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Predictive nomogram of the clinical outcomes of colorectal cancer based on methylated SEPT9 and intratumoral IL-10+ Tregs infiltration

Journal of Translational Medicine volume 22, Article number: 861 (2024) Cite this article

Abstract

Background

Gene methylation and the immune-related tumor microenvironment (TME) are highly correlated in tumor progression and therapeutic efficacy. Although both of them can be used to predict the clinical outcomes of colorectal cancer (CRC) patients, their predictive value is still unsatisfactory. Whether a combination risk model comprising these two prediction parameters performs better predictive effectiveness than independent factor is still unclear. Methylated Septin9 (mSEPT9) is an early diagnosis biomarker of CRC, in this study, we aimed to investigate mSEPT9-related biomarkers of immunosuppressive TME and identify the value of the combination risk model in predicting the clinical outcomes of CRC.

Methods

Immunofluorescence staining was performed to clarify the correlation between intratumoral IL-10+ Treg infiltration and mSEPT9 in peripheral blood. Survival time, response to 5-fluorouracil (5-FU)-based chemotherapy and PD-1 blockade, and the probability of recurrence or metastasis were analyzed in study (197 CRC samples) and validation (195 CRC samples) sets to evaluate the efficacy of combination risk model. Potential mechanisms were explored by mRNA sequencing.

Results

Hypermethylated SEPT9 in the peripheral blood of patients with CRC (stage I-III, and stage IV with resectable M1) before radical resection was positively correlated with high intratumoral IL-10+ Treg infiltration. The high-risk model revealed poor overall survival and progression-free survival, inferior therapeutic response to 5-FU-based chemotherapy and PD-1 blockade, and high probability of recurrence or metastasis. The underlying mechanisms may be associated with the increase in mSEPT9 and mediation of IL-10 via methionine metabolic reprogramming-induced infiltration of IL-10+ Tregs in the TME, which promotes tumor progression and resistance to 5-FU-based chemotherapy and PD-1 blockade.

Conclusions

The combination risk model of peripheral mSETP9 and intratumoral IL-10+ Treg infiltration in CRC can effectively predict prognosis, responsiveness to 5-FU-based chemotherapy and PD-1 blockade, and the probability of recurrence or metastasis. Therefore, this model can be used for precision treatment of CRC.

Background

According to the GLOBOCAN data for 2020, colorectal cancer (CRC) was ranked third in terms of incidence among malignant tumors, with approximately 1.9 million cases diagnosed, and second in terms of mortality among malignant tumors, with approximately 935,000 deaths recorded worldwide [1]. In recent years, comprehensive cancer treatments, such as neoadjuvant chemotherapy, surgery, targeted therapy, and immunotherapy, have significantly improved the prognosis of patients with CRC; however, the mortality of patients with CRC has not been effectively curbed [2]. This outcome might be highly related to tumor heterogeneity and the lack of effective biomarkers for individualized precision medicine. CRC is caused by the gradual accumulation and interaction of pathogenic mechanisms, such as polygenic mutations and epigenetic changes. Specific mechanisms include chromosome instability (CIN), microsatellite instability (MSI), and CpG island methylator phenotype (CIMP) [3,4,5]. Exploring risk models with high specificity and sensitivity for evaluating prognosis, predicting chemotherapy and immunotherapy sensitivity, and assessing the probability of recurrence and metastasis has great clinical significance in reducing CRC mortality as these models can be used to study the interrelationships among multiple mechanisms.

DNA methylation is an important epigenetic modification [6]. Hypermethylation of gene promoter regions leads to gene expression silencing and genome instability, and affects functions like cell death, DNA repair, and cell cycle regulation [7,8,9]. Septin9 (SEPT9) is a conserved protein skeleton gene with GTPase activity. Hypermethylation of CpG islands in the promoter region of SEPT9 were highly associated with the development of CRC [10, 11]. Currently, the detection of SEPT9 methylation in peripheral blood is used for the early diagnosis and monitoring of recurrence in CRC [12]. The expression level of peripheral mSEPT9 was association with clinicopathological characteristics of CRC and could be used to predict the prognosis of patients with CRC, but it did not perform better than TNM stage in the prognostic evaluation [13]. Whether peripheral mSEPT9 can be extended to predict the therapeutic efficacy and probability of recurrence or metastasis is still unclear. Notably, the effect of methylation on 5-FU drug resistance has been reported [14]. Some study showed that postoperative peripheral mSEPT9-positive patients benefited more from CAPEOX compared to FOLFOX [15], and patients with CRC and CIMP-positive tumor cells do not benefit from 5-FU-based chemotherapy [16], both suggesting that peripheral mSEPT9 could be used to assess sensitivity to 5-FU chemotherapy.

In addition to abnormal gene methylation, the immune-related tumor microenvironment (TME) is a crucial factor affecting prognosis and responsiveness to chemotherapy or immunotherapy [17, 18]. Tumor infiltrated Tregs play an essential role in the formation of immunosuppressive TME and suppress antitumor immunity. Tregs/Th17 balance predicts the efficacy of the folinic acid, fluorouracil, and oxaliplatin (FOLFOX) treatment regimen and PD-1 blockade [19, 20]. Combination therapy of anti-PD-L1 and Treg infiltration inhibitor can inhibit the progression of malignant tumor [21]. Due to diverse plasticity, regulatory T cells (Tregs) that infiltrate tumors are susceptible to TME stress mediated by tumor cells, differentiate into phenotypically and functionally heterogeneous subsets, express different levels of forkhead box P3 (Foxp3), and secret different cytokines, such as IL-10, TGF-β, and IFNγ [22, 23]. The differentiation and function of Tregs are regulated by abnormal tumor epigenetic modulation or metabolic modeling, which can regulate the expression of Foxp3 [24, 25]. Demethylase upregulated by cytokines in TME is essential for tumor Treg cell fitness and immune homeostasis [26]. These results reveal the underlying correlation between methylation and Treg-related immunosuppressive TME in tumor progression, and suggest the promising application in predicting the clinical outcomes of CRC patients. Most of the studies about peripheral mSETP9 and intratumoral IL-10 + Treg infiltration focused on molecular mechanisms in CRCs, and their clinical significance is not yet clear.

In order to identify whether the combination risk model based on peripheral mSETP9 and intratumoral IL-10 + Treg infiltration perform better predictive effectiveness than a single independent factor, the correlation between gene methylation and the Tregs-related immunosuppressive TME in tumor progression was explored, and a combination risk model was established to predict the clinical outcomes of CRCs based on peripheral mSEPT9 and intratumoral immunosuppressive Tregs infiltration. In this study, we proved that intratumoral IL-10+ Tregs infiltration was highly related to peripheral mSEPT9 in CRC, and established a combination risk model to facilitate the clinical application of peripheral mSEPT9 and IL-10+ Tregs as a predictive risk model for prognosis, resistance to 5-FU-based chemotherapy and PD-1 blockade, and the probability of recurrence or metastasis. Results of mRNA-sequencing revealed that methionine metabolism reprogramming might be associated with abnormal gene methylation and immunosuppressive TME, which may provide new insights for further mechanistic studies and investigations of new targets for individualized diagnosis and treatment. To our knowledge, this is the first integrated analysis of abnormal gene methylation, immune-related TME, molecular characteristics, and clinical outcomes in precision medicine for CRC.

Methods

Study population and tumor tissue samples

To evaluate the predictive value of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration, 197 patients diagnosed with CRC by endoscopic examination and biopsy histopathology between January 2017 and December 2018 at Tianjin Union Medical Center were enrolled and used as the study set. A total of 195 patients with CRC diagnosed between January 2019 and December 2020 were used as the validation set. All these patients with primary resectable CRC did not receive neoadjuvant therapy before surgery. The diagnosis and treatment programs were based on the guidelines of the National Comprehensive Cancer Network (NCCN). This study was performed in accordance with the principles of the Declaration of Helsinki and was approved by the Research Ethics Committee of Tianjin Union Medical Center (2024-B17). A general flowchart of the study is provided in Fig. 1A. Ten mL of blood was collected three days before radical resection to determine the levels of peripheral mSEPT9. After resection, cryopreserved CRC tissues from eight patients from January 2017 to December 2017 were used for transcriptome sequencing (mRNA-seq). Paraffin-embedded tumor specimens after two weeks of resection were used to analyze the histopathological characteristics. Then the treatment regimen (including 5-FU based chemotherapy or PD-1 blockade) of these CRC patients was recorded. Overall survival (OS) and progression-free survival (PFS) were assessed for 36 months using therapeutic regimens, imaging, and other clinical tests. OS was defined as time from the date of surgery to death. PFS was defined as time from the date of surgery to the verified first disease progression (including local recurrence and distant metastasis) or the date of death caused by CRC.

Fig. 1
figure 1

A. a Study design and flowchart of patient enrollment and study method. (a Created with BioRender.com) A total of 197 patients with CRC were enrolled between 2017 and 2018 and assigned to the study set. A total of 195 patients with CRC were enrolled between 2019 and 2020 and assigned to the validation set. Patients had clinical Stage I-III or stage IV disease with resectable M1 and were not treated with neoadjuvant therapy before radical resection. Ten mL of peripheral blood sample was collected before surgery and the Ct value of peripheral mSEPT9 was determined using RT-PCR. Paraffin-embedded tissues were collected for histopathological analysis. Fresh tissue was collected for mRNA sequencing. The treatment regimen was administered, imaging or other clinical tests were performed, and the OS and PFS were assessed for 36 months. b Graphical abstract of the study contents. (1) Biomarker selection for the immunosuppressive microenvironment: CIBERSORT calculation based on transcript difference analysis between patients with CRC that display different levels of peripheral mSEPT9, the intratumoral infiltration biomarker of immunosuppressive TME. Immunofluorescence staining was performed for validation. (2) Prognostic evaluation of peripheral mSEPT9 and IL-10+ Tregs: Prognostic value of peripheral mSEPT9 and IL-10+ Tregs in the study set and validation set, respectively. (3) Risk model construction using peripheral mSEPT9 and IL-10+ Tregs: nomogram of the combination of peripheral mSEPT9 and IL-10+ Tregs for predicting prognosis. (4) Risk model evaluation for predicting prognosis: Predictive value of the risk model for prognosis and probability of recurrence or metastasis in the study set and validation set. (5) Risk model for predicting chemotherapy and immunotherapy responsiveness: Predictive value of the risk model for responsiveness to 5-FU-based chemotherapy and PD-1 blockade in the study set and validation set. (6) Underlying molecular mechanism investigation: Transcript difference analysis between patients with CRC that display different levels of peripheral mSEPT9 and IL-10+ Tregs to elucidate the underlying mechanisms for abnormal gene methylation and Tregs-related immunosuppressive TME. B Representative immunofluorescence staining images of IL-10 and IL-10+ Tregs infiltration in tumor and normal tissues of patients with CRC (a CRC and normal tissue, b Lymph node metastatic tissue of CRC, red indicates IL-10, green indicates Foxp3, and blue indicates DAPI. magnification ×200). C Number of intratumoral IL-10+ Tregs infiltration in tumor tissues and corresponding peritumor tissues. D Ct value of mSEPT9 in peripheral blood is negatively correlated with intratumoral IL-10+ Tregs infiltration; the level of peripheral mSEPT9 is positively correlated with intratumoral IL-10+ Tregs infiltration. (Pearson r = − 0.373, P < 0.001)

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Quantification of methylated Septin9

SEPT9 gene methylation assay (Epigenomics AG for Epi ProColon 2.0) was performed. DNA was extracted from 3.5 mL of plasma isolated from 10 mL of peripheral blood using a plasma processing kit (BioChain Science and Technology, Inc., Beijing). The DNA was then incubated with bisulfite, and the methylated target sequences in the bisulfite-converted DNA template were amplified using real-time fluorescence PCR. The methylation of SEPT9 in plasma samples was measured using an ABI7500 fluorescent PCR instrument, and the Ct value for each patient was recorded.

Transcriptome sequencing (mRNA-seq)

Sample processing and sequencing

total RNA was isolated using an RNeasy Mini Kit (Qiagen). Paired-end libraries were synthesized using TruSeq® RNA Sample Preparation Kit (Illumina, USA), according to the TruSeq® RNA sample preparation guide. Briefly, poly (A)-containing mRNA was purified using poly (T) oligo-attached magnetic beads. Following purification, mRNA was fragmented into small pieces using divalent cations at 94 ℃ for 8 min. Cleaved RNA fragments were reverse transcribed into first-strand cDNA using reverse transcriptase and random primers. Second-strand cDNA was then synthesized using DNA Polymerase I and RNase H. After the cDNA fragments were subjected to an end repair process, a single ‘A’ base was added and then the adapters were ligated. The products were purified and enriched via PCR to create a final cDNA library. To confirm the insert size and calculate the mole concentration, purified libraries were quantified using the Qubit® 2.0 Fluorometer (Life Technologies, USA) and validated using the Agilent 2100 bioanalyzer (Agilent Technologies, USA). Clusters were generated using cBot with the library diluted to 10 pM and then sequenced on an Illumina NovaSeq6000 (Illumina, USA). Library construction and sequencing were performed by Shanghai Biotechnology Corporation.

Data analysis for gene expression

Raw sequencing reads were preprocessed by filtering out rRNA reads, sequencing adapters, short fragment reads, and other low-quality reads. Hisat2 (version 2.0.4) was used to map the clean reads to the human hg38 reference genome with two mismatches. After genome mapping, StringTie (version1.3.0) was run with reference annotations to generate FPKM values for known gene models. Differentially expressed genes (DEGs) were identified using edgeR software. The P-value significance threshold for multiple tests was set based on the false discovery rate (FDR). Fold changes were estimated according to the Fragments Per kilobase of exon per million mapped fragments (FPKM) in each sample. The DEGs were selected using the following filter criteria: FDR ≤ 0.05 and fold-change ≥ 2. The immune profile was analyzed using CIBERSORT calculations. Genes in the different groups were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses.

Immunofluorescence staining

CRC tissue sections with thickness of 4 μm were used for immunofluorescence staining to detect the expression of IL-10+Foxp3+ Tregs. The sections were incubated with primary antibodies against Foxp3 (dilution 1:80, ab20034, Abcam) and IL-10 (dilution 1:80, MAB91842, R&D Systems) overnight at 4 ℃, and then with the Alexa FluorTM488 and 594 conjugated secondary antibody (dilution 1:800, Invitrogen) for 1 h at room temperature. The nuclei were stained with DAPI for 10 min. Finally, fluorescence images were captured using a fluorescence microscope, processed and merged by Image J. Each result was confirmed and scored by two experienced pathologists. The mean number of evaluations was recorded. If the variation in count exceeded three or more cells, two pathologists reassessed the count of IL-10+ Tregs infiltration in the tumor until a consensus was reached.

Calculation of cut-off values

The cutoff values of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration were calculated using the highest point of the Jordan index, which was selected as the optimal cutoff value. The Ct value of peripheral mSEPT9 was 37. For the intratumoral IL-10+ Tregs, ≤ 5/high-power field (HPF) (at ×400 magnification) was defined as low and ≥ 6/HPF (at ×400 magnification) was defined as high.

Establishment of the combination risk model

Univariate and multivariate Cox analyses of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration were used to predict the OS and PFS of patients with CRC, and were incorporated into the risk score model to construct a clinical predictive nomogram. To quantify the differential performance of the nomogram, the ROC curve and Harrell’s consistency index (C-index) were estimated, a calibration curve was generated, and the capability of the nomogram was evaluated using the R package, rms (version 6.3-0).

Statistical analysis

Statistical analyses were performed using SPSS (version 25.0), R (version 6.3-0), and GraphPad Prism (version 10). CIBERSORT calculations based on mRNA-seq were used to analyze the infiltrated immune cells. Chi-square tests or Fisher’s exact tests were performed to compare the differences in IL-10+ Tregs levels according to different clinicopathological characteristics and the Ct values of peripheral mSEPT9. Survival analysis was based on OS and PFS. The influence of peripheral mSEPT9 and IL-10+ Tregs infiltration on OS and PFS was analyzed using Kaplan–Meier curves, and significance was determined using the log-rank test. Hazard ratios (HRs) and 95% confidence intervals (CIs) for CRC-related deaths were estimated using Cox regression analysis. Statistical significance was defined as a two-sided P value of < 0.05. Correlations between the risk model and molecular characteristics were evaluated using Pearson and Spearman correlation coefficients.

Data availability statement

Raw sequence data were uploaded to the NCBI Sequence Read Archive (SRA; submission number, SUB14206932). The data will be released upon publication.

Results

Peripheral m SEPT9  and intratumoral IL-10 + Tregs infiltration correlate with CRC progression

CIBERSORT based on mRNA-seq was performed to identify the underlying immune infiltration biomarker for the immunoevasive TME between the high peripheral mSEPT9 and low mSEPT9 groups. However, the CIBERSORT calculations revealed no significant differences in intratumoral immunocyte infiltration, suggesting the presence of different subtypes of infiltrated immunocytes. Tregs and macrophages were the main immunocytes with different subtypes and functions, with no significant differences found between M0, M1, and M2 macrophages. Therefore, we focused on the immunosuppressive subtypes of Tregs (Supplementary Fig. S1). IL-10, IL-35, and TGF-β are the main inhibitory cytokines secreted by immunosuppressive Tregs. As IL-10 has a relatively low expression in normal tissue [23, 27], IL-10+ Tregs is a more suitable biomarker of immunoevasive TME. IL-10 expression was higher in tumors than in normal tissues, and Tregs were more likely to infiltrate tumor tissues than normal tissues (Fig. 1B). By comparing the expression of IL-10+ Tregs in intratumoral and peritumoral tissues, we found that intratumoral IL-10+ Tregs infiltration was higher than matched peritumoral IL-10+ Tregs infiltration (Fig. 1C). Thus, intratumoral IL-10+ Tregs infiltration might be associated with tumor progression. The number of intratumoral IL-10+ Tregs infiltration was negatively correlated with the Ct value of peripheral mSEPT9, which indicates that intratumoral IL-10+ Tregs infiltration is positively correlated with the methylation level of SEPT9 (Figs. 1D and 2A). Peripheral mSEPT9 expression and intratumoral IL-10+ Tregs infiltration were found to be significantly associated with high T stage, lymph node metastasis, distant metastasis, and advanced American Joint Committee on Cancer (AJCC) disease stage (Fig. 2B; Table 1). Taken together, peripheral mSEPT9 is suggested to be associated with intratumoral IL-10+ Tregs infiltration, and is potentially involved in CRC progression.

Fig. 2
figure 2

Peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration were highly related to CRC progression. A Immunofluorescence staining for IL-10 (red) and the Tregs marker, Foxp3 (green), in tissues with different levels of peripheral mSEPT9. (a. mSEPT9 not detected in normal tissue, b-d. CRC tissue with gradually increasing levels of mSEPT9, magnification ×400). B Left, Ct value of peripheral mSEPT9 is negatively correlated with TNM stage (P < 0.01). Right, Number of intratumoral IL-10+ Tregs infiltration in CRC is positively correlated with TNM stage (P < 0.01)

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Table 1 Clinicopathological characteristics related to intratumoral IL-10+ Tregs infiltration in CRC for the study and validation sets
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Peripheral m SEPT9  and intratumoral IL-10 + Tregs infiltration are independent prognostic factor of CRC

The Kaplan-Meier log-rank test was conducted to assess the prognostic value of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration in the study and validation sets, respectively. High levels of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration predicted an unfavorable prognosis in patients with CRC (Fig. 3A–D). Further, peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration were identified as independent prognostic factors of CRC (Fig. 3E and F).

Fig. 3
figure 3

Peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration were independent prognostic factors of CRC. A, B Relationship between peripheral mSEPT9 and overall survival in the study set (A) and validation set (B). C, D Relationship between IL-10+ Tregs infiltration and overall survival in the study set (C) and validation set (D). Log-rank test was performed to assess the Kaplan-Meier curves. E, F Multivariate analysis of peripheral mSEPT9 and IL-10+ Tregs infiltration was conducted based on the clinicopathological characteristics of the study (E) and validation (F) sets

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Combined peripheral m SEPT9  and intratumoral IL-10 + Tregs infiltration risk model predicts unfavorable prognosis

Peripheral mSEPT9 was associated with intratumoral IL-10+ Tregs infiltration; however, the relative coefficient (Pearson r value = -0.373, P < 0.001) was not close to 1, which indicates that peripheral mSEPT9 did not have a strong negative correlation with intratumoral IL-10+ Tregs infiltration. Therefore, the combination of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration might be more sensitive than the individual factors. First, by investigating the predictive function of combined peripheral mSEPT9 and IL-10+ Tregs for prognosis, we proved that the ROC of the combination risk model was larger than that of the individual detection of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration (Fig. 4A). A nomogram was then used to establish a combination risk model (Fig. 4B). Patients with high-risk scores had worse OS and PSF than those with low-risk scores (Fig. 4C and D). Therefore, combining peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration can effectively predict the prognosis of patients with CRC, with better outcomes than individual detection.

Fig. 4
figure 4

Combined peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration model for predicting prognosis and responsiveness to 5-FU-based chemotherapy. A ROC curve of the combination risk model and signal for peripheral mSEPT9 and IL-10+ Tregs infiltration.(S9 represents mSEPT9, AUC = 0.73; IL-10 represents intratumoral IL-10+ Tregs infiltration, AUC = 0.73; Combine represents combination model based on peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration, AUC = 0.79; P1, peripheral mSEPT9 versus intratumoral IL-10+ Tregs infiltration; P2, peripheral mSEPT9 versus the combination model; P3, intratumoral IL-10+ Tregs infiltration versus the combination model). B Nomogram of the combination of peripheral mSEPT9 and IL-10+ Tregs infiltration. C, D Kaplan–Meier curves indicated poor OS (left) and PFS (right) in patients with high risk scores in the study set (C) and validation set (D). E, F OS (left) and RFS (right) curves indicated inferior responsiveness to 5-FU-based chemotherapy in patients with high risk scores in the study set (E) and validation set (F). G, H Kaplan–Meier curve indicated non-significant OS (left) and worse RFS (right) in high-risk patients with 5-FU-based chemotherapy relative to without 5-FU-based chemotherapy in the study set (G) and validation set (H). I Kaplan–Meier curve indicated better OS (left) and non-significant RFS (right) in low-risk patients with 5-FU-based chemotherapy relative to without 5-FU-based chemotherapy in the study set. J Kaplan–Meier curve indicated better OS (left) and RFS (right) in low-risk patients with 5-FU-based chemotherapy relative to without 5-FU-based chemotherapy in the validation set. K Kaplan–Meier curve indicated worse RFS in patients with high risk scores after radical resection in the study set (left) and validation set (right)

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Combined peripheral mSEPT9 and intratumoral IL-10+Tregs infiltration risk model predicts inferior responsiveness to 5-FU-based chemotherapy

Abnormal gene methylation and immune deployment are not only associated with tumor progression but are highly related to 5-FU-based chemotherapy [16]. To investigate the value of the risk model in predicting responsiveness to 5-FU-based chemotherapy, we analyzed the correlation of the combination risk model with the effect of 5-FU-based chemotherapy in patients with stages I-IV disease. 5-FU-based chemotherapy could improve both OS and RFS in patients with CRC and low-risk scores; however, no significant effect was found in patients with high risk scores in both the study and validation sets (Fig. 4E and F). Moreover, no statistically significant difference in OS and PFS was found between 5-FU-based chemotherapy and non-5-FU-based chemotherapy in patients with CRC and high risk scores in the study or validation sets, indicating a correlation with 5-FU resistance (Fig. 4G and H). Of patients with low-risk scores in study set, those that received 5-FU-based chemotherapy had a longer OS than those that did not receive 5-FU-based chemotherapy. However, no statistical difference in PFS was found in the study set, which may be related to the shorter follow-up time. In validation set, patients in the low-risk group who received 5-FU-based chemotherapy had better OS and PFS prognosis than patients who did not receive 5-FU chemotherapy. Therefore, the low-risk model was found to be sensitive to 5-FU-based chemotherapy in both the study and validation sets (Fig. 4I and J). These results highlight the role of combined peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration in predicting therapeutic responsiveness to 5-FU-based chemotherapy.

Combined peripheral mSEPT9 and intratumoral IL-10+Tregs infiltration risk model predicts high probability of recurrence or metastasis

We examined the role of the combined model in predicting the probability of recurrence or metastasis for patients with stages I-III disease after radical resection. High-risk patients had shorter PFS than low-risk patients (Fig. 4K), suggesting the potential function of combined peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration in predicting the probability of recurrence or metastasis after radical resection in patients with CRC.

Combined peripheral mSEPT9 and intratumoral IL-10+Tregs infiltration risk model predicts inferior responsiveness to PD-1 blockade

Currently, PD-1 blockade is increasingly used to treat advanced CRC; however, the objective response rate (ORR) is still relatively low. Abnormal gene methylation and immunosuppressive TME are crucial factors affecting immunotherapy [28, 29]. We analyzed the correlation of the combination risk model with responsiveness to PD-1 blockade in 36 patients with CRC and administered anti-PD-1. Contrast-enhanced computed tomography (CECT) was used to evaluate therapeutic efficacy. The ORR was 14% (3/21) for patients with high-risk scores and treated with anti-PD-1 and 60% (9/15) among patients with low-risk scores (Fig. 5A–C), indicating that a high-risk model based on peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration predicts inferior responsiveness to PD-1 blockade. By analyzing the results of mRNA-seq, we found a significant difference in several immunotherapy responsiveness-related genes, such as programmed cell death 1 (PDCD1), cytotoxic T-lymphocyte associated protein 4 (CTLA4), and indoleamine 2,3-dioxygenase 1 (IDO1), between the high- and low-risk model groups. This result indicates that high peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration might induce the formation of an immunosuppressive TME (Fig. 5D).

Fig. 5
figure 5

Combination model of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration model predicted the responsiveness to PD-1 blockade. A Waterfall plot of the responsiveness to PD-1 blockade according to the risk scores of the combination model. B Proportion of responder (CR&PR) and nonresponder (SD&PD) to PD-1 blockade in different risk groups. (CR: complete response, PR: partial response, SD: stable disease, PD: progressive disease). C Representative contrast-enhanced computed tomography (CECT) images of patients with low risk scores (a) and high risk scores (b) treated with PD-1 blockade. Arrows indicate the tumor. (left: baseline, middle: 2 treatment cycles, right: 4 treatment cycles). D Immunotherapy responsiveness-related genes in the high- and low-risk patients with CRC. Differences were considered significant at *P < 0.05, **P < 0.01, ***P < 0.001, compared to the low-risk group

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Peripheral mSEPT9 and IL-10+Tregs infiltration may be induced by competition between tumor and Tregs in one-carbon metabolism of methionine and folate cycle

To investigate the underlying mechanisms of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration-mediated tumor progression and resistance to 5-FU-based chemotherapy and PD-1 blockade, we analyzed the DEGs between the high- and low-risk groups using the nomogram of peripheral mSEPT9 and intratumoral IL-10+ Tregs. This nomogram was further validated by double IHC and nomogram evaluation. A total of 1705 DEGs were identified between the high- and low-risk groups (1,137 upregulated and 568 downregulated DEGs, Supplementary Table S1-2). The correlation between gene expression levels among samples was close to 1, indicating that the experimental detection was reliable and sample selection was reasonable and suitable for principal component analysis (PCA) to reduce data complexity (Supplementary Fig. S2A–C). Hierarchical clustering in the heat map distinguished the two groups based on their gene expression profiles, with reasonable gene expression distribution obtained (Fig. 6A, Supplementary Fig. S2D). Metabolic, immune, and cytoskeletal motor activity-related genes were the main DEGs. The genes upregulated in the high-risk group were cAMP responsive element binding protein 3 like 1 (CREB3L1), neurotrophic receptor tyrosine kinase 2 (NTRK2), aldehyde dehydrogenase 1 family member L1 (ALDH1L1), spondin 1 (SPON1), collagen type XVII alpha 1 chain (COL17A1), integrin binding sialoprotein (IBSP), matrix metallopeptidase 28 (MMP28), mucin 1 (MUC1), carbohydrate sulfotransferase 6 (CHST6), and extracellular matrix protein 1 (ECM1). The genes downregulated in the high-risk group were notum, palmitoleoyl-protein carboxylesterase (NOTUM), Wnt inhibitory factor 1 (WIF1), IDO1, adenosylhomocysteinase (AHCY), phospholipase A2 group IID (PLA2G2D), defensin alpha 5 (DEFA5), E2F transcription factor 5 (E2F5), PDCD1, methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), and SEPT9. Decreased septin9 expression indicated that hypermethylated SEPT9 inhibited its transcription. Overall, the DEGs were highly related to the one-carbon metabolism of methionine and the folate cycle, extracellular matrix (ECM) remodeling-related epithelial-mesenchymal transition (EMT), which regulates gene methylation, 5-FU-based chemotherapy responsiveness, and immunosuppressive-related gene expression that regulates responsiveness to PD-1 blockade [30] (Fig. 6B, C).

Fig. 6
figure 6

Enrichment analysis of genes between high-risk and low-risk group based on peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration, and underlying mechanisms analysis in tumor progression. A Heat map showing differences in gene expression profiles between the high-risk and low-risk groups. B Scatter plot of the expression correlation. (Pearson r = 0.936, P < 2.2e-16). C Volcano plot showing the upregulated and downregulated genes in the high-risk group versus the low-risk group. D GO enrichment analysis of DEGs in BP (a), CC (b), and MF (c), and GO enrichment scatterplot (d) between the high-risk and low-risk groups. (BP: biological process, CC: cellular components, MF: molecular functions). E Statistical map of DEGs between the high-risk group and low-risk group based on KEGG pathway classification. F Schematic representation of the underlying correlation and mechanisms. (F Created with BioRender.com)

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We analyzed the functions of the DEGs in the mRNA expression profiles of the high- and low-risk groups. In terms of biological process (BP), the enriched DEGs were involved in the one-carbon metabolic process, cytokine production in the inflammatory response, T cell apoptotic process, and epithelial to mesenchymal transition. In terms of cellular components (CC), the enriched DEGs were involved in the filopodium membrane, collagen trimer, integrin complex, and basement membrane. In terms of molecular functions (MF), the enriched DEGs were involved in laminin binding, Wnt protein binding, extracellular matrix binding, and neutral amino acid transmembrane transporter activity (Fig. 6D, Supplementary Fig. S2E, Supplementary Table S3).

Biological pathway analysis was used to determine the biological functions based on KEGG pathways (Supplementary Fig. S2F, Supplementary Table S4). The significantly different pathways included one-carbon metabolism of methionine and the folate cycle, which play important roles in regulating gene methylation and cytokine levels. This pathway might be the main upstream regulatory mechanisms of the hypermethylation of SEPT9 and increasing IL-10. Septin9 is a diaphragm protein of the cytoskeleton. Based on our results, cell adhesion, ECM-receptor interaction, and EMT were significantly different mechanisms enriched between the high peripheral mSEPT9 and low peripheral mSEPT9 groups. Therefore, hypermethylated SEPT9 promotes tumor progression by inducing abnormal cell polarity and ECM remodeling-related EMT, which is also highly related to immune escape. The main enriched signaling pathways included the TGF-beta, PI3K-Akt, Wnt, JAK-STAT, and T cell receptor signaling pathways. These pathways may contribute to the increases in IL-10. Tumor avidly consumed and outcompeted Tregs for one-carbon metabolisms. As a result, the immunosuppressive differentiation of IL-10+ Tregs and infiltration into TME were induced, and the tumor progression and resistance to 5-FU-based chemotherapy or PD-1 blockade were promoted (Fig. 6E, F).

Discussion

Due to diverse pathogenic mechanisms and the molecular heterogeneity of CRC, patients with same clinical stage might have different outcomes. Accordingly, establishing a combination risk model is feasible for increasing the sensitivity of predicting prognosis or response to therapy. Gene methylation is a mechanism of CIMP [31], which plays an important role in tumorigenesis and progression, and is highly related to 5-FU and immunotherapy resistance [32, 33]. Histological lymphocytic reactions are independent prognostic biomarkers of CRC and chemotherapeutic drugs rely on the induction of anticancer immune responses for therapeutic activity [34]. The overall findings highlight the correlation between abnormal gene methylation and immune-related TME in tumor progression, 5-FU-based chemotherapy, and immunotherapy, and the application prospects of combination risk models for precision medicine.

In this study, mRNA-seq was performed to screen out the potential biomarker of immunosuppressive TME in two groups with different levels of peripheral mSEPT9, and determine whether histological lymphocytic reaction is correlated with peripheral mSEPT9. The CIBERSORT result of the immune-related profile revealed no significant differences in intratumoral immunocyte infiltration, suggesting the existence of different subtypes of infiltrated immunocytes with diverse differentiation and functions. Tregs and macrophages are the main immunocytes with different subtypes and functions; however, no differences in macrophages M0, M1, and M2 were found based on the CIBERSORT calculation. Therefore, we focused on the immunosuppressive subtypes of Tregs. Previously, Tregs was found to be associated with a suppressive cytokine profile, which was characterized by high IL-10 and TGF-β, and low IFN-γ production, in gastric cancer [35]. IL-10 and TGF-β [27] are the main inhibitory cytokines secreted by immunosuppressive Tregs. The expression of IL-10 in normal tissue is relatively low. Based on our results, the expression of IL-10 in tumor is highly related to the TNM stage, which suggests that IL-10-related immune infiltration is an important characteristic and immunosuppressive biomarker of TME [23]. Therefore, we speculated that IL-10+ Tregs might play an important role in the progression of gastrointestinal tumors. Intratumoral IL-10+ Tregs infiltration may also be highly related to peripheral mSEPT9 in CRC. To validate the potential use of a combination risk model for prognostic evaluation and therapeutic guidance, we analyzed the correlation between the Ct value of peripheral mSEPT9 detected via RT-PCR and intratumoral IL-10+ Tregs infiltration detected via immunofluorescence staining.

In the study and validation sets, patients with CRC and high levels of peripheral mSEPT9 or high intratumoral IL-10+ Tregs infiltration exhibited inferior OS and PFS. Based on multivariate Cox regression analysis, peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration are independent prognostic factors for CRC. We compared the accuracy of combined detection with that of separate detection. Further, the ROC of the combination risk model based on peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration was larger than that of the individual factors, suggesting higher sensitivity of the combination risk model for prognostic evaluation than individual one. A nomogram was used to establish a combination risk model based on peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration. Patients with CRC and high risk scores had worse OS and PFS, and exhibited poor prognosis than those with low risk scores. After verifying the value of the combination risk model in the prognostic evaluation, we analyzed its value in predicting responsiveness to 5-FU-based chemotherapy, which is an important component of systemic chemotherapy for patients with CRC who are administered adjuvant regimens. However, only few patients benefit from adjuvant 5-FU-based chemotherapy, because the criteria for treatment are unclear. An effective biomarker for predicting therapeutic response to 5-FU-based chemotherapy is thus crucial for improving clinical outcomes [36, 37]. In our study, patients with high risk scores did not significantly benefit from 5-FU-based chemotherapy and exhibited inferior responsiveness compared to those with low risk scores. Moreover, the treatment of high-risk patients with or without 5-FU-based chemotherapy had no impact on OS and PFS. Patients with low risk scores significantly benefited from 5-FU-based chemotherapy and had a better OS than patients with low risk scores who did not receive 5-FU-based chemotherapy. High-risk patients also had a shorter PFS than low-risk patients with stages I-III disease after radical resection. This result indicates the value of predicting the probability of recurrence or metastasis after radical resection in patients with CRC. These results highlight the importance of combining gene methylation and Tregs-related immunosuppressive TME as a potential prognostic and therapeutic responsiveness-predictive model for CRC. To explain the discrepant clinical outcomes of mRNA-seq, we explored the potential mechanisms that regulate gene methylation and immunosuppressive TME in the promotion of tumorigenesis and progression. DEGs, such as AHCY, ALDH1L1, and MTHFD2, are crucial genes involved in one-carbon metabolism of methionine and folate that regulate methylation the response to 5-FU-based chemotherapy, and tumor progression [38,39,40,41,42]. GO and KEGG analyses also revealed enrichment of DEGs in the one-carbon metabolism of methionine and folate cycle, ECM-remodeling-related EMT, PI3K-AKT, and Wnt signaling pathways, which might be the mechanisms by which abnormal gene methylation induces tumor progression and chemoresistance [43,44,45].

Owing to the crucial role of immunosuppressive Tregs in regulating immune evasion and clinical efficacy of PD-1 blockade, tumor progression is facilitated by the immunosuppressive TME [46, 47], By examining the correlation between the combination risk model and responsiveness to PD-1 blockade, we found that patients with CRC and low risk scores responded better to PD-1 blockade than those with high risk scores. As a result, we proceeded to explore the underlying mechanisms. Tumor PD-L1 expression is induced by effective immune microenvironmental deployment [48, 49]. Based on mRNA-seq, the expression of immune-related genes, such as PDCD1, CTLA4, and IDO1, was negatively correlated with high levels of IL-10, peripheral mSEPT9, and intratumoral IL-10+Tregs infiltration. The KEGG pathways were the TGF-β signaling pathway, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation, TNF signaling pathway, JAK-STAT signaling pathway, T cell receptor signaling pathway, and natural killer cell mediated cytotoxicity. As these pathways are markedly related to the negative regulation of the immune system process, they might contributed to the IL-10-related immunosuppressive TME, causing poor anti-PD-1 efficacy in patients with high risk scores.

Overcoming metabolic plasticity is one of the main objectives of contemporary cancer therapies [50], and dietary intervention might be effective during the treatment of malignant tumors [51]. A recent study verified that the Solute Carrier Family, Amino Acid System L Transporter, and methionine transporters are overexpressed in tumors, and the high methionine cycle activity mediated by SLC family overexpression induces abnormal epigenetic modifications, especially gene methylation. Thus, this family plays an important role in regulating cytokines. The expression of the SLC family is higher in tumor cells than T cells, competing with T cells for methionine intake. Such competition leads to the dysfunction or abnormal differentiation of intratumoral T cells and contributes to the immunosuppressive TME [52, 53]. The abnormal amino acid intake by tumors is an underlying vulnerability, which suggests that amino acid depletion might be useful for improving the therapeutic efficacy of immunotherapy [54]. These findings provide a rationale for the application of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration models in predicting unfavorable survival outcomes, inferior response to 5-FU-based chemotherapy, and a high probability of recurrence or metastasis in patients with CRC.

In summary, to our knowledge, this study is the first to identify the combination of peripheral mSEPT9 and intratumoral IL-10+ Tregs infiltration as a sensitive predictive model for CRC. Thus, this model is significant for the evaluation of prognosis, responsiveness to 5-FU-based chemotherapy and PD-1 blockade, and probability of recurrence or metastasis. Of note, only the superficial relationship of the one-carbon metabolism of methionine and the folate cycle with IL-10 and peripheral mSEPT9 in tumors and IL-10+ Tregs infiltration in the TME was analyzed in this study. Furthermore, a relatively small size was employed for sequencing and appropriate in vitro and in vivo experiments were lacking to validate the underlying molecular mechanisms. The function and specific mechanisms of methionine metabolism reprogramming, which is mediated by the overexpression of SCL family members, in inducing the expression of immunosuppressive cytokines, such as IL-10, and abnormal gene methylation of SEPT9 in tumors will be analyzed in our subsequent study. We will assess the formation of IL-10-related TME and explore more sensitive biomarker models for predicting prognosis and response to 5-FU-based chemotherapy and immunotherapy in a larger cohort. We anticipate exploring new strategies to improve the therapeutic efficacy of treatments for CRC.

Data availability

Raw sequence data were uploaded to the NCBI Sequence Read Archive (SRA; submission number, SUB14206932). The data will be released upon publication.

Abbreviations

TME:

Tumor microenvironment

mSEPT9:

Methylated septin9

CRC:

Colorectal cancer

5-FU:

5-fluorouracil

CIN:

Chromosome instability

MSI:

Microsatellite instability

CIMP:

CpG island methylator phenotype

Tregs:

Regulatory T cells

FOXP3:

Forkhead box P3

mRNA-seq:

Transcriptome sequencing

OS:

Overall survival

PFS:

Progression-free survival

GO:

Gene ontology

KEGG:

Kyoto encyclopedia of genes and genomes

HFP:

High power field

ORR:

Objective response rate

CECT:

Contrast-enhanced computed tomography

PDCD1:

Programmed cell death 1

CTLA4:

Cytotoxic T-lymphocyte associated protein 4

IDO1:

Indoleamine 2,3-dioxygenase 1

DEGs:

Differentially expressed genes

PCA:

Principal component analysis

CREB3L1:

cAMP responsive element binding protein 3 like 1

NTRK2:

Neurotrophic receptor tyrosine kinase 2

ALDH1L1:

Aldehyde dehydrogenase 1 family member L1

SPON1:

Spondin 1

COL17A1:

Collagen type XVII alpha 1 chain

IBSP:

Integrin binding sialoprotein

MMP28:

Matrix metallopeptidase 28

MUC1:

Mucin 1

CHST6:

Carbohydrate sulfotransferase 6

ECM1:

Extracellular matrix protein 1

WIF1:

WNT inhibitory factor 1

AHCY:

Adenosylhomocysteinase

PLA2G2D:

Phospholipase A2 group IID

DEFA5:

Defensin alpha 5

E2F5:

E2F Transcription factor 5

MTHFD2:

Methylenetetrahydrofolate dehydrogenase 2

EMT:

Epithelial-mesenchymal transition 

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Acknowledgements

We acknowledge Editage service for the manuscript language edit.

Funding

This work was supported by grants from the National Science Foundation of China (#82173283) and the Foundation of the committee on science and technology of Tianjin (#21JCZDJC00230, 21JCQNJC01330, 21JCZDJC00990, 21JCYBJC01090). The funding bodies had no roles in the design of the study, data collection, analysis, interpretation, or decision to write and publish the work.

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Authors and Affiliations

  1. Nankai University School of Medicine, Nankai University, Tianjin, 300071, People’s Republic of China

    Jie Sun & Xiaohui Yang

  2. Department of Pathology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China

    Songli Shi, Zhengduo Yang & Shiwu Zhang

  3. Department of Radiology, Tianjin Union Medical Center, Tianjin, 300121, People’s Republic of China

    Chao Sun

  4. School of Integrative Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People’s Republic of China

    Jiangping Wang

  5. Department of General Surgery, Tianjin Union Medical Center, Nankai University, Tianjin, China

    Jing Xu

  6. Tianjin University of Traditional Chinese Medicine, Tianjin, 300121, People’s Republic of China

    Jiangping Wang

  7. Department of General Surgery, Tianjin University Medical Center, Tianjin, China

    Jing Xu

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Contributions

J. Sun: Data curation, formal analysis, investigation, writing–original draft, writing–review and editing. S. Shi and C. Sun: Software, data curation, methodology, writing–review and editing. J. Wang and X. Yang: Software, formal analysis, visualization. Z. Yang and J. Xu: Supervision, investigation, methodology. S. Zhang: Conceptualization, design, analysis, investigation, resources, supervision, project administration, writing–review and editing.

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Correspondence to Shiwu Zhang.

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Sun, J., Shi, S., Sun, C. et al. Predictive nomogram of the clinical outcomes of colorectal cancer based on methylated SEPT9 and intratumoral IL-10+ Tregs infiltration. J Transl Med 22, 861 (2024). https://doi.org/10.1186/s12967-024-05635-4

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  • DOI: https://doi.org/10.1186/s12967-024-05635-4

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Journal of Translational Medicine

ISSN: 1479-5876

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