Combined markers for predicting efficacy of targeted drugs for primary liver cancer and application thereof

Abstract

The present application relates to the field of medical diagnosis, and provides a combined marker for predicting the curative effect of target immune drug for primary liver cancer and application thereof, wherein the combined marker is folate receptor gamma gene FOLR3, stratified protein gene SFN and coiled-coil domain containing 9 gene CCDC9 derived from peripheral blood leukocyte mRNA.The present application performs combined detection based on multiple peripheral blood leukocyte markers, and compared with a single molecular marker, can reduce errors caused by individual expression difference of a single index to some extent, so that the detection result is more accurate.The curative effect prediction model constructed based on detection of peripheral blood leukocyte mRNA level change can specifically recognize and detect in the early stage of tumor formation, has high sensitivity and high specificity, provides an important means for reasonable use of target immune drug for liver cancer patients sensitive or resistant to target immune drug, and has great significance for effective treatment of liver cancer in China.

Description

Technical Field

[0001] This invention relates to the field of medical diagnostics, specifically to combined biomarkers for predicting the efficacy of targeted immunotherapy in primary liver cancer and their applications. Background Technology

[0002] Primary liver cancer is characterized by its insidious onset, high malignancy, and rapid progression. Most patients are diagnosed at a locally advanced stage or with metastasis, thus losing the opportunity for radical treatment. In recent years, targeted immunotherapy, represented by molecularly targeted drugs and immune checkpoint inhibitors, has brought new hope to patients with advanced liver cancer. However, clinical practice shows significant individual differences in patient responses to these drugs. A considerable number of patients face primary or secondary drug resistance, resulting in poor treatment outcomes and limited improvement in survival. This high heterogeneity in sensitivity to targeted immunotherapy has become one of the main bottlenecks restricting the improvement of treatment efficacy for advanced liver cancer. Therefore, accurately predicting the individual patient's sensitivity or resistance risk to a specific targeted immunotherapy regimen before treatment is crucial for developing personalized treatment strategies and avoiding the toxic side effects and economic burden caused by ineffective treatment.

[0003] Hepatocellular carcinoma (HCC) exhibits high heterogeneity at the genetic, epigenetic, and tumor microenvironment levels. This heterogeneity not only drives malignant progression but also directly determines the differences in response to targeted and immunotherapeutic drugs. The intrinsic gene mutation profile and signaling pathway activity within the tumor, as well as the state of immune cell infiltration and the expression levels of immunosuppressive molecules in the tumor microenvironment, collectively constitute the basis for drug sensitivity or resistance. Currently, there is a lack of sufficiently precise and universal biomarkers to effectively predict the efficacy of targeted immunotherapy in clinical practice. Although traditional methods such as imaging and serum alpha-fetoprotein (AFP) play a role in diagnosis and monitoring, they struggle to capture this complex molecular heterogeneity. Therefore, developing predictive models that integrate multi-omics data (such as genomics, transcriptomics, and proteomics) and clinicopathological features to decode the heterogeneity of HCC and thus provide a prospective assessment of patient drug responses before treatment has become an urgent and crucial research direction for improving the current state of targeted immunotherapy for HCC. Summary of the Invention

[0004] The purpose of this invention is to provide a combined biomarker for predicting the efficacy of targeted immunotherapy in primary liver cancer and its application. The efficacy prediction model constructed based on detecting changes in peripheral blood leukocyte mRNA levels can specifically identify and detect the effectiveness of targeted immunotherapy in early liver cancer, exhibiting high sensitivity and high specificity.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] In a first aspect, the present invention provides a combined biomarker for predicting the efficacy of targeted immunotherapy in primary liver cancer, wherein the combined biomarker is derived from peripheral blood leukocyte mRNA, namely, the folate receptor γ gene FOLR3, the layering protein gene SFN, and the coiled-coil domain protein 9 gene CCDC9.

[0007] Secondly, the present invention provides primers for detecting the above-mentioned combined biomarkers, comprising:

[0008] FOLR3 F: 5′-CTACACCTGCAAAAGCAACTGGC-3′ (SEQ ID NO: 1),

[0009] FOLR3 R: 5′- GGAAGTAGGACTCAAAGGTGCTG-3′ (SEQ ID NO: 2);

[0010] SFN F: 5′-ACTTTTCCGTCTTCCACTACGA-3′ (SEQ ID NO: 3),

[0011] SFN R: 5′-ACAGTTGTCAGGTTGTCTCGC-3′ (SEQ ID NO: 4);

[0012] CCDC9 F: 5′-GCTGGAGACATGACGTTGTCC-3′ (SEQ ID NO: 5),

[0013] CCDC9 R: 5′-GTCTTCTCGGCATCCCACTC-3′ (SEQ ID NO: 6).

[0014] Preferably, it also includes an internal reference primer:

[0015] HPRT1 F: 5′-GCCATCACATTGTAGCCCTC-3′ (SEQ ID NO:7),

[0016] HPRT1 R: 5′-GGTCATTACAATAGCTCTTCAGTC-3′ (SEQ ID NO:8).

[0017] Thirdly, the present invention provides a product for predicting the efficacy of targeted immunotherapy in primary liver cancer, including reagents for detecting the expression levels of the aforementioned combined biomarkers.

[0018] Preferably, the reagent comprises the following primers:

[0019] FOLR3 F: 5′-CTACACCTGCAAAAGCAACTGGC-3′ (SEQ ID NO: 1),

[0020] FOLR3 R: 5′- GGAAGTAGGACTCAAAGGTGCTG-3′ (SEQ ID NO: 2);

[0021] SFN F: 5′-ACTTTTCCGTCTTCCACTACGA-3′ (SEQ ID NO: 3),

[0022] SFN R: 5′-ACAGTTGTCAGGTTGTCTCGC-3′ (SEQ ID NO: 4);

[0023] CCDC9 F: 5′-GCTGGAGACATGACGTTGTCC-3′ (SEQ ID NO: 5),

[0024] CCDC9 R: 5′-GTCTTCTCGGCATCCCACTC-3′ (SEQ ID NO: 6).

[0025] More preferably, the primers further include an internal reference primer:

[0026] HPRT1 F: 5′-GCCATCACATTGTAGCCCTC-3′ (SEQ ID NO:7),

[0027] HPRT1 R: 5′-GGTCATTACAATAGCTCTTCAGTC-3′ (SEQ ID NO:8).

[0028] Preferably, the primary liver cancer includes early-stage primary liver cancer; the product is a reagent kit or a chip.

[0029] Fourthly, this invention provides the application of the above-mentioned combined biomarkers as auxiliary diagnostic indicators for the efficacy of targeted immunotherapy in primary liver cancer.

[0030] Fifthly, the present invention provides the application of the above-mentioned combined biomarkers in the preparation of products for predicting the efficacy of targeted immunotherapy for primary liver cancer.

[0031] Furthermore, the folic acid receptor γ gene FOLR3, the layering protein gene SFN, and the coiled-coil domain protein 9 gene CCDC9 are expressed at low levels in primary liver cancer.

[0032] The advantages and beneficial effects of this invention are as follows:

[0033] 1. The pathogenesis of primary liver cancer is complex, and liver cancer exhibits high heterogeneity at the genetic, epigenetic, histopathological, and immunological levels. Peripheral blood leukocyte markers are liquid biopsy biomarkers. Using liquid biopsy efficacy prediction models to screen patients sensitive or resistant to targeted immunotherapy offers timeliness and prospectivity, effectively avoiding ineffective treatment due to liver cancer heterogeneity.

[0034] 2. The efficacy prediction model constructed based on detecting changes in peripheral blood leukocyte mRNA levels can perform specific detection in the early stages of tumor formation.

[0035] 3. This invention is based on the combined detection of multiple peripheral blood leukocyte markers. Compared with a single molecular marker, it can reduce the error caused by individual differences in the expression of a single indicator to a certain extent, making the detection results more accurate. Attached Figure Description

[0036] Figure 1 This is a volcano diagram of differentially expressed genes in peripheral blood leukocytes from the effective group (PR+CR) and the ineffective group (PD+SD) in the sequencing set of this invention.

[0037] Figure 2 DNA electrophoresis images used to validate primers for 14 candidate genes obtained from high-throughput sequencing analysis show that 9 primer pairs are qualified.

[0038] Figure 3 This shows the relative expression levels of FLOR3 in liver cancer samples that responded to targeted immunotherapy (PR+CR) and liver cancer samples that did not respond to targeted immunotherapy (PD+SD).

[0039] Figure 4 This shows the relative expression levels of SFN in liver cancer samples that responded to targeted immunotherapy (PR+CR) and liver cancer samples that did not respond to targeted immunotherapy (PD+SD).

[0040] Figure 5 This shows the relative expression levels of CCDC9 in liver cancer samples that responded to targeted immunotherapy (PR+CR) and liver cancer samples that did not respond to targeted immunotherapy (PD+SD).

[0041] Figure 6 To predict the sensitivity or resistance of patients with primary liver cancer to targeted immunotherapy using peripheral blood leukocyte combination markers in a training set of 36 patients for ROC curve analysis.

[0042] Figure 7 After including sequencing samples (22 cases) in the ROC curve analysis, peripheral blood leukocyte combination markers were used to predict the sensitivity or resistance of patients with primary liver cancer to targeted immunotherapy.

[0043] Figure 8 The ROC curve analysis was performed using the traditional hepatocellular carcinoma serum marker alpha-fetoprotein (AFP) to identify the detection results in patients with primary hepatocellular carcinoma. Detailed Implementation

[0044] The technical solution of the present invention will be described in complete and clear form below with reference to the embodiments and accompanying drawings. The described embodiments are some embodiments of the present invention, but not all examples.

[0045] The reagent kits and reagent models used in the embodiments of this invention are as follows:

[0046] Human peripheral blood leukocyte isolation kit (YMB1001, Yimo, China); RNA reverse transcription kit (4368813, Thermo Fisher, USA); Q-PCR reagent (A25742, Thermo Fisher, USA); 100bp DNA ladder (MD104-02-AA, Thermo Fisher, USA).

[0047] In this embodiment of the invention, the instrument used to detect combined markers of human peripheral blood leukocytes is a Q-PCR instrument.

[0048] Example

[0049] I. Obtaining Subjects and Blood Samples

[0050] Blood samples were collected from the First Affiliated Hospital of Zhejiang University School of Medicine (Zhejiang University First Affiliated Hospital Ethics Approval No. 2023-0838-Fast) and the Affiliated Hospital of Hangzhou Normal University (Ethics Approval No.: 2022(E2)-KS-068), including 58 patients with primary liver cancer, of whom 37 patients responded to targeted immunotherapy and 21 patients did not respond to targeted immunotherapy.

[0051] II. Isolation of peripheral blood leukocytes

[0052] Isolate leukocytes according to the instructions of the human peripheral blood leukocyte isolation kit. The specific steps are as follows: 1) Add 10 mL of reagent A to a 15 mL centrifuge tube for later use. Use a 1 mL pipette tip with a filter to draw 1 mL of anticoagulated blood and slowly inject it into the 15 mL centrifuge tube along the tube wall; 2) Cap the tube, invert it 5 times, let it stand for 5 minutes, then invert it 5 times again, let it stand for 5 minutes, and centrifuge at 500 g for 5 minutes at room temperature; 3) Discard the waste liquid, add 10 mL of reagent B to resuspend the cells, and centrifuge at 500 g for 5 minutes at room temperature; 4) Discard the waste liquid, add 700 μL of reagent C to the white cell precipitate at the bottom of the centrifuge tube, and repeatedly pipet 10 times until the precipitate is completely dissolved; 5) Store at -80℃.

[0053] III. Extraction of RNA from Peripheral Blood Leukocytes

[0054] The specific steps are as follows: 1) Remove the white blood cell sample from the -80℃ freezer and thaw it completely at room temperature; 2) Add 280μL of DEPC water, vortex for 15 seconds, let stand at room temperature for 10 minutes, and centrifuge at 12000g for 15 minutes; 3) After centrifugation, transfer 850μL of supernatant to a new 1.5mL centrifuge tube, add 5μL of 4-bromoanisole, vortex for 15 seconds, let stand at room temperature for 5 minutes, and centrifuge at 12000g for 10 minutes; 4) After centrifugation, transfer 700μL of supernatant to a new 1.5mL low-adsorption centrifuge tube, add an equal volume of isopropanol and invert 20 times to mix, and let stand at -80℃ for 30 minutes; 5) After complete thawing, centrifuge at 12000g for 30 minutes; 6) Remove the supernatant, wash once with 1mL of 75% alcohol, and centrifuge at 12000g for 4 minutes; 7) Discard the supernatant, air dry in a fume hood for 1 minute, and add 20μL of DEPC water. Dissolve the RNA by blowing it 10-20 times with DEPC water and immediately place it on ice. The concentration of the extracted RNA was determined using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, USA).

[0055] IV. High-throughput sequencing analysis of differentially expressed genes in human peripheral blood leukocytes

[0056] The sequencing samples included 22 patients with primary liver cancer, of whom 14 responded to targeted immunotherapy and 8 did not. To ensure the use of qualified samples for transcriptome sequencing, RNA purity and concentration were detected using a NanoDrop 2000 spectrophotometer; RNA integrity was accurately detected using an Agilent 2100 bioanalyzer / LabChip GX automated electrophoresis system. Libraries were constructed using the NEBNextR Ultra™ Directional mRNA Library Prep Kit for IlluminaR (NEB, USA) for the IlluminaR sequencing platform, and the constructed libraries were sequenced using an Illumina NovaSeq 6000 gene sequencer.

[0057] The obtained data were analyzed for differentially expressed genes using DESeq2. Log2|FC|≥1 and p<0.05 were used as thresholds to screen for differentially expressed genes. The analysis results showed that 205 differentially expressed genes were screened in the effective group compared to the ineffective group, including 115 upregulated genes and 90 downregulated genes. Figure 1Furthermore, based on the FPKM mean value >1 in the sequencing results, 62 differentially expressed genes were obtained. Further, considering p < 0.05 and an area under the curve (AUC) > 0.7 or < 0.3 in the sequencing samples to distinguish between effective and ineffective patients, 14 differentially expressed genes were identified: KRT73 (cytokeratin 73 antibody), PF4V1 (platelet factor 4V1), HERC3 (a member of the E3 ubiquitin protein ligase family containing HECT and RLD domains), TNFAIP3 (tumor necrosis factor α-inducible protein 3), and YJEFN3 (containing Yjef...). N-terminal domain 3), CPT1B (carnitine palmitoyltransferase 1B antibody), FOLR3 (folate receptor γ), NPFF (neuropeptide FF), CLDN9 (blocking protein 9), GADD45G (growth arrest DNA damage inducible protein γ), SFN (layering protein), TNNI2 (skeletal muscle fast muscle troponin I), CCDC9 (coil-coil domain protein 9), TMEM176B (transmembrane protein 176B).

[0058] V. Constructing a predictive model for the sensitivity or resistance of primary liver cancer to targeted immunotherapy using a combination of peripheral blood leukocyte markers.

[0059] Primer design and specificity validation were performed on 14 candidate genes, and 9 pairs of qualified primers were obtained (PF4V1, HERC3, TNFAIP3, CPT1B, FOLR3, NPFF, SFN, CCDC9, TMEM176B). Figure 2 This data was used for subsequent training set analysis. The training set sample included 36 patients with primary liver cancer, of whom 23 responded to targeted immunotherapy and 13 did not.

[0060] RNA was extracted from the training set samples and reverse transcribed using an RNA reverse transcription kit, followed by Q-PCR detection. The results showed that the up- and down-regulation trends of five genes—CPT1B, FOLR3, NPFF, SFN, and CCDC9—were consistent with those of the sequencing samples in both effective and ineffective targeted immunotherapy liver cancer samples (Table 1).

[0061] Table 1. Summary of significance, trend, and AUC of individual genes for RT-qPCR validation using nine pairs of qualified primers.

[0062]

[0063] The AUC values ​​of five individual genes used to distinguish between effective and ineffective targeted immunotherapy in hepatocellular carcinoma samples were calculated and sorted in descending order of AUC value. Stepwise regression analysis was performed using SPSS Statistics software. A regression model was constructed by screening independent variables using both forward and backward methods. The final significant combination of forward and backward variables was FOLR3 (folate receptor γ, gene ID 2352 [NCBI]), SFN (stratification protein, gene ID 2810 [NCBI]), and CCDC9 (coil-coil domain protein 9, gene ID 26093 [NCBI]). The AUC of this three-gene combined model for distinguishing between effective and ineffective targeted immunotherapy in hepatocellular carcinoma samples was 0.804 (the specific gene sequences can be obtained from https: / / www.ncbi.nlm.nih.gov / ).

[0064] The primers used in this invention for detecting the combined biomarkers FOLR3, SFN, and CCDC9 in peripheral blood leukocyte mRNA, as well as the internal control primers, have the following sequences:

[0065] FOLR3 F: 5′-CTACACCTGCAAAAGCAACTGGC-3′ (SEQ ID NO: 1),

[0066] FOLR3 R: 5′- GGAAGTAGGACTCAAAGGTGCTG-3′ (SEQ ID NO: 2);

[0067] SFN F: 5′-ACTTTTCCGTCTTCCACTACGA-3′ (SEQ ID NO: 3),

[0068] SFN R: 5′-ACAGTTGTCAGGTTGTCTCGC-3′ (SEQ ID NO: 4);

[0069] CCDC9 F: 5′-GCTGGAGACATGACGTTGTCC-3′ (SEQ ID NO: 5),

[0070] CCDC9 R: 5′-GTCTTCTCGGCATCCCACTC-3′ (SEQ ID NO: 6);

[0071] Internal reference primer sequence:

[0072] HPRT1 F: 5′-GCCATCACATTGTAGCCCTC-3′ (SEQ ID NO:7),

[0073] HPRT1 R: 5′-GGTCATTACAATAGCTCTTCAGTC-3′ (SEQ ID NO:8).

[0074] In the training set, the relative expression levels of FOLR3, SFN, and CCDC9 in effective and ineffective samples of targeted immunotherapy for primary liver cancer were as follows: Figure 3 , Figure 4 , Figure 5 As shown. SPSS stepwise regression analysis showed that, using FOLR3, SFN, and CCDC9 in peripheral blood leukocytes as combined markers, when distinguishing between the effective group (PR+CR) and the ineffective group (PD+SD), HPRT1 (hypoxanthine phosphoribosyltransferase 1) as an internal control, the AUC was 0.792 (as shown). Figure 6 A co-analysis of the included sequencing samples (22 cases) showed that the AUC of 58 samples was 0.804, indicating that the model was relatively stable. Figure 7 The AUC for distinguishing between effective and ineffective patients using the serum biomarker alpha-fetoprotein (AFP) was only 0.659. Figure 8 ).

[0075] The embodiments of the present invention disclosed above are merely illustrative of the invention. These embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. It is impossible to exhaustively list all implementation methods here; any method that meets the requirements of the present invention falls within the protection scope of the present invention.

Claims

1. A combined biomarker for predicting the efficacy of targeted immunotherapy in primary liver cancer, characterized in that, The combined biomarkers are the folate receptor γ gene FOLR3, the layered protein gene SFN, and the coiled-coil domain protein 9 gene CCDC9, all derived from peripheral blood leukocyte mRNA.

2. Primers for detecting the combined marker of claim 1, characterized in that, include: FOLR3 F: 5′-CTACACCTGCAAAAGCAACTGGC-3′, FOLR3 R: 5′-GGAAGTAGGACTCAAAGGTGCTG-3′; SFN F: 5′-ACTTTTCCGTCTTCCACTACGA-3′, SFN R: 5′-ACAGTTGTCAGGTTGTCTCGC-3′; CCDC9 F: 5′-GCTGGAGACATGACGTTGTCC-3′, CCDC9 R: 5′-GTCTTCTCGGCATCCCACTC-3′.

3. The primer according to claim 2, characterized in that, It also includes internal reference primers: HPRT1 F: 5′-GCCATCACATTGTAGCCCTC-3′, HPRT1 R: 5′-GGTCATTACAATAGCTCTTCAGTC-3′.

4. A product for predicting the efficacy of targeted immunotherapy in primary liver cancer, characterized in that, Includes reagents for detecting the expression level of the combined biomarker as described in claim 1.

5. The product according to claim 4, characterized in that, The reagent includes the following primers: FOLR3 F: 5′-CTACACCTGCAAAAGCAACTGGC-3′, FOLR3 R: 5′-GGAAGTAGGACTCAAAGGTGCTG-3′; SFN F: 5′-ACTTTTCCGTCTTCCACTACGA-3′, SFN R: 5′-ACAGTTGTCAGGTTGTCTCGC-3′; CCDC9 F: 5′-GCTGGAGACATGACGTTGTCC-3′, CCDC9 R: 5′-GTCTTCTCGGCATCCCACTC-3′.

6. The product according to claim 5, characterized in that, The primers also include an internal reference primer: HPRT1 F: 5′-GCCATCACATTGTAGCCCTC-3′, HPRT1 R: 5′-GGTCATTACAATAGCTCTTCAGTC-3′.

7. The product according to claim 4, characterized in that, The primary liver cancer includes early-stage primary liver cancer; the product is a reagent kit or a chip.

8. The application of the combined biomarker as described in claim 1 as an auxiliary diagnostic indicator for the efficacy of targeted immunotherapy in primary liver cancer.

9. The use of the combined biomarker of claim 1 in the preparation of products for predicting the efficacy of targeted immunotherapy in primary liver cancer.

10. The application according to claim 9, characterized in that, The folate receptor γ gene FOLR3, the layering protein gene SFN, and the coiled-coil domain protein 9 gene CCDC9 are expressed at low levels in primary liver cancer.

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