A novel methylation site of SFRP1 Gene Promoter in colorectal cancer: Potential utility as biomarker for prognosis and immunotherapy

doi:10.21203/rs.3.rs-3758253/v1

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A novel methylation site of SFRP1 Gene Promoter in colorectal cancer: Potential utility as biomarker for prognosis and immunotherapy

https://doi.org/10.21203/rs.3.rs-3758253/v1

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The methylation level of the SFRP1 gene promoter is closely related to colorectal cancer. 205 stool samples were enrolled and were divided into positive and negative groups in the study. MassArray was used to screen the level of methylation on the SFRP1 gene promoter in the two groups. A random forest model was established to analyze and screen methylation sites of characteristic expression. A further 60 fecal samples were collected and divided into positive and negative groups to verify the specificity and sensitivity of the characteristic methylation sites by RT-PCR. TCGA database was used to analyze the relationship between tumor immunity and methylation sites in CRC patients. 12 differentially expressed MassArray detected methylation sites on SFRP1 promoter in colorectal cancer or adenoma stool specimens compared to normal. Random forest regression models built using the R package randomForest analysis (OOB error rate is 39.29%) located in the OOB error fastest changing area and have a good performance (accuracy: 0.757, sensitivity: 0.857, specificity: 0.696). The cg15839448, cg17816908 and cg14548509 showed better on specificity and sensitivity. Further, RT-PCR analysis showed that SFRP1_CpG_16.17.18 (cg17816908) performed best between the three CpG sites. The AUC of the ROC curve is 0.791, which was higher than the other two CpG sites. And cg17816908 is negatively correlated with prognosis and immune checkpoints with immunotherapy sensitivity in CRC patients. Our study demonstrated that SFRP1_CpG_16.17.18 (cg17816908) has a good clinical performance as a prognosis marker of colorectal cancer, and may provide a reference to immunotherapy in the clinic.

Biological sciences/Cancer

Health sciences/Biomarkers

SFRP1

colorectal cancer

methylation

MassARRAY

tumor immunity

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer-related deaths in the world with an estimated number of 1.8 million new cases and about 900,000 deaths worldwide¹. Colorectal cancer has served as a genetic and biological paradigm for the evolution of solid tumors, and these insights have illuminated early detection, risk stratification, prevention, and treatment principles².

Secreted frizzled-related protein 1 (SFRP1) is a member of the secretory glycoprotein SFRP family. SFRP1 has been classified as a tumor suppressor gene due to the loss of its expression in many human cancers, including gastric cancer, skin tumor and so on^3–5. Various mechanisms have been implicated in the loss of SFRP1 including epigenetic and genetic regulation^6,7. Endogenous SFRP1 expression increases in a dose-dependent manner after demethylating treatment, signifying DNA methylation as the main mechanism that is responsible for the silencing of SFRP1⁸.DNA methylation is one among the major grounds of cancer progression which is characterized by the addition of a methyl group to the promoter region of the gene thereby causing gene silencing or increasing the probability of mutations⁹. Therefore, DNA methylation markers are widely used in the diagnosis and prognosis of common cancers. Currently, an increasing number of studies have revealed that the specific methylated DNA could be used as a predictive marker in CRC, like SEPT9 and SDC2^10,11. Studies have shown that the methylation of the SFRP1 gene promoter is closely related to CRC^12,13. However, whether the methylation site on the SFRP1 promoter can be used as a biomarker for CRC prediction has not been studied.

Patients and samples

205 stool samples from inpatients admitted to the digestive department of Xijing Hospital from 2018 to 2019 were collected. These samples could be classified into 5 types including 20 adenoma samples, 50 CRC samples, 27 polyps’ samples, 34 high-risk samples, 60 normal samples, and 14 other gastrointestinal tumor samples. All samples were classified based on histological diagnoses by expert pathologists. 20 adenoma samples and 50 CRC samples were combined as the positive group. 27 polyps, 34 high-risk, and 60 normal samples were classified as the negative group (Table 1). The other 14 gastrointestinal tumor samples were excluded. The above two group samples were used in the classifier modeling and testing. Samples for RT-PCR analysis were collected from May to November 2021 including stool samples from 30 positive and 30 negative patients. All patients did not receive radiotherapy and chemotherapy. The study was approved by the local ethics committee and government authorities (Approval number: KY20223167-TEMP). The informed consent of all patients was obtained before collecting samples.

Table 1

Clinical characteristics of samples
Clinical feature	negative	positive	Other gastrointestinal tumors
Age range (N %)
< 45 years-old	27 (22.31)	20 (28.57)	3 (21.43)
45 ~ 65 years-old	79 (65.29)	37 (52.86)	9 (64.29)
≥ 65 years-old	15 (12.40)	13 (18.57)	2 (14.29)
Gender (N %)
Male	69 (57.02)	51 (72.86)	8 (57.14)
Female	52 (42.98)	19 (27.14)	6 (42.86)
Degree of danger (N %)
normal	60 (49.59)
high-risk	34 (28.68)
polyp	27 (22.13)
adenoma		20 (28.57)
CRC		50 (71.43)
Summary, N (%)	121 (59.31)	70 (34.31)	14 (6.86)

DNA Extraction and Bisulfite Conversion

Collected stool samples were weighted (200 mg) and centrifuged at 10,000 g for 10 min (Thermo Heraeus Multifuge X3R centrifuge, Thermo Scientific, USA) and 1 ml of the supernatant was transferred into a new centrifuge tube. Subsequently, each sample was mixed with 1 ml of adsorbent (TIANGEN, China) and further centrifuged at 13,500 rpm for 3 min (Thermo Sorvall Micro 21R centrifuge, Thermo Scientific, USA). For each sample, 800 µl of the supernatant was transferred into a new tube. Next, 200 µl of Lava-new buffer (TIANGEN, China) was added to each sample and incubated together at 70°C for 10 min (Thermomixer, Thermo Scientific, USA). 500 µl of chloroform (Sinopharm Chemical Reagent, China) were mixed with each sample with a thorough vortex and centrifuged at 13,500 rpm for 3 min (Thermo Sorvall Micro 21R centrifuge, Thermo Scientific, USA). Subsequently, 900 µl of the supernatant was transferred into a new tube and added with an equal volume of anhydrous ethanol (Sinopharm Chemical Reagent, China). After a thorough vortex, the solution and precipitation of each sample were added to a Spin Columns CB3 (TIANGEN, China) and centrifuged at 13,500 rpm for 30 s (Thermo Sorvall Micro 21R centrifuge, Thermo Scientific, USA) to discard the solution. Next, 500 µl of GD buffer (TIANGEN, China followed by 600 µl of PW buffer (TIANGEN, China) were subsequently added into the Spin Columns CB3 (TIANGEN, China) to remove impurities, further centrifuged at 13,500 rpm for 30 s (Thermo Sorvall Micro 21R centrifuge, Thermo Scientific, USA) and thoroughly dried. The stool DNA was finally eluted in 100 µl of TE buffer (TIANGEN, China) and stored at -20°C until further use. Bisulfite conversion of stool DNA was performed with the EZ DNA Methylation Direct Kit (Zymo Research, Irvine, CA, USA), using 1000 ng of DNA for each reaction.

Methylation analysis with MassArray

To assess methylation levels by MassArray, we first designed six primer pairs targeting shorter sequences overlapping on the SFRP1 promoter (Table 2). After PCR amplification (Table S1 and S2), experiments were carried out following the instruction manual (Epityper User Guide https://agenacx.com/wp-content/uploads/ 2017/10/ Mass ARRAY- EpiTYPER-User-Guide. pdf, Agena Bioscience), products were treated with shrimp alkaline phosphatase enzyme (Agena Bioscience) to dephosphorylate unincorporated deoxynucleotide triphosphate. (Table S3 and S4) Then, take 5 µL of the configured MassCLEAVE reaction system (Table S5) and directly add it into the mixed solution after the SAP reaction. The in vitro transcription and T-cleavage reaction were carried out as described in the instruction manual. Mass spectra were acquired through a MassARRAY mass spectrometer (Agena Bioscience) and analyzed using the EpiTYPER® MassARRAY® software, which provided a quantification of methylated/unmethylated CpGs, with values ranging from 0 to 1.

Table 2

Sequence of primers were used to assess methylation levels by MassArray
Amplicon	Left Primer	Right Primer
SFRP1_1	TTTTTTGAATTTTTGTTAGTTGGGA	CCTACCTTAACCTCCCAAACTTTTT
SFRP1_2	ATGGTGTTGTTTAATTTGTTGGAGT	AAAAAAAACAACCTTACCTTAAAACTT
SFRP1_3	TATATAAATAAAAGGGGGAGGAGGA	ACCAAAACAAAAACATCACCTAAAA
SFRP1_4	GGAGGTTGTAGGGTTGGAGTG	TTCTTTTTCTCCCCTTATCTCTTTC
SFRP1_5	AGTTGTTGGTTTGTTGTTTTATTTT	CACTCCAACCCTACAACCTCC
SFRP1_6	GTTTTGGGTTTTTGTTTTTTTT	CCAAAATAAAACTACCTCCTCCC

Methylation analysis with fluorescence PCR

Three important methylated CpG sites (cg17816908, cg14548509 and cg15839448) were screened by the informatic model. In order to validate their clinical performance in colorectal adenomas, two pairs of amplification primers, combined with three probes, were designed to detect and assess the methylation levels on specific CpG sites (Table 3). Locked nucleic acid was incorporated in the probes in order to strengthen the detection specificity. The fluorescence RT-PCR reactions are performed using KAPA PROBE FORCE qPCR Kits (KAPA Biosystems, USA) in LightCycler® 480 Instrument II (Roche, Switzerland), following its instructions.

Table 3

Sequence of RT-PCR probes and primers
Amplicon Name	SFRP1_CpG_16.17.18	SFRP1_CpG_12	SFRP1_CpG_13	ACTIN
Probe Sequence	ttt/LNA_Cg/LNA_Cgt/LNA_Cggtgacgga	c/LNA_Cgcact/LNA_Tg/LNA_Tta/LNA_Ccac	c/LNA_Tgcact/LNA_Cg/LNA_Tta/LNA_Ccac	TAATACCTACACCCACAACAC
Sense Primer	GGTGTTGAGTCGCGTTTG	GGTGTTGAGTCGCGTTTG	GGTGTTGAGTCGCGTTTG	GGTGTTGAGTCGCGTTTG
Anti-sense Primer	GAACCGCACTCGTTACCA	CGCAACCAATCAACTCCC	TTTGTTTTTTTGATTAGGTGTTTAAGA	CACCAACCTCATAACCTTATC

Bioinformatic Analyses

TCGA database (https://portal.gdc.cancer.gov) is a comprehensive resource for the systematical analysis of tumors. Colorectal cancer gene expression and methylation data were downloaded from the TCGA database. Relative survival data was downloaded from UCSC Xena database (http://xena.ucsc.edu/). Survival analysis of methylated sites was performed through survival package, including OS, PFS, and DFS. IOBR package was used to analyze ESTIMATE immunity score and CIBERSORT algorithm. Meanwhile, IPSs of TCGA CRC patients were downloaded from The Cancer Immunome Atlas (https://tcia.at/home).

Statistical Analysis

All statistical analyses were carried out by SPSS V.23 and R 4.0.5. A rank sum test was used to compare the difference in methylation level (β value) between positive samples and negative samples. ROC curve analysis was implemented using the R package pROC to determine AUC. P < 0.05 was considered as statistically significant.

SFRP1 promoter methylation in the stool of patients with colorectal cancer and adenoma

We detected the methylation of the SFRP1 promoter in stool specimens from 70 positive patients and 121 negative patients. As shown in Table 1, 45–60 years old is still the high incidence age of colorectal cancer. And the prevalence rate of men is almost three times that of women. The methylation pattern of each CpG site was determined separately by MassARRAY. There were 12 CpG sites on SFRP1 promoter region-04 sequence methylation levels in the clinical group were significantly higher than that in the negative group. The heatmaps for representing the methylation percentage are shown in Fig. 1. All analyzed CpG sites showed significantly increased methylation in stool specimens from colorectal cancer and adenoma patients compared to healthy normal samples (P < 0.01). In addition to our results, these CpG site were analyzed using Illumina HumanMethylation450 DNA methylation array data from The Cancer Genome Atlas (TCGA) database. Methylation levels (β-values) of 12 CpG sites located in the promoter sequences of SFRP1 genes were determined (Table 4). In the promoter region of SFRP1, 11 of the 12 studied CpG sites showed significantly higher methylation in the clinical group in comparison to the negative group excepting SFRP1_CpG_16.17.18 (Average Δβ = 0.15, P < 0.05).

Table 4

CpG sites were analyzed using Illumina HumanMethylation450 DNA methylation array data from The Cancer Genome Atlas (TCGA) database
Gene symbol	CG ID	NAT vs. CRC
Gene symbol	CG ID	p-value
SFRP1_CpG_1.2	cg01495122	P < 0.01
SFRP1_CpG_3.4	cg02388150	P < 0.01
SFRP1_CpG_5	cg04255616	P < 0.01
SFRP1_CpG_6.7.8	cg10406295	P < 0.01
SFRP1_CpG_9.10.11	cg13398291	P < 0.01
SFRP1_CpG_12	cg14548509	P < 0.01
SFRP1_CpG_13	cg15839448	P < 0.01
SFRP1_CpG_14.15	cg16662821	P < 0.01
SFRP1_CpG_16.17.18	cg17816908	0.15
SFRP1_CpG_19.20.21	cg21517947	P < 0.01
SFRP1_CpG_22.23	cg22418909	P < 0.01
SFRP1_CpG_24.25	cg24319902	P < 0.01

^* P < 0.05, ^**P < 0.01 and ^***P < 0.001. P < 0.05 is considered to be statistically significant. NAT, negative group; CRC, colorectal cancer.

Developing a prediction model based on the methylation biomarkers for distinguishing malignant from benign colorectal cancer and adenoma

We collected 205 intestinal cancer samples which were classified into 70 positive group and 121 negative group. The above two group samples were used in the classifier modeling and testing. The other 14 gastrointestinal tumor samples and 2 samples with the number of missing values were excluded. Before modeling, we ensured the 189 data was suitable for analysis through QC and preprocessing. The 189 samples were filled by the mean of this site in all 189 samples multiplied by a random number between 0 and 1. Then, we divided the 189 samples into a training dataset (152 samples) and a testing set (37 samples). The testing set has 37 samples (including positive group: 4 positive I + 10 positive II; negative group: 5 polyps + 7 high-risk + 11 negative) selected randomly from the 189 samples by stratified sampling. 56 positive group samples and 96 negative group samples remained as the training dataset for the random forest classifier modeling process. After modeling, we evaluated the classification model on an independent validation dataset including 20 samples filtered from 25 positive samples through the QC and preprocessing described in the methods. The analysis process included Data QC and preprocessing, classifier modeling and testing,validation.

Analysis Framework of the prediction model and Characteristic CpG sites in validation model

As shown in Fig. 2, there were 1000 iterations in the training process of our analysis. In each iteration, we built a novel training set, which consists of 56 positive group samples with 56 negative group samples randomly selected from the above 96 negative group samples, for learning a random forest classification model. Each run generated an optimal RF model by the R package 'randomForest' whose minimization criterion is out-of-bag (OOB) error based on out-of-bag samples from bootstrapping. After the iteration modeling process, all 1000 models were tested by the testing set which had 37 samples. Which had the best performance on both accuracy and robust. At last, we evaluated it on the independent validation dataset, which has 20 intestinal adenoma samples.

The final selected RF model (OOB error rate is 39.29%) is in the OOB error fastest changing area as shown in Fig. 3A. And the RF model has a good performance (accuracy: 0.757, sensitivity: 0.857, specificity: 0.696) on the testing set with 37 samples. The AUC value obtained by the RF model was 0.742 (Fig. 3B). On the validation dataset, The AUC value of the RF model was 0.791 (p < 0.01) similar to that on the testing set (Fig. 3C). This result could support the robust and generalization of this model. As shown in Fig. 4A, cg17816908, cg14548509 and cg15839448 are the most important sites. cg17816908 and cg14548509 are the TOP 2 features on both mean decrease accuracy and mean decrease Gini as criteria. cg15839448 is the fourth feature ranked by mean decrease accuracy and the third feature ranked by mean decrease Gini, which shows better performance than other sites such as cg10406295 and cg02388150. The three methylation sites were significantly compared between the negative and the positive group (Fig. 4B). There are almost no missing values on the above 3 CpG sites in all samples of our research.

The clinical performance of three important methylated CpG sites in colorectal cancer were validated by RT-PCR

Three important methylated CpG sites (cg17816908, cg14548509 and cg15839448) were screened by the informatic model. A new set of stool samples, containing 30 pathologically confirmed colorectal cancer patients and 30 normal volunteers were tested. There is no difference in gender and age between the two groups (Table 5). It showed that cg17816908 site is the best performer, while the other two sites showed companion methylation to cg17816908 (Fig. 5). Compared with the sensitivity (42,4% in advanced precancerous lesions) from well-known Cologuard, which contains FOBT, KRAS mutations, and methylated CpG site of NDRG4 and BMP3, the current discovery of SFRP1_CpG_16.17.18 (cg17816908) is still a clinical useful target for future development.

Table 5

Clinical characteristics of samples for fluorescent PCR
Clinical feature	negative	positive
Age range (N %)
< 45 years-old	3 (10.00)	4 (13.33)
45 ~ 65 years-old	26 (86.67)	22 (73.33)
≥ 65 years-old	1 (3.33)	4 (13.33)
Gender (N %)
Male	15 (50.00)	21 (70.00)
Female	15 (50.00)	9 (30.00)
Degree of danger (N %)
high-risk	N/A	N/A
polyp	N/A	N/A
adenoma	N/A	N/A
CRC	N/A	30 (100.00)
Summary, N (%)	30 (50.00)	30 (50.00)
N/A, not applicable

SFRP1_CpG_16.17.18 mediated tumor immunity and prognosis in CRC patients

Patients from the TCGA database are classified into low and high methylation groups according to the median cut-off value of cg15839448 methylation. The OS was not observe difference between low and high methylation groups. While the PFS (progression-free survival) and DFS (disease-free survival) were significantly longer for patients who had low methylation than high methylation groups (Fig. 6A-C). Then, we scored the immune scores of the two groups, it showed that Cg15839448 with the low methylation group had a higher Immune Score, Stromal Score, and ESTIMATE Score (Fig. 6D). Further, we evaluated the infiltration status of 22 types of immune cells in CRC patients between cg15839448 high and cg15839448 low groups via ESTIMATE algorithm. Relative fraction of the 22 types of immune cells in CRC patients were shown in Fig. 6E. Among them, the infiltration level of six immune cell types showed a remarkable difference, which includes B cells naïve, plasma cells, CD8 cells, Tregs, Macrophage M1 and Macrophage M2. It revealed a correlation between varied types of immune cells, suggesting the independent role of these immune cells. Moreover, it was revealed that high IPS score was associated with immunotherapy sensitivity. And of the combination pharmacy of cg15839448 with high methylation-expression group anti-PD1 and CTLA4 is effective (Fig. 7A-D). Then, we tried to elucidate the association between cg15839448 methylation and pivotal immune checkpoints, such as CD274, CTLA4, HAVCR2, PDCD1. As indicated in Fig. 7E-H, cg15839448 methylation was revealed to be negatively correlated with the expression of immune checkpoints CD274 (R = -0.36, P < 0.001), CTLA4 (R = -0.35, P < 0.001), HAVCR2 (R = -0.42, P < 0.001) and PDCD1(R = -0.37, P < 0.001). These results suggested that the hypermethylation of cg15839448 may be related to the suppression of immune microenvironment.

Although the screening and treatment strategies for CRC have been greatly improved in recent years, mortality from colorectal cancer remains high¹⁴. The existing colonoscopy and fecal occult blood test (FOBT) or FIT have certain limitations as a screening method for colorectal cancer^15–17. Therefore, robust diagnostic, prognostic and predictive biomarkers are clearly and urgently needed to detect advanced colon polyps and early-stage CRC, which are most effectively treated with current therapies, and to identify the most effective treatments for specific CRC patients.

DNA methylation was first found in cancer. Aberrant DNA methylation is thought to occur at very early stages of cancer development and specific genes seem to be methylated at different tumor stages⁹. SFRP1 has been classified as a tumor suppressor gene due to the loss of its expression in many human cancers¹⁸. Under physiological conditions, SFRP1 inactivates the canonical and non-canonical WNT/β-catenin pathway by directly binding to WNT proteins or the frizzled receptor¹⁹. Promoter methylation down-regulates the expression of SFRP1 in CRC, which relieves the negative regulation of Wnt/β-catenin, consequently altering the proliferation and differentiation of tumor cells²⁰. Xinyan Liu, Jinming Fu and others provide that SFRP1 promoter methylation may be related to the pathogenesis of CRC by cohort study method, and indicate that SFRP1 may become a valuable biomarker for early diagnosis of CRC²¹. Furthermore, Salehi et al. also supported the theory that aberrant promoter methylation of the SFRP1 gene may be used as a novel epigenetic biomarker in the diagnosis of CRC, especially as a noninvasive screening method for the detection of CRC²². In this experiment, we detected all methylation sites on the SFRP1 promoter in fecal samples of normal patients and CRC patients. Combined with the construction of the prediction model, three methylation sites with good accuracy and specificity were found.

Fecal samples are easier to obtain, non-invasive to patients and have better compliance compared with tissue samples. Most importantly, colorectal tumor cells continue to fall off and enter the cavity for CRC patients²³. Logically, the shedding of tumor cells into the feces occurs earlier during the progression of CRC development²⁴. This is why we did not select tissue and blood samples from patients with CRC. In addition, Massarray technology plays an important role in cancer biological diagnosis and individualized treatment, and it has been widely used in the detection of somatic mutation, genotyping, gene methylation and so on²⁵. MassARRAY has high accuracy and sensitivity for detecting DNA methylation, and can detect all sites at one time. This provides high reliability for us to determine the methylation sites of the SFRP1 promoter with differential expression in CRC patients. In our experiment, the methylation level of 12 methylation sites on the SFRP1 promoter in CRC patients was significantly higher than that in normal patients detected by MassARRAY.

Furthermore, a random forest model was constructed to seek out specific methylation sites and the model achieved high accuracy and specificity. The Random Forest regression model is a relatively new computer learning method, which is widely used to evaluate the significance of important predictors²⁶. In many current data sets, it has great advantages over other algorithms and performs well. A large number of studies have used bioinformatics to excavate clinical data²⁷. In our research, we use clinical experimental data combined with bioinformatics analysis to draw conclusions that may be more reliable. To predict early CRC lesions, Random Forest regression model was established based on 12 methylation sites on the SFRP1 promoter, with high accuracy and confirmation by the validation set. Random forest analysis identified the 3 highly discriminating methylation sites among 12 methylation sites. ROC analysis was conducted and showed an AUC of 0.742, implying high specificity and sensitivity.

Locked nucleic acid has higher stability and affinity compared with common fluorescent PCR²⁸. To validate the significance of methylation sites as potential CRC biomarkers, locked nuclear acid was incorporated in the PCR probe. Comparing three characteristic methylation sites in stool samples from 30 CRC patients and 30 normal patients as predictors, SFRP1_CpG_16.17.18 performed best and the AUC of the ROC curve is 0.791 (SFRP1_CpG_12: 0.728, SFRP1_CpG_13: 0.636). Research shows that mSEPT9 is a reliable blood-based marker in CRC detection, particularly advanced CRC¹⁰. In addition, Yoon DAE Han et al verified the capability of stool DNA based-SDC2 methylation for early detection of CRC patients with high specificity¹¹. Even though these two biological factors have been applied in clinics, each has certain disadvantages. Through clinical sample detection and bioinformatics analysis, it is concluded that the methylation site of SFRP1_CpG_16.17.18 on the SFRP1 promoter may be used as a potential biological site for early prevention of CRC. The methylation sites were shown to be associated with PFS and DFS of the patients. And Cg15839448 methylation is negatively correlated with immune checkpoints with immunotherapy sensitivity, indicating which might be a potential marker for immunotherapy.

However, some limitations need to be emphasized in the study. First, the samples included in the study and surveillance cohort were small, especially positive patient samples, so the data needs to be validated in a larger multicenter prospective study. Secondly, the effect of SFRP1 promoter site methylation on SFRP1 expression and function has not been deeply studied, so further experiments need to be carried out in the later stage.

In conclusion, our findings provide experimental evidence that the aberrant promoter methylation site of the SFRP1 gene has the potential to be a valuable biomarker for the early diagnosis of CRC. Meanwhile, we also found one of the methylation sites to be negatively associated with immune checkpoint, which may provide a reference to immunotherapy in the clinic.

Author contributions

SS and JL designed this study. YS, TH and XL performed experiments and acquired data. TH, XL, FW and HZ performed data analysis. YS, SS and JL wrote the manuscript. All authors read and approved the manuscript.

Funding

This study was supported by the Natural Science Foundation of Shaanxi Province, Key Industrial Innovation Project Fund (2023-ZDLSF-44); Airforce AoxiangFoundation and Young Changjiang Scholars of the Ministry of Education (JL); National Natural Science Foundation of China General Program (No. 82370588); National Natural Science Foundation of China Major Research Program Integration Project (No. 92259302); Independent Funds of the Key Laboratory (CBSKL2022ZZ34).

Ethics approval and consent participate

This study was conducted according to the guidelines of the Declaration of Helsinki. The study was approved by the local ethics committee and government authorities (Approval number: KY20223167-TEMP). The informed consent of all patients was obtained before collecting samples. All experiments were performed in accordance with relevant guidelines and regulations.

Animal ethics

Not applicable

Conflicts of Interest

The authors declare no competing interests.

Data Availability

The data used to support the findings are included in the main article. And the data are available from the corresponding author upon request.

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A novel methylation site of SFRP1 Gene Promoter in colorectal cancer: Potential utility as biomarker for prognosis and immunotherapy

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Abstract

Figures

Introduction

Methods

Patients and samples

DNA Extraction and Bisulfite Conversion

Methylation analysis with MassArray

Methylation analysis with fluorescence PCR

Bioinformatic Analyses

Statistical Analysis

Results

SFRP1 promoter methylation in the stool of patients with colorectal cancer and adenoma

Analysis Framework of the prediction model and Characteristic CpG sites in validation model

SFRP1_CpG_16.17.18 mediated tumor immunity and prognosis in CRC patients

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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