A triple negative breast cancer prognosis risk stratification model and application

Abstract

The application discloses a triple-negative breast cancer prognosis risk stratification model and application, and belongs to the technical field of biological medicine. According to obtained LncRNA expression profile data and complete clinical follow-up data of triple-negative breast cancer patients, seven LncRNAs significantly related to the survival prognosis of the triple-negative breast cancer patients are screened out through single-factor and multi-factor Cox proportional risk regression analysis, a specific marker combination is constituted, and the clinical pain point of low stratification precision of the existing triple-negative breast cancer prognosis is solved. Meanwhile, a prognosis risk scoring model, a specific primer group and a detection kit based on the marker combination are provided, a complete prognosis risk stratification system is formed, accurate prognosis risk stratification of the triple-negative breast cancer is realized, and guidance is provided for clinical individualized treatment, and the application has a very good application prospect.

Description

Technical Field

[0001] This invention belongs to the field of biomedical technology, specifically relating to a prognostic risk stratification model for triple-negative breast cancer and its application. Background Technology

[0002] Triple-negative breast cancer, which is highly malignant among breast cancers, is negative for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2. It is characterized by early onset, strong invasiveness, high recurrence and metastasis rate, and poor prognosis. Clinical treatment is still mainly based on chemotherapy, and there is a lack of effective targeted therapy.

[0003] Current prognostic assessments and risk stratification for triple-negative breast cancer primarily rely on traditional clinicopathological indicators such as TNM staging, lymph node metastasis status, and histological grade. However, these stratification methods have low accuracy and cannot effectively distinguish between patients with occult high-risk conditions and low-risk patients. Patients with triple-negative breast cancer at the same clinical stage show significant differences in prognosis. Some low-risk patients receive unnecessary high-intensity chemotherapy, leading to severe toxic side effects and reduced quality of life; while some high-risk patients, due to delayed identification, receive only conventional chemotherapy, missing the optimal window for intensive treatment, ultimately resulting in recurrence and metastasis, and a poor prognosis.

[0004] Long non-coding RNAs (lncRNAs) are a class of non-coding RNAs exceeding 200 nucleotides in length. Their abnormal expression is closely related to tumor occurrence, development, and prognosis, and they have become potential molecular biomarkers for precise prognostic stratification of tumors. Currently, some lncRNAs have been reported to be associated with triple-negative breast cancer, but there are no studies on the use of specific biomarker combinations composed of multiple lncRNAs for precise prognostic risk stratification of triple-negative breast cancer. Furthermore, there is a lack of risk scoring models and supporting detection products based on such biomarker combinations. Therefore, it is urgent to screen for lncRNA biomarker combinations with high specificity and sensitivity, construct precise prognostic risk stratification models, and provide molecular evidence for personalized diagnosis and treatment of triple-negative breast cancer. Summary of the Invention

[0005] To address the shortcomings of existing technologies that rely on traditional clinicopathological indicators such as TNM staging and lymph node metastasis status for prognostic assessment of triple-negative breast cancer, which suffer from low stratification accuracy and inability to effectively distinguish between occult high-risk and low-risk patients, leading to undertreatment of high-risk patients and overtreatment of low-risk patients, this invention aims to provide a combination of LncRNA biomarkers for prognostic risk stratification of triple-negative breast cancer. This solves the clinical pain point of low accuracy in existing prognostic stratification methods for triple-negative breast cancer. Furthermore, this invention provides a prognostic risk scoring model, specific primer set, detection kit, and prognostic risk stratification method based on this biomarker combination, forming a complete prognostic risk stratification system. This enables precise prognostic risk stratification of triple-negative breast cancer and provides guidance for individualized clinical treatment.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a combination of LncRNAs for survival prognostic assessment or auxiliary prognostic assessment of triple-negative breast cancer patients, including ENST00000662739.1 (AC004585.1), ENST00000572187.1 (AC004584.1), ENST00000583250.1 (AC104984.4), ENST00000619343.1 (AL031670.1), ENST00000591993.6 (LERFS), ENST00000580119.2 (AL035696.3) and ENST00000418803.1 (AL021707.3).

[0008] Based on the above technical solution, the nucleotide sequences of ENST00000662739.1(AC004585.1), ENST00000572187.1(AC004584.1), ENST00000583250.1(AC104984.4), ENST00000619343.1(AL031670.1), ENST00000591993.6(LERFS), ENST00000580119.2(AL035696.3), and ENST00000418803.1(AL021707.3) are shown in SEQ ID NO.1-7 respectively.

[0009] Secondly, the present invention provides the application of the above-mentioned LncRNA combination in the preparation of products for survival prognostic assessment or auxiliary prognostic assessment of triple-negative breast cancer patients.

[0010] Based on the above technical solution, the product further includes a reagent kit.

[0011] Thirdly, this invention provides a survival prognostic risk scoring model for triple-negative breast cancer patients. The scoring model is as follows: RiskScore = β1×Exp1+ β2×Exp2+ β3×Exp3+ β4×Exp4+ β5×Exp5+ β6×Exp6+ β7×Exp7; where β1~β7 are the Cox regression coefficients of the seven LncRNAs in the above LncRNA combination, and Exp1~Exp7 are the relative expression levels of the corresponding seven LncRNAs in tumor samples of triple-negative breast cancer patients (calculated using the 2^(-ΔΔCt) method, with β... Actin is used as an internal reference gene; patients are divided into high-risk and low-risk groups using the median risk score as the cutoff value. The overall survival rate and recurrence-free survival rate of patients in the high-risk group are significantly lower than those in the low-risk group, which can effectively distinguish the prognosis of patients.

[0012] Based on the above technical solution, the scoring model is further defined as follows: RiskScore = 0.512026×Exp(ENST00000662739.1)+0.315018×Exp(ENST00000572187.1)+0.634009×Exp(ENST00000583250.1)+0.173005×Exp(ENST00000619343.1)+0.515002×Exp(ENST00000591993.6)+0.331015×Exp(ENST00000580119.2)+0.714020×Exp(ENST00000418803.1).

[0013] Fourthly, the present invention provides specific primer pairs for amplifying the seven LncRNAs of the above-mentioned LncRNA combination. The nucleotide sequences of the primer pairs are shown in SEQ ID NO.8~SEQ ID NO.21. This primer set can specifically recognize the target LncRNA, without non-specific amplification, and the detection results are accurate and stable.

[0014] Fifthly, the present invention provides a detection kit for assessing or assisting in the prognostic evaluation of triple-negative breast cancer patients, wherein the detection kit contains the aforementioned specific primer pairs.

[0015] Based on the above technical solution, the detection kit further includes RNA extraction reagent, cDNA reverse transcription reagent, quantitative PCR reagent, internal reference primer and fluorescent dye.

[0016] Based on the above technical solution, further, the internal reference primer is β. Actin-specific primer pairs, nucleotide sequences as shown in SEQ ID NO.22~SEQ ID NO.23; the fluorescent dye is SYBR-Green dye.

[0017] Based on the above technical solution, the RNA extraction reagent further includes lysis buffer, digestion solution, RNA carrier, washing solution and elution solution.

[0018] Based on the above technical solution, the cDNA reverse transcription reagent further includes gDNA Clean ReactionMix, reverse transcriptase, and reverse transcription reaction buffer.

[0019] Based on the above technical solution, the quantitative PCR reagent further includes polymerase chain reaction buffer and dNTPs.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention is the first to discover that the combination of biomarkers composed of the above 7 LncRNAs is significantly associated with the prognosis of triple-negative breast cancer patients. All of these biomarkers are independent adverse prognostic factors with high specificity and sensitivity, providing new specific molecular biomarkers for prognostic risk stratification of triple-negative breast cancer.

[0021] 2. The prognostic risk scoring model constructed based on these 7 LncRNAs can accurately classify triple-negative breast cancer patients into high-risk and low-risk groups, effectively distinguishing the prognostic status of patients and solving the problem of existing stratification relying on traditional indicators and having low accuracy.

[0022] 3. The specific primer set provided by this invention has high specificity, and the matching detection kit is easy to operate, accurate and reproducible. It can realize the rapid detection of these 7 LncRNAs in clinical samples, providing a convenient technical means for prognostic risk stratification.

[0023] 4. The prognostic risk stratification results of this invention can serve as an important reference for treatment decisions for triple-negative breast cancer patients. When combined with traditional clinicopathological indicators, they can effectively guide clinicians to formulate treatment plans more scientifically. This not only helps to avoid undertreatment of high-risk patients but also reduces the toxic side effects of chemotherapy in low-risk patients, improving their quality of life. This aligns with the development trend of precision medicine in clinical practice and has significant clinical application value and industrialization prospects. Attached Figure Description

[0024] To more clearly illustrate the embodiments of the present invention, the accompanying drawings involved in the embodiments will be briefly described below.

[0025] Figure 1The results show the hazard ratios (HRs) and p-values ​​for the seven LncRNA biomarkers in Example 1.

[0026] Figure 2 This is the overall survival curve of the risk scoring model constructed based on a combination of 7 LncRNA biomarkers in Example 2.

[0027] Figure 3 This is a receiver operating characteristic (ROC) curve for the seven LncRNA biomarkers and their combination model in Example 2.

[0028] Figure 4 The figure shows the results of quantitative real-time PCR verification of the relative expression of seven LncRNA markers in triple-negative breast cancer tissue and adjacent normal tissue in Example 3.

[0029] Figure 5 This is a receiver operating characteristic (ROC) curve of the risk scoring model determination results in Example 3. Detailed Implementation

[0030] To facilitate a thorough understanding of the technical solutions of this application by those skilled in the art, the content of this application will be clearly and comprehensively described below in conjunction with specific embodiments and accompanying drawings. It should be noted that the embodiments presented herein are only some examples of this application, and not all embodiments. Based on the disclosed embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort should be included within the protection scope of this application. Furthermore, unless otherwise specified, the biological materials, experimental instruments, and reagents involved in the embodiments can all be obtained through commercial channels, and the experimental methods used are also conventional techniques in the art.

[0031] Example 1: LncRNA Model Construction and Analysis LncRNA expression profiles and clinical follow-up data of triple-negative breast cancer patients from tumor tissue and adjacent normal tissue were downloaded from the TCGA public database, including survival time, survival status, TNM stage, etc. After strict screening, 134 patient samples were included. The raw expression data obtained from TCGA were standardized using R language. Low-abundance expression genes were removed based on a mean expression level greater than 0 and variance filtering. Low-expression lncRNAs were filtered using a threshold (the threshold was set to retain lncRNAs with a transcriptome map per million bases (TPM) > 0.1 and effective expression in at least 20% of the samples). LncRNAs with extremely low expression levels and no biological significance were removed, and lncRNAs with stable expression levels were retained for subsequent analysis. Univariate / multivariate Cox proportional hazards regression analysis was performed, using stepwise regression to eliminate collinearity interference. Specifically, the expression level of candidate lncRNAs was used as the independent variable, and overall survival (OS) and relapse-free survival (RFS) were used as dependent variables. A significance level of α=0.05 was set, and univariate Cox proportional hazards regression analysis was performed to screen for lncRNAs significantly associated with patient survival prognosis (P<0.05). As potential prognostic biomarkers, these biomarkers were then subjected to collinearity testing. The variance inflation factor (VIF) was used to assess the degree of collinearity among variables, with VIF < 10 as the criterion for no significant collinearity. LncRNAs with VIF ≥ 10 were initially screened out as having strong collinearity. The remaining LncRNAs without significant collinearity were then included in the multivariate Cox regression analysis. Proportional hazard regression analysis was performed, and the model was optimized using a forward stepwise regression method. The inclusion criterion was set as P < 0.05, and the exclusion criterion as P > 0.1. Based on the likelihood ratio test results, variables were screened and eliminated one by one, gradually removing variables that did not significantly contribute to the model. Finally, seven independent adverse prognostic lncRNAs significantly associated with the prognosis of triple-negative breast cancer patients were identified, constituting the biomarker combination of this invention. Specifically, these are ENST00000662739.1 (AC004585.1), ENST00000572187.1 (AC004584.1), ENST00000583250.1 (AC104984.4), ENST00000619343.1 (AL031670.1), and ENST00000591993.6. (LERFS), ENST00000580119.2(AL035696.3), ENST00000418803.1(AL021707.3).

[0032] The nucleotide sequence of ENST00000662739.1 (AC004585.1) is shown below (SEQ ID NO.1):

[0033] The nucleotide sequence of ENST00000572187.1 (AC004584.1) is as follows (SEQ ID NO.2): ATGGTCCAGATGGGTATCATTAGGCTGCTGATGCCCTTTTGCGCCGCTCTGTAGTTCTTATCCTACACTACCTAAAAACAGGAACTATTTCACAGCCCACCAAGAGAAAAACTCTAAGGCTTGGTCCTATGAATGAATAATGAGGGCCATCTAGTGTCAAAAAAGGTAATTACAGGCTTTTTGTTTTTGAGATAGGGTATCACGTCTATAGTCCAGGCTGGAATGCAGTGGCATTCAGGACTCACTGCACCTTCCACCTCCTGGGCTCAAGCGATCCTCCTGCCTCGCCCCCACCACCCCCAAAGGTGGCTGAGGCAAGAGAATGGTGTGAACCCGGGAGGCGGAGCTTGCAGTGAGCGGAGATCACGCCACTGCACTCCAGCCTGGGTGACAGAGCTAGACTCTGTCTCAAAAAAAAAAAAAAAAAAAAAAGGTATAAACCATATACTTCCCAAAATGGCAATTTAAGGATTTAAAGAAAGTCAGAGGATGAGATCATCATTTTATGTCACTCTTCAAACTGTTGTGCTATTATAAGTTGGAACTCTC。

[0034] The nucleotide sequence of ENST00000583250.1 (AC104984.4) is as follows (SEQ ID NO.3): TTCTTTCTTTCTTTTTTGAGACAGAATCTGGCTCTGTCGCCCAGTGAGACTCCTATCTCTATTTTTAGAAATTAAAAATAAATAAGATAAATGTATATCTATACTGGTAGACATGAAGGGTTATCTAGTGGTTTTAGCATAACCAGTATTTTAACTGTATATGTGTACTGAGAAACTTGAAGCCAAGTTAATTACCACAGTTTAAATAAGAACATTCTTATATTTTACCTTTCTTCATAGATTCTTTTTCCATTCCCTCCTTGTATGTGTACAAGAGTTCATCAACTTCCTTCAGATCCAGGAAGCCTGAGCCATCACTGTCCCACTTCTGAAAGATGGCTTCTAGGAGAAGCCTCTGTCGGACTTGGAAAGCATTTTGGCTAAACTAGGATACAAACAGGATAATAAAGCACGGAAGATATATGTTCATTGTGCTAGCTAGCATTCTGTGTTTATTCCAAAAAAAGTCCAGCGTCTTCTATTAAAACAGGTTTACTTTGAGGCTAAGCATGATATTAAGGGAAAATGCAGTGATTCAGCAGTGGTGCATCCCTGTAAAGCCAGCCACTCAAGAGGCTGAGGCAGGAGTAT。

[0035] The nucleotide sequence of ENST00000619343.1 (AL031670.1) is as follows (shown in SEQ ID NO.4): CTTTGTTTCAGGTAGTACAGTCATGAAATTGAGTAAGTCTCTTAAAAAAATGAAAGTCCACAGATCCCATTGTGGAAGGTGCGGTGGCACGTGCCTGTAGTCCCATCTACTCAGGAGGCTTAGACAAGAGAATCACTTGAACCCAGGAGGCAGAAGTTAAAGTGAGCCGAGACTGCGCCACTGCACTCCAGCCTGGCTGACAGAGTAAGACCCCATCTCAAAAAACAAAAAAAAGAAAGGCATATTGACCATCTTTGGTTTTGGTGTGTATAAATAAAATAAAGGCAGCCACACCAATCTGTGATGTGAGGCGGCCAGCAGCTACAGGACAAGGCACAGCTGAAATACAGAGGCAAGGCTGTTTGTCTTAATAGTTCACTATACAGTTTGTTTTAGGCAGCTTTCTGTACCTTTTATTTTATAATAAAAATATAGAAGATACAGTTAACCATTATATATATATATGGGGGGGGTCCACTGAAGACTAGGATGCTACTATCAAGACAGAAACAGGTGGTTATAAACTACAACGTGCTAAGATGAGAATAAAGGATCAAAGAGGAAACGCTTGAGAGAATCTATACGAAGCTTTCACAACAAAAGTTAAAGTGACTTCAATGTGAGGGCAAGAAAATATTTCAAGGCTCAGAGAGGCTAAAATAATCAAAG。

[0036] The nucleotide sequence of ENST00000591993.6 (LERFS) is shown below (SEQ ID NO.5):

[0037] The nucleotide sequence of ENST00000580119.2(AL035696.3) is shown below (SEQ ID NO.6): AAATTGAAAAATGGAGACGTCAAGTTCCCTAGTCACAGAGCAGCTGTGATGGGACCCACGTAAACAGCCCTGATCAGTAGTGAAGAGCAACATGTGGCACACAGGGCCTCCTGCACAAGCAACAGTCGCTGCTTGCTGTTACTGTGGGCTCCACGTTGGGATCAGACACACCTGTCTTCAGTGGCTTGCAGTTGCAGCAAGGACCGAATGGTCTATTCTAAAAGAAAACATTACAGGCAGGCAGTACCAGACCACTGTGCTTGCAGCCCTCATGAGAAGGCTCAGAAGACACAGGAGCTG CCCTGGGTGTGAATTTCGTGTCCATGCTGGCTTCTCGCAGTGGCCCTTGGCATAGGTTGGGATCTTGCAGCAAGATTCTTCATCGGTTTCTCCATCTCTGAAGACTTCTTGCCAACAGCTGTACACAGGTTTGTTCTAATTCCCCAACCTTGCCACGACTGACCTGGGTCATCACCATGTGCTTATTGAAGAGCCTTGGAATACTGTATAGACCCGTACAGAGTTGTTCAAATAGACAATTGAGGACGACTATAATAGTTTCTCATTAGAAATACATGCTAATAAACATCATGTCTTTGGTATTAAGTGGCAATTAGATGAAACAACCTGAAATTCTGTCTGATACCAAGATTGAACTTTGCTGAGATTACTGTTTCACTCACCGAAAATATCAGAATACTCAAGGGAGTTATTTTTTTTCTTAATGTAACTTTCATAAAGGAAATGAAAGAAATGTGGAGACTG。

[0038] The nucleotide sequence of ENST00000418803.1 (AL021707.3) is as follows (SEQ ID NO.7): .

[0039] The hazard ratios (HRs) of these seven LncRNAs were all greater than 1, and the p-values ​​were all less than 0.001, all of which were independent adverse prognostic factors for triple-negative breast cancer. Figure 1 ).

[0040] Example 2: Construction of a prognostic risk scoring model Based on the HR values ​​of the seven LncRNAs in Example 1, the regression coefficient β corresponding to each LncRNA was calculated using the Cox proportional hazards regression model. The Cox proportional hazards regression coefficient of each LncRNA was calculated using the formula RiskScore=∑(βi×Expi). Based on the regression coefficients, a prognostic risk scoring model was constructed. RiskScore=0.512026×Exp(ENST00000662739.1)+0.315018×Exp(ENST00000572187.1)+0.634009×Exp(ENST00000583250.1)+0.173005×Ex p(ENST00000619343.1)+0.515002×Exp(ENST00000591993.6)+0.331015×Exp(ENST00000580119.2)+0.714020×Exp(ENST00000418803.1).

[0041] Substituting the relative LncRNA expression levels of the 134 patients into the model, a risk score was calculated for each patient. The median of all risk scores was 2.35, which was determined as the predefined risk score cutoff value. Based on this cutoff value, the 134 patients were divided into a high-risk group (n=67) and a low-risk group (n=67). Kaplan-Meier survival analysis showed a significant difference in prognosis between the two groups (p=0.002). The results are as follows... Figure 2 As shown in the figure; ROC curve analysis shows AUC=0.973, indicating that the model has good prognostic discrimination ability, and the results are as follows. Figure 3 As shown.

[0042] Example 3: Construction of LncRNA-specific primer set for validation After approval by the Medical Ethics Committee of the Affiliated Zhongshan Hospital of Dalian University, postoperative tumor specimens and adjacent normal tissues were collected from 100 patients diagnosed with triple-negative breast cancer at the Affiliated Zhongshan Hospital of Dalian University from January 2020 to December 2024. Quantitative PCR was performed to determine the expression level of the target LncRNA in the tissues.

[0043] The forward and reverse primer sequences for the seven designed LncRNAs are as follows: ENST00000662739.1 (AC004585.1)-F:5'-GTGGCTTTGATCTGCAGCCT-3' (SEQ IDNO.8); ENST00000662739.1 (AC004585.1)-R: 5'-CCCCGTGACACCTGTTCATA-3' (SEQ IDNO.9); ENST00000572187.1 (AC004584.1)-F:5'-ATTAGGCTGCTGATGCCCTT-3' (SEQ IDNO.10); ENST00000572187.1 (AC004584.1)-R: 5'- TTTCTCTTGGTGGGCTGTGAA -3' (SEQ ID NO. 11); ENST00000583250.1 (AC104984.4)-F: 5'- GCCTCTGTCGGACTTGGAAA -3' (SEQ ID NO. 12); ENST00000583250.1 (AC104984.4)-R:5'- TGCTTAGCCTCAAAGTAAACCTG -3'(SEQID NO.13); ENST00000619343.1 (AL031670.1)-F:5'- AAAGGCAGCCACACCAATCT -3'(SEQ IDNO.14); ENST00000619343.1 (AL031670.1)-R: 5'- ACAAACAGCCTTGCCTCTGT -3' (SEQ ID NO. 15); ENST00000591993.6 (LERFS)-F:5'- ACTGGTGCTGAAGTGTTGGC -3'(SEQ IDNO.16); ENST00000591993.6 (LERFS)-R:5'- GCTCCAATCAGCCCTACTCC -3'(SEQ IDNO.17); ENST00000580119.2 (AL035696.3)-F: 5'- TTGCAGCCCTCATGAGAAGG -3' (SEQ ID NO. 18); ENST00000580119.2 (AL035696.3)-R:5'- GATGACCCAGGTCAGTCGTG -3'(SEQ IDNO.19); ENST00000418803.1(AL021707.3)-F: 5'-CAGCTCTTGGGCGTCATTTAT-3'(SEQ ID NO.20); ENST00000418803.1(AL021707.3)-R: 5'-GGTCACCCTTGGACTGGAAG -3' (SEQ ID NO.21); β Actin F: 5' CATGTACGTTGCTATCCAGGC 3', (SEQ ID NO.22); β Actin R: 5' CTCCTTAATGTCACGCACGAT 3', (SEQ ID NO.23).

[0044] 1. Total RNA extraction (using the Trizol method as an example) Sample processing: Take about 50 mg of tumor tissue sample from a triple-negative breast cancer patient, place it in a pre-cooled mortar, add liquid nitrogen and grind it quickly into powder; place it in a 1.5 mL centrifuge tube without RNase, add 1 mL of pre-cooled Trizol lysis buffer to the powder sample, mix thoroughly by pipetting, and let stand at room temperature for 5 minutes to allow the cells to fully lyse.

[0045] Layering and extraction: Add 200 μL of chloroform, shake vigorously for 15 seconds, and let stand at room temperature for 2 minutes; centrifuge at 12000g for 15 minutes at 4℃; carefully aspirate the upper aqueous phase (about 400 μL) and transfer it to a new RNase-free centrifuge tube, avoiding aspiration of the middle and lower organic phases.

[0046] RNA precipitation: Add an equal volume (about 400 μL) of pre-cooled isopropanol to the aqueous phase, gently invert to mix, and let stand at room temperature for 10 minutes; centrifuge at 12000g for 10 minutes at 4℃; discard the supernatant, and white RNA precipitate will be visible at the bottom of the tube.

[0047] Washing and elution: Add 1 mL of 75% ethanol aqueous solution (prepared with DEPC water) and gently wash the precipitate; centrifuge at 7500g for 5 minutes at 4℃; discard the supernatant and air-dry the precipitate at room temperature for 5-10 minutes, avoiding complete drying; add 30-50 μL of RNase-free ddH2O and incubate in a water bath at 55-60℃ for 10 minutes to fully dissolve the RNA.

[0048] RNA quality testing: RNA concentration and purity were measured using a NanoDrop spectrophotometer. The RNA concentration and purity were then analyzed. 260 / A 280 The ratio is between 1.8 and 2.0, A 260 / A 230 The ratio is ≥2.0.

[0049] 2. cDNA synthesis (two-step process: removal of gDNA + reverse transcription) Genomic DNA removal: Prepare the reaction system (10 μL) as shown in Table 1 in an RNase-free PCR tube.

[0050] Table 1. Reaction System

[0051] After gently mixing, briefly centrifuge, place in a PCR instrument, incubate at 42°C for 2 minutes, and immediately cool on ice.

[0052] Reverse transcription reaction: Add the reagent shown in Figure 2 (10 μL) to the reaction tube above, making the total volume 20 μL. Table 2. Reagents added to the reaction tube

[0053] After gently mixing, briefly centrifuge and place in a PCR instrument. Follow the procedure below: incubate at 42°C for 15 minutes (reverse transcription), heat at 85°C for 5 seconds (to terminate the reaction), and store at 4°C for later use (or use immediately for qPCR).

[0054] 3. Real-time quantitative PCR detection The PCR template is reverse transcribed cDNA. After adding the following components to the PCR tube and mixing well to avoid foaming, the PCR reaction is carried out. The PCR reaction system is shown in Table 3.

[0055] Table 3. Reaction System

[0056] Real-time PCR amplification was performed using an ABI 7500fast instrument. The specific reaction procedure is shown in Table 4.

[0057] Table 4. Amplification Procedure

[0058] After the experimental procedure was fully completed, the generated raw data was first exported, categorized, and archived. Then, professional data analysis software was used to systematically process and analyze the exported data to extract relevant research information. The experimental data obtained through qPCR technology in this study can directly reflect the expression of relevant indicators. Specific results are as follows: Figure 4 As shown, the expression of all seven LncRNAs was significantly upregulated in breast cancer tissues, showing a clear difference compared to adjacent normal tissues.

[0059] 4. Risk score calculation: The relative expression levels of the seven LncRNAs were substituted into the risk score model of Example 2 to calculate the patient's risk score.

[0060] 5. Risk Level Determination: Patient risk scores were compared with a predefined threshold (2.35) to determine whether patients were in the high-risk or low-risk group (risk scores ≥2.35 were considered high-risk, and risk scores <2.35 were considered low-risk). Receiver operating characteristic (ROC) curve analysis was performed on the determination results (plotting the patient's survival status against the risk score). The results are as follows: Figure 5 As shown, the results indicate that the area under the curve (AUC) is 9.13, the sensitivity is 86.3%, and the specificity is 87.8%, confirming that the risk level determination method with a threshold of 2.35 has good discriminative power and the determination results are accurate and reliable.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A combination of lncRNAs for assessing or assisting in the prognostic evaluation of triple-negative breast cancer patients, characterized in that, The LncRNA combinations include ENST00000662739.1 (AC004585.1), ENST00000572187.1 (AC004584.1), ENST00000583250.1 (AC104984.4), ENST00000619343.1 (AL031670.1), ENST00000591993.6 (LERFS), ENST00000580119.2 (AL035696.3), and ENST00000418803.1 (AL021707.3).

2. The LncRNA combination according to claim 1, characterized in that, The nucleotide sequences of ENST00000662739.1 (AC004585.1), ENST00000572187.1 (AC004584.1), ENST00000583250.1 (AC104984.4), ENST00000619343.1 (AL031670.1), ENST00000591993.6 (LERFS), ENST00000580119.2 (AL035696.3), and ENST00000418803.1 (AL021707.3) are shown in SEQ ID NO.1-7, respectively.

3. The use of the LncRNA combination according to claim 1 or 2 in the preparation of products for survival prognostic assessment or auxiliary prognostic assessment of triple-negative breast cancer patients.

4. A survival prognostic risk scoring model for triple-negative breast cancer patients, characterized in that, The scoring model is: RiskScore = β1×Exp1+ β2×Exp2+ β3×Exp3+ β4×Exp4+ β5×Exp5+ β6×Exp6+ β7×Exp7; where β1~β7 are the Cox regression coefficients of the 7 LncRNAs in the LncRNA combination described in claim 1 or 2, and Exp1~Exp7 are the relative expression levels of the corresponding 7 LncRNAs in tumor samples from triple-negative breast cancer patients.

5. The survival prognostic risk scoring model for triple-negative breast cancer patients according to claim 4, characterized in that, The scoring model is as follows: RiskScore = 0.512026×Exp(ENST00000662739.1)+0.315018×Exp(ENST00000572187.1)+0.634009×Exp(ENST00000583250.1)+0.173005×Exp(ENST00000619343.1)+0.515002×Exp(ENST00000591993.6)+0.331015×Exp(ENST00000580119.2)+0.714020×Exp(ENST00000418803.1).

6. A specific primer pair for amplifying the seven LncRNAs of the LncRNA combination as described in claim 1 or 2, characterized in that, The nucleotide sequences of the primer pairs are shown in SEQ ID NO.8~SEQ ID NO.

21.

7. A diagnostic kit for assessing or assisting in the prognostic evaluation of triple-negative breast cancer patients, characterized in that, The detection kit contains the specific primer pair as described in claim 6.

8. The detection kit according to claim 7, characterized in that, The detection kit also includes RNA extraction reagent, cDNA reverse transcription reagent, quantitative PCR reagent, internal reference primers, and fluorescent dye.

9. The detection kit according to claim 8, characterized in that, The internal reference primer is β. Actin-specific primer pairs, nucleotide sequences as shown in SEQ ID NO.22~SEQ ID NO.23; the fluorescent dye is SYBR-Green dye.

10. The detection kit according to claim 8, characterized in that, RNA extraction reagents include lysis buffer, digestion solution, RNA carrier, washing buffer, and elution buffer; cDNA reverse transcription reagents include gDNA Clean Reaction Mix, reverse transcriptase, and reverse transcription reaction buffer; quantitative PCR reagents include polymerase chain reaction buffer and dNTPs.

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