Marker and kit for predicting risk of non-small cell lung cancer and application of marker and kit

Abstract

The invention discloses a marker and a kit for predicting the risk of non-small cell lung cancer and application of the marker and the kit, and belongs to the technical field of biology, the marker is an RELA gene existing in lower-layer platelets, the kit comprises a primer and a probe for detecting the RELA gene, and the nucleotide sequence of the primer and the nucleotide sequence of the probe are as follows: RELA-F: GCTACACAGGACCAGGGACAGGT; the RELA-R is GCAGAGCCGCACAGCATTCA, the RELA-R is RELA-R, and the Wherein, RELA-oligo: ACCGGCCTACACCCCACCGAG, RELA-oligo: The application is used for predicting the risk of non-small cell lung cancer. Based on dPLTs RELA expression level detection, the specificity of the kit is superior to that of a traditional serum tumor marker, reliable risk assessment can be provided in the early stage of tumor occurrence, and the kit has important value and significance for clinical early screening, layered diagnosis and treatment and reduction of unnecessary invasive inspection.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and more particularly to a biomarker, reagent kit, and application for predicting the risk of non-small cell lung cancer. Background Technology

[0002] Lung cancer has become the leading cause of cancer death in my country. According to the 2022 National Cancer Statistics Report released by the National Cancer Center, the mortality rate of lung cancer is as high as 27%. Lung cancer includes small cell lung cancer and non-small cell lung cancer (NSCLC). NSCLC accounts for more than 85% of all lung cancer deaths, and the 5-year survival rate after treatment for early-stage lung cancer can reach 70%. Therefore, early diagnosis and treatment are crucial for the prognosis of NSCLC. With the widespread use of LDCT in lung cancer screening in recent years, more and more lung cancers can be detected at an early stage. However, LDCT has certain radiation effects on the human body, and in addition to detecting early-stage lung cancer, it can also detect many lung nodules that cannot be accurately defined. Therefore, the USPSTF recommends conducting lung cancer risk assessments on the general population first, and only conducting LDCT screening on high-risk groups. Defining high-risk groups for lung cancer in China requires consideration of the specific circumstances in my country. The "Chinese Expert Consensus on the Diagnosis and Treatment of Lung Nodules (2024 Edition)" recommends that, where conditions permit, blood tumor markers be tested to provide a reference for the diagnosis and differential diagnosis of lung nodules. Therefore, liquid biopsy based on the detection of tumor markers in blood is widely used in research on the early diagnosis and prognosis of tumors due to its non-invasiveness, fast detection speed and variety of detection methods.

[0003] Studies have found that platelets play a crucial role in tumor growth and metastasis, although their earliest known physiological functions are blood clotting and thrombus formation. In the tumor microenvironment, platelets can not only transfer mRNA and non-coding RNA into tumor cells, but also take up RNA from tumor cells in complex ways. Therefore, the nucleic acids within platelets not only undergo dynamic changes but are also related to tumor development and progression. Some studies have used the nucleic acids of peripheral blood TEPs as a liquid biopsy marker, finding that it has good diagnostic efficiency in tumor diagnosis.

[0004] Carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), cytokeratin 19 fragment 21-1 (CYFRA21-1), progastrin-releasing peptide (Pro-GRP), and squamous cell carcinoma antigen (SCC) are currently commonly used serum biomarkers for detecting lung cancer in clinical practice. However, the sensitivity and specificity of these indicators in early-stage lung cancer remain unsatisfactory. Studies have shown that although CEA and CYFRA21-1 levels are higher in NSCLC than in healthy individuals, their diagnostic AUC is less than 60%, and the sensitivity is only around 50%. Therefore, in clinical practice for predicting the risk of non-small cell lung cancer, there is an urgent need to find a simple, easy-to-detect, and standardized liquid biopsy technique that can replace serum tumor markers. Summary of the Invention

[0005] One of the objectives of this invention is to provide a biomarker for predicting the risk of non-small cell lung cancer, thereby addressing the aforementioned problems.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: a biomarker for predicting the risk of non-small cell lung cancer, wherein the biomarker is the RELA gene present in the lower platelets.

[0007] The inventors of this application, through extensive experimental research, have demonstrated that the RELA gene, present in lower platelets, has high specificity as a biomarker for predicting NSCLC risk. It should be noted that the purpose of this invention in predicting the risk of non-small cell lung cancer is not to directly obtain a diagnosis of non-small cell lung cancer, but merely to predict the risk.

[0008] The RELA gene, short for Homo sapiens RELA proto-oncogene, NF-κB subunit, also known as NFKB3 or p65, is one of the key subunits of the NF-κB transcription factor complex. The RELA gene sequence involved in this application, with NCBI reference number NG_029971.1, encodes the RELA protein. RELA primarily forms a heterodimer with NFKB1 (p50) and is bound to the repressor protein I-κB in the cytoplasm during resting. When cells are subjected to inflammation, immune stimulation, or stress signals, I-κB is phosphorylated and degraded, releasing the RELA-p50 complex into the nucleus where it binds to specific κB DNA elements, thereby regulating the transcription of downstream genes. RELA is widely involved in various biological processes, including inflammatory responses, immune regulation, cell proliferation, differentiation, and tumorigenesis. Due to its important role in signal transduction and gene expression regulation, it has become a potential drug target and biomarker in research on immune diseases, inflammatory diseases, and various cancers.

[0009] Although high expression of RELA in NSCLC tissues has been confirmed, the expression of RELA in NSCLC platelets and its correlation with NSCLC have not yet been reported. Furthermore, this invention uses the platelet RELA gene as a liquid biopsy marker, which is more easily detectable and standardized compared to traditional methods of detecting RELA genes in tissues.

[0010] A second objective of this invention is to provide a kit for detecting the aforementioned markers.

[0011] As a preferred technical solution, the kit includes primers and probes for detecting the RELA gene, and the nucleotide sequences of the primers and probes are as follows:

[0012] RELA-F: GCTACACAGGACCAGGGACAGT;

[0013] RELA-R:GCAGAGCCGCACAGCATTCA;

[0014] RELA-oligo:ACCGGCCTCACCCCCACGAG.

[0015] The present invention also provides the use of the above-described biomarkers in predicting the risk of non-small cell lung cancer, and the use of the above-described kit in preparing reagents for predicting the risk of non-small cell lung cancer.

[0016] Through extensive experimentation, the inventors have demonstrated that the characteristic genes of dPLTs can be used as novel molecular markers to assess the malignant risk of NSCLC in patients with pulmonary nodules. The molecular markers were validated using platelets collected from clinical samples, providing a new direction for platelet liquid biopsy vector research. Probes and primers designed based on the characteristic gene sequences can be used for reagent kit development, and the detection limits, detection ranges, and reference intervals of the kits can be determined based on clinical data on the expression levels of the characteristic genes.

[0017] Compared with existing technologies, the advantages of this invention are as follows: This invention utilizes the RELA gene, a characteristic gene of lower platelets (dPLTs), as a novel molecular marker to assess the malignancy risk of non-small cell lung cancer (NSCLC) in patients with pulmonary nodules. Platelets, as a liquid biopsy carrier, have advantages such as convenient collection, high stability, and resistance to plasma degradation factors, and can be enriched and reflect tumor-related signals in the early stages of disease. This invention, based on the detection of dPLTs RELA expression levels, not only surpasses traditional serum tumor markers in specificity but also provides reliable risk assessment in the early stages of tumor development, which is of significant value and importance for early clinical screening, stratified diagnosis and treatment, and reducing unnecessary invasive examinations. Attached Figure Description

[0018] Figure 1 A comparison of the FSC-A distribution characteristics in uPLTs and dPLTs between the NSCLC group and the healthy group;

[0019] Figure 2 A comparison of the proportion of activated platelets (CD41⁺CD62P⁺) in uPLTs and dPLTs between the NSCLC group and the healthy group;

[0020] Figure 3 Venn plot of differentially expressed genes (DEGs) in uPLTs and dPLTs between the NSCLC group and the healthy group;

[0021] Figure 4 Figure 1 shows the results of Hallmark pathway functional enrichment analysis of differentially expressed genes in the NSCLC group and the healthy group of dPLTs.

[0022] Figure 5 According to Figure 4 Hallmark enrichment analysis results, and a heatmap of differentially expressed genes involved in significant pathways was drawn.

[0023] Figure 6 The figure shows the results of analyzing the difference in RELA gene expression between LUAD patients and normal tissues using the GEPIA platform;

[0024] Figure 7 To analyze the survival prognostic value of RELA in LUAD patients using the GEPIA platform;

[0025] Figure 8 The image shows the expression heatmap of TNFα-NFκB pathway-related genes in six samples based on sequencing results. The darker the red, the higher the expression, and the darker the blue, the lower the expression. In the figure, S1-S3 are the NSCLC group and S4-S6 are the healthy group.

[0026] Figure 9 To compare RELA expression levels in different platelet subtypes (dPLTs and uPLTs) between the NSCLC group and the healthy group;

[0027] Figure 9 In the study, RELA expression levels in NSCLC patients and healthy controls were compared in lower platelet (dPLTs) and upper platelet (uPLTs) respectively. Each platelet type was compared between groups separately to highlight the expression changes of different platelet subsets under disease conditions.

[0028] Figure 10The difference in RELA expression between platelet subsets (dPLTs vs uPLTs) between the NSCLC group and the healthy group;

[0029] Figure 10 In the NSCLC patient group and the healthy control group, the RELA expression difference between dPLTs and uPLTs was compared to highlight the difference in expression patterns of different platelet subsets in the same subject population.

[0030] Figure 11 The figure shows the overall comparison of RELA expression levels between dPLTs and uPLTs.

[0031] Figure 12 ROC curves for RELA to distinguish NSCLC patients from healthy individuals in dPLTs and uPLTs, and ROC curves for common lung cancer serological markers CA199 and CEA to distinguish NSCLC patients from healthy individuals. Detailed Implementation

[0032] To explain the technical content, objectives, and effects of the present invention in detail, the following specific embodiments are provided to further illustrate the content of the present invention. However, the content of the present invention is far more than the following examples.

[0033] In the following embodiments,

[0034] The healthy control group sample used was derived from volunteers who underwent routine health checkups at a tertiary hospital's health checkup center. Inclusion criteria: age 18–79 years, Han nationality, normal nutrition and coagulation function, and no systemic diseases.

[0035] Exclusion criteria: Imaging findings of signs of malignancy (such as pulmonary nodules), abnormal platelet count (<90×10⁻⁶) 9 / L or >330×10 9 (L), history of surgery within 6 months, history of blood transfusion or blood donation within 4 months, smoking history, history of drinking alcohol history, history of use of oral contraceptives or antiplatelet drugs.

[0036] The NSCLC (non-small cell lung cancer) patient sample used was from patients who were admitted for the first time and had not received any anti-tumor treatment. After admission, pulmonary nodules were found on imaging and pathological diagnosis confirmed lung cancer.

[0037] Exclusion criteria: incomplete clinical data, coexisting malignant tumors, presence of active inflammation, or recent blood transfusion history.

[0038] Example 1

[0039] Analysis of upper and lower platelet layers

[0040] Current research on TEPs as diagnostic biomarkers largely focuses on platelet extraction from peripheral blood using the PRP method. This involves centrifuging whole peripheral blood to separate it into a PRP layer and a blood cell layer, then centrifuging the PRP layer again to obtain the precipitate, which is the extracted platelets. However, the inventors discovered in practice that platelets in the peripheral blood of both healthy individuals and lung cancer patients cannot be completely extracted using the PRP method, and the proportion of unextracted platelets is even higher in lung cancer patients. This may be due to methodological limitations of centrifugation; the PRP method can only extract platelets from the upper PRP layer (uPLTs), while some platelets deposit in the lower red blood cell layer (dPLTs) after centrifugation and are not extracted. Therefore, are uPLTs and dPLTs different platelet subsets, causing them to distribute into different cell layers under the same centrifugal force? This study included 8 subjects, including 5 healthy controls and 3 NSCLC patients. The phenotypic characteristics of uPLTs and dPLTs were explored using flow cytometry analysis. The specific method was as follows:

[0041] 1.1 Sample preparation and staining

[0042] For each sample, 10 μL of platelet-rich plasma (PRP) was taken, and 2 μL of fluorescently labeled antibody mixture was added, including:

[0043] Anti-CD41-APC (platelet marker);

[0044] Anti-CD62P-PE (platelet activation marker).

[0045] After mixing, incubate at room temperature in the dark for 15–20 minutes, then dilute with 500 μL of sterile physiological saline.

[0046] 1.2 Flow cytometry detection:

[0047] Data were acquired using a BD FACSCanto™ flow cytometer (BD Life Sciences, Milpitas, CA 95035, USA), with at least 10,000 events recorded for each sample.

[0048] Platelet population definition: Platelet population is defined within the CD41-positive phylum;

[0049] Physical property analysis: FSC (forward scattering) and SSC (side scattering) parameters were analyzed within the platelet gill to compare the relative volume and particle size / content differences between uPLTs and dPLTs;

[0050] Activation status assessment: The proportion of CD62P / CD41 double positivity was calculated to assess the degree of activation of the two platelet layers.

[0051] 1.3 Flow cytometry results showed that dPLTs exhibited lower FSC-A values ​​than uPLTs, indicating that dPLTs were smaller in size. Furthermore, the proportion of CD62P+ platelets was relatively higher in dPLTs, suggesting that dPLTs exhibited higher activation levels and smaller size compared to uPLTs. Figure 1 and Figure 2 As shown.

[0052] Furthermore, the inventors discovered through flow cytometry analysis that dPLTs in NSCLC patients could reach over 30%, while in healthy individuals it was only around 13.5%. Figure 2 As shown in the figure. These results suggest that the differences in size and activation status between dPLTs and uPLTs may be partly attributable to tumor-associated platelet activation in NSCLC. This increases the likelihood that dPLTs, as a more activated subpopulation, could better reflect the systemic effects of tumors.

[0053] Furthermore, the inventors explored the differentially expressed genes of dPLTs and uPLTs between NSCLC patients and healthy individuals through transcriptome sequencing. This experiment included a total of 6 subjects (3 NSCLC patients and 3 healthy controls).

[0054] The specific experimental method is as follows:

[0055] 2.1 Experimental Grouping and Sample Processing

[0056] Four mL of peripheral venous blood was collected from each subject. Following the aforementioned platelet separation method, platelet-rich plasma (PRP) was divided into two parts: an upper layer of unactivated platelets (uPLTs) and a lower layer of derived platelets (dPLTs). The two groups of samples were then used to generate the following four experimental groups: uPLTs from NSCLC patients; dPLTs from NSCLC patients; uPLTs from healthy controls; and dPLTs from healthy controls.

[0057] 2.2 RNA extraction and quality control, library construction and sequencing, data processing and differential gene analysis, functional enrichment and signaling pathway analysis of differential genes. Reference: Best MG, In 't Veld SGJG, Sol N, Wurdinger T. RNA sequencing and swarm intelligence-enhanced classification algorithm development for blood-based disease diagnostics using spliced ​​blood platelet RNA. Nat Protoc. 2019 Apr;14(4):1206-1234. doi: 10.1038 / s41596-019-0139-5.Epub 2019 Mar 20. PMID: 30894694. Differential gene analysis screened out the differential genes in NSCLC and healthy individuals in dPLTs and uPLTs, respectively. It can be seen that the DEGs of the two platelet subsets are significantly different, such as Figure 3 As shown. Figure 3 In the diagram, the circles representing dPLTs indicate that there are 1634 differentially expressed genes between NSCLC and healthy individuals in the lower platelet layer, while the circles representing uPLTs indicate that there are 586 differentially expressed genes between NSCLC and healthy individuals in the upper platelet layer. The intersection of the two circles represents 48 genes that are differentially expressed genes in both dPLTs and uPLTs.

[0058] 2.3 Based on the results of differential gene analysis by combined flow cytometry and sequencing, it can be determined whether uPLTs and dPLTs are different platelet subsets.

[0059] Example 2

[0060] Specificity of RELA gene in dPLTs of NSCLC patients

[0061] 1.1 Functional enrichment of differentially expressed genes

[0062] Differentially expressed genes were enriched using the Metascape online platform (https: / / metascape.org / gp / index.html# / main / step1). The MSigDB Hallmark gene set was selected for pathway annotation and significance analysis (p < 0.05 was considered significant). Figure 4 As shown.

[0063] Among the differentially expressed genes in dPLTs, 14 genes were enriched in the TNFα–NFκB signaling pathway, including JUN, MYC, RELA, etc. Figure 4 , 5 As shown.

[0064] 1.2 Expression and survival analysis of the RELA gene (GEPIA database)

[0065] To further validate the clinical significance of RELA in NSCLC, the GEPIA platform (http: / / gepia.cancer-pku.cn / ) was used to analyze its expression differences in NSCLC (LUAD and LUSC) and normal tissues, as well as its survival prognostic value in NSCLC patients.

[0066] Sample information: LUAD (n = 483) were obtained from the TCGA database, and the control group was normal lung tissue from the GTEx database (n = 347).

[0067] Analysis methods: The differential analysis used the ANOVA model built into GEPIA. The Kaplan-Meier survival curves were grouped by median expression value. The overall survival (OS) was compared between the high expression and low expression groups. p < 0.05 was considered statistically significant.

[0068] Figure 6 The results showed that RELA was significantly upregulated in NSCLC tissues; Figure 7 To analyze the prognostic value of RELA in NSCLC patients using the GEPIA platform, Kaplan-Meier curves showed the relationship between high RELA expression and overall survival. This suggests that RELA expression may be associated with tumor progression and could serve as a biomarker for predicting NSCLC risk.

[0069] Although 14 genes in the DEGs of dPLTs may be associated with the TNFα–NFκB pathway in tumors, the inventors found, based on expression level analysis after normalization of sequencing results, that only the RELA gene was highly expressed in dPLTs of NSCLC patients and lowly expressed in healthy individuals. Figure 8 As shown in Figure 8, the darker the red, the higher the expression level, and the darker the blue, the lower the expression level.

[0070] Example 3

[0071] Clinical validation trials of RELA in dPLT expression

[0072] The inventors validated RELA gene expression in dPLTs and uPLTs samples from 4 pathologically confirmed NSCLC patients and 5 healthy controls using real-time quantitative reverse transcription PCR (qRT-PCR). GAPDH was used as the internal reference gene, and the specific method is described in the reference [Zu R, Ren H, Yin X, Zhang X, Rao L, Xu P, Wang D, Li Y, Luo H. The FLNA Gene in Tumour-Educated Platelets Can Be Utilised to Identify High-Risk Populations for NSCLCs. J Cell Mol Med. 2025 Apr;29(7):e70544. doi:10.1111 / jcmm.70544. PMID: 40208200; PMCID: PMC11984322.]. The results showed that there was no significant difference in RELA gene expression between NSCLC patients and healthy controls in uPLTs; however, in dPLTs, RELA expression in NSCLC patients was significantly higher than that in healthy controls (P=0.032). Figure 9 As shown. Furthermore, within the NSCLC patient group, RELA showed an increasing trend in dPLTs, but in healthy individuals, there was no significant difference in RELA expression between dPLTs and uPLTs, as... Figure 10 As shown. Comparison of RELA expression levels between dPLTs and uPLTs revealed no statistically significant difference, as... Figure 11 As shown.

[0073] In summary, the RELA gene showed a specific increase in dPLTs from NSCLC patients, suggesting that it has a potential differential expression profile in this platelet subset.

[0074] Example 4

[0075] Exploring the diagnostic efficiency of RELA for dPLTs in differentiating between NSCLC and healthy individuals

[0076] The inventors used real-time quantitative reverse transcription PCR (qRT-PCR) to detect the expression level of the RELA gene in dPLTs and uPLTs samples from 4 pathologically confirmed non-small cell lung cancer (NSCLC) patients and 5 healthy controls. Based on the relative expression level of RELA in dPLTs, receiver operating characteristic (ROC) curves were plotted to distinguish between NSCLC patients and healthy controls. The results showed that the area under the curve (AUC) was as high as 0.95. Figure 12 As shown, it exhibits high diagnostic efficacy.

[0077] Simultaneously, the detection results of two commonly used lung cancer serum tumor markers, CA19-9 and CEA, were collected from these 9 subjects, and corresponding ROC curves were plotted. Comparative analysis showed that the AUC of RELA expression in dPLTs was significantly better than that of CA19-9 (AUC=0.75) and CEA (AUC=0.85), see [link to analysis]. Figure 12 This suggests that RELA gene expression has potential advantages as a novel molecular marker in the diagnosis of NSCLC.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A risk marker for predicting non-small cell lung cancer, characterized by, The marker is a RELA gene present in the underlying platelets.

2. A kit for detecting the marker of claim 1.

3. The kit of claim 2, wherein The kit comprises primers and probes for detecting the RELA gene, and the nucleotide sequences of the primers and probes are as follows: RELA-F: GCTACACAGGACCAGGGACAGT; RELA-R: GCAGAGCCGCACAGCATTCA; RELA-oligo: ACCGGCCTCACCCCCACGAG.

4. Use of the marker of claim 1 in predicting the risk of non-small cell lung cancer.

5. Use of the kit of claim 2 in the preparation of a reagent for predicting the risk of non-small cell lung cancer.

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