Serum microRNA markers associated with liver cancer and a novel method for diagnosing liver cancer

Abstract

The present invention provides serum microRNA markers associated with liver cancer and novel methods for diagnosing liver cancer. [Solution] The liver cancer-associated serum microRNA markers include hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499. The relative expression levels of each liver cancer-associated serum microRNA marker in serum are examined using real-time PCR, and the test reagent contains primers for the liver cancer-associated serum microRNA markers. The present invention can be used to diagnose hepatocellular carcinoma, particularly early-stage hepatocellular carcinoma, or to differentiate the serum of at least one hepatocellular carcinoma patient from the serum of at least one healthy individual, at least one chronic hepatitis B patient, or at least one cirrhosis patient.

Description

[Technical Field]

[0001] The present invention belongs to the fields of genetic engineering and clinical medicine and relates to serum microRNA markers associated with human liver cancer and novel methods for diagnosing liver cancer, where serum microRNAs include hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499. [Background technology]

[0002] Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. According to data published by the World Health Organization, there were approximately 19.29 million new cases of cancer and approximately 9.66 million deaths worldwide in 2020. Malignant tumors are a significant threat to human health and pose a heavy economic burden to society. The development of liver cancer is a multifactorial, multistage process. Chronic HBV and HCV infection are major risk factors for primary hepatocellular carcinoma (HCC) worldwide. Chronic HBV infection, in particular, is highly correlated with the development of HCC geographically, accounting for 75% of liver cancer cases worldwide and even 85% in developing countries. Liver cancer is one of the most common malignant tumors in China. According to the 2018 China Liver Cancer Big Data Report, there were 841,000 new cases of primary liver cancer each year worldwide, of which 393,000 in China, accounting for approximately 46.7% of the global total. Chronic hepatitis B virus (HBV) infection is the leading cause of liver cancer in China. Liver cancer mainly includes hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), with the former accounting for 83.9% to 92.3% of cases. Approximately 85% of HCC patients are infected with HBV. Although the 5-year relative survival rate for liver cancer in China is only 12.1%, early diagnosis and effective treatment can achieve a relatively good prognosis, with small liver cancers (<5 cm) achieving a 5-year survival rate of over 80%. Currently, liver cancer screening tests are not effective, with relatively low detection and early diagnosis rates. Therefore, the development of early screening methods applicable to a broad population is of great significance for the effective prevention and treatment of liver cancer in China.

[0003] Liver cancer does not have obvious symptoms in its early stages. Traditional early screening methods for high-risk groups involve combining alpha-fetoprotein (AFP), a serological diagnostic marker for liver cancer, with B-mode ultrasound of the liver. As the most widely used serological marker, alpha-fetoprotein plays an important role in diagnosing and monitoring liver cancer; however, its detection rate for early-stage liver cancer is unsatisfactory. Currently, domestic and international studies have demonstrated that alpha-fetoprotein (AFP) still lacks both specificity and sensitivity. In clinical practice, approximately 30% to 40% of patients are alpha-fetoprotein negative. The sensitivity of B-mode ultrasound is related to tumor location, and the skill requirements of the examiner are relatively high, making early screening and diagnosis of liver cancer challenging.

[0004] MicroRNAs (miRNAs) are a series of evolutionarily conserved single-stranded RNA molecules with a length of 20–25 nucleotides, belonging to the endogenous non-coding RNA (NCR). Through imperfect complementary pairing with target gene mRNA, miRNAs either inhibit protein translation or promote mRNA degradation. The regulatory mechanisms of miRNAs are complex and diverse. By regulating target gene expression, miRNAs can induce a range of biological mechanisms, including embryonic development, cell proliferation, apoptosis, and cell differentiation. As tumor markers, miRNAs have the following five advantages: (1) They are stably expressed and present in the peripheral blood of healthy individuals, with no significant interindividual differences. (2) MiRNAs in serum or plasma are resistant to degradation when incubated at room temperature for more than 24 hours, repeatedly frozen and thawed, or under hyperacidic or hyperalkaline conditions. (3) Changes in miRNA expression levels are closely related to the pathological processes of malignant tumors. (4) Abnormal expression of miRNAs in serum can provide direct evidence of the presence of tumor cells, making them valuable biological markers. (5) miRNA not only has higher testing accuracy than protein markers, but also allows for easier simultaneous testing of multiple components, demonstrating its obvious advantages. miRNA is involved in many cellular activities in the human body, such as hyperplasia, apoptosis, and migration. Furthermore, miRNA plays an important role in cancer proliferation and metastasis, and can be used as a biomarker for tumor initiation, growth, and prognosis.

[0005] In preliminary studies, we have found that hsa-miR-2115 and hsa-miR-4499 have significantly different expression levels in the serum of liver cancer patients compared with controls, regulating liver cancer cell activity and providing a reference for early diagnosis of liver cancer. These findings suggest that they have potential as early diagnostic markers for HCC. Combining hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-211, hsa-miR-27, and hsa-miR-4499 can further enhance diagnostic efficacy and increase diagnostic value. The use of multiple miRNA biomarkers in combination enables rapid, accurate, and low-cost early diagnosis of HCC. [Prior art documents] [Non-patent literature]

[0006] [Non-Patent Document 1] Xu J, Wu C, Che X, et al. Circulating microRNAs, miR-21, miR-122, and miR-223, in patients with hepatocellular carcinoma or chronic hepatitis. [J]. Molecular Carcinogenesis, 2011(2):50. [Non-patent document 2] Zuo D, Chen L, Liu X, et al. Combination of miR-125b and miR-27a enhances sensitivity and specificity of AFP-based diagnosis of hepatocellular carcinoma [J]. Tumor Biology, 2016, 37(5):6539-6549. DOI:10.1007 / s13277-015-4545-1. [Non-patent document 3] Wang C, Hann HW, Ye Z, et al. Prospective evidence of a circulating microRNA signature as a non-invasive marker of hepatocellular carcinoma in HBV patients [J]. Oncotarget, 2017, 8(10): -. DOI:10.18632 / oncotarget.9429. [Non-patent document 4] Shaker O, AlhelfM, Morcos G, et al. miRNA-101-1 and miRNA-221 expressions and their polymorphisms as biomarkers for early diagnosis of hepatocellular carcinoma [J]. Infection, genetics and evolution: journal of molecular epidemiology and evolutionary genetics in infectious diseases, 2017, 51:173-181. DOI: 10.1016 / j.meegid.2017.03.030. Summary of the Invention [Problem to be solved by the invention]

[0007] The objective of the present invention is to provide a set of serum microRNA markers associated with liver cancer and a novel method for diagnosing liver cancer, which can further improve the diagnostic efficacy and value of liver cancer. [Means for solving the problem]

[0008] The technical solutions adopted by the present invention are as follows: The liver cancer-associated serum microRNA markers provided by the present invention are characterized as follows: The liver cancer-associated serum microRNA markers consist of hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499. The sequence of hsa-miR-122 is tggagtgtgacaatggtgtttg (SEQ ID NO: 1), the sequence of hsa-miR-21 is tagcttatcagactgatgttga (SEQ ID NO: 1), the sequence of hsa-miR-2115 is catcagaattcatggaggctag (SEQ ID NO: 3), the sequence of hsa-miR-221 is agctacattgtctgctgggtttc (SEQ ID NO: 4), the sequence of hsa-miR-27 is ttcacagtggctaagttccgc (SEQ ID NO: 5), and the sequence of hsa-miR-4499 is gattgctctgcgtgcggaatcgac (SEQ ID NO: 6).

[0009] The novel method for diagnosing liver cancer of the present invention is characterized as follows: After sampling and processing the serum to be tested, the relative expression levels of serum microRNA markers associated with liver cancer in the serum are measured using real-time PCR. The reagents for testing the serum microRNA markers associated with liver cancer, hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499, contain primers for the above markers and are specifically configured as follows: The sequence of the upstream primer hsa-miR-122-F for the marker hsa-miR-122 (SEQ ID NO: 1) is gccgagtggagtgtgacaatg (SEQ ID NO: 7), the sequence of the upstream primer hsa-miR-21-F for the marker hsa-miR-21 (SEQ ID NO: 2) is tcggcaggtagcttatcagac (SEQ ID NO: 8); the sequence of the upstream primer hsa-miR-2115-F for the marker hsa-miR-2115 (SEQ ID NO: 3) is catcagaattcatggaggct (SEQ ID NO: 9); the sequence of the upstream primer hsa-miR-221 ( The sequence of the upstream primer hsa-miR-221-F for the marker hsa-miR-27 (sequence number 4) is gccgagagctacattgtctgc (sequence number 10); the sequence of the upstream primer hsa-miR-27-F for the marker hsa-miR-27 (sequence number 5) is gccgagttcacagtggctaag (sequence number 11); the sequence of the upstream primer hsa-miR-4499-F for the marker hsa-miR-4499 (sequence number 6) is gattgctctgcgtgcggaatc (sequence number 12); and the sequence of the universal downstream primer Universal Rp for the above markers is cagtgcagggtccgaggt (sequence number 13).

[0010] The novel method for diagnosing liver cancer of the present invention is characterized as follows: liver cancer-related serum microRNA markers hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499 are measured, and the ΔCt values ​​are calculated and then input into a discrimination formula. Overall judgment value = -1.772 + 0.097 × ΔCthsa-miR-122 + 0.295 × ΔCthsa-miR-21 - 1.123 × ΔCthsa-miR-2115 - 0.322 × ΔCthsa-miR-221 + 0.606 × ΔCthsa-miR-27 + 0.057 × ΔCthsa-miR-4499 The overall score ≧0 is positive, indicating a relatively high risk of developing liver cancer; the overall score <0 is negative, indicating a relatively low risk of developing liver cancer.

[0011] The serum microRNA markers associated with liver cancer of the present invention are a plurality of markers selected from hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27 and hsa-miR-4499.

[0012] To facilitate convenient testing, a kit containing reagents and tools for measuring serum microRNA markers associated with liver cancer is prepared. The included reagents can measure the serum expression levels of these serum microRNA markers and include primers for the above-mentioned serum microRNA markers associated with liver cancer (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499). Specific implementation is as follows: The human liver cancer diagnostic kit contains primers for the above-mentioned serum microRNA markers associated with liver cancer, and the assay reagents corresponding to the microRNA markers hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499 each contain an upstream primer and a downstream primer. The six pairs of primers are specifically as follows: The sequence of the upstream primer hsa-miR-122-F for the marker hsa-miR-122 (SEQ ID NO: 1) is gccgagtggagtgtgacaatg (SEQ ID NO: 7), and the sequence of the universal downstream primer Universal Rp is cagtgcagggtccgaggt (SEQ ID NO: 13). The sequence of the upstream primer hsa-miR-21-F for the marker hsa-miR-21 (SEQ ID NO: 2) is tcggcaggtagcttatcagac (SEQ ID NO: 8), and the sequence of the universal downstream primer Universal Rp is cagtgcagggtccgaggt (SEQ ID NO: 13). The sequence of the upstream primer hsa-miR-2115-F for the marker hsa-miR-2115 (SEQ ID NO: 3) is catcagaattcatggaggct (SEQ ID NO: 9), and the sequence of the universal downstream primer Universal Rp is cagtgcagggtccgaggt (SEQ ID NO: 13). The sequence of the upstream primer hsa-miR-221-F for the marker hsa-miR-221 (SEQ ID NO: 4) is gccgagagctacattgtctgc (SEQ ID NO: 10), and the sequence of the universal downstream primer Universal Rp is cagtgcagggtccgaggt (SEQ ID NO: 13). The sequence of the upstream primer hsa-miR-27-F for the marker hsa-miR-27 (SEQ ID NO: 5) is gccgagttcacagtggctaag (SEQ ID NO: 11), and the sequence of the universal downstream primer Universal Rp is cagtgcagggtccgaggt (SEQ ID NO: 13). The sequence of the upstream primer hsa-miR-4499-F for the marker hsa-miR-4499 (SEQ ID NO: 6) is gattgctctgcgtgcggaatc (SEQ ID NO: 12), and the sequence of the universal downstream primer Universal Rp is cagtgcagggtccgaggt (SEQ ID NO: 13).

[0013] The other reagents in the kit other than the primers can be those commonly used in the corresponding testing techniques in the existing art.

[0014] The inventors used standard operating procedures (SOPs) to collect standard blood samples, and the system collected complete basic and clinical information of the population, and then performed tests using multiple methods: RT-PCR, TaqMan miRNA Array, and Real-time PCR (TaqMan probe).

[0015] Specifically, the experimental method of the research mainly includes the following parts: 1. Criteria for selecting and grouping research subjects Group A: control group (n=345, including 150 healthy controls, 150 patients with chronic hepatitis B, and 145 patients with liver cirrhosis), with no other systemic serious diseases. Group B: liver cancer patients (n=233) without other serious systemic diseases.

[0016] 2. Blood serum separation and pretreatment (1) Withdraw 2 ml of peripheral blood and place it in a blood collection tube containing a separating agent. Gently invert the tube 180° up and down to mix. Repeat this process 5 to 6 times. Within 12 hours, the blood collection tube will be coagulated. Centrifuge the tube at 3,000 g for 10 minutes. Immediately transfer the supernatant to a clean tube and store at -80°C. (2) Total RNA is extracted using the miRNeasy Serum / Plasma Advanced Kit according to the manufacturer's instructions (Qiagen), and its concentration is quantitatively tested using a Qubit 4.0 fluorometric assay.

[0017] 3. Measure serum miRNA expression levels using real-time PCR 1. Take the pretreated serum and obtain a cDNA sample by RNA reverse transcription. Prepare the reverse transcription system according to the table below.

[0018] [Table 1]

[0019] 2. Mix the PCR tube by inverting it 8 times, then briefly centrifuge it and place it on ice for 3 minutes. 3. Place the PCR tube in a PCR machine and perform reverse transcription under the following reaction conditions: 42°C, 60 min; 85°C, 5 min; hold at 4°C The reverse transcription product is stored in a refrigerator at 4°C and used for the next pre-amplification. 4. Prepare the reverse-transcribed cDNA according to the reaction system in the table below and perform pre-amplification.

[0020] [Table 2]

[0021] The reaction conditions for pre-amplification are as shown in the table below.

[0022] [Table 3]

[0023] After lowering the temperature to 4°C, the pre-amplification product is stored at 4°C and used in the subsequent real-time PCR reaction. 5. After briefly centrifuging the pre-amplified product, add 75 μl of 0.1×TE (pH 8.0), mix by inversion, and then briefly centrifuge again. The pre-amplified product can be used directly in the subsequent real-time PCR. For real-time PCR of the pre-amplification products, prepare the reaction system as shown in the table below.

[0024] [Table 4]

[0025] 6. Measure and compare the differences in miRNA expression levels in serum samples from healthy controls and liver cancer patients. Serum miRNAs detected to be differentially expressed in control and liver cancer patients include hsa-miR-107, hsa-miR-122, hsa-miR-1246, hsa-miR-192, hsa-miR-199, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-223, hsa-miR-26, hsa-miR-27, hsa-miR-4499, and hsa-miR-801. The copy numbers of all of these miRNAs were significantly higher in liver cancer patients than in the control group.

[0026] 4. Real-time PCR was used to verify the expression levels of serum miRNAs in the training set. 1. Design primers for the 13 target miRNAs: Design primers using poly(A) tailing PCR. 2. Real-time PCR reactions were performed with the addition of fluorescent probes. Differences in miRNA expression levels were examined and compared in serum samples from healthy controls, patients with chronic hepatitis B, patients with liver cirrhosis, and patients with liver cancer (68 healthy controls, 62 patients with chronic hepatitis B, 61 patients with liver cirrhosis, and 101 patients with liver cancer). 3. Real-time PCR testing revealed that the results were consistent for six miRNAs (abbreviated as 6miRNAs), specifically hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499.

[0027] Therefore, we conclusively confirmed that differentially expressed serum miRNAs in healthy controls and liver cancer patients include hsa-miR-122 (SEQ ID NO: 1), hsa-miR-21 (SEQ ID NO: 2), hsa-miR-2115 (SEQ ID NO: 3), hsa-miR-221 (SEQ ID NO: 4), hsa-miR-27 (SEQ ID NO: 5), and hsa-miR-4499 (SEQ ID NO: 6).

[0028] The above significantly differentially expressed miRNAs were incorporated into the Logistics regression equation, resulting in Logit(p=HCC)=-1.772+0.097×hsa-miR-122+0.295×hsa-miR-21-1.123×hsa-miR-2115-0.322×hsa-miR-221+0.606×hsa-miR-27+0.057×hsa-miR-4499.

[0029] 5. Evaluate the performance of the above miRNA combinations in the validation set. The performance of the six miRNA combination was verified in 298 independent serum samples (70 healthy controls, 65 patients with chronic hepatitis B, 63 patients with cirrhosis, and 100 patients with liver cancer), thereby evaluating the ability of the six miRNA combination to predict liver cancer patients.

[0030] 6. Method for preparing diagnostic kits Based on the above experimental results, the present inventors have further developed a microRNA diagnostic kit for human liver cancer. The diagnostic kit includes primers and tools for measuring measurable mature hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499 in the serum of subjects. The diagnostic kit includes a set of serum miRNA primers and may also include reagents such as Taq polymerase and deoxynucleotide triphosphates. [Brief explanation of the drawings]

[0031] [Figure 1] FIG. 1 is a flowchart of the experimental design for the screening, training, and validation stages of the microRNA combinations used in the present invention to identify the presence of at least one hepatocellular carcinoma target serum, particularly early stage hepatocellular carcinoma (BCLC stages 0 and A). [Figure 2] Figure 2 shows a flow chart of the main method for determining the combination of microRNAs in blood used in the present invention to diagnose patients with hepatocellular carcinoma, particularly early stage hepatocellular carcinoma (BCLC stages 0 and A). Control groups include healthy subjects, subjects with chronic hepatitis B, and subjects with liver cirrhosis. [Figure 3] Figure 3 illustrates a logistic regression model including preferred microRNA combinations (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499) used to identify at least one target serum for hepatocellular carcinoma in the present invention. [Figure 3A]Figure 3A shows the ROC curve evaluating the diagnostic value of the microRNA combination in comparing HCC and control groups in the training set (n = 292). Compared with AFP (AUC = 0.801), the microRNA combination had significantly higher diagnostic accuracy (AUC = 0.937) when distinguishing between serum from HCC and control groups. [Figure 3B] Figure 3B shows the ROC curve evaluating the diagnostic value of the microRNA combination in the validation set (n = 298) for HCC and control groups. Compared with AFP (AUC = 0.747), the microRNA combination had significantly higher diagnostic accuracy (AUC = 0.938) when distinguishing between serum from HCC and control groups. [Figure 4] Figure 4 illustrates a logistic regression model including a preferred combination of microRNAs (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499) used in the present invention to further differentiate sera from hepatocellular carcinoma patients from sera from healthy individuals, chronic hepatitis B patients, or cirrhosis patients. [Figure 4A] Figure 4A shows the ROC curve evaluating the diagnostic value of the microRNA panel when comparing HCC and healthy subjects in the validation set (n = 298). Compared with AFP (AUC = 0.793), the microRNA panel had significantly higher diagnostic accuracy (AUC = 0.990) when distinguishing between serum from HCC patients and serum from healthy individuals. [Figure 4B] Figure 4B shows the ROC curve evaluating the diagnostic value of the microRNA panel when comparing HCC and chronic hepatitis B patients in the validation set (n = 298). Compared with AFP (AUC = 0.734), the microRNA panel had significantly higher diagnostic accuracy (AUC = 0.887) when distinguishing between sera from HCC patients and chronic hepatitis B patients. [Figure 4C]Figure 4C shows the ROC curve evaluating the diagnostic value of the microRNA panel in comparing HCC and cirrhosis groups in the validation set (n = 298). Compared with AFP (AUC = 0.712), the microRNA panel had significantly higher diagnostic accuracy (AUC = 0.826) when distinguishing between serum from HCC patients and serum from cirrhosis patients. [Figure 5] Figure 5 illustrates a logistic regression model including preferred microRNA combinations (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499) used in the present invention to identify at least one target serum for hepatocellular carcinoma of different BCLC stages. [Figure 5A] Figure 5A shows the ROC curve evaluating the diagnostic value of the microRNA panel when comparing very-early stage HCC (BCLC stage 0) and control groups. Compared with AFP (AUC = 0.670), the microRNA panel had significantly higher diagnostic accuracy (AUC = 0.955) when distinguishing between serum from very-early stage HCC patients and control groups. [Figure 5B] Figure 5B shows the ROC curve evaluating the diagnostic value of the microRNA panel when comparing early-stage HCC (BCLC stage A) and control groups. Compared with AFP (AUC = 0.719), the microRNA panel had significantly higher diagnostic accuracy (AUC = 0.982) when distinguishing sera from early-stage HCC patients and control groups. [Figure 5C] Figure 5C shows the ROC curve evaluating the diagnostic value of the microRNA panel in comparing intermediate-stage HCC (BCLC stage B) and control groups. Compared with AFP (AUC = 0.815), the microRNA panel had significantly higher diagnostic accuracy (AUC = 0.912) when distinguishing between sera from intermediate-stage HCC patients and control groups. [Figure 5D]Figure 5D shows the ROC curve evaluating the diagnostic value of the microRNA panel in comparing advanced-stage HCC (BCLC stage C) and control groups. Compared with AFP (AUC = 0.830), the microRNA panel had significantly higher diagnostic accuracy (AUC = 0.955) when distinguishing between sera from patients with advanced HCC and those from controls. [Figure 6] Figure 6 illustrates a logistic regression model including a preferred combination of microRNAs (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499) used in the present invention to identify at least one target serum for hepatocellular carcinoma with AFP≦20 ng / mL and AFP>20 ng / mL. [Figure 6A] Figure 6A shows the ROC curve evaluating the diagnostic value of the microRNA combination when comparing HCC sera with AFP levels < 20 ng / mL and control sera. Compared with AFP (AUC = 0.676), the microRNA combination had significantly higher diagnostic accuracy (AUC = 0.956) when distinguishing between HCC sera with AFP levels < 20 ng / mL and control sera. [Figure 6B] Figure 6B shows the ROC curve evaluating the diagnostic value of the microRNA combination when comparing HCC sera with AFP > 20 ng / mL and control sera. Compared with AFP (AUC = 0.940), the microRNA combination did not significantly differentiate between HCC sera with AFP > 20 ng / mL and control sera (AUC = 0.968). [Figure 7] Figure 7 shows Table 2. Characteristics of subjects on the microRNA chip. [Figure 8] Figure 8 shows Table 3. Characteristics of subjects in the training and validation sets. [Figure 9] Figure 9 shows Table 6. Expression status and diagnostic efficiency of microRNAs in the training set. DETAILED DESCRIPTION OF THE INVENTION

[0032] The present invention will be further clarified by the following examples. Example 1: Selection and grouping criteria for study subjects The experimental design for the screening, training, and validation stages of the microRNA biomarkers in this invention is shown in Figure 1. The main steps of the method for identifying serum samples of hepatocellular carcinoma patients using the recommended serum microRNA combination are shown in Figure 2.

[0033] Between January 2022 and May 2023, 688 blood samples meeting the eligibility criteria (Table 1) were prospectively collected from Qilu Hospital of Shandong University and Shandong Provincial Hospital. These samples included 150 healthy donors (HC healthy group), 150 patients with chronic hepatitis B (CHB group), 145 patients with liver cirrhosis after HBV infection (LC cirrhosis group), and 233 patients with HCC associated with HBV infection (HCC group). These samples were divided into three time periods (Figure 1). The clinical symptoms of the patients are summarized in Tables 2 and 3 and specifically shown in Figures 7 and 8.

[0034] Table 1. Patient selection criteria

[0035] [Table 5]

[0036] Example 2 Serum Separation and Pretreatment of Blood Peripheral blood (2 ml) was withdrawn and placed in a blood collection tube containing a separating agent. The tube was gently inverted 180° up and down to mix, and this process was repeated 5 to 6 times. After 12 hours, the blood collection tube was allowed to clot. It was then centrifuged at 3,000 g for 10 minutes. The supernatant was then immediately transferred to a clean tube and stored at -80°C.

[0037] Total RNA is extracted using the miRNeasy Serum / Plasma Advanced Kit according to the manufacturer's instructions (Qiagen), and its concentration is quantitatively tested using a Qubit 4.0 fluorometric assay.

[0038] Example 3: Analyzing differential miRNA expression using microRNA chips MicroRNA expression differences were compared between HCC, healthy controls, chronic hepatitis B, and cirrhosis groups using microRNA chip analysis. Serum miRNAs screened for differential expression in HCC patients, healthy controls, chronic hepatitis B, and cirrhosis patients included hsa-miR-107, hsa-miR-122, hsa-miR-1246, hsa-miR-192, hsa-miR-199, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-223, hsa-miR-26, hsa-miR-27, hsa-miR-4499, and hsa-miR-801 (microRNA sequences are listed in Table 4). The copy numbers of these miRNAs were significantly higher in HCC patients than in healthy controls, chronic hepatitis B, and cirrhosis patients.

[0039] Table 4. MicroRNA sequences

[0040] [Table 6]

[0041] Example 4 Validating microRNAs screened with microRNA chips in a training set of 292 samples Primers were designed (miRNA primer sequences are shown in Table 5), and quantitative real-time PCR testing of each miRNA was performed on the serum of 68 healthy controls, 62 patients with chronic hepatitis B, 61 patients with liver cirrhosis, and 101 patients with liver cancer.

[0042] (1) Preparation of cDNA samples: (a) Using the miRNeasy Serum / Plasma Advanced Kit (Qiagen), small RNAs were extracted according to the manufacturer's instructions, and their concentrations were quantitatively assayed using a Qubit 4.0 fluorometric assay. (b) Small RNAs were tailed using a tailing reagent (E. coli Poly(A) Polymerase, Vazyme). (c) The tailed small RNAs were reverse-transcribed using a reverse transcription reagent (HiScript® II Reverse Transcriptase, Vazyme) to obtain cDNA.

[0043] (2) Real-time PCR: Using a probe-based real-time fluorescent quantitative PCR (Taq Pro U Multiple Probe qPCR MIX, Vazyme), changes in miRNA expression levels in serum samples from control groups (healthy controls, patients with chronic hepatitis B, and patients with liver cirrhosis) and liver cancer patients will be examined and compared. The serum miRNA expression levels of each group sample will be examined using RNU6-1 as an internal standard.

[0044] The Kruskal-Wallis test was used to compare the HCC group and the control group, and the Mann-Whitney U test was used to compare the HCC group and the control group. A stepwise logistic regression model was used to select microRNA diagnostic markers based on the training set. Using the predicted probability of HCC as a surrogate marker, a receiver operating characteristic (ROC) curve was constructed. The area under the ROC curve (AUC) was used to evaluate the diagnostic efficiency of the serum AFP and microRNA combination. ROC and regression analyses were performed using MedCalc (19.8) software. Analysis of the results showed that six miRNAs, hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499, were significantly different between the control group and the liver cancer patients.

[0045] The expression profiles and diagnostic efficiencies of the six candidate microRNAs in the training set are shown in Table 6 of FIG. 6 microRNA combinations * AUC = 0.937 (0.940, 0.912) The miRNAs with significant expression differences were incorporated into the Logistics regression equation. * Logit(p=HCC)=-1.772+0.097×hsa-miR-122+0.295×hsa-miR-21-1.123×hsa-miR-2115-0.322×hsa-miR-221+0.606×hsa-miR-27+0.057×hsa-miR-4499 is obtained.

[0046] The AUC of the microRNA combination was significantly greater than that of AFP (0.937 vs. 0.801, p<0.001, Figure 3-A), as shown in Figure 3.

[0047] Table 5. miRNA primer sequences

[0048] [Table 7]

[0049] Example 5 The above microRNA combinations are validated in a validation set of 298 samples. This embodiment is shown in FIGS. The parameters estimated from the training set were used to predict the probability of being diagnosed with HCC in an independent validation set (298 serum samples). Experiments were also conducted using quantitative RT-PCR assays. The predicted probabilities were used to construct receiver operating characteristic curves. Comparing the AUC between the microRNA combination and AFP in the validation set revealed that the diagnostic accuracy of the microRNA combination was significantly higher than that of AFP (AUC: 0.938 vs. 0.747, p<0.0001, Figure 3B).

[0050] The effectiveness of the microRNA combination and AFP in distinguishing HCC from healthy, CHB, and cirrhosis groups was also compared, as shown in Figure 4. The analysis demonstrated that both the microRNA combination and AFP could distinguish HCC from non-HCC. However, the AUC of the microRNA combination was significantly greater than that of AFP (HCC and healthy: 0.990 vs. 0.793, p<0.0001; HCC and CHB: 0.887 vs. 0.734, p<0.0001; HCC and cirrhosis: 0.826 vs. 0.712, p<0.0001; Figures 4A, 4B, and 4C).

[0051] Example 6 Diagnostic performance of microRNA combinations and AFP for different BCLC stages The diagnostic performance of the microRNA combination and AFP for different BCLC stages was further evaluated, as shown in Figure 5. In the diagnosis of early, intermediate, and late HCC (BCLC stages 0, A, B, and C), the diagnostic performance of the microRNA combination was significantly higher than that of AFP (BCLC stage 0, AUC 0.955 vs. 0.670, p<0.001; BCLC stage A, AUC 0.982 vs. 0.719, p<0.001; BCLC stage B, AUC 0.912 vs. 0.815, p<0.001; BCLC stage C, AUC 0.955 vs. 0.830, p<0.001; Figures 5A, 5B, 5C, and 5D).

[0052] Example 7 Diagnostic performance of microRNA combinations for normal AFP (≦20 ng / mL) and high AFP (>20 ng / mL) groups Example 7 is shown in Figure 6. The diagnostic accuracy of the microRNA panel was evaluated based on AFP levels. In the normal AFP (≦20 ng / mL) group, the AUC of the microRNA panel was significantly greater than that of AFP (0.956 vs. 0.676, p<0.001, Figure 6A). In the high AFP (>20 ng / mL) group, the difference between the AUC of the microRNA panel and that of AFP was not significant (0.968 vs. 0.940, p=0.26, Figure 6B).

[0053] The results demonstrated the specific expression of microRNAs in the serum of patients with hepatocellular carcinoma. Therefore, the microRNA set defined here can be used as a unique microRNA biomarker. Analysis of serum microRNA expression profiles in hepatocellular carcinoma patients not only enables early diagnosis of liver cancer, but also helps distinguish hepatocellular carcinoma from chronic hepatitis B and liver cirrhosis.

[0054] The present invention provides unique molecular markers for the identification and validation of serum microRNA expression biomarkers that can be screened in blood samples for early diagnosis and differential diagnosis of hepatocellular carcinoma.

Claims

1. Consists of hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27 and hsa-miR-4499; the sequence of hsa-miR-122 is tggagtgtgacaatggtgtttg (SEQ ID NO: 1), the sequence of hsa-miR-21 is tagcttatcagactgatgttga (SEQ ID NO: 2), and the sequence of hsa-miR-2115 is catcagaat A serum microRNA marker associated with liver cancer, characterized in that the sequence of hsa-miR-221 is agctacattgtctgctgggttttc (SEQ ID NO: 4), the sequence of hsa-miR-27 is ttcacagtggctaagttccgc (SEQ ID NO: 5), and the sequence of hsa-miR-4499 is gattgctctgcgtgcggaatcgac (SEQ ID NO: 6).

2. After sampling and processing the serum to be tested, The PCR method is used to examine the relative expression levels in serum of the liver cancer-related serum microRNA markers of claim 1; the reagent for examining the liver cancer-related serum microRNA markers hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27 and hsa-miR-4499 contains primers for the markers and is specifically configured as follows: the sequence of the upstream primer hsa-miR-122-F for marker hsa-miR-122 (SEQ ID NO: 1) is gccgagtggagtgtgacaatg (SEQ ID NO: 7), and the sequence of the upstream primer hsa-miR-21-F for marker hsa-miR-21 (SEQ ID NO: 2) is tcggcaggtagcttatcagac (SEQ ID NO: 8). 8); the sequence of the upstream primer hsa-miR-2115-F of the marker hsa-miR-2115 (SEQ ID NO: 3) is catcagaattcatggaggct (SEQ ID NO: 9); the sequence of the upstream primer hsa-miR-221-F of the marker hsa-miR-221 (SEQ ID NO: 4) is gccgagagctacattgtctgc (SEQ ID NO: 10); the marker hsa-miR-2 A novel method for diagnosing liver cancer, characterized in that the sequence of the upstream primer hsa-miR-27-F of marker hsa-miR-27 (SEQ ID NO: 5) is gccgagttcacagtggctaag (SEQ ID NO: 11); the sequence of the upstream primer hsa-miR-4499-F of marker hsa-miR-4499 (SEQ ID NO: 6) is gattgctctgcgtgcggaatc (SEQ ID NO: 12); and the sequence of the universal downstream primer Universal Rp of the marker is cagtgcagggtccgaggt (SEQ ID NO: 13).

3. Serum microRNA markers associated with liver cancer, hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499, were measured, and the ΔCt values ​​were calculated and then input into the following discrimination formula: Overall judgment value = −1.772 + 0.097 × ΔCthsa-miR-122 + 0.295 × ΔCthsa-miR-21 − 1.123 × ΔCthsa-miR-2115 − 0.322 × ΔCthsa-miR-221 + 0.606 × ΔCthsa-miR-27 + 0.057 × ΔCthsa-miR-4499; The novel method for diagnosing liver cancer according to claim 2, characterized in that a comprehensive judgment value of ≧0 is positive, indicating a relatively high risk of liver cancer; a comprehensive judgment value of <0 is negative, indicating a relatively low risk of liver cancer.

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