Multiomics-based Analysis Reveals Differences in intratumoral microbiota and Prognostic in Gastric Cancer Patients with Different BMI

doi:10.21203/rs.3.rs-4856834/v1

Download PDF

Article

Multiomics-based Analysis Reveals Differences in intratumoral microbiota and Prognostic in Gastric Cancer Patients with Different BMI

https://doi.org/10.21203/rs.3.rs-4856834/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Body mass index (BMI) is considered a significant prognostic factor for tumor outcomes;however, the role of BMI in gastric cancer (GC) remains controversial. Currently, there is a lack of research investigating the impact of BMI on GC from the perspective of intratumoral microbiota. This study aimed to compare and analyze the differences in and functions of intratumoral microbiota among GC patients with varying BMIs, aiming to ascertain whether specific microbial features are associated with prognosis in low-BMI gastric cancer patients.A retrospective analysis of the clinicopathological features and prognosis of 5567 patients with different BMIs were performed between January 2010 and December 2019. Tumor tissues from 189 GC patients were collected for 16S rRNA sequencing, 64 samples were selected for transcriptome sequencing, and 57 samples were selected for untargeted metabolomic analysis.Clinical cohort analysis revealed that GC patients with a low BMI(LBMI) presented poorer clinical and pathological characteristics than those with a nonlow- BMI༈NLBMI༉. LBMI has as a significant independent risk factor for adverse prognosis, potentially exerting immunosuppressive effects on postoperative adjuvant chemotherapy. 16S rRNA sequencing revealed no significant differences in the alpha and beta diversity of the intratumoral microbiota between the two groups of GC patients. However, LEfSe analysis revealed 32 differential intratumoral microbiota between the LBMI and NLBMI groups. Notably, g_Abiotrophia was significantly enriched in the LBMI group. In GC patients with LBMI, g_Abiotrophia was negatively correlated with the eosinophil, P2RY12, and SCN4B genes but positively correlated with LGR6. Metabolomic analysis further revealed a positive correlation between g_Abiotrophia and the purine metabolism products guanine and idp.LBMI is an independent risk factor for poor prognosis in patients with gastric cancer and may have inhibitory effects on postoperative adjuvant chemotherapy. There are differences in intratumoral microbiota between GC patients with different BMIs, along with distinct immune cell infiltration and metabolic characteristics.g_Abiotrophia may promote the occurrence and development of GC by regulating eosinophils and purine metabolism pathways, providing new solutions for precision treatment of GC.

Health sciences/Anatomy/Gastrointestinal system

Biological sciences/Microbiology

Biological sciences/Microbiology/Clinical microbiology

Biological sciences/Microbiology/Communities

BMI

intratumoral microbiota

immune cells

metabolome

Gastric cancer (GC) is the fifth most common malignancy globally and the fifth leading cause of cancer-related deaths¹.Over the past two decades, the 5-year survival rate of patients with GC has significantly improved due to various factors such as early detection, improvement in surgical techniques, improvement in nutritional care, and widespread use of systemic chemotherapy and immune-targeted therapy². However, in China, most GC patients are diagnosed at an advanced or even late stage, with a higher proportion of patients experiencing significant weight loss and worse prognosis³.

BMI is a measure of body weight.It is an important prognostic factor for various tumors, such as colorectal cancer, breast cancer, and pancreatic cancer⁴.However, its role in regulating the prognosis of patients with tumors including those with GC, is still controversial ^5–8.Ma et al⁵.demonstrated that GC patients with LBMI had a poor long-term prognosis, while Feng et al⁶.found that GC patients with a high BMI had a better long-term prognosis. Interestingly, Taghizadeh et al⁷.found that obese patients had a high risk of death and poor prognosis. However, Zhao et al⁸.showed no association between BMI and GC prognosis.

Previous studies have shown that intratumoral microbiota may contribute to tumorigenesis and progression and impact prognosis by inducing genomic instability and mutations affecting epigenetic modifications, promoting inflammatory responses, averting immune destruction, regulating metabolism, and activating invasion and metastasis^9–11. For example, Fusobacterium nucleatum is more abundant in various tumors such as colorectal cancer(CRC), oral cancer, and gastric cancers and affects long-term prognosis^12–14, A novel virulence protein of Fusobacterium nucleatum, Fn-Dps, has been found to promote invasion and metastasis of CRC cells by inducing EMT through upregulation of the chemokine CCL2/CCL7¹⁴. Interestingly, two recent studies have demonstrated the heterogeneity of microorganisms at different BMI states^15,16. In one of them, Huang et al¹⁵.Similarly,in their study of CRC patients with different BMIs states similarly found the same significant enrichment at the portal level was detected in hyper-reorganized CRC patients, with significant enrichment of Actinobacteria spp,Desulfovibrio spp, and Mycobacterium spp at the genus level. A study on intratumoral microbiota and GC revealed that Methylobacterium tumefaciens was significantly associated with poor prognosis in gastric cancer patients and was negatively correlated with CD8⁺ tissue-resident memory T (TRM) cells and TGF-β in the TME.Experimental methods verified that Methylobacterium could reduce TGF-β expression and the number of CD8⁺ TRM cells in tumors. These findings suggest that intratumoral microbiota may regulate the development of GC by influencing the tumor immune microenvironment¹⁷.

Therefore,intratumoral microbiota have attracted increasing a ttention as influencing factors of the tumor immune microenvironment. However, few studies have been conducted on GC, especially on LBMI GC patients with associated immunosuppression or intolerance¹⁸. Therefore, in this study, we performed a multiomics analysis based on intratumoral microbiotas combined with transcriptomics and metabolomics to analyze intratumoral microbes and their functions in GC patients with different BMIs to understand the characteristics of the differential intratumoral microbes of LBMI GC patients, i.e., the mechanism of potential modulation of GC, and to provide a new solution for the precision treatment of GC.

LBMI is an independent prognostic risk factor for poor prognosis in patients with GC

In this study, data from 5567 patients who met the criteria and had complete follow-up information were collected from 7192 hospitalized patients with GC(Fig. 1A). There were no statistically significant differences between the two groups of BMI patients in terms of smoking history, alcohol consumption history, extent of resection, type of pathology, pM stage or recurrent metastasis (all P > 0.05). Analysis revealed that relative to NLBMI patients, LBMI patients had a smaller percentage of family history of GC; more tumors were located in the lower 1/3 and the whole stomach and less in the upper 1/3, and there was a greater percentage of larger and more poorly differentiated tumors, and a greater percentage of open surgeries (all P < 0.05); the level of pre-CA199 positivity was significantly greater (P = 0.039), and the pre-CEA positivity level was similar; and the percentage of nerve invasion was greater (P = 0.011), while there was no significant difference in vascular invasion. Moreover, in the LBMI group, the percentage of female patients aged ≥ 60 years, incidence of complications, deep tumor infiltration, high number of lymph node metastases, late pathological stage and low percentage of receiving postoperative adjuvant chemotherapy were significantly greater than that those in the NLBMI group (P < 0.001). (Table S1).

Univariate and multivariate COX analysis revealed that LBMI is an independent poor prognostic factor for overall survival (OS) in GC patients (HR = 1.28, 95%CI: 1.13–1.45, P < 0.001) (Table 1). Kaplan-Meier survival analysis based on BMI classification showed that LBMI patients had worse prognosis compared to NLBMI patients before PSM (5-yr OS: 50.8% vs. 66.2%, P < 0.001) (Fig. 1B). After adjusting for clinicopathological characteristics that influence prognosis (P < 0.05) using PSM (ratio 1:4), the clinical characteristics of the two groups were comparable (P > 0.05, Table S2). Similarly, LBMI patients had worse prognosis (5-yr OS: 50.8% vs. 60.5%, P < 0.001) (Fig. 1C). Stratified analysis by TNM stage show no significant difference in OS between the two BMI groups in stage I and IV disease (Fig. 1D,G); however, in stage II and III patients, LBMI disease have worse OS compared to NLBMI patients (Fig. 1E,F). Subgroup analysis based on receiving postoperative chemotherapy set the PSM ratio to 1:4,and included clinicopathological data that influence prognosis. After PSM, the clinicopathological characteristics of the two BMI groups were comparable (Table S3), and LBMI patients had worse OS both before and after PSM (Fig. 1H, I).

Table 1

Single factor and multi-factors Cox analysis risk factor for gastric cancer OS
Parameters	Univariate	P value	Multivariate	P value
Parameters	HR (95%CI)	P value	HR (95%CI)	P value
Gender(Male vs Female )	1.21 (1.10 ~ 1.34)	< 0.001
Age (≥ 60years vs<60years )	1.65 (1.51 ~ 1.80)	< 0.001	1.37 (1.25 ~ 1.51)	< 0.001
BMI(<18.5 vs ≥ 18.5 )	1.60 (1.41 ~ 1.80)	< 0.001	1.28 (1.13 ~ 1.45)	< 0.001
Family.history	0.91 (0.83 ~ 0.99)	0.041
Smoking.history	1.06 (0.97 ~ 1.15)	0.171
Drinking.history	1.03 (0.94 ~ 1.13)	0.531
Surgery.methods(Laparoscopy vs Open)	0.50 (0.43–0.58)	< 0.001	0.78 (0.68–0.91)	< 0.001
Tumor location		< 0.001		0.002
Upper1/3	Ref		Ref
Middle1/3	0.53 (0.46 ~ 0.61)	< 0.001	0.88 (0.76 ~ 1.02)	0.097
Lower1/3	0.61 (0.55 ~ 0.67)	< 0.001	0.90 (0.82 ~ 0.99)	0.039
Total	2.26 (1.84 ~ 2.77)	< 0.001	1.46 (1.18 ~ 1.80)	< 0.001
Pathological type		0.042		< 0.001
Adenocarcinoma	Ref		Ref
MGC	0.87 (0.66 ~ 1.13)	0.296	0.62 (0.47 ~ 0.81)	< 0.001
SRCC	1.23 (1.05 ~ 1.43)	0.010	1.34 (1.14 ~ 1.57)	< 0.001
Differentiation		< 0.001
Poorly	Ref
Moderately	0.37 (0.23 ~ 0.59)	< 0.001
Well	0.79 (0.70 ~ 0.89)	< 0.001
Vascular.tumor.thrombus	2.45 (2.25 ~ 2.68)	< 0.001	1.38 (1.26 ~ 1.52)	< 0.001
Nerve.invasion	2.95 (2.68 ~ 3.24)	< 0.001	1.42 (1.28 ~ 1.58)	< 0.001
Maximum tumor diameter(≥ 5cm vs <5cm)	2.92 (2.68 ~ 3.18)	< 0.001	1.57 (1.43 ~ 1.72)	< 0.001
pTNM Satge		< 0.001		< 0.001
I	Ref		Ref
II	3.14 (2.53 ~ 3.91)	< 0.001	2.23 (1.77 ~ 2.79)	< 0.001
III	9.31 (7.72 ~ 11.23)	< 0.001	4.90 (3.95 ~ 6.07)	< 0.001
IV	18.67 (14.43 ~ 24.15)	< 0.001	10.59 (8.00 ~ 14.02)	< 0.001
Pre-CEA	1.89 (1.72 ~ 2.08)	< 0.001	1.31 (1.19 ~ 1.45)	< 0.001
Pre-CA199	2.13 (1.93 ~ 2.34)	< 0.001	1.26 (1.14 ~ 1.39)	< 0.001
Postoperative adjuvant therapy	1.14 (1.05 ~ 1.24)	0.002	0.71 (0.65 ~ 0.78)	< 0.001
BMI:Body Mass Index,MGC:Mucinous adenocarcinoma,SRCC:signet-ring cell carcinoma,Pre-:Pre-operation.P < 0.05 was considered significant.

Intratumoral Microbiome Landscape in LBMI and NLBMI Gastric Cancer Patients

To evaluate whether there were differences in microbial diversity, abundance, and composition between LBMI and NLBMI gastric cancer patients, we included 189 eligible gastric cancer patients, including 27 in the LBMI cohort and 162 in the NLBMI cohort (Fig.S1). As shown in Table S4, the clinicopathologic data were balanced and comparable between the two cohorts. On the basis of the species sparsity curves (Fig.S2B,C), we found that the curves of the four groups in both metrics flattened out. The Venn diagram (Fig.S2A) revealed that there are many overlaps in the microbial environments among the four groups.The diversity within the cancer tissues was significantly greater in both LBMI and NLBMI carcinomas than in the LBMI and NLBMI paracarcinomas, whereas there was no significant difference between the two cohorts of LBMI and NLBMI cancer tissues (Fig. 2A,B). Principal coordinate analysis (PCoA) revealed significant differences in both BMI carcinomas and paracancers in both groups;however, there was no significant difference between LBMI-CT and NLBMI-CT (P = 0.855) (Fig. 2C).PLS-DA analysis, revealed that the intratumoral microbiome of two groups of BMI carcinomas could be divided into two different clusters (Fig. 2D).Regarding species composition, the differences were a smaller between the tumor tissues of different BMI groups, while the differences were a greater between tumor and peritumoral tissues of the same BMI group (Fig. 2E,F).

LBMI intratumor g_Abiotrophia was significantly elevated

To determine the differentially dominant flora in GC patients with different BMIs, LEfSe analysis was performed (LDA > 2.0, P < 0.05), which revealed 59(Fig.S3) and 230(Fig.S4) differentially dominant flora in the LBMI group and the NLBMI group, respectively, compared with the paracancerous tissues .There were 32 differentially dominant flora in the LBMI-CT group compared with the NLBMI-CT group. At the phylum level only p_Nitrospinae dominated the flora in LBMI, whereas at the genus level g_Lachnoanaerobaculum,g_Brevundimonas and g_Stomatobaculum dominated the flora in the NLBMI group, whereas g_Acidiphilium,g_Thiobacillus and g_Abiotrophia and 9 other genera were the dominant flora in the LBMI group. At the species level, s_Knoelia_sp_BA2_2011 and 15 other species were dominant flora in the LBMI group (Fig. 3A;Fig.S5). At the genus level, the abundances of two groups of differentially bacteria, g_Abiotrophia and g_Lachnoanaerobaculum,significantly differed (Fig. 3B-J). Spearman correlation analysis revealed that g_Abiotrophia was positively correlated with g_Lachnoanaerobaculum and g_Stomatobaculum and that g_Brevundimonas was negatively correlated. These findings suggest a possible complementary relationship between the dominant differential flora between the two BMI groups (Fig.S2D).

LBMI intratumoral g_Abiotrophia negatively correlates with P2RY12

RNA sequencing analysis was performed on 64 tumor tissues from both groups, and PCA revealed that there was no significant difference in BMI the between the two groups (P = 0.136) (Fig. 4A). Compared with NLBMI, 343 genes were significantly upregulated and 320 genes in LBMI were significantly downregulated(Fig. 4B). KEGG and GO analyses were performed on the BMI differential genes of the two groups, and KEGG analysis revealed that the LBMI group was enriched mainly in the Wnt signaling pathway,gastric cancer, and African trypanosomiasis (Fig. 4C);similarly GO enrichment analysis was performed mainly in the extracellular region, extracellular space, plasma membrane and Wnt signaling pathway (Fig. 4D).Correlation analysis of the DEGs associated with the dominant flora at the genus level revealed that g_Abiotrophia was significantly positively correlated with 11 genes, such as LGR6 ,and significantly negatively correlated with 30 genes ,such as P2RY12 and SCN4B ,in the LBMI group (Fig. 4E; for details, see Additional file S1). The above results revealed that GC patients with different BMIs presented different transcriptomic landscapes and that many of these genes were closely related to differential intratumoral microbiota, suggesting that differential intratumoral microbiota may regulate the progression of GC by influencing the genes of the host.

LBMI intratumoral g_Abiotrophia negatively correlates with eosinophils

To explore the associations between BMI-associated intratumoral microbiota and tumor-infiltrating immune cells, we analyzed the composition of immune cells in 64 GC patients via transcriptome sequencing information and BMI data and plotted bar graphs of immune cell abundance (Fig. 5A, B) to discover the unique features of the tumor immune microenvironment of GC patients with different BMIs.

Correlation analysis revealed that in the NLBMI group, g_Lachnoanaerobaculum showed a significant positive correlation with T cell follicular helper, while Mast cells resting exhibited a significant negative correlation. g_Stomatobaculum demonstrated a significant positive correlation with T cell follicular helper, whereas T cells CD4 memory resting showed a significant negative correlation. In the LBMI group, g_Enterobacter displayed a significant positive correlation with B cells naive, while Dendritic cells activated and T cells CD4 memory resting showed significant negative correlations.g_Abiotrophia showed a significant negative correlation with eosinophils (Fig. 5C). The above results indicated that the BMI-related dominant intratumoral microbiota of GC patients were significantly associated with various tumor-infiltrating immune cells, suggesting that they may play a role in regulating the immune microenvironment of GC.

High purine metabolism in LBMI tumors

Untargeted metabolomic analysis was performed on 57 tumor tissues, and a total of 2688 metabolites were identified, of which 122 metabolites were significantly different between the LBMI and NLBMI groups (P < 0.05,FC ≥ 2 or FC ≤ 0.5) (Fig. 6A, Table S2), and the PLS-DA scoring plot revealed that the different metabolites in the LBMI versus NLBMI tumors could be classified into two different clusters ( R2Y = 0.432,Q2Y = 0.368) (Fig. 6B). Tests of the PLS-DA model revealed that R2 > Q2 and the Q2 regression line had a negative intercept (R2 = [0.0, 0.354] ,Q2 = [0.0, -0.421]) (Fig. 6C). The heatmap revealed that compared to the NLBMI group, the LBMI group had higher abundance of intratumoral purine metabolites,such as idp (Fig.S6).Differentially abundant metabolite KEGG enrichment analysis revealed that the LBMI group was enriched mainly in pathways such as purine metabolism and the caffeine metabolism pathway(Fig. 6D). Genus-level differential dominant bacteria and differentially abundant metabolite correlation analysis, as shown in Fig. 6E, revealed that the differential dominant bacteria in the NLBMI group, g_Lachnoanaerobaculum, were significantly negatively correlated with 12 differentially abundant metabolites,such as 8-methoxykynurenate;g_Stomatobaculum was significantly negatively correlated;g_Brevundimonas was significantly positively correlated with eleutheroside b1 and 2-dodecylbenzenesulfonic acid and significantly negatively correlated with mimosine and latamoxef. g_Abiotrophia in the LBMI group presented a significant negative correlation with demethoxyfumitremorgin c, whereas it presented a significant positive correlation with guanine and idp;g_Dubosiella presented a significant positive correlation with caffeine and four others;g_Enterobacter presented a significant positive correlation with cyclic n-acetylserotonin glucuronide and 8-methoxykynurenate presented a significant positive correlation; g_Sphingopyxis showed significant negative correlation with 2-piperidinone;g_Prevotellaceae_UCG-001 and g_Methylocystis demonstrated a significant negative correlation with differentially abundant metabolites that were not significantly correlated. The above results revealed significant correlations between the two groups of intratumoral microbiota and metabolites, suggesting that they may further affect the biological process of gastric cancer by influencing metabolites.

In recent years, the relationship between GC and BMI has been studied with varying results^5–8. The long-term prognosis of patients with different BMIs remains unclear.Therefore, the present study was conducted to investigate BMI and GC in a large cohort. In this study, LBMI was found to be an independent risk predictor of poor prognosis, and when PSM was used to adjust for confounders and K‒M survival curve analysis, it was observed that the LBMI group had a worse long-term prognosis in all patients than did the NLBMI group. This result is consistent with the findings of Feng et al⁶.Several other studies have concluded that patients with LBMI have a poor prognosis^5,18,21. When specific subgroups, such as stage I versus stage IV patients,were analyzed, there was no significant difference in prognosis between the two groups. In contrary in stage II and III patients,LBMI patients had a significantly worse prognosis than NLBMI patients did. This finding is consistent with those of Spyrou et al²¹.and may be because BMI has little effect on long-term prognosis in stage I versus stage IV patients. Moreover, among patients receiving postoperative adjuvant therapy, LBMI patients had worse overall survival rates and fewer benefits than NLBMI patients did, possibly because preoperative cancer-related malignancies are almost always associated with some degree of weight loss, which makes patients intolerant of postoperative adjuvant therapy side effects^5,18. Therefore, special perioperative nutritional support therapy and meticulous follow-up treatment for this special population with LBMI may improve the clinical outcome of patients.

Intratumoral microbiota are microorganisms present in tumor tissues and are now considered important regulators of many tumors, especially those of the gastrointestinal tract²². In the present study, we found significant differences in the alpha and beta diversity of the microbiota between tumor tissue and peritumoral tissue in the two groups, whereas there were no differences between intratumoral microbiota(Fig. 2A-C). A 16S rRNA study evaluating the differences in gastric flora between 229 tumor tissues and 247 peritumoral tissues revealed that the Shannon and Simpson indices of the alpha and beta diversity of gastric intratumoral microbiota in patients with GC were significantly greater than those in paraneoplastic tissues, which is in line with the results of the present study²³. In addition, two studies sequencing the biodiversity of obese and nonobese patients with CRC with fecal samples did not find a difference between them^15,16. Moreover analysis(Fig. 3A,B) revealed a greater abundance of the differentially dominant bacterium g_Abiotrophia in LBMI than in NLBMI.g_Abiotrophia is a nutrient-variant Streptococcus species that is most commonly found in the oral cavity, frequently observed in nutritionally deficient states, and results in infective endocarditis²⁴. g_Abiotrophia can promote fibronectin-mediated adhesion of HUVECs via DnaK and induce a proinflammatory response, leading to infective endocarditis in patients ²⁵. Two studies have shown that this bacterium is highly abundant in patients with oral cancer ²⁶and gastric cancer ²⁷ and promotes tumor development and metastasis.

To further explore the intratumoral transcriptomic differences between the different BMI groups, a gene correlation analysis (Fig. 4E) was performed, and the present findings revealed a significant negative correlation between g_Abiotrophia and P2RY12. P2RY12 was initially identified on platelets and plays an important role in platelet activation, which is also important in inflammation through the regulation of the innate and adaptive immune response²⁸. Indeed, following ADP-induced activation of P2RY12, platelets release mediators from their granules, including a variety of cytokines and chemokines, which recruit and activate leukocytes²⁹. Widespread expression is also now present in many immune cells³⁰ and it has been shown that activation of this P2RY12 receptor on dendritic cells promotes specific T- cell activation by increasing antigen endocytosis³¹, whereas P2RY12 inhibition induces immunosuppressive effects by decreasing antigen uptake³². Several recent studies have demonstrated that P2RY12 is a favorable factor for long-term prognosis in brain gliomas³³, lung cancer³⁴, and hepatocellular carcinoma³⁵. In conclusion, we hypothesize that the inhibition of P2RY12 expression by g_Abiotrophia promotes the development and metastasis of GC, which leads to a poor prognosis in patients with LBMI.

As an important influence on the tumor immune microenvironment, intratumoral microbiota can play important roles in tumor development and metastasis by influencing immune cells³⁶. In this study, g_Abiotrophia in the LBMI group was negatively correlated with eosinophils (Fig. 5C). Eosinophils were first identified in peripheral blood, and it is commonly believed that eosinophils and their mediators are usually associated with deleterious effects in allergic diseases but can also induce a protective host immune response against microbial pathogens³⁷. Interestingly, a review reported that eosinophils have a beneficial effect on probiotics and may respond to local immunity by modulating homeostasis between pro- and antiinflammatory effects³⁸. Many studies have investigated the role of eosinophils in tumor growth control, and a review³⁹ summarizing these studies reported that the presence of eosinophils at the tumor site or in the peripheral blood is a favorable prognostic factor for most cancers.Although there is evidence that eosinophils are tumorigenic, this review demonstrated that eosinophils have an antitumor effect on patients with gastric cancer with a better prognosis via the GEO database. Two reports also reached the same conclusion^40,41. In addition, eosinophils can act as nonspecialized antigen-presenting cells (APCs), and upon activation by certain cytokines or other inflammatory stimuli, eosinophils can upregulate MHC class II or costimulatory markers and stimulate an initiated CD4 + T-cell response in vitro and in vivo⁴². These findings suggest that eosinophils may act as helper cells in cancer and play an antitumor role. Taken together, these findings indicate that g_Abiotrophia may lead to tumor development and metastasis by affecting eosinophils, thus contributing to the poor prognosis of patients with LBMI gastric cancer.

Intratumoral microbiota can modulate tumor cell function by producing specific metabolites such as polyamines and short-chain fatty acids (SCFAs)⁴³. In this study, we found that LBMI-CT purine metabolism was enriched (Fig. 6D) and that g_Abiotrophia was positively correlated with guanine and idp (Fig. 6E). Purine nucleotides, such as RNA and DNA, are critical for synthesis, signaling, metabolism and energy homeostasis⁴⁴.Nutrients are required for the proliferation and differentiation of tumor cells, and guanine and idp are purine metabolites that can be synthesized into purine nucleotides through the purine metabolic pathway, which further provides nutrients for the proliferation and differentiation of tumor tissues and their development and metastasis⁴⁵. Two recent studies reported elevated nucleoside levels in GC tumor tissues^46,47. One study, Kaji et al⁴⁷.reported that nucleoside concentrations were higher in GC patients with peritoneal recurrence than in GC patients without peritoneal recurrence. It is possible that increased levels of nucleosides, especially adenosine, lead to shorter survival in gastric cancer patients. Notably, a recent study⁴⁸ reported that feeding nucleosides to mice accelerated tumor growth, whereas inhibition of purine remediation slowed tumor progression, revealing a critical role of the purine remediation pathway in tumor metabolism. Interestingly, this study revealed that g_Abiotrophia was negatively correlated with P2RY12 (Fig. 4E). The P2RY12 gene expresses a receptor that is a purinergic receptor and the gene is coupled to a Gi protein, resulting in reduced cAMP production⁴⁹. A recent study reported that decreased expression of P2RY12 resulted in decreased ligand production and increased cAMP production, which further led to increased synthesis of purine nucleotides or other purine metabolites within tumor tissues, thereby providing energy for tumor growth and development and promoting tumor development⁵⁰. Therefore, g_Abiotrophia may provide nutrients to tumor tissues by affecting P2RY12, which in turn affects on the conversion of guanine and IDP to purine nucleotides through the purine metabolic pathway.

There are several limitations to this study. First, weight loss is a common symptom in patients with GC, leading to significant differences in the distribution of BMI compared with healthy controls. This difference introduces a potential source of analytic inaccuracy and is unavoidable given the high degree of heterogeneity among GC patients. Second, the limited sample size of this study and the fact that it was a single-center retrospective analysis and that some of the missing data were not included in this study may have resulted in some selection bias. Third, compared with macrogenome sequencing, 16S rRNA gene sequencing was unable to annotate certain species at the species level, and the depth of species identification by 16S rRNA gene sequencing was relatively shallow. Lastly, basic experimental validation was not performed to draw relevant conclusions from the analysis of the histological data. Therefore, to overcome these limitations, further data validation of large-scale and prospective multicenter studies, which are combined with basic experimental validation, are needed to further validate the findings. The aforementioned limitations also offer valuable insights for future research aimed at enhancing treatment strategies for these patients.

LBMI is an important independent risk factor for poor prognosis and possible immunosuppression or intolerance to postoperative adjuvant chemotherapy. g_Abiotrophia, a high-abundance dominant bacterium in LBMI with a negative correlation between LBMI and eosinophils, may inhibit P2RY12 to promote purine metabolism, modulate the tumor immune microenvironment and thus contribute to gastric cancer development.

Clinical Cohort Data Collection and Definitions

A retrospective analysis was conducted on 7,192 patients who underwent gastrectomy at Zhejiang Cancer Hospital from January 2010 to December 2019. Among them, 5,567 patients met the following inclusion criteria: 1.Preoperative pathological biopsy confirmed primary gastric cancer; 2.Underwent radical or palliative gastrectomy; 3.No concomitant severe diseases such as acute cardiovascular and cerebrovascular diseases, liver cirrhosis, and chronic renal failure. Exclusion criteria: 1.Received neoadjuvant treatments such as preoperative radiotherapy, chemotherapy, or immunotherapy; 2.Number of dissected lymph nodes < 16; 3.Presence of other heterogeneous tumors; 4.Other types of gastric cancer (e.g.,neuroendocrine carcinoma, squamous cell carcinoma, adenosquamous carcinoma); 5.Patients with missing critical clinical data. The median follow-up time was 85 months (interquartile range:71 months). All eligible patients underwent radical gastrectomy according to the Japanese gastric cancer treatment guidelines¹⁹. Surgical methods included proximal, total, and distal gastrectomy. Postoperatively, specimens were reviewed by pathology experts at the Cancer Hospital of the Chinese Academy of Sciences. Pathological tumor-lymph node metastasis (pTNM) staging was based on the 8th edition of the American Joint Committee on Cancer (AJCC) TNM staging system²⁰. Potential curative resection was defined as R0 resection. Survival time was calculated from the date of surgery to the date of GC-related death or the most recent follow-up. The follow-up cut-off date was August 1, 2023. Perioperative management followed routine procedures, with no differences between groups. Patients meeting the above criteria were divided into two groups according to the BMI standards set by WHO: the low BMI group (BMI < 18.5 kg/m²) and the non-low BMI group (BMI ≥ 18.5 kg/m²). Various clinicopathological characteristics, surgery-related indicators, and postoperative outcome factors were collected for analysis, including gender, height, and weight. BMI was calculated based on the patients' height and weight. Tumor location was classified according to the center of the lesion as Upper 1/3 (cardia, fundus), Middle 1/3 (body), Lower 1/3 (antrum, including the angular incisure and pylorus), or involving the entire stomach (Total) (tumor involving more than 2/3 of the stomach wall). Tumor size was determined by the maximum diameter of the tumor. The positive levels of tumor markers were defined as CA199 ≥ 37 U/ml and CEA ≥ 5 ng/ml.

Clinical specimen collection and preparation

Samples were collected from 335 patients at Zhejiang Cancer Hospital between January 2013 and December 2018. After screening according to the aforementioned clinical cohort criteria and ensuring that no antibiotics or intestinal microecological agents were used within one month prior, 198 eligible GC patients were included in the final analysis. All patients were followed up through telephone and outpatient visits, with the follow-up cut-off date being August 1, 2023. This study was approved by the Ethics Committee of Zhejiang Cancer Hospital (Approval No. : IRB-2023-791) and written informed consent was obtained from all participants. Gastric samples were collected from patients undergoing gastrectomy, with the peritumoral tissue being 2–5 cm from the tumor margin. Notably, for metabolomic analysis, tissue specimens were subjected to cold ischemia for less than 30 minutes before being frozen at -80°C. For 16S rRNA sequencing and transcriptomic analysis, tissue specimens were immersed in RNA protective solution at 4°C overnight and then frozen at -80°C. Histological sections from the top and bottom of each specimen were reviewed by board-certified senior pathologists to confirm whether the tissues were tumor or adjacent non-tumor tissues. In this study, tumor samples had to have an average of 60% tumor cell nuclei and less than 20% necrosis to be considered acceptable.

16S rRNA sequencing, RNA-seq assays and Metabolomic assays

The methods and analytical steps for 16S rRNA sequencing, RNA-seq assays and Metabolomic assays are detailed in the supplementary materials.

statistical method

Continuous variables with normal distribution are expressed as mean ± standard deviation (x ± s) or Mean ± SD and analyzed using t-test or Mann-Whitney U test. Categorical variables are presented as counts (n, %) and analyzed using Chi-square test or Fisher's exact test. Propensity score matching (PSM) was used to account for differences in patient backgrounds, with a 1:4 ratio set to minimize selection bias between the two groups. Survival rates were calculated using the Kaplan-Meier method and survival curves were compared using the log-rank test. A Cox proportional hazards model with forward stepwise regression was employed to identify independent prognostic factors. Spearman correlation was used for the joint analysis of microbiome with transcriptome and metabolome. All data were analyzed using SPSS software version 26.0 (IBM USA), the Medsta statistical platform (www.medsta.cn/software), OmicStudio tools (https://www.omicstudio.cn/tool), and R version 4.3.1. All statistical tests were two-sided, and a p-value < 0.05 was considered statistically significant.

16S rRNA-seq, 16S ribosomal RNA sequencing;BMI,Body mass index;GC,gastric cancer;CRC,colorectal cancer;DEGs,differentially expressed genes; KEGG,kyoto encyclopedia of genes and genomes; GO, Gene Ontology; PCoA, principal coordinates analysis; PC, Polymerase Chain Reaction; QC,PSM,Propensity score matching;Quality control; RNA-seq, RNA sequencing; TRM,tissue-resident memory T;TIME, tumor immune microenvironment; TNM, tumor-node-metastasis.COX,Cox regression model;OS,overall survival.

Availability of data and materials

The raw reads of 16S rDNA and the raw data of transcriptome were deposited into the NCBI SRA database (Accession Number: Bioproject PRJNA1061213). The raw data of metabolome deposited into the MetaboLights database (Accession Number: MTBLS9211). The sample groupings used for the microbiome, transcriptome, and metabolome in this study are detailed in Supplementary File 3. The code used in this study and all supporting data are available upon request.

Acknowledgements

The authors would like to thank all of the participants who recruited patients in this study and the colleagues at Zhejiang Cancer Hospital.

Funding

The National Key R&D Program of China (2021YFA0910100), Medical Science and Technology Project of Zhejiang Province (2022KY114, WKJ-ZJ-2104), Natural Science Foundation of Zhejiang Province (HDMY22H160008), National Natural Science Foundation of China (82074245, 81973634, 82204828). Innovation Team and Talents Cultivation Program of National Administration of Traditional Chinese Medicine (No: ZYYCXTD-C-202208)。Medical Science and Technology Project of Zhejiang Province (2018KY305, 2021KY582)。

Author contributions

Zhengchen Jiang, Bo Zhang, Li Yuan, and Shi Wang conceived and designed the study.Kang Liu,Yubo Ma ,Pang Chuhong,Yingsong Zheng,Kailai Yin,and Ruihong Xia acquired the clinical data.Kang Liu, Yubo Ma, and Zhengchen Jiang analyzed the data.Kang Liu,and Zhengchen Jiang completed the data graphic display and drafted the manuscript. Zhou Liu, Li Yuan, and Xiangdong Cheng acquired the funding.All authors read and approved the final manuscript.

Abbreviations：

Competing interests

The authors declare that they have no conflict of interest.

Ethics approval and consent to participate

The Research Ethics Committees of Zhejiang Cancer Hospital approved the study (No. IRB-2023-791), and all patients provided written informed consent.

Consent for publication

Not applicable.

Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 74, 229–263, doi:10.3322/caac.21834 (2024).
Ahn, H. S. et al. Changes in clinicopathological features and survival after gastrectomy for gastric cancer over a 20-year period. British Journal of Surgery 98, 255–260, doi:10.1002/bjs.7310 (2011).
Li, Y. et al. Gastric cancer incidence trends in China and Japan from 1990 to 2019: Disentangling age–period–cohort patterns. Cancer 129, 98–106, doi:10.1002/cncr.34511 (2022).
Chen, Y. et al. Anthropometrics and cancer prognosis: a multicenter cohort study. The American Journal of Clinical Nutrition 120, 47–55, doi:10.1016/j.ajcnut.2024.05.016 (2024).
Ma, S. et al. Low body mass index is an independent predictor of poor long-term prognosis among patients with resectable gastric cancer. World Journal of Gastrointestinal Oncology 13, 161–173, doi:10.4251/wjgo.v13.i3.161 (2021).
Feng, F. et al. Impact of body mass index on surgical outcomes of gastric cancer. BMC Cancer 18, doi:10.1186/s12885-018-4063-9 (2018).
Schooling, C. M. et al. BMI and Lifetime Changes in BMI and Cancer Mortality Risk. Plos One 10, doi:10.1371/journal.pone.0125261 (2015).
Zhao, W. et al. Effects of a high body mass index on the short-term outcomes and prognosis after radical gastrectomy. Surgery Today 51, 1169–1178, doi:10.1007/s00595-021-02259-9 (2021).
Cao, Y. et al. Intratumoural microbiota: a new frontier in cancer development and therapy. Signal Transduction and Targeted Therapy 9, doi:10.1038/s41392-023-01693-0 (2024).
Liu, Z., Zhang, D. & Chen, S. Unveiling the gastric microbiota: implications for gastric carcinogenesis, immune responses, and clinical prospects. Journal of Experimental & Clinical Cancer Research 43, doi:10.1186/s13046-024-03034-7 (2024).
Wang, G., He, X. & Wang, Q. Intratumoral bacteria are an important “accomplice” in tumor development and metastasis. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1878, doi:10.1016/j.bbcan.2022.188846 (2023).
Hsieh, Y.-Y., Kuo, W.-L., Hsu, W.-T., Tung, S.-Y. & Li, C. Fusobacterium Nucleatum-Induced Tumor Mutation Burden Predicts Poor Survival of Gastric Cancer Patients. Cancers 15, doi:10.3390/cancers15010269 (2022).
Mitsuhashi, K. et al. Association ofFusobacteriumspecies in pancreatic cancer tissues with molecular features and prognosis. Oncotarget 6, 7209–7220, doi:10.18632/oncotarget.3109 (2015).
Mima, K. et al. Fusobacterium nucleatumin colorectal carcinoma tissue and patient prognosis. Gut 65, 1973–1980, doi:10.1136/gutjnl-2015-310101 (2016).
15 Li, J. et al. Microbial and metabolic profiles unveil mutualistic microbe-microbe interaction in obesity-related colorectal cancer. Cell Reports Medicine 5, doi:10.1016/j.xcrm.2024.101429 (2024).
Huang, Y. et al. Analysis of differences in intestinal flora associated with different BMI status in colorectal cancer patients. Journal of Translational Medicine 22, doi:10.1186/s12967-024-04903-7 (2024).
Peng, R. et al. Gastric Microbiome Alterations Are Associated with Decreased CD8 + Tissue-Resident Memory T Cells in the Tumor Microenvironment of Gastric Cancer. Cancer Immunology Research 10, 1224–1240, doi:10.1158/2326-6066.Cir-22-0107 (2022).
Indini, A. et al. Impact of BMI on Survival Outcomes of Immunotherapy in Solid Tumors: A Systematic Review. International Journal of Molecular Sciences 22, doi:10.3390/ijms22052628 (2021).
Association, J. G. C. Japanese gastric cancer treatment guidelines 2018 (5th edition). Gastric Cancer 24, 1–21, doi:10.1007/s10120-020-01042-y (2020).
Amin, M. B. et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA: A Cancer Journal for Clinicians 67, 93–99, doi:10.3322/caac.21388 (2017).
Spyrou, N., Vallianou, N., Kadillari, J. & Dalamaga, M. The interplay of obesity, gut microbiome and diet in the immune check point inhibitors therapy era. Seminars in Cancer Biology 73, 356–376, doi:10.1016/j.semcancer.2021.05.008 (2021).
Galeano Niño, J. L. et al. Effect of the intratumoral microbiota on spatial and cellular heterogeneity in cancer. Nature 611, 810–817, doi:10.1038/s41586-022-05435-0 (2022).
Liu, X. et al. Alterations of gastric mucosal microbiota across different stomach microhabitats in a cohort of 276 patients with gastric cancer. EBioMedicine 40, 336–348, doi:10.1016/j.ebiom.2018.12.034 (2019).
Mosca, A. M., Mané, F., Marques Pires, C. & Medeiros, P. Infective endocarditis by a rare and fastidious agent:Abiotrophia defectiva. BMJ Case Reports 14, doi:10.1136/bcr-2021-241964 (2021).
Sasaki, M., Shimoyama, Y., Kodama, Y. & Ishikawa, T. Abiotrophia defectiva DnaK Promotes Fibronectin-Mediated Adherence to HUVECs and Induces a Proinflammatory Response. International Journal of Molecular Sciences 22, doi:10.3390/ijms22168528 (2021).
Mäkinen, A. I. et al. Salivary microbiome profiles of oral cancer patients analyzed before and after treatment. Microbiome 11, doi:10.1186/s40168-023-01613-y (2023).
Wu, J. et al. Tongue Coating Microbiota Community and Risk Effect on Gastric Cancer. Journal of Cancer 9, 4039–4048, doi:10.7150/jca.25280 (2018).
Ferrari, D. et al. Eosinophils and Purinergic Signaling in Health and Disease. Frontiers in Immunology 11, doi:10.3389/fimmu.2020.01339 (2020).
Gómez Morillas, A., Besson, V. C. & Lerouet, D. Microglia and Neuroinflammation: What Place for P2RY12? International Journal of Molecular Sciences 22, doi:10.3390/ijms22041636 (2021).
Li, X., Zhang, G. & Cao, X. The Function and Regulation of Platelet P2Y12 Receptor. Cardiovascular Drugs and Therapy 37, 199–216, doi:10.1007/s10557-021-07229-4 (2021).
Ben Addi, A., Cammarata, D., Conley, P. B., Boeynaems, J.-M. & Robaye, B. Role of the P2Y12 Receptor in the Modulation of Murine Dendritic Cell Function by ADP. The Journal of Immunology 185, 5900–5906, doi:10.4049/jimmunol.0901799 (2010).
Mansour, A., Bachelot-Loza, C., Nesseler, N., Gaussem, P. & Gouin-Thibault, I. P2Y12 Inhibition beyond Thrombosis: Effects on Inflammation. International Journal of Molecular Sciences 21, doi:10.3390/ijms21041391 (2020).
Noorani, I. et al. Clinical impact of anti-inflammatory microglia and macrophage phenotypes at glioblastoma margins. Brain Communications 5, doi:10.1093/braincomms/fcad176 (2023).
Yu, L. et al. Prognostic value and immune infiltration of a novel stromal/immune score-related P2RY12 in lung adenocarcinoma microenvironment. International Immunopharmacology 98, doi:10.1016/j.intimp.2021.107734 (2021).
Ma, C. et al. Platelets control liver tumor growth through P2Y12-dependent CD40L release in NAFLD. Cancer Cell 40, 986–998.e985, doi:10.1016/j.ccell.2022.08.004 (2022).
Zhou, Y., Cheng, L., Liu, L. & Li, X. NK cells are never alone: crosstalk and communication in tumour microenvironments. Molecular Cancer 22, doi:10.1186/s12943-023-01737-7 (2023).
Yousefi, S. et al. Catapult-like release of mitochondrial DNA by eosinophils contributes to antibacterial defense. Nature Medicine 14, 949–953, doi:10.1038/nm.1855 (2008).
Rosenberg, H. F., Masterson, J. C. & Furuta, G. T. Eosinophils, probiotics, and the microbiome. Journal of Leukocyte Biology 100, 881–888, doi:10.1189/jlb.3RI0416-202R (2016).
Varricchi, G. et al. Eosinophils: The unsung heroes in cancer? OncoImmunology 7, doi:10.1080/2162402x.2017.1393134 (2017).
Iwasaki, K., Torisu, M. & Fujimura, T. Malignant tumor and eosinophils: I. Prognostic significance in gastric cancer. Cancer 58, 1321–1327, doi:10.1002/1097-0142(19860915)58:6<1321::Aid-cncr2820580623>3.0.Co;2-o (1986).
Cuschieri, A., Talbot, I. C. & Weeden, S. Influence of pathological tumour variables on long-term survival in resectable gastric cancer. British Journal of Cancer 86, 674–679, doi:10.1038/sj.bjc.6600161 (2002).
Farhan, R. K. et al. Effective antigen presentation to helper T cells by human eosinophils. Immunology 149, 413–422, doi:10.1111/imm.12658 (2016).
Natarajan, N. & Pluznick, J. L. From microbe to man: the role of microbial short chain fatty acid metabolites in host cell biology. American Journal of Physiology-Cell Physiology 307, C979-C985, doi:10.1152/ajpcell.00228.2014 (2014).
Xie, J., Liu, J., Chen, X. & Zeng, C. Purinosomes involved in the regulation of tumor metabolism: current progress and potential application targets. Frontiers in Oncology 14, doi:10.3389/fonc.2024.1333822 (2024).
Yin, J. et al. Potential Mechanisms Connecting Purine Metabolism and Cancer Therapy. Frontiers in Immunology 9, doi:10.3389/fimmu.2018.01697 (2018).
Dai, D. et al. Interactions between gastric microbiota and metabolites in gastric cancer. Cell Death & Disease 12, doi:10.1038/s41419-021-04396-y (2021).
Kaji, S. et al. Metabolomic profiling of gastric cancer tissues identified potential biomarkers for predicting peritoneal recurrence. Gastric Cancer 23, 874–883, doi:10.1007/s10120-020-01065-5 (2020).
Tran, D. H. et al. De novo and salvage purine synthesis pathways across tissues and tumors. Cell 187, 3602–3618.e3620, doi:10.1016/j.cell.2024.05.011 (2024).
Borea, P. A., Gessi, S., Merighi, S., Vincenzi, F. & Varani, K. Pharmacology of Adenosine Receptors: The State of the Art. Physiological Reviews 98, 1591–1625, doi:10.1152/physrev.00049.2017 (2018).
Burnstock, G. & Di Virgilio, F. Purinergic signalling and cancer. Purinergic Signalling 9, 491–540, doi:10.1007/s11302-013-9372-5 (2013).

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Multiomics-based Analysis Reveals Differences in intratumoral microbiota and Prognostic in Gastric Cancer Patients with Different BMI

Status:

Version 1

Abstract

Figures

Introduction

Results

LBMI is an independent prognostic risk factor for poor prognosis in patients with GC

Intratumoral Microbiome Landscape in LBMI and NLBMI Gastric Cancer Patients

LBMI intratumor g_Abiotrophia was significantly elevated

LBMI intratumoral g_Abiotrophia negatively correlates with P2RY12

LBMI intratumoral g_Abiotrophia negatively correlates with eosinophils

High purine metabolism in LBMI tumors

Discussion

Conclusion

Material and Methods

Clinical Cohort Data Collection and Definitions

Clinical specimen collection and preparation

16S rRNA sequencing, RNA-seq assays and Metabolomic assays

statistical method

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1