Liver cancer diagnosis marker based on exosome miRNA and application thereof

Abstract

The invention provides a liver cancer diagnosis or prognosis marker, a liver cancer diagnosis kit based on the same and application of the liver cancer diagnosis or prognosis marker. The liver cancer diagnosis or prognosis marker relates to a miRNA pair with biological significance in serum or plasma exosome, and a liver cancer diagnosis model constructed by taking the expression quantity ratio of the miRNA pair as a model feature has relatively high diagnosis sensitivity and specificity on liver cancer, has great potential in early screening of liver cancer, and can be applied to liver cancer diagnosis or prognosis. Important practical significance is realized on early diagnosis or prognosis prediction of the liver cancer.

Description

[0001] This application is a divisional application of the Chinese invention patent application with the application date of November 17, 2023, application number 202311541101.5, and invention name “A liver cancer diagnostic marker based on exosomal miRNA and its application”. Technical Field

[0002] The present invention belongs to the field of medical detection technology, and specifically relates to a liver cancer diagnostic marker based on exosome miRNA and its application. Background Art

[0003] Liver cancer is one of the most common malignant tumors clinically.

[0004] Due to the hidden nature of liver cancer, early liver cancer is difficult to detect, while advanced liver cancer progresses rapidly, has limited treatment methods, is difficult to cure, and has a poor prognosis, which has led to the low survival rate of liver cancer in my country. According to research, patients diagnosed with early liver cancer can increase their 5-year survival rate to 70-75% through radical treatments such as liver transplantation, tumor surgical resection, or local ablation therapy. Therefore, in addition to basic prevention and control such as vaccination, early screening for liver cancer is of great significance in reducing its morbidity and mortality.

[0005] At present, the most commonly used and traditional liver cancer diagnostic techniques include imaging and serum alpha-fetoprotein (AFP). Imaging diagnosis includes ultrasound imaging and CT, etc. However, imaging is usually used to diagnose intrahepatic space-occupying lesions, and is not sensitive and specific for the detection of early liver cancer or nodular cirrhosis without substantial space-occupying lesions. In addition, the above diagnostic methods are subjective and depend on the operator's experience and the sensitivity of the equipment. Serum AFP is currently a common and important indicator for diagnosing liver cancer and monitoring efficacy. However, the sensitivity of AFP alone is only 40-65%, so AFP is not ideal as an early diagnostic indicator. The GALAD model uses protein markers such as AFP and DCP to pioneer the domestic multi-indicator joint modeling, improving the sensitivity and specificity of early liver cancer. Therefore, molecular diagnostic methods are a powerful supplement to the auxiliary diagnosis of liver cancer.

[0006] Exosomes are small vesicles that are secreted by cells and enter body fluids or the extracellular environment. They are only 30-100 nanometers in diameter and play an important role in intercellular communication. Due to the protection of the lipid bilayer, exosomes can exist stably in various biological fluids and cell culture fluids. miRNA exists in the contents of exosomes and can be released from cancer cells into body fluids through the encapsulation of exosomes without being degraded by ribosomal enzymes. Studies have shown that more than half of the protein molecules in the serum of patients with liver cancer are fucosylated. The relationship between these proteins with specific glycosylation changes and the occurrence and development of liver diseases may play a very important role in the diagnosis, treatment and prognosis of liver diseases. Therefore, analyzing the relationship between miRNA in fucosylated exosomes and the occurrence and development of liver cancer will help develop more targeted liver cancer diagnostic markers. Summary of the invention

[0007] Purpose of the Invention

[0008] In view of the problems existing in the above-mentioned prior art methods, the present invention aims to provide a highly sensitive and specific diagnostic or prognostic marker for liver cancer, a kit based thereon or its application, including: application in constructing a model for liver cancer diagnosis, efficacy or prognosis evaluation, or application in the preparation of a drug or kit for liver cancer diagnosis, efficacy or prognosis evaluation.

[0009] Solution

[0010] To achieve the above object, the present invention provides the following technical solutions:

[0011] In a first aspect, the present invention provides a diagnostic or prognostic marker for liver cancer, wherein the diagnostic or prognostic marker for liver cancer comprises a miRNA pair consisting of any two of the following miRNAs, or any combination of the miRNA pairs:

[0012] hsa-let-7a, hsa-miR-21, hsa-miR-125a, hsa-miR-150, hsa-miR-200a, hsa-miR-483, hsa-miR-199a, hsa-miR-200a, hsa-miR-429, hsa-miR-126, hsa-miR-381, hsa-miR-185, hsa-miR-215, hsa-miR-374a.

[0013] In a preferred embodiment, the diagnostic or prognostic marker for liver cancer comprises a miRNA pair selected from the following:

[0014] hsa-miR-200a / hsa-miR-150;

[0015] hsa-miR-483 / hsa-miR-199a;

[0016] hsa-miR-200a / hsa-miR-199a;

[0017] hsa-miR-150 / hsa-miR-429;

[0018] hsa-miR-126 / hsa-miR-200a;

[0019] hsa-miR-199a / hsa-miR-429;

[0020] hsa-miR-381 / hsa-miR-200a;

[0021] hsa-miR-185 / hsa-miR-429;

[0022] hsa-miR-215 / hsa-miR-199a;

[0023] hsa-miR-125a / hsa-miR-215;

[0024] hsa-let-7a / hsa-miR-21;

[0025] hsa-miR-200a / hsa-miR-374a;

[0026] hsa-miR-125a / hsa-miR-21;

[0027] hsa-miR-21 / hsa-miR-150; and

[0028] Any one, several or all of the above combinations.

[0029] In a preferred embodiment, the diagnostic or prognostic marker for liver cancer comprises a miRNA pair selected from the following:

[0030] hsa-let-7a / hsa-miR-21;

[0031] hsa-miR-125a / hsa-miR-21;

[0032] hsa-miR-21 / hsa-miR-150;

[0033] hsa-miR-200a / hsa-miR-150; and,

[0034] Any one, two, three or all of the above.

[0035] In some specific embodiments, the diagnostic or prognostic markers for liver cancer comprise a combination of hsa-let-7a / hsa-miR-21, hsa-miR-125a / hsa-miR-21, hsa-miR-21 / hsa-miR-150, and hsa-miR-200a / hsa-miR-150.

[0036] In other specific embodiments, the diagnostic or prognostic markers for liver cancer comprise a combination of hsa-let-7a / hsa-miR-21, hsa-miR-200a / hsa-miR-150, and hsa-miR-125a / hsa-miR-21.

[0037] In other specific embodiments, the diagnostic or prognostic marker for liver cancer comprises a combination of hsa-miR-125a / hsa-miR-21 and hsa-miR-21 / hsa-miR-150.

[0038] Preferably, the miRNA in the miRNA pair is a miRNA in serum or plasma, preferably a miRNA in serum or plasma fucosylated exosomes.

[0039] Further preferably, the diagnostic or prognostic marker for liver cancer also comprises any one or more of AFP, AFP-L3 and DCP; preferably, the AFP, AFP-L3 or DCP is the corresponding protein in serum, plasma or whole blood.

[0040] In a second aspect, the present invention provides the use of the diagnostic or prognostic marker for liver cancer as described in the first aspect above in constructing a model for liver cancer diagnosis, efficacy or prognosis evaluation.

[0041] In a specific embodiment, the expression ratio of the miRNA pair is used as a model feature, and a machine learning method is used to perform modeling.

[0042] The machine learning method may be a machine learning method commonly used in the field of modeling, for example, logistic regression, support vector machine, tree model, neural network, etc.

[0043] In a third aspect, the present invention provides the use of the diagnostic or prognostic marker of liver cancer or its detection reagent as described in the first aspect above in the preparation of a drug or kit for liver cancer diagnosis, efficacy or prognosis evaluation.

[0044] In a fourth aspect, the present invention provides a drug or a kit for diagnosis, efficacy or prognosis evaluation of liver cancer, wherein the drug or the kit comprises the diagnostic or prognostic marker of liver cancer or a detection reagent thereof as described in the first aspect above.

[0045] In patients with liver cancer, the expression ratio of the above miRNA pairs in their serum fucosylated exosomes was significantly different from that in healthy humans.

[0046] Beneficial Effects

[0047] The diagnostic or prognostic markers for liver cancer provided by the present invention involve miRNA pairs in serum or plasma exosomes with biological significance. The expression ratio of the miRNA pair is used as the feature of the liver cancer diagnostic model, avoiding the instability when a single miRNA expression level is used as a feature, thereby enabling more accurate diagnosis or prognostic evaluation; the liver cancer diagnostic model constructed using the expression ratio of the miRNA pair as the model feature has high diagnostic sensitivity and specificity (above 85% and 90%, respectively), and also has great potential in the screening of early liver cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] One or more embodiments are exemplarily described by the pictures in the accompanying drawings, and these exemplary descriptions do not constitute limitations on the embodiments. Here, the special word "exemplary" means "used as an example, embodiment, or illustrative". Here, any embodiment described as "exemplary" is not necessarily interpreted as being superior or better than other embodiments.

[0049] Figure 1 This is a heat map of the expression ratios of the 14 paired miRNAs screened in Example 1 on NGS samples.

[0050] Figure 2 The model effect of the combination of the expression ratios of the 14 paired miRNAs screened in Example 1 on NGS samples: (a) shows the ROC curve, and (b) shows the confusion matrix.

[0051] Figure 3 It is a box plot of the expression ratios of the four paired miRNAs screened in Example 1 on the NGS data, as described in Example 2; wherein T and N represent the liver cancer group and the non-liver cancer control group, respectively.

[0052] Figure 4 It is a box plot of the CT value difference of the three paired miRNAs screened in Example 1 by qPCR, as described in Example 3; wherein T and N represent the liver cancer group and the non-liver cancer control group, respectively.

[0053] Figure 5 The figure shows the liver cancer diagnosis effect of the liver cancer diagnosis model constructed based on the preferred combination of paired miRNAs. Among them, (a) shows the ROC curve, (b) shows the confusion matrix, and (c) shows the Venn diagram of positive samples.

[0054] Figure 6 The figure shows the effects of samples of different stages of liver disease on the liver cancer diagnosis model constructed based on the preferred combination of paired miRNAs; wherein (a) shows the ROC curve, and (b) shows the confusion matrix.

[0055] Figure 7 The figure shows the liver cancer diagnosis effect of the liver cancer diagnosis model constructed based on the preferred combination of paired miRNAs and the liver cancer triple detection indicators; wherein, (a) shows the ROC curve, (b) shows the confusion matrix, and (c) shows the Venn diagram of positive samples.

[0056] Figure 8 The figure shows the effect of samples of different stages of liver disease on the liver cancer diagnosis model constructed based on the preferred combination of paired miRNAs and the liver cancer triple detection index; wherein (a) shows the ROC curve, and (b) shows the confusion matrix.

[0057] Fig. 9 It is a box plot of the CT value difference of the two paired miRNAs screened in Example 1 by qPCR, as described in Example 6; wherein T and N represent the liver cancer group and the non-liver cancer control group, respectively.

[0058] Fig.10 The figure shows the liver cancer diagnosis effect of the liver cancer diagnosis model constructed based on the preferred combination 2 of paired miRNAs; wherein (a) shows the ROC curve, and (b) shows the confusion matrix.

[0059] Fig.11 The figure shows the liver cancer diagnosis effect of the liver cancer diagnosis model constructed based on the preferred combination of paired miRNAs and the liver cancer triple detection index; wherein, (a) shows the ROC curve, and (b) shows the confusion matrix.

[0060] Fig.12 The figure shows the liver cancer diagnosis effect of the liver cancer diagnosis model constructed based on the preferred combination of paired miRNA and AFP; wherein (a) shows the ROC curve, and (b) shows the confusion matrix.

[0061] Fig.13 A Venn diagram showing the predictions of 160 liver cancer samples based on the AFP indicator alone (i.e., “AFP”), the diagnostic model based on the preferred combination of paired miRNAs (i.e., “miR”), and the diagnostic model based on the preferred combination of paired miRNAs combined with AFP (i.e., “miR+AFP”). DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Unless otherwise expressly stated, throughout the specification and claims, the term "including" or its variations such as "comprising" or "including" will be understood to include the stated elements or components, without excluding other elements or other components.

[0063] In addition, in order to better illustrate the present invention, numerous specific details are provided in the specific embodiments below. It should be understood by those skilled in the art that the present invention can also be implemented without certain specific details. In some embodiments, raw materials, elements, methods, means, etc. well known to those skilled in the art are not described in detail, so as to highlight the main purpose of the present invention.

[0064] The reagents, kits, raw materials and equipment used in the following examples, unless otherwise specified, can all be purchased through commercial channels. The experiments or detection methods involved in the present invention, unless otherwise specified, are all conventional experiments or detection methods in the art, or are performed with reference to the corresponding kits or product instructions.

[0065] Example 1: Screening of characteristic miRNA pairs for liver cancer based on serum exosome data

[0066] 1. Collection and preparation of clinical data and samples

[0067] A retrospective analysis was performed to collect serum samples from patients with liver cancer, patients with benign liver diseases, and healthy people. The sample information is shown in Table 1.

[0068] As shown in Table 1 , the dataset was divided into six cohorts. The NGS cohort was used to discover characteristic miRNA pairs for liver cancer; the five PCR cohorts were used for modeling and verification of two preferred combinations.

[0069] Liver cancer patients met the following inclusion criteria: mainly early cases that can be cured by surgery; pathological diagnosis of the tumor pathology (Edmoson) stage; all cases had complete basic information; chronic liver diseases caused by other reasons, such as alcoholic fatty liver disease and autoimmune liver disease, were excluded; pregnancy, reproductive embryonic tumors, other organ malignancies, severe infectious diseases, other important organ diseases, etc. were excluded. Benign liver disease samples included hepatitis, cirrhosis, intrahepatic hemangioma, etc., and patients after liver cancer surgery, liver transplantation, and other organ malignancies were excluded.

[0070] Table 1 Statistics of selected cases:

[0071]

[0072]

[0073] 2. Isolation and purification of exosomes

[0074] Blood was collected in a 5 mL vacuum tube (coagulant + separation gel) and centrifuged at 1800 × g for 10 minutes at room temperature within 2 hours after sampling; the resulting serum was centrifuged at 3000 × g for 10 minutes at 4 ° C to remove any cell debris and apoptotic bodies; then, the precipitate was discarded, the upper serum was drawn and stored at -80 ° C, and repeated freezing and thawing was strictly prohibited. Exosomes were isolated using the GlyExo-Capture kit, in which the magnetic beads used were the company's patented "A lectin-magnetic carrier coupling complex for isolating glycosylated exosomes in clinical samples".

[0075] 3. Extraction and NGS sequencing of exosomal miRNA

[0076] use Mini Kit, follow the procedure in the kit manual to extract total RNA from extracellular vesicles, and evaluate it using the high-sensitivity RNA kit of the Qsep100 fully automated nucleic acid analysis system. Then, the exosomal small RNA was compiled into a cDNA library using the Illumina NEBNext small RNA library preparation kit, and the library of the target size fragment was selected and purified using the E-Gel Power Snap electrophoresis system and E-Gel SizeSelect II gel. After checking the quality and concentration of the cDNA library, 75nt, single-end sequencing was performed on the Illumina NextSeq 550 sequencing system, and the sequencing data volume of a single library was greater than 10M reads.

[0077] 4. Construct miRNA interaction network and analyze NGS sequencing data to obtain characteristic miRNA pairs for liver cancer through single factor screening and genetic algorithm screening.

[0078] (1) Construction of miRNA interaction network

[0079] 1) Obtain miRNA targets from the miRTarBase database;

[0080] 2) Based on the target information of miRNA obtained in step 1), the transcription factors that can serve as the targets of miRNA are screened from the human transcription factor database hTFtarget and AnimalTFDB;

[0081] 3) Based on the transcription factors screened in step 2), the miRNAs further regulated by them are obtained through bioinformatics methods or public databases, thereby constructing the interaction relationship between miRNA-TF-miRNA and obtaining the miRNA interaction network.

[0082] (2) Analyze NGS sequencing data and prepare miRNA quantification data

[0083] By analyzing the above NGS sequencing data, the miRNA quantitative data of each sample in the comprehensive sample set including disease samples and control samples were obtained:

[0084] First, the original sequencing file in fastq format is required for quality control. Based on the quality control results, cutadapt software is used to remove adapters and low-quality reads. For data that pass the quality control, the exceRpt small RNA analysis process is used for annotation and quantification to obtain the expression matrix. According to the expression level of the expression matrix, miRNAs with counts that are too low are filtered out (specific analysis is required for specific situations) to obtain corrected miRNA quantitative data.

[0085] (3) Construction of miRNA expression ratio features

[0086] Based on the miRNA interaction network constructed above and the prepared miRNA quantitative data, the expression ratio of the miRNA pairs with interacting relationships in each sample was calculated.

[0087] In order to avoid the phenomenon of denominator being 0, when calculating the miRNA expression ratio, the denominator is uniformly added with "1". The calculation formula is as follows:

[0088] miRNA_a / miRNA_b=counts a / (count b +1)

[0089] (4) Screening of characteristic miRNA pairs

[0090] Through single factor screening and genetic algorithm screening, characteristic miRNA pairs of liver cancer were obtained. The basic process is as follows:

[0091] i) Single factor screening: Compare the expression ratios of each miRNA pair in the disease biological sample group relative to the normal biological sample group, use python's scipy.stats.ttest_ind to calculate the p value, and use statsmodels.stats.multitest.fdrcorrection to correct the p value. For the corrected p value p-adjusted, screening is performed based on a threshold of 0.05;

[0092] ii) For the screened miRNA pairs, the logarithm of the change fold of the expression ratio in the disease biological sample group relative to the expression ratio in the normal biological sample group, i.e., log2FoldChange, is calculated, and a suitable threshold for log2FoldChange is selected according to the actual situation to further screen suitable targets;

[0093] The genetic algorithm screening was performed more than 100 times.

[0094] After the above screening procedure, 14 paired miRNAs were obtained, and their detailed information is shown in the following Table 2:

[0095] hsa-miR-200a / hsa-miR-150 hsa-miR-199a / hsa-miR-429 hsa-let-7a / hsa-miR-21 hsa-miR-483 / hsa-miR-199a hsa-miR-381 / hsa-miR-200a hsa-miR-200a / hsa-miR-374a hsa-miR-200a / hsa-miR-199a hsa-miR-185 / hsa-miR-429 hsa-miR-125a / hsa-miR-21 hsa-miR-150 / hsa-miR-429 hsa-miR-215 / hsa-miR-199a hsa-miR-21 / hsa-miR-150 hsa-miR-126 / hsa-miR-200a hsa-miR-125a / hsa-miR-215 .

[0096] In addition, a heat map of the expression ratios of these 14 paired miRNAs on the NGS cohort was drawn, see Figure 1 ; Figure 1 The heat map shows that the expression ratio of these 14 paired miRNAs performs well in the grouping and clustering of cancer and non-cancer samples in the NGS cohort. It can be inferred that these 14 miRNA pairs and any combination selected from these 14 miRNA pairs have the potential to become liver cancer markers.

[0097] To verify this, the model performance of the combination of these 14 miRNA pairs was verified by example; specifically, the training set and test set were randomly divided into 7:3 on the NGS cohort, and the random forest model was used to build the model on the training set, and then verified on the test set. The ROC curve and confusion matrix of the model built based on the combination of the above 14 miRNA pairs are shown in Figure 2 ; Figure 2 The results showed that the AUC of the training set and validation set were 0.98 and 0.93, respectively, and the sensitivity and specificity were 90.12%, 96.04% and 88.24%, 90.91%, respectively, indicating that the constructed model has good classification performance.

[0098] 5. Further screening of characteristic miRNA pairs for liver cancer

[0099] Recursive feature elimination was performed on the expression ratios of the 14 paired miRNAs screened above, and finally the 8 paired miRNA combinations with the highest cross-validation AUC were obtained; the 8 paired miRNAs were verified using samples of the qPCR confirmation cohort, and 4 paired miRNAs with consistent trends and significant differences between cancer and control groups were selected, as shown below:

[0100] hsa-let-7a / hsa-miR-21;

[0101] hsa-miR-125a / hsa-miR-21;

[0102] hsa-miR-200a / hsa-miR-150;

[0103] hsa-miR-21 / hsa-miR-150.

[0104] Example 2: Box plot display of four paired miRNAs using NGS quantitative data

[0105] The box plots of the four paired miRNAs screened in Example 1 on the NGS cohort were tested and drawn. The results are as follows: Figure 3 shown.

[0106] Depend on Figure 3 As can be seen from the box plot, there is a significant difference in the expression ratio of these four paired miRNAs between the liver cancer group and the control group (p<0.5), indicating that these four paired miRNAs have the potential to become liver cancer markers.

[0107] Example 3: Verification of the diagnostic performance of liver cancer by RT-qPCR of the preferred combination of paired miRNAs

[0108] Among the four paired miRNAs screened in Example 1, three paired miRNAs hsa-let-7a / hsa-miR-21, hsa-miR-125a / hsa-miR-21, and hsa-miR-200a / hsa-miR-150 were selected as the preferred combination one; they were verified by RT-qPCR in the in-center and multi-center verification cohorts, and the sample information is shown in Table 1.

[0109] The specific verification method is as follows:

[0110] First, the first-strand cDNA synthesis was performed, and the reaction system and reaction conditions were as follows:

[0111] Reaction system and volume:

[0112]

[0113]

[0114] Reaction conditions and time:

[0115] Reaction conditions Time (minutes) 42℃ 60 85℃ 5 4℃ ∞ .

[0116] Then, miRNA qPCR reaction was performed on the ABI 7500 real-time fluorescence quantitative PCR system. The reaction system and reaction conditions were as follows:

[0117] Reaction system and volume:

[0118]

[0119] Reaction conditions and time:

[0120]

[0121] In the above RT-qPCR procedure, the primer sequences used are as follows:

[0122]

[0123]

[0124] The results are as follows:

[0125] Box plots of the differences in CT values ​​of paired miRNA qPCR for each qPCR cohort are shown in Figure 4 ;Depend on Figure 4 It can be seen that the trends of these three paired miRNAs are consistent with the trends of NGS data both within the center and in other centers, and are relatively stable in different centers; this indicates that the three paired miRNAs screened are also applicable to the RT-qPCR cohort, with significant differences between the liver cancer group and the non-liver cancer control group, without any preference for the detection method and obvious individual differences.

[0126] Example 4: Construction of a liver cancer diagnosis model based on the paired miRNA preferred combination 1 and verification of its diagnostic performance

[0127] In order to detect the diagnostic effect of the preferred combination of the above three paired miRNAs on liver cancer, this example uses logistic regression to build a model on the training cohort and verifies it on the in-center validation set and multi-center validation set. The specific method is as follows:

[0128] The sample information used is shown in Table 1: the qPCR confirmation cohort was used as the training set, and the in-center validation cohort and multicenter validation cohort were used as validation sets. A logistic regression model was established for the three paired miRNAs in the training set, and the cutoff was determined according to the maximum Youden index principle of the training set. The overall performance of the model was evaluated by the AUC, sensitivity, and specificity of the ROC curve. A Venn diagram was used to view the intersection of the model prediction positive and AFP positive for samples with the actual diagnosis result of liver cancer. According to the above method, statistical analysis was performed on the samples of early liver cancer and benign liver disease in the entire cohort and separately extracted.

[0129] The diagnostic effect of the model is as follows Figure 5 As shown; Figure 5The results show that the AUCs of the training set, the intra-center validation set, and the multi-center validation set were 0.996, 0.951, and 0.944, respectively. Figure 5 (a)), the sensitivity and specificity at a specific cutoff were 94.34%, 87.50%, 86.76% and 96.20%, 93.48%, 93.14%, respectively ( Figure 5 (b)). This shows that the model effect of the three paired miRNAs is at a high level and the effect of the data in different centers is basically stable.

[0130] At the same time, this embodiment also detected the AFP of positive samples and compared it with the prediction effect of the above model. It was found that the total sensitivity of the model in predicting liver cancer can reach 88.54%, while the sensitivity of AFP is only 61.26% ( Figure 5 (c)).

[0131] In addition, by Figure 5 (c) It can also be concluded that the liver cancer diagnostic model based on the combination of the three paired miRNAs has strong complementarity with AFP and can detect 87.76% of samples that AFP did not detect. This shows that the model based on the above three paired miRNAs has a strong predictive effect on liver cancer and can be used as a diagnostic marker for liver cancer.

[0132] Since the purpose of early screening for liver cancer is to detect early liver cancer lesions from patients with benign liver diseases, this example separately extracted samples of early liver cancer and benign liver diseases, and checked the prediction effect on the model. The results are as follows: Figure 6 As shown; Figure 6 The results showed that the AUC of early liver cancer samples and benign liver disease samples was 0.936, and the sensitivity and specificity were 84.29% and 92.20%, respectively, both at a high level. This shows that the preferred combination of three paired miRNAs we screened has great potential in the screening of early liver cancer.

[0133] Example 5: Construction of a liver cancer diagnosis model based on a paired miRNA preferred combination and liver cancer triple detection indicators (AFP, AFP-L3 and DCP)

[0134] In this embodiment, the above three paired miRNAs and liver cancer triple detection indicators (AFP, AFP-L3 and DCP) are used for joint modeling, and the sample information of the training set and validation set used is consistent with that of Example 4. The specific modeling method is as follows:

[0135] The sample information used is shown in Table 1: the qPCR confirmation cohort was used as the training set, and the in-center validation cohort and multicenter validation cohort were used as validation sets. A logistic regression model was established for the three paired miRNAs in the training set, and the cutoff was determined according to the maximum Youden index principle of the training set. The overall performance of the model was evaluated by the AUC, sensitivity, and specificity of the ROC curve. A Venn diagram was used to view the intersection of the model prediction positive and AFP positive for samples with the actual diagnosis result of liver cancer. According to the above method, statistical analysis was performed on the samples of early liver cancer and benign liver disease in the entire cohort and separately extracted.

[0136] The diagnostic effect of the model is as follows Figure 7 shown by Figure 7 From the ROC curve of (a), it can be concluded that the AUCs of the training set, the intra-center validation set, and the multi-center validation set are 1, 0.983, and 0.969, respectively. Figure 7 From the confusion matrix of (b), it can be concluded that the sensitivity and specificity under a specific cutoff are 100%, 92.19%, 90.44% and 97.47%, 100%, 95.67% respectively; and, Figure 7 The positive sample Venn diagram in (c) shows that this model can detect 95.48% of AFP-positive and 88.78% of AFP-negative cancer samples. This shows that the model based on the above three paired miRNA pairs and liver cancer triple detection indicators has a strong predictive effect on liver cancer.

[0137] In addition, this example also tested the effect of early liver cancer samples on the model relative to benign liver disease samples. The results are as follows: Figure 8 As shown; Figure 8 The results showed that the AUC of early liver cancer samples and benign liver disease samples was 0.958, with a sensitivity and specificity of 91.43% and 95.41%, respectively. This indicates that the three paired miRNAs combined with the liver cancer triple detection index we screened have great potential in the screening of early liver cancer.

[0138] Example 6: Verification of the diagnostic performance of liver cancer by RT-qPCR of the preferred combination of paired miRNAs

[0139] Among the four paired miRNAs screened in Example 1, two paired miRNAs (hsa-miR-125a / hsa-miR-21 and hsa-miR-21 / hsa-miR-150) were selected as the preferred combination 2. RT-qPCR was used to verify them in the center and multi-center verification cohorts, and the sample information is shown in Table 1. The amplification primers and reaction system are shown in Example 3, and the box plot of the CT value difference of the qPCR of the above two paired miRNAs is shown in Fig. 9 ; Fig. 9The qPCR validation results of the preferred combination 2 consisting of these two paired miRNAs showed significant differences between the liver cancer group and the non-liver cancer control group.

[0140] Example 7: Construction of a liver cancer diagnosis model based on the preferred combination of paired miRNAs and verification of its diagnostic performance

[0141] In order to detect the diagnostic effect of the preferred combination 2 consisting of the above two paired miRNAs on liver cancer, this example uses logistic regression to build a model on the training cohort and verify it on the validation set. The specific method is as follows:

[0142] The sample information used is shown in Table 1: The combination of two qPCR training cohorts is preferably used as the training set, and the combination of two qPCR validation cohorts is preferably used as the validation set. A logistic regression model was established for the two paired miRNAs in the training set, and the cutoff was determined according to the maximum Youden index principle of the training set. The overall performance of the model was evaluated by the AUC, sensitivity and specificity of the ROC curve.

[0143] The diagnostic effect of the model is as follows Fig.10 As shown; Fig.10 The AUC of the training set and validation set are 0.916 and 0.922 respectively ( Fig.10 (a)), the sensitivity and specificity under the specific cutoff were 84.38%, 84.38% and 88.05%, 91.59% respectively ( Fig.10 (b)). This shows that the diagnostic effect of the model based on two paired miRNAs is at a high level and the effect is basically stable in different batches of data.

[0144] Example 8: Construction of a liver cancer diagnosis model based on the paired miRNA preferred combination II and liver cancer triple detection indicators (AFP, AFP-L3 and DCP), and verification of its diagnostic performance

[0145] In this embodiment, the above two paired miRNAs and liver cancer triple detection indicators (AFP, AFP-L3 and DCP) are used for joint modeling, and the sample information of the training set and validation set used is consistent with that of Example 7. The specific modeling method is as follows:

[0146] The sample information used is shown in Table 1: The combination of two qPCR training cohorts is preferred as the training set, and the combination of two qPCR validation cohorts is preferred as the validation set. A logistic regression model was established for the two paired miRNAs and triple tests in the training set, and the cutoff was determined according to the maximum Youden index principle of the training set. The overall performance of the model was evaluated by the AUC, sensitivity and specificity of the ROC curve.

[0147] The diagnostic effect of the model is as follows Fig.11 As shown; Fig.11The AUC of the training set and validation set are 0.968 and 0.959 respectively ( Fig.11 (a)), the sensitivity and specificity under the specific cutoff were 91.67%, 85.94% and 94.34%, 94.39% respectively ( Fig.11 (b)). This shows that the model based on these two paired miRNAs and liver cancer triple detection indicators has a strong predictive effect on liver cancer.

[0148] Example 9: Construction of a liver cancer diagnosis model based on the paired miRNA preferred combination II and liver cancer indicator (AFP), and verification of its diagnostic performance

[0149] In this embodiment, the above two paired miRNAs and liver cancer index (AFP) are used for joint modeling, and the sample information of the training set and validation set used is consistent with that of Example 7. The specific modeling method is as follows:

[0150] The sample information used is shown in Table 1: The combination of two qPCR training cohorts is preferably used as the training set, and the combination of two qPCR validation cohorts is preferably used as the validation set. A logistic regression model was established for the two paired miRNAs and AFPs in the training set, and the cutoff was determined according to the maximum Youden index principle of the training set. The overall performance of the model was evaluated by the AUC, sensitivity, and specificity of the ROC curve.

[0151] The diagnostic effect of the model is as follows Fig.12 As shown; Fig.12 The AUC of the training set and validation set are 0.966 and 0.942 respectively ( Fig.12 a), the sensitivity and specificity under the specific cutoff were 90.63%, 85.94% and 91.20%, 92.52% ( Fig.12 b). This shows that the model constructed based on these two paired miRNAs and the liver cancer indicator AFP also has a good predictive effect on liver cancer.

[0152] In addition, in this example, the diagnostic performance of the model based on the two paired miRNAs, the model based on AFP alone, and the model based on the two paired miRNAs combined with AFP were compared. Fig.13 As shown; Fig.13 The results showed that among 160 liver cancer samples, the sensitivity of AFP was only 60.62%, while the model based on two paired miRNAs combined with AFP could detect 97.94% of AFP-positive samples and 74.60% of AFP-negative samples. This shows that the preferred combination of paired miRNAs 2 has a good supplementary effect on the clinically commonly used liver cancer indicator AFP, and the model based on paired miRNA combined with AFP has a higher detection rate for liver cancer.

[0153] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A diagnostic or prognostic marker for liver cancer, characterized in that: The diagnostic or prognostic marker for liver cancer comprises a miRNA pair consisting of any two of the following miRNAs, or any combination of the miRNA pairs: hsa-let-7a, hsa-miR-21, hsa-miR-125a, hsa-miR-150, hsa-miR-200a, hsa-miR-483, hsa-miR-199a, hsa-miR-200a, hsa-miR-429, hsa-miR-126, hsa-miR-381, hsa-miR-185, hsa-miR-215, hsa-miR-374a.

2. The diagnostic or prognostic marker for liver cancer according to claim 1, characterized in that: The diagnostic or prognostic marker for liver cancer comprises a miRNA pair selected from the following: hsa-miR-200a / hsa-miR-150; hsa-miR-483 / hsa-miR-199a; hsa-miR-200a / hsa-miR-199a; hsa-miR-150 / hsa-miR-429; hsa-miR-126 / hsa-miR-200a; hsa-miR-199a / hsa-miR-429; hsa-miR-381 / hsa-miR-200a; hsa-miR-185 / hsa-miR-429; hsa-miR-215 / hsa-miR-199a; hsa-miR-125a / hsa-miR-215; hsa-let-7a / hsa-miR-21; hsa-miR-200a / hsa-miR-374a; hsa-miR-125a / hsa-miR-21; hsa-miR-21 / hsa-miR-150; and Any one, several or all of the above combinations.

3. The diagnostic or prognostic marker for liver cancer according to claim 2, characterized in that: The diagnostic or prognostic marker for liver cancer comprises a miRNA pair selected from the following: hsa-let-7a / hsa-miR-21; hsa-miR-125a / hsa-miR-21; hsa-miR-21 / hsa-miR-150; hsa-miR-200a / hsa-miR-150; and, Any one, two, three or all of the above.

4. The diagnostic or prognostic marker for liver cancer according to claim 3, characterized in that: The diagnostic or prognostic markers for liver cancer include a combination of hsa-let-7a / hsa-miR-21, hsa-miR-125a / hsa-miR-21, hsa-miR-21 / hsa-miR-150 and hsa-miR-200a / hsa-miR-150.

5. The diagnostic or prognostic marker for liver cancer according to claim 3, characterized in that: The diagnostic or prognostic markers for liver cancer include a combination of hsa-let-7a / hsa-miR-21, hsa-miR-200a / hsa-miR-150 and hsa-miR-125a / hsa-miR-21.

6. The diagnostic or prognostic marker for liver cancer according to claim 3, characterized in that: The diagnostic or prognostic marker for liver cancer comprises a combination of hsa-miR-125a / hsa-miR-21 and hsa-miR-21 / hsa-miR-150.

7. The diagnostic or prognostic marker for liver cancer according to claim 1, characterized in that: The miRNA in the miRNA pair is a miRNA in serum or plasma.

8. The diagnostic or prognostic marker for liver cancer according to claim 7, characterized in that: The miRNA in the miRNA pair is a miRNA in serum or plasma fucosylated exosomes.

9. The diagnostic or prognostic marker for liver cancer according to any one of claims 1 to 8, characterized in that: The liver cancer diagnosis or prognosis markers further include any one or more of AFP, AFP-L3 and DCP.

10. The diagnostic or prognostic marker for liver cancer according to claim 9, characterized in that The AFP, AFP-L3 or DCP is the corresponding protein in serum, plasma or whole blood.

11. Use of the diagnostic or prognostic marker for liver cancer according to any one of claims 1 to 10 in constructing a model for liver cancer diagnosis, efficacy or prognosis assessment.

12. The use according to claim 11, characterized in that: The expression ratio of the miRNA pair is used as a model feature, and a machine learning method is used to perform modeling.

13. Use of the liver cancer diagnosis or prognosis marker or detection reagent thereof according to any one of claims 1 to 10 in the preparation of a drug or kit for liver cancer diagnosis, efficacy or prognosis evaluation.

14. A drug or kit for liver cancer diagnosis, efficacy or prognosis evaluation, characterized in that: The medicine or kit comprises the diagnosis or prognosis marker for liver cancer or a detection reagent thereof according to any one of claims 1 to 10.

15. The drug or kit according to claim 14, characterized in that: In patients with liver cancer, the expression ratio of the miRNA pair in serum fucosylated exosomes is significantly different from that in healthy subjects.

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