Biomarkers for predicting resistance to cdk4 / 6 inhibitors and uses thereof

Abstract

The application discloses non-coding RNA tRF-18-EY0VWUD2 as a biomarker for predicting breast cancer CDK4 / 6 inhibitor drug resistance. The application firstly finds that tRNA-derived small RNA, i.e., tRF-18-EY0VWUD2, can be used as a biomarker for predicting breast cancer CDK4 / 6 inhibitor drug resistance. Research results show that tRF-18-EY0VWUD2 is significantly highly expressed in CDK4 / 6 inhibitor drug resistance cell lines and patient serum, the PFS of patients with high expression of tRF-18-EY0VWUD2 is significantly poorer than that of patients with low expression of tRF-18-EY0VWUD2, and tRF-18-EY0VWUD2 is an independent prognostic factor for the curative effect of CDK4 / 6 inhibitor treated patients.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, and more specifically to a biomarker for predicting resistance to CDK4 / 6 inhibitors and its application. Background Technology

[0002] Breast cancer is the most common malignant tumor in women. The latest national cancer report released by the National Cancer Center in 2024 showed that 357,200 new cases of breast cancer were diagnosed in China in 2022, posing a serious threat to the lives and health of women. Approximately 70% of breast cancer patients are hormone receptor (HR) positive, and endocrine therapy is the cornerstone of treatment for HR-positive breast cancer.

[0003] Cyclin-dependent kinase 4 / 6 (CDK4 / 6) inhibitors are targeted drugs that regulate the cell cycle. They arrest the cell cycle in the G1 phase by blocking the phosphorylation of retinoblastoma (Rb) gene products mediated by downstream CDK4 / 6.

[0004] The clinical application of CDK4 / 6 inhibitors has changed the treatment landscape for HR-positive breast cancer. Currently, CDK4 / 6 inhibitor-targeted therapy combined with endocrine therapy has become the standard treatment for HR-positive advanced breast cancer. However, in clinical practice, nearly 50% of HR-positive breast cancer patients develop resistance to CDK4 / 6 inhibitors, and there is still a lack of effective biomarkers to predict whether a patient is sensitive to or resistant to CDK4 / 6 inhibitor therapy.

[0005] Therefore, how to effectively reverse CDK4 / 6 inhibitor resistance and find accurate biomarkers for efficacy are urgent clinical problems to be solved in HR-positive breast cancer. Summary of the Invention

[0006] The purpose of this invention is to provide a biomarker for predicting CDK4 / 6 inhibitor resistance and its application, which can predict the resistance of HR-positive breast cancer patients to CDK4 / 6 inhibitors and can serve as a biomarker for predicting the sensitivity of breast cancer to CDK4 / 6 inhibitor treatment and a potential target for drug resistance treatment.

[0007] According to a first aspect of the present invention, a biomarker for predicting resistance to CDK4 / 6 inhibitors is provided, said biomarker being a non-coding RNA tRF-18-EY0VWUD2, the nucleotide sequence of which is shown in SEQ ID NO.1.

[0008] In a second aspect of the present invention, the use of the aforementioned biomarker in the preparation of a reagent for predicting CDK4 / 6 inhibitor resistance is provided.

[0009] As an optional implementation, the reagent contains primers that can specifically recognize tRF-18-EY0VWUD2.

[0010] According to a third aspect of the present invention, a reagent for predicting resistance to CDK4 / 6 inhibitors is provided, the reagent containing primers capable of specifically recognizing tRF-18-EY0VWUD2.

[0011] According to a fourth aspect of the present invention, a kit for predicting CDK4 / 6 inhibitor resistance is provided, the kit comprising the aforementioned reagents.

[0012] According to a fifth aspect of the present invention, a method for determining the expression level of the aforementioned biomarker is provided, comprising the following steps:

[0013] Total RNA was extracted from cells / serum / patient tissues;

[0014] Add reverse transcription primers to reverse transcribe RNA into cDNA;

[0015] The expression level of tRF-18-EY0VWUD2 was determined by quantitative PCR after adding the tRF-18-EY0VWUD2 pre-primer and the tRF-18-EY0VWUD2 post-primer.

[0016] As an optional implementation, the nucleotide sequence of the reverse transcription primer is shown in SEQ ID NO.2, the nucleotide sequence of the tRF-18-EY0VWUD2 pre-primer is shown in SEQ ID NO.3, and the nucleotide sequence of the tRF-18-EY0VWUD2 post-primer is shown in SEQ ID NO.4.

[0017] According to a sixth aspect of the present invention, a method for evaluating resistance to CDK4 / 6 inhibitors is provided, comprising the following steps:

[0018] The expression level of RNA tRF-18-EY0VWUD2 was determined using the aforementioned method;

[0019] The ΔCt value between the target gene and the internal reference gene was used as a relative quantitative indicator. When ΔCt≤7.4, it indicated that the patient was resistant to CDK4 / 6 inhibitors.

[0020] According to a seventh aspect of the present invention, a method for knocking down the expression level of the aforementioned biomarker is provided, comprising the following steps:

[0021] In CDK4 / 6 inhibitor-resistant cell lines MCF7 and T47D, the small nucleic acid inhibitor tRF-inhibitor targeting tRF-18-EY0VWUD2 was transfected, and a negative control was set up.

[0022] After transfection, cells are cultured for an appropriate time, then cells are collected and total RNA is extracted.

[0023] The extracted total RNA was reverse transcribed to synthesize cDNA;

[0024] Using cDNA as a template, the expression level of tRF-18-EY0VWUD2 was detected by qPCR to verify the knockdown efficiency of tRF-18-EY0VWUD2.

[0025] As an optional implementation, the nucleotide sequence of the tRF-inhibitor is shown in SEQ ID NO.5.

[0026] This invention has the following outstanding substantive features and significant progress:

[0027] This study is the first to discover that tRNA-derived small RNA, tRF-18-EY0VWUD2, can serve as a biomarker for predicting CDK4 / 6 inhibitor resistance in breast cancer. The results showed that tRF-18-EY0VWUD2 was significantly overexpressed in CDK4 / 6 inhibitor-resistant cell lines and patient serum. Patients with high tRF-18-EY0VWUD2 expression had significantly worse progression-free survival (PFS) than patients with low tRF-18-EY0VWUD2 expression, and it was an independent prognostic factor for the efficacy of CDK4 / 6 inhibitor treatment.

[0028] tRF-18-EY0VWUD2 plays an important biological role in CDK4 / 6 inhibitor resistance in breast cancer and can serve as a biomarker for predicting the sensitivity of breast cancer to CDK4 / 6 inhibitor treatment, providing new clues to CDK4 / 6 inhibitor resistance in breast cancer. Attached Figure Description

[0029] Figure 1 This invention relates to the screening of differentially derived tRNA fragments in the serum of CDK4 / 6 inhibitor-sensitive and drug-resistant patients; wherein, Figure 1 Part A in the figure represents the differential expression of tRNA-derived fragments in the serum of CDK4 / 6 inhibitor-sensitive and drug-resistant patients screened by high-throughput sequencing. Figure 1 Part B of the image shows the 20 tRFs with the most significant differential expressions in the heatmap.

[0030] Figure 2 This invention relates to the correlation analysis of differential tRF expression in CDK4 / 6 inhibitor sensitivity and resistance, as well as patient efficacy and prognosis; wherein,Figure 2 Parts A and B in the figure are for qRT-PCR detection of differential tRF expression levels in CDK4 / 6 inhibitor-sensitive and drug-resistant cell lines; Figure 2 Part C in the figure represents the difference in tRF-18-EY0VWUD2 expression in the serum of CDK4 / 6 inhibitor-sensitive and drug-resistant patients; Figure 2 Part D in the diagram represents the ROC curve analysis of the sensitivity and specificity of tRF-18-EY0VWUD2 in predicting CDK4 / 6 inhibitor resistance. Figure 2 Part E in the figure represents the effect of tRF-18-EY0VWUD2 expression level on PFS in patients analyzed by KM curve. ("*": p < 0.05, "**": p < 0.01, "***": p < 0.001, "****": p < 0.0001).

[0031] Figure 3 This invention relates to the overexpression and knockdown effects of tRF-18-EY0VWUD2 and the CCK8 cell proliferation experiment; wherein, Figure 3 Parts A and B in the figure are qRT-PCR detection of the expression level of tRF-18-EY0VWUD2 in CDK4 / 6 inhibitor sensitive and drug-resistant cell lines after overexpression and knockdown of tRF-18-EY0VWUD2, respectively; Figure 3 Parts C and D in the figure represent the cell viability of CDK4 / 6 inhibitor-sensitive cell lines after overexpression of tRF-18-EY0VWUD2, as detected by CCK8 assay. Figure 3 Parts E and F in the figure represent the cell viability of tRF-18-EY0VWUD2 knocked down in CDK4 / 6 inhibitor-resistant cell lines as detected by CCK8 assay. ("*": P < 0.05, "**": P < 0.01, "***": P < 0.001, "****": P < 0.0001).

[0032] Figure 4 In this embodiment of the invention, tRF-18-EY0VWUD2 regulates the resistance of breast cancer cells to CDK4 / 6 inhibitors; wherein, Figure 4 Parts A and C in the figure are for the colony formation assay to detect the effect of tRF-18-EY0VWUD2 overexpression on the colony formation ability of MCF7 and T47D sensitive cell lines; Figure 4 The colony formation assays of parts B and D were used to examine the effect of tRF-18-EY0VWUD2 knockdown on the colony formation ability of MCF7 / PR and T47D / PR drug-resistant cell lines. Figure 4The E and G portions of the EdU assay were used to detect the effect of tRF-18-EY0VWUD2 overexpression on the proliferation of MCF7 and T47D sensitive cell lines; Figure 4 The F and H portions of the EdU assay were used to examine the effect of tRF-18-EY0VWUD2 knockdown on the proliferation of MCF7 / PR and T47D / PR drug-resistant cell lines. (Scale bar: 10 μm; "*": P < 0.05, "**": P < 0.01, "***": P < 0.001).

[0033] Figure 5 In this embodiment of the invention, tRF-18-EY0VWUD2 promotes CDK4 / 6 inhibitor resistance in breast cancer in animals; wherein, Figure 5 Part A in the figure represents the changes in tumor volume in mice during CDK4 / 6 inhibitor treatment; Figure 5 Part B in the figure represents the volume and weight of tumor tissue removed after euthanasia of mice; Figure 5 Part C describes the detection of Ki67 expression levels in mouse tumor tissues using HE staining and immunohistochemistry. (Scale bar: 10 μm; "*": P < 0.05, "**": P < 0.01, "***": P < 0.001). Detailed implementation method.

[0034] To better understand the technical content of the present invention, specific embodiments are described below in conjunction with the accompanying drawings.

[0035] Various aspects of the invention are described in this disclosure with reference to the accompanying drawings, in which numerous illustrative embodiments are shown. The embodiments of this disclosure are not necessarily intended to encompass all aspects of the invention. It should be understood that the various concepts and embodiments described above, as well as those described below in more detail, can be implemented in any of a number of ways.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; where specific conditions are not specified in the examples, conventional conditions or conditions recommended by the manufacturer shall apply. Reagents or instruments whose manufacturers are not specified are all commercially available products.

[0037] Any step described in any method or process claim may be performed in any order, and is not limited to the order stated in the claims.

[0038] In this invention, the term "drug resistance" refers to the phenomenon that tumor cells develop resistance to drugs that were originally effective, leading to a decrease or failure of treatment efficacy.

[0039] In this invention, the term "non-coding RNAs" refers to RNA that does not encode proteins. The human genome is predominantly composed of non-coding RNAs; only 2% of the transcripts produced in the human genome are coding RNAs, while the remaining 98% are non-coding RNAs. These are functional RNA molecules that cannot be translated into proteins. These non-coding RNAs are widely involved in human physiological and pathological activities and are closely related to many tumors.

[0040] In this invention, the term "prognosis" refers to the prediction of disease progression based on existing diagnostic evidence.

[0041] In this invention, when it is mentioned that the biomarker is used, it means that the biomarker is measured in an in vitro sample (i.e., in vitro) of a biological tissue or organ.

[0042] Novel small noncoding RNAs (sncRNAs) originate from tRNA derivative fragments, which can be divided into two main categories: tiRNAs (tRNA half-molecules) and tRFs (small RNA molecules derived from tRNA). tRFs can be further divided into four types: ① tRF-1: derived from the 3' end of the precursor tRNA; ② tRF-3: derived from the 3' end of the mature tRNA; ③ tRF-5: derived from the 5' end of the mature tRNA; ④ i-tRF: derived from the middle region of the mature tRNA.

[0043] Currently, CDK4 / 6 inhibitors combined with endocrine therapy have become the standard treatment strategy for HR-positive advanced breast cancer. However, research on whether tRFs are related to CDK4 / 6 inhibitor resistance in breast cancer remains limited. Therefore, this invention aims to screen for differential tRF expression in CDK4 / 6 inhibitor-sensitive and resistant breast cancer patients using high-throughput sequencing, further validate differentially expressed tRFs at the cellular and clinical levels, and evaluate the correlation between tRFs and the efficacy of CDK4 / 6 inhibitor treatment and patient prognosis in HR-positive breast cancer through survival analysis, univariate analysis, and multivariate analysis.

[0044] In one embodiment of the present invention, a biomarker for predicting CDK4 / 6 inhibitor resistance is provided. The biomarker is a non-coding RNA named tRF-18-EY0VWUD2 in the tRF database MINTbase, and its nucleotide sequence is shown in SEQ ID NO.1 (Table 1).

[0045] Table 1 tRF-18-EY0VWUD2 sequence

[0046] SEQ ID NO:1 ACTTGACCGCTCTGACCA

[0047] In another embodiment of the invention, the use of non-coding RNA tRF-18-EY0VWUD2 in the preparation of a reagent for predicting CDK4 / 6 inhibitor resistance is provided.

[0048] In some embodiments, the reagent used to predict CDK4 / 6 inhibitor resistance contains primers that specifically recognize tRF-18-EY0VWUD2.

[0049] In another embodiment of the present invention, a reagent for predicting CDK4 / 6 inhibitor resistance is provided, the reagent containing primers capable of specifically recognizing tRF-18-EY0VWUD2.

[0050] In another embodiment of the present invention, a kit for predicting CDK4 / 6 inhibitor resistance is also provided, the kit comprising the aforementioned reagent for predicting CDK4 / 6 inhibitor resistance.

[0051] In other embodiments of the present invention, a method for determining the expression level of non-coding RNA tRF-18-EY0VWUD2 is also provided, the method comprising:

[0052] (1) Extract total RNA from cells / serum / patient tissue;

[0053] (2) Add reverse transcription primers to reverse transcribe RNA into cDNA;

[0054] (3) Add the tRF-18-EY0VWUD2 front primer and the tRF-18-EY0VWUD2 back primer, and determine the expression level of tRF-18-EY0VWUD2 by quantitative PCR.

[0055] In some embodiments, total RNA is extracted from cells / serum / patient tissue using the Trizol method.

[0056] In some embodiments, the nucleotide sequence of the reverse transcription primer is shown in SEQ ID NO.2 (Table 2).

[0057] In some embodiments, the nucleotide sequence of the tRF-18-EY0VWUD2 preprime is shown in SEQ ID NO.3 (Table 2).

[0058] In some embodiments, the nucleotide sequence of the tRF-18-EY0VWUD2 primer is shown in SEQ ID NO.4 (Table 2).

[0059] In some embodiments, the ΔCt value is used as a relative quantitative indicator, where ΔCt is the difference between the circulation threshold Ct of the target gene tRF-18-EY0VWUD2 and the circulation threshold Ct of the internal reference gene in the same reaction system.

[0060] Table 2

[0061] SEQ ID NO:2 GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGGTCA SEQ ID NO:3 GCGCGACTTGACCGCTC SEQ ID NO:4 AGTGCAGGGTCCGAGGTATT

[0062] This invention, based on the non-coding RNA tRF-18-EY0VWUD2 biomarker and the aforementioned method for measuring non-coding RNA tRF-18-EY0VWUD2, can rapidly and sensitively measure tRF-18-EY0VWUD2 expression, thereby enabling the assessment of CDK4 / 6 inhibitor resistance.

[0063] Therefore, in another example of the present invention, a method for evaluating resistance to CDK4 / 6 inhibitors is also provided, the method comprising the following steps:

[0064] (1) The expression level of tRF-18-EY0VWUD2 was detected using the aforementioned method for determining the expression level of non-coding RNA tRF-18-EY0VWUD2;

[0065] (2) The ΔCt value between the target gene and the internal reference gene is used as a relative quantitative indicator. When ΔCt≤7.4, it indicates that the patient is resistant to CDK4 / 6 inhibitors.

[0066] In other embodiments of the present invention, a method for knocking down the expression level of non-coding RNA tRF-18-EY0VWUD2 is also provided, specifically including the following steps:

[0067] (1) Transfect the small nucleic acid inhibitor tRF-inhibitor targeting tRF-18-EY0VWUD2 into CDK4 / 6 inhibitor resistant cell lines MCF7 and T47D, and set up a negative control at the same time;

[0068] (2) After transfection, culture the cells for an appropriate time, collect the cells and extract total RNA;

[0069] (3) The extracted total RNA was reverse transcribed to synthesize cDNA; wherein the nucleotide sequence of the reverse transcription primer is shown in SEQ ID NO.2 (Table 2);

[0070] (4) Using cDNA as a template, the expression level of tRF-18-EY0VWUD2 was detected by qPCR to verify the knockdown efficiency of tRF-18-EY0VWUD2.

[0071] In some embodiments, the nucleotide sequence of the tRF-inhibitor is shown in SEQ ID NO.5 (Table 3).

[0072] Table 3

[0073] SEQ ID NO:5 mU*mG*mG*mU mC mA mG mA mG mC mG mG mU mC mA mA mG mU*dTdT

[0074] To facilitate better understanding, the present invention will be further illustrated below with several specific examples, but the preparation process is not limited to these examples, and the content of the present invention is not limited to these examples.

[0075] Unless otherwise specified, the following embodiments are all conventional methods.

[0076] Unless otherwise specified, all materials and reagents used in the following examples are commercially available.

[0077] The corresponding primers used in the following examples are shown in Table 5.

[0078] Example 1

[0079] I. Materials

[0080] (1) Screening and collection of clinical samples

[0081] The clinical samples from breast cancer patients included in this study were all obtained from the Department of Oncology, First Affiliated Hospital of Nanjing Medical University. Serum samples were collected from 93 patients with HR-positive advanced breast cancer who visited the hospital from January 1, 2020 to December 31, 2022 and were using CDK4 / 6 inhibitors (all palbociclib). Follow-up continued until March 30, 2024. High-throughput sequencing was performed on the serum of 6 patients to screen for differentially expressed tRNA-derived fragments, and serum samples from 87 patients underwent subsequent validation and clinical relevance analysis. All samples included in the study had complete clinicopathological data, including age, menstrual status, clinical TNM stage, Ki67 index, HR expression status, HER2 expression status, and metastatic sites (detailed clinical data are shown in Table 4). Blood samples were collected before the patient's first use of a CDK4 / 6 inhibitor. Whole blood samples were collected using purple blood collection tubes containing EDTA, incubated at room temperature for 15 minutes, centrifuged at 5000 g for 10 minutes, and the serum supernatant was transferred to a new EP tube and centrifuged at 16000 g for 15 minutes. Finally, the separated serum was stored at -80°C. This study has been ethically approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University, and all patients signed informed consent forms before blood collection.

[0082] Table 4

[0083] (2) Cell line origin

[0084] The human MCF7 and T47D breast cancer cells involved in this study were purchased from the American College of Cell Bank (ATCC) in the United States. The MCF7 and T47D cells were cultured for more than 6 months in cell culture medium containing increasing concentrations of palbociclib (PD 0332991) to construct CDK4 / 6 inhibitor-resistant cell lines (MCF / PR and T47D / PR). Ultimately, the drug-resistant cells were able to survive stably in a medium with a PD 0332991 concentration of 0.1 μmol / L.

[0085] (3) Main reagents

[0086] DMEM high-glucose medium (Gibco, USA), culture dishes (Corning, USA), PBS phosphate-buffered saline (Gibco, USA), South American fetal bovine serum (Gibco, USA), penicillin-streptomycin antibiotic solution (Syntox, China), phenol red EDTA trypsin digestion solution (Syntox, China), serum-free cell cryopreservation solution (Syntox, China), RNase-freed H2O (Takara, China), DEPC (Sigma, USA), PD 0332991 (Pfizer, USA), Trizol and Trizol LS (Life, USA), reverse transcription kit (Novizan, China), SYBR Green PCR Kit (Novizan, China).

[0087] (4) Main instruments

[0088] Carbon dioxide incubator (Thermo Fisher Scientific, USA), 37°C incubator (Foma, China), -80°C freezer (Sanyo, Japan), ultra-high speed centrifuge (L-100XP) (Beckman, USA), PCR synthesizer (Longi, China), quantitative fluorescence PCR instrument (Roche, USA).

[0089] II. Experimental Methods

[0090] (1) RNA-Seq sequencing

[0091] High-throughput sequencing of tRFs was performed at Shanghai Kangcheng Biotechnology Co., Ltd., using the Illumina Nextseq 500 sequencing platform. The MINTbase (https: / / cm.jefferson.edu / MINTbase / ), tRFdb (http: / / genome.bioch.virginia.edu / trfdb), and tsRBase (http: / / www.tsrbase.org) databases were used for tRNA-derived fragment sequence alignment. Gene read count analysis was performed using HTseq, and differential gene screening was performed using the DEseq2 algorithm.

[0092] (2) Cell culture, resuscitation, cryopreservation and passage

[0093] 2.1 Cell Culture

[0094] All cells were cultured in a 37°C incubator containing 5% CO2 in DMEM medium containing 10% fetal bovine serum and 1% penicillin-streptomycin solution. Additionally, 0.1 μmol / L PD 0332991 was routinely added to the culture medium of CDK4 / 6 inhibitor-resistant cells to maintain resistance.

[0095] 2.2 Cell passage

[0096] Remove the culture dish from the incubator and wash twice with 2 mL of PBS solution. Aspirate the PBS, add 1 mL of trypsin to the culture dish, and then return the dish to the incubator. After 2-3 minutes, add 2 mL of complete culture medium to stop digestion and mix thoroughly. Transfer the liquid from the culture dish to a 15 mL centrifuge tube and centrifuge at 800 rpm for 3 minutes. Remove the supernatant, add 1 mL of complete culture medium, and mix thoroughly. Transfer the liquid to the culture dish containing the complete culture medium, and finally place the culture dish in the incubator for incubation.

[0097] 2.3 Cell cryopreservation

[0098] Remove the culture medium from the culture dish, wash twice with 2 mL of PBS, then add 1 mL of trypsin, place in an incubator, add 2 mL of complete culture medium after 2 minutes to stop digestion, mix well and transfer to a centrifuge tube, centrifuge at 800 rpm for 5 minutes, remove the supernatant, add cell cryopreservation solution, mix well and place in a -80℃ freezer overnight, and transfer to a liquid nitrogen tank for storage the next day.

[0099] 2.4 Cell resuscitation

[0100] Quickly place the cryovials into a 37°C water bath to thaw completely. Immediately dry them and sterilize the outside with 75% alcohol. Then, aspirate the cell suspension from the cryovials into a centrifuge tube containing complete culture medium and centrifuge at 800 rpm for 5 minutes. Remove the supernatant, add preheated complete culture medium, mix well, and transfer the resuspended cells to a new culture dish. Finally, incubate in an incubator.

[0101] (3) RNA extraction and RT-qPCR experiment

[0102] 3.1 Total RNA extraction from cells using the Trizol method

[0103] (1) After centrifuging the cells, discard the culture medium, wash twice with PBS and collect them in a 1.5 mL RNase-free EP tube. Then add 1 mL of Trizol, mix by pipetting and let stand at room temperature for 15 minutes.

[0104] (2) Add 0.2 mL of chloroform, mix by inverting, then let stand at room temperature for 5 minutes, and place the centrifuge tube in a pre-cooled 4°C centrifuge and centrifuge at 12000 g for 15 minutes.

[0105] (3) After centrifugation, the liquid separates into layers. The uppermost aqueous phase containing RNA is transferred to a new 1.5 mL EP tube, and an equal volume of isopropanol is added. The tube is then incubated at 4°C for 10 minutes.

[0106] (4) Place the EP tube in a centrifuge at 4°C and centrifuge at 12000 g for 15 minutes. Remove the supernatant and carefully avoid removing the RNA precipitate at the bottom of the centrifuge tube.

[0107] (5) Add 1 mL of 75% ethanol to resuspend the RNA precipitate, gently invert to mix, and place in a centrifuge at 4°C. Centrifuge at 7500g for 5 minutes.

[0108] (6) Remove the supernatant and let the RNA precipitate stand for 5 minutes to dry;

[0109] (7) Add a certain amount of DEPC water, mix well, and determine the concentration and purity of RNA;

[0110] (8) Freeze the RNA at -80℃, or proceed directly with subsequent steps such as cDNA reversal.

[0111] 3.2 Total RNA extraction from serum using the Trizol method

[0112] (1) After the specimen is thawed, 0.25 mL of serum is aspirated into a 1.5 mL centrifuge tube, 0.75 mL of Trizol LS is added, the mixture is pipetted and mixed, and the tube is placed on ice for 10 minutes.

[0113] (2) Add 0.2 mL of chloroform, mix by inverting, then let stand at room temperature for 5 minutes, and place the centrifuge tube in a pre-cooled 4°C centrifuge and centrifuge at 12000 g for 15 minutes.

[0114] (3) After centrifugation, transfer the supernatant to a new 1.5 mL EP tube, add an equal volume of isopropanol to the supernatant, mix by inverting, and place on ice for 10 minutes.

[0115] (4) Place the EP tube into a pre-cooled 4°C centrifuge and centrifuge at 12000 g for 15 minutes;

[0116] (5) Discard the supernatant, carefully avoid aspirating the RNA precipitate at the bottom of the centrifuge tube, add 1 mL of 75% ethanol, mix by inverting, place in a centrifuge at 4℃, and centrifuge at 7500 g for 15 minutes.

[0117] (6) Remove the supernatant, invert it onto clean filter paper, centrifuge at 5000 g for 3 minutes, then discard the supernatant, let it stand to dry, and evaporate the ethanol completely.

[0118] (7) Add a certain amount of DEPC water, mix well, determine the concentration and purity of RNA, freeze the RNA at -80℃, or directly carry out subsequent cDNA reversal and other steps.

[0119] 3.3 Real-time quantitative PCR detection of tRF expression using stem-loop method

[0120] Total RNA was extracted from cells and serum using the Trizol method. One ng of RNA was converted to cDNA using random primers and a reverse transcription kit. The cDNA template was diluted and mixed with a SYBR Green PCR Kit, then incubated in a real-time PCR instrument. U6 and GAPDH were used as internal controls. Specific primers were synthesized by Ribobio, and their sequences are listed in Table 5. Each sample group was prepared in triplicate or at least, with a negative control included.

[0121] Table 5

[0122] tRF-Pro-AGG-005 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAGCGAG tRF-Pro-AGG-005 Forward TCGTTGGTCTAGGGGTATGATT tRF-Pro-AGG-005 Reverse AGTGCAGGGTCCGAGGTATT tRF-Val-CAC-001 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGGTGT tRF-Val-CAC-001 Forward GCGCGCCCGGGCGGAA tRF-Val-CAC-001 Reverse AGTGCAGGGTCCGAGGTATT tRF-Ala-CGC-012 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAAAATC tRF-Ala-CGC-012 Forward CGCGAGGCGATCACGTA tRF-Ala-CGC-012 Reverse AGTGCAGGGTCCGAGGTATT tRF-Leu-TAG-002 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACGCTCGG tRF-Leu-TAG-002 Forward CGCGCGGGTAGTGTGG tRF-Leu-TAG-002 Reverse AGTGCAGGGTCCGAGGTATT tRF-Val-TAC-001 GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGGTCA tRF-Val-TAC-001 Forward GCGCGAACTTGACCGCTC tRF-Val-TAC-001 Reverse AGTGCAGGGTCCGAGGTATT tRF-Val-TAC-002 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGGTCA tRF-Val-TAC-002 Forward GCGCGACTTGACCGCTC tRF-Val-TAC-002 Reverse AGTGCAGGGTCCGAGGTATT tRF-Thr-AGT-019 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACTGGAGG tRF-Thr-AGT-019 Forward CGTCGAATCCCAGCGGTG tRF-Thr-AGT-019 Reverse AGTGCAGGGTCCGAGGTATT tRF-Gln-TTG-012 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACATTCTC tRF-Gln-TTG-012 Forward GGGTGTGATAGGTGGCACG tRF-Gln-TTG-012 Reverse AGTGCAGGGTCCGAGGTATT tRF-Ser-GCT-107 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAGCAGT tRF-Ser-GCT-107 Forward CGCGGAGAAAGCTCACAAGA tRF-Ser-GCT-107 Reverse AGTGCAGGGTCCGAGGTATT tRF-Glu-TTC-003 RT GTCGTATCCAGTGCAGGGTCCGAGGTATTCGCACTGGATACGACAACAGA tRF-Glu-TTC-003 Forward CGCGATGATGTATGCTTTGTT tRF-Glu-TTC-003 Reverse AGTGCAGGGTCCGAGGTATT U6 Forward CTCGCTTCGGCAGCACA U6 Reverse AACGCTTCACGAATTTGCGT U6 RT AACGCTTCACGAATTTGCGT GAPDH Forward ATCAAGTGGGGCGATGCTG GAPDH Reverse ACCCATGACGAACATGGGG GAPDH RT

[0123] The reverse transcription system is shown in Table 6. After the system was mixed evenly, it was briefly centrifuged and reverse transcribed according to the program of reacting at 42℃ for 6 minutes and 70℃ for 10 minutes. The RT product after the reaction was temporarily stored in a 4℃ refrigerator and then verified by stem-loop method real-time quantitative PCR (qPCR system is shown in Table 7; qPCR program: 95℃ pre-denaturation for 30 s, followed by heating at 95℃ for 10 s, cooling at 60℃ for 30 s, 40 cycles).

[0124] Table 6

[0125] RNA Template (1 ug) × μL Bulge-Loop RT Primer 2 μL Reverse Transcription Buffer 1 μL RNase Mix 1 μL <![CDATA[RNase-free H2O]]> Up to 20 μL

[0126] Table 7

[0127] 5×SYBR Green Mix 10 μL RT products 2 μL PCR Forward Primer 1 μL PCR Reverse Primer 1 μL <![CDATA[ddH2O]]> Up to 20 μL

[0128] (4) Statistical analysis

[0129] Statistical analysis was performed using SPSS Statistics 26.0 and Graphpad Prism V8.0.2 software. The differences in tRF expression in CDK4 / 6 inhibitor-sensitive and drug-resistant cell lines and patient serum were calculated using t-tests. The correlation between tRF expression, patient clinicopathological factors and prognosis of CDK4 / 6 inhibitor-treated breast cancer patients was analyzed using log-rank test. Multivariate analysis was performed using Cox model, and P < 0.05 was considered statistically significant.

[0130] III. Results Analysis

[0131] (1) Screening and validation of differentially derived tRNA fragments in CDK4 / 6 inhibitor-sensitive and drug-resistant patients

[0132] To detect the expression of differentially expressed tRNA-derived fragments in CDK4 / 6 inhibitor-sensitive and CDK4 / 6 inhibitor-resistant patients with HR-positive breast cancer, high-throughput sequencing was performed on serum samples from three CDK4 / 6 inhibitor-sensitive and three CDK4 / 6 inhibitor-resistant patients.

[0133] Sequencing results as follows Figure 1 As shown, the results indicated that 570 differentially expressed tRNA derivative fragments were screened from the serum of patients sensitive and resistant to CDK4 / 6 inhibitors. Figure 1 Part A of the graph shows the 20 tRFs with the most significant differences in expression. Figure 1 Part B of the document.

[0134] By combining data from tRF databases {including MINTbase (https: / / cm.jefferson.edu / MINTbase), tRFdb (http: / / genome.bioch.virginia.edu / trfdb / search.php), and tsRBase (http: / / www.tsrbase.org)}, tRNA-derived fragments with incomplete information were removed. Furthermore, based on the principles of large intergroup fold differences, small intragroup differences, and high copy numbers, 10 tRFs with the most significant differences were finally selected for further validation.

[0135] Subsequently, differential tRF expression was validated in CDK4 / 6 inhibitor-sensitive cell lines (MCF7 and T47D) and CDK4 / 6 inhibitor-resistant cell lines (MCF7 / PR and T47D / PR). The results are as follows: Figure 2 As shown in Parts A and B, the qRT-PCR results showed that tRF-VAL-TAC-002 was significantly and stably highly expressed in CDK4 / 6 inhibitor-resistant cell lines compared with CDK4 / 6 sensitive cell lines.

[0136] Therefore, we selected tRF-VAL-TAC-002 as the target for CDK4 / 6 inhibitor resistance and conducted subsequent clinical sample validation.

[0137] (2) Correlation analysis of tRF-18 expression with efficacy and prognosis in patients treated with CDK4 / 6 inhibitors

[0138] tRF-VAL-TAC-002 originates from the 3' end of tRNAVal and is named tRF-18-EY0VWUD2 in the MINTbase database. To further validate the expression level of tRF-18-EY0VWUD2 in CDK4 / 6 inhibitor-sensitive and CDK4 / 6 inhibitor-resistant breast cancer patients, serum samples were collected from 87 CDK4 / 6 inhibitor-sensitive and 87 CDK4 / 6 inhibitor-resistant patients. The clinicopathological characteristics of the patients are shown in Table 1.

[0139] qRT-PCR results showed that tRF-18-EY0VWUD2 was significantly highly expressed in the serum of patients resistant to CDK4 / 6 inhibitors. Figure 2 Part C of the data; Receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) was 0.9032 (P < 0.0001). Figure 2 Part D of the data suggests that tRF-18-EY0VWUD2 expression levels have good specificity and accuracy in distinguishing between CDK4 / 6 inhibitor-sensitive and CDK4 / 6 inhibitor-resistant patients; Kaplan-Meier survival curve analysis shows that patients with high tRF-18-EY0VWUD2 expression have significantly worse progression-free survival (PFS) than patients with low expression. Figure 2 (Part E in the text).

[0140] Further univariate analysis revealed that tRF-18-EY0VWUD2 expression level, disease-free survival (DFS), and Ki67 proliferation index may be associated with patient prognosis, with statistically significant differences (Table 6). Other clinicopathological factors such as age (≤50 years, >50 years), menstrual status (premenopausal, postmenopausal), primary tumor stage (I / II, III / IV), metastatic site (visceral metastasis, non-visceral metastasis), and HER2 expression status (positive, negative) were not significantly associated with patient prognosis. Cox multivariate analysis showed that tRF-18-EY0VWUD2 and DFS were independent prognostic factors for the efficacy of CDK4 / 6 inhibitor treatment (Table 8).

[0141] The above results indicate that high expression of tRF-18-EY0VWUD2 is significantly associated with CDK4 / 6 inhibitor resistance in breast cancer and poor patient prognosis. tRF-18-EY0VWUD2 can serve as a potential biomarker for predicting CDK4 / 6 inhibitor resistance in breast cancer. Furthermore, when ΔCt ≤ 7.4 was detected in the serum of patients treated with CDK4 / 6 inhibitors by qRT-PCR, resistance was indicated.

[0142] Table 8. Analysis of the correlation between clinicopathological factors and prognosis in patients with advanced breast cancer treated with CDK4 / 6 inhibitors.

[0143] Example 2

[0144] I. The impact of aberrant tRF-18-EY0VWUD2 expression at the cellular level on CDK4 / 6 inhibitor resistance

[0145] First, the mimic (sequence 3' - dTdT UGGUCAGAGCGG UC AAGU - 5'), inhibitor (sequence 5' - mU*mG*mG*mU mC mA mG mA mG mC mG mG mU mCmA mA mG mU*dTdT - 3') and their respective control groups of tRF-18-EY0VWUD2 were transfected into the parental and drug-resistant cell lines MCF7 and T47D, respectively. (The tRF-mimic / inhibitor cell transfection method was as follows: sensitive and drug-resistant cells were cultured in an incubator until the cell density reached 70-90%. Then, the cell transfection reagent Lipofectamine 3000, tRF-mimic / tRF-inhibitor, and culture medium were mixed evenly, incubated at room temperature, and then placed in an incubator for further culture.)

[0146] Overexpression and knockdown effects were detected by RT-qPCR. The results confirmed that tRF-18-EY0VWUD2 expression was significantly increased in MCF7 and T47D parental cells transfected with tRF-18-EY0VWUD2 mimic; and significantly decreased in drug-resistant MCF7 / PR and T47D / PR cells transfected with tRF-18-EY0VWUD2 inhibitor. This demonstrates that tRF-18-EY0VWUD2 mimic and inhibitor can achieve significant overexpression and knockdown effects, respectively. Figure 3 Part A of the text - Figure 3 Part B of the middle section.

[0147] II. Biological functions of aberrant tRF-18-EY0VWUD2 expression in breast cancer

[0148] The effects of overexpression or knockdown of tRF-18-EY0VWUD2 on the sensitivity of CDK4 / 6 inhibitor drugs in breast cancer cells were examined using CCK8, clonogenic, and EdU assays.

[0149] CCK8 experimental results showed that overexpression of tRF-18-EY0VWUD2 significantly promoted MCF7 ( Figure 3 Part C of the text) and T47D ( Figure 3 The E-part of the cell line showed resistance to CDK4 / 6 inhibitors, while inhibition of tRF-18-EY0VWUD2 expression significantly enhanced MCF7 / PR ( Figure 3 Part D in the middle) and T47D / PR ( Figure 3 The F-part cell line in the study of drug sensitivity to CDK4 / 6 inhibitors.

[0150] Clonogenic assays showed that overexpression of tRF-18-EY0VWUD2 enhanced the clonogenic ability of the parental cell line. Figure 4 Part A of the Figure 4 (Part C of the text), while knocking down tRF-18-EY0VWUD2 weakened the clonogenic ability of drug-resistant cell lines (part C of the text). Figure 4 Part B of the text Figure 4 (Part D in the text).

[0151] EdU experiments showed that overexpression of tRF-18-EY0VWUD2 enhanced the proliferation ability of the parental cell line. Figure 4 Part E in Figure 4 Knocking down the G portion of tRF-18-EY0VWUD2 inhibited the proliferation of drug-resistant cell lines. Figure 4 The F part in Figure 4 (The H part in the text).

[0152] The above results indicate that high expression of tRF-18-EY0VWUD2 in CDK4 / 6 inhibitor-resistant cells can promote cell proliferation and thus reduce the sensitivity of breast cancer cells to CDK4 / 6 inhibitors.

[0153] III. Biological functions of tRF-18-EY0VWUD2 in vivo

[0154] Nude mice were used to construct an orthotopic breast cancer tumor model by transfecting MCF7 / PR and T47D / PR cells with tRF-18-EY0VWUD2 inhibitor or inhibitor-NC, with 5 mice in each group; the tumors were induced to grow to a size of 200 mm. 3Subsequently, mice were treated with CDK4 / 6 inhibitors (PD concentration of 5 mg / kg) once daily by gavage.

[0155] The results showed that, compared with the inhibitor-NC group, the tRF-18-EY0VWUD2 inhibitor group had smaller tumor volume, lighter weight, significantly slower growth rate, and higher sensitivity to CDK4 / 6 inhibitors. Figure 5 Part A of the text - Figure 5 Part B of the document.

[0156] In addition, HE staining and immunohistochemistry were performed on paraffin-embedded and sectioned mouse tumor tissues to analyze the expression level of Ki67 in mouse tumor tissues. The results showed that mice transfected with tRF-18-EY0VWUD2 inhibitor had the lowest Ki67 expression level after treatment with CDK4 / 6 inhibitor. Figure 5 (Part C of the text).

[0157] The above in vivo and in vitro biological function experiments show that tRF-18-EY0VWUD2 can promote CDK4 / 6 inhibitor resistance in breast cancer, and inhibiting its expression can reverse CDK4 / 6 inhibitor resistance.

[0158] While the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Those skilled in the art can make various modifications and refinements without departing from the spirit and scope of the invention. Therefore, the scope of protection of the present invention shall be determined by the claims.

Claims

1. A biomarker for predicting CDK4 / 6 inhibitor resistance, characterized in that, The biomarker is the non-coding RNA tRF-18-EY0VWUD2, whose nucleotide sequence is shown in SEQ ID NO.

1.

2. The use of the biomarker of claim 1 in the preparation of reagents for predicting CDK4 / 6 inhibitor resistance.

3. The application according to claim 2, characterized in that, The reagent contains primers that can specifically recognize tRF-18-EY0VWUD2.

4. A reagent for predicting CDK4 / 6 inhibitor resistance, characterized in that, The reagent contains primers that can specifically recognize tRF-18-EY0VWUD2.

5. A kit for predicting CDK4 / 6 inhibitor resistance, characterized in that, The kit includes the reagent as described in claim 4.

6. A method for determining the expression level of the biomarker according to claim 1, characterized in that, Includes the following steps: Total RNA was extracted from cells / serum / patient tissues; Add reverse transcription primers to reverse transcribe RNA into cDNA; The expression level of tRF-18-EY0VWUD2 was determined by quantitative PCR after adding the tRF-18-EY0VWUD2 pre-primer and the tRF-18-EY0VWUD2 post-primer.

7. The method according to claim 6, characterized in that, The nucleotide sequences of the reverse transcription primers are shown in SEQ ID NO.2, the nucleotide sequences of the tRF-18-EY0VWUD2 pre-primer are shown in SEQ ID NO.3, and the nucleotide sequences of the tRF-18-EY0VWUD2 post-primer are shown in SEQ ID NO.

4.

8. A method for evaluating resistance to CDK4 / 6 inhibitors, characterized in that, Includes the following steps: The expression level of RNA tRF-18-EY0VWUD2 was determined using the method described in claim 6; The ΔCt value between the target gene and the internal reference gene was used as a relative quantitative indicator. When ΔCt≤7.4, it indicated that the patient was resistant to CDK4 / 6 inhibitors.

9. A method for knocking down the expression level of the biomarker of claim 1, characterized in that, Includes the following steps: In CDK4 / 6 inhibitor-resistant cell lines MCF7 and T47D, the small nucleic acid inhibitor tRF-inhibitor targeting tRF-18-EY0VWUD2 was transfected, and a negative control was set up. After transfection, cells were cultured for an appropriate time, and then the cells were collected and total RNA was extracted. The extracted total RNA was reverse transcribed to synthesize cDNA; Using cDNA as a template, the expression level of tRF-18-EY0VWUD2 was detected by qPCR to verify the knockdown efficiency of tRF-18-EY0VWUD2.

10. The method according to claim 9, characterized in that, The nucleotide sequence of tRF-inhibitor is shown in SEQ ID NO.5.

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