PCR (Polymerase Chain Reaction) array kit for detecting tumor lipid metabolism state and application

Abstract

The invention provides a PCR array kit for detecting tumor metabolism state and application thereof, the kit comprises 96 pairs of specific primers, 86 lipid metabolism related genes and 7 reference genes can be detected at the same time, the key pathway of lipid metabolism is fully covered, the problem of limited gene detection flux in the prior art is solved, and the kit can be used for detecting the tumor metabolism state. The detection efficiency is greatly improved, the panorama of the tumor lipid metabolism state can be quickly obtained, and a reliable molecular basis is provided for personalized precise treatment. Compared with the prior art, the invention has the advantages of prominent technical innovation, remarkable clinical practical value, wide application prospect and good technical economy, and is expected to become an important auxiliary tool for tumor diagnosis and treatment.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and in particular to a PCR array kit for detecting tumor lipid metabolism status and an application thereof. Background Art

[0002] With the continuous advancement of molecular tumor biology research, the key role of lipid metabolic reprogramming in tumorigenesis and progression has been gradually revealed and confirmed. Tumor cells not only obtain the energy required for survival and proliferation by regulating pathways such as fatty acid synthesis, cholesterol metabolism, and lipid oxidation, but also utilize lipid metabolic intermediates to participate in signal transduction and epigenetic regulation. Abnormal lipid metabolism has become a prominent metabolic hallmark of cancer. Cancer cells rely on lipid metabolism for energy, biomembrane synthesis, and significantly enhance their proliferation, invasion, and metastasis. Furthermore, abnormal lipid metabolism profoundly impacts the tumor microenvironment and its response to cancer therapy. Disturbances in tumor cell lipid metabolism lead to an immunosuppressive phenotype and extensively interact with the complex tumor immune microenvironment. For example, CD36 promotes tumor growth and mediates drug resistance in the tumor microenvironment by targeting lipids in tumor-associated macrophages. Furthermore, CD36 plays a key role in myeloid-derived suppressor cells by increasing fatty acid uptake and oxidation, thereby supporting their immunosuppressive function. Abnormal lipid metabolism in tumor-infiltrating regulatory T cells significantly enhances their immunosuppressive function. Disturbed lipid metabolism in the tumor microenvironment also inhibits the recruitment of CD8+ T cells and impairs their tumor-killing capacity. Therefore, accurate quantification of tumor lipid metabolism status has become a key molecular basis for predicting treatment response and prognosis.

[0003] However, current clinical detection methods still mainly rely on traditional serum biochemical indicators. These methods are difficult to fully, dynamically and accurately reflect the complex metabolic state of tumor cells, which seriously restricts the precise diagnosis and treatment of tumors. Existing detection technologies such as microarray technology and transcriptome sequencing technology can cover the expression detection of all genes, but they are still insufficient in accuracy, and have disadvantages such as high cost, cumbersome operation and long time consumption. The test results still need to rely on real-time fluorescence quantitative PCR (qPCR) technology for review. Although qPCR technology is currently the most mature and accurate method for gene quantitative detection, it also has limitations such as low throughput and cumbersome operation.

[0004] Therefore, there is an urgent need for an innovative tool that can detect lipid metabolic reprogramming in a multi-dimensional and high-throughput manner, which can not only accurately evaluate the metabolic characteristics of tumor cells, but also predict the patient's treatment response and prognosis, and provide clinicians with a molecular basis for personalized treatment. Summary of the Invention

[0005] In view of this, the object of the present invention is to provide a PCR array kit for detecting tumor lipid metabolism status and its application.

[0006] To achieve the above object, the technical scheme of the present application is as follows:

[0007] The present application provides a PCR array kit for detecting the metabolic state of tumor, which comprises 96 pairs of pre-packed dry powder specific primers, which are arranged in a 96-well plate according to function, including a gene detection area and a quality control area.

[0008] The gene detection area comprises 86 pairs of target gene primers and 7 pairs of internal reference gene primers, the target genes are ACSS1, ADGRD1, ADM, AGER, ALDH2, AMT, ANKRD29, ANKRD65, ANLN, ATP8A1, BIRC5, ARNTL2, BUB1B, CACNA2D2, CCNA2, CCNB1, CCNB2, CDC6, CDCA5, CDK1, CDKN3, CYP4B1, DKK1, DLGAP5, DNALI1, FAM189A2, ERO1A, ESYT3, EXO1, FAAH, FAM83A, FAM83D, FBP1, FOXM1, GGT6, GJB2, GJB3, GPD1L, HJURP, HLF, HMMR, HSD17B6, IRX2, IRX3, IRX5, ITGB1, KIF11, KIF23, KPNA2, KRT16, KRT6A, LOXL2, MAD2L1, MKI67, MS4A15, NEK2, NPC2, NR0B2, NUSAP1, PCLAF, PKP2, PLK1, PLPP4, PNMA2, PRC1, PXMP4, RHOV, RNASE1, RRM2, SCNN1B, SERPINB5, SFTPB, SKA3, SLC2A1, SNX30, SPC25, SPOCK1, STEAP1, TCN1, TMEM125, TMEM130, TNS4, TPX2, TTK, UCK2, UNC13B; and the internal reference genes are ACTB, B2M, GAPDH, GUSB, HPRT1, PGK1, PPIA.

[0009] The quality control area comprises:

[0010] NPC background well: no primer pair;

[0011] Negative control GDC: primer pair for detecting ACTB gene promoter sequence;

[0012] Positive control PPC: primer pair for detecting positive control pCDH-CMV-MCS-EF1-copGFP plasmid.

[0013] Furthermore, the target gene primer sequences are shown as SEQ ID NO.1 to SEQ ID NO.172, the internal reference gene primer sequences are shown as SEQ ID NO.173 to SEQ ID NO.186, the quality control region negative control GDC primer sequences are shown as SEQ ID NO.187 to SEQ ID NO.188, and the quality control region positive control PPC primer sequences are shown as SEQ ID NO.189 to SEQ ID NO.190.

[0014] Furthermore, the primer pairs of the gene detection region are arranged in a specific order in a 96-well plate, and the specific arrangement order is as follows:

[0015] Row A, columns 1 to 12: ACSS1, ADGRD1, ADM, AGER, ALDH2, AMT, ANKRD29, ANKRD65, ANLN, ATP8A1, BIRC5, BMAL2;

[0016] Row B, columns 1 to 12: BUB1B, CACNA2D2, CCNA2, CCNB2, CCNB2, CDC6, CDCA5, CDK1, CDKN3, CYP4B1, DKK1, DLGAP5;

[0017] Row C, columns 1 to 12: DNALI1, ENTREP1, ERO1A, ESYT3, EXO1, FAAH, FAM83A, FAM83D, FBP1, FOXM1, GGT6, GJB2;

[0018] Row D, columns 1 to 12: GJB3, GPD1L, HJURP, HLF, HMMR, HSD17B6, IRX2, IRX3, IRX5, ITGB1, KIF11, KIF23;

[0019] Row E, columns 1 to 12: KPNA2, KRT16, KRT6A, LOXL2, MAD2L1, MKI67, MS4A15, NEK2, NPC2, NROB2, NUSAP1, PCLAF;

[0020] Row F, columns 1 to 12: PKP2, PLK1, PLPP4, PNMA2, PRC1, PXMP4, RHOV, RNASE1, RRM2, SCNN1B, SERPINB5, SFTPB;

[0021] Row G, Column 1-12: SKA3, SLC2A1, SNX30, SPC25, SPOCK1, STEAP1, TCN1, MEM125, TMEM130, TNS4, TPX2, TTK;

[0022] Row H, Column 1-9: UCK2, UNC13B, ACTB, B2M, GAPDH, GUSB, HPRT1, PGK1, PPIA;

[0023] Further, the primer pairs of the quality control area are arranged in a specific order in the 96-well plate, and the specific arrangement order is as follows:

[0024] Row H, Column 10: NPC background well, no primer pair;

[0025] Row H, Column 11: negative control GDC, primer pair for detecting ACTB gene promoter sequence;

[0026] Row H, Column 12: positive control PPC, primer pair for detecting positive control pCDH-CMV-MCS-EF1-copGFP plasmid.

[0027] The application also provides a preparation method of the PCR array kit for detecting tumor metabolic state, comprising the following steps:

[0028] The primers are divided into 20 muL / well and placed in the corresponding hole positions of the 96-well plate, and the plate is pasted with a sealing film and centrifuged;

[0029] The 96-well plate is pre-frozen at-80 DEG C for 1 hour;

[0030] The 96-well plate is taken out and placed in a vacuum freeze-drying device for freeze-drying to remove water;

[0031] After freeze-drying is completed, the 96-well plate is sealed and packaged using a vacuum sealing machine.

[0032] The application also provides a method for detecting tumor lipid metabolism state using the above kit, comprising the following steps:

[0033] Extracting sample RNA and reverse transcribing into cDNA;

[0034] Preparing a PCR reaction system, including cDNA template, ultrapure water, qPCR premix;

[0035] Adding the prepared PCR reaction system to the 96-well plate preloaded with primers, and adding pCDH-CMV-MCS-EF1-copGFP plasmid to the positive control well;

[0036] Performing qPCR reaction;

[0037] Gene expression levels were calculated and differentially expressed genes were screened to evaluate lipid metabolism status.

[0038] Furthermore, the total preparation volume of the PCR reaction system is 2500 μL, and the component ratio is: 250 μL of cDNA sample, 1000 μL of ultrapure water, and 1250 μL of qPCR premix. The final reaction system in each well is 20 μL.

[0039] Furthermore, the qPCR reaction program was as follows: pre-denaturation at 95°C for 10 minutes; followed by 40 cycles of denaturation at 95°C for 10 seconds and annealing / extension at 60°C for 30 seconds; and the melting curve was performed using default parameters.

[0040] The present invention also provides a method for constructing a tumor lipid metabolism status scoring system, comprising:

[0041] (1) Based on the TCGA dataset, tumor lipid metabolism subtypes were divided into Cluster 1 and Cluster 2 by consensus cluster analysis;

[0042] (2) Screening lipid metabolism genes significantly associated with prognosis;

[0043] (3) Principal component analysis was performed on the differential gene expression data, and the first principal component was extracted as the LipidMet Score;

[0044] (4) The high / low score groups were divided according to the median value, and the prognostic prediction efficacy of the score was verified using independent datasets GSE68465, GSE72094, GSE42127, and GSE50081.

[0045] Beneficial effects of the present invention: The present invention provides a PCR array kit for detecting tumor metabolic status and its application, the kit includes 96 pairs of specific primers, which can simultaneously detect 86 lipid metabolism-related genes and 7 internal reference genes, comprehensively covering the key pathways of lipid metabolism, and solving the problem of limited gene detection throughput in the prior art. In addition, the present invention realizes the synchronous detection and quantitative analysis of 86 lipid metabolism-related genes and 7 internal reference genes through PCR array technology, greatly improving the detection efficiency, and can quickly obtain a panoramic view of the tumor lipid metabolic state. In addition, the primers of the kit of the present invention are obtained through strict screening and optimization, ensuring the specific amplification of the target gene, avoiding the interference of non-specific products, and improving the accuracy of the test results. In addition, the present invention enables the kit to detect the expression changes of low-abundance genes through the optimized qPCR reaction system and conditions, with high sensitivity, and can accurately reflect the metabolic state of tumor cells.

[0046] Overall, the present invention provides a comprehensive detection kit for lipid metabolic reprogramming status and its application. This method transcends the limitations of traditional biochemical indicators, enabling a multidimensional and precise assessment of tumor metabolic status. With both high sensitivity and specificity, it can accurately predict a patient's treatment response and prognosis, providing a reliable molecular basis for personalized precision therapy. Compared with existing technologies, this invention boasts outstanding technological innovation, significant clinical utility, broad application prospects, and excellent technical and economic efficiency, and is expected to become an important auxiliary tool for tumor diagnosis and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Figure 1 is the identification and survival analysis of lipid metabolism subtypes; 1A is the consistent clustering result of lipid metabolism gene expression profiles based on the TCGA dataset; 1B is the Kaplan-Meier curve of overall survival (OS) of Cluster 1 and Cluster 2; 1C is the heat map of lipid metabolism gene expression; 1D is the difference in clinical feature distribution.

[0048] Figure 2 The figure shows the biological characteristics of lipid metabolism subtypes; 2A is the HALLMARK pathway GSVA enrichment heat map; 2B-C are the differences in the infiltration scores of 28 immune cells; 2D-G are the comparison of microenvironment scores, 2D is the matrix score, 2E is the immune score, 2F is the ESTIMATE total score, and 2G is the tumor purity.

[0049] Figure 3 3A is the ROC curve of the TCGA training set; 3B-E are independent data set validations, 3B is GSE68465 validation, 3C is GSE72094 validation, 3D is GSE42127 validation: 3E is GSE50081 validation.

[0050] Figure 4 The independent prognostic value of LipidMet Score.

[0051] Figure 5 Figure 5A-J shows the association between score groups and clinical characteristics, and Figure 5K shows the Sankey diagram of multi-omics association.

[0052] Figure 6 RNA quality control gel run results.

[0053] Figure 7 The results were calculated as the mean value of the internal reference for qPCR detection.

[0054] Figure 8 The CT value and melting curve results of qPCR detection are shown.

[0055] Figure 9Volcano plot of differentially expressed genes. DETAILED DESCRIPTION

[0056] The technical solutions of the present invention will be described in further detail below with reference to specific embodiments. It should be understood that the following examples are merely illustrative and explanations of the present invention and should not be construed as limiting the scope of protection of the present invention. All technologies implemented based on the above-mentioned contents of the present invention are encompassed within the scope that the present invention is intended to protect. It should be noted that, unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art. The experimental reagents in the examples, unless otherwise specified, can all be obtained commercially. The experimental methods in the examples, unless otherwise specified, are all conventional methods.

[0057] Example 1 Screening of key genes for tumor lipid metabolism

[0058] First, the complete set of genes related to lipid metabolism pathways was downloaded from public databases. Based on the lipid metabolism gene expression profiles of the TCGA dataset samples, consensus cluster analysis was performed using the R package ConsensusClusterPlus. Finally, two lipid metabolism subtypes with significant differences were identified (Cluster 1 and Cluster 2, sample size N = 268 / 229; see Appendix). Figure 1 A). Survival analysis showed that there was a significant difference in the prognosis of the two subtypes, with the overall survival (OS) of patients in Cluster 2 being significantly worse than that in patients in Cluster 1 (see Appendix Figure 1 B). The expression profiles of lipid metabolism genes can effectively distinguish these two different lipid metabolism patterns (see Appendix Figure 1 C). Analysis of the clinical characteristics of different subgroups of patients showed that there were no significant differences in the distribution of characteristics such as age group, stage, and sex, but there were significant differences in the distribution of immunophenotyping (p < 0.05; see Appendix). Figure 1 D).

[0059] To explore the differences in biological behavior between different lipid metabolism subtypes, gene set variation analysis (GSVA) ​​based on the HALLMARK gene set was performed. The results showed that most of the 50 HALLMARK pathways were significantly enriched between the two groups, among which Cluster2 was significantly enriched in pathways such as DNA damage repair and hypoxia (see Appendix). Figure 2 A). Further analysis of the distribution of 28 immune cell infiltration scores, immune scores, stromal scores, and tumor purity in samples of different subtypes revealed significant differences in most immune cell infiltration scores between the two groups (see Appendix). Figure 2 Cluster 1 had significantly lower matrix scores and total ESTIMATE scores than Cluster 2 (see Appendix Figure 2 D, F), the tumor purity was significantly higher than that of Cluster 2 (see Appendix Figure 2 G), while there was no significant difference in the immune scores between the two subgroups (see Appendix Figure 2 E).

[0060] Based on the differentially expressed genes obtained from the above analysis, univariate Cox proportional hazard regression analysis was performed, and 137 genes significantly associated with prognosis were screened out (p < 0.01). The expression data of these genes were used for principal component analysis (PCA) to calculate the lipid metabolism status score (LipidMet Score) of each sample. According to the median value of the Score (-0.04613714), the samples were divided into a high score group (High) and a low score group (Low). Survival analysis showed that the OS of patients in the High group was significantly worse than that in the Low group (see Appendix Figure 3 A). Receiver operating characteristic (ROC) curve analysis showed that the score had a predictive efficacy (AUC) of 0.694, 0.644, and 0.623 for the 1-year, 3-year, and 5-year OS of the sample, respectively, demonstrating that the score can effectively characterize the OS risk of the sample.

[0061] To verify the universality of the scoring system, the same method was used to calculate the scores in four independent validation datasets (GSE68465_GPL96, GSE72094_GPL15048, GSE42127_GPL6884, and GSE50081_GPL570). The results showed that all validation datasets showed good prediction consistency (see Appendix Figure 3 BE), its 1-year, 3-year and 5-year OS prediction AUCs were:

[0062] GSE68465_GPL96: 0.700, 0.674, 0.620

[0063] GSE72094_GPL15048: 0.696, 0.671, 0.774

[0064] GSE42127_GPL6884: 0.891, 0.723, 0.725

[0065] GSE50081_GPL570: 0.675, 0.722, 0.730

[0066] Univariate and multivariate Cox regression analyses were further used to evaluate the independent prognostic value of the Score model relative to other clinical factors. After adjusting for confounding factors such as age, gender, and stage, both univariate and multivariate Cox analyses confirmed that LipidMetScore was an independent risk factor for patient prognosis (see Appendix Figure 4In addition, the sample distribution of different lipid metabolism patterns (Cluster), genomic phenotypes (geneCluster), immune subtypes and Score groups (High / Low) is shown (see Appendix Figure 5 K). The distribution differences of LipidMet Score among different groups were compared: the score of Cluster2 was significantly higher than that of Cluster1, and the score of geneCluster2 was significantly higher than that of geneCluster1; there were also significant differences in the scores among different clinical characteristics groups such as stage, age group and immunophenotype (see Appendix Figure 5 Finally, based on the results of univariate and multivariate Cox analysis, 86 key genes in tumor lipid metabolism were screened and identified (see Table 1-1, Table 1-2).

[0067] Table 1-1 Key genes for tumor lipid metabolism

[0068] Serial number Gene Species Gene ID Ensembl Transcript 1 DKK1 Homo sapiens 22943 NM_012242.4 2 TMEM125 Homo sapiens 128218 NM_144626.3 3 ANLN Homo sapiens 54443 NM_018685.5 4 IRX5 Homo sapiens 10265 NM_005853.6 5 CCNA2 Homo sapiens 890 NM_001237.5 6 STEAP1 Homo sapiens 26872 NM_012449.3 7 ADGRD1 Homo sapiens 283383 NM_198827.5 8 GJB3 Homo sapiens 2707 NM_024009.3 9 ANKRD65 Homo sapiens 441869 NM_001145210.3 10 PRC1 Homo sapiens 9055 NM_003981.4 11 CDK1 Homo sapiens 983 NM_001786.5 12 GJB2 Homo sapiens 2706 NM_004004.6 13 DLGAP5 Homo sapiens 9787 NM_014750.5 14 NUSAP1 Homo sapiens 51203 NM_016359.5 15 FAM83D Homo sopiens 81610 NM_030919.3 16 SLC2A1 Homo sapiens 6513 NM_006516.4 17 HMMR Homo sapiens 3161 NM_001142556.2 18 PLK1 Homo spiens 5347 NM_005030.6 19 PKp2 Homo sapiens 5318 NM_001005242.3 20 MKI67 Homo sapiens 4288 NM_002417.5 21 SFTPB Homo sapiens 6439 NM_000542.5 22 DNAU1 Homo sapiens 7802 NM_003462.5 23 ESYT3 Homo sapiens 83850 NM_031913.5 24 TNS4 Homo sapiens 84951 NM_032865.6 25 KIF23 Homo sapiens 9493 NM_001367805.3 26 NEK2 Homo sapiens 4751 NM_002497.4 27 RRM2 Homo sapiens 6241 NM_001034.4 28 SERPINB5 Homo saplens 5268 NM_002639.5 29 TTK Homo sapliens 7272 NM_003318.5 30 FBP1 Homo sapiens 2203 NM_000507.4 31 ERO1A Homo sapliens 30001 NM_014584.3 32 MAD2L1 Homo sapiens 4085 NM_002358.4 33 HJURP Homo sapiens 55355 NM_018410.5 34 UNC13B Homo sapiens 10497 NM_001371189.2 35 SCNN1B Homo sapiens 6338 NM_000336.3 36 ACSS1 Homo sapiens 84532 NM_032501.4 37 CDKN3 Homo sapiens 1033 NM_005192.4 38 TMEM130 Homo sapiens 222865 NM_152913.3 39 SPOCK1 Homo sapiens 6695 NM_004598.4 40 ATP8A1 Homo sapiens 10396 NM_006095.2 41 BUB1B Homo sapiens 701 NM_001211.6 42 CCNB1 Homo sapiens 891 NM_031966.4 43 NPC2 Homo sapiens 10577 NM_006432.5

[0069] Table 1-2 Key genes for tumor lipid metabolism

[0070] 44 KRT16 Homo sapiens 3868 NM_005557.4 45 PCLAF Homo sapiens 9768 NM_014736.6 46 GPD1L Homo sapiens 23171 NM_015141.4 47 GGT6 Homo sapiens 124975 NM_001288702.2 48 CYP481 Homo sapiens 1580 NM_001099772.2 49 BIRC5 Homo sapiens 332 NM_001168.3 50 FOXM1 Homo sapiens 2305 NM_021953.4 51 PNMA2 Homo sapiens 10687 NM_007257.6 52 UCK2 Homo sapiens 7371 NM_012474.5 53 KPNA2 Homo sapiens 3838 NM_002266.4 54 LOXL2 Homo sapiens 4017 NM_002318.3 55 CACNA2D2 Homo sapiens 9254 NM_006030.4 56 MS4A15 Homo sapiens 219995 NM_001098835.2 57 PXMP4 Homo sapiens 11264 NM_007238.5 58 FAM83A Homo sapiens 84985 NM_001394396.1 59 ARNTL2 Homo sapiens 56938 NM_020183.6 60 HSD17B6 Homo sapiens 8630 NM_003725.4 61 AMT Homo sapiens 275 NM_000481.4 62 SKA3 Homo sapiens 221150 NM_145061.6 63 RHOV Homo sapiens 171177 NM_133639.4 64 ADM Homo sapiens 133 NM_001124.3 65 PLPP4 Homo sapiens 196051 NM_0010300593 66 IRX2 Homo sapiens 153572 NM_033267.5 67 HLF Homo sapiens 3131 NM_002126.5 68 CDCA5 Homo sapiens 113130 NM_080668.4 69 ITGB1 Homo sapiens 3688 NM_002211.4 70 NR0B2 Homo sapiens 8431 NM_021969.3 71 IRX3 Homo sapiens 79191 NM_024336.3 72 KIF11 Homo sapiens 3832 NM_004523.4 73 AGER Homo sapiens 177 NM_001136.5 74 CDC6 Homo sapiens 990 NM_001254.4 75 SNX30 Homo sapiens 401548 NM_001012994.2 76 FAM 189A2 Homo sapiens 9413 NM_001347995.2 77 KRT6A Homo sapiens 3853 NM_005554.4 78 RNASE1 Homo sapiens 6035 NM_002933.5 79 ALDH2 Homo sapiens 217 NM_000690.4 80 TPX2 Homo sapiens 22974 NM_012112.5 81 ANKRD29 Homo sapiens 147463 NM_173505.4 82 FAAH Homo sapiens 2166 NM_001441.3 83 CCNB2 Homo sapiens 9133 NM_004701.4 84 TCN1 Homo sapiens 6947 NM_001062.4 85 EXO1 Homo sapiens 9156 NM_130398.4 86 SPC25 Homo sapiens 57405 NM_020675.4

[0071] Example 2 Preparation of a PCR Array Kit for Detecting Tumor Lipid Metabolism

[0072] (1) Aliquot the primers for the gene detection region and the quality control region into the corresponding wells of a 96-well plate at 20 μL / well, seal the plate with film, and centrifuge at 3000 rpm for 3 minutes;

[0073] The specific arrangement order of the gene detection area and the quality control area is shown in Table 2 below:

[0074] Table 2 Ranking of gene detection areas and quality control areas

[0075] 1 2 3 4 5 6 7 8 9 10 11 12 A ACSS1 ADGRD1 ADM AGER ALDH2 AMT ANKRD29 ANKRD65 ANLN ATP8A1 BIRC5 BMAL2 B BUB1B CACNA2D2 CCNA2 CCNB1 CCNB2 CDC6 CDCA5 CDK1 CDKN3 CYP4B1 DKK1 DLGAP5 C DNALI1 ENTREP1 DIFFERENCE1A ESYT3 EXO1 FAAtH FAM83A FAM83D FBP1 FOXM1 GGT6 GJB2 D GJB3 GPD1L HELP HLF HMM HSD17B6 IRX2 IRX3 IRX5 ITGB1 KIF11 KIF23 E KPNA2 KRT16 KRT6A LOXL2 MAD2L1 MKI67 MS4A15 NEK2 NPC2 NR0B2 NUSAP1 PCLAF F PKP2 PLK1 PLPP4 PNMA2 PRC1 PXMP4 RHOV RNASE1 RRM2 SCNNIB SERPINB5 SFTPB G SKA3 SLC2A1 SNX30 SPC25 SPOCK1 STEAP1 TCN1 TMEM125 TMEM130 TNS4 TPX2 RTD H UCK2 UNC13B ACTB B2M GAPDH GUSB HPRT1 PGK1 PPIA NPCs GDC PPC

[0076] The specific sequences of the primers in the gene detection region and the primers in the quality control region are shown in Table 3.

[0077] Table 3 Primer pair sequences

[0078]

[0079]

[0080]

[0081]

[0082] (2) Prefreeze the 96-well plate at -80°C for 1 hour;

[0083] (3) The 96-well plate was taken out and placed in a vacuum freeze-drying apparatus for freeze-drying to remove moisture. The freeze-drying parameters were set as follows: sample temperature ≤ −20° C., chamber pressure ≤ 10 Pa, and holding time ≥ 3 h.

[0084] (4) After freeze-drying, use a vacuum sealer to seal the 96-well plate.

[0085] Example 3 Lung cancer tissue test

[0086] The sample groups in this example are divided into: cancerous tissue, adjacent tissue (>2 cm away from the tumor lesion)

[0087] 1. Extract sample RNA and reverse transcribe it into cDNA

[0088] After the tissue was frozen and ground in liquid nitrogen, ~20 mg was taken and RNA was extracted using a kit (vazyme#RC113-01). The gel was run and the results showed that the RNA was free of protein or phenol contamination and had good purity (see Figure 6 ), genomic DNA digestion and reverse transcription reaction were performed to prepare cDNA.

[0089] The genomic DNA digestion method is as follows:

[0090] Prepare the mixture shown in the table below in an RNase-free centrifuge tube and mix thoroughly by gently pipetting. Incubate at 37°C for 2 minutes.

[0091]

[0092] The reverse transcription reaction method is as follows:

[0093] Prepare the reverse transcription reaction system according to the table below, prepare 20 μL of the system, incubate at 50°C for 2 minutes and then at 85°C for 5 seconds.

[0094]

[0095] 2. PCR testing

[0096] Configure the PCR reaction system according to the table below:

[0097] Components Dosage cDNA 250 μL Ultrapure Water 1000μL 2×qPCR Mix 1250μL

[0098] Use a pipette to gently pipette 10 times, invert 30 times to mix, and then transfer all to a single slot of the 12-channel loading tank.

[0099] Use a dispenser to add 20 μL / well to the PCR array kit prepared in Example 2, wherein 2.5 μL of the positive control pCDH-CMV-MCS-EF1-copGFP plasmid is added to the positive control well (PPC-H12). The plate is sealed with film and pressed tightly, followed by centrifugation at 3000 rpm for 3 minutes.

[0100] Carefully remove the PCR array kit from the centrifuge and use ABI StepOnePlus TM Wait for qPCR instrument to detect, wherein, the qPCR reaction conditions are as follows:

[0101]

[0102] The default parameters were used for the melting curve.

[0103] The experimental results are as follows Figure 7-8 The distribution of CT values ​​in the experimental and control groups was similar, with the expression of most genes concentrated in the CT value range of 25-30, followed by low-expression genes (CT: 30-35), high-expression genes (CT < 25), and absent genes (CT > 35).

[0104] Combine the p-value of the independent sample t-test for each gene and draw a volcano plot ( Figure 8 Differentially expressed genes were identified when the absolute value of the fold difference was ≥1.5 and the t-test p-value was less than 0.01. Changes in the expression of genes related to lipid metabolism were screened, and the differentially expressed genes screened were downregulated (ADGRD1, FAM189A2, AGER, LOXL2, GGT6, and UNC13B), and upregulated (TNS4, GJB2, SLC2A1, PCLAF, NEK2, KPNA2, RRM2, FOXM1, CDC6, and NPC2).

[0105] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. All modifications, equivalent substitutions, improvements, etc. within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A PCR array kit for detecting tumor metabolic status, characterized in that: The kit includes 96 pairs of pre-packed dry powder specific primers, which are arranged in a 96-well plate according to their functions, including a gene detection area and a quality control area; The gene detection area contains 86 pairs of target gene primers and 7 pairs of internal reference gene primers. The target genes are ACSS1, ADGRD1, ADM, AGER, ALDH2, AMT, ANKRD29, ANKRD65, ANLN, ATP8A1, BIRC5, ARNTL2, BUB1B, CACNA2D2, CCNA2, CCNB1, CCNB2, CDC6, CDCA5, CDK1, CDKN3, CYP4B1, DKK1, DLGAP5, DNALI1, FAM189A2, ERO1A, ESYT3, EXO1, FAAH, FAM83A, FAM83D, FBP1, FOXM1, GGT6, GJB2, GJB3, GPD1L, HJURP, HLF, HMMR, HSD17B6, IRX2, and IRX3. , IRX5, ITGB1, KIF11, KIF23, KPNA2, KRT16, KRT6A, LOXL2, MAD2L1, MKI67, MS4A15, NEK2, NPC2, NR0B2, NUSAP1, PCLAF, PKP2, PLK1, PLPP4, PNMA2, PRC1, PXMP4, RHOV, RNASE1, RRM2, SCNN1B, SERPINB5, SFTPB, SKA3, SLC2A1, SNX30, SPC25, SPOCK1, STEAP1, TCN1, TMEM125, TMEM130, TNS4, TPX2, TTK, UCK2, UNC13B; the internal reference genes are ACTB, B2M, GAPDH, GUSB, HPRT1, PGK1, PPIA; The quality control area includes: NPC background wells: no primer pair; Negative control GDC: primer pair used to detect the promoter sequence of ACTB gene; Positive control PPC: Primer pair used to detect the positive quality control pCDH-CMV-MCS-EF1-copGFP plasmid.

2. The kit according to claim 1, wherein The target gene primer sequences are shown in SEQ ID NO.1 to SEQ ID NO.172, the internal reference gene primer sequences are shown in SEQ ID NO.173 to SEQ ID NO.186, the quality control region negative control GDC primer sequences are shown in SEQ ID NO.187 to SEQ ID NO.188, and the quality control region positive control PPC primer sequences are shown in SEQ ID NO.189 to SEQ ID NO.

190.

3. The kit according to claim 1, wherein The primer pairs of the gene detection region are arranged in a specific order in a 96-well plate. The specific arrangement order is as follows: Row A, columns 1 to 12: ACSS1, ADGRD1, ADM, AGER, ALDH2, AMT, ANKRD29, ANKRD65, ANLN, ATP8A1, BIRC5, BMAL2; Row B, columns 1 to 12: BUB1B, CACNA2D2, CCNA2, CCNB2, CCNB2, CDC6, CDCA5, CDK1, CDKN3, CYP4B1, DKK1, DLGAP5; Row C, columns 1 to 12: DNALI1, ENTREP1, ERO1A, ESYT3, EXO1, FAAH, FAM83A, FAM83D, FBP1, FOXM1, GGT6, GJB2; Row D, columns 1 to 12: GJB3, GPD1L, HJURP, HLF, HMMR, HSD17B6, IRX2, IRX3, IRX5, ITGB1, KIF11, KIF23; Row E, columns 1 to 12: KPNA2, KRT16, KRT6A, LOXL2, MAD2L1, MKI67, MS4A15, NEK2, NPC2, NROB2, NUSAP1, PCLAF; Row F, columns 1 to 12: PKP2, PLK1, PLPP4, PNMA2, PRC1, PXMP4, RHOV, RNASE1, RRM2, SCNN1B, SERPINB5, SFTPB; Row G, columns 1 to 12: SKA3, SLC2A1, SNX30, SPC25, SPOCK1, STEAP1, TCN1, MEM125, TMEM130, TNS4, TPX2, TTK; Row H, columns 1 to 9: UCK2, UNC13B, ACTB, B2M, GAPDH, GUSB, HPRT1, PGK1, PPIA; The primer pairs of the quality control region are arranged in a specific order in a 96-well plate. The specific arrangement order is as follows: Row H, column 10: NPC background well, no primer pair; Row H, column 11: negative control GDC, primer pair used to detect the promoter sequence of the ACTB gene; Row H, column 12: Positive control PPC, primer pair used to detect the positive quality control pCDH-CMV-MCS-EF1-copGFP plasmid.

4. A method for preparing a PCR array kit for detecting tumor metabolic status according to any one of claims 1 to 3, comprising the following steps: The primers were dispensed into corresponding wells of a 96-well plate at 20 μL / well, sealed with film, and centrifuged; The 96-well plate was pre-frozen at -80°C for 1 hour; The 96-well plate was taken out and placed in a vacuum freeze-drying device for freeze-drying to remove moisture; After freeze-drying, the 96-well plate was sealed using a vacuum sealer.

5. A method for detecting tumor lipid metabolism status using the kit according to any one of claims 1 to 3, comprising the following steps: Extract sample RNA and reverse transcribe it into cDNA; Prepare PCR reaction system, including cDNA template, ultrapure water, and qPCR premix; The prepared PCR reaction system was added to a 96-well plate preloaded with primers, and the pCDH-CMV-MCS-EF1-copGFP plasmid was added to the positive control well; Perform qPCR reactions; Gene expression levels were calculated and differentially expressed genes were screened to evaluate lipid metabolism status.

6. The method according to claim 5, characterized in that The total preparation volume of the PCR reaction system is 2500 μL, and the component ratio is: 250 μL of cDNA sample, 1000 μL of ultrapure water, and 1250 μL of qPCR premix. The final reaction system in each well is 20 μL.

7. The method according to claim 5, characterized in that The qPCR reaction program was as follows: pre-denaturation at 95°C for 10 minutes; followed by 40 cycles of denaturation at 95°C for 10 seconds and annealing / extension at 60°C for 30 seconds; the melting curve used default parameters.

8. A method for constructing a tumor lipid status scoring system, characterized in that: include: (1) Based on the TCGA dataset, tumor lipid metabolism subtypes were divided into Cluster 1 and Cluster 2 by consensus cluster analysis; (2) Screening lipid metabolism genes significantly associated with prognosis; (3) Principal component analysis was performed on the differential gene expression data, and the first principal component was extracted as the LipidMet Score; (4) The high / low score groups were divided according to the median value, and the prognostic prediction efficacy of the score was verified using independent datasets GSE68465, GSE72094, GSE42127, and GSE50081.

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