced proliferation inhibition in colon cancer cells harboring the BRAFV29E mutation, compared to PLX7904 [43]. Additionally, the Survivin inhibitor YM-155 has been reported to suppress the PI3K/AKT/Survivin pathway, thereby enhancing the antiapoptotic effects of miR-542–3p in CRC [44]. Nevertheless, additional clinical studies are warranted to confirm the extensive promise of YM-155 and PLX-4720 in CRC, particularly among patients with high GARS scores.

This study has several limitations. Firstly, public datasets were used to construct the model, and future validation should be conducted in more prospective and multicenter CRC cohorts. Importantly, this study indirectly assessed the predictive capacity of GARS for immunotherapy response as it did not directly assessed patients undergoing immunotherapy. Secondly, we only investigated the potential prognostic value of GARS, further studies are needed to explore the underlying mechanisms of this signature in CRC development.

In summary, based on multiple bioinformatics and machine learning algorithms, we successfully developed and validated a unique prognostic model comprising 22 GTs. This model shows surprising prospects as a prognostic indicator and predictor of immunotherapy response for CRC patients. GARS is a promising tool for optimizing therapeutic decision-making and monitoring regimens for individual CRC patients.

Conclusions

This study underscores the crucial role of GTs in the progression and treatment of CRC. Utilizing single-cell RNA sequencing of 61 CRC samples combined with machine learning techniques, the research developed the GARS model, which serves as a robust predictor of patient outcomes. The investigation also delves into the TME, uncovering significant gene expression linkages. For patients with elevated GARS scores, the study suggests personalized treatment approaches, highlighting PLX-4720 and YM-155 as potential key therapeutic agents. Ultimately, this research provides a novel prognostic tool and therapeutic direction for personalized CRC therapy.

Web references

The Cancer Genome Atlas database. https://portal.gdc.cancer.gov. Accessed 7 November 2023.

The Cancer Therapeutics Response Portal database. http://portals.broadinstitute.org/ctrp. Accessed 7 November 2023.

The Tumor Immune Estimation Resource database. http://timer.cistrome.org. Accessed 3 November 2023.

The Tumor Immune Dysfunction and Exclusion database. http://tide.dfci.harvard.edu. Accessed 5 November 2023.

The Molecular Signatures Database. http://www.gsea-msigdb.org/gsea/msigdb. Accessed 3 November 2023.

The gene interaction database. GeneMANIA. http://genemania.org. Accessed 5 November 2023.

The Anti-cancer drug screening database. CTRP. https://discover.nci.nih.gov/cellminer. Accessed 9 November 2023.

Data availability

Public data used in this work are available from the TCGA Research Network portal (https://portal.gdc.cancer.gov/) and the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). Please contact the authors for more information.

Publication consent

The manuscript has received approval for submission from all authors.

Ethics approval and consent to participate

Not applicable.

Funding

This study was supported by the Translational Medicine and Interdisciplinary Research Joint Fund of Zhongnan Hospital of Wuhan University (Grant No. ZNJC202006 )

CRediT authorship contribution statement

Xin Chen: Writing – review & editing, Writing – original draft, Visualization, Methodology, Investigation, Conceptualization. Dan Zhang: Writing – review & editing, Writing – original draft, Visualization, Investigation, Conceptualization. Haibin Ou: Writing – review & editing, Methodology, Conceptualization. Jing Su: Writing – review & editing, Visualization, Conceptualization. You Wang: Writing – review & editing, Investigation, Conceptualization. Fuxiang Zhou: Writing – review & editing, Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix Supplementary materials

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Acknowledgements

Not applicable.

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.tranon.2024.102093.
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