Liver-cancer-related serum microrna marker and new method for diagnosing liver cancer

Abstract

Provided in the present invention are a liver-cancer-related serum microRNA marker and a new method for diagnosing liver cancer. The liver-cancer-related serum microRNA marker is composed of hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27 and hsa-miR-4499. The relative expression level of each liver-cancer-related serum microRNA marker in serum is detected by means of a real-time PCR method, and the detection reagent comprises primers of the above-described liver-cancer-related serum microRNA markers. The present invention can be used for diagnosing hepatocellular carcinoma, especially early hepatocellular carcinoma; or for distinguishing serum of at least one hepatocellular carcinoma patient from serum of at least one healthy individual, serum of at least one chronic hepatitis B patient or serum of at least one liver cirrhosis patient.

Description

Liver cancer-related serum microRNA markers and a new method for diagnosing liver cancer Technical Field

[0001] The present invention belongs to the fields of genetic engineering and clinical medicine, and relates to a serum microRNA marker related to human liver cancer and a new method for diagnosing liver cancer. The serum microRNA includes hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499. Background Art

[0002] Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. According to data released by the World Health Organization, in 2020, there were approximately 19.29 million new cancer cases and 9.96 million deaths worldwide. Malignant tumors are a major threat to human health and impose a heavy economic burden on society. The development of liver cancer is a multifactorial, multi-stage process. Globally, chronic HBV and HCV infection are major risk factors for primary hepatocellular carcinoma (HCC). Chronic HBV infection, in particular, shows high regional consistency in the incidence of HCC, accounting for 75% of liver cancer cases worldwide and as much as 85% in developing countries. Liver cancer is one of the most common malignant tumors in my country. According to the 2018 "China Liver Cancer Big Data Report," 841,000 new cases of primary liver cancer are reported annually worldwide, with 393,000 occurring 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 my country. Liver cancer primarily includes hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). The HCC accounts for 83.9% to 92.3% of all HCC cases, and approximately 85% of HCC patients are infected with HBV. The five-year relative survival rate for HCC in my country is only 12.1%. However, early diagnosis and effective treatment can achieve a relatively good prognosis, especially for small HCCs less than 5 cm in diameter, which can reach a five-year survival rate of over 80%. Currently, HCC screening is suboptimal, with relatively low detection and early diagnosis rates. Developing a broad-based early screening method is crucial for the effective prevention and treatment of HCC in my country.

[0003] Liver cancer has no obvious symptoms in its early stages. The traditional early screening method involves combining liver cancer serological diagnostic marker alpha-fetoprotein (AFP) with liver ultrasound examination for high-risk groups. As the most widely used serological marker, alpha-fetoprotein plays an important role in the diagnosis and monitoring of liver cancer, but its detection rate for early liver cancer is not satisfactory. Current domestic and international studies have shown that alpha-fetoprotein (AFP) still lacks specificity and sensitivity. Clinically, approximately 30% to 40% of patients are alpha-fetoprotein negative. The sensitivity of ultrasound testing is related to the location of the tumor, and it requires a high level of skill from the examining physician, all of which contribute to the difficulty of early screening and diagnosis of liver cancer.

[0004] MicroRNA (miRNA) is a family of evolutionarily conserved, single-stranded RNA molecules of 20 to 25 nucleotides in length, classified as endogenous noncoding RNA. Through imperfectly complementary pairing with target gene mRNAs, miRNAs inhibit protein translation or promote mRNA degradation. The regulatory mechanisms of miRNAs are complex and diverse. By regulating target gene expression, miRNAs can direct a range of biological processes, such as embryonic development, cell growth, apoptosis, and cell differentiation. As tumor markers, miRNAs possess the following five advantages: ① miRNAs are stably expressed and maintained in normal human peripheral blood, with no significant individual variability; ② miRNAs in serum or plasma are not easily degraded by incubation at room temperature for 24 hours or longer, repeated freeze-thaw cycles, or exposure to acidic or alkaline conditions; ③ Changes in miRNA expression levels are closely correlated with the pathological process of malignant tumors; ④ Abnormal expression of miRNAs in serum can serve as direct evidence of the presence of tumor cells, making them valuable biomarkers; ⑤ Compared to protein markers, miRNAs offer not only higher detection accuracy but also greater ease of simultaneous multi-component detection, making them significantly superior. miRNAs are involved in numerous cellular processes in the human body, such as proliferation, apoptosis, and migration. Moreover, miRNA plays an important role in the proliferation and metastasis of cancer and can be used as a biomarker for tumor occurrence, development and prognosis.

[0005] In previous studies, our company discovered for the first time that hsa-miR-2115 and hsa-miR-4499 showed significant differences in serum expression levels in liver cancer patients compared to controls. These findings suggest that hsa-miR-2115 and hsa-miR-4499 regulate liver cancer cell activity, providing guidance for early diagnosis of liver cancer and demonstrating their potential as biomarkers for the early diagnosis of HCC. By combining hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499, we can further enhance diagnostic efficacy and value. The combined application of multiple miRNA biomarkers allows for rapid, accurate, and cost-effective early diagnosis of HCC patients.

[0006] References: [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.

[0007] [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.

[0008] [3] Wang C, Hann H W, 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.

[0009] [4]Shaker O, Alhelf M, Morcos G, et al.miRNA-101-1and miRNA-221expressions 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.

[0010] Summary of the Invention

[0011] The purpose of the present invention is to provide a group of liver cancer-related serum microRNA markers and a new method for diagnosing liver cancer, which can further improve the diagnosis effect of liver cancer and enhance the diagnostic value.

[0012] The technical solution adopted in the present invention is as follows:

[0013] The present invention provides a liver cancer-related serum microRNA marker, characterized in that the liver cancer-related serum microRNA marker is composed of hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27 and hsa-miR-4499; wherein 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), and the sequence of hsa-miR-27 is ttcacagtggctaagttccgc (SEQ ID No. 5). No.5), and the sequence of hsa-miR-4499 is gattgctctgcgtgcggaatcgac (SEQ ID No.6).

[0014] The present invention provides a novel method for diagnosing liver cancer, characterized in that, after the serum to be tested is sampled and processed, the relative expression levels of liver cancer-related serum microRNA markers in the serum are measured by a real-time PCR method; a reagent for detecting 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 above-mentioned markers, and is specifically composed as follows: the sequence of the upstream primer hsa-miR-122-F of the 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 of 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 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 sequence of the upstream primer hsa-miR-27-F of the 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 the marker hsa-miR-4499 (SEQ ID No.6) is gattgctctgcgtgcggaatc (SEQ ID No.12); the universal downstream primers for the above markers are Universal The Rp sequence is cagtgcagggtccgaggt (SEQ ID No. 13).

[0015] The present invention provides a novel method for diagnosing liver cancer, characterized by measuring 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, and inputting the measured ΔCt values ​​into a discriminant formula:

[0016] Comprehensive judgment value = -1.772 + 0.097*ΔCt hsa-miR-122 + 0.295*ΔCt hsa-miR-21 -1.123*ΔCt hsa-miR-2115 -0.322*ΔCt hsa-miR-221 + 0.606*ΔCt hsa-miR-27 + 0.057*ΔCt hsa-miR-4499; comprehensive judgment value ≥ 0, positive, high risk of liver cancer; comprehensive judgment value < 0, negative, low risk of liver cancer.

[0017] The liver cancer-related serum microRNA markers of the present invention are selected from multiple groups including hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27 and hsa-miR-4499.

[0018] To facilitate detection and use, the reagents and tools used to measure liver cancer-related serum microRNA markers were prepared into a kit. The reagents included are capable of measuring the expression levels of these serum microRNA markers in serum, and contain primers for the above-mentioned 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). The specific implementation is as follows:

[0019] A human liver cancer diagnostic kit, comprising primers for the liver cancer-related serum microRNA markers, and measurement 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, respectively, comprising upstream primers and downstream primers. The six primer pairs are as follows: the upstream primer hsa-miR-122-F for the marker hsa-miR-122 (SEQ ID No. 1) has a sequence of gccgagtggagtgtgacaatg (SEQ ID No. 7), and the universal downstream primer Universal Rp has a sequence of cagtgcagggtccgaggt (SEQ ID No. 13);

[0020] The upstream primer hsa-miR-21-F of the marker hsa-miR-21 (SEQ ID No. 2) has a sequence of tcggcaggtagcttatcagac (SEQ ID No. 8), and the universal downstream primer Universal Rp has a sequence of cagtgcagggtccgaggt (SEQ ID No. 13);

[0021] 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), and the sequence of the universal downstream primer Universal Rp is cagtgcagggtccgaggt (SEQ ID No. 13);

[0022] The upstream primer hsa-miR-221-F of the marker hsa-miR-221 (SEQ ID No. 4) has a sequence of gccgagagctacattgtctgc (SEQ ID No. 10), and the universal downstream primer Universal Rp has a sequence of cagtgcagggtccgaggt (SEQ ID No. 13);

[0023] The sequence of the upstream primer hsa-miR-27-F of 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);

[0024] The sequence of the upstream primer hsa-miR-4499-F of 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).

[0025] The other reagents in the kit except the primers can be commonly used reagents for the corresponding detection technology in the prior art.

[0026] The inventors collected standard blood samples according to standard operating procedures (SOPs), systematically collected complete population basic information and clinical data, and used RT-PCR, TaqMan miRNA Array, and Real-time PCR (TaqMan probe) methods for detection.

[0027] Specifically, the experimental methods of the study mainly include the following parts:

[0028] 1. Selection of research subjects and basis for grouping

[0029] Group A: control group (n=345, 150 healthy controls, 150 chronic hepatitis B patients, and 145 cirrhosis patients), without other major systemic diseases;

[0030] Group B: patients with liver cancer (n=233) without other major systemic diseases.

[0031] 2. Blood serum separation and pretreatment

[0032] (1) Draw 2 ml of peripheral blood into a separating gel blood collection tube and gently mix by inverting 180° for 5-6 times. Within 12 hours, centrifuge the blood collection tube at 3,000 g for 10 minutes. Then, transfer the supernatant to a clean tube and store at -80°C.

[0033] (2) Total RNA was extracted using the miRNeasy Serum / Plasma Advanced Kit according to the manufacturer's instructions (Qiagen), and its concentration was quantified using a Qubit 4.0 fluorescence quantifier.

[0034] 3. Real-time PCR method to measure serum miRNAs expression

[0035] 1. Take the pre-treated serum and obtain cDNA sample by RNA reverse transcription reaction; prepare the reverse transcription system as shown in the following table:

[0036] 2. Invert the PCR tube 8 times to mix thoroughly, then centrifuge briefly and place on ice for 3 minutes.

[0037] 3. Place the PCR tube into a gene amplifier for reverse transcription. The reaction conditions are as follows: 42°C, 60 min; 85°C, 5 min; 4°C, forever;

[0038] The reverse transcription products were stored in a refrigerator at 4°C for the next step of pre-amplification.

[0039] 4. After reverse transcription, the cDNA was pre-amplified using the reaction system in the table below:

[0040] The reaction conditions for pre-amplification are shown in the following table:

[0041] After cooling to 4°C, the pre-amplification product was stored at 4°C for use in the next Real-time PCR reaction.

[0042] 5. Briefly centrifuge the pre-amplified product, then add 75 μl of 0.1× TE (pH 8.0), invert and mix thoroughly, and briefly centrifuge again. The pre-amplified product can be used directly in the following real-time PCR.

[0043] For real-time PCR of pre-amplified products, prepare the reaction system as shown in the following table:

[0044] 6. Detect and compare the differences in miRNAs expression levels in serum samples of healthy control group and liver cancer patients.

[0045] The differentially expressed serum miRNAs detected between the control group and HCC 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.

[0046] The copy numbers of these miRNAs in HCC patients were significantly higher than those in the control group.

[0047] 4. Verification of serum miRNA expression using real-time PCR in the training set

[0048] 1. Design primers for 13 target miRNAs: Use the PolyA-tailed PCR method to design primers.

[0049] 2. Real-time PCR was performed by adding fluorescent probes. The expression levels of miRNAs in serum samples from healthy controls, chronic hepatitis B patients, cirrhosis patients, and liver cancer patients were detected and compared (68 healthy controls, 62 chronic hepatitis B patients, 61 cirrhosis patients, and 101 liver cancer patients).

[0050] 3. After real-time PCR detection, the results of 6 miRNAs (abbreviated as 6miRNAs) were consistent, namely: hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499.

[0051] Therefore, the serum miRNAs finally confirmed to be differentially expressed in the healthy control group 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).

[0052] The above miRNAs with significant expression differences were included in the logistics regression equation, and the result was: 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.

[0053] 5. Evaluating the performance of the above miRNAs combination in the validation set

[0054] The performance of the 6-miRNA panel was validated in 298 independent serum samples (70 healthy controls, 65 chronic hepatitis B patients, 63 cirrhosis patients, and 100 HCC patients) to evaluate the ability of the 6-miRNA panel to predict HCC patients.

[0055] VI. Preparation of diagnostic kit

[0056] Based on the above experimental results, the inventors also developed a microRNA diagnostic kit for human liver cancer. The kit contains primers and tools for detecting mature hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499 detectable in serum. The kit includes a batch of serum miRNA primers and may also include Taq enzyme, deoxynucleotide triphosphates, and other reagents. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] FIG1 is a flowchart of the experimental design of the present invention for identifying at least one hepatocellular carcinoma target serum, particularly a microRNA combination present in early hepatocellular carcinoma (BCLC stage 0 and A), during the screening, training, and validation stages.

[0058] Figure 2 is a diagram illustrating the main steps of the method for determining the microRNA profile in the blood of patients with hepatocellular carcinoma, particularly those with early-stage hepatocellular carcinoma (BCLC stage 0 and A). The control group included healthy subjects, subjects with chronic hepatitis B, and subjects with cirrhosis.

[0059] FIG3 illustrates a logistic regression model comprising a preferred microRNA combination (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499) for determining at least one hepatocellular carcinoma target serum of the present invention;

[0060] Figure 3-A: Receiver operating characteristic (ROC) curves showing the diagnostic value of the microRNA panel in the HCC group compared to the control group in the training set (n=292). Compared to AFP (AUC=0.801), the microRNA panel demonstrated significantly higher diagnostic accuracy (AUC=0.937) in differentiating HCC sera from control sera.

[0061] Figure 3-B: ROC curve of the diagnostic value of the microRNA panel in the HCC group compared with the control group in the validation set (n=298). Compared with AFP (AUC=0.747), the microRNA panel had significantly higher diagnostic accuracy in distinguishing HCC group sera from control group sera (AUC=0.938).

[0062] FIG4 illustrates a logistic regression model comprising a preferred microRNA combination (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499) of the present invention for further distinguishing serum from hepatocellular carcinoma patients and serum from healthy individuals, chronic hepatitis B patients, or cirrhosis patients;

[0063] Figure 4-A: Receiver operating characteristic (ROC) curves of the diagnostic value of the microRNA panel in the HCC group compared with the healthy control group in the validation set (n=298). Compared with AFP (AUC=0.793), the microRNA panel demonstrated significantly higher diagnostic accuracy (AUC=0.990) in distinguishing serum from HCC patients and healthy individuals.

[0064] Figure 4-B: Receiver operating characteristic (ROC) curves of the diagnostic value of the microRNA panel in the HCC group compared with the chronic hepatitis B group in the validation set (n=298). Compared with AFP (AUC=0.734), the microRNA panel demonstrated significantly higher diagnostic accuracy (AUC=0.887) in distinguishing serum from serum of HCC patients and chronic hepatitis B patients.

[0065] Figure 4-C: ROC curve plot of the diagnostic value of the microRNA panel in the HCC group compared with the cirrhosis group in the validation set (n=298). Compared with AFP (AUC=0.712), the microRNA panel had significantly higher diagnostic accuracy in distinguishing between HCC patient sera and cirrhosis patient sera (AUC=0.826).

[0066] FIG5 illustrates a logistic regression model comprising a preferred microRNA combination (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499) of at least one target serum for identifying hepatocellular carcinoma of different BCLC stages of the present invention;

[0067] Figure 5-A: Receiver operating characteristic (ROC) curves showing the diagnostic value of the microRNA panel for very early-stage HCC (BCLC 0) compared to controls. Compared to AFP (AUC = 0.670), the microRNA panel demonstrated significantly higher diagnostic accuracy (AUC = 0.955) in distinguishing serum from patients with very early-stage HCC and controls.

[0068] Figure 5-B: Receiver operating characteristic (ROC) curve of the diagnostic value of the microRNA panel for early-stage HCC (BCLC A) compared with controls. Compared with AFP (AUC = 0.719), the microRNA panel demonstrated significantly higher diagnostic accuracy (AUC = 0.982) in distinguishing serum from early-stage HCC patients and controls.

[0069] Figure 5-C: Receiver operating characteristic (ROC) curve of the diagnostic value of the microRNA panel for patients with intermediate-stage HCC (BCLC B) compared to controls. Compared to AFP (AUC = 0.815), the microRNA panel demonstrated significantly higher diagnostic accuracy (AUC = 0.912) in distinguishing serum from patients with intermediate-stage HCC and controls.

[0070] Figure 5-D: ROC curve of the diagnostic value of the microRNA panel when comparing advanced HCC (BCLC C) with the control group. Compared with AFP (AUC = 0.830), the microRNA panel had a much higher diagnostic accuracy (AUC = 0.955) in distinguishing serum from serum of patients with advanced HCC and controls.

[0071] FIG6 illustrates a logistic regression model comprising a preferred microRNA combination (hsa-miR-122, hsa-miR-21, hsa-miR-2115, hsa-miR-221, hsa-miR-27, and hsa-miR-4499) for identifying hepatocellular carcinoma with AFP ≤ 20 ng / mL and AFP > 20 ng / mL according to the present invention;

[0072] Figure 6-A: Receiver Operating Characteristic (ROC) curve comparing the diagnostic value of the microRNA panel in the HCC group with AFP ≤ 20 ng / mL and the control group. Compared with AFP (AUC = 0.676), the microRNA panel demonstrated significantly higher diagnostic accuracy (AUC = 0.956) in distinguishing serum from the HCC group with AFP ≤ 20 ng / mL and the control group.

[0073] Figure 6-B: ROC curve of the diagnostic value of the microRNA panel when comparing the HCC group with AFP > 20 ng / mL and the control group. Compared with AFP (AUC = 0.940), the microRNA panel showed no significant difference in differentiating between the serum of the HCC group with AFP > 20 ng / mL and the control group (AUC = 0.968).

[0074] Figure 7 is Table 2. Characteristics of microRNA chip subjects;

[0075] Figure 8 is Table 3. Characteristics of subjects in the training and validation sets;

[0076] Figure 9 shows Table 6. MicroRNA expression and diagnostic efficacy in the training set. DETAILED DESCRIPTION

[0077] The present invention is further illustrated by the following examples.

[0078] Example 1 Selection and Grouping Basis of Research Subjects

[0079] The experimental design of the screening, training, and validation phases of the microRNA biomarkers of the present invention is shown in Figure 1. The main steps of the method for identifying serum samples of patients with hepatocellular carcinoma using the recommended serum microRNA combination are shown in Figure 2.

[0080] Between January 2022 and May 2023, 688 blood samples meeting the eligibility criteria (Table 1) were collected from Qilu Hospital of Shandong University and Shandong Provincial Hospital. These samples were obtained from 150 healthy donors (HC healthy group), 150 patients with chronic hepatitis B (CHB group), 145 patients with HBV-induced cirrhosis (LC cirrhosis group), and 233 patients with HBV-related HCC (HCC group). These samples were chronologically divided into three stages (Figure 1). The clinical manifestations of the patients are summarized in Tables 2 and 3, with details in Figures 7 and 8.

[0081] Table 1 - Eligibility criteria for patient selection

[0082] Example 2 Blood serum separation and pretreatment

[0083] Draw 2 ml of peripheral blood into a separating gel blood collection tube and gently mix by inverting 180° for 5-6 times. Within 12 hours, centrifuge the procoagulant blood collection tube at 3,000 g for 10 minutes. Transfer the supernatant to a clean tube and store at -80°C.

[0084] Total RNA was extracted using the miRNeasy Serum / Plasma Advanced Kit according to the manufacturer's instructions (Qiagen), and its concentration was quantified using a Qubit 4.0 fluorometer.

[0085] Example 3: Analysis of miRNA expression differences using microRNA chips

[0086] MicroRNA microarray analysis compared microRNA expression levels in HCC, healthy controls, and the combined CHB and cirrhosis groups. Serum miRNAs identified as differentially expressed in HCC, healthy controls, CHB, and cirrhosis 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 shown in Table 4). The copy numbers of these miRNAs were significantly higher in HCC patients than in healthy controls, CHB patients, and cirrhosis patients.

[0087] Table 4. microRNA sequences

[0088] Example 4 Verification of microRNAs screened by microRNA chip in a training set of 292 samples

[0089] Primers were designed (miRNA primer sequences are shown in Table 5 ), and quantitative real-time PCR detection of miRNAs was performed on the sera of 68 healthy controls, 62 chronic hepatitis B patients, 61 cirrhosis patients, and 101 liver cancer patients.

[0090] (1) Preparation of cDNA samples: (a) Using the miRNeasy Serum / Plasma Advanced Kit (Qiagen), small RNA was extracted according to the manufacturer's instructions, and its concentration was quantified using a Qubit 4.0 fluorescence quantifier. (b) The small RNA was tailed using a tailing reagent (E. coli Poly(A) Polymerase, Vazyme). (c) The tailed small RNA was reverse transcribed using a reverse transcription reagent ( II Reverse Transcriptase, Vazyme) to obtain cDNA.

[0091] (2) Real-time PCR: Taq Pro U Multiple Probe qPCR MIX, Vazyme was used to detect and compare the changes in miRNA expression in serum samples of the control group (healthy controls, chronic hepatitis B patients, and cirrhosis patients) and liver cancer patients. The serum miRNA expression levels of each group of samples were detected using RNU6-1 as the internal reference.

[0092] The HCC group and the control group were compared using the Kruskal-Wallis test, and the unpaired Mann-Whitney 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. The predicted probability of HCC was used as a surrogate marker to construct a receiver operating characteristic (ROC) curve. The area under the ROC curve (AUC) was used to evaluate the diagnostic efficacy of the serum AFP and microRNA combination. ROC and regression analyses were performed using MedCalc (19.8) software. 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 HCC patients.

[0093] The expression profiles and diagnostic efficacy of the six candidate microRNAs in the training set are shown in Table 6 of Figure 9 , where the 6-microRNA combination *AUC = 0.937 (0.940, 0.912)

[0094] The above miRNAs with significant expression differences were included in the logistics regression equation, and the result was: *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.

[0095] The AUC of the microRNA combination was significantly greater than that of AFP (0.937 vs. 0.801, p < 0.001, FIG3-A ), see FIG3 .

[0096] Table 5 miRNA primer sequences

[0097] Example 5 validated the above microRNA combination in a validation set of 298 samples.

[0098] For this embodiment, please refer to FIG. 3 and FIG. 4 .

[0099] 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). Similarly, quantitative RT-PCR was used for the experiments. The predicted probabilities were used to construct receiver operating characteristic curves.

[0100] Comparison of the AUC between the microRNA panel and AFP in the validation set showed that the diagnostic accuracy of the microRNA panel was significantly higher than that of AFP (AUC: 0.938 vs. 0.747, p < 0.001, Figure 3-B).

[0101] The ability of the microRNA panel to distinguish HCC from healthy controls, chronic hepatitis (CHB) controls, and cirrhosis was also compared with that of AFP, as shown in Figure 4. This analysis demonstrated that both the microRNA panel and AFP could distinguish HCC from non-HCC. However, the AUC of the microRNA panel was significantly greater than that of AFP (HCC vs. healthy controls: 0.990 vs. 0.793, p < 0.001; HCC vs. chronic hepatitis (CHB): 0.887 vs. 0.734, p < 0.001; HCC vs. cirrhosis: 0.826 vs. 0.712, p < 0.001; Figures 4-A, 4-B, and 4-C).

[0102] Example 6 Diagnostic performance of microRNA combination and AFP in different BCLC stages

[0103] The diagnostic performance of the microRNA panel and AFP in different BCLC stages was further evaluated, as shown in Figure 5. In the diagnosis of early, intermediate, and advanced HCC (BCLC stages 0, A, B, and C), the microRNA panel had significantly higher diagnostic performance than 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 5-A, 5-B, 5-C, and 5-D).

[0104] Example 7 Diagnostic Performance of MicroRNA Combination in Normal AFP (≤20 ng / mL) and High AFP (>20 ng / mL) Groups

[0105] For Example 7, see Figure 6 . The diagnostic accuracy of the microRNA combination was assessed based on AFP levels. In the group with normal AFP (≤20 ng / mL), the AUC of the microRNA combination was significantly greater than that of AFP (0.956 vs. 0.676, p < 0.001, Figure 6-A). In the group with high AFP (>20 ng / mL), the AUC of the microRNA combination was not significantly different from that of AFP (0.968 vs. 0.940, p = 0.26, Figure 6-B).

[0106] The results obtained confirmed the specific expression of microRNAs in the serum of patients with hepatocellular carcinoma. Therefore, the microRNA set defined here can serve as a unique microRNA biomarker. Analysis of serum microRNA expression profiles in hepatocellular carcinoma can not only diagnose the development of liver cancer at an early stage, but also distinguish hepatocellular carcinoma from chronic hepatitis B and cirrhosis.

[0107] The present invention provides unique molecular markers for the identification and verification of serum microRNA expression biomarkers that can be used for screening, early diagnosis, and differential diagnosis of hepatocellular carcinoma in blood samples.

Claims

1. A serum microRNA marker associated with liver cancer, characterized in that: The liver cancer-related serum microRNA markers consisted 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 was tggagtgtgacaatggtgtttg (SEQ ID No. 1), the sequence of hsa-miR-21 was tagcttatcagactgatgttga (SEQ ID No. 2), the sequence of hsa-miR-2115 was catcagaattcatggaggctag (SEQ ID No. 3), the sequence of hsa-miR-221 was agctacattgtctgctgggtttc (SEQ ID No. 4), and the sequence of hsa-miR-27 was ttcacagtggctaagttccgc (SEQ ID No. 5). No.5), and the sequence of hsa-miR-4499 is gattgctctgcgtgcggaatcgac (SEQ ID No.6).

2. A new method for diagnosing liver cancer, characterized in that: After the serum to be tested is sampled and processed, the relative expression of the liver cancer-related serum microRNA markers in serum in right 1 is detected by the Real-time PCR method; the reagent for detecting 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 above markers, and the specific composition is as follows: the sequence of the upstream primer hsa-miR-122-F of the 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 of the marker hsa-miR-21 (SEQ ID No.2) is tcggcaggtagcttatcagac (SEQ ID No.8); the marker hsa-miR-2115 (SEQ ID The sequence of the upstream primer hsa-miR-2115-F of the marker hsa-miR-2115-F (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 sequence of the upstream primer hsa-miR-27-F of the 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 the marker hsa-miR-4499 (SEQ ID No.6) is gattgctctgcgtgcggaatc (SEQ ID No.12); the sequence of the universal downstream primer Universal Rp of the above markers is cagtgcagggtccgaggt (SEQ ID No.13).

3. A new method for diagnosing liver cancer according to claim 2, characterized in that: 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 were detected, and the ΔCt values ​​were measured and input into the discrimination formula: Comprehensive judgment value=-1.772+0.097*ΔCt hsa-miR-122+0.295*ΔCt hsa-miR-21-1.123*ΔCt hsa-miR-2115-0.322*ΔCt hsa-miR-221+0.606*ΔCt hsa-miR-27+0.057*ΔCt hsa-miR-4499; A comprehensive judgment value ≥ 0 is positive, indicating a higher risk of liver cancer; a comprehensive judgment value < 0 is negative, indicating a lower risk of liver cancer.

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