Marker for predicting drug resistance and poor survival prognosis of kidney cancer immunotherapy and application of marker

Abstract

The invention discloses a marker for predicting drug resistance and poor survival prognosis of kidney cancer immunotherapy, the marker is composed of the following eight genes: SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59, and the invention also relates to application of a detection reagent composed of reagents with the expression quantities of the eight genes in preparation of a kit for evaluating drug resistance and prognosis survival of kidney cancer immunotherapy. As a kit, the detection reagent is the only key component for evaluating the anti-immunotherapy drug resistance and prognosis survival function of the kidney cancer. According to the invention, a kidney cancer specimen based on tumor immunotherapy is screened and constructed after being subjected to whole transcriptome sequencing and machine learning, so that the drug resistance and clinical prognosis condition of a kidney cancer patient receiving anti-tumor immunotherapy can be efficiently and accurately predicted, and an effective guidance opinion is provided for a clinician to make a treatment decision on the kidney cancer patient; the occurrence of invalid treatment is reduced, so that the treatment cost and uncomfortable experience of the patient are reduced.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and specifically relates to a marker for predicting drug resistance and poor survival prognosis of renal cancer immunotherapy and an application thereof. Background Art

[0002] Kidney malignancy is one of the most common malignant tumors of the urinary system worldwide, accounting for 5% and 3% of all tumors in men and women in the United States, respectively. Among them, renal cell carcinoma (RCC, hereinafter referred to as renal cancer) is the most common pathological type of renal malignancy, ranking 13th in the world's cancer deaths, with about 140,000 kidney cancer-related deaths each year. Because the disease has an insidious onset and is often asymptomatic, about 1 / 3 of patients have developed metastatic renal cancer at the time of diagnosis, and 1 / 4 of renal cancer patients who undergo radical nephrectomy will also experience recurrence and metastasis. Therefore, it is of great clinical significance to explore targeted and effective treatments to improve the survival rate and quality of life of renal cancer. However, drug resistance is one of the key reasons for the failure of targeted and immunotherapy for renal cell carcinoma, but its mechanism has not yet been fully elucidated.

[0003] Although anti-angiogenic tyrosine kinase inhibitors have been approved as first-line treatment for metastatic RCC in recent years, only 38% of patients have an objective response rate, and the vast majority of patients who respond to treatment still develop tumor resistance and progression in the later stages of treatment. Immunotherapy has also become the first choice recommended by guidelines for the second-line treatment of advanced renal cancer, but it also faces the problems of resistance and low responsiveness, and may even develop resistance and disease progression in the later stages, placing a heavy burden on patients' families and society. Therefore, biomarkers for evaluating the efficacy of immunotherapy for renal cancer and patient selectivity have always been the focus of research in this field.

[0004] Chinese patent document: CN116179707A, publication date: 2023-05-30, discloses a renal cancer prognosis marker, in particular, a renal cancer prognosis marker for predicting anti-PD 1 treatment and its application. The invention discloses that the higher the gene expression of transketolase-like protein TKTL1 in the cancer tissue of renal cancer patients, the better the prognosis of renal clear cell carcinoma patients. And in a joint study with anti-PD 1 treatment, it was found that the expression of TKTL1 was positively correlated with the prognosis of patients with renal clear cell carcinoma who responded to anti-PD 1 treatment. However, this scheme only involves a simple survival prediction of patients with renal cancer, and does not provide an accurate prediction for the clinical difficulty of treatment resistance. The study only involves a single gene prediction, and the expression of TKTL1 is only positively correlated with the prognosis of patients with renal clear cell carcinoma who respond to anti-PD 1 treatment. Its accuracy and clinical practicality need further research, which does not meet the development prospects of precision medicine today. Chinese patent document: CN118127159A, publication date: 2024-06-04, the invention discloses a biomarker of immune cell infiltration in renal cancer and its use, and proposes the use of KMT2D detection reagent in the preparation of products for evaluating tumor immune infiltration levels. The invention found that KMT2D is a promising biomarker for evaluating tumor immune infiltration levels. By detecting the expression of KMT2D in tumor tissues of patients with renal cancer, the tumor type can be evaluated and clinical decisions can be made based on the KMT2D expression level. However, the invention failed to directly predict the resistance to immunotherapy of renal cancer, and its clinical practicality needs to be verified. Chinese patent document: CN117844924A, publication date: 2024-04-09, the present invention discloses a biomarker BUB1 for renal cell carcinoma, which found through integrated data research that BUB1 is overexpressed in KIRP, KIRC, and KICH tissues relative to normal tissues, and analyzed specificity, sensitivity, and accuracy, suggesting that BUB1 can be used as a molecular marker for diagnosing renal cell carcinoma, providing new ideas for the diagnosis and prognosis prediction of renal cell carcinoma. However, the invention is only aimed at the diagnosis and prediction of renal cell carcinoma, and fails to directly predict the resistance to immunotherapy of renal cancer and the prediction of poor clinical prognosis, and its clinical practicality needs to be verified.

[0005] In response to the deficiencies of the prior art, the present invention provides a marker for predicting renal cancer immunotherapy resistance and poor survival prognosis, which provides effective guidance for clinical physicians on anti-tumor immunotherapy for renal cancer patients, effectively reduces the failure of treatment, and thus reduces patients' treatment costs and discomfort experience. Summary of the invention

[0006] In view of the deficiencies of the prior art and actual needs, the present invention provides a marker for evaluating immunotherapy resistance and prognosis survival of renal cancer patients and its application.

[0007] To achieve the above object, the technical solution adopted by the present invention is:

[0008] On the one hand, the present invention provides a biomarker for evaluating renal cancer immunotherapy resistance and prognosis survival, wherein the biomarker comprises the following eight gene combinations: SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59.

[0009] Another aspect of the present invention provides a kit for evaluating the immunotherapy resistance and prognosis survival of renal cancer, wherein the kit comprises detection reagents capable of detecting the expression levels of eight genes: SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59.

[0010] Furthermore, the detection reagent includes the following primers:

[0011] Upstream primer for detecting SFTPA1: AGGACGTTTGTGTTGGAAGC (SEQ ID NO. 1);

[0012] Downstream primer for detecting SFTPA1: GAAGTCCCACCCAGCTTACT (SEQ ID NO. 2);

[0013] Upstream primer for detecting AC104451.2: TACCAATGACAGCAGAGGGG (SEQ ID NO. 3);

[0014] Downstream primer for detecting AC104451.2: TGCCTACACTCAAGCACCAT (SEQ ID NO. 4);

[0015] Upstream primer for detecting RPS27P29: GGTGATGACAACCTTCCCAC (SEQ ID NO. 5);

[0016] Downstream primer for detecting RPS27P29: AGCAGCCAACACACAAAAACT (SEQ ID NO. 6);

[0017] Upstream primer for detecting AC133965.1: CCATGGAGAACTGGGAGTCA (SEQ ID NO. 7);

[0018] Downstream primer for detecting AC133965.1: GCCCAGCAACAGAATAAGCA (SEQ ID NO. 8);

[0019] Upstream primer for detecting FAM86B2: ACAGTGCAGGGTGGGAATAA (SEQ ID NO. 9);

[0020] Downstream primer for detecting FAM86B2: AACCCAGAGACGGACAAGAG (SEQ ID NO. 10);

[0021] Upstream primer for detecting USP17L14P: ACCACCCTCATCCACCAAAT (SEQ ID NO. 11);

[0022] Downstream primer for detecting USP17L14P: TCCATTGAGTTCTTCGGGCT (SEQ ID NO. 12);

[0023] Upstream primer for detecting TPM3P6: GACCAGACTCTGCTTGACCT (SEQ ID NO. 13);

[0024] Downstream primer for detecting TPM3P6: AGGCAGAGACAGTTTGGTGA (SEQ ID NO. 14);

[0025] Upstream primer for detecting C15orf59: TGCGGTTCAGTGACAAAGTG (SEQ ID NO. 15);

[0026] Downstream primer for detecting C15orf59: ACCCTTCGAGTCTGTTCAGG (SEQ ID NO. 16).

[0027] Preferably, the detection reagent is the only key component of the kit for evaluating the anti-immunotherapy resistance and prognosis survival function of renal cancer.

[0028] Preferably, the determination of renal cancer immunotherapy resistance and prognosis survival risk comprises the following steps:

[0029] 1) Detect the expression levels of eight genes, including SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59;

[0030] 2) Based on the expression data of the eight genes obtained in step 1), the immunotherapy resistance and prognosis survival risk of renal cancer were evaluated.

[0031] Preferably, in step 2), the following formula is used to evaluate the resistance to immunotherapy and the prognosis survival risk of renal cancer:

[0032] Renal cancer immunotherapy resistance and prognostic survival score = (0.032934531 × SFTPA1) + (0.009387296 × AC104451.2) + (0.008590676 × RPS27P29) - (0.016760224 × AC133965.1) - (0.017838574 × FAM86B2) - (0.020450004 × USP17L14P) - (0.004624503 × TPM3P6) - (0.018495423 × C15orf59);

[0033] In the formula, SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6, and C15orf59 represent the expression levels of eight genes, respectively;

[0034] If the renal cancer immunotherapy resistance score is less than 0.3978, it means that the patient is predicted to have high resistance to immunotherapy and a high-risk group for prognosis survival; if the renal cancer immunotherapy resistance score is greater than 0.3978, it means that the patient is predicted to have low resistance to immunotherapy and a low-risk group for prognosis survival.

[0035] Preferably, the immunotherapy is the use of anti-PD-1 antibodies to treat renal cell carcinoma.

[0036] Preferably, the test sample is derived from a patient whose disease has progressed after receiving at least one systemic anti-angiogenic therapy.

[0037] The present invention also provides a use of a detection reagent in preparing a product for evaluating the immunotherapy resistance and prognosis survival of renal cancer, wherein the detection reagent is composed of reagents for detecting the expression amounts of the following eight genes: SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59.

[0038] Preferably, the detection reagent serves as the only key component for realizing the function of evaluating the anti-immunotherapy resistance and prognosis survival of renal cancer.

[0039] Preferably, the determination of renal cancer immunotherapy resistance and prognosis survival risk comprises the following steps:

[0040] 1) Detect the expression levels of eight genes, including SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59;

[0041] 2) Based on the expression data of the eight genes obtained in step 1), the immunotherapy resistance and prognosis survival risk of renal cancer were evaluated.

[0042] Preferably, in step 2), the anti-immunotherapy resistance and prognostic survival function of renal cancer are evaluated by the following method: renal cancer immunotherapy resistance and prognostic survival score = (0.032934531 × SFTPA1) + (0.009387296 × AC104451.2) + (0.008590676 × RPS27P29) - (0.016760224 × AC133965.1) - (0.017838574 × FAM86B2) - (0.020450004 × USP17L14P) - (0.004624503 × TPM3P6) - (0.018495423 × C15orf59);

[0043] In the formula, SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6, and C15orf59 represent the expression levels of eight genes, respectively;

[0044] If the renal cancer immunotherapy resistance score is less than 0.3978, it means that the patient is predicted to have high resistance to immunotherapy and a high-risk group for prognosis survival; if the renal cancer immunotherapy resistance score is greater than 0.3978, it means that the patient is predicted to have low resistance to immunotherapy and a low-risk group for prognosis survival.

[0045] Preferably, the immunotherapy is the use of anti-PD-1 antibodies to treat renal cell carcinoma.

[0046] Preferably, the test sample is derived from a patient whose disease has progressed after receiving at least one systemic anti-angiogenic therapy.

[0047] Compared with the prior art, the technical effect of the present invention is positive and obvious. The present invention involves a group of markers for evaluating the resistance to immunotherapy and poor survival prognosis of renal cancer and their applications, which are based on whole transcriptome sequencing of renal cancer specimens for tumor immunotherapy, screening and construction after machine learning, and can efficiently and accurately predict the resistance and clinical prognosis of renal cancer patients receiving anti-tumor immunotherapy, providing effective guidance for clinicians to make treatment decisions for renal cancer patients, reducing the occurrence of ineffective treatment, and thus reducing the treatment costs and discomfort experience of patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 : Receiver operating characteristic curve results of immunotherapy resistance score in renal cancer patient cohort.

[0049] Figure 2: Kaplan-Meier survival analysis curve of the correlation between immunotherapy resistance score and overall survival prognosis in the training cohort of patients with renal cancer.

[0050] Figure 3 : This is a Kaplan-Meier survival analysis curve chart of the correlation between immunotherapy resistance score and disease-free survival prognosis in the training cohort of renal cancer patients.

[0051] Figure 4 : This is a Kaplan-Meier survival analysis curve chart of the correlation between immunotherapy resistance score and disease-free survival prognosis in the validation cohort of renal cancer patients. DETAILED DESCRIPTION

[0052] The following is a further description of the specific embodiments and technical solutions of the present invention in conjunction with the accompanying drawings and specific examples, and the specific examples provide preferred embodiments. The reagents, equipment and methods used in the present invention are all conventional reagents, equipment and methods in the art, and all conventional commercial products with source and specific models marked, and their test methods and specific conditions are carried out according to the test methods and conditions in the instructions of the corresponding products, and if there are no special instructions, the raw materials (actual, methods and equipment) used can be purchased from conventional commercial products, and the reagents, equipment and test methods are all conventional reagents, equipment and methods in the art.

[0053] Example 1: Model construction and effect verification

[0054] 1. Experimental subjects

[0055] The study subjects were patients from three prospective clinical trials using the anti-PD-1 antibody (nivolumab) in renal cell carcinoma: CheckMate 009, CheckMate 010, and CheckMate 025. These patients had renal cell carcinoma with clear histology and disease progression after at least one systemic antiangiogenic therapy. In addition, the remaining inclusion criteria for the patients were as follows:

[0056] (1) Complete information on immunotherapy efficacy and survival follow-up data;

[0057] (2) Having whole transcriptome sequencing data;

[0058] (3) Patients with unknown immunotherapy results and incomplete survival data were excluded.

[0059] 2. Experimental process

[0060] According to the above inclusion and exclusion criteria, this study enrolled a total of 150 patients with renal cell carcinoma who received immunotherapy, and collected their complete transcriptome sequencing data for subsequent analysis.

[0061] 2.1 Randomization

[0062] This study uses a random number grouping method: first, a set of random numbers is generated using a random number generator, and then the members of each group are determined according to these numbers. Then we number the subjects (for example, from 1 to n), then randomly select a starting number from the random number table, and select random numbers that are the same as the number of subjects in a certain direction, and then group them according to the nature of the random numbers, and randomly assign them to the training cohort and the validation cohort in a ratio of 2:1. Finally, 150 patients who met the above criteria were randomly divided into a training cohort (100 cases) and a validation cohort (50 cases) for subsequent analysis.

[0063] 2.2 Screening of differentially expressed genes

[0064] We normalized the transcriptome sequencing data of all cohort patients and used the limma package in the R language environment to identify differentially expressed genes (DEGs) between immunotherapy-resistant and non-resistant patients. Genes with a P value less than 0.05 and a difference multiple greater than 1.5 were defined as DEGs, and 127 DEGs were obtained. Immunotherapy resistance was defined as non-response or disease progression after treatment with the PD-1 inhibitor NIVOLUMAB. The top 20 genes with high differential expression between immunotherapy-resistant and non-resistant patients were extracted for subsequent analysis.

[0065] 2.3 Machine learning to screen candidate genes

[0066] We further analyzed the above 40 DEGs significantly associated with renal cancer immunotherapy responsiveness using the least absolute shrinkage and selection operator (LASSO) algorithm. By introducing the L1 regularization term in the loss function, the model coefficients were forced to be sparse and some coefficients were compressed to zero, thereby achieving the effect of feature selection. This study finally screened out 8 genes (SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59) that were highly correlated with the outcome of renal cancer immunotherapy as candidate genes for the construction of a prediction model.

[0067] SFTPA1 is also called surfactant protein A1. Its main functions include participating in immune regulation and regulating the synthesis and secretion of pulmonary surfactant. AC104451.2 is the complete sequence of RP11-632N clone located on human chromosome 3. RPS27P29, also known as ribosomal protein S27 pseudogene 29, is located on the 19p12 site of human chromosome 19. Its variant expression has been found to be associated with abnormal intellectual development. AC133965.1 is a cloned fragment of RP11-781F15 located on human chromosome 2. FAM86B2 is located on the site of human chromosome 88, which can affect the activity of protein lysine n-methyltransferase and is involved in the process of methylation. Related; USP17L14P gene is located on human chromosome 4p16.1, which is involved in ubiquitination-related cellular regulatory mechanisms and can affect protein homeostasis and signal transduction processes in cells by regulating protein degradation; TPM3P6, also known as tropomyosin 3 pseudogene 6, is located on human chromosome 19 at 19q13.42; C15orf59, also known as INSYN1, has the main function of regulating synaptic transmission and synaptic plasticity, thereby regulating information transmission and connection strength between neurons (https: / / www.ncbi.nlm.nih.gov / gene / , https: / / www.genenames.org).

[0068] 2.4 Construction of a scoring model for predicting immunotherapy resistance in renal cancer

[0069] By using the RMS and glmnet packages in the R language environment, we used the expression levels of eight candidate genes in the training cohort as the basis, introduced regularization terms in the loss function to control the complexity of the model and prevent overfitting, and calculated their respective weights to construct a renal cancer immunotherapy resistance scoring model. Renal cancer immunotherapy resistance score = (0.032934531×SFTPA1)+(0.009387296×AC104451.2)+(0.008590676×RPS27P29)-(0.016760224×AC133965.1)-(0.017838574×FAM86B2)-(0.020450004×USP17L14P)-(0.004624503×TPM3P6)-(0.018495423×C15orf59). The lower the renal cancer immunotherapy resistance score, the higher its immunotherapy resistance.

[0070] 2.5 Testing of the renal cancer immunotherapy resistance scoring model

[0071] The receiver operating characteristic curve (ROC) of the renal cell carcinoma immunotherapy resistance score had an area under the curve of 96.3% in the training cohort. When the critical value was set at 0.3978, the prediction model achieved the best prediction performance. In the entire cohort of renal cell carcinoma patients, the area under the curve reached 87.5%, the specificity reached 74.4%, and the sensitivity reached 91.0% ( Figure 1 ). According to the optimal critical value of the renal cancer immunotherapy resistance score, we divided renal cancer patients into high-risk and low-risk groups, and combined them with the patients' treatment outcomes and survival prognostic indicators for correlation verification. Kaplan-Meier survival analysis showed that in the training cohort of renal cancer patients, the overall survival prognosis of patients in the high-risk group of renal cancer immunotherapy resistance score was significantly worse than that in the low-risk group (hazard ratio = 2.09, 95% confidence interval: 1.24-3.50, P = 0.0208) ( Figure 2 ), their disease-free survival prognosis was also significantly worse than that of the low-risk group (hazard ratio = 3.32, 95% confidence interval: 2.16-5.12, P < 0.0001) ( Figure 3 ). These results suggest that patients with low immunotherapy resistance scores in renal cancer have higher immunotherapy resistance and worse survival benefit from immunotherapy.

[0072] 2.6 Validation of the renal cancer immunotherapy resistance scoring model

[0073] The ROC curve of the renal cancer immunotherapy resistance score had an area under the curve of 70.8%, a sensitivity of 100%, and a specificity of 36.8% in the validation cohort. According to the optimal critical value of the renal cancer immunotherapy resistance score mentioned above, we also divided renal cancer patients into high-risk and low-risk groups, and combined them with the patients' treatment outcomes and survival prognostic indicators for correlation verification. Kaplan-Meier survival analysis showed that the disease-free survival prognosis of patients in the high-risk group of renal cancer immunotherapy resistance score was also significantly worse than that in the low-risk group (hazard ratio = 2.11, 95% confidence interval: 1.10-4.05, P = 0.0494) ( Figure 4 ). These results suggest that the renal cancer immunotherapy resistance scoring model has good generalizability and clinical applicability.

[0074] Example 2: Kit (I)

[0075] The invention comprises detection reagents and instructions, wherein the detection reagents are composed of reagents for detecting the expression amounts of the following eight genes: SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59.

[0076] Total RNA was extracted from tissue samples using the TRIzol reagent method, and ribosomal RNA was removed to retain all non-ribosomal RNA. Reverse transcriptase was then used to reverse transcribe the RNA into complementary DNA for constructing a sequencing library, which was then amplified by PCR to increase the library concentration. Finally, a complementary DNA library for sequencing was obtained. Subsequently, high-throughput sequencing platforms such as Illumina were used to perform single-stranded or double-stranded sequencing, data quality control, and alignment with reference genes on the complementary DNA library. Finally, the expression levels of the eight genes were quantitatively analyzed using the quantitative HTSeq method.

[0077] The primers for the eight genes are as follows:

[0078] SFTPA1: Upstream primer: AGGACGTTTGTGTTGGAAGC (SEQ ID NO. 1); Downstream primer: GAAGTCCCACCCAGCTTACT (SEQ ID NO. 2);

[0079] AC104451.2: Upstream primer: TACCAATGACAGCAGAGGGG (SEQ ID NO. 3); Downstream primer: TGCCTACACTCAAGCACCAT (SEQ ID NO. 4);

[0080] RPS27P29: Upstream primer: GGTGATGACAACCTTCCCAC (SEQ ID NO. 5); Downstream primer: AGCAGCCAACACACAAAAACT (SEQ ID NO. 6);

[0081] AC133965.1: Upstream primer: CCATGGAGAACTGGGAGTCA (SEQ ID NO. 7); Downstream primer: GCCCAGCAACAGAATAAGCA (SEQ ID NO. 8);

[0082] FAM86B2: upstream primer: ACAGTGCAGGGTGGGAATAA (SEQ ID NO.9); downstream primer: AACCCAGAGACGGACAAGAG (SEQ ID NO.10);

[0083] USP17L14P: Upstream primer: ACCACCCTCATCCACCAAAT (SEQ ID NO. 11); Downstream primer: TCCATTGAGTTCTTCGGGCT (SEQ ID NO. 12);

[0084] TPM3P6: upstream primer: GACCAGACTCTGCTTGACCT (SEQ ID NO. 13); downstream primer: AGGCAGAGACAGTTTGGTGA (SEQ ID NO. 14);

[0085] C15orf59: Upstream primer: TGCGGTTCAGTGACAAAGTG (SEQ ID NO. 15); Downstream primer: ACCCTTCGAGTCTGTTCAGG (SEQ ID NO. 16);

[0086] The manual states the following:

[0087] Renal cancer immunotherapy resistance score = (0.032934531 × SFTPA1) + (0.009387296 × AC104451.2) + (0.008590676 × RPS27P29) - (0.016760224 × AC133965.1) - (0.017838574 × FAM86B2) - (0.020450004 × USP17L14P) - (0.004624503 × TPM3P6) - (0.018495423 × C15orf59);

[0088] If the renal cancer immunotherapy resistance score is less than 0.3978, it means that the patient is predicted to have high resistance to immunotherapy and a high-risk prognosis survival group; if the renal cancer immunotherapy resistance score is greater than 0.3978, it means that the patient is predicted to have low resistance to immunotherapy and a low-risk prognosis survival group.

[0089] Example 3-9 Kit (two to nine)

[0090]

[0091] The above kits also include instructions, which record the following content: when the gene expression level is higher than the normal value of the single gene expression level, it belongs to the low-risk group and has a good treatment responsiveness.

[0092] Example 10-17 Kit (ten to seventeen).

[0093]

[0094]

[0095] The above kits also include instructions, which record the following contents:

[0096] Renal cancer immunotherapy resistance score = (0.032934531 × SFTPA1) + (0.009387296 × AC104451.2) + (0.008590676 × RPS27P29) - (0.016760224 × AC133965.1) - (0.017838574 × FAM86B2) - (0.020450004 × USP17L14P) - (0.004624503 × TPM3P6) - (0.018495423 × C15orf59);

[0097] If the renal cancer immunotherapy resistance score is less than 0.3978, it means that the patient is predicted to have high immunotherapy resistance and high-risk survival; if the renal cancer immunotherapy resistance score is greater than 0.3978, it means that the patient is predicted to have low immunotherapy resistance and low-risk survival. Note: When the genes not involved in the above kit are substituted into the formula for calculation, their values ​​are calculated as 0.

[0098] Example 18 Control Test

[0099] 1 Information

[0100] The subjects of this study were from the data of patients with renal cell carcinoma who were treated with the anti-PD-1 antibody (nivolumab). These patients had renal cell carcinoma with clear histology and disease progression after at least one systemic antiangiogenic therapy. In addition, the remaining inclusion criteria of the patients were as follows:

[0101] (1) Complete information on immunotherapy efficacy and survival follow-up data;

[0102] (2) Having whole transcriptome sequencing data;

[0103] (3) Patients with unknown immunotherapy results and incomplete survival data were excluded.

[0104] 2. Experimental methods

[0105] Twenty-two patients who met the above criteria were included in the study as a test cohort, and the experimental group testing scheme and comparison group one to eleven testing schemes were implemented.

[0106] Experimental group: The kit described in Example 2 was used and the evaluation was performed according to the instructions, and the results were recorded.

[0107] Comparative Example 1: Kit 2 was used to perform evaluation according to the instructions, and the results were recorded.

[0108] Comparative Example 2: Kit 5 was used and the evaluation was performed according to the instructions, and the results were recorded.

[0109] Comparative Example 3: Kit 9 was used to perform evaluation according to the instructions, and the results were recorded.

[0110] Comparative Example 4: Kit 10 was used to perform evaluation according to the instructions, and the results were recorded.

[0111] Comparative Example 5: Kit 11 was used to perform evaluation according to the instructions, and the results were recorded.

[0112] Comparative Example Group 6: Kit 12 was used to perform evaluation according to the instructions, and the results were recorded.

[0113] Comparative Example Group 7: Use Kit 13 and perform evaluation according to the instructions, and record the results.

[0114] Comparative Example Group 8: Kit 14 was used to conduct evaluation according to the instructions, and the results were recorded.

[0115] Comparative Example Group 9: Use Kit 15 and perform evaluation according to the instructions, and record the results.

[0116] Comparative Example 10: Kit 16 was used to perform evaluation according to the instructions, and the results were recorded.

[0117] Comparative Example 11: Kit 17 was used to perform evaluation according to the instructions, and the results were recorded.

[0118] After completing the above recording results, compare them with the original actual statistical results and calculate the accuracy of each group.

[0119] 3. Results

[0120] The results showed that the area under the ROC curve of the experimental group scheme for predicting tumor immunotherapy responsiveness was as high as 79.2%, the areas under the curve of the comparison groups one to three were 52.8%, 50.7% and 57.6% respectively, the areas under the curve of the comparison groups four to seven were 76.4%, 66.7%, 69.4% and 66.7% respectively, and the areas under the curve of the comparison groups eight to eleven were 74.7%, 72.2%, 73.6% and 72.2% respectively.

[0121] 4. Conclusion

[0122] The above results show that the experimental group has the highest accuracy rate. Based on rich clinical and research experience and a large number of cases in the hospital over the years, the inventor of this application selected the best combination of indicators and confirmed that it has excellent evaluation effects. It can provide effective guidance for clinical physicians in treatment decisions for patients with advanced renal disease, reduce the occurrence of ineffective treatments, and thus reduce patients' treatment costs and discomfort experience. It has strong practicality.

[0123] The above detailed description of the preferred specific embodiments of the present invention is only the preferred implementation of the present invention. It should be pointed out that ordinary technicians in this field can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in this technical field through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the existing technology should be within the scope of protection determined by the claims.

Claims

1. Biomarkers for evaluating immunotherapy resistance and prognosis survival in renal cancer, the biomarkers comprising the following eight gene combinations: SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59.

2. A kit for evaluating the immunotherapy resistance and prognosis survival of renal cancer, characterized in that: The kit comprises detection reagents capable of detecting the expression amounts of eight genes, namely, SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59.

3. The kit according to claim 2, characterized in that The detection reagent includes the following primers: Upstream primer for detecting SFTPA1: AGGACGTTTGTGTTGGAAGC; Downstream primers for detecting SFTPA1: GAAGTCCCACCCAGCTTACT; Upstream primer for detecting AC104451.2: TACCAATGACAGCAGAGGGG; Downstream primer for detecting AC104451.2: TGCCTACACTCAAGCACCAT; Upstream primer for detecting RPS27P29: GGTGATGACAACCTTCCCAC; Downstream primers for detecting RPS27P29: AGCAGCCAACACACAAAAACT; Upstream primer for detecting AC133965.1: CCATGGAGAACTGGGAGTCA; Downstream primers for detecting AC133965.1: GCCCAGCAACAGAATAAGCA; Upstream primer for detecting FAM86B2: ACAGTGCAGGGTGGGAATAA; Downstream primer for detecting FAM86B2: AACCCAGAGACGGACAAGAG; Upstream primer for detecting USP17L14P: ACCACCCTCATCCACCAAAT; Downstream primer for detecting USP17L14P: TCCATTGAGTTCTTCGGGCT; Upstream primer for detecting TPM3P6: GACCAGACTCTGCTTGACCT; Downstream primers for detecting TPM3P6: AGGCAGAGACAGTTTGGTGA; Upstream primer for detecting C15orf59: TGCGGTTCAGTGACAAAGTG; Downstream primer for detecting C15orf59: ACCCTTCGAGTCTGTTCAGG.

4. The kit according to claim 3, characterized in that The detection reagent is the only key component of the kit for evaluating the anti-immunotherapy resistance and prognosis survival function of renal cancer.

5. The kit according to any one of claims 3 to 4, characterized in that The determination of renal cancer immunotherapy resistance and prognosis survival risk includes the following steps: 1) Detect the expression levels of eight genes, including SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59; 2) Based on the expression data of the eight genes obtained in step 1), the immunotherapy resistance and prognosis survival risk of renal cancer were evaluated.

6. The kit according to claim 5, characterized in that In step 2), the following formula is used to evaluate the risk of renal cancer immunotherapy resistance and prognosis survival: Renal cancer immunotherapy resistance and prognostic survival score = (0.032934531 × SFTPA1) + (0.009387296 × AC104451.2) + (0.008590676 × RPS27P29) - (0.016760224 × AC133965.1) - (0.017838574 × FAM86B2) - (0.020450004 × USP17L14P) - (0.004624503 × TPM3P6) - (0.018495423 × C15orf59); In the formula, SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6, and C15orf59 represent the expression levels of eight genes, respectively; If the renal cancer immunotherapy resistance score is less than 0.3978, it means that the patient is predicted to have high resistance to immunotherapy and a high-risk group for prognosis survival; if the renal cancer immunotherapy resistance score is greater than 0.3978, it means that the patient is predicted to have low resistance to immunotherapy and a low-risk group for prognosis survival.

7. Use of a detection reagent in the preparation of a product for evaluating the drug resistance and prognosis survival of renal cancer immunotherapy, characterized in that: The detection reagent is composed of reagents for detecting the expression amounts of the following eight genes: SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59.

8. The use according to claim 7, characterized in that The detection reagent serves as the only key component for realizing the function of evaluating the anti-immunotherapy resistance and prognosis survival of renal cancer.

9. The use according to any one of claims 7-8, characterized in that: The determination of renal cancer immunotherapy resistance and prognosis survival risk includes the following steps: 1) Detect the expression levels of eight genes, including SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6 and C15orf59; 2) Based on the expression data of the eight genes obtained in step 1), the immunotherapy resistance and prognosis survival risk of renal cancer were evaluated.

10. The use according to claim 9, characterized in that In step 2), the anti-immunotherapy resistance and prognostic survival function of renal cancer were evaluated by the following method: renal cancer immunotherapy resistance and prognostic survival score = (0.032934531 × SFTPA1) + (0.009387296 × AC104451.2) + (0.008590676 × RPS27P29) - (0.016760224 × AC133965.1) - (0.017838574 × FAM86B2) - (0.020450004 × USP17L14P) - (0.004624503 × TPM3P6) - (0.018495423 × C15orf59); In the formula, SFTPA1, AC104451.2, RPS27P29, AC133965.1, FAM86B2, USP17L14P, TPM3P6, and C15orf59 represent the expression levels of eight genes, respectively; If the renal cancer immunotherapy resistance score is less than 0.3978, it means that the patient is predicted to have high resistance to immunotherapy and a high-risk group for prognosis survival; if the renal cancer immunotherapy resistance score is greater than 0.3978, it means that the patient is predicted to have low resistance to immunotherapy and a low-risk group for prognosis survival.

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