A cervical cancer plasma circulating microorganism marker combination, reagent, kit, risk assessment model and application thereof

Abstract

The application discloses a cervical cancer plasma circulating microbial marker combination, a reagent, a kit, a risk assessment model and application thereof, and belongs to the technical field of liquid biopsy and microbiome. The cervical cancer plasma circulating microbial marker combination comprises Prevotella, Sneathia, Chlamydia trachomatis, Peptostreptococcus, Fusobacterium, Campylobacter and Haemophilus. The cervical cancer plasma circulating microbial marker combination is used for verifying cervical cancer patients and healthy people, and the AUC value is 0.939, the sensitivity is 81.58%, the specificity is 90.00%, the accuracy is 85.29%, the positive predictive value is 91.18%, and the negative predictive value is 79.41%, which indicates that the cervical cancer plasma circulating microbial marker combination can effectively distinguish cervical cancer patients from healthy people, is suitable for early screening of cervical cancer, and has significant clinical screening application value.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of liquid biopsy and microbiome, and particularly relates to a combination of circulating microbial biomarkers in cervical cancer plasma, reagents, kits, risk assessment models and their applications. Background Technology

[0002] Although HPV vaccination is effective in preventing cervical cancer, current vaccine coverage is insufficient and ineffective for those already infected. Early screening remains a crucial means of reducing morbidity and mortality. However, early-stage cervical cancer often presents with no significant clinical symptoms, and approximately 20%-30% of cases progress to advanced stages due to missed or delayed detection by existing screening technologies, leading to a significantly worse prognosis. This situation underscores the urgent need for highly sensitive and specific screening technologies.

[0003] Currently, clinical practice mainly relies on three types of screening methods, but all have significant drawbacks. The Pap test, while having high specificity (86%-100%), has a sensitivity of only 30%-87% and is highly dependent on manual interpretation, leading to poor reproducibility; 27% of cases show discrepancies between cytological and histological diagnoses. HPV DNA testing has a sensitivity of 90% for high-grade lesions, but its specificity is low in young women, with a 22% decrease in positive predictive value. Since HPV infection is often transient, it can easily lead to unnecessary colposcopy and overtreatment. Liquid-based cytology (LBC), while reducing sample error and supporting combined testing (such as p16 / Ki67), offers limited sensitivity improvement and is costly; results are still influenced by the pathologist's experience. The common pain points of these technologies are: they all require invasive cervical sampling, resulting in poor patient compliance, and the difficulty in balancing sensitivity and specificity, leading to a persistently high rate of missed diagnoses of early-stage cancer.

[0004] In recent years, liquid biopsy technology has become a research hotspot in the field of early cancer screening due to its advantages such as being non-invasive and repeatable. This technology mainly achieves early cancer diagnosis by detecting biomarkers such as circulating tumor DNA (ctDNA), circulating tumor cells (CTC), and exosomes in the blood. Among them, ctDNA detection technology is the most mature and has been widely used in clinical research. PCR-based ctDNA detection methods (such as ddPCR and ARMS-PCR) have the advantages of low cost and short detection time, but their throughput is low, and they can only measure known gene mutations or methylation levels of some target sites. They cannot comprehensively detect multiple mutations, unknown mutations, and new mutations, so they are not suitable for large-scale cancer screening. In contrast, next-generation sequencing (NGS)-based ctDNA detection methods (such as targeted panel sequencing and WGBS) can provide more comprehensive genomic information, but they also face many challenges in clinical application. First, the content of ctDNA in blood is extremely low, accounting for only 0.01%-1% of circulating cell-free DNA (cfDNA). Therefore, NGS detection usually requires ultra-high depth sequencing (usually >10000X), resulting in high costs and difficulty in popularization. Secondly, the ctDNA signal released by early-stage tumors is weak, limiting detection sensitivity and making false negatives common. Furthermore, non-tumor factors such as clonal hematopoiesis (CHIP) may introduce gene mutation noise, leading to false positives and further affecting detection accuracy.

[0005] Recent studies have found that circulating cell-free microbial DNA (cmDNA) in plasma can dynamically reflect the systemic microbiota-host interaction state, providing new insights for early cancer screening. Circulating HPV DNA (such as HPV16 / 18 / 33 / 52 / 58) is specifically enriched in the plasma of cervical cancer patients, with a detection rate of 63%-90% in advanced cases and 10%-32% in early cases, and is significantly correlated with tumor burden, but its sensitivity for early cases is insufficient when detected alone. On the other hand, vaginal microbiota (such as a reduction in protective Lactobacillus and an enrichment in pathogenic Gardnerella / Prevotella) is clearly associated with cervical cancer progression, but its metastatic characteristics in plasma remain unclear.

[0006] Therefore, there is an urgent need to find a highly sensitive, highly specific, highly accurate, low-cost, and non-invasive technical solution that can be used for early cervical cancer screening to make up for the shortcomings of existing technologies. Summary of the Invention

[0007] In view of this, the purpose of this invention is to provide a combination of circulating microbial markers in cervical cancer plasma, which has high sensitivity, specificity, and accuracy, and has the advantages of being non-invasive and low in detection cost, making it suitable for large-scale early screening of cervical cancer.

[0008] Another object of the present invention is to provide a reagent.

[0009] Another object of the present invention is to provide a reagent kit.

[0010] Another object of the present invention is to provide the application of the combination of cervical cancer plasma circulating microbial markers, the reagent, or the kit in constructing a cervical cancer risk assessment model.

[0011] Another objective of this invention is to provide a cervical cancer risk assessment model.

[0012] Another object of the present invention is to provide the application of the combination of cervical cancer plasma circulating microbial markers, the reagent, the kit, or the cervical cancer risk assessment model in the preparation of cervical cancer screening and diagnostic products.

[0013] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0014] This invention provides a combination of circulating microbial markers in cervical cancer plasma, comprising Prevotella spp., Sneavia spp., Chlamydia trachomatis, Peptostreptococcus spp., Fusobacterium spp., Campylobacter spp., and Haemophilus spp.

[0015] The present invention also provides a reagent comprising a reagent for detecting the content or abundance of the combination of circulating microbial markers in cervical cancer plasma.

[0016] The present invention also provides a kit comprising the reagents.

[0017] The present invention also provides the application of the combination of cervical cancer plasma circulating microbial markers, the reagent, or the kit in constructing a cervical cancer risk assessment model.

[0018] This invention also provides a cervical cancer risk assessment model, the risk assessment model comprising:

[0019] Risk score = Σ(A_i × S_i);

[0020] Where i ranges from 1 to 7, corresponding to the 7 target microorganisms in the cervical cancer plasma circulating microbial biomarker combination; A_i represents the abundance of each target microorganism in the sample to be tested; and S_i represents the feature weight of each target microorganism.

[0021] Preferably, the feature weights are as follows: Prevotella spp. 0.210; Snaezium spp. 0.110; Chlamydia trachomatis 0.060; Peptostreptococcus spp. 0.030; Fusobacterium spp. 0.080; Campylobacter spp. 0.020; and Haemophilus spp. 0.010.

[0022] Preferably, a risk score ≥ 0.1082 is considered positive, and otherwise it is considered negative.

[0023] Preferably, the sample to be tested includes peripheral blood of the subject.

[0024] The present invention also provides the application of the combination of cervical cancer plasma circulating microbial markers, the reagent, the kit, or the cervical cancer risk assessment model in the preparation of cervical cancer screening and diagnostic products.

[0025] Preferably, the method for cervical cancer screening and diagnosis includes: extracting plasma DNA from peripheral blood and sequencing it; determining the abundance of each target microorganism in the cervical cancer plasma circulating microbial marker combination; and determining whether the peripheral blood comes from a cervical cancer patient based on a cervical cancer risk assessment model.

[0026] The beneficial effects of this invention are:

[0027] This invention presents a plasma cell-free microbial DNA (cmDNA) detection system based on low-depth whole-genome sequencing. It captures microbial characteristics at low cost through low-depth whole-genome sequencing and combines this with decontamination bioinformatics algorithms to overcome low biomass interference, breaking through the high-cost bottleneck of traditional next-generation sequencing (NGS) liquid biopsy. Furthermore, through machine learning, seven target microorganisms are screened from plasma to form a cervical cancer plasma circulating microbial biomarker combination. This biomarker exhibits excellent diagnostic performance in the training set (AUC = 0.938) and remains stable in the independent validation set (AUC = 0.939). This invention, through technological innovation, solves the problems of invasiveness, low sensitivity, and high cost of existing screening methods, providing a new solution for early cervical cancer screening. Attached Figure Description

[0028] Figure 1 The flowcharts for bioinformatics analysis and decontamination are shown in Example 1.

[0029] Figure 2 This is a flowchart of the screening process for combined circulating microbial markers in plasma for cervical cancer in Example 1;

[0030] Figure 3 The combination of circulating microbial biomarkers in cervical cancer plasma and their characteristic weights in Example 1;

[0031] Figure 4 This is the ROC curve of the training set in Example 1;

[0032] Figure 5 This is the ROC curve of the validation set in Example 1. Detailed Implementation

[0033] This invention provides a combination of circulating microbial markers in cervical cancer plasma, comprising Prevotella spp., Sneavia spp., Chlamydia trachomatis, Peptostreptococcus spp., Fusobacterium spp., Campylobacter spp., and Haemophilus spp.

[0034] This invention involved nucleic acid extraction, library construction, and MGISEQ2000 sequencing of plasma samples from 84 pathologically confirmed cervical cancer patients and 60 healthy individuals. The MaAsLin multivariate statistical method was used for differential analysis of the sequencing data. A random forest machine learning algorithm combined with rigorous 10-fold cross-validation was employed to identify seven highly discriminative target microorganisms. The cervical cancer plasma circulating microbial biomarker combination of this invention was validated in 38 pathologically confirmed cervical cancer patients and 30 healthy individuals. The AUC value was 0.939, sensitivity was 81.58%, specificity was 90.00%, and accuracy was 85.29%, indicating that the seven target microorganisms screened by this invention can effectively distinguish cervical cancer patients from healthy individuals, demonstrating significant clinical screening application value.

[0035] The present invention also provides a reagent comprising a reagent for detecting the content or abundance of the combination of circulating microbial markers in cervical cancer plasma.

[0036] In this invention, the reagent preferably includes primers, probes, aptamers, or antibodies that are specific to the seven target microorganisms in the combination of circulating microbial markers in cervical cancer plasma.

[0037] In this invention, the detection sample of the reagent preferably includes peripheral blood of the subject, and more preferably includes plasma DNA in the peripheral blood of the subject.

[0038] The present invention also provides a kit comprising the reagents.

[0039] The present invention also provides the application of the combination of cervical cancer plasma circulating microbial markers, the reagent, or the kit in constructing a cervical cancer risk assessment model.

[0040] This invention also provides a cervical cancer risk assessment model, the risk assessment model comprising:

[0041] Risk score = Σ(A_i × S_i);

[0042] Where i ranges from 1 to 7, corresponding to the 7 target microorganisms in the cervical cancer plasma circulating microbial biomarker combination; A_i represents the abundance of each target microorganism in the sample to be tested; and S_i represents the feature weight of each target microorganism.

[0043] In some embodiments of this invention, the abundance is preferably the percentage of reads obtained from sequencing the target microorganism out of all microbial reads; the feature weights of each target microorganism preferably include: Prevotella spp. 0.210; Snaezium spp. 0.110; Chlamydia trachomatis 0.060; Peptostreptococcus spp. 0.030; Fusobacterium spp. 0.080; Campylobacter spp. 0.020; and Haemophilus spp. 0.010.

[0044] In this invention, a risk score of ≥0.1082 is preferably considered positive, and a score below 0.1082 is considered negative. That is, a risk score of ≥0.1082 is preferably considered a high risk for cervical cancer, and a score below 0.1082 is considered a low risk for cervical cancer.

[0045] In this invention, the sample to be tested preferably includes peripheral blood of the subject.

[0046] The cervical cancer risk assessment model of this invention was validated in 38 pathologically diagnosed cervical cancer patients and 30 healthy individuals. The AUC value was 0.939, sensitivity was 81.58%, specificity was 90.00%, accuracy was 85.29%, positive predictive value was 91.18%, and negative predictive value was 79.41%. This indicates that the cervical cancer risk assessment model of this invention can significantly distinguish between cervical cancer patients and healthy individuals, highlighting its potential as a non-invasive screening tool for cervical cancer.

[0047] The present invention also provides the application of the combination of cervical cancer plasma circulating microbial markers, the reagent, the kit, or the cervical cancer risk assessment model in the preparation of cervical cancer screening and diagnostic products.

[0048] In this invention, the preferred method for cervical cancer screening and diagnosis includes: extracting plasma DNA from peripheral blood and sequencing it; determining the abundance of each target microorganism in the combination of circulating microbial markers in cervical cancer plasma; and determining whether the peripheral blood comes from a cervical cancer patient based on a cervical cancer risk assessment model.

[0049] In this invention, the cervical cancer screening and diagnosis preferably includes early cervical cancer screening and diagnosis, and the early cervical cancer preferably includes stage I or stage II cervical cancer.

[0050] The verification experiments of this invention show that, among all the cervical cancer samples included, the proportion of early stage (I / II) cervical cancer samples reached 65.57%, indicating that the combination of circulating microbial markers in plasma for cervical cancer of this invention can serve as a reliable tumor marker and is suitable for non-invasive early screening of cervical cancer.

[0051] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0052] Unless otherwise specified, the following embodiments are all conventional methods.

[0053] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0054] Example 1

[0055] 1. Sample selection

[0056] Plasma samples were collected from 122 pathologically confirmed cervical cancer patients (who had not received treatment prior to surgery) and 90 healthy individuals. All cancer cases were confirmed by imaging, laboratory testing, and pathology. Healthy individuals were from routinely examined populations. The samples were divided into a training set (84 cancer patients / 60 healthy individuals) and a validation set (38 cancer patients / 30 healthy individuals). There were no significant differences in age or sex between the two groups. Cancer patients were staged as follows:

[0057] Training set: 20 cases in stage I (23.81%), 33 cases in stage II (39.29%), and 31 cases in stage III and above (36.90%).

[0058] Validation set: 12 cases (31.58%) in stage I, 15 cases (39.47%) in stage II, and 11 cases (28.95%) in stage III and above.

[0059] 2. Low-pass whole-genome sequencing (WGS) wet experimental workflow

[0060] (1) Plasma separation: 10 mL of peripheral blood sample was collected using a cell-free DNA collection tube (Streck tube). The sample was transported to the laboratory at 6℃-26℃ and plasma separation was completed within 72 hours. Severely hemolyzed samples could not be used for the experiment. A two-step centrifugation method was used for plasma separation. The first step was low-speed centrifugation (4℃, 1600×g, 15 min) to remove whole blood cells and collect the supernatant plasma; the second step was high-speed centrifugation (4℃, 16000×g, 15 min) to remove platelets, cell debris, and apoptotic bodies, while retaining cell-free nucleic acids. 3-5 mL of plasma can usually be obtained from 10 mL of peripheral blood.

[0061] (2) Cell-free nucleic acid extraction: QIAamp Circulating Nucleic Acid Kit (Kaijie, 55114) was used, with the addition of carrier RNA to improve the recovery rate of small nucleic acid fragments and enhance the enrichment of microbial nucleic acids. The concentration of cfDNA was determined using Qubit. If the total extraction volume was >300 ng, fragment detection was performed using Agilent 2100 to confirm the presence of cell-free DNA in the extract.

[0062] (3) Library Construction: Reagents used for library construction include the xGenPrism DNALibraryPrep Kit (manufacturer: IDT), HyperPure magnetic beads (manufacturer: KAPA), HiFi Hotstart ReadyMix (manufacturer: KAPA), and PCR Index Primer. The amount of cfDNA used for library construction is 50-200 ng. The experimental steps are: end repair, magnetic bead purification, ligation reaction 1 (20℃ 15 min → 65℃ 15 min → 4℃ maintenance), ligation reaction 2 (65℃ 30 min → 4℃ maintenance), magnetic bead purification, library amplification (using KAPA HiFi Hotstart ReadyMix, 10-12 cycles), and magnetic bead purification to obtain the library sample. An 8 bp paired-end UMI adapter is added during library construction.

[0063] (4) Sequencing: The sequencing equipment platform was the MGISEQ-2000RS (MGI Tech) platform, and the sequencing strategy was PE150 (paired-end 150bp) sequencing. Sterile water was added as a control during each sequencing process, and quality control was carried out by monitoring reagent / environmental microbial contamination.

[0064] 3. Low-pass whole-genome sequencing (WGS) dry experimental workflow

[0065] (1) Data quality control: The raw sequencing data was filtered using FASTP software to remove low-quality (Q value <15) bases and reads, and to remove short sequences (length <50bp) and adapter contamination sequences to ensure the accuracy of subsequent analysis.

[0066] (2) Human sequence removal: Bowtie2 was used to compare the quality-controlled data with the human reference genome GRCh38 to effectively remove human sequence interference and improve the signal-to-noise ratio of microbial signals.

[0067] (3) Microbial sequence enrichment: BBmap software was used to further filter repetitive sequences and mitochondrial contamination. Based on the microbial species database, Kraken2 was used for k-mer classification analysis, and Bracken was used for species abundance correction to ensure the comprehensiveness and accuracy of microbial identification.

[0068] (4) Contamination control: Filter low abundance microbial signals (relative abundance <0.01% or number of reads <10); remove low frequency microorganisms (if the frequency of a microorganism in the same type of sample is less than 20% of the total number of samples, the microorganism is considered a contaminant and filtered); use Decontam software in combination with negative control samples (P value <0.5) to remove potential exogenous contamination.

[0069] (5) Data analysis: The final obtained microbial characteristic data are modeled and analyzed using machine learning algorithms to establish a diagnostic prediction model.

[0070] This analytical workflow, through multiple quality control and decontamination steps, significantly improves the specificity and reliability of microbial detection, providing a high-quality data foundation for subsequent disease biomarker screening and diagnostic model construction. See below for the bioinformatics analysis and decontamination procedures. Figure 1 .

[0071] 4. Screening and risk assessment model construction of plasma circulating microbial biomarker combinations for cervical cancer

[0072] Using 144 samples (84 cervical cancer patients and 60 healthy controls) as the training set, we screened combinations of cervical cancer indicative microbial biomarkers based on circulating plasma microorganisms, constructed a cervical cancer screening risk assessment model, and designed the following workflow ( Figure 2 ).

[0073] A cervical cancer risk assessment model based on plasma microbial biomarkers was established through a constructed bioinformatics analysis workflow and systematic microbiome analysis. First, the MaAsLin multivariate statistical method was used to perform differential analysis on sequencing data, strictly controlling for the influence of confounding factors such as gender and age, to initially screen for microbial species significantly associated with cervical cancer. Subsequently, using a random forest machine learning algorithm combined with rigorous 10-fold cross-validation, the seven most discriminative target microorganisms and their feature weights were finally determined, as shown in [link to relevant documentation]. Figure 3 .

[0074] The seven target microorganisms obtained through screening include: Prevotella spp., Sneavia spp., Chlamydia trachomatis, Peptostreptococcus spp., Fusobacterium spp., Campylobacter spp., and Haemophilus spp. These seven target microorganisms constitute a composite of circulating microbial markers in cervical cancer plasma.

[0075] The feature weights of the seven target microorganisms in the cervical cancer plasma circulating microbial marker combination were as follows: Prevotella spp. 0.210; Seneca spp. 0.110; Chlamydia trachomatis 0.060; Peptostreptococcus spp. 0.030; Fusobacterium spp. 0.080; Campylobacter spp. 0.020; and Haemophilus spp. 0.010.

[0076] Based on the above-mentioned iconic microorganisms and feature weights, a risk assessment model was constructed. In the model construction process, the abundance (A_i) of each target microorganism was first determined, representing the proportion of reads obtained from the target microorganism to all microbial reads. Subsequently, each microorganism was assigned a statistically validated feature weight (S_i), which reflects the strength of the association between the microorganism and the occurrence of cervical cancer.

[0077] The final risk score is calculated as follows: Risk score = Σ(A_i×S_i), where i ranges from 1 to 7, representing 7 target microorganisms.

[0078] By analyzing the distribution characteristics of the risk scores, 0.1082 was determined to be the optimal cutoff value. The specific criteria are: a subject's risk score ≥ 0.1082 is considered positive, and vice versa.

[0079] 5. Validation of the combination of circulating microbial biomarkers in cervical cancer plasma and the risk assessment model.

[0080] In the training set, the risk model achieved an AUC of 0.938, while maintaining 86.9% sensitivity and 95.0% specificity, with an overall accuracy of 90.3%, and positive predictive value (96.1%) and negative predictive value (83.8%). Figure 4 ).

[0081] In the independent validation set, the model performance remained stable, with an AUC of 0.939, sensitivity of 81.58%, specificity of 90.00%, accuracy of 85.29%, positive predictive value of 91.18%, and negative predictive value of 79.41%. Figure 5 ).

[0082] The above performance demonstrates that the combination of circulating microbial biomarkers and risk assessment model for cervical cancer plasma of this invention can significantly distinguish between cervical cancer patients and healthy individuals, highlighting its potential as a non-invasive screening tool for cervical cancer.

[0083] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. The application of a reagent or kit in the preparation of cervical cancer screening and diagnostic products, characterized in that, The reagents include those for detecting the content or abundance of a combination of circulating microbial markers in cervical cancer plasma; The kit includes the reagent; The cervical cancer plasma circulating microbial marker combination is Prevotella spp. ( Prevotella spp.), snesium ( Sneathia spp.), Chlamydia trachomatis ( Chlamydia trachomatis ), Peptostreptococcus ( Peptostreptococcus spp.), Fusobacterium ( Fusobacterium spp.), Campylobacter spp. Campylobacter spp.) and Haemophilus spp. Haemophilus spp.); The method for cervical cancer screening and diagnosis includes: extracting plasma DNA from peripheral blood and sequencing it; determining the abundance of each target microorganism in the cervical cancer plasma circulating microbial marker combination; and determining whether the peripheral blood comes from a cervical cancer patient based on a cervical cancer risk assessment model. The cervical cancer risk assessment model includes: Risk score = Σ(A_i × S_i); Where i ranges from 1 to 7, corresponding to the 7 target microorganisms in the cervical cancer plasma circulating microbial biomarker combination; A_i represents the abundance of each target microorganism in the sample to be tested; and S_i represents the feature weight of each target microorganism.

2. The application according to claim 1, characterized in that, The characteristic weights for Prevotella spp. are 0.210; for Snaezium spp., they are 0.110; for Chlamydia trachomatis, they are 0.060; for Peptostreptococcus spp., they are 0.030; for Fusobacterium spp., they are 0.080; for Campylobacter spp., they are 0.020; and for Haemophilus spp., they are 0.

010.

3. The application according to claim 1, characterized in that, A risk score ≥ 0.1082 is considered positive, and a score ≤ 0.1082 is considered negative.

4. The application according to claim 1, characterized in that, The sample to be tested was the peripheral blood of the subject.

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