IncRNA biomarker for breast cancer and application of lncRNA biomarker

Abstract

The invention relates to the technical field of biotechnology and medical diagnosis, in particular to lncRNA biomarkers for breast cancer and application of the lncRNA biomarkers in breast cancer diagnosis. The biomarker is an lncRNA (long non-coding ribonucleic acid) combination, and the combination comprises at least one of AL360012.1, Z93241.1, FTX (fourier transform X), LINC01275 (LINC01275), AC008083.4 and BAIAP2-DT (Basic International Analysis Protein 2-DT). The sensitivity of the lncRNA biomarker panel is as high as 87.2%, the specificity is 91.9%, and the lncRNA biomarker panel can effectively assist in early diagnosis of breast cancer patients. Meanwhile, the lncRNA biomarker can effectively help to screen out patients with negative conventional tumor markers at present. The panel accuracy of the lncRNA biomarker is also up to 89.4%, and great convenience is provided for helping patients to accurately diagnose and treat.

Description

Technical Field

[0001] The present application belongs to the field of biotechnology and medical diagnosis technology, and in particular, relates to a lncRNA biomarker for breast cancer and their corresponding application in breast cancer diagnosis. Background Art

[0002] Breast cancer is one of the most common cancers in the world and one of the leading causes of cancer death. At the same time, breast cancer patients also bear a lot of hidden costs and related pain in multiple dimensions such as economic, physical, psychological, emotional and social fields. Therefore, timely diagnosis and treatment are the key to optimizing breast cancer management and improving the quality of life. The implementation of breast imaging screening has undoubtedly completely improved the detection rate of asymptomatic breast cancer, provided more treatment opportunities for breast cancer patients, and reduced the morbidity and mortality rate. However, for patients with dense breast tissue, the effect of mammography will be greatly reduced, only 47.8% to 64.4%, especially in my country, nearly half of women with breast cancer have dense breasts; and there is a possibility of false positives, leading to unnecessary invasive examinations. The results of ultrasound examinations are highly dependent on the operator's experience and level, and cannot store complete images. Breast MRI is not suitable for routine screening due to its expensive equipment and high examination costs.

[0003] Tissue biopsy is the gold standard for diagnosing breast cancer and its subtype classification, but a single biopsy can only provide limited information about the tumor at a specific time and location, and cannot capture its inherent complexity and heterogeneity. Compared with tissue biopsy, liquid biopsy has many advantages, such as minimally invasive, easy to collect continuously, and real-time acquisition of tumor genetic information. These characteristics make liquid biopsy an important tool for tumor molecular profiling and provide the possibility for accurate tumor diagnosis. The application of liquid biopsy markers is expected to improve the effect of breast cancer screening, reduce the cost of testing, and improve patient compliance due to its minimal invasiveness. At present, several common blood-based biomarkers include circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), circulating tumor RNA (ctRNA), and extracellular vesicles (EVs). However, the levels of these markers in the blood of patients with early breast cancer are low, and breast cancer itself is heterogeneous, which limits their application in breast cancer screening. In addition, classic circulating tumor-related markers such as CEA, CA15-3, and CA125 have low sensitivity and specificity for breast cancer diagnosis, so they are also limited in population screening.

[0004] In addition to their established roles in maintaining vascular hemostasis and promoting wound healing after vascular injury, platelets also play a role in tumorigenesis and development that cannot be ignored. Platelets can promote processes such as angiogenesis, tumor cell growth, spread, and distant metastasis, so platelets can be used as potential biomarkers. Platelets capture RNA from tumor cells in the blood circulation and modify their own transcripts according to external signals from tumor cells, so that platelets are "educated". These tumor-educated platelets (TEPs) can provide specific information about tumor localization and molecular characteristics. It is worth noting that the RNA profile of TEPs has become a new liquid biopsy tool that can be used for early cancer detection, progression monitoring, and treatment response assessment. A major advantage of using platelet RNA profiles to detect cancer is that platelets are the second most abundant cell type in the blood, and their high stability, easy detection, and minimally invasive characteristics provide more advantages for liquid biopsy applications. Although TEPs RNA profiles have shown high sensitivity and accuracy in early cancer detection and patient tumor staging, these studies have mainly focused on TEPs mRNA and have not been systematically and comprehensively evaluated clinically or compared with traditional markers. Whether long noncoding RNA (lncRNA) in platelets can detect breast cancer as accurately as mRNA in platelets as a diagnostic biomarker has not been reported. Summary of the invention

[0005] The purpose of the present application is to provide a lncRNA biomarker for breast cancer and their corresponding application in the diagnosis of breast cancer, aiming to solve the above-mentioned problems existing in the current diagnostic methods for breast cancer and provide a new diagnostic method to improve the diagnostic ability of breast cancer, especially for CA15-3 negative and / or CEA negative breast cancer; or various molecular subtypes of breast cancer; or BI-RADS (2-4a) staging; or according to the TNM staging including stage I and stage II breast cancer.

[0006] In order to achieve the above application purpose, the technical solution adopted in this application is as follows:

[0007] One object of the present invention is to provide a lncRNA biomarker for breast cancer, which is a lncRNA combination. Another object of the present invention is to provide a reagent for detecting the lncRNA biomarker, such as a nucleotide sequence specifically complementary to the lncRNA; and to provide the use of the lncRNA biomarker or reagent in the preparation of products for breast cancer risk prediction, diagnosis, prognosis evaluation, treatment effect monitoring or recurrence monitoring. Another object of the present invention is to provide a kit and a corresponding method for detecting the lncRNA biomarker.

[0008] The technical solution of the present invention is as follows.

[0009] In one aspect, the present invention provides a lncRNA biomarker for breast cancer, wherein the biomarker is a lncRNA combination, and the combination includes at least one of AL360012.1, Z93241.1, FTX, LINC01275, AC008083.4 and BAIAP2-DT.

[0010] It should be noted that the six lncRNAs can be used alone or in combination of any two or more.

[0011] Preferably, the combination comprises (1) FTX, Z93241.1 and BAIAP2-DT;

[0012] (2) FTX, Z93241.1, BAIAP2-DT, and AC008083.4;

[0013] (3) FTX, Z93241.1, BAIAP2-DT, AC008083.4, and LINC01275;

[0014] (4) Z93241.1, AL360012.1 and BAIAP2-DT;

[0015] (5) Z93241.1, AL360012.1, AC008083.4, and LINC01275;

[0016] (6) Z93241.1, AL360012.1, BAIAP2-DT, AC008083.4, and LINC01275; or

[0017] (7) AL360012.1, Z93241.1, FTX, LINC01275, AC008083.4, and BAIAP2-DT.

[0018] It should be noted that the fasta data of the above lncRNA (from the Ensembl genome database) are as follows:

[0019] AC008083.4:

[0020] ENSG00000276454, RP11-493L12.7, lncRNA;

[0021] AL360012.1:

[0022] ENSG00000270103, RNU11, lncRNA;

[0023] BAIAP2-DT:

[0024] ENSG00000226137, BAIAP2-AS1, lncRNA;

[0025] FTX:

[0026] ENSG00000230590, FTX, lncRNA;

[0027] LINC01275:

[0028] ENSG00000237595, RP11-112L6.3, lncRNA;

[0029] Z93241.1:

[0030] ENSG00000270022, RNU12, lncRNA;

[0031] In another aspect, the present invention provides use of the lncRNA biomarker or reagent in preparing a product for predicting the risk of breast cancer, diagnosing, evaluating prognosis, monitoring treatment effects, or monitoring recurrence.

[0032] Preferably, the breast cancer is various molecular subtypes of breast cancer; or, according to the histological classification, includes invasive cancer or non-invasive cancer; or according to the TNM staging, includes stage I, stage II, stage III, and stage IV breast cancer, wherein stage I-stage II is early breast cancer, and stage III-stage IV is advanced breast cancer.

[0033] Preferably, the breast cancer is CA15-3 negative and / or CEA negative breast cancer; or various molecular subtypes of breast cancer; or BI-RADS (2-4a) staging; or TNM staging including stage I and stage II breast cancer.

[0034] In another aspect, the present invention provides a reagent for detecting the aforementioned lncRNA biomarker.

[0035] Preferably, the reagent is a reagent for detecting the lncRNA by qPCR, and the reagent includes a reverse transcription primer, a PCR amplification primer pair and / or a probe.

[0036] Preferably, the reagent comprises the following qPCR amplification primer pair for specifically amplifying the lncRNA:

[0037] (1) For AL360012.1: the upstream primer is 5'-CGTGCGGAATCGACATCAAG-3' (SEQ ID NO: 1), and the downstream primer is 5'-CGGGACCAACGATCACCAG-3' (SEQ ID NO: 2);

[0038] (2) For Z93241.1: the upstream primer is 5'-TGAGACGAATTTTTGAGCGGG-3' (SEQ ID NO: 3), and the downstream primer is 5'-ATCGCAACTCCCAGGCATC-3' (SEQ ID NO: 4);

[0039] (3) For FTX: the upstream primer is 5'-AGGTTGTCCTTTTCTGGCATCA-3' (SEQ ID NO: 5), and the downstream primer is 5'-TAGCGGCAATGGACAGCAG-3' (SEQ ID NO: 6);

[0040] (4) For LINC01275: the upstream primer is 5'-TAACGGGCATGCAGGCAA-3' (SEQ ID NO: 7), and the downstream primer is 5'-CTTTGACTCATCCGGCCCTC-3' (SEQ ID NO: 8);

[0041] (5) For AC008083.4; the upstream primer is 5'-GTCTTCCACTCATGCCTTGC-3' (SEQ ID NO: 9), and the downstream primer is 5'-TGGAGCAACTCCTTCTGCTAC-3' (SEQ ID NO: 10);

[0042] (6) For BAIAP2-DT: the upstream primer is 5'-CATCCAGAGATCGCCCTGAC-3' (SEQ ID NO: 11), and the downstream primer is 5'-GTCAGGTTCCACAGCTACCC-3' (SEQ ID NO: 12).

[0043] In yet another aspect, the present invention provides use of the aforementioned biomarkers or the aforementioned reagents in the preparation of products for predicting the risk of breast cancer, diagnosing, evaluating the prognosis, monitoring the therapeutic effect, or detecting the recurrence of breast cancer.

[0044] Preferably, the product is a kit, more preferably, the product is a qPCR kit.

[0045] Preferably, the breast cancer is various molecular subtypes of breast cancer; or, according to the histological classification, includes invasive cancer or non-invasive cancer; or according to the TNM staging, includes stage I, stage II, stage III, stage IV breast cancer, wherein stage I-stage II is early breast cancer, and stage III-stage IV is advanced breast cancer; preferably, the breast cancer is CA15-3 negative and / or CEA negative breast cancer; or various molecular subtypes of breast cancer; or BI-RADS (2-4a) staging; or according to the TNM staging, includes stage I and stage II breast cancer.

[0046] In the last aspect, the present invention provides a kit comprising the aforementioned reagents; preferably, the kit is a PCR kit.

[0047] The beneficial effects of the present invention are:

[0048] The lncRNA biomarkers for breast cancer provided by the present invention are a new group of lncRNAs. Experiments have shown that the sensitivity of the lncRNA biomarker panel is as high as 87.2% and the specificity is 91.9%. It can effectively assist in the early diagnosis of breast cancer patients. At the same time, lncRNA biomarkers can also effectively help screen out patients who are currently negative for conventional tumor markers. In addition, platelet lncRNA detection has many advantages such as easy sample acquisition, fast results, and economy. It also reduces the inconvenience and long cycle of imaging examinations such as breast mammography and ultrasound, reduces the exposure of the patient's body, and improves the patient's enthusiasm for treatment and reexamination. And the panel accuracy of lncRNA biomarkers is as high as 89.4%, which provides great convenience for helping patients with accurate diagnosis and treatment.

[0049] In addition, the lncRNA biomarkers disclosed in the present invention can diagnose or identify CA15-3 negative and / or CEA negative breast cancer; or various molecular subtypes of breast cancer; or BI-RADS (2-4a) staging; or according to TNM staging including stage I and stage II breast cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 This is a flowchart of the clinical discovery and validation of lncRNA biomarkers in breast cancer diagnosis in the examples of this application.

[0052] Figure 2is a graph showing the relationship between platelet parameters and breast cancer in the embodiment of the present application, wherein Figure 2 (A) Real-world data distribution of participant identity, age, and patient TNM stage. (B) Differential expression of platelet parameters in 6202 breast cancer patients and 8768 non-breast cancer patients. (C) Correlation of platelet parameters with breast cancer prognosis.

[0053] Figure 3 : The graphs of breast cancer-related lncRNA biomarkers in the examples of the present application, including (A) the graph features of breast cancer-derived platelet lncRNAs. (B) Subtype statistics of breast cancer-derived platelet lncRNAs. PCA (C) and unsupervised hierarchical clustering heat map (D) of platelet DElncRNAs (differentially expressed lncRNAs). (E) Volcano plot showing the significant differential expression of platelet lncRNAs in breast cancer patients and non-tumor patients. (F) The number of platelet DElncRNAs in the breast cancer group and the non-breast cancer group.

[0054] Figure 4 : is a machine learning model diagram for breast cancer diagnosis in the embodiment of the present application, wherein (A) the box diagram shows the expression levels of 6 lncRNAs in breast cancer and non-breast cancer cohorts. (B) ROC curve is used to show the performance of 6 platelet lncRNAs and traditional tumor markers in detecting breast cancer. (C) Model performance of eight machine learning algorithms in the training cohort. (D) Classification error rate of eight machine learning algorithms in the training cohort.

[0055] Figure 5 It is a machine learning model optimization diagram for breast cancer diagnosis in the embodiment of the present application, wherein the sample statistics for constructing the model of the training cohort (A), the test cohort (E), and the external validation cohort (I). Confusion matrix and model performance of lncRNA panel in the training cohort (B), the test cohort (F), and the external validation cohort (J). Receiver operating characteristic (ROC) curve of the diagnostic performance of lncRNA panel in the training cohort (C), the test cohort (G), and the external validation cohort (K). Decision curve analysis (DCA) to evaluate the clinical benefit of platelet lncRNA panel in the training cohort (D), the test cohort (H), and the external validation cohort (L).

[0056] Figure 6The following are data presentations of the platelet-derived lncRNA panel in detecting breast cancer subgroups in the examples of the present application, including (AC) distribution of TNM staging, molecular subtypes, and BI-RADS staging. (DE) Distribution of CA153 and CEA in TNM staging. (FI) Receiver operating characteristic (ROC) curves of the diagnostic performance of the lncRNA panel in the early breast cancer (I-II) cohort (F), BI-RADS stage 2-4a cohort (G), CEA or / and CA153 negative cohort (H), and different molecular subtype cohorts (I). DETAILED DESCRIPTION

[0057] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0058] In this application, the term "and / or" describes the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. The character " / " generally indicates that the associated objects are in an "or" relationship.

[0059] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, "at least one of a, b, or c", or "at least one of a, b, and c" can all mean: a, b, c, ab (i.e., a and b), ac, bc, or abc, where a, b, c can be single or multiple, respectively.

[0060] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution, some or all of the steps can be executed in parallel or sequentially, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0061] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0062] The weight of the relevant components mentioned in the embodiment description of the present application can not only refer to the specific content of each component, but also represent the proportional relationship between the weights of the components. Therefore, as long as the content of the relevant components is proportionally enlarged or reduced according to the embodiment description of the present application, it is within the scope disclosed in the embodiment description of the present application. Specifically, the mass described in the embodiment description of the present application can be a mass unit known in the chemical industry such as μg, mg, g, kg, etc.

[0063] The term "PET" is the abbreviation of "Polyethylene terephthalate", which means polyethylene terephthalate; the term "PU" is the abbreviation of "polyurethane", which means polyurethane material and is the abbreviation of polyurethane.

[0064] Example 1

[0065] This example focuses on clinical observation and validation studies to establish platelet lncRNA as a biomarker for diagnosing breast cancer. Figure 1 As shown. In the clinical discovery phase, a retrospective case-control cohort of 6202 breast cancer patients, 2924 benign tumor patients, and 5844 healthy subjects from a certain cancer hospital was analyzed for real-world data of platelet parameters to explore the value of these features for breast cancer diagnosis and survival. In the platelet biomarker identification phase, whole-genome screening of platelet transcriptome sequencing was performed on 14 breast cancer patients and 5 healthy subjects to identify a set of candidate lncRNAs. In the platelet biomarker validation phase, a generalizable model was established by integrating multiple lncRNA biomarkers, and its diagnostic performance was evaluated in different centers. This included an internal cohort (177 tumor cases and 213 non-tumor cases) and an independent cohort from Chengdu Second People's Hospital (29 tumor cases and 53 non-tumor cases). The internal cohort was used for model training and internal validation, and the independent cohort was used for external validation.

[0066] Example 2

[0067] This example establishes platelet lncRNA as a biomarker for diagnosing breast cancer

[0068] 1. Relationship between platelet parameters and breast cancer

[0069] Platelet parameters, including platelet counts (PLTs), mean platelet volume (MPV), and platelet distribution width (PDW), were analyzed in 6202 breast cancer patients, 2924 benign tumor patients, and 5844 healthy subjects. The median age of participants in the tumor group was 48.0 years (interquartile range [IQR]: 43.0-55.0), 43.5 years (interquartile range [IQR]: 33.0-51.0) in the benign tumor group, and 43.0 years (interquartile range [IQR]: 32.0-54.0) in the normal group. Overall, the median age of participants in the tumor group was higher than that in the benign or normal groups. In addition, it was observed that most patients were in the early stage (I-III) of the AJCC8th TNM stage ( Figure 2 A).

[0070] When benign patients and healthy individuals were combined into a new non-tumor group, significant differences in MPV and PDW were observed between the tumor and non-tumor groups ( Figure 2 B), PLTs and MPV have important guiding significance for the prognosis of breast cancer patients ( Figure 2 C). This phenomenon suggests that platelets may be involved in the occurrence and development of breast cancer, or that the internal components of platelets have undergone a certain degree of change during the development of breast cancer.

[0071] 2. Identification of lncRNA biomarkers associated with breast cancer

[0072] RNA sequencing was performed on platelets from 14 breast cancer patients (HER2, n=3; Luminal A, n=4; Luminal B, n=4; TN breast cancer, n=3) and 5 healthy subjects. After excluding genes with low coverage, 6088 lncRNAs were finally obtained and analyzed as follows.

[0073] By analyzing breast cancer-derived lncRNAs in platelets, we observed that 62% of breast cancer lncRNAs were identified as novel lncRNAs, while 38% were annotated lncRNAs ( Figure 3 A). Further grouping and analysis of these 38% annotated lncRNAs revealed that 43.5% were intergenic lncRNAs (lincRNAs), 40.6% were antisense lncRNAs, 9.3% were sense or intronic lncRNAs, 4.8% were protein-coding lncRNAs, and the rest were classified as other subtypes ( Figure 3 B).

[0074] Differential gene expression analysis was performed and 28 differentially expressed lncRNAs were found, of which 17 were upregulated and 11 were downregulated ( Figure 3 EF). Principal component analysis (PCA) ( Figure 3C) and unsupervised hierarchical clustering heatmap ( Figure 3 D) Analysis showed that the expression patterns of these DElncRNAs in platelets could accurately identify breast cancer patients.

[0075] 3. Build and validate a machine learning model for breast cancer diagnosis

[0076] Their expression patterns were confirmed in an in-house cohort (177 breast cancer patients and 213 non-tumor controls) by RT-qPCR assay, and the expression change trends of six lncRNA biomarkers (FTX, Z93241.1, BAIAP2-DT, AC008083.4, LINC01275, and AL360012.1) were consistent in the RNA-seq-based cohort ( Figure 4 A).

[0077] Among them, the fasta data of six lncRNA biomarkers (from the Ensembl genome database) are as follows:

[0078] AC008083.4:

[0079] ENSG00000276454, RP11-493L12.7, lncRNA;

[0080] AL360012.1:

[0081] ENSG00000270103, RNU11, lncRNA;

[0082] BAIAP2-DT:

[0083] ENSG00000226137, BAIAP2-AS1, lncRNA;

[0084] FTX:

[0085] ENSG00000230590, FTX, lncRNA;

[0086] LINC01275:

[0087] ENSG00000237595, RP11-112L6.3, lncRNA;

[0088] Z93241.1:

[0089] ENSG00000270022, RNU12, lncRNA.

[0090] The primer information of the six lncRNA biomarkers is shown in Table 1.

[0091] Table 1

[0092]

[0093] Expression status: FTX, Z93241.1, and AL360012.1 were significantly upregulated in breast cancer patients, while BAIAP2-DT, AC008083.4, and LINC01275 were significantly downregulated.

[0094] These six lncRNA biomarkers showed strong ability in distinguishing breast cancer patients from non-tumor controls, with AUC values ​​ranging from 0.640 to 0.810, and they showed higher or comparable diagnostic accuracy in identifying breast cancer patients compared with traditional tumor markers ( Figure 4 B).

[0095] The RT-qPCR data were randomly divided into training and test sets (internal validation) in a ratio of 7:3. Seven machine learning algorithms and classical logistic regression were evaluated to build models on the training set. Hyperparameters were tuned using 10-fold cross-validation to optimize model performance. The results showed that the performance metrics of these models were comparable, while the ensemble method slightly outperformed the simple model ( Figure 4 C). Among them, the classification error rate of random forest is the lowest ( Figure 4 D). Therefore, random forest was selected as the preferred model for further investigation.

[0096] To further optimize the performance, 10-fold cross validation with 5 resampling iterations was used for hyperparameter tuning. The performance of the random forest model is shown in Figure 5 In the training cohort (124 tumor cases and 149 non-tumor cases; Figure 5 AB) and the test cohort (53 tumor cases and 64 non-tumor cases; Figure 5 EF), all six platelet lncRNAs showed strong discrimination ability in distinguishing breast cancer from non-tumor cases. The diagnostic AUC value of the training group was 0.937 ( Figure 5 C), and the test group is 0.923 ( Figure 5 G). In the training set, the classification accuracy, sensitivity, and specificity reached 89.4%, 87.2%, and 91.9%, respectively. Similarly, the accuracy of the test (internal validation) set was 87.2%, the sensitivity was 84.4%, and the specificity was 90.6%. To verify the reliability of these platelet lncRNA biomarkers in breast cancer diagnosis, their expression levels in platelet samples from an independent cohort (29 tumor cases and 53 non-tumor cases) from Chengdu Second People's Hospital were detected ( Figure 5IJ). The platelet lncRNA biomarkers identified in the external validation set also showed consistent expression patterns and achieved significantly higher diagnostic performance in the external validation set, with an AUC of 0.913 ( Figure 5 K and S1). The classification accuracy, sensitivity, and specificity of the external dataset were 80.5%, 94.3%, and 55.2%, respectively.

[0097] Example 3

[0098] Diagnostic reliability of breast cancer-related lncRNA biomarkers in detecting breast cancer subgroups

[0099] To comprehensively evaluate the clinical utility of platelet-derived lncRNA panel for the detection of early breast cancer patients, we combined all cohorts into one dataset and performed a comprehensive analysis. Figure 6 AE summarized the distribution of TNM stage, molecular subtype, BI-RADS stage, CEA and CA153 levels. The results showed that the patients were mainly in early stage (stage I and II) ( Figure 6 A), more than 70% of patients have BI-RADS stage greater than 4a ( Figure 6 C). It is worth noting that with the progress of TNM staging, CEA levels gradually increased, while CA15-3 levels did not show an obvious upward trend from stage I to stage III ( Figure 6 DE). In all stages, the mean values ​​of CEA and CA15-3 were lower than the established positive reference thresholds (CA15-3>30U / mL, CEA>5ng / mL) ( Figure 6 Subsequent analysis showed that the platelet lncRNA panel had significant diagnostic performance, with an AUC of 0.916 ( Figure 6 F), and the AUC for breast cancer patients with BI-RADS stage 2 to 4a was 0.886 ( Figure 6 G), the AUC values ​​for CA153-negative, CEA-negative, and CA153 and CEA double-negative breast cancer were 0.897, 0.905, and 0.910, respectively ( Figure 6 H). In addition, the kit is applicable to various breast cancer molecular subtypes, with AUC values ​​of 0.903, 0.924, 0.891 and 0.896 for Luminal A, Luminal B, HER2-positive and TNBC breast cancer, respectively ( Figure 6 I). To illustrate the clinical application prospects of the constructed platelet lncRNAs panel in the diagnosis of early breast cancer.

[0100] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A lncRNA biomarker for breast cancer, characterized in that: The biomarker is a lncRNA combination, and the combination includes at least one of AL360012.1, Z93241.1, FTX, LINC01275, AC008083.4 and BAIAP2-DT.

2. The lncRNA biomarker for breast cancer according to claim 1, characterized in that The combination includes (1) FTX, Z93241.1 and BAIAP2-DT; (2) FTX, Z93241.1, BAIAP2-DT, and AC008083.4; (3) FTX, Z93241.1, BAIAP2-DT, AC008083.4, and LINC01275; (4) Z93241.1, AL360012.1 and BAIAP2-DT; (5) Z93241.1, AL360012.1, AC008083.4, and LINC01275; (6) Z93241.1, AL360012.1, BAIAP2-DT, AC008083.4, and LINC01275; or (7) AL360012.1, Z93241.1, FTX, LINC01275, AC008083.4, and BAIAP2-DT.

3. The lncRNA biomarker for breast cancer according to claim 1 or 2, characterized in that: The breast cancer is various molecular subtypes of breast cancer; or, according to histological classification, includes invasive cancer or non-invasive cancer; or according to TNM staging, includes stage I, stage II, stage III, and stage IV breast cancer, wherein stage I-stage II is early breast cancer, and stage III-stage IV is advanced breast cancer.

4. The lncRNA biomarker for breast cancer according to claim 3, characterized in that: The breast cancer is CA15-3 negative and / or CEA negative breast cancer; or various molecular subtypes of breast cancer; or BI-RADS (2-4a) staging; or according to TNM staging including stage I and stage II breast cancer.

5. A reagent for detecting the lncRNA biomarker according to any one of claims 1 to 4.

6. The reagent according to claim 5, characterized in that The reagent is a reagent for detecting the lncRNA by qPCR, and the reagent includes a reverse transcription primer, a PCR amplification primer pair and / or a probe.

7. The reagent according to claim 5 or 6, characterized in that The reagents include the following qPCR amplification primer pairs for specifically amplifying the lncRNA: (1) For AL360012.1: the upstream primer is 5′-CGTGCGGAATCGACATCAAG-3′, and the downstream primer is 5′-CGGGACCAACGATCACCAG-3′; (2) For Z93241.1: the upstream primer is 5′-TGAGACGAATTTTTGAGCGGG-3′, and the downstream primer is 5′-ATCGCAACTCCCAGGCATC-3′; (3) For FTX: the upstream primer is 5′-AGGTTGTCCTTTTCTGGCATCA-3′, and the downstream primer is 5′-TAGCGGCAATGGACAGCAG-3′; (4) For LINC01275: the upstream primer is 5′-TAACGGGCATGCAGGCAA-3′, and the downstream primer is 5′-CTTTGACTCATCCGGCCCTC-3′; (5) For AC008083.4; the upstream primer is 5'-GTCTTCCACTCATGCCTTGC-3', and the downstream primer is 5'-TGGAGCAACTCCTTCTGCTAC-3'; (6) For BAIAP2-DT: the upstream primer is 5'-CATCCAGAGATCGCCCTGAC-3', and the downstream primer is 5'-GTCAGGTTCCACAGCTACCC-3'.

8. Use of the biomarker according to any one of claims 1 to 4 or the reagent according to any one of claims 5 to 7 in the preparation of a product for predicting the risk of breast cancer, diagnosis, prognosis assessment, monitoring the therapeutic effect or detecting recurrence.

9. The use according to claim 8, characterized in that The breast cancer is various molecular subtypes of breast cancer; or, according to histological classification, includes invasive cancer or non-invasive cancer; or according to TNM staging, includes stage I, stage II, stage III, and stage IV breast cancer, wherein stage I-stage II is early breast cancer, and stage III-stage IV is advanced breast cancer; preferably, the breast cancer is CA15-3 negative and / or CEA negative breast cancer; or various molecular subtypes of breast cancer; or BI-RADS (2-4a) staging; or according to TNM staging, includes stage I and stage II breast cancer.

10. A kit, characterized in that: The kit comprises the reagents according to any one of claims 5 to 7, and preferably, the kit is a PCR kit.

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