Biomarker for predicting benefit of hepatocellular carcinoma immunotherapy, RNA (Ribonucleic Acid) model and application of biomarker

Abstract

The invention discloses a biomarker for predicting benefit of hepatocellular carcinoma immunotherapy, an RNA (Ribonucleic Acid) model and application. 49 markers related to the curative effect of hepatocellular carcinoma immunotherapy are screened out, and a RNA model-HIRE scoring model for predicting the curative effect of immunotherapy based on a tumor microenvironment is established by applying a machine learning algorithm. The HIRE scoring model is constructed according to characteristic genes related to immunotherapy prognosis of hepatocellular carcinoma patients, tumor microenvironment activation conditions are reflected to a certain extent, meanwhile, tumor cells and immune checkpoint gene expression levels are combined, tumors and tumor microenvironments can be evaluated more comprehensively, and the HIRE scoring model has a good application prospect in immunotherapy prognosis of the hepatocellular carcinoma patients. And thus, potential people beneficial to immunotherapy can be more accurately screened out, more individualized immune benefit evaluation is provided for hepatocellular carcinoma patients, and the accurate progress of hepatocellular carcinoma immunotherapy is greatly promoted.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to a biomarker, RNA model and application for predicting the benefit of immunotherapy for hepatocellular carcinoma. Background Art

[0002] Hepatocellular carcinoma (HCC) is a global health problem with increasing morbidity and mortality. Although surgical treatment and local therapy are widely used worldwide, it is estimated that about 50-60% of HCC patients will eventually receive systemic therapy. There are many prognostic factors for HCC, and HCC patients with the same clinical stage and histological grade may have different prognoses even if they receive the same treatment.

[0003] In fact, for more than a decade, systemic molecular targeted therapy has been the main treatment for advanced HCC; some drugs have been shown to provide significant survival benefits as single drugs, with a median overall survival (OS) of 11-14 months for first-line sorafenib and lenvatinib (including China's independently developed donafenib), and a median overall survival of 8-11 months for second-line regorafenib, cabozantinib, and ramucirumab. In recent years, immunotherapy based on immune checkpoint inhibitors (ICBs) has made breakthrough progress in advanced hepatocellular carcinoma. Compared with sorafenib, ICI atezolizumab (an anti-PD-L1 antibody) and anti-VEGFA antibody bevacizumab have produced superior results to sorafenib in patients with advanced HCC, setting a new first-line benchmark. The median OS duration of this treatment regimen is 19 months, which brings more survival benefits to patients and changes the overall treatment landscape of hepatocellular carcinoma. Similarly, the anti-PD-1 antibody sintilimab combined with a bevacizumab biosimilar (IBI305) has been reported to improve OS in Chinese patients with advanced hepatitis B virus (HBV)-related HCC compared with sorafenib. The establishment of the atezolizumab-bevacizumab regimen as a standard of care marks the advent of a new era in which ICI-based combination therapies dominate clinical research in all stages of HCC. In addition to approving atezolizumab-bevacizumab for first-line treatment, the FDA has also accelerated the approval of the anti-PD-1 antibody pembrolizumab as a monotherapy based on efficacy data from early trials, and nivolumab (another anti-PD-1 antibody) combined with the anti-CTLA4 antibody ipilimumab for second-line treatment of advanced HCC. However, these data have not yet been confirmed in phase III studies. But the biggest challenge of this therapy is the huge disparity in patient efficacy, and multiple studies have shown that only some hepatocellular carcinoma patients can benefit from this therapy. Therefore, it is necessary to understand the determinants of HCC patients' response and resistance to these different drugs and / or combinations, to find biomarkers that can accurately predict the treatment response, to develop corresponding individualized treatment plans, and to avoid the harm and burden caused to patients by overtreatment and inappropriate treatment.

[0004] According to the TNM staging system, different patients with the same type of tumor and the same stage have significantly different clinical outcomes after the same or similar treatment, which indicates that this staging system has limitations in providing prognostic information and guiding postoperative chemotherapy decisions. Currently, imaging has become the standard technology for the diagnosis and follow-up of HCC. However, imaging technology has its limitations, especially in the early detection of HCC. Therefore, reliable, non- / minimally invasive biomarkers are urgently needed. To date, alpha-fetoprotein (AFP) is the only serum biomarker used for HCC management in clinical practice. However, the specificity and sensitivity of AFP are relatively low. Current research on biomarkers for predicting the efficacy of immune checkpoint inhibitors in advanced hepatocellular carcinoma is mainly focused on the field of liquid biopsy. PD-L1 expression was initially considered as a potential predictive biomarker for immunotherapy in phase I trials and was subsequently validated in larger clinical trials, paving the way for the use of immunohistochemical evaluation of PD-L1 in clinical practice for multiple cancer types, including NCSLC, breast cancer, and advanced esophageal cancer. However, the utility of PD-L1 as a biomarker for immunotherapy response in HCC is unclear.

[0005] A meta-analysis exploring the clinical significance of PD-L1 expression in HCC found that PD-L1 expression was not significantly correlated with improved survival or response rate. In the phase I Checkmate-040 trial, which was designed to evaluate the efficacy and safety of nivolumab alone or in combination with ipilimumab in patients with advanced HCC, there was no difference in PD-L1 expression between responders, and both PD-L1-positive and -negative patients showed comparable response rates. In addition, a phase 1b study evaluating the efficacy and safety of atezolizumab combined with bevacizumab retrospectively analyzed the expression of PD-L1 in 86 tumor samples by immunohistochemical staining and found that the patient's clinical response was not related to the expression of PD-L1. The KEYNOTE-224 study analyzed 52 patients by immunohistochemistry to evaluate the expression of PD-L1 in immune and tumor cells, and no correlation was observed between the response rate and PD-L1 expression in tumor cells.

[0006] Tumor mutation burden (TMB) is defined as the total number of somatic gene coding errors, base substitutions, gene insertions or deletions detected per million bases. Tumors with high TMB are associated with higher expression of new antigens, which will be more likely to cause T lymphocyte recognition to activate immune responses. Therefore, TMB-high (TMB-H) status has been explored as a predictive biomarker for response to PD-1 / PD-L1 inhibitors. The KEYNOTE-158 phase 2 study showed that pembrolizumab significantly improved in a variety of advanced TMB-H solid tumors, with an ORR of 29% in TMB-H patients and 6% in the TMB-low subgroup. These findings led to the FDA approval of pembrolizumab for the treatment of unresectable or metastatic TMB-H cancers. However, due to the rare finding of TMB-H status in HCC, no HCC patients were included in the trial. Evidence to date suggests that TMB-H corresponds to a minority of HCC patients, limiting the clinical applicability of TMB as a predictive biomarker for HCC.

[0007] The probability of mutations arising during DNA synthesis is limited by the immediate proofreading of replication errors and the post-replication mismatch repair (MMR) mechanism. Microsatellites are a class of short tandemly repeated DNA sequences in the genome that are inherently more susceptible to repair of damaged DNA. Loss of the MMR pathway (dMMR) leads to microsatellite instability (MSI) status. MSI status is considered a potential biomarker for response to ICPi treatment. In 2015, the FDA granted the first accelerated approval of pembrolizumab for the treatment of any MSI-H or dMMR unresectable tumor type based on the response rates observed in the Phase I KEYNOTE-016 study. However, MSI-H is a rare feature in HCC patients, resulting in limited utility as a predictor of response to ICPi, with only <3% of HCC having MSI status.

[0008] In summary, the clinical application of existing biomarkers including PD-L1 expression level, TMB and MSI-H in liver cancer faces challenges, suggesting that there is still a need to develop more accurate and comprehensive markers to predict the efficacy of immunotherapy for hepatocellular carcinoma. Developing new and robust biomarkers and exploring effective biomarker combined detection strategies may be the key to meeting this challenge.

[0009] As technology advances, so does our understanding of the complexity and diversity of the tumor microenvironment and its impact on treatment response. In-depth analysis of the complexity of the tumor microenvironment is likely to reveal advanced biomarkers that will yield results in identifying patient populations that respond to current ICB therapy and will facilitate the search for new therapeutic modulatory targets. Therefore, predicting the responsiveness of immune checkpoint inhibitors to PD-1 / PD-L1 monoclonal antibodies based on relevant tumor immune infiltration features is an important measure to improve the success rate of current immune checkpoint inhibitor treatment for hepatocellular carcinoma and to develop next-generation immunotherapies.

[0010] In order to predict the efficacy of immunotherapy more accurately and comprehensively, international scholars have predicted the efficacy from the perspective of tumor immune microenvironment (including TIDE, IPS, Pan-F-TBRs and other scores). Among them, the TIDE score integrates the expression characteristics of T cell dysfunction and T cell rejection to simulate tumor immune escape. The TIDE score is trained from untreated tumor data and can predict the clinical response of ICB based on pre-treatment tumor characteristics. However, in some cancer types such as renal cell carcinoma, which has a large number of CD8+T cell infiltrations, higher CTLs may not be associated with survival benefits. In addition, depending on the sample size or characteristics of a specific data set, there may not be any statistically significant genes that interact with CTLs to affect survival. Therefore, it is urgent to design and develop a multi-gene expression profile and prognostic scoring system for liver cancer based on existing research and technology. Summary of the invention

[0011] In view of the above-mentioned current technical deficiencies, the purpose of the present invention is to provide a biomarker, RNA model and application for predicting the benefit degree and prognosis of hepatocellular carcinoma immunotherapy. The specific scheme is as follows.

[0012] In a first aspect of the present invention, a marker for predicting the benefit degree and / or prognosis of liver cancer immunotherapy is provided, wherein the marker comprises the following genes:

[0013] SNORA74B, GBP1, CXCL9, CXCL10, TRBC2, UBE2D3P2, HNRNPA1P21, CD24, SSR2, TRGV2, GBP4, GBP5 , PSMB9, TTYH3, PRF1, TRBC1, LAPTM4B, CD8A, SLC39A4, ACVR1B, TRGC2, GPR171, CEP41, ZC3HAV1 L, DDR2, XPR1, PLCE1, GAL3ST4, PWWP2B, HSPB7, TMEM267, SLC12A8, IDO1, B3GNT5, SLC6A9, FCRL 3. IFNG, GDPD5, LGI2, ZFAT, AGPAT4, DTHD1, ERFE, ARHGAP39, SPATA12, KLRD1, EPGN, ROR1, TLL2.

[0014] The second aspect of the present invention provides an RNA model for predicting the benefit degree and / or prognosis of liver cancer immunotherapy, wherein the model generates a risk score based on the expression level of the above-mentioned marker genes through the Gscore() function of the R package "HIRE".

[0015] In some embodiments of the present invention, the RNA model is constructed by the following method, comprising the following steps:

[0016] (1) Sequencing data of hepatocellular carcinoma patients were collected and divided into a training group and an internal validation group;

[0017] (2) Genes associated with progression-free survival and overall survival with statistical significance were screened using the batch_sur() function of the R package “IOBR”;

[0018] (3) Based on the gene expression levels obtained in step (2), a random survival forest model was constructed using the rfsrc() function of the R package “randomForestSRC”.

[0019] In some embodiments of the present invention, the cut-off value of the risk score in the RNA model is the average of the risk scores.

[0020] In some embodiments of the present invention, the relevant code for generating a risk score based on the RNA model is as follows:

[0021] #Install the HIRE R package

[0022] devtools::install_github("LiaoWJLab / HIRE")

[0023] # Load R package

[0024] Library(“HIRE”)

[0025] # Input gene expression matrix - and calculate HIRE model risk score

[0026] HIRE<-Gscore(eset=eset,scale=TRUE)

[0027] For detailed code and parameters, please refer to the link: https: / / github.com / LiaoWJLab / HIRE

[0028] The third aspect of the present invention further provides a computer-readable storage medium storing a computer program, which, when executed by a processor, can implement the functions of the RNA model of the second aspect of the present invention.

[0029] The fourth aspect of the present invention also provides the use of the marker in the preparation of a product for predicting the degree of survival benefit and / or prognosis of liver cancer immunotherapy.

[0030] The fifth aspect of the present invention also provides the use of a substance for detecting the marker in the preparation of a product for predicting the degree of benefit and / or prognosis of liver cancer immunotherapy.

[0031] The sixth aspect of the present invention also provides a product, which includes the above-mentioned substance for detecting markers.

[0032] In some embodiments of the present invention, in the third to sixth aspects of the present invention, the product includes reagents, instruments, software, etc., such as a test kit, a diagnostic reagent, a detection reagent, a test paper, a gene chip or a protein chip, etc.

[0033] In some embodiments of the present invention, in the third to sixth aspects of the present invention, the substance includes a substance for detecting the expression level of a marker gene by sequencing technology, nucleic acid hybridization technology, nucleic acid amplification technology, and the like.

[0034] In some embodiments of the present invention, in the third to sixth aspects of the present invention, the substances include primers, antibodies, probes, etc. For example, in a specific embodiment of the present invention, the substances include primers with sequences such as SEQ ID NO. 1-98.

[0035] In some embodiments of the present invention, in the third to sixth aspects of the present invention, the primer further includes an internal reference primer.

[0036] In some embodiments of the present invention, in the third to sixth aspects of the present invention, the internal reference genes include TUBB and ACTB. For example, in a specific embodiment of the present invention, the primer sequences of the internal reference genes TUBB and ACTB are shown in SEQ ID NOs. 99-102.

[0037] In some embodiments of the present invention, the liver cancer is hepatocellular carcinoma.

[0038] In some embodiments of the invention, the immunotherapy comprises immune checkpoint inhibitor therapy.

[0039] In some embodiments of the present invention, the immune checkpoint inhibitor comprises a PD-1 / PD-L1 antibody.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] At present, the biomarkers for predicting the efficacy of immunotherapy in the field of hepatocellular carcinoma mainly include the expression levels of AFP and PD-L1, but these two indicators mainly rely on the characteristics of tumor cells, ignoring the key role of the tumor microenvironment in mediating tumor immunotherapy response. The present invention screens out 49 related markers by analyzing the transcriptome spectrum of hepatocellular patients receiving immunotherapy, and uses a machine learning algorithm to establish a molecular scoring RNA model based on the tumor microenvironment (HIRE scoring model). The HIRE scoring model of the present invention is constructed based on characteristic genes related to the prognosis of immunotherapy in patients with hepatocellular carcinoma, which reflects the activation of the tumor microenvironment to a certain extent. At the same time, combined with tumor cell characteristics and immune checkpoint gene expression levels, it can more comprehensively evaluate tumors and tumor microenvironments, and then more accurately screen out potential populations that benefit from immunotherapy.

[0042] Compared with existing molecular markers, this model is not only an independent prognostic predictor of progression and death risk in patients with hepatocellular carcinoma after immunotherapy, but also an effective tool for predicting patients' response to immunotherapy, providing a more personalized immune benefit assessment for patients with hepatocellular carcinoma, which has certain clinical translational significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The HIRE score of the training group was used to predict the efficacy of immunotherapy for hepatocellular carcinoma. Figure 1 A. OS survival curves of HCC patients with high and low HIRE scores receiving immunotherapy. Figure 1 B PFS survival curves of HCC patients with high and low HIRE scores receiving immunotherapy. Figure 1 C ROC curve evaluated the accuracy of HIRE score in predicting the efficacy of immunotherapy for hepatocellular carcinoma in the training group.

[0044] Figure 2The HIRE score was used as the internal validation group to predict the efficacy of immunotherapy for hepatocellular carcinoma. Figure 2 A. OS survival curves of HCC patients with high and low HIRE scores receiving immunotherapy. Figure 2 B PFS survival curves of HCC patients with high and low HIRE scores receiving immunotherapy. Figure 2 C ROC curve evaluated the accuracy of HIRE score in predicting the efficacy of immunotherapy for hepatocellular carcinoma in the validation group.

[0045] Figure 3 To compare the efficacy of HIRE score with other common immunological biomarkers in predicting therapeutic efficacy.

[0046] Figure 4 The HIRE score was used as an external validation group to predict the therapeutic efficacy of hepatocellular carcinoma. DETAILED DESCRIPTION

[0047] The following will be combined with the embodiments to clearly and completely describe the concept of the present invention and the technical effects produced, so as to fully understand the purpose, characteristics and effects of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative work are all within the scope of protection of the present invention.

[0048] Unless otherwise specified, the reagents, methods and equipment used in the present invention are conventional reagents, methods and equipment in the art. Unless otherwise specified, the reagents and materials used in the following examples are commercially available.

[0049] Example 1 Construction of a Hepatocellular Carcinoma Patient Cohort

[0050] 1. Construction of training and validation group patient cohorts

[0051] The applicant first applied for data from the Genentech research team in the United States, which has two major immunotherapy cohort data for hepatocellular carcinoma: the IMbrave150 patient cohort published in the New England Journal of Medicine and the GO30140A / F group patient cohort published in The Lancet Oncology. The patient inclusion criteria for the above two studies are as follows: ① Patients are 18 years old or older; ② Locally advanced metastatic or unresectable hepatocellular carcinoma (or coexisting); ③ Previously untreated and unsuitable systemic treatment for liver cancer, or progression after treatment; ④ There are measurable lesions; ⑤ ECOG score 0 or 1; ⑥ Child-Pugh liver function class A; ⑦ Adequate hematological and organ function. ⑧ Among them, the two groups of patients in the GO30140 cohort have additional inclusion criteria. GO30140 Group A: Child-Pugh score reaches B7, and the platelet count is at least 100,000 platelets per μL. GO30140 F group: Child-Pugh liver function class A, life expectancy of at least 3 months, platelet count of at least 75,000 platelets per μL. After combining the inclusion criteria and relevant screening, a total of 246 patients were obtained in the cohort receiving atezolizumab combined with bevacizumab. The data were randomly divided into a training group and an internal validation group (7:3), with 173 patients in the training group and 73 patients in the validation group.

[0052] Example 2: Screening of candidate genes for modeling

[0053] The transcriptome data of the immunotherapy cohort of hepatocellular carcinoma patients provided by the Genentech research team were analyzed to identify gene signatures related to tumor microenvironment, metabolism, and tumor intrinsic pathways. By analyzing gene signatures related to gene differences and treatment responses, feature engineering was performed to screen target genes.

[0054] In this study, we first used the batch_surv() function of the R package “IOBR” to obtain genes with p-values ​​< 0.01 that were associated with progression-free survival and overall survival. The obtained genes were divided into high expression group and low expression group according to the average value. From the high expression group, 49 genes with p-values ​​< 0.001 were selected as input genes. The input genes include: SNORA74B, GBP1, CXCL9, CXCL10, TRBC2, UBE2D3P2, HNRNPA1P21, CD24, SSR2, TRGV2, GBP4, GBP5, PSMB9, TTYH3, PRF1, TRBC1, LAPTM4B, CD8A, SLC39A4, ACVR1B, TRGC2, GPR171, CEP41, ZC3HAV1L, DDR2, XPR1, PLCE1, GAL3ST4, PWWP2B, HSPB7, TMEM267, SLC12A8, IDO1, B3GNT5, SLC6A9, FCRL3, IFNG, GDPD5, LGI2, ZFAT, AGPAT4, DTHD1, ERFE, ARHGAP39, SPATA12, KLRD1, EPGN, ROR1, and TLL2. Finally, based on the expression levels of the input genes, a random survival forest model was constructed using the rfsrc() function of the R package “randomForestSRC”, which generated a risk score based on the expression levels of these genes.

[0055] The relevant code reference for the model to generate risk scores based on gene expression is as follows:

[0056] #Install the HIRE R package

[0057] devtools::install_github("LiaoWJLab / HIRE")

[0058] # Load R package

[0059] Library(“HIRE”)

[0060] # Input gene expression matrix - and calculate HIRE model score

[0061] HIRE<-Gscore(eset=eset,id_pdata="ID")

[0062] Example 3: Predictive effect of HIRE scoring model on the efficacy and prognosis of hepatocellular carcinoma patients receiving immunotherapy in the training group

[0063] According to the method of Example 2, the HIRE scores of 173 patients in the training group were calculated, and the effect of using the HIRE score was explored in the training group. The average HIRE score was set as the cut-off value, and the patients were divided into a high HIRE group and a low HIRE group. The results of survival analysis of patients with OS data showed that patients with low HIRE had significantly longer overall survival ( Figure 1 A). Patients with high HIRE had a significantly higher survival risk (HR=5.78, 95%CI=2.97-11.22) than those with low HIRE. Subsequently, survival analysis of patients with PFS data showed that patients with hepatocellular carcinoma with low HIRE had significantly longer progression-free survival after immunotherapy ( Figure 1 B). The risk of progression in patients with high HIRE (HR=7.26, 95%CI=4.65-11.34) was significantly higher than that in patients with low HIRE. The ROC curve was used to evaluate the efficacy of HIRE score in predicting the response to immunotherapy in patients with hepatocellular carcinoma. The analysis showed that the AUC value of HIRE score prediction was 0.9455, indicating that the accuracy of HIRE score in predicting the efficacy of immunotherapy was 94.55% ( Figure 1 C).

[0064] Example 4 Predictive effect of HIRE scoring model on the efficacy of immunotherapy in patients with hepatocellular carcinoma in the validation group

[0065] The effect of using HIRE score was explored in the validation group. The internal validation group (n=73) was selected as the validation cohort. The mean HIRE score was set as the cut-off value, and the patients were divided into high HIRE group and low HIRE group based on it. The results of survival analysis of patients with OS data showed that patients with hepatocellular carcinoma with low HIRE had significantly longer overall survival ( Figure 2 A). Patients with high HIRE had a significantly higher survival risk (HR=3.17, 95%CI=1.14-8.8) than those with low HIRE. Subsequently, survival analysis of patients with PFS data showed that patients with hepatocellular carcinoma with low HIRE had significantly longer progression-free survival (PFS) after receiving immunotherapy. Figure 2 B). The risk of progression in patients with high HIRE (HR=3.8, 95%CI=1.91-7.58) was significantly higher than that in patients with low HIRE. The ROC curve was used to evaluate the efficacy of HIRE score in predicting the response to immunotherapy in patients with hepatocellular carcinoma. The analysis showed that the AUC value of HIRE score prediction was 0.8307, indicating that the accuracy of HIRE score in predicting the efficacy of immunotherapy was 83.07% ( Figure 2C). Next, the HIRE score was compared with other common immunological biomarkers in predicting efficacy and found that the AUC value of the HIRE score in predicting the efficacy of immunotherapy for hepatocellular carcinoma was higher than that of other biomarkers ( Figure 3 ).

[0066] The accuracy of HIRE score in predicting the efficacy of immunotherapy for hepatocellular carcinoma was further verified in the external validation cohort (GSE14520, TCGA-LIHC). Similarly, the mean HIRE score was set as the cut-off value, and the patients were divided into high HIRE group and low HIRE group accordingly. Among hepatocellular carcinoma patients receiving immunotherapy, the HIRE score distribution in the non-responder group was significantly higher than that in the responder group ( Figure 4 ).

[0067] In summary, the HIRE score has a good predictive effect in both the training group and the internal and external validation groups, and its accuracy in predicting the efficacy of immunotherapy for hepatocellular carcinoma is better than other common immunological biomarkers.

[0068] Example 5: Construction of PCR kit and detection implementation method

[0069] (1) Design of PCR primers

[0070] ① In order to realize the clinical transformation of the gene set identified in the above research, so as to achieve real-time detection of individual patients, the primer sequences of the above gene set were predicted using the Primer designing tool. The primer sequences corresponding to each marker gene are SEQ ID NO.1-98 (see Table 1 for details);

[0071] ② In order to achieve the standardization of the PCR detection results of the above-mentioned tag genes, TUBB and ACTB were selected as internal reference genes, and the corresponding primer sequences were SEQ ID NO.99-102 (see Table 1 for details). The expression level of each tag gene was calculated based on the average expression level of these two internal reference genes.

[0072] Note: The internal reference gene is expressed in all cells and is a basic gene for normal life activities. The standardization parameters of each sample are calculated by summing the internal reference genes. The combination of multiple internal reference genes can better standardize the expression level of tumor microenvironment genes. In qPCR detection technology, internal reference genes are required for relative quantification of target genes.

[0073] Table 1: qRT-PCR primer sequences for gene sets

[0074]

[0075]

[0076]

[0077] (2) PCR kit detection and implementation method (RNA extraction, obtaining standard values)

[0078] 1. Extract RNA from tumor tissue

[0079] 1) Extract RNA from fresh or frozen tissue specimens

[0080] ① First, place the tumor sample tissue directly into a mortar, add a small amount of liquid nitrogen, and grind it quickly. When the tissue becomes soft, add a small amount of liquid nitrogen and grind it again. Repeat this three times.

[0081] ② Add 1ml Trizol to 50-100mg of tissue sample and transfer to a centrifuge tube. In addition, the volume of tissue should not exceed 10% of the volume of Trizol. Then use an electric homogenizer to fully homogenize for 1-2 minutes;

[0082] ③ Then, centrifuge at 12000r / min for 5min, discard the precipitate, add 200μL of chloroform per ml of Trizol, cover the centrifuge tube tightly, shake by hand for 15s, and leave at room temperature for 10min;

[0083] ④ Then, centrifuge at 4°C, 12000r / min for 15min, aspirate the upper aqueous phase, transfer to another new centrifuge tube, add 0.6mL of isoamyl alcohol per mL of Trizol, mix well and place at room temperature for 5-10min;

[0084] ⑤ Then, centrifuge at 4°C, 12,000 rpm for 10 min, discard the supernatant, add 1 mL of 75% ethanol per mL of Trizol, and oscillate gently to suspend the precipitate;

[0085] ⑥Then, air dry or vacuum dry at room temperature for 5-10 minutes, and then measure the absorbance at 260nm to quantify the RNA concentration;

[0086] ⑦Finally, RNA can be isolated from mRNA or stored in 70% ethanol and stored at -70°C. The homogenate before adding chloroform can be stored for more than 1 month.

[0087] 2) Extraction of RNA from formalin-fixed paraffin-embedded tissue (FFPE) specimens

[0088] Note: For FFPE samples, it is recommended that the sample storage time is less than 2 years;

[0089] ① After removing the surface layer of the wax block that is in contact with the air, cut 5-8 paraffin sections (5-10 μm thick);

[0090] ② Add PBS, vortex and mix, centrifuge at room temperature, discard the supernatant, and repeat 3 times;

[0091] ③Put the sample in a sterile centrifuge tube, add xylene, and vortex vigorously;

[0092] ④Use Roche's High Pure FFPET RNA Isolation Kit to extract high-purity RNA.

[0093] 2. RNA quantitative detection

[0094] ① Reverse transcription: According to the steps of the reverse transcription commercial kit, the extracted total cell RNA is reverse transcribed into the corresponding cDNA. The reverse transcription system is 10μL, specifically including: 2μL buffer, 0.5μL reverse transcriptase, 0.5μL oligonucleotide primer, 0.5μL random primer, 1μL total cell RNA with a concentration of about 1000ng (the volume of the added RNA sample is adjusted according to the detected RNA concentration to ensure the RNA amount), and the remaining system is supplemented with RNase-free water. After the reverse transcription system is prepared, reverse transcription is performed.

[0095] ② Configure the real-time quantitative PCR reaction system: Perform the real-time quantitative PCR experiment according to the instructions of the Roche fluorescent quantitative PCR kit. The reaction system is 10 μL, including: 3.6 μL of DEPC water, 0.2 μL of upstream primer, 0.2 μL of downstream primer, 5 μL of SYBR Green I fluorescent reaction solution, and 1 μL of cDNA sample. Use Roche quantitative fluorescent PCR instrument to amplify cDNA.

[0096] ③ Calculate gene expression using the comparative method (ΔΔCt) and compare the cycle threshold (Ct) of the target gene and the housekeeping gene.

[0097] 3. Selection of reference genes and label genes

[0098] ① To evaluate the consistency between PCR test results and high-throughput sequencing data, 65 patients with remaining specimens were randomly selected from the above cohort data, and the above-mentioned tag genes and internal reference genes were tested in the tissue specimens;

[0099] ②By evaluating the expression abundance and gene expression stability of the internal reference genes, ACTB and TUBB were selected as reference genes for subsequent standardization; at the same time, the tag genes were used as components of the PCR detection kit. Tag genes: SNORA74B, GBP1, CXCL9, CXCL10, TRBC2, UBE2D3P2, HNRNPA1P21, CD24, SSR2, TRGV2, GBP4, GBP5, PSMB9, TTYH3, PRF1, TRBC1, LAPTM4B, CD8A, SLC39A4, ACVR1B, TRGC2, GPR171, CEP41, ZC3HAV1L, DDR 2. XPR1, PLCE1, GAL3ST4, PWWP2B, HSPB7, TMEM267, SLC12A8, IDO1, B3GNT5, SLC6A9, FCRL3, IFNG, GDPD5, LGI2, ZFAT, AGPAT4, DTHD1, ERFE, ARHGAP39, SPATA12, KLRD1, EPGN, ROR1, TLL2; internal reference genes: ACTB and TUBB.

[0100] 4. Calculation of HIRE score

[0101] The following formula was used to calculate the expression level of each gene: Nrel = N01 / N02 = N×2-Ct1 / N×2-Ct2 = 2-(Ct1-Ct2) = 2-ΔCt (1 is the detected gene, 2 is the average value of the internal reference gene). The following method was then used to calculate the score of each tumor patient: the patient's gene expression level matrix was input into the Gscore() function of the R package HIRE to calculate the patient's HIRE score.

[0102] The above specific implementations have been described in detail for the present invention, but the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in the relevant technical field without departing from the purpose of the present invention. In addition, the embodiments of the present invention and the features in the embodiments can be combined with each other without conflict.

Claims

1. A marker for predicting the benefit degree and / or prognosis of liver cancer immunotherapy, characterized in that: The markers include the following genes: SNORA74B, GBP1, CXCL9, CXCL10, TRBC2, UBE2D3P2, HNRNPA1P21, CD24, SSR2, TRGV2, GBP4, GBP5 , PSMB9, TTYH3, PRF1, TRBC1, LAPTM4B, CD8A, SLC39A4, ACVR1B, TRGC2, GPR171, CEP41, ZC3HAV1 L, DDR2, XPR1, PLCE1, GAL3ST4, PWWP2B, HSPB7, TMEM267, SLC12A8, IDO1, B3GNT5, SLC6A9, FCRL 3. IFNG, GDPD5, LGI2, ZFAT, AGPAT4, DTHD1, ERFE, ARHGAP39, SPATA12, KLRD1, EPGN, ROR1, TLL2.

2. An RNA model for predicting the degree of benefit and / or prognosis of hepatocellular carcinoma immunotherapy, characterized in that: The model generates a risk score based on the marker gene expression level as described in claim 1 through the Gscore() function of the R package "HIRE".

3. The RNA model according to claim 2, characterized in that The risk scoring model is constructed by the following method, including the following steps: (1) Sequencing data of HCC patients were collected and divided into a training group and an internal validation group; (2) The batch_surv() function of the R package "IOBR" was used to screen genes that were statistically associated with progression-free survival and overall survival; (3) Based on the gene expression levels obtained in step (2), a random survival forest model was constructed using the rfsrc() function of the R package "randomForestSRC".

4. The RNA model according to claim 2, characterized in that The cut-off value of the risk score of the model is the average of the risk scores.

5. A computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the function of the RNA model as described in any one of claims 2 to 4 can be realized.

6. Use of the marker as claimed in claim 1 in the preparation of a product for predicting the degree of survival benefit and / or prognosis of liver cancer immunotherapy.

7. Use of a substance for detecting the marker as claimed in claim 1 in the preparation of a product for predicting the degree of benefit and / or prognosis of liver cancer immunotherapy.

8. The use according to claim 7, characterized in that: The substance includes a substance for detecting the expression level of the marker gene by at least one method selected from the group consisting of sequencing technology, nucleic acid hybridization technology, and nucleic acid amplification technology.

9. A product comprising the substance for detecting a marker according to any one of claims 7 to 8.

10. The marker according to claim 1, the RNA model according to any one of claims 2 to 4, the storage medium according to claim 5, the use according to any one of claims 6 to 8, or the product according to claim 9, characterized in that: The liver cancer is hepatocellular carcinoma.

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