Loss of GGT6 promotes colorectal cancer progression and correlates with poor prognosis: a study based on multi-database mining and functional validation

Introduction

Colorectal cancer (CRC) ranks as the third most commonly diagnosed cancer worldwide and is the second leading cause of cancer deaths from malignant tumors (1). In recent years, the prevalence of CRC among individuals under 50 has continued to rise (2,3), while diagnostic screening is not routinely recommended until patients reach 45–50 years of age (4). The absence of reliable early-detection biomarkers frequently results in most patients being diagnosed only when the disease has progressed to advanced stages (5). Although the survival of patients diagnosed with metastatic disease has improved over the past 20 years, significant heterogeneity in survival outcomes remains (6). Cancer immunotherapy based on immune checkpoint inhibition has significantly improved the prognosis of patients with advanced or metastatic mismatch repair-deficient (dMMR) or microsatellite instability-high (MSI-H) CRC (7). For example, targeting the BRAF D594A mutation can improve the efficacy of immunotherapy (8). However, approximately 90% of CRC cases with proficient mismatch repair (pMMR) mechanisms do not respond to immune checkpoint therapy (9,10). BRAF mutations are also present in only about 10% of CRCs (11). Diagnostic screening is crucial for improving the survival rate of patients with CRC, as it can reduce the annual incidence of CRC by more than 25% (12-14). Hence, there is a critical imperative to discover and clinically validate innovative molecular markers that can facilitate the early detection and personalized treatment of CRC.

Gamma-glutamyl transferase 6 (GGT6) belongs to the GGT family (15) and its functional mechanisms in CRC are still not well understood in the academic community. This study aims to fill this research gap by examining the clinical relevance and biological effects of GGT6. In non-tumor diseases, GGT6 can influence the occurrence and progression of periodontitis through glutathione metabolism (16). In hepatocellular carcinoma, the GGT family genes are strongly associated with prognosis, with GGT6 also associated with deoxyribonucleic acid (DNA) methylation and immune cell infiltration (17). Recent studies have also emphasized the prognostic role of GGT6 messenger ribonucleic acid (mRNA) expression in head and neck squamous cell carcinoma, papillary renal cell carcinoma, thyroid carcinoma, and glioma (18,19), suggesting that GGT6 may serve as a novel prognostic marker for malignant tumors. However, GGT6 expression and its potential prognostic relevance in CRC have not been extensively elaborated.

This study elucidates the expression profile and functional significance of GGT6 in CRC, revealing a consistent downregulation of this gene in cancerous tissues. This diminished expression significantly correlates with aggressive clinicopathological features and serves as an independent indicator of unfavorable prognosis. Interestingly, our findings suggest that GGT6 deficiency may impair the efficacy of immunotherapy. Functional assays further demonstrate that GGT6 modulates the proliferation, migration, and invasive potential of CRC cells. Collectively, these results identify GGT6 as a critical tumor-suppressive factor in CRC development, highlighting its promise as a robust prognostic biomarker and a novel therapeutic target. We present this article in accordance with the MDAR and TRIPOD reporting checklists (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0095/rc).

Methods

Data collection and preprocessing

The RNA sequencing data of CRC patients used in this article were all extracted from the official The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/), University of California, Santa Cruz Xena Browser (UCSC Xena) (https://xenabrowser.net/) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database. Immunohistochemistry images of GGT6 protein expression in normal colon (antibody: HPA027213, patient ID: 1832, 1990, 3266) and CRC patient tissues (antibody: HPA027213, patient ID: 2707, 1958, 2735) were obtained from the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/). Genes with 0 expression in 50% of the samples were excluded prior to transcriptome analysis. Only patients possessing both complete transcriptomic datasets and relevant clinical information were considered eligible for inclusion in the analytical phases of this investigation (samples with an overall survival (OS) of 0 were also excluded from the survival analyses), and data cleaning was performed using R software (4.3.1). This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

GGT6 expression and survival analysis

The pan-cancer expression profile of GGT6 was systematically explored using the TIMER platform (https://cistrome.shinyapps.io/timer/). Furthermore, mRNA and protein expression patterns, along with their clinico-pathological correlations in CRC, were characterized through an integrative analysis of the TCGA, GEO, and HPA databases. This study, based on the optimal cutoff values obtained using the “survival” and “survminer” R packages, divided CRC patients into high and low GGT6 expression groups and explored the differences in OS between the two groups. Univariate and multivariate Cox regression analyses, integrating tumor stage, demographic characteristics, and GGT6 expression levels, identified independent prognostic factors. The detailed results of the regression analysis (such as grouping, hazard ratios, and 95% confidence intervals) were visualized using forest plots.

Differentially expressed genes (DEGs) analysis and biological function analysis

The “DESeq2” R package was used to identify gene expression differences between high and low GGT6 expression groups. After filtering for DEGs that met the criteria of |log2 fold change (FC)| ≥1 and false discovery rate (FDR) <0.05, this study further used the “ggplot2” package to construct a volcano plot for visualizing these significantly DEGs. Gene Ontology (GO) (20) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (21) enrichment analyses were performed using the R packages “ggplot2” and “clusterProfiler” (22). Gene Set Enrichment Analysis (GSEA) (23) analysis of DEGs was performed using the software GSEA_4.3.3, and relevant pathways were visualized using the R package “GseaVis” (24).

Somatic mutation analysis

The DNA mutation data was obtained from the TCGA database, and the “maftools” package (25) was used to visualize the mutation rates between high and low GGT6 expression groups.

Establishment of nomogram

Based on the findings of Cox regression analysis, this study developed a nomogram model for predicting 1-, 3-, and 5-year OS in CRC patients using the “regplot” and “rms” packages. The model’s performance was evaluated using multi-dimensional indicators, including calibration curves to test the predictive values, decision curves to assess clinical utility, and dynamic receiver operating characteristic (ROC) curves and area under the curve (AUC) values for 1 to 5 years generated by the “riskRegression” package, thus comprehensively validating the robustness of this prognostic model.

Estimation of the tumor immune microenvironment landscape

To analyze differences in the immune microenvironment, this study utilized the CIBERSORT (26) and single sample GSEA (ssGSEA) algorithms to obtain immune cell infiltration abundance and immune function scores in CRC patients (27,28). Subsequently, Pearson correlation coefficients were used to assess the correlation between GGT6 expression and 22 types of immune cells and immune function indicators. These analyses were conducted based on high and low GGT6 expression groups from the TCGA database to reveal its potential role in immune regulation.

Tumor Immune Dysfunction and Exclusion (TIDE), Immunophenoscore (IPS) scores and drug sensitivity analysis

TIDE scores were computed via the TIDE platform (http://tide.dfci.harvard.edu/). Simultaneously, the IPS for each CRC patient was retrieved from The Cancer Immunome Atlas (TCIA, https://tcia.at/). Furthermore, we estimated individual therapeutic sensitivities by leveraging the Genomics of Drug Sensitivity in Cancer (GDSC) database, where the half-maximal inhibitory concentration (IC₅₀) of representative CRC agents was calculated using the ‘oncoPredict’ R package (29).

Cell transfection

The CRC cell lines HT-29 and DLD-1 (Procell, Wuhan, China) were grown in DMEM/High Glucose and RPMI-1640 media, respectively, under standard conditions (37 ℃, 5% CO₂). Each medium was enriched with 10% fetal bovine serum (FBS) and 100 IU/mL penicillin-streptomycin. Regular media replenishment and passaging were performed to maintain cellular health. Upon reaching the logarithmic phase, cells were dissociated, resuspended, and accurately counted before being plated for functional assays. The si-negative control (NC), si-GGT6-1 (F: 5'-CAGAGGCGCUGGUUCUAAATT-3'; R: 5'-UUUAGAACCAGCGCCUCUGTT-3') si-GGT6-2 (F: 5'-CAGAAGAGCCCGUGGUCUATT-3'; R: 5'-UAGACCACGGGCUCUUCUGTT-3') and si-GGT6-3 (F: 5'-CUGUGAGGCAGCUCCAGAATT-3'; R: 5'-UUCUGGAGCUGCCUCACAGTT-3') (OBIO Technology, Shanghai, China) were transfected with the Lipofectamine^® 3000 Reagent (ThermoFisher Scientific, Waltham, Massachusetts, USA) at 50–60% cell density on the next day.

Reverse transcription quantitative polymerase chain reaction (RT-qPCR)

This study used RNA isolater Total RNA Extraction Reagent to extract total RNA from the samples (Vazyme, R401-01). Complementary DNA (cDNA) was synthesized from the extracted RNA using the PrimeScript™ RT Master Mix (Takara). Quantitative real-time PCR (qPCR) was then performed with TransStart Tip Green qPCR SuperMix (TransGen) on a real-time PCR system. The following human-specific primers were used: GGT6 (forward: 5'-AATTCCACGGCCCTGACATC-3', reverse: 5'-CCATCAGCATGGCAAAGTAGT-3') and GAPDH (forward: 5'-TCAAGGCTGAGAACGGGAAG-3', reverse: 5'-CGCCCCACTTGATTTTGGAG-3').

Cell Counting Kit-8 (CCK-8) assay

WT and si-GGT6 cells were seeded into 96-well flat-bottom plates (100 µL of culture medium and 3,000 cells per well). Every 24 hours, 10 µL of CCK-8 (APExBIO) solution was added to each well. After incubation, the absorbance of each well was measured at a wavelength of 450 nm.

Colony formation assay

Transfected HT29 and DLD-1 cells (500 cells per well, 2 mL of culture medium) were seeded into six-well plates and cultured for 10 days, with the culture medium changed every 3 days. When cell colonies were visible to the naked eye, the plates were washed, and the cells were stained with Coomassie Brilliant Blue R250 solution for 15 minutes. After staining, the culture plates were air-dried and photographed to record the growth of cell colonies for subsequent statistical analysis.

Transwell migration and invasion assay

Different tumor cells were resuspended in serum-free culture medium at a concentration of 1×10⁴ cells/mL. In the migration assay, cells were added to the upper chamber, and the lower chamber contained medium supplemented with 10% FBS. In the invasion assay, the upper chamber membrane was first coated with Matrigel and incubated at 37 ℃ for 2 hours. After incubation for 24 hours, the cells were stained with Coomassie Brilliant Blue R250. Finally, cells that successfully migrated through the membrane were photographed under a microscope.

Immunofluorescence staining and analysis

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. Tissue microarrays containing 80 colon specimens (cohort ID: AF-CocSur2201) were commercially obtained from Hunan Aifang Biotechnology Co., Ltd. (Hunan, China). The supplier confirmed that all human tissue samples were collected under protocols approved by its institutional ethics committee (approval No. HN20250401), and that written informed consent was obtained from all patients prior to sample collection. All samples were anonymized prior to being provided to the authors, and no identifiable personal information was accessible. Clinicopathological information, including pathological diagnosis between January and September 2018 and follow-up data until September 2022, was collected and provided by the supplier. Immunofluorescence staining of the tissue microarrays was performed by the supplier using a GGT6-specific primary antibody (ThermoFisher, Cat# PA5-55546). Images were acquired using a Zeiss LSM 980 laser confocal microscope equipped with a multispectral imaging system. Quantitative analysis of fluorescence intensity was subsequently performed by the authors using ImageJ FIJI (v2.14.0/1.54f).

Statistical analysis

Statistical analysis and graph plotting were performed using R 4.3.1 and GraphPad Prism 10.0 software. Student’s t-test was used for comparisons between two groups. Wilcoxon rank-sum test was used for comparisons among three or more groups. Kaplan-Meier (K-M) survival curves were plotted and compared using the log-rank test. Univariate and multivariate analyses of various clinicopathological factors were performed using the Cox proportional hazards model. In addition, the correlation between variables was assessed using Pearson correlation analysis. A P value less than 0.05 was considered statistically significant. Statistical significance levels are as follows: ns, not significant; *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001.

Results

GGT6 is downregulated in CRC and is associated with poor prognosis

Through the TIMER2.0 online platform, we found that GGT6 mRNA expression was downregulated in most tumor types (Figure 1A). Using the TCGA dataset, we performed a comparative analysis of GGT6 mRNA levels between colorectal malignant tumor groups and normal control groups. The results showed that GGT6 expression was significantly lower in tumor tissues compared to normal tissues (Figure 1B). Similar results were obtained in the analysis of paired samples (Figure 1C). Furthermore, the expression results of GGT6 in the GSE39582 dataset were consistent with the TCGA dataset (Figure 1D). Immunohistochemical staining in the HPA database further confirmed the trend of downregulated GGT6 protein levels in CRC (Figure 1E,1F). These data indicate that GGT6 is downregulated in CRC.

Figure 1 Expression of GGT6 in cancer and adjacent normal tissues. (A) Differential expression of GGT6 between cancer and adjacent normal tissues based on the TIMER2.0 database, utilizing data from the TCGA database. (B) Expression levels of GGT6 in tumor and normal tissues in TCGA-COAD dataset. (C) Comparison of GGT6 expression in paired cancer and adjacent normal tissue samples from TCGA-COAD. (D) Expression levels of GGT6 in cancer and adjacent normal tissues in the GSE39582 dataset. (E,F) Immunohistochemical staining of GGT6 in normal colon tissues (https://images.proteinatlas.org/27213/58075_A_9_3.jpg, https://images.proteinatlas.org/27213/58075_A_7_3.jpg, https://images.proteinatlas.org/27213/58075_A_8_3.jpg) and colorectal cancer samples (https://images.proteinatlas.org/27213/58073_A_1_7.jpg, https://images.proteinatlas.org/27213/58073_A_1_2.jpg, https://images.proteinatlas.org/27213/58073_A_1_5.jpg) from the HPA database. **, P<0.01; ***, P<0.001. GGT6, gamma-glutamyl transferase 6; HPA, Human Protein Atlas; TCGA, The Cancer Genome Atlas; TPM, transcript per million.

Low GGT6 expression is connected with adverse outcomes in CRC

Consistently, K-M analysis indicated that low GGT6 expression correlates with shortened OS in CRC patients across multiple datasets, including TCGA and two GEO cohorts (GSE39582 and GSE17538) (Figure 2A-2C). Multivariate Cox regression analysis confirmed that GGT6 is an independent predictor of OS in CRC patients, even after adjusting for potential confounding clinico-pathological variables in the TCGA dataset (Figure 2D,2E). In the GSE39582 dataset, GGT6 showed the same results (Figure 2F,2G). Multivariate Cox regression analysis confirmed that GGT6 is an independent predictor of OS in CRC patients.

Figure 2 Prognostic analysis and Cox regression results of GGT6 in TCGA and GEO datasets. (A) OS analysis of GGT6 expression in TCGA-COAD dataset. (B) OS analysis of GGT6 expression in GSE39582 dataset. (C) OS analysis of GGT6 expression in GSE17538 dataset. (D,E) Forest plots of univariate (D) and multivariate (E) Cox regression analysis of GGT6 expression and clinical factors in the TCGA-COAD dataset. (F,G) Forest plot showing the results of univariate (F) and multivariate (G) Cox regression analysis of GGT6 expression and clinical factors in the GSE39582 dataset. CI, confidence interval; GEO, Gene Expression Omnibus; GGT6, gamma-glutamyl transferase 6; OS, overall survival; TCGA, The Cancer Genome Atlas.

GGT6 is associated with various clinicopathological variables of CRC

This study analyzed the relationship between GGT6 levels and various clinicopathological characteristics in TCGA-CRC. The results showed that CRC patients with higher tumor stage, T stage, and N stage had lower GGT6 expression levels. In contrast, GGT6 expression was not associated with the gender, age, or M stage of CRC patients (Figure 3A-3F). Similarly, in the GSE39582 dataset, CRC patients with advanced tumor stage, T stage, N stage, and M stage showed lower GGT6 expression, with no correlation to gender or age (Figure 3G-3L).

Figure 3 Association between GGT6 expression and clinical pathological factors in the TCGA and GSE39582 datasets. (A-F) Association between GGT6 expression and clinical factors [(A) age (years), (B) gender, (C) tumor stage, (D) T stage, (E) N stage, and (F) M stage] in the TCGA dataset. (G-L) Association between GGT6 expression and clinical factors [(G) age, (H) gender, (I) tumor stage, (J) T stage, (K) N stage, and (L) M stage] in the GSE39582 dataset. GGT6, gamma-glutamyl transferase 6; M, metastasis; N, node; T, tumor; TCGA, The Cancer Genome Atlas.

Construction and validation of a prognostic nomogram

The nomogram was constructed based on a multivariate Cox regression analysis of the TCGA database (the patients included complete clinical and pathological data, as well as prognostic data, n=421), combining several predictors. The development of a GGT6-based nomogram further validated its potential in clinical settings, providing reliable 1-, 3-, and 5-year prognostic forecasts for the CRC cohort (Figure 4A). This is also verified by the calibration curve, the decision curve, and the area under the 1 to 5-year ROC curve (Figure 4B-4D).

Figure 4 Construction of a nomogram based on the TCGA database. (A) Nomogram for predicting 1-, 3-, and 5-year OS based on age (years), tumor stage, and GGT6 expression. (B) AUC values of the nomogram and individual clinical factors (age, tumor stage, GGT6 expression) for 1–5-year OS. (C) Calibration curve of the TCGA nomogram for predicting 1-, 3-, and 5-year survival probabilities. (D) Decision curve analysis for the nomogram and individual factors, showing the net benefit at various thresholds of risk. AUC, area under the curve; GGT6, gamma-glutamyl transferase 6; OS, overall survival; TCGA, The Cancer Genome Atlas.

Functional analysis and mutational characterization of GGT6

We differentially analyzed samples with different GGT6 expression based on the TCGA database. GO analysis indicates that GGT6 is related to the functions of alcohol metabolic process, hormone metabolic process, apical part of cell, apical plasma membrane, channel activity, passive transmembrane transporter activity, and other functions are closely related (Figure 5A). KEGG analysis indicates that the abnormally reduced GGT6 expression was enriched in signaling pathways such as taste transduction, steroid hormone biosynthesis, retinol metabolism, renin secretion, peroxisome proliferator-activated receptor (PPAR) signaling pathway and other pathways (Figure 5B). Furthermore, GSEA analysis revealed that epithelial-mesenchymal transition (EMT), angiogenesis, and KRAS signaling pathways in the low GGT6 expression group (Figure 5C-5E). We also analyzed the mutation map between the high and low GGT6 expression groups. Figure 5F shows the mutational characteristics of genes with mutation frequencies in the top 20 in the TCGA database in different expression subpopulations of GGT6.

Figure 5 Functional enrichment analysis and somatic mutation analysis based on GGT6 expression. (A) GO analysis for DEGs between high and low GGT6 groups. Significant GO terms are shown with p.adjust values and gene counts. (B) KEGG analysis for DEGs between high and low GGT6 expression groups. The plot shows significant pathways with corresponding P values and gene ratios. (C-E) GSEA of the HALLMARK gene set for the angiogenesis pathway, epithelial mesenchymal transition pathway and KRAS signaling pathway in the high and low GGT6 expression groups. (F) Somatic mutation landscape of genes in the high and low GGT6 expression groups, showing the types of mutations (e.g., missense, nonsense) and their frequencies in each group. BP, biological process; CC, cellular component; DEGs, differentially expressed genes; GGT6, gamma-glutamyl transferase 6; GO, Gene Ontology; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; NES, normalized enrichment score; PPAR, peroxisome proliferator-activated receptor.

Immune infiltration analysis of GGT6

The CIBERSORT algorithm was used to assess the distribution of 22 immune cell types in the TCGA-CRC dataset, aiming to analyze the correlation between GGT6 levels and the immune microenvironment. A stacked histogram was generated to summarize these infiltrative profiles (Figure 6A). GGT6 expression was found to show positive correlation with plasma cells and Treg cells, and negative correlation with monocytes, M2-type macrophages, M1-type macrophages, neutrophils, and activated natural killer (NK) cells (Figure 6B). In the GGT6 high expression group, there were more Treg cell infiltration. In the GGT6 low-expression group, there were more macrophages M1 and neutrophils infiltration (Figure 6B). In addition, we scored the TCGA dataset for 12 immune functions by ssGSEA and found that GGT6 expression was negatively correlated with the scores of T-cell co-inhibition, pro-inflammatory, cytolytic activity, and antigen-presenting cell co-inhibition (Figure 6C).

Figure 6 Immune infiltration analysis and immune response pathways based on GGT6 expression in the TCGA dataset. (A) Proportions of immune cell types in the high and low GGT6 expression groups based on CIBERSORT analysis. (B) Correlation analysis between GGT6 expression and various immune cell types. (C) Box plots showing the abundance of different immune cell types in the high and low GGT6 expression groups. *, P<0.05, **, P<0.01; ns, not significant, P>0.05. APC, antigen-presenting cell; CCR, C-C chemokine receptor; GGT6, gamma-glutamyl transferase 6; HLA, human leukocyte antigen; IFN, interferon; MHC, major histocompatibility complex; NK, natural killer; TCGA, The Cancer Genome Atlas.

Analysis of the benefits of immunotherapy

To explore the potential role of GGT6 in CRC immunotherapy, the correlation between GGT6 expression and immune checkpoint markers was first analyzed. The high GGT6 group correlated with LGALS9, HAVCR1, TIMD4 and TNFRSE14 (Figure 7A). These suggest that the high GGT6 expression group may have a poorer immune microenvironment, but is also more likely to benefit from immunotherapy (30). TIDE algorithm was employed to predict immunotherapy response, where elevated TIDE scores reflect a greater potential for immune evasion and a diminished likelihood of therapeutic benefit. Within the TCGA-CRC cohort, GGT6-high patients exhibited significantly lower TIDE and exclusion scores, alongside markedly higher microsatellite instability (MSI) scores. Notably, no significant disparity was observed in T-cell dysfunction scores between different GGT6 expression groups (Figure 7B), suggesting that patients with high GGT6 expression are more easy to benefit from immunotherapy.

Figure 7 Analysis of the benefits of immunotherapy. (A) Expression levels of immune checkpoint genes (LGALS9, TIMD4, HAVCR1, TNFRSF14) in high and low GGT6 expression groups. (B) TIDE scores comparing high and low GGT6 expression groups, indicating potential response to immune checkpoint blockade therapy. (C-F) IPS analysis for immune therapy efficacy in high and low GGT6 expression groups, focusing on different immune response subtypes. (G-K) Drug sensitivity analysis for common CRC chemotherapeutics, comparing high and low GGT6 expression groups. *, P<0.05; **, P<0.01; ***, P<0.001. CTLA-4, cytotoxic T-lymphocyte antigen-4; GGT6, gamma-glutamyl transferase 6; IC50, half-maximal inhibitory concentration; IPS, immunophenoscore; MSI, microsatellite Instability; PD-1, programmed cell death protein 1; TIDE, tumor immune dysfunction and exclusion.

In addition, the IPS, derived from a sophisticated machine learning scoring system, was calculated to predict clinical sensitivity to immune checkpoint inhibitors. The distribution of IPS scores was assessed across different GGT6 expression groups to further validate the role of GGT6 in modulating the CRC immune landscape. Four IPS scores in CRC patients in the high and low GGT6 groups (Figure 7C-7F), analysis of the four IPS subcategories revealed that high GGT6 expression was positively correlated with higher scores in the group with better efficacy of immunotherapy (all P<0.05). These results collectively indicate a potentially superior response to immune checkpoint blockade in the GGT6-high population, reinforcing its role as a predictive biomarker for CRC immunotherapy.

Cancer drug sensitivity analysis

Patients have different sensitivities to commonly used treatment drugs for CRC and have different clinical characteristics. Therefore, in order to analyze the clinical significance of GGT6, We compared the sensitivity of the GGT6 high expression group and the GGT6 low expression group to commonly used drugs in the TCGA dataset, and found that the high expression group was more sensitive to oxaliplatin and lapatinib (Figure 7G-7K). This provides a favorable contribution to the subsequent combination therapy in CRC patients.

Tissue microarray validation of GGT6 expression and prognostic value

To validate the conclusions obtained from public databases, we performed immunofluorescence staining on cancer and adjacent tissues from 80 pairs of CRC patient specimens using a tissue microarray. We found that GGT6 expression was significantly reduced in tumor tissues (Figure 8A,8B). Meanwhile, ROC curve analysis showed that low GGT6 expression effectively distinguished tumor from adjacent tissues (Figure 8C) (AUC =0.696) as well as survival status (Figure 8D) (AUC =0.622). Using the optimal cutoff determined by the ROC curve, 80 CRC patients were divided into a GGT6-low expression group (n=43) and a GGT6-high expression group (n=37). The associations between GGT6 levels and various clinicopathological parameters are summarized in Table 1. Our analysis shows that GGT6 is significantly correlated with patient survival (P=0.04). However, no significant correlations were observed with other clinical characteristics, underscoring the specific prognostic relevance of GGT6 in CRC. The survival curve indicated that low GGT6 expression is associated with poorer prognosis (Figure 8E). Additionally, both univariate (Figure 8F) and multivariate (Figure 8G) Cox regression analyses revealed that low GGT6 expression is an independent prognostic risk factor. This further confirms the prognostic value of GGT6 in clinical settings.

Figure 8 Validation of GGT6 expression and prognostic significance in CRC using tissue microarray analysis. (A) Representative immunofluorescence images of GGT6 in normal and tumor tissues. GGT6 is stained in red, and nuclei are counterstained with DAPI (blue). Scale bar, 300 µm. (B) Normalized expression levels of GGT6 in tumor and paired normal tissues from 80 cases. (C) ROC curve analysis of GGT6 expression for distinguishing cancer vs. adjacent normal tissues, with an AUC of 0.696. (D) ROC curve analysis of GGT6 expression for predicting OS based on survival status, with an AUC of 0.622. (E) OS analysis of GGT6 expression based on tissue microarrays. (F,G) Forest plot showing the results of univariate (F) and multivariate (G) Cox regression analysis of GGT6 expression and clinical factors based on tissue microarrays. ****, P<0.0001. AUC, area under the curve; CI, confidence interval; CRC, colorectal cancer; DAPI, 4',6-diamidino-2-phenylindole; GGT6, gamma-glutamyl transferase 6; OS, overall survival; ROC, receiver operating characteristic.

GGT6 regulates CRC cell malignant phenotypes

To explore the biological role of GGT6, we performed siRNA-mediated knockdown in HT-29 and DLD-1 cells. Among the tested sequences, si-GGT6-2 showed the highest interference efficiency at the mRNA level (Figure 9A,9B). Notably, loss of GGT6 promoted the oncogenic phenotype of CRC cells, as evidenced by increased proliferation in CCK-8 and colony formation assays (Figure 9C-9H). Moreover, Transwell assays indicated that GGT6 silencing enhanced cellular migration and invasion (Figure 9I,9J). These suggest that GGT6 may be a tumor suppressor in CRC.

Figure 9 Knockdown of GGT6 promoted cell proliferation invasion and migration. (A,B) Gene silencing efficiency was verified by mRNA expression analysis in HT29 and DLD-1 cells after siRNA transfection. (C,D) The colony formation assay was performed to evaluate the proliferation of HT29 and DLD-1 cells after silencing GGT6, cells were stained with Coomassie Brilliant Blue R250. (E,F) Statistical analysis of the clone formation assay. (G,H) The CCK-8 assay was used to detect the proliferation of HT29 and DLD-1 cells after GGT6 gene silencing. (I,J) Transwell migration and invasion assays were performed after transfection with si-GGT6 in HT29 and DLD-1 cells, cells were stained with Coomassie Brilliant Blue R-250 and visualized under a microscope at a magnification of 400×. Data are presented as mean ± standard error of the mean from three independent biological replicates. *, P<0.05; **, P<0.01; ****, P<0.0001. CCK-8, Cell Counting Kit-8; GGT6, gamma-glutamyl transferase 6; mRNA, messenger ribonucleic acid; NC, negative control; OD, optical density.

Discussion

In 2022, CRC accounted for approximately 1.9 million new cancer cases and 900,000 related deaths worldwide, remaining one of the most common malignancies globally (1). Despite the emergence of novel therapies such as targeted therapy and immunotherapy, the prognosis for patients with advanced CRC remains poor (31). Recurrence and metastasis are the main causes of death in CRC patients, resulting in a 5-year survival rate of less than 20% (32). Therefore, it is crucial to investigate and identify potential biomarkers to guide clinical diagnosis and improve treatment.

This study utilized public databases and bioinformatics methods to analyze the expression of GGT6 in various tumors. Results revealed that GGT6 expression is downregulated in most tumors. In CRC, our analyses using multiple public databases demonstrated that both transcriptional and protein levels of GGT6 were decreased in tumor. Analysis of GGT6 expression and patient prognosis in the TCGA database, as well as in the GSE39582 and GSE17538 datasets, showed that low GGT6 expression is associated with poor OS. Interestingly, previous research has shown that low GGT6 expression correlates with poor prognosis in papillary renal cell carcinoma (19). Meanwhile, GGT6 expression shows an excellent AUC value for the diagnosis of prostate cancer and is associated with improved progression-free survival (33). Consistent with these findings, our analysis further highlights the critical role of GGT6 in inhibiting CRC progression.

To further explore the relationship between GGT6 and CRC, we conducted a systematic analysis of transcriptional data and clinical characteristics in the TCGA database and the GSE39582 dataset. The results revealed that GGT6 expression decreases as tumor stage advances. Cox regression analysis identified low GGT6 expression as an independent risk factor for CRC. Using GGT6 expression, age, and tumor stage from the TCGA database as indicators, a nomogram was constructed to predict patient prognosis. Analysis using ROC curves, decision curve analysis (DCA) curves, and calibration curves demonstrated that the nomogram has predictive value. A risk model constructed from a gene set that includes GGT6 can effectively predict the prognosis of clear cell renal cell carcinoma (34,35). Nomograms are effective tools for predicting tumor patient prognosis, and the one constructed in this study showed good predictive capability. It also underscored the potential of GGT6 as a prognostic biomarker for CRC.

The tumor microenvironment contains various immune cells, which play a crucial role in the effectiveness of treatment (36,37). This study found that M1 macrophages and neutrophils infiltrated less in the high GGT6 expression group. Previous studies have highlighted the role of neutrophils in microbial pathogen defense, but their involvement in promoting cancer growth and dissemination is now widely recognized (38). For example, neutrophils have been shown to resist ferroptosis via aconitate decarboxylase 1, thereby promoting breast cancer metastasis (39). Furthermore, our study revealed elevated levels of antigen-presenting cell co-inhibitory signals and T-cell co-inhibitory signals in the low GGT6 expression group, while T-cell co-stimulatory signals were reduced. T cells play a crucial role in antitumor immunity and immunotherapy (40). The crosstalk between T cells and dendritic cells (DCs) within the tumor microenvironment is crucial for antitumor responses (41-43). DCs present antigens to T cells, which then become activated and kill tumor cells (44). The reduction of GGT6 expression impairs the functionality of these interactions between DCs and T cells. Based on these findings, low GGT6 expression in tumor cells may foster an immunosuppressive environment, disrupt CD8⁺ T cell and DC interactions, and promote immune evasion, ultimately contributing to tumor progression.

Immune checkpoint therapy has achieved remarkable success in CRC patients, especially those with MSI or MMR deficiency (10). However, not all MSI-high patients experience a durable response (45). Therefore, identifying new predictive targets for immunotherapy remains essential. Immunotherapy analyses, including immune checkpoint analysis, TIDE scoring, and IPS scoring, showed that the high GGT6 expression group had a better response to immunotherapy. These results suggest that GGT6 plays a crucial role in the immune microenvironment of CRC and could serve as a novel biomarker for immunotherapy in the future.

To elucidate the biological functions of GGT6 in glioma development, patients from the TCGA database were divided into high and low GGT6 expression groups based on optimal cutoff values from survival analyses. DEGs between the two groups were analyzed, followed by functional enrichment analyses.

GO analysis revealed that low GGT6 expression is associated with the alcohol metabolic process, hormone metabolic process, ion channels, and transmembrane transport proteins. KEGG analysis identified pathways such as neuroactive ligand-receptor interaction, metabolism of xenobiotics by cytochrome P450, PPAR signaling, fat digestion and absorption, and steroid hormone biosynthesis. The PPAR is a lipid-sensing transcription factor activated by fatty acids, which regulates lipid homeostasis and cell fate. PPARγ activation in cancer cells modulates fatty acid uptake, de novo fatty acid synthesis, cholesterol uptake, and lipolysis, promoting cancer progression and metastasis (46). Recent studies have also found that GGT6 influences tumor progression by regulating metabolic reprogramming in tumor cells. Knockdown of GGT6 promotes the proliferation and invasion of clear cell renal cell carcinoma cells (47). Similarly, in our experiments, we also found that knocking down GGT6 promotes the proliferation, invasion, and migration of CRC cells. These findings suggest that GGT6 may influence CRC progression by modulating lipid and steroid metabolism through the PPAR pathway.

GSEA analyses revealed significant enrichment of EMT, angiogenesis, and KRAS signaling pathways in the low GGT6 expression group. Aberrant EMT reactivation is associated with the malignant characteristics of cancer cells during progression and metastasis, including enhanced migration, invasion, stemness, and resistance to chemotherapy and immunotherapy (48). KRAS, a common oncogenic mutation in CRC, occurs in approximately 40% of cases and leads to constitutive activation of downstream signaling pathways, promoting tumorigenesis. KRAS mutations are linked to poor prognosis (49). Additionally, KRAS is involved in the AKT-GSK3β pathway, inhibiting STAT3 phosphorylation and impairing IFN responses in CRC, resulting in an immunosuppressive microenvironment. Angiogenesis, a hallmark of solid cancers (50), is a fundamental pathological process in CRC development (51). Therefore, downregulation of GGT6 represents a potential mechanism underlying colorectal tumorigenesis and progression.

Drug sensitivity analyses revealed that the high GGT6 expression group was more sensitive to common CRC chemotherapeutic agents, such as oxaliplatin and lapatinib. Thus, GGT6 may serve as a novel predictor of chemotherapy efficacy in clinical settings.

A tissue microarray consisting of 80 colon cancer tissues and their paired normal colon tissues yielded similar results, showing decreased GGT6 expression in tumor tissues. Moreover, ROC curve analysis demonstrated that GGT6 effectively distinguishes cancerous tissues from adjacent normal tissues and predicts patient prognosis. K-M and Cox regression analyses indicated that low GGT6 expression in this set of colon cancer tissues is associated with poor prognosis and serves as an independent risk factor for unfavorable outcomes.

Although this study establishes an association between GGT6 and CRC, certain limitations exist. The bioinformatics analyses relied on transcriptomic sequencing data and clinical information from public databases, which may introduce confounding biases. Future prospective studies are necessary to address these issues. Furthermore, although our GSEA results link GGT6 to the PPAR signaling pathway, EMT, and KRAS signaling pathways, these potential mechanisms were not experimentally validated in vitro or in vivo in the present study. While these bioinformatic findings provide a valuable theoretical framework for understanding the tumor-suppressive role of GGT6, future studies are required to confirm these specific molecular interactions and downstream cascades through experimental approaches, such as Western blotting for EMT markers (e.g., E-cadherin and Vimentin) and in vivo functional assays. Moreover, our findings regarding the tumor immune microenvironment—specifically the correlations between GGT6 expression and the infiltration of Tregs, M1 macrophages, and neutrophils—were derived from the CIBERSORT algorithm using TCGA data. While these computational predictions offer valuable preliminary insights, they remain to be experimentally confirmed. Future research employing more direct techniques, such as multiplex immunohistochemistry or flow cytometry on clinical patient samples, is required to validate these specific immune cell correlations and their biological significance in CRC. Additionally, validation of GGT6 expression in a larger cohort of clinical CRC samples is required to enhance reliability. While in vitro experiments preliminarily explored the role of GGT6 in CRC proliferation, invasion, and migration, its specific mechanisms and broader functions remain to be elucidated.

Conclusions

In conclusion, GGT6 is downregulated in CRC tumor tissues, and low GGT6 expression is associated with poor prognosis, immune cell infiltration, and tumor immunosuppressive pathways in CRC patients. Moreover, GGT6 expression is related to the sensitivity of CRC to drug treatments, including immunotherapy. In summary, the results of this study suggest that GGT6 has the potential to be an important prognostic biomarker and a potential therapeutic target for CRC. However, further experimental validation is needed to further elucidate the precise biological effects of GGT6 in the development and progression of CRC and its underlying molecular mechanisms.

Acknowledgments

We would like to thank TCGA, UCSC Xena, HPA, GEO and GDSC for providing open-access data and analysis platforms that supported this study.

Footnote

Reporting Checklist: The authors have completed the MDAR and TRIPOD reporting checklists. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0095/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0095/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0095/prf

Funding: This study was supported by grants from Jiangsu Provincial Department of Science and Technology (grant No. BE2023717), Suzhou Municipal Science and Technology Bureau (grant No. SKY2022006), Jiangsu Commission of Health (grant No. M2020043), and China Digestive Tumor Clinical Research Public Welfare Project (grant No. P014-038).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0095/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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