Impaired microbiota in breast cancer patients: In association with high lipopolysaccharide levels and a low-fibre diet

Impaired microbiota in breast cancer patients: In association with high lipopolysaccharide levels and a low-fibre diet | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impaired microbiota in breast cancer patients: In association with high lipopolysaccharide levels and a low-fibre diet Fatma Hümeyra Yerlikaya, Mustafa Topkafa, Hande Köksal, Salih Maçin, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5145862/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Recent studies have demonstrated that the intestinal microbiota, and in particular the metabolites produced by the microbiota, play a role in the pathogenesis of cancer. We hypothesized that gut microbiota and microbiota metabolites may change negatively in breast cancer patients. The trial included 65 women with breast cancer and 50 women as a control group. A Food Frequency Questionnaire characterised daily macronutrient intake over the past 1 month. A metagenomic analysis was conducted based on all hypervariable regions (V1-V9) of the 16S rRNA gene with the objective of investigating the composition and diversity of the microbiome. Results A microbiome analysis revealed that the composition of the gut microbiome differed between cases and controls. In particular, the diversity of microbes in the gut was reduced in breast cancer patients. The present study has demonstrated that serum lipopolysaccharide levels and the Firmicutes/Bacteroidetes ratio are elevated in breast cancer patients in comparison to the control group. We found that breast cancer patients who participated in our study had non-ideal dietary patterns. The relationship between diet and gut microbiota exerts a profound influence on the complex interplay between these two factors. This relationship may have an impact on the development of breast cancer and the likelihood of cancer metastasis. Conclusions It can be postulated that interventions that enhance microbial diversity through dietary recommendations may have a beneficial impact on health, particularly in patients with breast cancer. Microbiota breast cancer short chain fatty acid lipopolysaccharide zonulin diet fiber Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Breast cancer is a complex disease influenced by many factors, including genetics, lifestyle and environmental exposures [ 1 ]. Microorganisms play an indirect role in influencing the incidence, natural history and/or severity of various cancers [ 2 ]. For example, microbes in the gut affect dormant cancer cells, which can cause the disease to return [ 1 ]. Research into the link between microbes and cancer has mainly focused on bacteria and their role in different types of cancer [ 3 ]. Although the potential carcinogenic role of the gut or breast microbiome in the development of breast cancer or the pathogenic role in the progression of an established breast cancer lesion has not yet been confirmed, emerging data suggest that processes derived from the gut microbiome may play a role in breast carcinogenesis [ 1 ]. The relationship between dietary intake, gut microbiota diversity and function, and their importance in human health is currently an active area of research [ 4 ]. An increasing number of studies suggest that metabolites produced by microorganisms are important mediators of dietary microbial effects on the host [ 5 – 7 ]. The main problem with a Western diet, typically high in fat and digestible saccharides, is that most nutrients are absorbed in the duodenum, leaving little substrate for bacteria in the colon [ 6 ]. One of the most relevant therapeutic pathways of microbiome modulation involves restoring levels of short-chain fatty acids (SCFAs), microbial metabolites essential for human health [ 7 ]. A diet comprising vegetables and fruit rich in complex carbohydrates is associated with an abundance of SCFA-producing bacteria, while a higher intake of unhealthy fats and simple sugars is associated with an increase in facultative anaerobic bacteria and a decrease in SCFA [ 8 ]. SCFAs play a key role in regulating colonic physiology and modifying the intestinal environment. Recent research has shown that SCFAs not only affect the signalling pathway in the gut, but also reach tissues and organs outside the gut by circulating in the blood [ 9 ]. Breast cancer is one of the most common malignant tumours in women [ 10 ]. There is increasing evidence that the composition and diversity of the gut microbiota influence tumour immune responses, but a detailed mechanistic understanding of these responses is lacking. Furthermore, the majority of current studies on cancer microbiota have concentrated on the investigation of colon, lung, kidney, and bladder cancers. Until recently, little attention has been paid to the microbiota and cancers that respond less favourably to immune checkpoint therapies, such as breast cancer. There is a lack of in-depth research into the relationship between SCFA levels and breast cancer in particular. We hypothesized that gut microbiota and microbiota metabolites may change negatively in breast cancer patients. In this context, our study aims to find answers to questions to develop new predictive methods and treatments for breast cancer with a holistic approach that integrates gut microbiota, dysbiosis, microbiota metabolites and diet in newly diagnosed breast cancer cases before starting any treatment. Material and Method Participants Newly diagnosed early stage breast cancer patients admitted to the Department of General Surgery, Faculty of Medicine, Selçuk University were included in the study as 'cases' after obtaining informed consent. Healthy women of the same age who attended the same outpatient clinic without any clinical complaints were included in the study as a 'control group'. The trial included 65 women with breast cancer and 50 women as a control group. Clinical information and treatment history were obtained from patients' medical records. A analysis was conducted on the pathology reports of breast cancer patients. The data included in the report comprised histopathological diagnosis, tumour grade, stage and hormone receptor status (estrogen receptor - ER, progesterone receptor - PR and Her2neu). Women with a history of cancer or an advanced tumour stage, or women who had received antibiotic therapy or any neoadjuvant treatment within the previous six months, were excluded from the study. The women participating in the study were in the perimenopausal stage. Demographic and clinical details of the subjects are shown in Table 1 . Table 1 Demographic and Clinical Details of in Breast Cancer Patients and Healthy Controls. 1 Breast Cancer Patients Control subjects p value Number of Subjects n = 65 n = 50 Age (years) 48.59 ± 14.92 50.45 ± 9.12 0.852 Body Mass Index 27.60 ± 5.49 25.58 ± 6.54 0.143 Glucose (mg/dL) 107.0 ± 33.8 83.98 ± 16.30 p < 0.001 Total Cholesterol (mg/dL) 198.43 ± 38.91 180.48 ± 30.20 0.030 Triglyceride (mg/dL) 135.05 ± 76.13 115.35 ± 61.54 0.287 Low-density Lipoprotein Cholesterol (mg/dL) 118.10 ± 32.03 110.40 ± 19.15 0.316 High-density Lipoprotein Cholesterol (mg/dL) 54.84 ± 15.28 58.58 ± 20.62 0.597 Cancer antigen 15 − 3 23.21 ± 10.20 14.73 ± 8.48 p < 0.001 White Blood Cell (cells/mL) 7.86 ± 4.11 6.43 ± 1.46 0.020 Metabolic Diseases (%) Hypertension - - - Diabetes 4.6 - - Hypercholesterolemia - - - Histopathological diagnosis of Breast Cancer Patients Frequency (%) Invasive Breast Carcinoma Nonspecific Type 88.52 Invasive Lobular Carcinoma 1.63 Invasive Mucinous Carcinoma 1.63 Adenoid Cystic Carcinoma 1.63 ER Status Negative 19.67 Positive 75.40 PR Status Negative 18.03 Positive 77.04 HER2 Negative 62.29 Positive 31.14 Ki-67 >14 37.70 <14 42.62 1 Data are presented as mean ± SD. Abbreviations: ER Status, estrogen receptor; PR Status, progesterone receptor; HER2, human epidermal growth factor receptor 2; Ki-67, cell proliferation index. Nutritional Assessment and Anthropometric Evaluation In the study, the Food Frequency Questionnaire (FFQ) was employed to obtain general information about the participants and to determine their dietary habits. The questionnaire was administered by the researchers through a face-to-face interview. The initial section of the questionnaire comprises two subsections, the first of which gathers basic demographic data, including age, gender vs. under the heading of general information. The second subsection comprises strategically selected questions pertaining to physical activity levels and health-related lifestyle preferences. The second section of the questionnaire comprises the FFQ, which inquires about the frequency of consumption of specific foods relative to the previous month. The FFQ form inquired about the frequency and amounts of consumption of a total of 36 foods and beverages. The foods included in the FFQ form were selected based on a review of the relevant literature, with the objective of serving the purpose of the study and to explain the research problem [ 11 , 12 ]. In the course of the comprehensive retrospective interviews, the participants are requested to provide detailed data on the manner in which the food is prepared and the ingredients used. In order to ascertain the quantities of food and beverages consumed with precision and accuracy, the Food and Nutrition Photo Catalogue, a frequently employed resource in similar studies, was utilised [ 13 ]. The body mass index (BMI) was calculated by dividing body weight by the square of height (kg/m²) in breast cancer patients participating in the study and healthy individuals in the control group. Chemical Analyses Venous blood samples were collected after 12 hours of fasting and separated into serum and plasma samples in accordance with standard laboratory procedures. The samples were maintained on ice and centrifuged (1000 x g, 15 min) within a period of 2–4 h following collection. The plasma and serum samples were stored at a temperature of -20°C until the analyses were performed. The serum glucose, total cholesterol, high-density lipoproteins (HDL), low-density lipoproteins (LDL), triglycerides, cancer antigen 15 − 3 (CA 15.3) and white blood cells (WBC) were determined by standard methods. Serum lipopolysaccharide-binding protein (LBP) and serum zonulin concentrations were quantified using a commercially available kit based on the enzyme-linked immunosorbent assay (ELISA) method (Elabscience Biotechnology Co.). This technique employs a two-sided sandwich approach, utilising two selected polyclonal antibodies that bind to distinct epitopes of human LBP and zonulin. SCFAs were extracted and analyzed according to a modified version of the method described by Abd El-Wahab et al. and Kahraman et al. [ 14 , 15 ]. The faecal sample was placed in sterile containers and immediately frozen at -20°C, after which it was transported to the laboratory. In the laboratory, 1 g of human faeces sample was homogenised with 10 mL of water at room temperature. One millilitre of the homogenate was extracted, 200 microlitres of 25% ortho-phosphoric acid was added, and the mixture was vortexed. Subsequently, the supernatant was subjected to centrifugation at 13,000 rpm/min, after which it was passed through a 0.22 polytetrafluoroethylene syringe filter. The SCFAs were analysed using an Agilent 8860 series gas chromatograph, which was equipped with a flame ionisation detector (GC-FID) and a DB-FFAP column (30 m, 0.32 mm, 1 µm, J&W Scientific, USA). SCFAs were dissolved in water and injected in a splitless injection mode (1 µL). A flow rate of 1 ml min-1 of hydrogen was employed as the carrier gas. The injection port and detector temperatures were recorded as 270 and 300°C, respectively. The initial oven temperature of 60°C was maintained for a period of five minutes, after which it was increased to 140°C at a rate of 5°C per minute. Chromatograms were recorded utilising an OpenLAB CDS data processor (Agilent Technologies Inc., Wilmington, DE, USA). The identification and calibration of authentic SCFAs peaks was performed by comparing their retention times with those of authentic standards. Microbiota analyses DNA isolation In order to optimise the study, a commercially available DNA isolation kit was utilised, with DNA isolation conducted in accordance with the company's recommendations. A volume of 400 µl of binding solution (comprising 4 M guanidine thiocyanate, 20 mM Tris-HCl, pH 8) was then added to the sample. Following incubation of the sample at 95°C for 10 minutes, 400 µl of 2-propanol was added and loaded onto a silica column. The DNAs in the sample were separated from the other components by centrifugation at 10,000 g for one minute. They were then retained on the silica column and washed twice with washing solution (20 mM NaCl, 2 mM Tris-HCl, pH 8; 80% v/v ethanol). The silica column was subjected to centrifugation in order to facilitate the drying process. The DNA retained on the silica column was collected from the column with 100 µl of nuclease-free, sterile, deionised water (pH 7). The quality of the DNA samples obtained was evaluated using the NanoDrop device, with the concentration and 260/280nm and 230/260nm absorbance values recorded. Next-generation sequencing and real-time polymerase chain reaction (PCR) of DNA samples were conducted on a QIAGEN device. A total of 17 µl of PCR mix and 3 µl of bacterial DNA were transferred to PCR tubes. The mixture was prepared and the PCR samples were dispensed in a cold block. A DNA-free negative control and a positive control were used in each ‘real-time PCR’ test. The 16S Study Protocol, barcoding and sequencing In order to perform a metagenomic analysis of the 16S rRNA, previously described workflows were utilised. The primer pair to be employed for the construction of amplicon libraries has been designed to target a region of approximately 1500 bp, encompassing the V1-V9 region of the 16S rRNA gene. The Oxford Nanopore Technologies Nanopore barcode DNA sequences of the generated library were added to the 5' end of the target-specific primer pairs. The target-specific primer-connector sequences for the 16S rRNA are TTTCTGTTGGTGCTGATATATTGC and AGRGTTTGATYHTGGCTCAG for the forward primer and 5’-ACTTGCCTGTCGCTCGCTCTATCTTC-TACCTTGTTAYGACTT-3' for the reverse primer. The initial PCR was conducted using the Proof Reading DNA Polymerase 2x Reaction Mix and 200 nanomoles of each primer. The following thermal cycling programme was implemented in the PCR device: 95°C for 3 minutes; 25 cycles of 95°C for 30 seconds, 55°C for 30 seconds and 72°C for 90 seconds; 72°C for 5 minutes. The PCR product was subjected to electrophoresis on an agarose gel in order to ascertain its size, which was found to be approximately 1450 base pairs (bp). It was then purified using a PCR product purification kit. The ligation sequencing kit (SQK-LSK109; Oxford Nanopore Technologies) and native barcoding kit (EXP-NBD104-114; Oxford Nanopore Technologies) protocols were employed for the preparation of the amplicon library. Firstly, the ends of the 60 µL final volume samples (100–200 fmol) were prepared, a dA tail was added and fixed. Subsequently, the 0.5X MagBeads (MobiomX) kit was employed for the purpose of purification. The native barcodes were maintained at room temperature for a period of 10 minutes in order to facilitate their binding to the prepared tips. Subsequently, the samples were purified and subjected to spectrofluorimetric analysis. A total of 35 µL of DNA was extracted from each of the barcoded samples, with an equal volume of DNA from each sample being combined in a single tube. The adapters were connected to the ends of the barcoded samples, which were then purified and measured. Following the preparation stage, 50 fmol of the library was loaded into a Spot-On flow cell (FLO-MIN106D). The sequencing run was initiated on a Mk1C™ instrument (Oxford Nanopore Technologies) utilising MinKNOW™ software. Sequencing was terminated either when the requisite data had been obtained or when the maximum permitted run time of 72 hours had elapsed. Bioinformatics analysis Following the sequencing process, the results were converted from fast5 format to fastq format using the guppy software (base-calling and de-multiplexing) in order to facilitate further analysis. Given that the 16S rRNA region is on average 1500 bp, reads between 1250 and 1750 bp in length were subjected to filtration using Trimmomatic, with the remaining reads being excluded from the subsequent analysis. The cleaned reads were subjected to analysis using a bespoke workflow developed in the Python programming language. In accordance with this workflow, each sequence was subjected to BLAST algorithmic matching during the filtering process. An OTU was created by taking the taxonomic data of sequences with more than 60% reference coverage and 80% pairwise similarity in the matching results. A variety of analytical techniques were employed, including alpha diversity analysis, principal component analysis (PCA), principal coordinate analysis (PCoA), beta diversity analysis, biomarker analysis, and phenotype analysis. These techniques were applied using a range of indexes in conjunction with the tools provided by the QIIME2 platform for phylogenetic analyses with the OTU (.biom) file. The Mothur platform was employed to organise taxonomic classifications and to prepare dynamic crown charts. The graphs and tables in the analyses were generated using the Python programming language libraries. Statistical Analysis The results were subjected to statistical analysis using IBM SPSS Statistics 26.0 (IBM Corp., Armonk, New York, USA), a software package designed for the analysis of quantitative data. Accuracy, specificity, linearity, limit of determination, recovery and reproducibility of the measurement method used were tested. Student t-test was used for parametric distribution. For non-parametric distribution, the Mann-Whitney U test was used. All fatty acid (molar %) values were subjected to statistical analysis using MedCalc Statistical Software version 19.2.6 (MedCalc Software bv, Ostend, Belgium; https://www.medcalc.org ; 2020). This involved the use of the Chi-square test and proportions. A p-value of less than < 0.05 was considered statistically significant. Results Baseline characteristics The SCFA levels of the Breast cancer group and the Control group are presented in Table 2 , while the FFQ questionnaire assessment is presented in Table 1 . No statistically significant difference was observed between the breast cancer patient and control groups in SFCAs measurements. As demonstrated in Table 2 , the results of the statistical evaluation of the ELISA studies revealed that serum LPS levels were significantly elevated in the breast cancer group in comparison to the control group (p = 0.007). There was no significant difference between serum zonulin levels (p = 0.690). Table 2 Chemical analysis of in Breast Cancer Patients and Healthy Controls. 1 Breast Cancer Patients (n = 65) Control subjects (n = 50) p value The sum of SCFA 10.66 ± 7.2 11.94 ± 9.2 0.443 Acetic acid (mM) 6.34 ± 4.0 7.87 ± 6.3 0.455 Propionic acid (mM) 1.78 ± 1.5 1.95 ± 1.7 0.509 Butyric acid (mM) 1.64 ± 1.5 1.29 ± 1.2 0.230 Isobutyric acid (mM) 0.20 ± 0.1 0.23 ± 0.2 0.754 Valeric acid (mM) 0.23 ± 0.2 0.23 ± 0.2 0.998 Isovaleric acid (mM) 0.26 ± 0.2 0.30 ± 0.2 0.212 Acetic acid (molar %) 65.02 ± 9.5 66.62 ± 7.4 0.863 Propionic acid (molar %) 17.36 ± 5.0 17.87 ± 6.1 0.586 Butyric acid (molar %) 15.11 ± 6.2 11.52 ± 5.4 0.944 Isobutyric acid (molar %) 2.49 ± 2.2 2.04 ± 0.9 0.876 Valeric acid (molar %) 2.45 ± 1.1 2.12 ± 1.2 0.909 Isovaleric acid (molar %) 3.03 ± 1.7 2.32 ± 1.8 0.821 Zonulin (ng/ml) 77.5 ± 34.3 75.1 ± 26.9 0.690 Lipopolysaccharide (ng/ml) 206.6 ± 143.1 139.3 ± 101.1 0.007 1 Data are presented as mean ± SD. Abbreviations: SCFA, short-chain fatty acids. Table 3 Nutritional Evaluation of in Breast Cancer Patients and Healthy Controls. 1 FFQ item Breast Cancer Patients (n = 65) Control subjects (n = 50) p value Dairy product (milk, yoghurt, kefir) (ml) 2848.30 ± 1914.15 3586.75 ± 1465.50 0.032 Cheese (g) 587.11 ± 363.84 601.29 ± 287.60 0.840 Red meat (g) 1101.29 ± 615.19 797.14 ± 492.53 0.009 Chicken (g) 1344.00 ± 763.40 1245.94 ± 817.37 0.965 Fish (g) 286.48 ± 200.22 303.70 ± 351.35 0.820 Egg (g) 416.56 ± 253.67 420.27 ± 270.14 0.946 Beans (g) 873.43 ± 679.92 1016.66 ± 1097.65 0.481 Dried chickpeas (g) 666.61 ± 539.68 929.06 ± 898.56 0.138 Lentil (g) 1875.48 ± 1345.06 2126.25 ± 1003.98 0.312 Processed meat (salami, sausage) (g) 346.24 ± 225.56 191.51 ± 193.45 0.001 Roast potatoes (g) 1732.58 ± 1182.15 1296.28 ± 1100.75 0.072 Leek (g) 172.22 ± 50.68 150.00 ± 10.1 0.225 Onion (g) 284.92 ± 264.97 310.00 ± 263.12 0.653 Garlic (g) 5.75 ± 3.28 5.80 ± 5.39 0.963 Tomato (g) 894.19 ± 426.95 765.71 ± 328.29 0.208 Green beans (g) 214.28 ± 179.28 219.31 ± 167.43 0.908 Peas (g) 187.50 ± 75.00 200.00 ± 75.00 0.791 Banana (g) 411.57 ± 377.88 557.42 ± 536.65 0.188 Red plum (g) 68.14 ± 35.14 69.67 ± 30.38 0.850 Pomegranate (g) 149.71 ± 88.43 153.63 ± 109.02 0.872 Grape (g) 95.40 ± 42.14 166.06 ± 130.28 0.005 White bread (g) 1307.54 ± 594.65 973.38 ± 461.10 0.006 Whole grain bread (g) 1099.35 ± 474.62 853.75 ± 781.10 0.183 Pasta (g) 439.50 ± 203.43 427.27 ± 159.51 0.750 Rice (g) 558.30 ± 237.12 563.51 ± 208.10 0.910 Olive oil (ml) 95.30 ± 30.05 104.59.41 ± 72.72 0.462 Other oils (ml) 796.15 ± 383.97 888.23 ± 401.33 0.294 Olives (g) 254.67 ± 81.85 329.26 ± 419.75 0.312 Butter (g) 60.96 ± 26.14 78.23 ± 77.90 0.220 Chocolate (g) 429.21 ± 492.21 273.33 ± 187.79 0.043 Tea (ml) 3629.06 ± 1151.95 2903.24 ± 1065.84 0.002 Coffee (ml) 730.55 ± 650.61 625.42 ± 417.22 0.356 Fast food (pizza, hamburger) (g) 1483.75 ± 1298.04 797.50 ± 675.95 0.001 Fresh juice (ml) 973.91 ± 780.43 814.28 ± 1083.10 0.500 Green leafy vegetable (g) 1878.46 ± 1259.89 2567.56 ± 1803.70 0.004 Other fresh fruits (g) 1089.91 ± 642.90 1547.16 ± 870.70 0.014 1 Data are presented as mean ± SD. Metagenomic analysis A comparative metagenomic analysis was conducted on the bacterial compositions of 30 randomly selected samples from the main study group. The relationships between the metadata used to group the samples and the bacterial composition of the samples were examined by various analyses, and the findings were presented. The results of the comparative metagenomic analyses are presented below. The analyses performed were as follows: Chrona Analysis, Genus-Based Analyses, Similarity Analyses, Alpha Diversity Analyses, PCA-Based Analyses, Beta Diversity Analyses and finally LEfSe Analysis. In chrona analysis, the metagenome is represented as a series of nested and concentric rings forming a circle. Each of the concentric rings represents a single taxonomic level. The further a ring is from the centre, the lower the taxonomic level. The taxa at each level are represented as part of the ring in proportion to the abundance of that taxonomic level found in the sample. Consequently, this visualisation provides a multi-level view of the community structure. The Krona software employs a hierarchical interactivity, whereby clicking on a circle slice or segment displays another pie chart showing the embedded taxonomic hierarchy of that fragment. This enables the examination of each taxon in a metagenome in detail, with the levels of its member taxa also being visible. A comparison of the intestinal microbiota composition at the phylum level between the groups revealed a lower detection rate of Bacteroidota in breast cancer patients compared to healthy controls. However, this difference did not reach the level of statistical significance. The detection rates of Bacillota (Firmicutes), Pseudomonadota (Proteobacteria), Actinomycetota, Verrucomicrobiota and Thermodesulphobacteriota were comparable between breast cancer patients and healthy controls. The intestinal microbiota composition of the study groups was analysed at the class level, revealing a statistically significant reduction in the detection rates of Erysipelotrichia (p = 0.041) and Negativicutes (p = 0.004) in breast cancer patients compared to healthy controls. Nevertheless, the detection rates of Clostridia, Bacteroidia, Bacilli, Gammaproteobacteria, Coriobacteriia, Verrucomicrobiae, Actinomycetes, Tissierellia, Betaproteobacteria and Desulfovibrionia were comparable between breast cancer patients and healthy controls. A statistical analysis of the intestinal microbiota composition at the family level revealed a significantly lower prevalence of Prevotellaceae in breast cancer patients compared to healthy controls (p = 0.002). The detection rates of Clostridiaceae, Lachnospiraceae, Streptococcaceae and Enterobacteriaceae were found to be similar in breast cancer patients and healthy controls. Upon analysis of the intestinal microbiota composition at the genus level, it was observed that the detection rate of Escherichia was statistically significantly higher in breast cancer patients compared to healthy controls (p = 0.046). Nevertheless, the detection rates of Bifidobacterium, Ruminococcus, Bacteroides, Faecalibacterium, Blautia and Streptococcus were comparable between breast cancer patients and healthy controls. Upon analysis of the intestinal microbiota composition at the species level, Escherichia coli (p = 0.046) and Bacteroides cellulosilyticus (p = 0.038) were found to be statistically significantly higher in breast cancer patients compared to healthy controls. The detection rate of Faecalibacterium prausnitzii was comparable between breast cancer patients and healthy controls. In our study, we observed a reduction in the Akkermansia muciniphila species in breast cancer patients compared to the control group, although this was not statistically significant (p = 0.081). The proportion of Akkermansia muciniphila in the patient group was 0.48%, while in the control group it was 1.68%. In five of the fifteen women with breast cancer, the bacterium was not detected at all. The data on the intestinal microbiota composition of the study groups at the phylum, class, family, genus and species levels are presented in Table 4 . Table 4 Evaluation of Intestinal Microbiota Composition of in Breast Cancer Patients and Healthy Controls. 1 Breast Cancer Patients (n = 15) Control subjects (n = 15) p value Investigation of gut microbiota composition at the phylum level Bacillota (Firmicutes) 86.62 ± 12.87 78.34 ± 19.90 0.116 Bacteroidota 6.38 ± 6.74 13.80 ± 15.94 0.106 Pseudomonadota (Proteobacteria) 3.65 ± 10.74 2.98 ± 5.79 0.148 Actinomycetota 2.81 ± 1.92 3.24 ± 2.62 0.806 Verrucomicrobiota 0.48 ± 1.44 1.63 ± 4.12 0.207 Thermodesulfobacteriota 0.06 ± 0.22 0.01 ± 0.04 1.000 Bacillota (Firmicutes)/ Bacteroidota 13.57 5.67 0.161 Studying the composition of the microbiota in the classroom Clostridia 73.86 ± 18.39 68.38 ± 18.81 0.305 Bacteroidia 6.38 ± 6.74 13.78 ± 15.95 0.106 Bacilli 9.65 ± 17.34 4.25 ± 5.19 0.345 Gammaproteobacteria 3.63 ± 10.74 2.93 ± 5.69 0.174 Erysipelotrichia 2.54 ± 3.99 3.73 ± 2.69 0.041 Coriobacteriia 2.66 ± 1.95 3.11 ± 2.61 1.000 Verrucomicrobiae 0.48 ± 1.44 1.63 ± 4.12 0.207 Negativicutes 0.57 ± 0.79 1.92 ± 1.66 0.004 Actinomycetes 0.19 ± 0.43 0.13 ± 0.24 0.878 Tissierellia 0.00 ± 0.00 0.05 ± 0.21 0.351 Betaproteobacteria 0.02 ± 0.05 0.05 ± 0.12 0.565 Desulfovibrionia 0.06 ± 0.22 0.01 ± 0.04 1.000 Investigation of gut microbiota composition at the family level Clostridiaceae 2.36 ± 2.75 2.67 ± 3.11 0.351 Lachnospiraceae 56.09 ± 20.44 47.19 ± 18.16 0.305 Streptococcaceae 1.81 ± 1.34 2.47 ± 3.45 0.595 Prevotellaceae 0.83 ± 1.65 5.59 ± 11.60 0.002 Enterobacteriaceae 3.63 ± 11.03 2.78 ± 5.76 0.106 Investigation of gut microbiota composition at the genus level Escherichia 2.87 ± 9.46 2.04 ± 4.73 0.046 Bifidobacterium 0.21 ± 0.45 0.10 ± 0.21 0.852 Ruminococcus 1.79 ± 1.59 1.49 ± 1.12 0.539 Bacteroides 2.68 ± 4.05 1.39 ± 1.76 0.787 Faecalibacterium 6.48 ± 6.05 7.18 ± 3.75 0.267 Blautia 31.18 ± 16.78 22.11 ± 11.85 0.174 Streptococcus 1.81 ± 1.34 2.42 ± 3.44 0.461 Investigation of gut microbiota composition at the species level Escherichia coli 2.74 ± 8.96 1.98 ± 4.52 0.046 Bacteroides cellulosilyticus 0.25 ± 0.74 0.00 ± 0.00 0.038 Faecalibacterium prausnitzii 5.71 ± 5.41 6.23 ± 3.36 0.285 1 Data are presented as mean ± SD. Genus-Based Analyses The graphs presented herein illustrate the differentiation of the specimens at the level of phylum, genus, etc. These analyses are based on genus. The graphs illustrate the variation of distinct organisms, demonstrating the diversity of the samples in percentage terms. The various colours employed in the graphs represent the different phyla (Fig. 1 ). Alpha diversity analyses Alpha diversity is defined as the diversity of species found within a given sample or ecosystem. The calculation of alpha diversity employs a variety of indices. One such index is Shannon's index, which measures alpha diversity in terms of species richness and diversity. The number of different species present in a sample is referred to as species richness, while the distribution of microorganisms within the sample is designated as species diversity. The Chao1 index is employed to estimate the diversity of the data set, with a view to identifying species that may be overlooked due to the limitations of the available data. Another index that may be employed is Simpson's index. This index calculates the richness of the sample based on the relative abundance of the different species that comprise it. Upon analysis of Fig. 2 , it can be concluded that the alpha diversity index of patient sample number 1, coded with H1, is notably low in comparison to the other samples. This indicates the presence of dysbiosis, characterised by low species diversity and abundance. A comparison of Shannon indices representing alpha diversity between breast cancer and control groups reveals a reduction in species diversity in the patient group, although this difference is not statistically significant (p = 0.35) (Fig. 3 ). Beta Diversity Analysis The principal objective of beta diversity analysis is to ascertain the structural relationships between groups or samples by comparing the samples both within themselves and between the groups they are in. In order to achieve this objective, PCoA analyses, which are basic coordinate analyses, were conducted, as it was also necessary to compare the samples with the bacteria they had. A principal coordinate analysis (PCoA) differs from a principal component analysis (PCA) in that the distances between the bacteria in the samples are also taken into account. Permanova test was applied in PCoA analysis and no statistically significant difference was found between the groups ( p = 0.088) (Fig. 4 ). LEfSe analysis In LEfSe analysis, the differences between the metagroups to which the bacteria identified in the samples belong are compared via a relative abundance table (Fig. 5 ). LEfSe is an algorithm for biomarker discovery that identifies genomic features (genes, pathways or taxa) that characterise differences between metagroups. LEfSe first robustly identifies features that are statistically different between metagroups. Subsequently, additional tests are conducted to ascertain whether the observed differences are consistent with the expected biological behaviour. The LEfSe algorithm employs LDA to estimate the effect size of each differentially abundant feature. LDA scores of 2 and above may provide insight into potential biomarkers. In the present study, the LDA score for Escherichia coli bacteria at the species level in the patient group was found to be 4 in LEfSe analysis. The Bacteroides cellulosilyticus species was also identified as a significant biomarker in the LefSe analysis, with an LDA score exceeding 2. Both Escherichia coli and Bacteroides cellulosilyticus were identified as potential biomarkers for breast cancer. Discussion The relationship between the human gut microbiota and cancers has emerged as a significant area of biomedical research in recent years [ 16 ]. The gut microbiota is a collective term for the microorganisms present in the gastrointestinal tract that interact with the host to regulate behaviour and biochemical processes in the gut [ 17 ]. This symbiotic physiological interaction between the gut and the microbiota plays an important role in the modulation of gut homeostasis. Disturbances in the microbiota, also known as dysbiosis, have been linked to the onset of various diseases, including cancer [ 16 ]. The intestinal microbiota exerts a profound influence on fundamental metabolic processes, including the production of essential vitamins, the protection against the infiltration of pathogenic microorganisms, and the metabolism of compounds derived from the host's diet [ 16 ]. Furthermore, research has demonstrated that commensal bacteria residing in the gut can serve as an essential indicator of the health or pathogenic states of the host [ 16 ]. It is well established that the Firmicutes/Bacteroidetes (F/B) ratio plays a pivotal role in maintaining intestinal homeostasis [ 17 ]. For instance, the association between a high F/B ratio and obesity and metabolic syndrome has been repeatedly observed [ 18 ]. The F/B ratio increases from birth to adulthood and undergoes further changes with age [ 19 ]. A state of imbalance in the F/B ratio can give rise to a number of pathological conditions, including inflammatory disorders, autoimmune diseases and neoplastic growths. In the present study, the F/B ratio was found to be 5.57 in the control group and 13.57 in patients with breast cancer. However, this finding was accompanied by elevated levels of lipopolysaccharide and a reduction in microbial diversity. These results support our hypothesis that microbiota balance may be disrupted in breast cancer patients. The gut is directly connected to a number of body systems, including the enteric nervous system, the central nervous system, the autonomic nervous system, the entero-endocrine system and the hypothalamic-pituitary-adrenal axis [ 20 ]. The bidirectional relationship between the gut and these body systems forms the gut-brain axis, which secretes various hormones and neurohormones to regulate the metabolic and digestive gastrointestinal tract [ 20 ]. Although the precise mechanism by which microbiota-hormonal signalling occurs is yet to be determined, it is evident that there is a correlation between the composition of the gut microbiota and fluctuations in hormone levels that influence the host's immunity and metabolism [ 21 ]. Furthermore, research has indicated a potential association between the regulation of hormone activity by the gut microbiota and the development of cancer, with particular relevance to colorectal, breast and prostate cancers [ 16 ]. Laborda-Illanes et al. have proposed that the gut microbiota may exert a multifactorial influence on breast cancer through the regulation of steroid hormone metabolism and mucosal and systemic immune responses [ 22 ]. A higher F/B ratio has also been associated with a relatively higher abundance of gut microbial β-glucuronidase-producing species, which may be attributed to gut dysbiosis and lower gut microbiota diversity caused by the aforementioned ratio [ 23 , 24 ]. Beta-glucuronidase has the capacity to deconjugate estrogen metabolites, thereby facilitating their reabsorption in the circulation. This process results in elevated circulating estrogen levels. [ 24 ]. This metabolic pathway facilitates the accelerated development of hormone receptor-positive breast cancers [ 23 ]. These findings collectively indicate that in addition to the microbial composition of the gut, a change in the F/B ratio may also contribute to an increased risk of breast cancer. The breasts are situated at a considerable distance from the colon, and it can be reasonably assumed that the colon does not exert a direct influence on breast disease. Nevertheless, it is estimated that 99% of the microbial mass in the human body is located in the gastrointestinal tract. Furthermore, the symbiotic microbiota in the colon affects the whole body through various bacterial metabolites [ 25 , 26 ]. Bacterial extracellular vesicles are present in all body fluids, including blood, lymphatic fluid, amniotic fluid and breast milk [ 27 ]. Bacterial metabolites present in the body can be attributed to extracellular vesicles produced by microorganisms. These extracellular vesicles can influence host pathology through direct or indirect interactions with host cells [ 28 ]. While systemic LPS is typically regarded as a soluble product neutralised or unneutralised by lipoproteins and endotoxin core antibodies, LPS is also released through extracellular vesicles as a membrane-associated pathogen-associated molecular pattern [ 29 ]. The results of our study indicated that lipopolysaccharide levels were statistically significantly higher in the breast cancer group compared to the control group. Our findings are in agreement with previous studies that have suggested that intestinal metagenomes in postmenopausal breast cancer patients are rich in genes encoding the biosynthesis of lipopolysaccharide, a potent trigger of systemic inflammation that may play a role in promoting neoplastic [ 10 , 30 ]. It has been demonstrated that LPS can enhance the invasiveness and metastatic potential of breast cancer cells by activating a multitude of inflammatory and oncogenic signalling pathways [ 31 , 32 ]. As a potent inflammatory mediator, LPS not only promotes tumour formation but also enhances cancer metastasis through NF-κB, β1 integrin-mediated cell adhesion and monocyte-induced interactions between cancer and endothelial cells [ 33 ]. Nevertheless, LPS has also been demonstrated to induce apoptosis in fused hybrid cells and epithelial cells of human breast cancer [ 34 ]. Finally, the microbiota exerts immunomodulatory effects through interaction with antigen-presenting cells, such as dendritic cells, which emit very long projections that can reach the intestinal lumen. It also interacts with Toll-like cells, among others. In this context, LPS can stimulate the Toll-like receptor 4 on the surface of the host cell, thereby initiating an immune response mediated by T cells against cancer cells [ 35 ]. Biodiversity plays a pivotal role in maintaining a balanced ecosystem, contributing to its stability and ecological function. In general, high biodiversity provides a robust stability to the ecosystem [ 36 ]. Alpha diversity, the most commonly employed indicator for assessing the health of the gut microbiota, is demonstrably correlated with disease status. In the present study, a comparison of the Shannon indices representing alpha diversity between the patient and control groups revealed a lower species diversity in the patient group, although this difference was not statistically significant. In a study by Goedert et al., it was found that the composition of the gut microbiota of postmenopausal women with breast cancer was altered and had low diversity, regardless of estrogen levels [ 37 ]. Byrd et al. observed an inverse association between breast cancer risk and alpha diversity, independent of age, menopausal status, BMI, tumour grade, estrogen receptor status and molecular subtype [ 38 ]. Aarnoutse et al. and He et al. reported no significant difference in alpha diversity between breast cancer cases and controls in their respective studies [ 19 , 39 ]. We believe that these differences in the studies may be due to the different numbers of participants in the study and other study characteristics (e.g. antibiotic use). It is postulated that the discrepancies observed in the aforementioned studies may be attributed to the disparate numbers of participants enrolled in each study and other study-specific variables, such as the administration of antibiotics [ 40 ]. The discovery that disruption of the gut microbiome can influence the dissemination of a tumour at a distant site provides evidence to support the hypothesis that the gut microbiome can be regarded as an endocrine gland [ 3 , 5 ]. A number of metabolites associated with the activity of gut bacterial species have been observed to circulate through the blood and affect the function of breast cancer cells and immune cells in vitro. SCFAs, including acetate, propionate, and butyrate, are produced by bacterial species during the fermentation of undigested carbohydrates [ 41 ]. Given that these bacterial metabolites can enter the circulation, reach distant targets and affect mitochondrial metabolism, they can act as a kind of hormone and regulate the metabolism of cancer cells [ 42 ]. SCFAs are of particular interest in the context of carcinogenesis, as they exhibit significant activity on immune signalling and cell division processes [ 5 ]. In the present study, although there was no statistically significant difference in fecal total short-chain fatty acid levels between breast cancer patients and the control group, there was a 10% decrease in the former. In a study by Alvandi et al., it was found that low faecal concentrations of the three main SCFAs were associated with an increased risk of and incidence of colorectal cancer [ 43 ]. In a study by Botticelli et al., it was demonstrated that a group of non-small cell lung cancer patients with early progression exhibited low levels of certain SCFAs [ 44 ]. In 2021, He et al. published a study comparing the composition and symbiosis of gut microbiota between healthy premenopausal women and premenopausal breast cancer patients [ 19 ]. In this study, the levels of SCFAs were found to be decreased in women with premenopausal breast cancer compared to the control group, although the difference was not statistically significant. Zhu et al. conducted non-targeted metabolomic, targeted metabolomic (SCFAs) and 16S rDNA sequencing studies on faecal samples from 14 breast cancer patients and 14 healthy volunteers [ 10 ]. The SCFA content in faecal samples was quantified. The analysis included the following acids: acetic, propanoic, isobutyric, butyric, isovaleric and pentanoic acids. The findings of this study indicated that the levels of acetic, propanoic, butyric and isovaleric acid were significantly lower in breast cancer patients compared to the control group [ 10 ]. SCFAs have been demonstrated to exhibit potent anti-inflammatory effects by inhibiting the release of pro-inflammatory cytokines from macrophages and neutrophils. Acetate has been demonstrated to facilitate the release of reactive oxygen species (ROS) when introduced to mouse neutrophils by activating GPR43. ROS are effective bactericidal factors that play a role in the clearance of pathogens [ 45 ]. SCFAs may therefore be master regulators of inflammatory diseases, exerting their effects by tightly controlling the migration of immune cells towards inflammatory sites and modulating their activation state. This enables accelerated pathogen clearance via ROS activation. The aforementioned processes will result in a reduction in host damage, which will permit not only the survival of the host but also the survival of the SCFA-producing bacteria. Consequently, SCFAs are essential for maintaining normal metabolism and preventing and treating diseases when present in appropriate concentrations [ 45 ]. The structural integrity of the intestine and the layers of mucus that protect it are of paramount importance in maintaining symbiotic homeostasis. SCFA stimulates the production of mucus through the epigenetic regulation of mucin (Muc2) expression in goblet cells. This is achieved by controlling the activities of histone acetyl transferase and histone deacetylase enzymes [ 6 ]. The production of increased mucus by colonic epithelial cells serves to protect the intestinal lining from infiltration and exposure to both pathogenic and commensal bacteria. Furthermore, it suppresses the innate immune response and reduces the overall inflammatory tone [ 6 , 7 ]. It is possible that some of the effects of SCFA may be due to low colorectal pH, rather than any specific SCFA. At a pH of 6, bile acids are largely protonated and insoluble, thus unable to be taken up by colonocytes. Furthermore, the low pH inhibits the bacterial conversion of primary bile acids to secondary bile acids, thereby reducing their carcinogenicity [ 46 ]. It is also postulated that lower pH values (and elevated SCFA) prevent the overgrowth of pH-sensitive pathogenic bacteria, such as Escherichia coli or Salmonella [ 46 ]. A LDA score of 2 or above may provide insight into potential biomarkers. In our study, Escherichia coli bacteria at the species level in the patient group were found to have an LDA score of 4 in LEfSe analysis. This suggests that they may be potential biomarkers for breast cancer. In their study, AlMalki et al. demonstrated that the secretome of Escherichia coli, which is linked to breast inflammation and cancer development, affects important metabolic processes [ 47 ]. In accordance with our findings, the levels of Escherichia coli and Staphylococcus epidermidis, which are known to cause double-stranded breaks in DNA, were found to be elevated in HeLa cells (a type of cultured human cell) in women with breast cancer [ 48 ]. Given the established relationship between gut dysbiosis and breast cancer, further research is required to investigate the potential benefits of combining this approach with dietary patterns and a greater focus on nutrition. The available data primarily concern the correlational relationship between the microbiome and established breast cancer. However, a causal relationship has not yet been elucidated. For this purpose, it is necessary to collect a large amount of relevant information, such as lifestyle and dietary habits, medical history, etc., in order to adjust for confounding factors. The composition of the gut microbiota is influenced by a multitude of factors, including genetic (e.g., genotype of the individual) and epigenetic (e.g., maternal microbiota, diet, lifestyle, and antibiotic treatment) elements [ 16 ]. In the course of our study, we employed a comprehensive nutrition questionnaire to ascertain the dietary habits of our participants. The results of our study indicated that the consumption of fruit and vegetables was significantly lower in the breast cancer group compared to the control group. In a study investigating the association between increased fruit intake during adolescence and early adulthood and breast cancer risk, Farvid et al. found that increased fruit consumption led to lower breast cancer incidence rates [ 49 ]. These foods may be important in reducing the risk of breast cancer, as they contain components such as antioxidants and other micronutrients (minerals, phytosterols, polyphenols, sulphides, vitamins, salicylates, glucosinolates, phytoestrogens, lectins, etc.) [ 50 ]. Nevertheless, the abundance of dietary fibre in fruits and vegetables can induce bacterial enzyme selectivity by influencing the microbial composition. Additionally, it can affect bile acid metabolism by interrupting enterohepatic circulation. Dietary habits exert a direct influence on the composition of the gut microbiota, estrogen levels and the production of microbial-derived metabolites, particularly short-chain fatty acids. These metabolites may, in turn, influence the risk of developing breast cancer [ 31 ]. Although red meat is an important source of protein, there is a correlation between increased daily consumption of red meat and processed meat and an increased risk of developing breast cancer. A number of potential mechanisms have been proposed to explain the association between red meat intake and breast cancer. The consumption of processed meat or red meat cooked at high temperatures has been linked to an increased risk of developing cancer. This is due to the presence of carcinogens, such as heterocyclic amines and polycyclic aromatic hydrocarbons, in these foods [ 50 ]. Furthermore, the consumption of red meat and processed meat, which are high in cholesterol content, saturated fat and haem iron, has been linked to an increased risk of developing malignant tumours [ 49 ]. The study by Farvid et al. found that consumption of unprocessed red meat was associated with an increased risk of breast cancer [ 49 ]. It has been postulated that a Western lifestyle, characterised by a diet high in sugar and fat, low in fibre and minimal activity, may increase the risk of breast cancer. The typical Western diet is associated with an increased proliferation of unhealthy bacteria that contain high levels of β-glucuronidase. As previously stated, this enzyme is responsible for deconjugating estrogen and returning it to the circulatory system, thereby increasing its availability to further nourish estrogen-sensitive cancers. In their study, Pierce and colleagues demonstrated that the adoption of a healthy lifestyle following a diagnosis of breast cancer can result in a reduction in mortality rates of up to 50% [ 51 ]. A case-control study was conducted in which 1010 patients with breast cancer and 1950 controls were interviewed about their dairy consumption habits. The results indicated that there was a significantly reduced risk of breast cancer in those who consumed more yoghurt, which may be attributed to the presence of probiotics [ 52 ]. In a case-control study of postmenopausal women, Veer van't et al. reported that the consumption of fermented dairy products was higher in the control group than in breast cancer patients. Furthermore, the authors suggested that the consumption of these products may have a protective role in both pre-menopausal and postmenopausal women [ 53 ]. Conclusions In conclusion, there is a compelling need to identify new biomarkers and predictors of treatment benefit and/or toxicity in breast cancer. The field of microbiome research in breast cancer is relatively novel, with data emerging only recently. It is evident that this area warrants further investigation with regard to cancer prevention, identification and the development of therapeutic strategies. The microbiota and their metabolites have the potential to address knowledge gaps in the pathogenesis of breast cancer. Lifestyle factors, such as diet and physical activity, exert a profound influence on the intricate relationship between gut microbiota and estrogen metabolism. This, in turn, affects the recurrence of breast cancer and the likelihood of metastasis. It has been demonstrated that interventions which increase microbial diversity through dietary recommendations may impact health, particularly in patients with breast cancer. A diet that is unhealthy and high in processed meat, simple sugars and low in fibre has a significant negative impact on the gut microbiota, especially in newly diagnosed breast cancer patients. Abbreviations ER Status estrogen receptor PR Status progesterone receptor HER2 human epidermal growth factor receptor 2 Ki-67 cell proliferation index. Declarations Funding This study was financially supported by Selçuk University Scientific Research Projects (BAP) coordinatorship [23401019] and Tübitak 1002-B Emergency Support modüle [123S453]. Availability of data and materials All data generated or analyzed during this study are included in this published article and are also available from the corresponding author on reasonable request. Ethics approval and consent to participate Our study was approved by decision number E-70632468-050.01.04-471808 of Selçuk University, Faculty of Medicine, Pharmaceutical and Non-Medical Device Research Ethics Committee. Consent for publication Not applicable. Acknowledgment The authors have no acknowledgments to declare. Declaration of competing interest The authors declare no competing interests. References Papakonstantinou A, Nuciforo P, Borrell M, Zamora E, Pimentel I, Saura C, et al. The conundrum of breast cancer and microbiome - A comprehensive review of the current evidence. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5145862","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":364667176,"identity":"0dcfb1b7-f7df-4f89-ad0f-13dcdbea4e77","order_by":0,"name":"Fatma Hümeyra Yerlikaya","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIie3OMUvDQBjG8SdLspz7QYX4EVKCh2ClX+UthbhkC2TqECjcVHS13yIunQuBZCnOCYXSTl0c4pZBwWsr2c5kFLw/3PHecT84wGT6gzkv7ZQAe8DDZeljZTutAWoJ9SGc+pLB4jb9kDvXq46HPc0yH858xa1mpyfXG1EtZTRMt6HvUZ4JsDzmoEhLxjwU2ytJliI2nyTZCOpGEf3P2Il8SRqnVXFszsR970EsSZO0hIAiApx1kSCuFm80XW5Cn1P+6NssiO4o+I1MV2UT08NTURzqenY/fHay17Ie6ck5ywZu1j8H+7R1ANUn4Cadr0wmk+m/9g3SrFYmusP0YgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of Biochemistry","correspondingAuthor":true,"prefix":"","firstName":"Fatma","middleName":"Hümeyra","lastName":"Yerlikaya","suffix":""},{"id":364667177,"identity":"f1c0955a-0545-42cc-96af-a44f8b392b0a","order_by":1,"name":"Mustafa Topkafa","email":"","orcid":"","institution":"Konya Technical University, Vocational School of Technical Sciences, Chemistry and Chemical Processing Technologies","correspondingAuthor":false,"prefix":"","firstName":"Mustafa","middleName":"","lastName":"Topkafa","suffix":""},{"id":364667178,"identity":"85376a49-38f6-465b-a6b0-89b654963214","order_by":2,"name":"Hande Köksal","email":"","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of General Surgery","correspondingAuthor":false,"prefix":"","firstName":"Hande","middleName":"","lastName":"Köksal","suffix":""},{"id":364667179,"identity":"25928beb-10cc-4bf5-92ad-33c4d5664d2b","order_by":3,"name":"Salih Maçin","email":"","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of Microbiology","correspondingAuthor":false,"prefix":"","firstName":"Salih","middleName":"","lastName":"Maçin","suffix":""},{"id":364667180,"identity":"0f8d56bf-283b-488a-b6e0-152941a3d3ac","order_by":4,"name":"Serdar Yormaz","email":"","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of General Surgery","correspondingAuthor":false,"prefix":"","firstName":"Serdar","middleName":"","lastName":"Yormaz","suffix":""},{"id":364667181,"identity":"975b35d0-99b1-47ed-9f29-915ceafa24b4","order_by":5,"name":"Abdullah Sivrikaya","email":"","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of Biochemistry","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Sivrikaya","suffix":""},{"id":364667182,"identity":"efbd2a10-5a07-4468-9579-89d26e08a7eb","order_by":6,"name":"Sedat Abuşoğlu","email":"","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of Biochemistry","correspondingAuthor":false,"prefix":"","firstName":"Sedat","middleName":"","lastName":"Abuşoğlu","suffix":""},{"id":364667183,"identity":"e8727073-8d6a-4f9e-8920-3298da8507da","order_by":7,"name":"Duygu Eryavuz Onmaz","email":"","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of Biochemistry","correspondingAuthor":false,"prefix":"","firstName":"Duygu","middleName":"Eryavuz","lastName":"Onmaz","suffix":""},{"id":364667184,"identity":"92f895b8-a849-4455-9acf-6a4d9086339c","order_by":8,"name":"Uğur Arslan","email":"","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of Microbiology","correspondingAuthor":false,"prefix":"","firstName":"Uğur","middleName":"","lastName":"Arslan","suffix":""},{"id":364667185,"identity":"e9522758-a956-46b6-bbcf-67e6bb781ee6","order_by":9,"name":"Nurullah Çiftçi","email":"","orcid":"","institution":"University of Selcuk, Faculty of Medicine, Department of Microbiology","correspondingAuthor":false,"prefix":"","firstName":"Nurullah","middleName":"","lastName":"Çiftçi","suffix":""}],"badges":[],"createdAt":"2024-09-24 14:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5145862/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5145862/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70207958,"identity":"e4c82f54-6aa1-4609-9a32-31c3aa4b49a7","added_by":"auto","created_at":"2024-11-29 13:58:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78902,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of samples from breast cancer patients and control groups at the phyla level\u003c/p\u003e","description":"","filename":"Binder31.png","url":"https://assets-eu.researchsquare.com/files/rs-5145862/v1/353ecb97b90cb14ae164fc55.png"},{"id":70207959,"identity":"8350c58f-4d43-483a-975d-ef6789225f4a","added_by":"auto","created_at":"2024-11-29 13:58:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":131972,"visible":true,"origin":"","legend":"\u003cp\u003eShannon index distributions in Breast Cancer Patients and Healthy Controls (G1: Breast cancer group, G2: Control group)\u003c/p\u003e","description":"","filename":"Binder32.png","url":"https://assets-eu.researchsquare.com/files/rs-5145862/v1/07c6e4c9212a2ed907f1f4ff.png"},{"id":70208278,"identity":"96d1c586-3607-4c64-b73f-ed7cd8462f5b","added_by":"auto","created_at":"2024-11-29 14:06:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12520,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Shannon indices of samples of Breast Cancer and control group\u003c/p\u003e","description":"","filename":"Binder33.png","url":"https://assets-eu.researchsquare.com/files/rs-5145862/v1/8eac09c9afbcae76e773a04f.png"},{"id":70208280,"identity":"f0337715-2a4c-4ec5-980d-c72e052c2721","added_by":"auto","created_at":"2024-11-29 14:06:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134341,"visible":true,"origin":"","legend":"\u003cp\u003eBeta diversity analysis in Breast Cancer Patients and Healthy Controls (Bray-Curtis)\u003c/p\u003e","description":"","filename":"Binder34.png","url":"https://assets-eu.researchsquare.com/files/rs-5145862/v1/a4ace7a66482ff43b2e54c7b.png"},{"id":70209147,"identity":"a7108560-14ab-4386-a630-3f2b119c42d1","added_by":"auto","created_at":"2024-11-29 14:14:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87507,"visible":true,"origin":"","legend":"\u003cp\u003eLEfSe analysis in Breast Cancer Patients and Healthy Controls\u003c/p\u003e","description":"","filename":"Binder35.png","url":"https://assets-eu.researchsquare.com/files/rs-5145862/v1/4736bb2bb8c4ead60cf0c43d.png"},{"id":70207963,"identity":"b7815ce9-fecd-42ff-ad36-a6aeacd5440f","added_by":"auto","created_at":"2024-11-29 13:58:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":146799,"visible":true,"origin":"","legend":"\u003cp\u003eThis study indicated that serum lipopolysaccharide levels and the Firmicutes/Bacteroidetes ratio were higher in breast cancer patients than in the control group, and that the diversity of microbes in the gut appeared to be reduced. It seems that a diet high in unhealthy and processed meat, simple sugars and low in fibre may have a significant negative impact on gut microbiota, especially in newly diagnosed breast cancer patients.\u003c/p\u003e","description":"","filename":"figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5145862/v1/ca56389ab1865116c34b73fa.png"},{"id":70210318,"identity":"68ce4e4c-1179-4101-b2f3-ade58045dbca","added_by":"auto","created_at":"2024-11-29 14:30:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1630778,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5145862/v1/3cf1cd23-20b2-4c91-9aa0-a962f94da3e2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impaired microbiota in breast cancer patients: In association with high lipopolysaccharide levels and a low-fibre diet","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is a complex disease influenced by many factors, including genetics, lifestyle and environmental exposures [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Microorganisms play an indirect role in influencing the incidence, natural history and/or severity of various cancers [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For example, microbes in the gut affect dormant cancer cells, which can cause the disease to return [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Research into the link between microbes and cancer has mainly focused on bacteria and their role in different types of cancer [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although the potential carcinogenic role of the gut or breast microbiome in the development of breast cancer or the pathogenic role in the progression of an established breast cancer lesion has not yet been confirmed, emerging data suggest that processes derived from the gut microbiome may play a role in breast carcinogenesis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe relationship between dietary intake, gut microbiota diversity and function, and their importance in human health is currently an active area of research [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. An increasing number of studies suggest that metabolites produced by microorganisms are important mediators of dietary microbial effects on the host [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The main problem with a Western diet, typically high in fat and digestible saccharides, is that most nutrients are absorbed in the duodenum, leaving little substrate for bacteria in the colon [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. One of the most relevant therapeutic pathways of microbiome modulation involves restoring levels of short-chain fatty acids (SCFAs), microbial metabolites essential for human health [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A diet comprising vegetables and fruit rich in complex carbohydrates is associated with an abundance of SCFA-producing bacteria, while a higher intake of unhealthy fats and simple sugars is associated with an increase in facultative anaerobic bacteria and a decrease in SCFA [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. SCFAs play a key role in regulating colonic physiology and modifying the intestinal environment. Recent research has shown that SCFAs not only affect the signalling pathway in the gut, but also reach tissues and organs outside the gut by circulating in the blood [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBreast cancer is one of the most common malignant tumours in women [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. There is increasing evidence that the composition and diversity of the gut microbiota influence tumour immune responses, but a detailed mechanistic understanding of these responses is lacking. Furthermore, the majority of current studies on cancer microbiota have concentrated on the investigation of colon, lung, kidney, and bladder cancers. Until recently, little attention has been paid to the microbiota and cancers that respond less favourably to immune checkpoint therapies, such as breast cancer. There is a lack of in-depth research into the relationship between SCFA levels and breast cancer in particular. We hypothesized that gut microbiota and microbiota metabolites may change negatively in breast cancer patients. In this context, our study aims to find answers to questions to develop new predictive methods and treatments for breast cancer with a holistic approach that integrates gut microbiota, dysbiosis, microbiota metabolites and diet in newly diagnosed breast cancer cases before starting any treatment.\u003c/p\u003e"},{"header":"Material and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003eNewly diagnosed early stage breast cancer patients admitted to the Department of General Surgery, Faculty of Medicine, Sel\u0026ccedil;uk University were included in the study as \u0026apos;cases\u0026apos; after obtaining informed consent. Healthy women of the same age who attended the same outpatient clinic without any clinical complaints were included in the study as a \u0026apos;control group\u0026apos;. The trial included 65 women with breast cancer and 50 women as a control group. Clinical information and treatment history were obtained from patients\u0026apos; medical records. A analysis was conducted on the pathology reports of breast cancer patients. The data included in the report comprised histopathological diagnosis, tumour grade, stage and hormone receptor status (estrogen receptor - ER, progesterone receptor - PR and Her2neu). Women with a history of cancer or an advanced tumour stage, or women who had received antibiotic therapy or any neoadjuvant treatment within the previous six months, were excluded from the study. The women participating in the study were in the perimenopausal stage. Demographic and clinical details of the subjects are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDemographic and Clinical Details of in Breast Cancer Patients and Healthy Controls.\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBreast Cancer Patients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eControl subjects\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Subjects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.59\u0026thinsp;\u0026plusmn;\u0026thinsp;14.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.45\u0026thinsp;\u0026plusmn;\u0026thinsp;9.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBody Mass Index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.60\u0026thinsp;\u0026plusmn;\u0026thinsp;5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.58\u0026thinsp;\u0026plusmn;\u0026thinsp;6.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlucose (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107.0\u0026thinsp;\u0026plusmn;\u0026thinsp;33.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e83.98\u0026thinsp;\u0026plusmn;\u0026thinsp;16.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e198.43\u0026thinsp;\u0026plusmn;\u0026thinsp;38.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180.48\u0026thinsp;\u0026plusmn;\u0026thinsp;30.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTriglyceride (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e135.05\u0026thinsp;\u0026plusmn;\u0026thinsp;76.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.35\u0026thinsp;\u0026plusmn;\u0026thinsp;61.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow-density Lipoprotein Cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.10\u0026thinsp;\u0026plusmn;\u0026thinsp;32.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110.40\u0026thinsp;\u0026plusmn;\u0026thinsp;19.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-density Lipoprotein Cholesterol (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.84\u0026thinsp;\u0026plusmn;\u0026thinsp;15.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58.58\u0026thinsp;\u0026plusmn;\u0026thinsp;20.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancer antigen 15\u0026thinsp;\u0026minus;\u0026thinsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.21\u0026thinsp;\u0026plusmn;\u0026thinsp;10.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.73\u0026thinsp;\u0026plusmn;\u0026thinsp;8.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite Blood Cell (cells/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.86\u0026thinsp;\u0026plusmn;\u0026thinsp;4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetabolic Diseases (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypercholesterolemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathological diagnosis of Breast Cancer Patients\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrequency (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInvasive Breast Carcinoma Nonspecific Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e88.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInvasive Lobular Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInvasive Mucinous Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAdenoid Cystic Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e1.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eER Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e19.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e75.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePR Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e77.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e62.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e31.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eKi-67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026gt;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e37.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e42.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAbbreviations: ER Status, estrogen receptor; PR Status, progesterone receptor; HER2, human epidermal growth factor receptor 2; Ki-67, cell proliferation index.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eNutritional Assessment and Anthropometric Evaluation\u003c/h3\u003e\n\u003cp\u003eIn the study, the Food Frequency Questionnaire (FFQ) was employed to obtain general information about the participants and to determine their dietary habits. The questionnaire was administered by the researchers through a face-to-face interview. The initial section of the questionnaire comprises two subsections, the first of which gathers basic demographic data, including age, gender vs. under the heading of general information. The second subsection comprises strategically selected questions pertaining to physical activity levels and health-related lifestyle preferences.\u003c/p\u003e\n\u003cp\u003eThe second section of the questionnaire comprises the FFQ, which inquires about the frequency of consumption of specific foods relative to the previous month. The FFQ form inquired about the frequency and amounts of consumption of a total of 36 foods and beverages. The foods included in the FFQ form were selected based on a review of the relevant literature, with the objective of serving the purpose of the study and to explain the research problem [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the course of the comprehensive retrospective interviews, the participants are requested to provide detailed data on the manner in which the food is prepared and the ingredients used. In order to ascertain the quantities of food and beverages consumed with precision and accuracy, the Food and Nutrition Photo Catalogue, a frequently employed resource in similar studies, was utilised [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. The body mass index (BMI) was calculated by dividing body weight by the square of height (kg/m\u0026sup2;) in breast cancer patients participating in the study and healthy individuals in the control group.\u003c/p\u003e\n\u003ch3\u003eChemical Analyses\u003c/h3\u003e\n\u003cp\u003eVenous blood samples were collected after 12 hours of fasting and separated into serum and plasma samples in accordance with standard laboratory procedures. The samples were maintained on ice and centrifuged (1000 x g, 15 min) within a period of 2\u0026ndash;4 h following collection. The plasma and serum samples were stored at a temperature of -20\u0026deg;C until the analyses were performed. The serum glucose, total cholesterol, high-density lipoproteins (HDL), low-density lipoproteins (LDL), triglycerides, cancer antigen 15\u0026thinsp;\u0026minus;\u0026thinsp;3 (CA 15.3) and white blood cells (WBC) were determined by standard methods.\u003c/p\u003e\n\u003cp\u003eSerum lipopolysaccharide-binding protein (LBP) and serum zonulin concentrations were quantified using a commercially available kit based on the enzyme-linked immunosorbent assay (ELISA) method (Elabscience Biotechnology Co.). This technique employs a two-sided sandwich approach, utilising two selected polyclonal antibodies that bind to distinct epitopes of human LBP and zonulin.\u003c/p\u003e\n\u003cp\u003eSCFAs were extracted and analyzed according to a modified version of the method described by Abd El-Wahab et al. and Kahraman et al. [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. The faecal sample was placed in sterile containers and immediately frozen at -20\u0026deg;C, after which it was transported to the laboratory. In the laboratory, 1 g of human faeces sample was homogenised with 10 mL of water at room temperature. One millilitre of the homogenate was extracted, 200 microlitres of 25% ortho-phosphoric acid was added, and the mixture was vortexed. Subsequently, the supernatant was subjected to centrifugation at 13,000 rpm/min, after which it was passed through a 0.22 polytetrafluoroethylene syringe filter. The SCFAs were analysed using an Agilent 8860 series gas chromatograph, which was equipped with a flame ionisation detector (GC-FID) and a DB-FFAP column (30 m, 0.32 mm, 1 \u0026micro;m, J\u0026amp;W Scientific, USA). SCFAs were dissolved in water and injected in a splitless injection mode (1 \u0026micro;L). A flow rate of 1 ml min-1 of hydrogen was employed as the carrier gas. The injection port and detector temperatures were recorded as 270 and 300\u0026deg;C, respectively. The initial oven temperature of 60\u0026deg;C was maintained for a period of five minutes, after which it was increased to 140\u0026deg;C at a rate of 5\u0026deg;C per minute. Chromatograms were recorded utilising an OpenLAB CDS data processor (Agilent Technologies Inc., Wilmington, DE, USA). The identification and calibration of authentic SCFAs peaks was performed by comparing their retention times with those of authentic standards.\u003c/p\u003e\n\u003ch3\u003eMicrobiota analyses\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eDNA isolation\u003c/h2\u003e\n \u003cp\u003eIn order to optimise the study, a commercially available DNA isolation kit was utilised, with DNA isolation conducted in accordance with the company\u0026apos;s recommendations. A volume of 400 \u0026micro;l of binding solution (comprising 4 M guanidine thiocyanate, 20 mM Tris-HCl, pH 8) was then added to the sample. Following incubation of the sample at 95\u0026deg;C for 10 minutes, 400 \u0026micro;l of 2-propanol was added and loaded onto a silica column. The DNAs in the sample were separated from the other components by centrifugation at 10,000 g for one minute. They were then retained on the silica column and washed twice with washing solution (20 mM NaCl, 2 mM Tris-HCl, pH 8; 80% v/v ethanol). The silica column was subjected to centrifugation in order to facilitate the drying process. The DNA retained on the silica column was collected from the column with 100 \u0026micro;l of nuclease-free, sterile, deionised water (pH 7). The quality of the DNA samples obtained was evaluated using the NanoDrop device, with the concentration and 260/280nm and 230/260nm absorbance values recorded. Next-generation sequencing and real-time polymerase chain reaction (PCR) of DNA samples were conducted on a QIAGEN device. A total of 17 \u0026micro;l of PCR mix and 3 \u0026micro;l of bacterial DNA were transferred to PCR tubes. The mixture was prepared and the PCR samples were dispensed in a cold block. A DNA-free negative control and a positive control were used in each \u0026lsquo;real-time PCR\u0026rsquo; test.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eThe 16S Study Protocol, barcoding and sequencing\u003c/h2\u003e\n \u003cp\u003eIn order to perform a metagenomic analysis of the 16S rRNA, previously described workflows were utilised. The primer pair to be employed for the construction of amplicon libraries has been designed to target a region of approximately 1500 bp, encompassing the V1-V9 region of the 16S rRNA gene. The Oxford Nanopore Technologies Nanopore barcode DNA sequences of the generated library were added to the 5\u0026apos; end of the target-specific primer pairs. The target-specific primer-connector sequences for the 16S rRNA are TTTCTGTTGGTGCTGATATATTGC and AGRGTTTGATYHTGGCTCAG for the forward primer and 5\u0026rsquo;-ACTTGCCTGTCGCTCGCTCTATCTTC-TACCTTGTTAYGACTT-3\u0026apos; for the reverse primer. The initial PCR was conducted using the Proof Reading DNA Polymerase 2x Reaction Mix and 200 nanomoles of each primer. The following thermal cycling programme was implemented in the PCR device: 95\u0026deg;C for 3 minutes; 25 cycles of 95\u0026deg;C for 30 seconds, 55\u0026deg;C for 30 seconds and 72\u0026deg;C for 90 seconds; 72\u0026deg;C for 5 minutes. The PCR product was subjected to electrophoresis on an agarose gel in order to ascertain its size, which was found to be approximately 1450 base pairs (bp). It was then purified using a PCR product purification kit.\u003c/p\u003e\n \u003cp\u003eThe ligation sequencing kit (SQK-LSK109; Oxford Nanopore Technologies) and native barcoding kit (EXP-NBD104-114; Oxford Nanopore Technologies) protocols were employed for the preparation of the amplicon library. Firstly, the ends of the 60 \u0026micro;L final volume samples (100\u0026ndash;200 fmol) were prepared, a dA tail was added and fixed. Subsequently, the 0.5X MagBeads (MobiomX) kit was employed for the purpose of purification. The native barcodes were maintained at room temperature for a period of 10 minutes in order to facilitate their binding to the prepared tips. Subsequently, the samples were purified and subjected to spectrofluorimetric analysis.\u003c/p\u003e\n \u003cp\u003eA total of 35 \u0026micro;L of DNA was extracted from each of the barcoded samples, with an equal volume of DNA from each sample being combined in a single tube. The adapters were connected to the ends of the barcoded samples, which were then purified and measured. Following the preparation stage, 50 fmol of the library was loaded into a Spot-On flow cell (FLO-MIN106D). The sequencing run was initiated on a Mk1C\u0026trade; instrument (Oxford Nanopore Technologies) utilising MinKNOW\u0026trade; software. Sequencing was terminated either when the requisite data had been obtained or when the maximum permitted run time of 72 hours had elapsed.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eBioinformatics analysis\u003c/h3\u003e\n\u003cp\u003eFollowing the sequencing process, the results were converted from fast5 format to fastq format using the guppy software (base-calling and de-multiplexing) in order to facilitate further analysis. Given that the 16S rRNA region is on average 1500 bp, reads between 1250 and 1750 bp in length were subjected to filtration using Trimmomatic, with the remaining reads being excluded from the subsequent analysis. The cleaned reads were subjected to analysis using a bespoke workflow developed in the Python programming language. In accordance with this workflow, each sequence was subjected to BLAST algorithmic matching during the filtering process. An OTU was created by taking the taxonomic data of sequences with more than 60% reference coverage and 80% pairwise similarity in the matching results.\u003c/p\u003e\n\u003cp\u003eA variety of analytical techniques were employed, including alpha diversity analysis, principal component analysis (PCA), principal coordinate analysis (PCoA), beta diversity analysis, biomarker analysis, and phenotype analysis. These techniques were applied using a range of indexes in conjunction with the tools provided by the QIIME2 platform for phylogenetic analyses with the OTU (.biom) file. The Mothur platform was employed to organise taxonomic classifications and to prepare dynamic crown charts. The graphs and tables in the analyses were generated using the Python programming language libraries.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eThe results were subjected to statistical analysis using IBM SPSS Statistics 26.0 (IBM Corp., Armonk, New York, USA), a software package designed for the analysis of quantitative data. Accuracy, specificity, linearity, limit of determination, recovery and reproducibility of the measurement method used were tested. Student t-test was used for parametric distribution. For non-parametric distribution, the Mann-Whitney U test was used. All fatty acid (molar %) values were subjected to statistical analysis using MedCalc Statistical Software version 19.2.6 (MedCalc Software bv, Ostend, Belgium; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medcalc.org\u003c/span\u003e\u003c/span\u003e; 2020). This involved the use of the Chi-square test and proportions. A p-value of less than \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eThe SCFA levels of the Breast cancer group and the Control group are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, while the FFQ questionnaire assessment is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No statistically significant difference was observed between the breast cancer patient and control groups in SFCAs measurements. As demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the results of the statistical evaluation of the ELISA studies revealed that serum LPS levels were significantly elevated in the breast cancer group in comparison to the control group (p\u0026thinsp;=\u0026thinsp;0.007). There was no significant difference between serum zonulin levels (p\u0026thinsp;=\u0026thinsp;0.690).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChemical analysis of in Breast Cancer Patients and Healthy Controls. \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast Cancer Patients (n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl subjects (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThe sum of SCFA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.66\u0026thinsp;\u0026plusmn;\u0026thinsp;7.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.94\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetic acid (mM)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.34\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropionic acid (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButyric acid (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.64\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsobutyric acid (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValeric acid (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsovaleric acid (mM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.212\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcetic acid (molar %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.02\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.62\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePropionic acid (molar %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.36\u0026thinsp;\u0026plusmn;\u0026thinsp;5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.87\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButyric acid (molar %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.11\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.52\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsobutyric acid (molar %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValeric acid (molar %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsovaleric acid (molar %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZonulin (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.1\u0026thinsp;\u0026plusmn;\u0026thinsp;26.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLipopolysaccharide (ng/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206.6\u0026thinsp;\u0026plusmn;\u0026thinsp;143.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.3\u0026thinsp;\u0026plusmn;\u0026thinsp;101.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: SCFA, short-chain fatty acids.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNutritional Evaluation of in Breast Cancer Patients and Healthy Controls. \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFFQ item\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast Cancer Patients\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;65)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl subjects (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDairy product (milk, yoghurt, kefir) (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2848.30\u0026thinsp;\u0026plusmn;\u0026thinsp;1914.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3586.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1465.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCheese (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e587.11\u0026thinsp;\u0026plusmn;\u0026thinsp;363.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e601.29\u0026thinsp;\u0026plusmn;\u0026thinsp;287.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed meat (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1101.29\u0026thinsp;\u0026plusmn;\u0026thinsp;615.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e797.14\u0026thinsp;\u0026plusmn;\u0026thinsp;492.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1344.00\u0026thinsp;\u0026plusmn;\u0026thinsp;763.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1245.94\u0026thinsp;\u0026plusmn;\u0026thinsp;817.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFish (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e286.48\u0026thinsp;\u0026plusmn;\u0026thinsp;200.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e303.70\u0026thinsp;\u0026plusmn;\u0026thinsp;351.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgg (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e416.56\u0026thinsp;\u0026plusmn;\u0026thinsp;253.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e420.27\u0026thinsp;\u0026plusmn;\u0026thinsp;270.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeans (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e873.43\u0026thinsp;\u0026plusmn;\u0026thinsp;679.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1016.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1097.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDried chickpeas (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e666.61\u0026thinsp;\u0026plusmn;\u0026thinsp;539.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e929.06\u0026thinsp;\u0026plusmn;\u0026thinsp;898.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLentil (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1875.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1345.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2126.25\u0026thinsp;\u0026plusmn;\u0026thinsp;1003.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcessed meat (salami, sausage) (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346.24\u0026thinsp;\u0026plusmn;\u0026thinsp;225.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191.51\u0026thinsp;\u0026plusmn;\u0026thinsp;193.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoast potatoes (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1732.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1182.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1296.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1100.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeek (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.22\u0026thinsp;\u0026plusmn;\u0026thinsp;50.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150.00\u0026thinsp;\u0026plusmn;\u0026thinsp;10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnion (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e284.92\u0026thinsp;\u0026plusmn;\u0026thinsp;264.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e310.00\u0026thinsp;\u0026plusmn;\u0026thinsp;263.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGarlic (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.75\u0026thinsp;\u0026plusmn;\u0026thinsp;3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.80\u0026thinsp;\u0026plusmn;\u0026thinsp;5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTomato (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e894.19\u0026thinsp;\u0026plusmn;\u0026thinsp;426.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e765.71\u0026thinsp;\u0026plusmn;\u0026thinsp;328.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen beans (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e214.28\u0026thinsp;\u0026plusmn;\u0026thinsp;179.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219.31\u0026thinsp;\u0026plusmn;\u0026thinsp;167.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeas (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187.50\u0026thinsp;\u0026plusmn;\u0026thinsp;75.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200.00\u0026thinsp;\u0026plusmn;\u0026thinsp;75.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBanana (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e411.57\u0026thinsp;\u0026plusmn;\u0026thinsp;377.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e557.42\u0026thinsp;\u0026plusmn;\u0026thinsp;536.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed plum (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.14\u0026thinsp;\u0026plusmn;\u0026thinsp;35.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.67\u0026thinsp;\u0026plusmn;\u0026thinsp;30.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePomegranate (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e149.71\u0026thinsp;\u0026plusmn;\u0026thinsp;88.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153.63\u0026thinsp;\u0026plusmn;\u0026thinsp;109.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.872\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrape (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.40\u0026thinsp;\u0026plusmn;\u0026thinsp;42.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e166.06\u0026thinsp;\u0026plusmn;\u0026thinsp;130.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite bread (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1307.54\u0026thinsp;\u0026plusmn;\u0026thinsp;594.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e973.38\u0026thinsp;\u0026plusmn;\u0026thinsp;461.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole grain bread (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1099.35\u0026thinsp;\u0026plusmn;\u0026thinsp;474.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e853.75\u0026thinsp;\u0026plusmn;\u0026thinsp;781.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePasta (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e439.50\u0026thinsp;\u0026plusmn;\u0026thinsp;203.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e427.27\u0026thinsp;\u0026plusmn;\u0026thinsp;159.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e558.30\u0026thinsp;\u0026plusmn;\u0026thinsp;237.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e563.51\u0026thinsp;\u0026plusmn;\u0026thinsp;208.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlive oil (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.30\u0026thinsp;\u0026plusmn;\u0026thinsp;30.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104.59.41\u0026thinsp;\u0026plusmn;\u0026thinsp;72.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther oils (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e796.15\u0026thinsp;\u0026plusmn;\u0026thinsp;383.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e888.23\u0026thinsp;\u0026plusmn;\u0026thinsp;401.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOlives (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254.67\u0026thinsp;\u0026plusmn;\u0026thinsp;81.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329.26\u0026thinsp;\u0026plusmn;\u0026thinsp;419.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eButter (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.96\u0026thinsp;\u0026plusmn;\u0026thinsp;26.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.23\u0026thinsp;\u0026plusmn;\u0026thinsp;77.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChocolate (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e429.21\u0026thinsp;\u0026plusmn;\u0026thinsp;492.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e273.33\u0026thinsp;\u0026plusmn;\u0026thinsp;187.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTea (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3629.06\u0026thinsp;\u0026plusmn;\u0026thinsp;1151.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2903.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1065.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e730.55\u0026thinsp;\u0026plusmn;\u0026thinsp;650.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e625.42\u0026thinsp;\u0026plusmn;\u0026thinsp;417.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFast food (pizza, hamburger) (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1483.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1298.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e797.50\u0026thinsp;\u0026plusmn;\u0026thinsp;675.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFresh juice (ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e973.91\u0026thinsp;\u0026plusmn;\u0026thinsp;780.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e814.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1083.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreen leafy vegetable (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1878.46\u0026thinsp;\u0026plusmn;\u0026thinsp;1259.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2567.56\u0026thinsp;\u0026plusmn;\u0026thinsp;1803.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther fresh fruits (g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1089.91\u0026thinsp;\u0026plusmn;\u0026thinsp;642.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1547.16\u0026thinsp;\u0026plusmn;\u0026thinsp;870.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic analysis\u003c/h2\u003e \u003cp\u003eA comparative metagenomic analysis was conducted on the bacterial compositions of 30 randomly selected samples from the main study group. The relationships between the metadata used to group the samples and the bacterial composition of the samples were examined by various analyses, and the findings were presented. The results of the comparative metagenomic analyses are presented below. The analyses performed were as follows: Chrona Analysis, Genus-Based Analyses, Similarity Analyses, Alpha Diversity Analyses, PCA-Based Analyses, Beta Diversity Analyses and finally LEfSe Analysis. In chrona analysis, the metagenome is represented as a series of nested and concentric rings forming a circle. Each of the concentric rings represents a single taxonomic level. The further a ring is from the centre, the lower the taxonomic level. The taxa at each level are represented as part of the ring in proportion to the abundance of that taxonomic level found in the sample. Consequently, this visualisation provides a multi-level view of the community structure. The Krona software employs a hierarchical interactivity, whereby clicking on a circle slice or segment displays another pie chart showing the embedded taxonomic hierarchy of that fragment. This enables the examination of each taxon in a metagenome in detail, with the levels of its member taxa also being visible.\u003c/p\u003e \u003cp\u003eA comparison of the intestinal microbiota composition at the phylum level between the groups revealed a lower detection rate of Bacteroidota in breast cancer patients compared to healthy controls. However, this difference did not reach the level of statistical significance. The detection rates of Bacillota (Firmicutes), Pseudomonadota (Proteobacteria), Actinomycetota, Verrucomicrobiota and Thermodesulphobacteriota were comparable between breast cancer patients and healthy controls. The intestinal microbiota composition of the study groups was analysed at the class level, revealing a statistically significant reduction in the detection rates of Erysipelotrichia (p\u0026thinsp;=\u0026thinsp;0.041) and Negativicutes (p\u0026thinsp;=\u0026thinsp;0.004) in breast cancer patients compared to healthy controls. Nevertheless, the detection rates of Clostridia, Bacteroidia, Bacilli, Gammaproteobacteria, Coriobacteriia, Verrucomicrobiae, Actinomycetes, Tissierellia, Betaproteobacteria and Desulfovibrionia were comparable between breast cancer patients and healthy controls. A statistical analysis of the intestinal microbiota composition at the family level revealed a significantly lower prevalence of Prevotellaceae in breast cancer patients compared to healthy controls (p\u0026thinsp;=\u0026thinsp;0.002). The detection rates of Clostridiaceae, Lachnospiraceae, Streptococcaceae and Enterobacteriaceae were found to be similar in breast cancer patients and healthy controls. Upon analysis of the intestinal microbiota composition at the genus level, it was observed that the detection rate of Escherichia was statistically significantly higher in breast cancer patients compared to healthy controls (p\u0026thinsp;=\u0026thinsp;0.046). Nevertheless, the detection rates of Bifidobacterium, Ruminococcus, Bacteroides, Faecalibacterium, Blautia and Streptococcus were comparable between breast cancer patients and healthy controls. Upon analysis of the intestinal microbiota composition at the species level, Escherichia coli (p\u0026thinsp;=\u0026thinsp;0.046) and Bacteroides cellulosilyticus (p\u0026thinsp;=\u0026thinsp;0.038) were found to be statistically significantly higher in breast cancer patients compared to healthy controls. The detection rate of Faecalibacterium prausnitzii was comparable between breast cancer patients and healthy controls. In our study, we observed a reduction in the Akkermansia muciniphila species in breast cancer patients compared to the control group, although this was not statistically significant (p\u0026thinsp;=\u0026thinsp;0.081). The proportion of Akkermansia muciniphila in the patient group was 0.48%, while in the control group it was 1.68%. In five of the fifteen women with breast cancer, the bacterium was not detected at all. The data on the intestinal microbiota composition of the study groups at the phylum, class, family, genus and species levels are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of Intestinal Microbiota Composition of in Breast Cancer Patients and Healthy Controls. \u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBreast Cancer Patients (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl subjects (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eInvestigation of gut microbiota composition at the phylum level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacillota (Firmicutes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e86.62\u0026thinsp;\u0026plusmn;\u0026thinsp;12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.34\u0026thinsp;\u0026plusmn;\u0026thinsp;19.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.80\u0026thinsp;\u0026plusmn;\u0026thinsp;15.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePseudomonadota (Proteobacteria)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;5.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinomycetota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.24\u0026thinsp;\u0026plusmn;\u0026thinsp;2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerrucomicrobiota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThermodesulfobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacillota (Firmicutes)/ Bacteroidota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eStudying the composition of the microbiota in the classroom\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClostridia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.86\u0026thinsp;\u0026plusmn;\u0026thinsp;18.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.38\u0026thinsp;\u0026plusmn;\u0026thinsp;18.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroidia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;6.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.78\u0026thinsp;\u0026plusmn;\u0026thinsp;15.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacilli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.65\u0026thinsp;\u0026plusmn;\u0026thinsp;17.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.25\u0026thinsp;\u0026plusmn;\u0026thinsp;5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGammaproteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErysipelotrichia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.54\u0026thinsp;\u0026plusmn;\u0026thinsp;3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoriobacteriia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.66\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerrucomicrobiae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegativicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinomycetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissierellia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBetaproteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDesulfovibrionia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInvestigation of gut microbiota composition at the family level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClostridiaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.36\u0026thinsp;\u0026plusmn;\u0026thinsp;2.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.351\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.09\u0026thinsp;\u0026plusmn;\u0026thinsp;20.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.19\u0026thinsp;\u0026plusmn;\u0026thinsp;18.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStreptococcaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevotellaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.59\u0026thinsp;\u0026plusmn;\u0026thinsp;11.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnterobacteriaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.63\u0026thinsp;\u0026plusmn;\u0026thinsp;11.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.78\u0026thinsp;\u0026plusmn;\u0026thinsp;5.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInvestigation of gut microbiota composition at the genus level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEscherichia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;9.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.04\u0026thinsp;\u0026plusmn;\u0026thinsp;4.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBifidobacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRuminococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaecalibacterium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.48\u0026thinsp;\u0026plusmn;\u0026thinsp;6.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.18\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlautia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.18\u0026thinsp;\u0026plusmn;\u0026thinsp;16.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.11\u0026thinsp;\u0026plusmn;\u0026thinsp;11.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStreptococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.42\u0026thinsp;\u0026plusmn;\u0026thinsp;3.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInvestigation of gut microbiota composition at the species level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEscherichia coli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.74\u0026thinsp;\u0026plusmn;\u0026thinsp;8.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.98\u0026thinsp;\u0026plusmn;\u0026thinsp;4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteroides cellulosilyticus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFaecalibacterium prausnitzii\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.71\u0026thinsp;\u0026plusmn;\u0026thinsp;5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenus-Based Analyses\u003c/h2\u003e \u003cp\u003eThe graphs presented herein illustrate the differentiation of the specimens at the level of phylum, genus, etc. These analyses are based on genus. The graphs illustrate the variation of distinct organisms, demonstrating the diversity of the samples in percentage terms. The various colours employed in the graphs represent the different phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAlpha diversity analyses\u003c/h2\u003e \u003cp\u003eAlpha diversity is defined as the diversity of species found within a given sample or ecosystem. The calculation of alpha diversity employs a variety of indices. One such index is Shannon's index, which measures alpha diversity in terms of species richness and diversity. The number of different species present in a sample is referred to as species richness, while the distribution of microorganisms within the sample is designated as species diversity. The Chao1 index is employed to estimate the diversity of the data set, with a view to identifying species that may be overlooked due to the limitations of the available data. Another index that may be employed is Simpson's index. This index calculates the richness of the sample based on the relative abundance of the different species that comprise it. Upon analysis of Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be concluded that the alpha diversity index of patient sample number 1, coded with H1, is notably low in comparison to the other samples. This indicates the presence of dysbiosis, characterised by low species diversity and abundance. A comparison of Shannon indices representing alpha diversity between breast cancer and control groups reveals a reduction in species diversity in the patient group, although this difference is not statistically significant (p\u0026thinsp;=\u0026thinsp;0.35) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eBeta Diversity Analysis\u003c/h2\u003e \u003cp\u003eThe principal objective of beta diversity analysis is to ascertain the structural relationships between groups or samples by comparing the samples both within themselves and between the groups they are in. In order to achieve this objective, PCoA analyses, which are basic coordinate analyses, were conducted, as it was also necessary to compare the samples with the bacteria they had. A principal coordinate analysis (PCoA) differs from a principal component analysis (PCA) in that the distances between the bacteria in the samples are also taken into account. Permanova test was applied in PCoA analysis and no statistically significant difference was found between the groups ( p\u0026thinsp;=\u0026thinsp;0.088) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLEfSe analysis\u003c/h2\u003e \u003cp\u003e In LEfSe analysis, the differences between the metagroups to which the bacteria identified in the samples belong are compared via a relative abundance table (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). LEfSe is an algorithm for biomarker discovery that identifies genomic features (genes, pathways or taxa) that characterise differences between metagroups. LEfSe first robustly identifies features that are statistically different between metagroups. Subsequently, additional tests are conducted to ascertain whether the observed differences are consistent with the expected biological behaviour. The LEfSe algorithm employs LDA to estimate the effect size of each differentially abundant feature. LDA scores of 2 and above may provide insight into potential biomarkers. In the present study, the LDA score for Escherichia coli bacteria at the species level in the patient group was found to be 4 in LEfSe analysis. The Bacteroides cellulosilyticus species was also identified as a significant biomarker in the LefSe analysis, with an LDA score exceeding 2. Both Escherichia coli and Bacteroides cellulosilyticus were identified as potential biomarkers for breast cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe relationship between the human gut microbiota and cancers has emerged as a significant area of biomedical research in recent years [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The gut microbiota is a collective term for the microorganisms present in the gastrointestinal tract that interact with the host to regulate behaviour and biochemical processes in the gut [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This symbiotic physiological interaction between the gut and the microbiota plays an important role in the modulation of gut homeostasis. Disturbances in the microbiota, also known as dysbiosis, have been linked to the onset of various diseases, including cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The intestinal microbiota exerts a profound influence on fundamental metabolic processes, including the production of essential vitamins, the protection against the infiltration of pathogenic microorganisms, and the metabolism of compounds derived from the host's diet [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, research has demonstrated that commensal bacteria residing in the gut can serve as an essential indicator of the health or pathogenic states of the host [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. It is well established that the Firmicutes/Bacteroidetes (F/B) ratio plays a pivotal role in maintaining intestinal homeostasis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. For instance, the association between a high F/B ratio and obesity and metabolic syndrome has been repeatedly observed [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The F/B ratio increases from birth to adulthood and undergoes further changes with age [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. A state of imbalance in the F/B ratio can give rise to a number of pathological conditions, including inflammatory disorders, autoimmune diseases and neoplastic growths. In the present study, the F/B ratio was found to be 5.57 in the control group and 13.57 in patients with breast cancer. However, this finding was accompanied by elevated levels of lipopolysaccharide and a reduction in microbial diversity. These results support our hypothesis that microbiota balance may be disrupted in breast cancer patients.\u003c/p\u003e \u003cp\u003eThe gut is directly connected to a number of body systems, including the enteric nervous system, the central nervous system, the autonomic nervous system, the entero-endocrine system and the hypothalamic-pituitary-adrenal axis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The bidirectional relationship between the gut and these body systems forms the gut-brain axis, which secretes various hormones and neurohormones to regulate the metabolic and digestive gastrointestinal tract [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Although the precise mechanism by which microbiota-hormonal signalling occurs is yet to be determined, it is evident that there is a correlation between the composition of the gut microbiota and fluctuations in hormone levels that influence the host's immunity and metabolism [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Furthermore, research has indicated a potential association between the regulation of hormone activity by the gut microbiota and the development of cancer, with particular relevance to colorectal, breast and prostate cancers [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Laborda-Illanes et al. have proposed that the gut microbiota may exert a multifactorial influence on breast cancer through the regulation of steroid hormone metabolism and mucosal and systemic immune responses [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA higher F/B ratio has also been associated with a relatively higher abundance of gut microbial β-glucuronidase-producing species, which may be attributed to gut dysbiosis and lower gut microbiota diversity caused by the aforementioned ratio [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Beta-glucuronidase has the capacity to deconjugate estrogen metabolites, thereby facilitating their reabsorption in the circulation. This process results in elevated circulating estrogen levels. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This metabolic pathway facilitates the accelerated development of hormone receptor-positive breast cancers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings collectively indicate that in addition to the microbial composition of the gut, a change in the F/B ratio may also contribute to an increased risk of breast cancer.\u003c/p\u003e \u003cp\u003eThe breasts are situated at a considerable distance from the colon, and it can be reasonably assumed that the colon does not exert a direct influence on breast disease. Nevertheless, it is estimated that 99% of the microbial mass in the human body is located in the gastrointestinal tract. Furthermore, the symbiotic microbiota in the colon affects the whole body through various bacterial metabolites [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Bacterial extracellular vesicles are present in all body fluids, including blood, lymphatic fluid, amniotic fluid and breast milk [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Bacterial metabolites present in the body can be attributed to extracellular vesicles produced by microorganisms. These extracellular vesicles can influence host pathology through direct or indirect interactions with host cells [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile systemic LPS is typically regarded as a soluble product neutralised or unneutralised by lipoproteins and endotoxin core antibodies, LPS is also released through extracellular vesicles as a membrane-associated pathogen-associated molecular pattern [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The results of our study indicated that lipopolysaccharide levels were statistically significantly higher in the breast cancer group compared to the control group. Our findings are in agreement with previous studies that have suggested that intestinal metagenomes in postmenopausal breast cancer patients are rich in genes encoding the biosynthesis of lipopolysaccharide, a potent trigger of systemic inflammation that may play a role in promoting neoplastic [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt has been demonstrated that LPS can enhance the invasiveness and metastatic potential of breast cancer cells by activating a multitude of inflammatory and oncogenic signalling pathways [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As a potent inflammatory mediator, LPS not only promotes tumour formation but also enhances cancer metastasis through NF-κB, β1 integrin-mediated cell adhesion and monocyte-induced interactions between cancer and endothelial cells [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Nevertheless, LPS has also been demonstrated to induce apoptosis in fused hybrid cells and epithelial cells of human breast cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Finally, the microbiota exerts immunomodulatory effects through interaction with antigen-presenting cells, such as dendritic cells, which emit very long projections that can reach the intestinal lumen. It also interacts with Toll-like cells, among others. In this context, LPS can stimulate the Toll-like receptor 4 on the surface of the host cell, thereby initiating an immune response mediated by T cells against cancer cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBiodiversity plays a pivotal role in maintaining a balanced ecosystem, contributing to its stability and ecological function. In general, high biodiversity provides a robust stability to the ecosystem [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Alpha diversity, the most commonly employed indicator for assessing the health of the gut microbiota, is demonstrably correlated with disease status. In the present study, a comparison of the Shannon indices representing alpha diversity between the patient and control groups revealed a lower species diversity in the patient group, although this difference was not statistically significant. In a study by Goedert et al., it was found that the composition of the gut microbiota of postmenopausal women with breast cancer was altered and had low diversity, regardless of estrogen levels [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Byrd et al. observed an inverse association between breast cancer risk and alpha diversity, independent of age, menopausal status, BMI, tumour grade, estrogen receptor status and molecular subtype [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Aarnoutse et al. and He et al. reported no significant difference in alpha diversity between breast cancer cases and controls in their respective studies [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. We believe that these differences in the studies may be due to the different numbers of participants in the study and other study characteristics (e.g. antibiotic use). It is postulated that the discrepancies observed in the aforementioned studies may be attributed to the disparate numbers of participants enrolled in each study and other study-specific variables, such as the administration of antibiotics [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe discovery that disruption of the gut microbiome can influence the dissemination of a tumour at a distant site provides evidence to support the hypothesis that the gut microbiome can be regarded as an endocrine gland [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A number of metabolites associated with the activity of gut bacterial species have been observed to circulate through the blood and affect the function of breast cancer cells and immune cells in vitro. SCFAs, including acetate, propionate, and butyrate, are produced by bacterial species during the fermentation of undigested carbohydrates [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Given that these bacterial metabolites can enter the circulation, reach distant targets and affect mitochondrial metabolism, they can act as a kind of hormone and regulate the metabolism of cancer cells [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. SCFAs are of particular interest in the context of carcinogenesis, as they exhibit significant activity on immune signalling and cell division processes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In the present study, although there was no statistically significant difference in fecal total short-chain fatty acid levels between breast cancer patients and the control group, there was a 10% decrease in the former. In a study by Alvandi et al., it was found that low faecal concentrations of the three main SCFAs were associated with an increased risk of and incidence of colorectal cancer [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In a study by Botticelli et al., it was demonstrated that a group of non-small cell lung cancer patients with early progression exhibited low levels of certain SCFAs [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In 2021, He et al. published a study comparing the composition and symbiosis of gut microbiota between healthy premenopausal women and premenopausal breast cancer patients [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this study, the levels of SCFAs were found to be decreased in women with premenopausal breast cancer compared to the control group, although the difference was not statistically significant. Zhu et al. conducted non-targeted metabolomic, targeted metabolomic (SCFAs) and 16S rDNA sequencing studies on faecal samples from 14 breast cancer patients and 14 healthy volunteers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The SCFA content in faecal samples was quantified. The analysis included the following acids: acetic, propanoic, isobutyric, butyric, isovaleric and pentanoic acids. The findings of this study indicated that the levels of acetic, propanoic, butyric and isovaleric acid were significantly lower in breast cancer patients compared to the control group [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSCFAs have been demonstrated to exhibit potent anti-inflammatory effects by inhibiting the release of pro-inflammatory cytokines from macrophages and neutrophils. Acetate has been demonstrated to facilitate the release of reactive oxygen species (ROS) when introduced to mouse neutrophils by activating GPR43. ROS are effective bactericidal factors that play a role in the clearance of pathogens [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. SCFAs may therefore be master regulators of inflammatory diseases, exerting their effects by tightly controlling the migration of immune cells towards inflammatory sites and modulating their activation state. This enables accelerated pathogen clearance via ROS activation. The aforementioned processes will result in a reduction in host damage, which will permit not only the survival of the host but also the survival of the SCFA-producing bacteria. Consequently, SCFAs are essential for maintaining normal metabolism and preventing and treating diseases when present in appropriate concentrations [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe structural integrity of the intestine and the layers of mucus that protect it are of paramount importance in maintaining symbiotic homeostasis. SCFA stimulates the production of mucus through the epigenetic regulation of mucin (Muc2) expression in goblet cells. This is achieved by controlling the activities of histone acetyl transferase and histone deacetylase enzymes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The production of increased mucus by colonic epithelial cells serves to protect the intestinal lining from infiltration and exposure to both pathogenic and commensal bacteria. Furthermore, it suppresses the innate immune response and reduces the overall inflammatory tone [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is possible that some of the effects of SCFA may be due to low colorectal pH, rather than any specific SCFA. At a pH of 6, bile acids are largely protonated and insoluble, thus unable to be taken up by colonocytes. Furthermore, the low pH inhibits the bacterial conversion of primary bile acids to secondary bile acids, thereby reducing their carcinogenicity [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. It is also postulated that lower pH values (and elevated SCFA) prevent the overgrowth of pH-sensitive pathogenic bacteria, such as Escherichia coli or Salmonella [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. A LDA score of 2 or above may provide insight into potential biomarkers. In our study, Escherichia coli bacteria at the species level in the patient group were found to have an LDA score of 4 in LEfSe analysis. This suggests that they may be potential biomarkers for breast cancer.\u003c/p\u003e \u003cp\u003eIn their study, AlMalki et al. demonstrated that the secretome of Escherichia coli, which is linked to breast inflammation and cancer development, affects important metabolic processes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In accordance with our findings, the levels of Escherichia coli and Staphylococcus epidermidis, which are known to cause double-stranded breaks in DNA, were found to be elevated in HeLa cells (a type of cultured human cell) in women with breast cancer [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Given the established relationship between gut dysbiosis and breast cancer, further research is required to investigate the potential benefits of combining this approach with dietary patterns and a greater focus on nutrition. The available data primarily concern the correlational relationship between the microbiome and established breast cancer. However, a causal relationship has not yet been elucidated. For this purpose, it is necessary to collect a large amount of relevant information, such as lifestyle and dietary habits, medical history, etc., in order to adjust for confounding factors. The composition of the gut microbiota is influenced by a multitude of factors, including genetic (e.g., genotype of the individual) and epigenetic (e.g., maternal microbiota, diet, lifestyle, and antibiotic treatment) elements [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the course of our study, we employed a comprehensive nutrition questionnaire to ascertain the dietary habits of our participants. The results of our study indicated that the consumption of fruit and vegetables was significantly lower in the breast cancer group compared to the control group. In a study investigating the association between increased fruit intake during adolescence and early adulthood and breast cancer risk, Farvid et al. found that increased fruit consumption led to lower breast cancer incidence rates [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. These foods may be important in reducing the risk of breast cancer, as they contain components such as antioxidants and other micronutrients (minerals, phytosterols, polyphenols, sulphides, vitamins, salicylates, glucosinolates, phytoestrogens, lectins, etc.) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Nevertheless, the abundance of dietary fibre in fruits and vegetables can induce bacterial enzyme selectivity by influencing the microbial composition. Additionally, it can affect bile acid metabolism by interrupting enterohepatic circulation. Dietary habits exert a direct influence on the composition of the gut microbiota, estrogen levels and the production of microbial-derived metabolites, particularly short-chain fatty acids. These metabolites may, in turn, influence the risk of developing breast cancer [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough red meat is an important source of protein, there is a correlation between increased daily consumption of red meat and processed meat and an increased risk of developing breast cancer. A number of potential mechanisms have been proposed to explain the association between red meat intake and breast cancer. The consumption of processed meat or red meat cooked at high temperatures has been linked to an increased risk of developing cancer. This is due to the presence of carcinogens, such as heterocyclic amines and polycyclic aromatic hydrocarbons, in these foods [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Furthermore, the consumption of red meat and processed meat, which are high in cholesterol content, saturated fat and haem iron, has been linked to an increased risk of developing malignant tumours [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The study by Farvid et al. found that consumption of unprocessed red meat was associated with an increased risk of breast cancer [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt has been postulated that a Western lifestyle, characterised by a diet high in sugar and fat, low in fibre and minimal activity, may increase the risk of breast cancer. The typical Western diet is associated with an increased proliferation of unhealthy bacteria that contain high levels of β-glucuronidase. As previously stated, this enzyme is responsible for deconjugating estrogen and returning it to the circulatory system, thereby increasing its availability to further nourish estrogen-sensitive cancers. In their study, Pierce and colleagues demonstrated that the adoption of a healthy lifestyle following a diagnosis of breast cancer can result in a reduction in mortality rates of up to 50% [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. A case-control study was conducted in which 1010 patients with breast cancer and 1950 controls were interviewed about their dairy consumption habits. The results indicated that there was a significantly reduced risk of breast cancer in those who consumed more yoghurt, which may be attributed to the presence of probiotics [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. In a case-control study of postmenopausal women, Veer van't et al. reported that the consumption of fermented dairy products was higher in the control group than in breast cancer patients. Furthermore, the authors suggested that the consumption of these products may have a protective role in both pre-menopausal and postmenopausal women [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, there is a compelling need to identify new biomarkers and predictors of treatment benefit and/or toxicity in breast cancer. The field of microbiome research in breast cancer is relatively novel, with data emerging only recently. It is evident that this area warrants further investigation with regard to cancer prevention, identification and the development of therapeutic strategies. The microbiota and their metabolites have the potential to address knowledge gaps in the pathogenesis of breast cancer. Lifestyle factors, such as diet and physical activity, exert a profound influence on the intricate relationship between gut microbiota and estrogen metabolism. This, in turn, affects the recurrence of breast cancer and the likelihood of metastasis. It has been demonstrated that interventions which increase microbial diversity through dietary recommendations may impact health, particularly in patients with breast cancer. A diet that is unhealthy and high in processed meat, simple sugars and low in fibre has a significant negative impact on the gut microbiota, especially in newly diagnosed breast cancer patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eER Status\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eestrogen receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePR Status\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eprogesterone receptor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHER2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ehuman epidermal growth factor receptor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKi-67\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecell proliferation index.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was financially supported by Sel\u0026ccedil;uk University Scientific Research Projects (BAP) coordinatorship [23401019] and T\u0026uuml;bitak 1002-B Emergency Support mod\u0026uuml;le [123S453].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and are also available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study was approved by decision number E-70632468-050.01.04-471808 of Sel\u0026ccedil;uk University, Faculty of Medicine, Pharmaceutical and Non-Medical Device Research Ethics Committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no acknowledgments to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePapakonstantinou A, Nuciforo P, Borrell M, Zamora E, Pimentel I, Saura C, et al. 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PMID: 2736542.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Microbiota, breast cancer, short chain fatty acid, lipopolysaccharide, zonulin, diet, fiber","lastPublishedDoi":"10.21203/rs.3.rs-5145862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5145862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRecent studies have demonstrated that the intestinal microbiota, and in particular the metabolites produced by the microbiota, play a role in the pathogenesis of cancer. We hypothesized that gut microbiota and microbiota metabolites may change negatively in breast cancer patients. The trial included 65 women with breast cancer and 50 women as a control group. A Food Frequency Questionnaire characterised daily macronutrient intake over the past 1 month. A metagenomic analysis was conducted based on all hypervariable regions (V1-V9) of the 16S rRNA gene with the objective of investigating the composition and diversity of the microbiome.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA microbiome analysis revealed that the composition of the gut microbiome differed between cases and controls. In particular, the diversity of microbes in the gut was reduced in breast cancer patients. The present study has demonstrated that serum lipopolysaccharide levels and the Firmicutes/Bacteroidetes ratio are elevated in breast cancer patients in comparison to the control group. We found that breast cancer patients who participated in our study had non-ideal dietary patterns. The relationship between diet and gut microbiota exerts a profound influence on the complex interplay between these two factors. This relationship may have an impact on the development of breast cancer and the likelihood of cancer metastasis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIt can be postulated that interventions that enhance microbial diversity through dietary recommendations may have a beneficial impact on health, particularly in patients with breast cancer.\u003c/p\u003e","manuscriptTitle":"Impaired microbiota in breast cancer patients: In association with high lipopolysaccharide levels and a low-fibre diet","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 13:58:13","doi":"10.21203/rs.3.rs-5145862/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"830af5ec-2d38-4c0d-8b4f-d32d3fa109f2","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-29T13:58:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-29 13:58:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5145862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5145862","identity":"rs-5145862","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}