Application of a fungal microbiome as a marker for gastric cancer diagnosis

Abstract

The application discloses a kind of fungi microbial community as the application of gastric cancer diagnosis marker, the fungi microbial community is selected from one or more in fungi door level, genus level and species level.By the ITS sequencing of fungal DNA and the taxonomic annotation of reading of GC tissue, cancer adjacent tissue and healthy (HC) gastric tissue, a total of 4.5 million valid tags are generated, divided into 1631 classification units (OTUs), and 10 door levels and 301 genus levels of fungal subgroups are identified.The proportion of Basidiomycota and Ascomycota in GC group increases, and the high expression of Cystobasidium, Cutaneotrichosporon, Apodus, Apiotrichum, Simplicillium, Lecanactis, Rhizopus, Rhodotorula, Exophiala and Sarocladium in GC can be used as the fungal microbial community marker for gastric cancer diagnosis, and is closely related to the occurrence and development of gastric cancer.

Description

Technical Field

[0001] This invention belongs to the field of microbiome or gastric cancer technology, specifically relating to the application of fungal microbiome as a biomarker for gastric cancer diagnosis. Background Technology

[0002] Gastric cancer (GC) is one of the most common malignant tumors, ranking second among all cancer-related deaths and fifth most widely diagnosed cancer. The development of GC typically involves multiple stages, from atrophic gastritis (AG) to intestinal metaplasia (IM), eventually progressing to GC. While the etiology and pathogenesis of gastric cancer remain unclear, exposure to environmental carcinogens (such as toxic chemicals and Helicobacter pylori) has been shown to increase cancer risk, and the gut microbiota can also adapt to changing environmental conditions by altering its composition and gene expression. The stomach has long been considered a "hostile site" for bacteria because the highly acidic gastric juices are unsuitable for microbial growth. Recent evidence suggests that alterations in the gastric microbiota are associated with the onset and progression of gastric cancer. Since the 1980s, Helicobacter pylori (H. pylori, HP) has been identified as the most common pathogenic bacterium for gastric cancer over the past 13 years. However, only 1-3% of all H. pylori infections ultimately develop into GC; meanwhile, 15% of H. pylori patients who have eradicated H. pylori with medication still develop GC, indicating that other microorganisms can also reside in the stomach and play a role in the occurrence and development of GC.

[0003] Advances in high-throughput sequencing technology have provided strong technical support for exploring the differences in the composition and abundance of the gastric microbiome in healthy individuals and gastric cancer patients. One study demonstrated that oral bacteria are more likely to aggregate in gastric cancer samples, with Streptococcus, Neisseria, Haemophilus, and Porphyromonas being the most dominant species in gastric cancer (GC). Emerging or re-emerging fungi are becoming a global public health threat closely related to host immune regulation. Recent studies have confirmed alterations in the fungal composition in colorectal adenomas, Crohn's disease, and ulcerative colitis patients, offering opportunities to discover new relationships between host-fungal microbiome interactions. However, research understanding the functional role of the gastric fungal microbiota in GC is limited, particularly from the perspective of the potential diagnostic value of fungi in GC screening.

[0004] my country has a high incidence of gastric cancer (GC), and current GC screening still relies on gastroscopy: a complex procedure with poor patient experience and low compliance. Clinical serum biomarkers for GC mainly include CA19-9, CEA, and CA72-4; however, these biomarkers have low sensitivity and specificity for GC. This means that serum tumor markers may be normal, even though some cancer patients have progressed to advanced stages, or even experienced tumor recurrence and metastasis, yet their tumor markers remain at normal levels. Therefore, finding new biomarkers with higher sensitivity and specificity is an effective way to achieve early diagnosis and provide timely treatment recommendations for gastric cancer patients. Summary of the Invention

[0005] Objective of the Invention: This invention comprehensively analyzes the fungal microbiome in GC tissue, adjacent GC tissue, and healthy gastric tissue to explain the relationship between changes in the gastric fungal microbiome and the occurrence and development of GC. Results show that the abundance of gastric flora, particularly Basidiomycota, in GC patients is significantly higher than in the HC group. Ten highly abundant fungal genera in the GC group can serve as biomarkers for the prediction and diagnosis of GC. Furthermore, this invention proposes for the first time the interaction between the host fungal microbiome and the occurrence and development of GC, providing a new explanation for the potential role of fungi in GC.

[0006] Based on the above-mentioned objectives, this invention discloses an application of fungal microbiome as a biomarker for gastric cancer diagnosis. The fungal microbiome comprises any one or more of Cystobasidium, Cutaneotrichosporon, Apodus, Apiotrichum, Simplicillium, Lecanactis, Rhizopus, Rhodotorula, Exophiala, and Sarocladium, wherein the fungal microorganisms are one or more at the phylum, genus, and species levels within the fungi.

[0007] The screening method for the fungal microorganisms is as follows: DNA is extracted from the tissue, ITS sequencing and reading taxonomic annotation are performed, and the difference and / or similarity of the fungal microorganisms between gastric cancer patients and healthy controls are determined to obtain fungal microbial markers.

[0008] Specifically, the tissues include gastric cancer tissue, adjacent tissue, and healthy gastric tissue.

[0009] The ITS sequencing process is as follows:

[0010] (1) Extracting DNA from tissues;

[0011] (2) Library construction and PCR amplification were performed using ITS primers;

[0012] (3) The concentration and purity of the PCR products were detected using a NanoDrop 2000 UV spectrophotometer and 1% agarose gel electrophoresis;

[0013] (4) Sequencing was performed using an Illumina MiSeqPE 300 sequencer.

[0014] Specifically, the ITS primers in step (2) are:

[0015] Upstream primer ITS3F: GCATCGATGAAGAACGCAGC,

[0016] Downstream primer ITS4R: TCCTCCGCTTATTGATATGC.

[0017] The aforementioned reading taxonomy annotation process is as follows:

[0018] (1) Delete mismatched, missing and duplicate sequences, and only allow 2 nucleotide mismatches;

[0019] (2) Use UPARSE.7 to cluster taxonomic units (OTUs) with a similarity of more than 97% into the same OTU;

[0020] (3) Use QIIME to calculate α diversity, including chao1, species and PD_whole_tree;

[0021] (4) The LefSe method was used to analyze the differentially expressed fungi in the GC and HC groups.

[0022] The results showed that Basidiomycota was more abundant than Ascomycota and was significantly enriched in gastric cancer tissues. Ten fungal biomarkers, namely Cystobasidium, Cutaneotrichosporon, Apodus, Apiotrichum, Simplicillium, Lecanactis, Rhizopus, Rhodotorula, Exophiala, and Sarocladium, were all highly expressed in GC tissues.

[0023] This application uses receiver operating characteristic (ROC) curves to analyze fungal biomarkers, and the analysis tools are Excel and Graphpad 8.0.

[0024] This application further proposes a kit for the diagnosis of gastric cancer, comprising a reagent capable of detecting the fungus to which the marker of claim 1 belongs.

[0025] Beneficial Effects: This invention elucidates the role of fungal communities in the gastric microenvironment during the development and progression of gastric globulin (GC), and discovers that the fungal content of a certain fungal microbiome in GC tissue can serve as an effective biomarker for GC detection. Using this biomarker, GC can be accurately and reliably screened and diagnosed. For example, by detecting the level of Cutaneotrichosporon in the tissues of GC patients, it is possible to accurately determine whether cancer is likely to occur or whether they have GC. Attached Figure Description

[0026] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0027] Figure 1 A flowchart for screening fungal biomarkers and its correlation with host immunity;

[0028] Figure 2 To illustrate the changes in bacterial microbiome diversity in gastric cancer, where GC represents the gastric cancer group and HC represents the normal group, (A, B, and C) Chao1, observed_species, and PD_whole_tree describe the alpha diversity of fungi between the GC and HC groups; (D and E) Principal component analysis of fungal composition in HC and GC samples;

[0029] Figure 3 The changes in fungal composition between the gastric cancer group and the normal group were shown. GC represented the gastric cancer group, and HC represented the normal group. (A) Venn analysis of OUT abundance among the three groups; (B) Comparison at the level of 17 dominant fungal phyla; (C) Comparison of relative abundance of Basidiomycota and Ascomycota; (D) Comparison of relative abundance of fungi at the genus level; (E and F) Differential expression of fungi at the genus level between the GC and HC groups.

[0030] Figure 4 To demonstrate that fungal species can serve as diagnostic markers for gastric cancer (GC), (A) a branching diagram of different fungal taxa, with differences represented by the color of the most abundant category (red, HC; green, GC; yellow, no significance). The diameter of each circle is proportional to the number of taxa. Each ring represents the next level of taxonomy. (B) Linearity Calculation (LDA) scores between the HC and GC groups using LEfSe analysis (LDA score >3, red, healthy group; green, gastric cancer group). (C) ROC curves for the top 10 fungal genera in diagnosing GC.

[0031] Figure 5 For fungal function prediction. (A) Fungal function prediction at the community level; (B) Fungal function prediction at the trophic level.

[0032] Figure 6Analysis of the diagnostic value of Cutaneotrichosporon in GC. (A) Differential expression graph of Cutaneotrichosporon in HC and GC; (B) ROC curve of Cutaneotrichosporon in diagnosing HC and GC. Detailed Implementation

[0033] To provide a more detailed understanding of the features and technical content of this invention, the implementation of the invention will be described in detail below with reference to the accompanying drawings. The drawings are for illustrative purposes only and are not intended to limit the invention. Those skilled in the art can make some non-essential improvements and adjustments to this invention based on the above description. In the following embodiments, unless otherwise specified, all reagents used are commercially available.

[0034] Example 1: Screening of fungal microbiomes.

[0035] 1.1 Collection of tissue samples and extraction of DNA:

[0036] Patients clinically diagnosed with gastric cancer were selected, and surgically removed gastric cancer tissue (GC, 22 cases) and adjacent cancerous tissue (PC, 22 cases) were collected. These tissues were immediately washed three times with sterile saline, 1 mL of tissue preservation solution was added, and the tissues were stored at -80°C. Simultaneously, healthy gastric tissue scraped from healthy individuals during gastroscopy (HC, 11 cases) was collected, washed, treated with tissue preservation solution, and stored at -80°C. Total RNA was extracted from three groups of tissues using a commercial soil DNA extraction kit and stored at -80°C for later use. The overall experimental procedure is as follows: Figure 1 As shown.

[0037] 1.2 ITS2 sequencing of fungi in tissues:

[0038] RNA concentration and purity were determined using a NanoDrop 2000 UV spectrophotometer and 1% agarose gel electrophoresis. The ITS2 rRNA PCR amplification procedure was followed: initial denaturation at 95°C for 3 min; followed by 35 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 45 s; finally, incubation at 72°C for 10 min and storage at 10°C until the reaction was terminated. The reaction mixture consisted of 10 μL of 2×proTaq buffer, 0.8 μL of 5 μM forward primer, 0.8 μL of 5 μM reverse primer, 10 ng / μL of template DNA, and finally, ddH2O to a final volume of 20 μL. The PCR products were purified using the AxyPrep DNA gel extraction kit (Axygen Biosciences, USA), and both ends were sequenced using an Illumina MiSeq PE 300 platform. The raw sequencing results are stored in the Sequence Reading Archive (SRA) database of the National Center for Biotechnology Information (NCBI) (access code: PRJNA797736).

[0039] 1.3 Taxonomic annotation of sequencing results:

[0040] The raw ITS2 rRNA sequencing data were processed as follows: Within a 50bp moving window, 300bp reads with an average quality score less than 20 were truncated, truncated reads less than 50bp were discarded, and blurry characters in the reads were discarded; based on overlapping sequences, only overlapping sequences longer than 10bp were assembled. The maximum mismatch ratio for overlapping regions was 0.2. Reads that could not be assembled were discarded; samples were differentiated based on barcodes and primers, sequence orientation was adjusted, and barcodes were precisely matched, allowing only single nucleotide mismatches during primer matching. Operational taxonomic units (OTUs) with 97% similarity were clustered into the same OTU using UPARSE 7.1, and chimeric sequences were identified and removed; based on sequencing accuracy, alpha diversity was calculated using QIIME, including the observed_species index, chao1, and PD_whole_tree; sample points were colored according to disease phenotype and inter-group differences, and principal component analysis (PCA) was performed based on Bray-Curtis distance; identified fungal OTUs were classified and annotated according to the UNITE database. Figure 2 As shown, the HC group had significantly higher alpha diversity than the GC group at the observed_species index, chao1, and PD_whole_tree levels, indicating that the HC group had higher fungal species abundance than the GC group. PCA results showed that the fungi in the HC and GC groups were clearly divided into two groups, indicating a high degree of uniformity in fungal composition within the same tissue type.

[0041] 1.4 Screening and diagnostic efficacy analysis of differentially expressed fungi in the GC group:

[0042] The differences in OUTs among the three groups were analyzed using the Venn distribution, such as Figure 3 As shown, 64 OUTs were shared across all three groups, while HC, GC, and Para-GC had 869, 213, and 339 unique OUTs, respectively. Compared to the HC group, Basidiomycota had the highest abundance in the GC group, followed by Ascomycota. Further analysis revealed that Apiotrichum, Cutaneotrichosporon, Sarocladium, and Malassezia were significantly more expressed in the GC group than in the HC group, while Rhizopus, Rhodotorula, Apodus, and Cytobasidium were significantly less expressed in the HC group than in the GC group. Finally, we used LefSe to analyze various fungal compositions in HC and GC, such as... Figure 4 As shown, 10 fungi in the GC group were selected: Cystobasidium (AUC = 0.8760), Cutaneotrichosporon (AUC = 1.000), Apodus (AUC = 0.9421), Apiotrichum (AUC = 0.9793), Simplicillium (AUC = 0.8182), Lecanactis (AUC = 0.8636), Rhizopus (AUC = 0.8884), Rhodotorula (AUC = 0.9525), Exophiala (AUC = 0.8926), and Sarocladium (AUC = 0.9318). AUC represents the area under the curve. These 10 genera of fungi have good diagnostic value for GC and are considered as a fungal microbiome in this invention.

[0043] 1.5 Functional Analysis and Annotation of Fungi in GC

[0044] like Figure 5 As shown, we analyzed the functions of fungi in gastric cancer (GC) in terms of both population and nutrition. The results showed that saprophytes were the most widely distributed (64.3%), while soil saprophytes were the most widespread (54.9%). Animal pathogens were the most diverse group between the two, indicating significant differences in fungal functions between HC and GC. This demonstrates the unique symbiotic relationship of fungi in gastric cancer development and suggests that the fungal community is essential for maintaining gastric homeostasis.

[0045] 1.6 Diagnostic value of Cutaneotrichosporon in gastric cancer

[0046] like Figure 6As shown, taking Cutaneotrichosporon as an example, we used real-time quantitative PCR to verify the expression level of Cutaneotrichosporon in 44 gastric cancer cases and 20 healthy tissues, using β-Actin as an internal reference gene. The expression of Cutaneotrichosporon showed a highly significant difference between HC and GC. **** (P<0.0001) When Cutaneotrichosporon was used as a diagnostic tool for gastric cancer, its area under the ROC curve was 0.9476, with a sensitivity of 100% and a specificity of 90%. These results indicate that a Cutaneotrichosporon expression level exceeding 11.54 is sufficient for diagnosing gastric cancer. These results demonstrate that the fungal microbiome we identified has good diagnostic potential for gastric cancer.

[0047] This invention provides a screening approach and method for biomarkers in the diagnosis of gastric cancer. Many methods and approaches exist for implementing this technical solution; the above description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies. sequence list <110> Nanjing Drum Tower Hospital <120> Application of a fungal microbiome as a biomarker for gastric cancer diagnosis <160> 2 <170> SIPOSequenceListing 1.0 <210> 1 <211> 20 <212> DNA <213> Artificial Sequence <400> 1 gcatcgatga agaacgcagc 20 <210> 2 <211> 20 <212> DNA <213> Artificial Sequence <400> 2 tcctccgctt attgatatgc 20

Claims

1. The application of a reagent capable of detecting fungal microbial markers in the preparation of a kit for gastric cancer diagnosis, characterized in that, The fungal microbial marker is Cutaneotrichosporon .

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