[go: up one dir, main page]
Skip to main content
Download PDF

Early and long-term responses of intestinal microbiota and metabolites to 131I treatment in differentiated thyroid cancer patients

BMC Medicine volume 22, Article number: 300 (2024) Cite this article

Abstract

Background

Multiple high doses of 131I therapy in patients with differentiated thyroid cancer (DTC) might disrupt the balance of gut microbiota and metabolites. This study aimed to investigate the alterations of intestinal bacteria and metabolism over two courses of 131I therapy, explore the interactions, and construct diagnostic models reflecting enteric microecology based on 131I therapy.

Methods

A total of 81 patients were recruited for the first 131I therapy (131I-1st), among whom 16 received a second course (131I-2nd) after half a year. Fecal samples were collected 1 day before (Pre-131I-1st/2nd) and 3 days after (Post-131I-1st/2nd) 131I therapy for microbiome (16S rRNA gene sequencing) and metabolomic (LC–MS/MS) analyses.

Results

A total of six microbial genera and 11 fecal metabolites enriched in three pathways were identified to show significant differences between Pre-131I-1st and other groups throughout the two courses of 131I treatment. In the Post-131I-1st group, the beneficial bacteria Bifidobacterium, Lachnoclostridium, uncultured_bacterium_f_Lachnospiraceae, and Lachnospiraceae_UCG004 were abundant and the radiation-sensitive pathways of linoleic acid (LA), arachidonic acid, and tryptophan metabolism were inhibited compared with the Pre-131I-1st group. Compared with the Pre-131I-1st group, the Pre-131I-2nd group exhibited a reduced diversity of flora and differentially expressed metabolites, with a low abundance of beneficial bacteria and dysregulated radiation-sensitive pathways. However, less significant differences in microbiota and metabolites were found between the Pre/Post-131I-2nd groups compared with those between the Pre/Post-131I-1st groups. A complex co-occurrence was observed between 6 genera and 11 metabolites, with Lachnoclostridium, Lachnospiraceae_UCG004, Escherichia-Shigella, and LA-related metabolites contributing the most. Furthermore, combined diagnostic models of charactered bacteria and metabolites answered well in the early, long-term, and dose-dependent responses for 131I therapy.

Conclusions

Different stages of 131I therapy exert various effects on gut microecology, which play an essential role in regulating radiotoxicity and predicting the therapeutic response.

Peer Review reports

Background

As the most common endocrine malignancy, thyroid cancer (TC) has an increasing annual global incidence, with differentiated thyroid cancer (DTC) accounting for approximately 90% of the cases [1]. Most patients with DTC exhibit positive outcomes after standard treatments, including surgery, 131I, and levothyroxine replacement for suppression of thyroid hormones. However, some patients require multiple 131I therapies with higher doses due to the residual thyroid tissue and local or distant metastatic mass, which increases the chances of adverse reactions in the hematopoietic, gastrointestinal, and even marrow systems [2, 3]. Nonetheless, few noninvasive tests are available to investigate the early and long-term responses of DTC patients under 131I therapy.

Since 131I is administered orally, it accumulates in the gastrointestinal tract and may affect gut microbes [4]. Until now, many studies have reported that ionizing radiation (IR) affects the structure, function, and species of the gut microbiota and its metabolites by triggering dysbiosis, further exacerbating damage induced by IR [5,6,7,8]. On the contrary, beneficial bacteria and favorable metabolites could rebuild the IR-injured gut microbial structure via anti-inflammatory and anti-oxidant mechanisms [6, 9, 10]. In addition, previous studies also revealed a significant difference in gut microbes and metabolites between DTC patients and health controls, with the imbalanced composition of microbiota and homeostasis of lipid metabolism possibly accelerating the progression of thyroid cancer [11, 12]. Interestingly, profiles of gut microbiota could also predict the treatment effect of 131I therapy in DTC patients [4]. All these findings indicated that the gut microbiome and its related metabolites were closely related to DTC and 131I therapy, highlighting its importance in the prevention of radiotoxicity and risk factors of DTC progression.

Nevertheless, the functional alterations in enteric microbiota and their radiation-associated metabolites at different stages of 131I treatment in DTC remain unclear. Hence, gut 16S-rDNA sequencing and metabolome analyses were performed in a cohort of patients with DTC over two courses to validate 131I-related changes in the microbial and metabolic signatures of DTC patients.

Methods

Study subjects and design

The study enrolled 81 patients with DTC undertaking the first 131I therapy (131I-1st) and 16 follow-ups among them after half a year undertaking the second 131I therapy (131I-2nd) from the Department of Nuclear Medicine, Shanghai Tenth People’s Hospital, between March 2021 and March 2022 (Table 1). The diagnosis of DTC was based on the following criteria: (i) clinically diagnosed with DTC within 6 months [13], (ii) age between 18 and 75 years, female (nonpregnant) or male without history of other malignancies, and (iii) receiving 131I treatment. The following patients were also excluded: (i) patients confirmed not to have thyroid cancer on postoperative pathology, (ii) subjects suffering from a gastrointestinal disease or any other severe physical or mental disease, and (iii) subjects with a history of probiotics, orally ingested antibiotics, or any other similar drug within 2 months before fecal sampling. Blood routine examination, thyroid function, and biochemical and inflammatory indicators in the blood samples were measured (Table 1). The study was carried out following the Declaration of Helsinki principles and approved by the Ethics Committee of the Shanghai Tenth People’s Hospital (No. SHSY-IEC-5.0/22K13/P01). Informed, written consent was obtained from all patients.

Table 1 Baseline characteristics of patients in 131I-1st and 131I-2nd groups
Full size table

Collection of fecal samples

Fecal samples were collected in the morning after an overnight fast (> 8 h) 1 day before (Pre-131I-1st/2nd) and 3 days after (Post-131I-1st/2nd) 131I treatment. Fecal samples were snap-frozen with liquid nitrogen, collected, and stored at − 80 °C (Haier, DW-86L626, China) [14].

Microbiomic analysis

Microbial DNA was extracted from fecal samples using a QIAamp Fast DNA stool Mini Kit (Qiagen, Cat# 51,604) and amplified with barcoded bacterial primers targeting the 16S rRNA variable region 3–4 (V3–V4): forward primer 338F:5′-ACTCCTACGGGAGGCAGCA-3′ and reverse primer 806R:5′-GGACTACHVGGGTWTCTAAT-3′ [15]. Illumina NovaSeq6000 was used for the construction of sequencing libraries and paired-end sequencing of samples. Paired-end reads were merged using FLASH v1.2.7, and tags with more than six mismatches were removed [16]. Using Trimmomatic, tags with a quality score of 20 within a sliding window of 50 bps were determined, and those shorter than 350 bps were removed [17]. A total of 172 chimeras were removed from the denoised sequences and they were clustered into operational taxonomic units (OTUs) with a 97% similarity using the USEARCH (version 10.2) program. All OTUs were assigned a taxonomy by searching the Silva databases (Release128) using QIIME.

Untargeted metabolomic analysis

Fifty milligrams of fecal sample was weighed into an EP tube, and 1000 μL of extract solution (methanol: acetonitrile: water = 2:2:1, with isotopically labeled internal standard mixture) was added. The samples were then homogenized at 35 Hz for 4 min and sonicated for 5 min in an ice-water bath. Homogenization and sonication cycles were repeated for three times. The samples were then incubated for 1 h at − 40 °C and centrifuged at 12,000 rpm for 15 min at 4 °C. The resulting supernatant was transferred into a fresh glass vial for further analysis. A quality control sample was prepared by mixing an equal aliquot of the supernatant from all samples.

LC–MS/MS analysis

LC–MS/MS analyses were performed using an UHPLC system (Vanquish, Thermo Fisher Scientific) with a UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 μm) coupled to Q Exactive HFX mass spectrometer (Orbitrap MS, Thermo). The mobile phase consisted of 25 mmol/L ammonium acetate and 25 ammonia hydroxides in water (pH = 9.75) (A) and acetonitrile (B). The auto-sampler temperature was 4 ℃, and the injection volume was 3 μL. The QE HFX mass spectrometer was used for its ability to acquire MS/MS spectra on information-dependent acquisition (IDA) mode in the control of the acquisition software (Xcalibur, Thermo). In this mode, the acquisition software continuously evaluates the full scan MS spectrum. The ESI source conditions were set as follows: sheath gas flow rate as 30 Arb, Aux gas flow rate as 25 Arb, capillary temperature 350 ℃, full MS resolution as 60,000, MS/MS resolution as 7500, collision energy as 10/30/60 in NCE mode, spray Voltage as 3.6 kV (positive) or − 3.2 kV (negative), respectively.

Statistical analysis

Data are presented as the mean ± standard deviation or median (interquartile range). To assess significance, the mean values of independent groups were compared using Student’s t test and Wilcoxon rank-sum test; categorical variables were assessed by the chi-square test as follows: *, p < 0.05; **, p < 0.01; ***, p < 0.001; ****, p < 0.0001. False discovery rate (FDR) was used to adjust p values in differential expressed bacteria and metabolite analyses. Statistical significance was set at p < 0.05.

Results

Clinical characteristics of the participants

The basic information of the 81 DTC patients receiving 131I therapy for the first time (131I-1st), among whom 16 received a second course (131I-2nd), are displayed in Table 1. Compared with the 131I-1st group, the 131I-2nd group showed a higher clinical stage (II/III/IV) and dose of 131I treatment, but lower percentage of lymphocytes. In contrast, gender, age, body mass index (BMI), thyroid function, inflammation, and liver function showed no significant difference between the groups.

Features of gut bacterial community composition and structure

Furthermore, microbiome analyses were performed on fecal samples from DTC patients before and after 131I therapy (Fig. 1). The results were processed for quality filtering, and chimeric sequences were removed, yielding an average of 58,152 sequences (derived from 67,859 raw reads) for abundance and diversity analyses. The rarefaction curve revealed great species richness and evenness (Fig. 2A) and the rank abundance curve (Fig. 2B) confirmed the thoroughness of the sample collection. The α diversity of Ace, Chao1, PD_whole_tree (Fig. 2C-E), Simpson, and Shannon (Additional file 1: Fig.S1A, B) indices were significantly decreased in the Post-131I-1st group compared to the Pre-131I-1st group and in the Post-131I-2nd group compared to the Pre-131I-2nd group. The Pre-131I-2nd group demonstrated a small reduction compared to the Pre-131I-1st group. In addition, principal coordinate analysis (PCoA) based on binary and unweighted distances (Fig. 2F, G) revealed significant differences between the Pre-131I-1st/2nd groups and Post-131I-1st/2nd groups.

Fig. 1
figure 1

Overview of the workflow for the microbiomic and metabolomic strategy used in this study

Full size image
Fig. 2
figure 2

Features of gut bacterial community composition and structure. α diversity analysis in rarefaction curve (A), rank abundance curve (B), Ace (C), Chao1 (D), and PD_whole_tree (E) indices among four groups. β diversity analysis on principal coordinates analysis (PCoA) for binary (F) and unweighted distances (G) among four groups. Abundance distribution on Phylum (H) and Family (I) level among four groups. J Annotated abundance heatmap of gut microbial features of the top 20 genera based on Ward hierarchical clustering. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patients with DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patients with DTC after the second 131I therapy

Full size image

Moreover, remarkable differences were observed in all groups from the Phylum to Genus levels (Fig. 2H, I). At the Phylum level, the relative abundance of Firmicutes was elevated in the Post-131I-1st/2nd groups compared to the Pre-131I-1st/2nd groups, which was inconsistent with Proteobacteria (Fig. 2H) and the ratio of Firmicutes/Bacteroidetes (F/B, Additional file 1: Fig.S1C). At the Family level, Enterobacteriaceae decreased in the Post-131I-1st group compared with Pre-131I-1st group, whereas Lachnospiraceae and Bifidobacteriaceae were enriched in the Post-131I-1st group (Fig. 2I). However, the differences between the Pre-and Post-131I-2nd groups were not considerable. A broad overview of the taxonomic data at the Genus level of all samples is shown in Fig. 2J.

Alterations in microbial genus compositions at different stages of DTC with 131I therapy

To investigate the difference in bacterial composition among the 4 groups, linear discriminate analysis effect size (LEfSe) analysis (Fig. 3A–C) and the Wilcoxon rank-sum test (Additional file 2: Fig.S2A-C) were conducted. According to the above results, two altered patterns were identified on the genus level. The first pattern showed an increased abundance, while the second pattern presented a decreased abundance in the Post-131I-1st/2nd groups compared to the Pre-131I-1st/2nd groups. The first pattern included four genera, including Bifidobacterium, Lachnoclostridium, uncultured_bacterium_f_Lachnospiraceae, and Lachnospiraceae_UCG004 (Fig. 3D). The second pattern included two genera, namely Akkermansia and Escherichia-Shigella (Fig. 3D). Intriguingly, uncultured_bacterium_f_Lachnospiraceae in the first pattern showed a lower abundance in the Pre-131I-2nd group compared to the Pre-131I-1st group, displaying an opposite trend in pattern2 (Fig. 3B). Similar with the results at the Family level, the differences between the Pre- and Post-131I-2nd groups were not obvious (Fig. 2H; Fig. 3D). Additionally, the Post-131I-1st/2nd groups were integrated and divided into the 131I-high dose (for metastatic diseases, n = 24, ≥ 150 mCi) and 131I-low dose (for thyroid remnants or adjuvant therapy, n = 73, < 150 mCi) groups [18, 19]. Surprisingly, the abundance of Lachnospiraceae_UCG004 was significantly improved in the high-dose group (Fig. 3E), showing reversed results to Escherichia-Shigella (Fig. 3F) and Akkermansia (Additional file 2: Fig.S2D).

Fig. 3
figure 3

Alterations in microbial genus compositions at different stages of DTC with 131I therapy

Full size image

Linear discriminate analysis (LDA) effect size (LEfSe) revealing differential microbiota on the genus level between Post- and Pre-131I-1st (A), Pre-131I-2nd and Pre-131I-1st (B), Post- and Pre-131I-2nd (C), charactered genera marked with red. D Taxonomic analysis showed six microbial genera were changed among four groups. Abundance of g_Lachnospiraceae_UCG004 (E) and g_Escherichia-Shigella (F) between low (< 150 mCi) and high (≥ 150 mCi) dose of 131I therapy. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patients with DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patients with DTC after the second 131I therapy.

Fecal metabolomic profiles based on 131I treatment progress

Next, the metabolome of the enteric flora was evaluated in DTC patients to reveal the alterations in metabolites during continued 131I therapy (Fig. 1). The score plots of principal components analysis (PCA) and partial least square regressions-discriminant analysis (PLS-DA) clearly separated the Pre-131I-1st/2nd groups from Post-131I-1st/2nd groups (Fig. 4A, B). These findings revealed an obvious metabolite variability across different stages of 131I treatment. According to the results of pairwise contrasts by volcano plots, a greater number of downregulated differential metabolites were observed in the Post-131I-1st group compared to the Pre-131I-1st group (Fig. 4C). In addition, fewer metabolites were upregulated in the Pre-131I-2nd group compared to the Pre-131I-1st group (Fig. 4D); however, no distinct difference was found between the Pre- and Post-131I-2nd groups (Fig. 4E). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted for all differential metabolites, further exploring their biological function (Fig. 4F–H). Notably, the two essential metabolic pathways involved in LA metabolism and arachidonic acid (ARA) metabolism were strongly downregulated in the Post-131I-1st/2nd groups compared to the Pre-131I-1st/2nd groups (Fig. 4F, H). The pathways of LA and tryptophan metabolism were downregulated in the Pre-131I-2nd group compared to the Pre-131I-1st group (Fig. 4G).

Fig. 4
figure 4

Fecal metabolomic profiles in accordance with 131I treatment progress. Principle components analysis (PCA) (A) and partial least square regressions-discriminant analysis (PLS-DA) (B) results among four groups. Volcano plot of differential metabolites (fold change ≤ 0.5 or > 2) between Post- and Pre-131I-1st (C), Pre-131I-2nd and Pre-131I-1st (D), Post-and Pre-131I-2nd (E). Differential abundance scores (DAS) of KEGG pathways between Post- and Pre-131I-1st (F), Pre-131I-2nd and Pre-131I-1st (G), Post- and Pre-131I-2nd (H), radiation-sensitive metabolic pathways marked red. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patients with DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patients with DTC after the second 131I therapy

Full size image

To investigate the influence of the differential metabolites in these three metabolic pathways (radiation-sensitive metabolic pathways), reaction pathways were manually constructed using the KEGG pathway reference map (Fig. 5). The results revealed that five metabolites (alpha-dimorphecolic acid, 13-OxoODE, 13-L-Hydroperoxylinoleic acid, PC(22:2(13Z,16Z)/14:0), and 13S-hydroxyoctadecadienoic acid) derived from the LA metabolism pathway, four metabolites (5,6-DHET, 20-Carboxy-leukotriene B4, 6-Ketoprostaglandin E1, delta-12-Prostaglandin J2) derived from the ARA metabolism pathway, and two metabolites (indoleacetic acid, formylanthranilic acid) derived from the tryptophan metabolism pathway were consistent with their pathways’ regulation by 131I therapy. Meanwhile, a close metabolic association was found between the LA and ARA metabolism pathways (Fig. 5). Our previous study and other research have reported that the administration of metabolites in ARA and tryptophan metabolism pathways could exert an anti-radiotoxicity effect [6, 9, 20, 21]. Additionally, anti-inflammatory and antioxidative effects have also been linked to intestinal LA metabolism pathways [22, 23]. Our study also preliminarily revealed that the supplementation of LA in mice undergoing 131I therapy could provide some protection against radiation toxicity (Additional file 3: Fig.S3A-S3F; Additional file 4: Supplemental Methods). Therefore, metabolites in these radiation-sensitive metabolic pathways might play a crucial role in radiation protection.

Fig. 5
figure 5

Changes of gut metabolites across the 131I therapy summarized in KEGG pathways. 131I therapy-related changes of gut metabolites summarized in radiation-sensitive metabolic pathways among four groups according to KEGG pathway reference map. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patients with DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patients with DTC after the second 131I therapy

Full size image

Disrupted gut microbiota interactions with fecal metabolism and clinical factors associated with DTC patients under 131I therapy

An integrated network analysis was performed according to the microbial and metabolic data to identify the possible mechanisms between differential intestinal flora and metabolites in 131I therapy (Fig. 6A, B; Additional file 5: Fig.S4A-C). Consequently, an elaborate co-existence pattern and a robust connection were found between 131I therapy progress-related microbes and metabolites from the three radiation-sensitive metabolic pathways (Fig. 6A). Meanwhile, Lachnoclostridium, Lachnospiraceae_UCG004, and Escherichia-Shigella counts were closely correlated with fecal metabolites in the LA metabolism pathways (Fig. 6B), which were recognized as main contributors in this integrated network, suggesting their crucial roles in the different phases of 131I therapy. Interestingly, Bifidobacterium, Lachnoclostridium, uncultured_bacterium_f_Lachnospiraceae, and Lachnospiraceae_UCG004 were negatively associated with the pathways of LA, ARA, and tryptophan metabolism, with a converse trend in Akkermansia and Escherichia-Shigella (Fig. 6A, B). Furthermore, the α diversity of all the indices was strongly negatively correlated with Escherichia-Shigella, but positively correlated with F/B, uncultured_bacterium_f_Lachnospiraceae, Lachnospiraceae_UCG004, and metabolites in 3 pathways (Fig. 6C). Moreover, the redundancy analysis was performed to verify the impact of metabolites (Fig. 6D) and clinical factors (Fig. 6E) on the bacteria sample distribution. The results indicated that metabolites from the LA metabolism pathway, tumor necrosis factor-alpha (TNFα), red blood cells (RBCs), and platelets (PLTs) contributed the most to the sample distribution. Moreover, these clinical factors (Fig. 6F) and other inflammation indicators (Fig. 6G–K) showed a high correlation with the identified bacteria and metabolites.

Fig. 6
figure 6

Disrupted gut microbiota interactions with fecal metabolism and clinical factors associated with DTC patients under 131I therapy. A Integrated network of 131I therapy -related microbial genera and fecal metabolites. Networks were constructed using NAMAP with Spearman’s rank correlations. B The SANKEY diagrams show associations between 131I therapy-related microbial genera and fecal metabolites. The most significant factors for the associations marked red. C Spearman correlation analyses between α diversity indices and 131I therapy-related microbial genera and fecal metabolites. Redundancy analysis (RDA) analysis of metabolites (D) and clinical factors (E) on the Genus level, red marked factors contributed most to sample distribution. F Spearman correlation analyses between clinical factors important in RDA and 131I therapy-related microbial genera and fecal metabolites. Abundance of Escherichia-Shigella (G), and levels of PC(22:2(13Z,16Z)/14:0) (H), Delta-12-Prostaglandin J2 (I), and Formylanthranilic acid (J) between low and high levels of interleukin 2 receptor (IL-2). K Abundance of Escherichia-Shigella between low and high levels of interleukin 6 (IL-6). * p < 0.05, ** p < 0.01, *** p < 0.001

Full size image

Predictive models for response to 131I therapy based on microbiome and metabolism

Classifiers of logistic regression and random forest (RF) were constructed to determine the non-invasive diagnostic markers based on gut microbial and metabolomic factors. The exploratory cohort (70%) was randomly generated from the two compared groups in the corresponding model (Fig. 7A). Model1 consisted of 7–8 factors in two classifiers and was constructed to distinguish the Post-131I-1st from the Pre-131I-1st group (Fig. 7B, C; Additional file 5: Fig.S5A), achieving a total area under the curve (AUC) of 0.96 (logistic) and 0.92 (RF) (Fig. 7D); this model was further validated (Z > 0.05, Fig. 7E). Over half of the common factors were found between two classifiers in Model1, including PD_whole_tree in α diversity, Akkermansia, Lachnospiraceae_UCG004, and 5,6-DHET (Fig. 7B, C). These results indicated significant alterations in the gut microenvironment after 131I-1st. In contrast, Model2 consisted of PC(22:2(13Z,16Z)/14:0) and delta-12-Prostaglandin J2 in logistic and RF classifiers, and was constructed to discriminate between the Pre-131I-2nd and Pre-131I-1st groups (Fig. 7F, G; Additional file 6: Fig.S5B). This model achieved a total AUC of 0.75 in the two classifiers (Fig. 7H) and showed good validation (Z > 0.05, Fig. 7I). Despite the small difference in clinical data between the two groups (Table 1), the changes in fecal metabolites in DTC patients undergoing 131I therapy showed larger changes. Furthermore, Model3 consisted of 2–3 factors in logistic and RF classifiers and was constructed to discriminate the 131I-high dose group from the 131I-low dose group (Fig. 7J, K; Additional file 5: Fig.S5C); the model achieved a total AUC of 0.72 in two classifiers (Fig. 7I) and showed good validation (Z > 0.05, Fig. 7J). Escherichia-Shigella was the only common genus in the two classifiers, which was sensitive to 131I therapy, consistent with the results in Fig. 3F. Besides, Model4 was aimed to distinguish between the Post-131I-2nd group and the Pre-131I-2nd group but could not be constructed as no factors were incorporated into models, indicating no significant differences between the two groups.

Fig. 7
figure 7

Predictive models combined with microbiome and metabolism for response to 131I therapy. A Schematic of sample distribution to exploratory cohort and validation cohort. B Eight factors were selected by the forward stepwise logistic regression in discriminations between Post- and Pre-131I-1st groups (Model1). C The importance ranking of the top seven factors in random forest model (RF) of Model1. Receiver operating characteristic (ROC) curves in exploration set (D) and validation set (E) of Model1. F Two factors were selected by the forward stepwise logistic regression in discriminations between Pre-131I-2nd and Pre-131I-1st groups (Model2). G The importance ranking of the top two factors in RF of Model2. ROC curves in exploration set (H) and validation set (I) of Model2. J Two factors were selected by the forward stepwise logistic regression in discriminations between high (≥ 150 mCi) and low (< 150 mCi) dose of 131I therapy groups (Model3). K The importance ranking of the top three factors in RF of Model3. ROC curves in exploration set (L) and validation set (M) of Model3. DTC, differentiated thyroid cancer; HR, hazard ratio; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patients with DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy

Full size image

Discussion

Recent research has revealed that gut microbiomes and metabolism are significantly altered by IR [5,6,7,8,9,10, 24,25,26]. However, few studies have focused on the effects of IR from radionuclides [4, 21, 27, 28]. According to a recent position article by the European Association of Nuclear Medicine [29], radiotherapy cannot be extrapolated to nuclear medicine directly, due to differences in dose-rate effects, linear energy transfer, dosimetry, fractionation, duration of treatment delivery, range, and target volume [30,31,32]. Therefore, an integrated study of fecal microbe genomics and fecal metabolomes was performed in a DTC cohort of multiple 131I therapies. The fluctuations of intestinal flora and fecal metabolites were investigated in the 131I treatment process, which might reflect the disrupted enteric microecology by radionuclides (Fig. 8). The results indicated that some gut bacteria and fecal metabolites might serve as noninvasive biomarkers for acute and chronic responses of DTC patients under 131I treatment.

Fig. 8
figure 8

Putative mechanism of disrupted gut microbiome accompanied with fecal metabolites in DTC patients undertaking twice 131I therapy. Fluctuations of intestinal flora and fecal radiation-sensitive metabolites in the 131I treatment progress, which might reflect and regulate enteric microecology disrupted by radionuclide interruption

Full size image

In the early response for 131I therapy (4 days), the findings suggested a decreased abundance of Akkermansia and Escherichia-Shigella and an increased abundance of Bifidobacterium, Lachnoclostridium, uncultured_bacterium_f_Lachnospiraceae, and Lachnospiraceae_UCG004. In the mouse model of radiation enteritis with 18 Gy irradiation, the richness of Akkermansia and Escherichia-Shigella after irradiation might contribute to radiation-induced pathogenesis [33, 34]. Regarding the ability to produce short-chain fatty acids (SCFA), Bifidobacterium and Lachnospiraceae are commonly recognized to play a beneficial role in gut health, further adopted in the treatment of gastrointestinal diseases induced by radiation [6, 35]. Our results indicated that 131I therapy (about 100 mCi) cleared harmful bacteria and preserved the beneficial bacteria in a short period of time. Additionally, our study identified the downregulated pathways of LA, ARA, and tryptophan metabolism after 131I therapy. Some metabolites involved in pathways of tryptophan have been found to significantly decrease the damage of radiation, such as 3-propionic acid [9], indole-3-carboxaldehyde [6], and kynurenic acid [6]. Moreover, increased production of LA, the precursor of ARA, strongly contributed to radiation resistance, with ARA-derived metabolite inhibitors exerting sensitizing effects, including aspirin [36, 37]. Wang’s research and our results further verified the radioprotective effect of supplementing ARA and LA [20, 21]. Based on these findings, all differential pathways in our study were hypothesized to be involved in radiation resistance, and inhibiting these radiation-sensitive metabolic pathways might result in radiation damage.

In the long-term response for 131I therapy (6 months), strikingly different results were found compared with the early response, including the deterioration of microbiome α diversity, with four beneficial bacteria reduced and two harmful bacteria improved; differentially expressed metabolites were downregulated and radiation-sensitive metabolic pathways were silenced; TC showed greater progression in the Pre-131I-2nd group compared to the Pre-131I-1st group, which was consistent with Zheng’s findings [4]. Gut microbiota might be a short-term protective factor against radionuclide toxicity due to their ability to self-regulate but cannot compensate over extended periods, leading to persistent metabolic damage. 131I therapy exerts a significant negative influence on gut microbiota and metabolism [4]. However, the alterations in bacteria or metabolites after the second 131I therapy with a higher dose were not as great as the first therapy, despite showing the same tendency. This indicated the limited ability of the body to regulate gut microbiota and metabolism against multiple radionuclide interventions. Gambale’s study also revealed that only a minority of cases benefit from the second 131I treatment [38]. Considering the non-significant response of intestinal ecology to the second 131I treatment, additional research should investigate the relations between the therapeutic effect of a second 131I and the regulation of gut microbiota and metabolism.

The integrated network analysis based on Spearman’s correlation revealed mutual interactions among the genera from two patterns and metabolic products from three radiation-sensitive metabolic pathways. In parallel, Lachnoclostridium, Lachnospiraceae_UCG004, Escherichia-Shigella, and LA-derived metabolites were the primary contributors to our integrated network, with high connection with inflammation factors across the 131I therapy. Escherichia-Shigella was reported to play a crucial role in proinflammatory responses [39]. Lachnospiraceae is a well-known SCFA producer, recognized as a regulator for intestinal inflammation [40, 41]. Metabolites in the LA metabolism pathway were found to have anti-inflammatory, anti-proliferative, and antithrombotic properties [22, 42]. Considering the proinflammatory and anti-inflammatory effects involved in these characterized bacteria and related metabolic pathways, and the potential function of LA pathway enrichment in repairing and promoting intestine recovery following 131I therapy [28], the replenishment of essential bacteria or metabolites were speculated to protect against radiation toxicity via inflammation resistance, needed to be investigated in future studies.

Finally, three diagnostic models were established, including key microbiota and metabolites for indicating the early (Model1), long-term (Model2), and dose-dependent (Model3) responses for 131I therapy by two classifiers. The reliability and accuracy of the three models were proved by the similar factors involved in each model by different classifiers and by their corresponding validation cohort. Model1 contains more than one-third of the factors of characterized bacteria and metabolites and showed a high AUC value, indicating the great discriminative ability of the model and the significant alterations in intestinal ecology in the early response to 131I therapy. Fewer factors were included in both Model2 and Model3 compared to Model1, which might be attributed to the lower number of patients in the analyzed cohorts. Nevertheless, the metabolites in the LA and ARA pathways were identified (PC(22:2(13Z,16Z)/14:0) and delta-12-Prostaglandin J2) as biomarkers for distinguishing the chronic response of 131I therapy, as opposed to the minor differences of clinical factors between DTC and Pre-131I-2nd groups. As the only common bacteria observed in Model3, Escherichia-Shigellas showed strong radiosensitivity under 131I therapy and might be recognized as an independent risk factor for screening the effective treatment dose with radionuclides in future studies.

Nevertheless, the limitations of the current study should be acknowledged. First, despite the integrated analyses of microbiomics and metabolomics, our study lacks novelty as the role of gut microbiota in response to 131I treatment in DTC patients has already been investigated. Second, only 16S rRNA gene sequencing and fecal metabolomic analyses were performed, and metagenomics and serum metabolomics are necessary for further investigation and functional analysis. Finally, the relationship between metabolites in radiation-sensitive metabolic pathways and other microbiota under 131I therapy remains unclear. Additional experimental verification might be required to further corroborate our findings.

Conclusions

Considerable changes in the gut microbes were identified with metabolic alterations in LA, ARA, and tryptophan metabolism in DTC patients during multiple 131I therapy courses. Additionally, the cross-interactions among characterized bacteria and metabolites from three radiation-sensitive metabolic pathways demonstrated a close association with different stages of 131I treatment. Moreover, the early and long-term 131I treatment-related changes in gut microbiota combined with fecal metabolites might represent a noninvasive diagnostic method for radionuclide damage.

Availability of data and materials

The original data presented in this study are available from the corresponding author upon reasonable request.

Abbreviations

131I-1st :

The first 131I therapy

131I-2nd :

The second 131I therapy

ARA:

Arachidonic acid

AUC:

Area under the curve

BMI:

Body mass index

CRP:

C-reactive protein

DBI:

Direct bilirubin

DTC:

Differentiated thyroid cancer

F/B:

Firmicutes/Bacteroidetes

FDR:

False discovery rate

Gran:

Neutrophil

Hb:

Hemoglobin

HDL:

High-density lipoprotein

IL-1:

Interleukin 1

IL-2:

Interleukin 2 receptor

IL-6:

Interleukin 6

IR:

Ionizing radiation

KEGG:

Kyoto Encyclopedia of Genes and Genomes

LA:

Linoleic acid

LDL:

Low-density lipoprotein

LEfSe:

Linear discriminate analysis effect size

Lymph:

Lymphocyte

OTU:

Operational taxonomic unit

PCA:

Principal components analysis

PCoA:

Principal coordinates analysis

PLS-DA:

Partial least square regressions-discriminant analysis

PLT:

Platelet

Post:

-131I-1stAfter the first 131I therapy

Post-131I-1st/2nd :

After the second 131I therapy

Pre-131I-1st :

Before the first 131I therapy

Pre-131I-2nd :

Before the second 131I therapy

RBC:

Red blood cell

RDA:

Redundancy analysis

RF:

Random forest

ROC:

Receiver operating characteristic

SCFA:

Short-chain fatty acids

TBI:

Total bilirubin

TC:

Thyroid cancer

TCh:

Total cholesterol

Tg:

Thyroglobulin

TG:

Triglyceride

TgAb:

Thyroglobulin antibodies

TNFα:

Tumor necrosis factor alpha

TSH:

Thyrotropin

WBC:

White blood cell

References

  1. Kitahara CM, Sosa JA. The changing incidence of thyroid cancer. Nat Rev Endocrinol. 2016;12(11):646–53. https://doi.org/10.1038/nrendo.2016.110.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Hurst Z, Liyanarachchi S, He H, Brock P, Sipos J, Nabhan F, et al. Risk Haplotypes Uniquely Associated with Radioiodine-Refractory Thyroid Cancer Patients of High African Ancestry. Thyroid. 2019;29(4):530–9. https://doi.org/10.1089/thy.2018.0687.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Fard-Esfahani A, Emami-Ardekani A, Fallahi B, Fard-Esfahani P, Beiki D, Hassanzadeh-Rad A, et al. Adverse effects of radioactive iodine-131 treatment for differentiated thyroid carcinoma. Nucl Med Commun. 2014;35(8):808–17. https://doi.org/10.1097/mnm.0000000000000132.

    Article  CAS  PubMed  Google Scholar 

  4. Zheng L, Zhang L, Tang L, Huang D, Pan D, Guo W, et al. Gut microbiota is associated with response to (131)I therapy in patients with papillary thyroid carcinoma. Eur J Nucl Med Mol Imaging. 2023;50(5):1453–65. https://doi.org/10.1007/s00259-022-06072-5.

    Article  CAS  PubMed  Google Scholar 

  5. Yu Y, Lin X, Feng F, Wei Y, Wei S, Gong Y, et al. Gut microbiota and ionizing radiation-induced damage: Is there a link? Environ Res. 2023;229:115947. https://doi.org/10.1016/j.envres.2023.115947.

    Article  CAS  PubMed  Google Scholar 

  6. Guo H, Chou WC, Lai Y, Liang K, Tam JW, Brickey WJ, et al. Multi-omics analyses of radiation survivors identify radioprotective microbes and metabolites. Science. 2020;370 6516 https://doi.org/10.1126/science.aay9097.

  7. Lavrinienko A, Mappes T, Tukalenko E, Mousseau TA, Møller AP, Knight R, et al. Environmental radiation alters the gut microbiome of the bank vole Myodes glareolus. Isme j. 2018;12(11):2801–6. https://doi.org/10.1038/s41396-018-0214-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kalkeri R, Walters K, Van Der Pol W, McFarland BC, Fisher N, Koide F, et al. Changes in the gut microbiome community of nonhuman primates following radiation injury. BMC Microbiol. 2021;21(1):93. https://doi.org/10.1186/s12866-021-02146-w.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Xiao HW, Cui M, Li Y, Dong JL, Zhang SQ, Zhu CC, et al. Gut microbiota-derived indole 3-propionic acid protects against radiation toxicity via retaining acyl-CoA-binding protein. Microbiome. 2020;8(1):69. https://doi.org/10.1186/s40168-020-00845-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cui M, Xiao H, Li Y, Zhou L, Zhao S, Luo D, et al. Faecal microbiota transplantation protects against radiation-induced toxicity. EMBO Mol Med. 2017;9(4):448–61. https://doi.org/10.15252/emmm.201606932.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lu G, Yu X, Jiang W, Luo Q, Tong J, Fan S, et al. Alterations of Gut Microbiome and Metabolite Profiles Associated With Anabatic Lipid Dysmetabolism in Thyroid Cancer. Front Endocrinol (Lausanne). 2022;13:893164. https://doi.org/10.3389/fendo.2022.893164.

    Article  PubMed  Google Scholar 

  12. Yu X, Jiang W, Kosik RO, Song Y, Luo Q, Qiao T, et al. Gut microbiota changes and its potential relations with thyroid carcinoma. J Adv Res. 2022;35:61–70. https://doi.org/10.1016/j.jare.2021.04.001.

    Article  CAS  PubMed  Google Scholar 

  13. Schlumberger M, Leboulleux S. Current practice in patients with differentiated thyroid cancer. Nat Rev Endocrinol. 2021;17(3):176–88. https://doi.org/10.1038/s41574-020-00448-z.

    Article  CAS  PubMed  Google Scholar 

  14. Vandeputte D, Falony G, Vieira-Silva S, Tito RY, Joossens M, Raes J. Stool consistency is strongly associated with gut microbiota richness and composition, enterotypes and bacterial growth rates. Gut. 2016;65(1):57–62. https://doi.org/10.1136/gutjnl-2015-309618.

    Article  CAS  PubMed  Google Scholar 

  15. Chen K, Luan X, Liu Q, Wang J, Chang X, Snijders AM, et al. Drosophila Histone Demethylase KDM5 Regulates Social Behavior through Immune Control and Gut Microbiota Maintenance. Cell Host Microbe. 2019;25(4):537-52.e8. https://doi.org/10.1016/j.chom.2019.02.003.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Yang C, Xu Z, Deng Q, Huang Q, Wang X, Huang F. Beneficial effects of flaxseed polysaccharides on metabolic syndrome via gut microbiota in high-fat diet fed mice. Food Res Int. 2020;131:108994. https://doi.org/10.1016/j.foodres.2020.108994.

    Article  CAS  PubMed  Google Scholar 

  17. Sheng D, Zhao S, Gao L, Zheng H, Liu W, Hou J, et al. BabaoDan attenuates high-fat diet-induced non-alcoholic fatty liver disease via activation of AMPK signaling. Cell Biosci. 2019;9:77. https://doi.org/10.1186/s13578-019-0339-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Song HJ, Qiu ZL, Shen CT, Wei WJ, Luo QY. Pulmonary metastases in differentiated thyroid cancer: efficacy of radioiodine therapy and prognostic factors. Eur J Endocrinol. 2015;173(3):399–408. https://doi.org/10.1530/eje-15-0296.

    Article  CAS  PubMed  Google Scholar 

  19. Ciarallo A, Rivera J. Radioactive Iodine Therapy in Differentiated Thyroid Cancer: 2020 Update. AJR Am J Roentgenol. 2020;215(2):285–91. https://doi.org/10.2214/ajr.19.22626.

    Article  PubMed  Google Scholar 

  20. Wang Q, Lin Y, Sheng X, Xu J, Hou X, Li Y, et al. Arachidonic Acid Promotes Intestinal Regeneration by Activating WNT Signaling. Stem Cell Reports. 2020;15(2):374–88. https://doi.org/10.1016/j.stemcr.2020.06.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lu G, Gao D, Jiang W, Yu X, Tong J, Liu X, et al. Disrupted gut microecology after high-dose (131)I therapy and radioprotective effects of arachidonic acid supplementation. Eur J Nucl Med Mol Imaging. 2024. https://doi.org/10.1007/s00259-024-06688-9.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Zhao J, Cheng W, Lu H, Shan A, Zhang Q, Sun X, et al. High fiber diet attenuate the inflammation and adverse remodeling of myocardial infarction via modulation of gut microbiota and metabolites. Front Microbiol. 2022;13:1046912. https://doi.org/10.3389/fmicb.2022.1046912.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Alarcon-Gil J, Sierra-Magro A, Morales-Garcia JA, Sanz-SanCristobal M, Alonso-Gil S, Cortes-Canteli M, et al. Neuroprotective and Anti-Inflammatory Effects of Linoleic Acid in Models of Parkinson's Disease: The Implication of Lipid Droplets and Lipophagy. Cells. 2022;11 15 https://doi.org/10.3390/cells11152297.

  24. Manichanh C, Varela E, Martinez C, Antolin M, Llopis M, Doré J, et al. The gut microbiota predispose to the pathophysiology of acute postradiotherapy diarrhea. Am J Gastroenterol. 2008;103(7):1754–61. https://doi.org/10.1111/j.1572-0241.2008.01868.x.

    Article  CAS  PubMed  Google Scholar 

  25. Yi Y, Shen L, Shi W, Xia F, Zhang H, Wang Y, et al. Gut Microbiome Components Predict Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer: A Prospective. Longitudinal Study Clin Cancer Res. 2021;27(5):1329–40. https://doi.org/10.1158/1078-0432.Ccr-20-3445.

    Article  CAS  PubMed  Google Scholar 

  26. Kumagai T, Rahman F and Smith AM. The Microbiome and Radiation Induced-Bowel Injury: Evidence for Potential Mechanistic Role in Disease Pathogenesis. Nutrients. 2018;10:10. https://doi.org/10.3390/nu10101405.

  27. Fernandes A, Oliveira A, Guedes C, Fernandes R, Soares R and Barata P. Ionizing Radiation from Radiopharmaceuticals and the Human Gut Microbiota: An Ex Vivo Approach. Int J Mol Sci. 2022;23:18. https://doi.org/10.3390/ijms231810809.

  28. Li W, Cheng F, Zhang J, Li C, Yu D, Simayijiang H, et al. Changes in Gut Microbiota and Metabolites in Papillary Thyroid Carcinoma Patients Following Radioactive Iodine Therapy. Int J Gen Med. 2023;16:4453–64. https://doi.org/10.2147/ijgm.S433433.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Aerts A, Eberlein U, Holm S, Hustinx R, Konijnenberg M, Strigari L, et al. EANM position paper on the role of radiobiology in nuclear medicine. Eur J Nucl Med Mol Imaging. 2021;48(11):3365–77. https://doi.org/10.1007/s00259-021-05345-9.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Terry SYA, Nonnekens J, Aerts A, Baatout S, de Jong M, Cornelissen B, et al. Call to arms: need for radiobiology in molecular radionuclide therapy. Eur J Nucl Med Mol Imaging. 2019;46(8):1588–90. https://doi.org/10.1007/s00259-019-04334-3.

    Article  PubMed  Google Scholar 

  31. Morris ZS, Wang AZ, Knox SJ. The Radiobiology of Radiopharmaceuticals. Semin Radiat Oncol. 2021;31(1):20–7. https://doi.org/10.1016/j.semradonc.2020.07.002.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Pouget JP, Lozza C, Deshayes E, Boudousq V, Navarro-Teulon I. Introduction to radiobiology of targeted radionuclide therapy. Front Med (Lausanne). 2015;2:12. https://doi.org/10.3389/fmed.2015.00012.

    Article  PubMed  Google Scholar 

  33. Li Y, Yan H, Zhang Y, Li Q, Yu L, Li Q, et al. Alterations of the Gut Microbiome Composition and Lipid Metabolic Profile in Radiation Enteritis. Front Cell Infect Microbiol. 2020;10:541178. https://doi.org/10.3389/fcimb.2020.541178.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Shakyawar SK, Mishra NK, Vellichirammal NN, Cary L, Helikar T, Powers R, et al. A Review of Radiation-Induced Alterations of Multi-Omic Profiles, Radiation Injury Biomarkers, and Countermeasures. Radiat Res. 2023;199(1):89–111. https://doi.org/10.1667/rade-21-00187.1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Yamanouchi K, Tsujiguchi T, Sakamoto Y, Ito K. Short-term follow-up of intestinal flora in radiation-exposed mice. J Radiat Res. 2019;60(3):328–32. https://doi.org/10.1093/jrr/rrz002.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Lewis JE, Kemp ML. Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance. Nat Commun. 2021;12(1):2700. https://doi.org/10.1038/s41467-021-22989-1.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Choy H, Milas L. Enhancing radiotherapy with cyclooxygenase-2 enzyme inhibitors: a rational advance? J Natl Cancer Inst. 2003;95(19):1440–52. https://doi.org/10.1093/jnci/djg058.

    Article  CAS  PubMed  Google Scholar 

  38. Gambale C, Prete A, Contartese L, Torregrossa L, Bianchi F, Molinaro E, et al. Usefulness of second 131I treatment in biochemical persistent differentiated thyroid cancer patients. Eur Thyroid J. 2023;12 6 https://doi.org/10.1530/etj-23-0052.

  39. Xie J, Liu Y, Chen B, Zhang G, Ou S, Luo J, et al. Ganoderma lucidum polysaccharide improves rat DSS-induced colitis by altering cecal microbiota and gene expression of colonic epithelial cells. Food Nutr Res. 2019;63 https://doi.org/10.29219/fnr.v63.1559.

  40. Borton MA, Sabag-Daigle A, Wu J, Solden LM, O’Banion BS, Daly RA, et al. Chemical and pathogen-induced inflammation disrupt the murine intestinal microbiome. Microbiome. 2017;5(1):47. https://doi.org/10.1186/s40168-017-0264-8.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Vacca M, Celano G, Calabrese FM, Portincasa P, Gobbetti M and De Angelis M. The Controversial Role of Human Gut Lachnospiraceae. Microorganisms. 2020;8 4 https://doi.org/10.3390/microorganisms8040573.

  42. Jang YO, Kim OH, Kim SJ, Lee SH, Yun S, Lim SE, et al. High-fiber diets attenuate emphysema development via modulation of gut microbiota and metabolism. Sci Rep. 2021;11(1):7008. https://doi.org/10.1038/s41598-021-86404-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank Home for Researchers editorial team (www.home-for-researchers.com) for language editing service and Shanghai Biotree Biomedical Technology CO., LTD for sequencing service.

Funding

This work was supported by the Science and Technology Commission of Shanghai Municipality [grant number 22YF1433600], National Natural Science Foundation of China [grant number: 82071964], Shanghai Leading Talent Program sponsored by Shanghai Human Resources and Social Security Bureau [grant number: 2019], Shanghai Municipal Health Commission [grant number: GWVI-11.2-YQ51].

Author information

Author notes

  1. Ganghua Lu, Dingwei Gao, and Yixian Liu contributed equally to this work.

Authors and Affiliations

  1. Clinical Nuclear Medicine Center, Imaging Clinical Medical Center, Institute of Nuclear Medicine, Institute of Clinical Mass Spectrometry Applied Research Center, Department of Nuclear Medicine, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200072, China

    Ganghua Lu, Dingwei Gao, Xiaqing Yu, Wen Jiang & Zhongwei Lv

  2. Department of Gynecology and Obstetrics, Shanghai Tenth People’s Hospital, School of Medicine, Tongji University, Shanghai, 200072, China

    Yixian Liu

  3. Department of Nuclear Medicine, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, China

    Wen Jiang

  4. Shanghai Public Health Clinical Center, Fudan University, Shanghai, 200003, China

    Zhongwei Lv

Authors
  1. Ganghua Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dingwei Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yixian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaqing Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wen Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhongwei Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors participated in the design of the study and/or patient enrolment and met the criteria for authorship. Z.L., W.J., and X.Y. contributed to the study design, study conduct and supervision, scientific overview, data analysis, and manuscript editing. G.L. and D.G. were directly involved in the clinical diagnosis of the patients, collected and analyzed the clinical data of the subjects, analyzed the microbial and LM-MS data, and drafted the original manuscript. Y.L. was involved in the revision of the manuscript and contributed to responding to the reviewers’ letters. The manuscript has been substantially revised by Z.L., W.J., and X.Y.. G.L., D.G., and Y.L. contributed equally to this work and shared first co-authorship. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Xiaqing Yu, Wen Jiang or Zhongwei Lv.

Ethics declarations

Ethics approval and consent to participate

The study was carried out following the Declaration of Helsinki principles and approved by the Ethics Committee of the Shanghai Tenth People’s Hospital (No. SHSY-IEC-5.0/22K13/P01). Informed, written consent was obtained from all patients.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

12916_2024_3528_MOESM1_ESM.jpg

Additional file 1: Fig.S1 Features of gut bacterial community composition and structure. Α diversity analysis in Simpson (A) and Shannon (B) indices among four groups. (C) Ratio of Firmicutes/Bacteroidetes (F/B) among four groups. * p < 0.05. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patients with DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patients with DTC after the second 131I therapy.

12916_2024_3528_MOESM2_ESM.jpg

Additional file 2: Fig.S2 Alterations in microbial genus compositions at different stages of DTC with 131I therapy. Wilcoxon rank-sum test between Post- and Pre-131I-1st (A), Pre-131I-2nd and Pre-131I-1st (B), Post- and Pre-131I-2nd (C) groups. (D) Abundance of g_Akkermansia between low (< 150 mCi) and high (≥ 150 mCi) dose of131I therapy. * p < 0.05. DTC, differentiated thyroid cancer; Pre-131I-1st group, patients with DTC before the first 131I therapy; Post-131I-1st group, patients with DTC after the first 131I therapy; Pre-131I-2nd group, patients with DTC before the second 131I therapy; Post-131I-2nd group, patients with DTC after the second 131I therapy.

12916_2024_3528_MOESM3_ESM.jpg

Additional file 3: Fig.S3 Linoleic acid as the potential radioprotectants under 131I therapy. (A) Schematic of linoleic acid (LA) treatment under 2 mCi 131I therapy. (B) Spleens stained with hematoxylin and eosin (H&E) (× 200 magnification). (C) The small intestines stained with H&E (× 200 magnification, broken intestinal epithelium, black arrow), (D) alcian blue/periodic acid-schiff (AB-PAS) (× 200 magnification, goblet cells, black arrow) and (E, F) immunohistochemistry (IHC) (× 200 magnification, stained with antibodies, black arrow). WP, white pulp.

12916_2024_3528_MOESM4_ESM.docx

Additional file 4: Supplemental Methods. Methods for the supplementation of linoleic acid in mice undergoing 131I therapy.

12916_2024_3528_MOESM5_ESM.jpg

Additional file 5: Fig.S4 Disrupted gut microbiota interactions with fecal metabolism and clinical factors associated with DTC patients under 131I therapy. Spearman correlation analyses among six charactered genera (A) and 11 radiation-sensitive metabolites(B). (C) Spearman correlation analyses between six charactered genera and 11 radiation-sensitive metabolites.* p < 0.05,** p < 0.01,*** p < 0.001.

12916_2024_3528_MOESM6_ESM.jpg

Additional file 6: Fig.S5 Predictive models combined with microbiome and metabolism for response to 131I therapy. The cross-validation process was repeated five times in random forest model of Model1 (A), Model2 (B) and Model3 (C).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, G., Gao, D., Liu, Y. et al. Early and long-term responses of intestinal microbiota and metabolites to 131I treatment in differentiated thyroid cancer patients. BMC Med 22, 300 (2024). https://doi.org/10.1186/s12916-024-03528-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12916-024-03528-3

Keywords

BMC Medicine

ISSN: 1741-7015

Contact us