Marker for detecting liver cancer and application
By detecting C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, Znf541 CFRNA, it solves the problem of early diagnosis of liver cancer in the existing technology, and achieves early diagnosis of efficient and accurate liver cancer, and has good clinical application prospects.
Patent Information
- Application Number
- CN202411732580.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Priority Date
- 2023-12-06
- Filing Date
- 2024-11-29
- Publication Date
- 2025-01-28
AI Technical Summary
Existing liver cancer diagnosis methods are difficult to detect liver cancer or high-risk people in the early stage. Traditional methods are mostly used for middle and late stage diagnosis, and there is a traumatic risk and false negative rate.
By detecting C1QTNF4, Setbp1, HMGA1, PCDHB3, CYBA, ZNF541 CFRNA, as early diagnosis of liver cancer as a logo. These genes can be used alone or in combination, and the existence of these logo in the samples through the kit can achieve efficient and accurate early diagnosis.
The verification of this method in patients with liver cancer and health groups shows that there are significant differences in the level of logo, high sensitivity, and fast detection speed. It is suitable for detecting early liver cancer patients from high -risk groups of hepatitis B, reducing the risk of trauma, suitable for large -scale people screening Essence
Smart Images
Figure CN119372318A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of liver cancer diagnosis, and in particular to markers and applications for detecting liver cancer. Background Art
[0002] Cancer is a major disease that poses a serious threat to human life and social development. Since the 20th century, the number of cancer patients has been increasing. Hepatocellular carcinoma (HCC) is the most common malignant tumor of the digestive system, with a poor prognosis and an increasing mortality rate. It has become one of the most common malignant tumors in the world. A variety of risk factors may lead to genetic or phenotypic changes and cause the progression of liver cancer, including viruses, fibrosis, metabolic disorders, and alcohol. Early HCC patients can be treated with resection, liver transplantation, or ablation, and the 5-year survival rate of patients can reach more than 50%.
[0003] Traditional methods for diagnosing liver cancer include imaging examinations, hematological examinations, and pathological examinations. Imaging examinations include B-ultrasound, CT, and MRI. These examinations are mainly for the display results after the lesions occur in the patient's lesions. They are mostly used for screening and diagnosis of patients diagnosed with cancer, and cannot screen out early patients or people at high risk of cancer; commonly used markers for hematological examinations include alpha-fetoprotein (AFP), but it is not only a diagnostic marker for liver cancer, but also exists in the blood of patients with other types of tumors; pathological examinations, such as puncture biopsy, are another method for diagnosing liver cancer, but they are traumatic examinations and there is a risk of bleeding or needle tract metastasis. At the same time, the pathological diagnosis of liver puncture has a certain degree of false negatives. In the existing diagnostic methods, the confirmed results mostly occur in the middle and late stages of liver cancer, and early detection, early diagnosis, and early treatment of liver cancer are the key to improving the efficacy of liver cancer.
[0004] Therefore, developing a method for early diagnosis of liver cancer and timely screening out people with early or high-risk liver cancer, thereby achieving the goal of early intervention and treatment, will have important clinical application value. Summary of the invention
[0005] In response to the above technical problems, the purpose of the present application is to provide a liver cancer marker and its application. The marker is 6 gene molecules, namely C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541. The 6 gene molecules can be used as diagnostic markers alone or as a combination as diagnostic markers. By detecting whether these markers are present in the sample, early diagnosis of liver cancer can be performed more efficiently and accurately.
[0006] To achieve the above objectives, this application provides the following technical solutions:
[0007] In a first aspect, an embodiment of the present application provides a marker for diagnosing liver cancer, wherein the marker includes at least one of six cfRNAs: C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541.
[0008] In a second aspect, the present application provides a nucleic acid molecule composition, which includes nucleotide sequences of six cfRNAs: C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541.
[0009] In a third aspect, the present application provides a kit for detecting the aforementioned markers or nucleic acid molecule compositions.
[0010] In a fourth aspect, the embodiments of the present application provide the use of the aforementioned markers or nucleic acid molecule compositions in the preparation of liver cancer diagnostic reagents.
[0011] The present application provides a marker for detecting liver cancer and its application. Compared with the prior art, the present application has at least the following advantages:
[0012] Beneficial effects:
[0013] (1) This application was verified during the course of liver cancer patients, as well as in healthy people, HCC patients and liver cancer patients, and it was proved that the levels of these markers were significantly different between the groups and were stable, indicating that the markers have high sensitivity and fast detection speed, and are suitable for detecting early liver cancer patients who have not yet shown clinical symptoms from the high-risk population of hepatitis B, thereby achieving the effect of early detection.
[0014] (2) The markers provided in this application provide more accurate and sensitive auxiliary diagnostic information for the early diagnosis of liver cancer in people at high risk of liver cancer, further realizing individualized diagnosis and treatment of liver cancer; diagnosis can be achieved through blood testing without the need for additional tissue sample collection, greatly reducing the risk of trauma, and is more suitable for large-scale population screening of early liver cancer, with good clinical use and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] Figure 1 Tissue deconvolution analysis of cfRNA in 16 normal serum samples provided in the examples of the present application.
[0016] Figure 2 The volcano plot analysis of differentially expressed genes between HCC and normal serum samples provided in the examples of the present application.
[0017] Figure 3 The overlapping features of differentially expressed genes between HCC and normal serum samples provided in the examples of the present application.
[0018] Figure 4This is an enrichment analysis of the differentially expressed gene set between HCC and normal serum samples provided in the examples of the present application.
[0019] Figure 5 A flowchart of the identification process of candidate cfRNA markers provided in the embodiments of the present application.
[0020] Figure 6 The expression level distribution diagram of 14 candidate cfRNAs in HCC, CHB and normal training groups provided in the examples of the present application.
[0021] Figure 7 1000 iterations of Lasso regression analysis of the candidate genes provided in the embodiments of the present application.
[0022] Figure 8 The expression level distribution diagram of the 6 candidate cfRNAs provided in the examples of the present application in HCC, CHB and normal training groups.
[0023] Fig. 9 The expression levels of the six candidate cfRNAs provided in the examples of the present application in HCC, CHB and normal training groups.
[0024] Fig.10 The expression levels of the 6 candidate cfRNAs provided in the examples of this application in the target GTEx dataset tissues.
[0025] Fig.11 This is a ROC curve analysis diagram of the 6 candidate cfRNAs provided in the examples of the present application.
[0026] Fig.12 The ROC curve analysis diagram of the 6 candidate cfRNAs provided in the examples of the present application for distinguishing HCC from CHB and normal groups.
[0027] Fig.13 The distribution of candidate cfRNA in HCC, CHB and healthy tissue samples provided in the embodiments of the present application.
[0028] Fig.14 The ROC curve analysis results of candidate cfRNA in HCC, CHB and healthy tissue samples provided in the examples of the present application. DETAILED DESCRIPTION
[0029] In order to make the purpose, technical solution and advantages of the present application more clear, the present application is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described here are only used to explain the present application and are not used to limit the present application.
[0030] The present application example analyzes the overall biological functions of differentially expressed cfRNAs between liver cancer and healthy controls through gene set enrichment analysis. In cancer, upregulated cfrRNAs are most significantly enriched in the activation of cancer pathways (e.g. Figure 1 In HCC samples, the increased cfRNA may not only come from tumor tissue, but it is still involved in the tumor progression pathway. Therefore, serum cfRNA contains liver cancer-specific cfRNA and has the potential to serve as a diagnostic biomarker for HCC.
[0031] Based on this, an embodiment of the present application provides a marker for diagnosing liver cancer, wherein the marker includes at least one of C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541 genes.
[0032] In certain embodiments, the markers include C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, ZNF541 genes.
[0033] Based on this, an embodiment of the present application provides a nucleic acid molecule composition, which includes the nucleotide sequences of C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541 genes.
[0034] Based on this, an embodiment of the present application provides a kit for detecting the aforementioned markers or nucleic acid molecule compositions.
[0035] Based on this, the embodiments of the present application provide the use of the aforementioned markers or nucleic acid molecule compositions in the preparation of liver cancer diagnostic reagents.
[0036] The following will illustrate the substantial features and significant effects of the present technical solution through more specific embodiments. It should be understood that the enumerated embodiments should not be construed as limiting the technical solution of the present application.
[0037] Screening of candidate cfRNA
[0038] In order to identify the tissue source of cfRNA, the present invention performs tissue deconvolution analysis of cfRNA on samples from healthy individuals. The results are as follows: Figure 1As shown in the figure, half of the cfRNA comes from blood cells circulating in the blood, and is mainly expressed in the transcriptional expression of lung, liver and kidney cells, etc. These organs have rich blood supply and important functions of blood metabolism. This is similar to the report on the source of cfDNA in plasma. Blood cells are the main contributors to the plasma cfDNA pool, followed by the placenta (in pregnant women) and liver. This shows that part of the cfRNA in the blood is derived from the expression of liver tissue cells, but this part of the cfRNA is covered by the transcription of blood cells.
[0039] The present invention example found that a considerable amount of cfRNA expression was upregulated in HCC samples compared with the healthy control group (e.g. Figure 2 However, in HCC tissues, these upregulated cfRNAs rarely overlap with upregulated mRNAs (e.g. Figure 3 As shown). There are two reasons for the low overlap: the first reason is that cell-free RNA (cfRNA) can represent a transcriptional mixture that reflects the health status of multiple tissues and therefore has a wide range of clinical utility; the other reason is that the present embodiment sets |logFC|>1 in order to obtain cfRNA with a greater degree of difference; at the same time, the present embodiment sets the base average value to more than 10 to obtain cfRNAs with relatively high base expression, thereby excluding a large number of cfRNAs in liver cancer tissues that may overlap with mRNA. Therefore, in HCC tissues, upregulated cfRNAs rarely overlap with upregulated mRNAs.
[0040] The present application example analyzes the overall biological functions of differentially expressed cfRNAs between liver cancer and healthy controls through gene set enrichment analysis. In cancer, upregulated cfrRNAs are most significantly enriched in the activation of cancer pathways (e.g. Figure 4 In HCC samples, the increased cfRNA may not only come from tumor tissue, but it is still involved in the tumor progression pathway. Therefore, serum cfRNA contains liver cancer-specific cfRNA, which has the potential to serve as a biomarker for HCC diagnosis or early liver cancer diagnosis.
[0041] Chronic hepatitis B (CHB) causes nearly 40% of hepatocellular carcinoma (HCC), so it is particularly important to monitor the progression of liver cancer in CHB patients. The present application example uses samples from CHB patients and healthy individuals to identify HCC-specific diagnostic biomarkers (such as Figure 5As shown in the figure, it can be seen that the present application example collected serum samples from 14 HCC patients, 17 CHB patients and 8 healthy individuals from Zhongnan Hospital as a training set (the population characteristics are listed in Table 1). cfRNA was extracted from 0.5 mL of serum from 39 serum samples for sequencing, and each sequencing library used about 10 ng of cfRNA.
[0042] Table 1
[0043]
[0044]
[0045] In the present application example, a total of 114 down-regulated cfRNAs and 559 up-regulated cfRNAs were identified in HCC and healthy tissue samples, such as Figure 3 As shown in Figure 2, 6878 downregulated cfRNAs and 293 upregulated cfRNAs were identified in HCC and CHB samples; in the TCGA target gene GTEx dataset, 5151 genes were downregulated and 4339 genes were upregulated between HCC tumor tissues and non-tumor tissues ( Figure 2 In order to obtain liver tissue cfRNA, the present application example collected the above-mentioned overlapping differentially expressed gene serum samples and tissue samples, and obtained 9 down-regulated genes and 16 up-regulated genes (such as Figure 3 Then, the present application example limited the basal expression level of cfRNA in blood and mRNA in tissues (cfRNA base average > 10, expressed in at least 80% of the samples), excluded some low-expression genes, and obtained 2 down-regulated genes and 12 up-regulated genes, as shown in Figure 2. Figure 6 As shown, the expression levels of cfRNA in these three groups of 14 genes were significantly different in HCC and non-HCC samples. It is worth noting that H19 and RP5-1171I10.5 are lncRNAs and the remaining 12 genes are protein-coding RNAs. The incorporation of lncRNAs is due to the comprehensive and efficient cfRNA extraction capability of the MOF method, which can obtain more than 90% of lncRNAs, but the kit method can only obtain a small amount of cfRNA, thereby losing many potential biomarkers.
[0046] In the present embodiment, to generate a diagnostic HCC model, the candidate cfRNAs were screened for HCC diagnosis by least absolute shrinkage and selection operator (lasso) regression analysis with 1000 iterations, and the coefficients obtained by lasso regression were used as weights to generate cfRNA scores for HCC diagnosis. Figure 7As shown in Figure 2, the combination of C1QTNF4, SETBP1, CYBA, PCDHB3, HMGHA1, ZNF541, and GBP2 is the most common. However, the diagnostic performance of the above 7 cfRNAs is only slightly improved compared with the first 6 cfRNAs, and significantly improved compared with the first 5 cfRNAs. Therefore, in the embodiment of the present application, the cfRNA score is generated based on the first 6 cfRNAs, that is, cfRNA-score = (-0.65310) * C1QTNF4 + 0.15879 * CYBA + 0.27373 * HMGA1 + 0.29307 * PCDHB3 + (-0.21822) * SETBP1 + 0.12482 * ZNF541 + (-2.25578). The 6 cfRNA markers and cfRNA scores screened in the embodiment of the present application show the ability to distinguish HCC from CHB and healthy tissue samples (such as Figure 8 , Fig. 9 As shown in Fig.10 As shown in Figure 3, the differential expression characteristics of these six genes in HCC tissues were also significantly present compared with healthy tissues. Fig. 9 and Fig.10 In the study, subtle differences in gene expression patterns between blood and tissues can be observed. The reason for this phenomenon is that half of the cfRNA is present in the blood circulation and comes from blood cells, and part comes from the liver. In addition, not all RNA from liver tumor cells enters the blood, but is selective. Therefore, there are subtle differences in the expression profiles of these six genes in blood and tissues.
[0047] In the embodiment of the present application, the library is constructed by extracting cfRNA from body fluids such as blood or bile using a reference kit (SMARTer Stranded Total RNA-Seq Kit-Pico Input Mammalian, Takara Bio USA, Inc., 635005); the library collects transcriptome sequencing data using the Illumina NovaSeq 6000 sequencing platform, and performs quantitative analysis and differential expression analysis of transcripts; by comparing the RNA expression levels between different samples, transcriptome markers associated with the disease can be screened out.
[0048] like Fig.11 As shown, the performance of the six cfRNA markers for diagnosing HCC was evaluated using the area under the receiver operating characteristic curves (AUROCs), with the AUROCs for distinguishing HCC from CHB and healthy tissues being 0.997, and CHB and healthy tissues being 0.996 and 1.00, respectively (see Figure 2). Fig.12 shown).
[0049] Validation of HCC-specific cfRNA biomarkers
[0050] The present embodiment was validated in an independent cohort of 19 serum samples (HCC, n=7; CHB, n=4; healthy, n=8), and the population characteristics are shown in Table 2.
[0051] Table 2
[0052]
[0053]
[0054] like Fig.13 , 14 As shown, the results showed that the above model effectively separated HCC and control samples (CHB and healthy tissue samples) with good diagnostic efficiency (AUROC=0.905). Therefore, the changes in candidate cfRNA can be used for non-invasive diagnosis of HCC patients.
[0055] In summary, the C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541 genes provided in the examples of the present application can be used as markers for diagnosing early HCC, have high diagnostic sensitivity, fast detection speed, are suitable for the development of products for diagnosing HCC, and have good clinical application prospects.
[0056] The above is only a preferred specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed in the present application should be covered within the protection scope of the present application.
Claims
1. A marker for diagnosing liver cancer, characterized in that: The markers include at least one of the six cfRNAs: C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541.
2. The marker according to claim 1, characterized in that The markers include 6 cfRNAs: C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541.
3. A nucleic acid molecule composition, characterized in that: The nucleic acid molecule composition includes nucleotide sequences of six cfRNAs, namely, C1QTNF4, SETBP1, HMGA1, PCDHB3, CYBA, and ZNF541.
4. A kit, characterized in that: The kit is used to detect the marker according to claim 1 or the nucleic acid molecule composition according to claim 3.
5. Use of the marker according to claim 1 or the nucleic acid molecule composition according to claim 3 in the preparation of a liver cancer diagnostic reagent.