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Abstract

Objective: Jingfang granule (JFG) is a modified formulation of Jingfang Baidu Power for the treatment of influenza and infectious diseases. Recent studies show that JFG may inhibit the development of cancer but the effect of JFG on methylation in early pancreatic cancer has remained unclear. In this study, we aim to investigate the potential effect of JFG on gene methylation of early pancreatic cancer. Methods: We treated golden hamsters with N-Nitrosobis(2-oxopropyl)amine (BOP) to induce early pancreatic cancer and detected the gene methylation of pancreatic cancer tissue. The gene ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genome (KEGG) analysis were performed in this study. Results: The GO analysis demonstrated that there was no significantly correlated GO term enrichment from differentially methylated genes. And the KEGG analysis found that the top enriched term was the Th1 and Th2 cell differentiation pathway. Four differentially methylated genes were discovered in the Th1 and Th2 cell differentiation pathway of which RUNX3 was associated with pancreatic cancer development. Conclusion: The results investigate that JFG may improve early pancreatic cancer by activating the immune response by inhibiting RUNX3 methylation. This research provides a theoretical basis for the prevention and improvement of early pancreatic cancer in clinical treatment with traditional Chinese medicine.

Keywords

Jingfang granules (JFG)methylation pancreatic cancer immune

Introduction

Pancreatic cancer is one of the most difficult cancers to diagnose and the most rapidly progressive malignancy.^1,2 Most pancreatic cancers are diagnosed as advanced malignant tumors due to a lack of accurate early diagnosis and the 5 years survival rate of patients is 5%∼10%.³ The poor prognosis of patients with pancreatic cancer is due to the ability of tumor cells to modulate the immune response which leads to suppression of the antitumor activity of the immune cells and inactivation of the immune response thereby establishing an immune escape mechanism that promotes tumor development in many malignancies.^4,5 The immune T cell differentiates into Th cell, CTL and regulatory T cell, of which Th cell in turn differentiates into Th1 cell and Th2 cell.⁶ The Th1 cell mediates inflammation-associated immune responses to enhance phagocytosis and killing of tumors by macrophages and natural killer (NK) cells by promoting IFN-γ, IL-2 secretion. Whereas tumor-mediated immune escape promotes and inhibits the proliferation of Th2 and Th1 cells, respectively, thereby preventing tumors from being recognized by immune cells.⁶ The predominance of Th2 cells over Th1 cells in pancreatic cancer promotes tumor growth, thus affecting the prognosis of patients.^7,8 Tumor cells secret antiinflammatory factors IL-10 and TGF-β, which stimulate differentiate T helper cells into Th2 phenotype and inhibit Th1 differentiate in pancreatic cancer. Abnormal expression of immune cytokines has been found to affect the immune response, leading to a skewing of immune function toward promoting tumor development.⁹ Therefore, focusing on the immune response may help us treat pancreatic cancer better.

Jingfang granule (JFG) is a modified formulation of Jingfang Baidu Power for the treatment of influenza and some infectious diseases.¹⁰ Research studies have shown that JFG may affect the PI3K-AKT pathway, MAPK pathway, and TNF pathway by IL-6, TNF, and VEGFA to treat coronavirus infections.¹¹ In addition, JFG inhibited the inflammatory response in models for mastitis. Inflammation response in the JFG group was significantly improved compared to the model group indicating that JFG may regulate the balance of immune factors to treat inflammation.¹² JFG modulates immune factors to treat and prevent inflammation-related diseases, which indicates the potential application of JFG in cancer. Therefore, the aim of this study was to investigate the effect of JFG on early pancreatic cancer and to analyze the potential application of JFG in the treatment of pancreatic cancer, so as to provide an effective treatment to improve the prognosis of patients with early pancreatic cancer.

Materials and Methods

Reagents

JFG was obtained by Shandong New Time Pharmaceutical Co., LTD. N-Nitrosobis(2-oxopropyl)amine (BOP) was obtained by Shandong Yunmo Medical Technology Co., LTD. (Weifang, China). Hematoxylin-Eosin (H&E) staining kit was supplied by Solarbio. 0.9% saline was purchased from Shandong Qidu Pharmaceutical Co., LTD.

Preparation and Compositional Analysis of JFG Extract

JFG is a patented Traditional Chinese Medicine derived from Jingfang Baidu Power. It is composed of 11 herbs which are Schizonepeta tenuifolia (Benth.) Briq. (Jing-Jie), Saposhnikovia divaricate (Turcz.) Schischk. (Fang-Feng), Ligusticum striatum DC. (Chuan-Xiong), Bupleurum chinense DC. (Chai-Hu), Peucedanum praeruptorum Dunn (Qian-Hu), Notopterygium incisum K.C. Ting ex H.T.Chang (Qiang-Huo), Heracleum hemsleyanum Diels (Du-Huo), Poria cocos (Schw.) Wolf. (Fu-Ling), Citrus aurantium L. (Zhi-Ke), Platycodon grandiflorus (Jacq.) A. DC. (Jie-Geng), and Glycyrrhiza uralensis Fisch. ex DC. (Gan-Cao).^13,14

We extracted the volatile oil from Jing-Jie, Fang-Feng, Chuan-Xiong, Qian-Hu, Qiang-Huo, Du Huo, and Zhi-Ke. The volatile oil was mixed in with Chai-Hu, Fu-Ling, Gan-Cao, and Jie-Geng.¹³ And the mixture of volatile oils was boiled in a vessel containing purified water, twice again.¹³ The mixture was cooled to about 80 °C and filtered then concentrated to a concentration of 1.30. The concentrate was mixed with volatile oil of JFG at a ratio of 500:1 (w:v) to use.¹³

Animals

Golden hamsters (female, 49∼55 day old, 95∼125 g) were purchased from Beijing Vital River Laboratory Animal Technology (certificate number was SCXK Beijing, 2021-0011; Beijing, China). All animal experiments were approved by the Experimental Animal Ethics Committee at State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine (Approved on March 08th, 2022; No. AH-IACUC-2022-027) and performed in accordance with the norms. Golden hamsters were acclimatized at a standard temperature of 20∼26 °C and relative humidity of 40%∼70% with a 12 h/12 h light and dark cycle. Feeding conditions and care of golden hamsters were conducted in compliance with the guidelines of the Experimental Animal Ethics Committee.

Animals Grouping and Model Establishment

After 7 days of acclimatization, the golden hamsters were divided according to body weights into the following groups (n = 10): normal control group (C) treated with sterilized purified water; model group (V) treated with sterilized purified water; JFG low dose group (L) treated with 0.6 g/kg (10 mL/kg) of JFG; JFG middle dose group (M) treated with 1.8 g/kg (10 mL/kg) of JFG; JFG high dose group (H) treated with 5.4 g/kg (10 mL/kg) of JFG. Golden hamsters were treated with JFG via intragastric administration (ig) every day and meanwhile, hamsters were treated with 10 mg/kg (10 mL/kg) of BOP once a week via subcutaneous injection (sc). At the appropriate time, the golden hamsters were anesthetized with zoletil 50 and xylazine hydrochloride mixture (1.5 mg/mL, 10 mL/kg) and then we collected the required samples for further study. All in vivo studies were carried out in accordance with the approved regulations by the Experimental Animal Ethics Committee at the State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine. In this study, no weight loss or treatment-related toxic responses were observed for golden hamsters treated with either test article.

Organ Index Calculation

The pancreas, heart, liver, spleen, lungs, kidneys, and thymus of golden hamsters were taken and weighed, and then the organ index was calculated by the following formula.

Organ index = \frac{organ mass (g)}{body mass (g) \times 100 %} .

Histopathology

Randomly selected portions of pancreatic tissue were fixed with 10% formaldehyde and then dehydrated. Tissues were embedded in paraffin and cut into slices. The slices were treated with xylene and alcohol and then rinsed with distilled water. Slices were stained with the H&E kit and microscopically examined.

Pancreatic DNA Methylation Detection

DNA methylation was detected by Beijing Novogene Co., LTD. DNA was verified by agarose gel. DNA purity was detected using NanoPhotometer^® spectrophotometer (IMPLEN, CA, USA). DNA concentration was measured using Qubit^® DNA Assay Kit in Qubit^® 2.0 Flurometer (Life Technologies, CA, USA).

A total of 1.5 μg genomic DNA was handled by Mspl which was mixed with appropriate lambda DNA, followed by end repair and adenylation. The cytosine methylation barcode was ligated to the DNA according to the manufacturer's instructions. Obtained DNA fragments were then bisulfited with the EZ DNA Methylation-GoldTM Kit (Zymo Research). The library was performed by Novogene Corporation (Beijing, China). Pair-end sequencing of the sample was sequenced on the Illumina platform (Illumina, CA, USA). Library quality was assessed on the Agilent Bioanalyzer 2100 system.

DNA Methylation Analysis

The DSS software was used to analyze differentially methylated regions (DMRs).^15–17 Based on the distribution of DMRs in the genome, the genes related to DMRs were defined if their genomic region (from TSS to TES) or promoter region (2 kb upstream of TSS) overlapped with the DMRs.¹⁸

Gene Ontology and Kyoto Encyclopedia of Genes and Genome Enrichment Analysis

Corrections were made for gene length bias and gene ontology (GO) enrichment analyses with DMR-related genes were performed with the GOseq R software package.¹⁹ GO terms with corrected P-values less than .05 were used for further analyses. DMR-related genes were tested the statistical enrichment in the Kyoto Encyclopedia of Genes and Genome (KEGG) pathway using the KOBAS software.²⁰

Statistical Analysis

Statistical analysis was performed using one-way ANOVA with Graph Prism Version 8.00 (https://www.graphpad.com GraphPad Software, La Jolla, CA) for differences between multiple groups.²¹ The data was shown as mean ± standard deviation (SD) and P-value <.05 was considered a significant difference.

Results

JFG Reduces the Morbidity of Pancreatic Cancer

There was no significant change in the body weight of hamsters after subcutaneous injection of BOP (Figure 1A). Intragastric administration of JFG had no effect on the body weight of hamsters compared with the C group (Figure 1A). The results showed that there were no significant changes in the weights of the pancreas, heart, spleen, lungs, kidneys, and thymus after administration of BOP and JFG. The weight of the liver was significantly increased in the V group and H group compared with the C group at D30 but there was no significant difference at D58 (Figure 1B and C). H&E staining images indicated the pancreatic tissue pathology at D58 (Figure 1D). Only one golden hamster had cancerous pancreatic tissue in the V group and L group, respectively. All other pancreatic tissues were normal and no pancreatic lesions were found in groups C, M, and H. The pancreatic cancer incidences were 0% (C group), 20% (V group), 20% (L group), 0% (M group), and 0% (H group), respectively. Compared to the BOP alone group (V group), there were no significant pancreatic carcinomas in the JFG middle dose group (M group) and the JFG high dose group (H group). Thus, we speculated that continuous administration of JFG may reduce pancreatic cancer morbidity.

Figure 1.

(A) Body weights of animals were measured on the indicated days. Data are shown as the M ± SD. The two-tailed unpaired t-test was performed. The arrow indicates the time of the first dissection. (B) Organ Index was measured on Day 30. Data are shown as the M ± SD. The two-tailed unpaired t-test was performed. ***, P < .001; ****, P < .0001. (C) Organ Index was measured on Day 58. Data are shown as the M ± SD. The two-tailed unpaired t-test was performed. (D) Representative H&E staining images on Day 58 (Scale bar = 500 μm, H&E ×20).

The Effect of BOP on Early Pancreatic Cancer Gene Methylation

To investigate the gene methylation by BOP-induced early pancreatic cancer, we dissected five randomly selected hamsters from each group at D30. The gene methylation levels in the pancreas were compared between the V group and the C group (Figure 2). About 2150 CpG sites, with significant differences in gene methylation, of which 661 CpG sites were hypermethylated representing 207 genes and 1489 CpG sites were hypomethylated representing 508 genes (Figure 2A and B). DMR gene region distribution of 661 hypermethylated CpG sites demonstrated that 56 of those sites were located in promoter regions representing 54 genes and 1489 hypomethylated CpG sites suggested that 136 of those sites were located in promoter regions representing 123 genes (Figure 2B).

Figure 2.

Differentially methylated regions (DMRs) of the V group compared to the C group in pancreatic cancer at D30. (A) Violin plots of DMR methylation level distribution. (B) Column diagram of DMR gene region distribution. (C) GO functional enrichment analysis of V group hypermethylation compared to C group. (D) GO functional enrichment analysis of V group hypomethylation compared to C group. (E) KEGG pathway enrichment analysis of V group hypermethylation compared to C group. (F) KEGG pathway enrichment analysis of V group hypomethylation compared to C group. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genome.

In order to further analyze the function of differentially methylated genes in the V group and C group, we performed GO functional enrichment analysis and KEGG analysis. The results of the GO enrichment analysis for the hypermethylated gene indicated that among the top 30 categories, there were 13 categories related to BP, including cellular protein modification process, protein modification process, macromolecule modification; 3 categories related to CC and 14 categories related to MF (Figure 2C). The top 30 categories from GO enrichment analysis of hypomethylated genes showed that there were 21 categories related to BP, including signal transduction, cell communication, cellular response to stimulus, etc; 1 category related to CC and 8 categories related to MF (Figure 2D).

Taking corrected P-value <.05 as the screening condition for KEGG analysis, the top 20 enrichment pathways are shown in Figure 2. KEGG analysis can provide some evidence of the biological processes and signal transduction pathways in which methylated genes may be involved. Compared with the C group, hypermethylated differential genes were not enriched for significantly relevant pathways, and we speculate that this may be related to the insufficient timing of BOP induction of pancreatic cancer (Figure 2E). Hypomethylated genes of the V group were enriched to a total of 42 pathways. We found that 1 pathway was associated with pancreatic cancer namely insulin secretion (Figure 2F).

After that, we dissected all the remaining hamsters at D58 and compared V group gene methylation levels with the C group in the pancreas (Figure 3). Of 20 310 CpG sites, significant differences in gene methylation, of which 1730 CpG sites were hypermethylated representing 612 genes and 18 580 CpG sites were hypomethylated representing 4404 genes (Figure 3A and B). By DMR gene region distribution analysis, about 1730 hypermethylated CpG sites showed that 56 of those sites were located in promoter regions representing 54 genes and 136 promoter regions among 1489 hypomethylated CpG sites representing 123 genes (Figure 3B).

Figure 3.

Differentially methylated regions (DMRs) of the V group compared to the C group in pancreatic cancer at D58. (A) Violin plots of DMR methylation level distribution. (B) Column diagram of DMR gene region distribution. (C) GO functional enrichment analysis of V group hypermethylation compared to C group. (D) GO functional enrichment analysis of V group hypomethylation compared to C group. (E) KEGG pathway enrichment analysis of V group hypermethylation compared to C group. (F) KEGG pathway enrichment analysis of V group hypomethylation compared to C group. GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genome.

Then we performed GO enrichment analysis and KEGG analysis for differently methylated genes. The results of the GO enrichment analysis for the hypermethylated gene indicated that among the top 30 categories, there were 21 categories related to BP, including cell communication, regulation of small GTPase-mediated signal transduction, signal transduction, intracellular signal transduction, etc and 9 categories related to MF including Rho GTPase binding, GTPase binding, enzyme binding, guanyl-nucleotide, exchange factor activity, etc (Figure 3C). The top 30 categories from GO enrichment analysis of hypomethylated genes demonstrated that there were 13 categories related to BP, including misregulation of small GTPase mediated signal transduction, Ras protein signal transduction, regulation of Ras protein signal transduction, regulation of intracellular signal transduction, etc; 17 categories related to MF, including protein binding, Rho GTPase binding, GTPase binding, guanyl-nucleotide exchange factor activity, etc (Figure 3D).

We performed KEGG analysis of differential methylation in groups V and C and corrected P-value <.05 was the data we required. The top 20 enrichment pathways are shown in Figure 3. Hypermethylated genes of the V group were enriched to a total of 33 pathways of which 2 were related to pancreatic cancer, including insulin secretion, and type II diabetes mellitus (Figure 3E). Hypomethylated genes were enriched to a total of 109 pathways among them 5 were related to pancreatic cancer, including insulin secretion, insulin resistance, insulin signaling pathways, type II diabetes mellitus, etc (Figure 3E). In summary, only 1 hypomethylated-enriched pathway was associated with pancreatic cancer at D30, whereas there were two hypermethylation-enriched pathways and five hypomethylated-enriched pathways at D58. This suggests that BOP-induced early pancreatic cancer is more effective at D58 compared to D30.

The Effect of JFG on Early Pancreatic Cancer Gene Methylation

The gene methylation levels were compared among the L group versus the V group, the M group versus the V group and the H group versus the V group at D30 to investigate the effect of JFG on early pancreatic cancer gene methylation (Figure 4). Approximately 5984 CpG sites were significantly differentially methylated between L and V groups (Figure 4A and B). DMR gene region distribution of 4769 hypermethylated CpG sites indicated that 472 of those sites were located in promoter regions representing 439 genes and 1215 hypomethylated CpG sites suggested that 86 of those sites were located in promoter regions representing 82 genes (Figure 4B). In the M group versus the V group, about 7717 CpG sites, significant differences in gene methylation, of which 1692 hypermethylated CpG sites indicated that 109 of those sites were located in promoter regions representing 102 genes and 6025 hypomethylated CpG sites suggested that 492 of those sites were located in promoter regions representing 456 genes (Figure 4C and D). Comparing H group with the V group, 2864 CpG sites were hypermethylated representing 995 genes and 14 353 CpG sites were hypomethylated representing 3739 genes (Figure 4E and F). Two thousand eight hundred sixty-four hypermethylated CpG sites demonstrated that 193 of those sites were located in promoter regions representing 179 genes and 14 353 hypomethylated CpG sites suggested that 1159 of those sites were located in promoter regions representing 1055 genes (Figure 4F).

Figure 4.

Differentially methylated regions (DMRs) in pancreatic cancer at D30. (A) Violin plots of DMR methylation level distribution of L versus V. (B) Column diagram of DMR gene region distribution of L versus V. (C) Violin plots of DMR methylation level distribution of M versus V. (D) Column diagram of DMR gene region distribution of M versus V. (E) Violin plots of DMR methylation level distribution of H versus V. (F) Column diagram of DMR gene region distribution of H versus V.

The gene methylation levels were compared among the L group versus the V group, the M group versus the V group and the H group versus the V group at D58 (Figure 5). In the L group versus the V group, about 4144 CpG sites, significant differences in gene methylation, of which 3341 hypermethylated CpG sites showed that 311 of those sites were located in promoter regions representing 293 genes and 803 hypomethylated CpG sites suggested that 83 of those sites were located in promoter regions representing 76 genes (Figure 5A and B). There were 3901 hypermethylated CpG sites and 1039 hypomethylated CpG sites compared with the M group and V group (Figure 5C and D) and in the H group versus V group, there were 361 hypermethylated CpG sites and 2336 hypomethylated CpG sites (Figure 5E and F).

Figure 5.

Differentially methylated regions (DMRs) in pancreatic cancer at D58. (A) Violin plots of DMR methylation level distribution of L versus V. (B) Column diagram of DMR gene region distribution of L versus V. (C) Violin plots of DMR methylation level distribution of M versus V. (D) Column diagram of DMR gene region distribution of M versus V. (E) Violin plots of DMR methylation level distribution of H versus V. (F) Column diagram of DMR gene region distribution of H versus V.

JFG May Improve Early Pancreatic Cancer Through Immune Response

Aiming to analyze the effect of JFG on gene methylation of early pancreatic cancer, we performed GO functional enrichment analysis using genes that were hypermethylated in V group versus hypomethylated in the administered group and hypomethylated in V group versus hypermethylated in the administered group. The results demonstrated that there was no significantly correlated GO term from hypermethylated in V group versus hypomethylated in the administered group (Figure 6A and C) and hypomethylated in V group versus hypermethylated in the administered group (Figure 6B and D).

Figure 6.

(A) GO functional enrichment analysis of V group hypermethylated versus the administered group hypomethylated at D30. (B) GO functional enrichment analysis of V group hypomethylated versus the administered group hypermethylated at D30. (C) GO functional enrichment analysis of V group hypermethylated versus the administered group hypomethylated at D58. (D) GO functional enrichment analysis of V group hypomethylated versus the administered group hypermethylated at D58. GO, gene ontology.

In order to improve the reliability of this study, we performed KEGG analysis on methylated genes which were differentially methylation both at D30 and at D58 and only at D58 (Figure 7). It was worth keeping in mind that 3 pathways had corrected P-value <.05 in hypermethylated in the V group versus hypomethylated in the administered group and the top enriched term was Th1 and Th2 cell differentiation pathway (Figure 7A and Table 1). Four differentially methylated genes were discovered in this pathway of which RUNX3 was the most important. RUNX3 is a key tumor suppressor in pancreatic cancer and its de-expression in pancreatic cancer promotes tumor growth and metastasis. Based on those results, we speculate that JFG may play a tumor-suppressive role by inhibiting RUNX3 methylation. On the other hand, the KEGG analysis of hypermethylated in the V group versus hypomethylated in the administered group revealed that 15 pathways had corrected P-value <.05 including MAPK signaling pathway, Glutamatergic synapse, Type II diabetes mellitus, etc (Figure 7B and Table 1). Collectively, our results demonstrated that JFG may play a tumor-suppressive role through the activation of immune response by RUNX3.

Figure 7.

(A) KEGG pathway enrichment analysis of V group hypermethylated versus the administered group hypomethylated. (B) KEGG pathway enrichment analysis of V group hypomethylated versus the administered group hypermethylated. KEGG, Kyoto Encyclopedia of Genes and Genome.

Table 1.

List of DMR-related Genes Enriched for KEGG.

	Category	Term	Count	P-value	Genes	Fold enrichment
Hyper	KEGG_PATHWAY	maua04658: Th1 and Th2 cell differentiation	4	.008135	NOTCH1, MAML3, JAK3, RUNX3	9.4054601
	KEGG_PATHWAY	maua04360: Axon guidance	5	.0094038	RGS3, SEMA3F, EPHB2, BMP7, PRKCZ	5.8466373
	KEGG_PATHWAY	maua04390: Hippo signaling pathway	4	.0357135	BMP7, PRKCZ, TP73, TEAD3	5.3745486
	KEGG_PATHWAY	maua04144: Endocytosis	5	.0409542	RAB31, VPS37D, AGAP1, PRKCZ, CYTH1	3.7297514
Hypo	KEGG_PATHWAY	maua04010: MAPK signaling pathway	10	.0027041	MAP4K1, NTRK2, PDGFD, RPS6KA2, CACNA2D3, PDGFA, CACNA1D, NFATC1, GNG14, FGFR1	3.34484
	KEGG_PATHWAY	maua04724: Glutamatergic synapse	6	.0053688	GRIK3, GRIK4, CACNA1D, ADCY7, GNG14, SHANK1	5.2553672
	KEGG_PATHWAY	maua04530: Tight junction	7	.0060032	RAP2C, MAGI1, TIAM1, CACNA1D, PRKCZ, TJP3, RUNX1	4.2063307
	KEGG_PATHWAY	maua04810: Regulation of actin cytoskeleton	8	.0078135	APC2, TIAM1, PDGFD, PDGFA, GNG14, SSH1, FGFR1, ITGA9	3.4596002
	KEGG_PATHWAY	maua04930: Type II diabetes mellitus	4	.0107374	PRKCD, IRS2, CACNA1D, PRKCZ	8.612963
	KEGG_PATHWAY	maua04020: Calcium signaling pathway	8	.0128134	NTRK2, PDGFD, PDGFA, CACNA1D, NFATC1, ADCY7, PLCD1, FGFR1	3.1438952
	KEGG_PATHWAY	maua05215: Prostate cancer	5	.0163462	TCF7L1, PDGFD, PDGFA, ERG, FGFR1	5.0664488
	KEGG_PATHWAY	maua04015: Rap1 signaling pathway	7	.019041	MAGI1, TIAM1, PDGFD, PDGFA, ADCY7, PRKCZ, FGFR1	3.2737054
	KEGG_PATHWAY	maua04929: GnRH secretion	4	.0231941	GABBR2, CACNA1D, KCNN3, KCNN4	6.4597222
	KEGG_PATHWAY	maua05221: Acute myeloid leukemia	4	.0281951	TCF7L1, SPI1, RARA, RUNX1	5.9916264
	KEGG_PATHWAY	maua04062: Chemokine signaling pathway	6	.0326416	TIAM1, NCF1, PRKCD, ADCY7, PRKCZ, GNG14	3.3340502
	KEGG_PATHWAY	maua04014: Ras signaling pathway	7	.0329071	NTRK2, TIAM1, RASA3, PDGFD, PDGFA, GNG14, FGFR1	2.8824259
	KEGG_PATHWAY	maua05412: Arrhythmogenic right ventricular cardiomyopathy	4	.0372921	TCF7L1, CACNA2D3, CACNA1D, ITGA9	5.3691198
	KEGG_PATHWAY	maua05202: Transcriptional misregulation in cancer	6	.0432795	SPI1, RARA, PDGFA, ERG, RUNX1, BAIAP3	3.0852405
	KEGG_PATHWAY	maua05200: Pathways in cancer	11	.0445794	APC2, TCF7L1, SPI1, NOTCH1, RARA, LOC106022749, PDGFA, ADCY7, GNG14, FGFR1, RUNX1	1.9980863

Discussion

The incidence of pancreatic cancer is increasing every year causing the treatment of pancreatic cancer to be a huge challenge and immune resistance leads to high mortality in pancreatic cancer.²² Pancreatic adenocarcinoma disrupts the inflammatory factor release and escapes the immune response from the early stages of tumor initiation, thereby avoiding recognition by T cells.^22,23 Genetic and epigenetic alterations are important factors in the acquisition of immune escape from pancreatic cancer and tumor suppressor genes can be inactivated in the early stages of pancreatic cancer to promote malignant tumor development.^23,24

DNA methylation is an essential epigenetic mechanism and it is relevant to early diagnosis of cancer.²⁵ Gene methylation aberrant is associated with the occurrence and development of cancer and suggests the possibility of early carcinogenesis.^26,27 Hypermethylation of promoter regions causes inactivation of tumor suppressors thereby promoting tumor development.^25,28,29 In this study, we found that about 661 CpG sites were hypermethylated representing 207 genes and 1489 CpG sites were hypomethylated representing 508 genes at D30 and approximately 1730 CpG sites were hypermethylated representing 612 genes and 18 580 CpG sites were hypomethylated representing 4404 genes at D58 in BOP-induced early pancreatic cancer.

Based on the above data, we performed GO functional enrichment analysis and KEGG pathways enrichment analysis using genes that were hypermethylated in the V group versus hypomethylated in the administered group and hypomethylated in the V group versus hypermethylated in the administered group. One pathway that deserves particular attention is the Th1 and Th2 cell differentiation pathway which was enriched in hypermethylated in the V group versus hypomethylated in the administered group. Four differentially methylated genes were discovered in this pathway, including RUNX3. RUNX3 is a key tumor suppressor and the low expression of RUNX3 may imply the development of tumor and a poor prognosis in pancreatic cancer.^30,31

Tumor growth and progression are partly due to immune modulation and evasion. Recently, a continuously growing number of studies are now focusing on the role of the immune response in tumors. This specific immunosuppressive microenvironment contributes to tumor growth.³² In this study, we found that differently methylated genes performing KEGG analysis were enriched to the Th1 and Th2 cell differentiation pathway in BOP-induced early pancreatic cancer after treatment with JFG. These indicated that JFG may affect the immune response through Th1 and Th2 cell differentiation pathway in early pancreatic cancer. On the other hand, RUNX3 was one of the differentially methylated genes enriched by KEGG analysis, which was included in the Th1 and Th2 cell differentiation pathway. The role of RUNX3 in this pathway is shown in Figure 8. This suggested that RUNX3 mainly inhibits the secretion of the Th2-type cytokine IL-4, thereby regulating the immune response and inhibiting tumor growth.

Figure 8.

Th1 and Th2 differentiation pathways.

The methylation level of RUNX3 was significantly increased after treatment with BOP and significantly decreased after therapy with JFG in early pancreatic cancer. Previous studies have already the demonstrated that promoter hypermethylation rather than mutation or deletion leads to the inactivation of RUNX3 in pancreatic cancer.^33–35 Multiple aspects of the immune response are regulated by RUNX3, including activation of NK cells, T cell differentiation and maturation^36–40 and RUNX3 silenced as an oncogene due to promoter hypermethylation in pancreatic cancer cells.³⁰ Furthermore, RUNX3 is involved in thymopoiesis to regulate the immune response and acted as an oncogene in many cancers, including pancreatic cancer, gastric cancer, breast cancer, and lung cancer.^36,41 Thus, the antitumor activity of RUNX3 was activated by promoter hypomethylation, which inhibits tumor growth. RUNX3 hypomethylation in pancreatic cancer tissue after treatment with JFG revealed that JFG may function as a tumor suppressor by activating the immune response through RUNX3.

In this study, we preliminarily explored the effect of JFG on gene methylation in early pancreatic cancer. We found that JFG may affect the immune response through Th1 and Th2 cell differentiation pathway and significantly decrease the methylation level of RUNX3. However, the experimental analysis of JFG altered RUNX3 methylation in this study was scarce, making mechanistic studies of JFG limited. Meaningfully, this study suggests that JFG may play a role in ameliorating pancreatic cancer through the immune system, which points the way for our future research. Next, we will focus on the role of JFG on the immune system and analyze the mechanism by which JFG ameliorates pancreatic cancer, so as to provide new therapeutic approaches for the prevention and treatment of pancreatic cancer.

Conclusion

Treatment with JFG for BOP-induced early pancreatic cancer found significant hypomethylation of RUNX3 and enrichment of Th1 and Th2 cell differentiation pathways by KEGG analysis. RUNX3 regulates the immune response and suppresses tumor growth mainly by inhibiting the secretion of the Th2-type cytokine IL-4. Therefore, we speculate that JFG may improve early pancreatic cancer by modulating immune response through RUNX3.

Footnotes

Acknowledgment

The authors are indebted to Shandong New Time Pharmaceutical Co., LTD. for providing all the materials required for this project. The study team thanks the State Key Laboratory of Generic Manufacture Technology of Chinese Traditional Medicine for providing facilities during the experimental process. The authors are grateful to the editor and reviewers for their suggestions, which are extremely helpful for this article.

Author Statement

Weiping Ge: conceptualization, methodology, investigation, validation, formal analysis, writing—original draft, and writing—review and editing; Yuting Li: conceptualization, methodology, investigation, validation, formal analysis, and writing—review and editing; Sina Pan: conceptualization, methodology, investigation, validation, formal analysis, and writing—review and editing; Fenghui Ma: conceptualization, methodology, investigation, validation, formal analysis, and writing—review and editing; Min Li: conceptualization, methodology, investigation, validation, and writing—review and editing; Lei Yan: conceptualization, methodology, investigation, validation, and writing—review and editing; Xue Meng: conceptualization, methodology, investigation, validation, and writing—review and editing; Huiying Ma: conceptualization, methodology, investigation, validation, and writing—review and editing; Guangyan Li: conceptualization, methodology, investigation, validation, resources, supervision, project administration, and writing—review and editing Jingchun; Yao: conceptualization, methodology, investigation, validation, resources, supervision, project administration, and writing—review and editing; Tao Li: conceptualization, methodology, investigation, validation, resources, supervision, project administration, and writing—review and editing

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability

The data that support the findings of this study are available from the corresponding author, Tao Li, upon reasonable request.

Ethics Approval

All animal experimental were approved by the Experimental Animal Ethics Committee at State Key Laboratory of Integration and Innovation of Classic Formula and Modern Chinese Medicine (Approved on March 08th, 2022; No. AH-IACUC-2022-027) and performed in accordance with the National Institute of Health Guide for the Care and Use of Laboratory Animals. No weight loss or treatment-related toxic responses were observed for golden hamsters treated with either test article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Weiping Ge

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Jingfang Granules May Improve Early Pancreatic Cancer by Modulating Immune Response Through RUNX3

Abstract

Keywords

Introduction

Materials and Methods

Reagents

Preparation and Compositional Analysis of JFG Extract

Animals

Animals Grouping and Model Establishment

Organ Index Calculation

Histopathology

Pancreatic DNA Methylation Detection

DNA Methylation Analysis

Gene Ontology and Kyoto Encyclopedia of Genes and Genome Enrichment Analysis

Statistical Analysis

Results

JFG Reduces the Morbidity of Pancreatic Cancer

The Effect of BOP on Early Pancreatic Cancer Gene Methylation

The Effect of JFG on Early Pancreatic Cancer Gene Methylation

JFG May Improve Early Pancreatic Cancer Through Immune Response

Discussion

Conclusion

Footnotes

Acknowledgment

Author Statement

Declaration of Conflicting Interests

Data Availability

Ethics Approval

Funding

ORCID iD

References