Small cell lung cancer prognostic marker and application thereof

Abstract

The invention belongs to the technical field of biological medicine, and particularly relates to a small cell lung cancer prognostic marker and application thereof. The molecular characteristics of blood sample proteins of small cell lung cancer patients are researched through a DIA analysis technology, and a small cell lung cancer serum protein expression difference atlas is drawn. The biomarker disclosed by the invention is simple and convenient to sample, the sample size required for detection is small, resources are saved, the serum B4GALT1 serving as a secretory marker of SCLC is easier to obtain compared with tissue biopsy, and the sensitivity is higher: the AUC of the B4GALT1 is 0.995, and the sensitivity and the specificity of the B4GALT1 are 1 and 0.95833 respectively.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biological medicine, and particularly relates to a small cell lung cancer prognosis marker and application thereof. BACKGROUND

[0002] Protein glycosylation is a common and important post-translational modification. In eukaryotic organisms, common glycosylation modifications of proteins mainly include two types: N-glycosylation formed by connecting with asparagine residues in protein or polypeptide sequences; O-glycosylation formed by connecting with serine or threonine in protein or polypeptide sequences (Zhou, Hui-min, Zhaohai Zhang, Chunqing Liu, Changgong Jin, Jianing Zhang, Xiaoyan Miao and Li Jia. “B4GALT1 gene knockdown inhibits the hedgehog pathway and reverses multidrug resistance in the human leukemia K562 / adriamycin-resistant cell line.” IUBMB Life 64 (2012): n. pag.). Related research results show (Lilkova E, Petkov P, Ilieva N, Krachmarova E, Nacheva G, Litov L. Molecular modeling of the effects of glycosylation on the structure and dynamics of human interferon-gamma. J Mol Model. 2019 Apr 25; 25(5): 127. doi: 10.1007 / s00894-019-4013-8. PMID: 31025190.) that glycosylation helps maintain the correct conformation of proteins, avoids the degradation of proteins through the proteasome and lysosomal pathway, and plays an important role in the realization of protein function. At the same time, cell membrane protein glycosylation plays an important role in the biological processes of pathogen and host cell recognition, fertilization and growth and development (Polenghi M, Taverna E. Intracellular traffic and polarity in brain development. Front Neurosci. 2023 Oct 4; 17: 1172016. doi: 10.3389 / fnins.2023.1172016. PMID: 37859764; PMCID: PMC10583573.). Changes in glycosyltransferase expression are the main cause of abnormal glycosylation formation in malignant tumors, and abnormal glycosylation modification accelerates the evolution of malignant tumors by affecting the function of glycoproteins.Therefore, understanding the biological functions of glycosyltransferases in malignant tumors and their regulatory mechanisms is of great significance for revealing the mechanisms of malignant tumors.

[0003] The β1,4 galactosyltransferase family has 7 members, all of which encode type II transmembrane proteins. The glycosyltransferases of this family use glycoproteins and glycosphingolipid sugar chains as substrates, covalently linking Gal to GlcNAc through a β1,4 glycosidic bond to form a Galβ-1→4GlcNAc structure. As an important member of the β1,4 galactosyltransferase family, B4GALT1 plays an important role in multiple biological processes such as cell growth, adhesion, and migration (Chen PD, Liao YY, Cheng YC, Wu HY, Wu YM, Huang MC. Decreased B4GALT1 promotes hepatocellular carcinoma cell invasiveness by regulating the laminin-integrin pathway. Oncogenesis. 2023 Oct 31; 12(1): 49. doi: 10.1038 / s41389-023-00494-y. PMID: 37907465; PMCID: PMC10618527.). B4GALT1 enhances PD-L1 protein stability by mediating N-glycosylation of PD-L1, inhibits CD8 + T cell activity, and promotes tumor immune escape. Current research on the abnormal expression of B4GALT1 gene and the clinical pathological significance of various malignant tumors is mainly based on gene chip and transcriptome sequencing at the mRNA level, and further verification and in-depth research at the protein level are needed.

[0004] Small cell lung cancer (SCLC) is the second most common thoracic malignancy, accounting for 13% of all lung cancers. It has characteristics of high invasiveness, rapid proliferation, and easy metastasis. One-third of patients are in the limited stage (LS-SCLC), and two-thirds are in the extensive stage (ES-SCLC). Currently, the treatment of LS-SCLC includes chemotherapy and early intervention thoracic hyperfractionated radiotherapy and brain prophylactic irradiation (PCI) after chemotherapy, but the optimal dose and schedule of thoracic radiotherapy have not been established. The main treatment for ES-SCLC is chemotherapy with or without PCI, but the value of radiotherapy is still unclear and needs further exploration. Moreover, SCLC has an initial response to chemotherapy and radiotherapy, but usually develops resistance, and most patients relapse after first-line treatment, and only about 10% of patients are disease-free at 2 years, resulting in a five-year overall survival rate of up to 10%. Therefore, there is an urgent need for prognostic biomarkers for small cell lung cancer. SUMMARY

[0005] The present application finds that high expression of serum B4GALT1 protein level is related to poor efficacy of radiotherapy and chemotherapy for small cell lung cancer patients, and based on this, the present application is completed.

[0006] In a first aspect, the present application provides a small cell lung cancer prognosis marker, which comprises B4GALT1.

[0007] Further, when the protein expression amount of B4GALT1 in the biological sample of the subject is greater than or equal to 29.207 ng / ml, the prognosis of the subject is poor.

[0008] Further, the biological sample of the subject is blood.

[0009] Still further, the blood sample of the subject is at least one of peripheral blood, plasma and / or serum.

[0010] In a second aspect, the present application provides use of the biomarker of the first aspect of the present application in preparation of a reagent for predicting efficacy of small cell lung cancer patients.

[0011] Further, when the protein expression amount of B4GALT1 in the biological sample of the patient is greater than or equal to 29.207 ng / ml, the prognosis of the patient is poor.

[0012] Further, the biological sample of the patient is blood.

[0013] Still further, the blood sample of the patient is at least one of peripheral blood, plasma and / or serum.

[0014] In a third aspect, the present application provides a kit for predicting efficacy of small cell lung cancer patients, which contains a reagent for detecting expression amount of the marker of the first aspect.

[0015] Further, when the protein expression amount of B4GALT1 in the biological sample of the patient is greater than or equal to 29.207 ng / ml, the prognosis of the patient is poor.

[0016] Further, the biological sample of the patient is blood.

[0017] Still further, the blood sample of the patient is at least one of peripheral blood, plasma and / or serum.

[0018] Further, the kit is selected from an ELISA detection kit, a colloidal gold detection kit and / or a flow detection kit.

[0019] Still further, the kit diagnosis method comprises a direct method, an indirect method, a double antibody sandwich method and / or a competition method.

[0020] Beneficial effects

[0021] The present application studies the molecular characteristics of the blood sample proteins of small cell lung cancer patients by DIA analysis technology, and draws a serum protein expression difference atlas of small cell lung cancer. The biomarker of the present application is simple to sample, and the required sample amount for detection is small, which saves resources. Serum B4GALT1 is a "secretion type marker" of SCLC, which is more easily obtained than tissue biopsy, and has higher sensitivity: the AUC of B4GALT1 is 0.995, and the sensitivity and specificity of B4GALT1 are 1 and 0.95833, respectively. BRIEF DESCRIPTION OF DRAWINGS

[0022] Figure 1 For serum proteomics of small cell lung cancer.

[0023] Note: A is the clustering result; B is the volcano plot result; C is the protein GO enrichment analysis result; D is the KEGG enrichment analysis result; E is the Reactome enrichment analysis result.

[0024] Figure 2 For multi-omics analysis results.

[0025] Figure 3 For the correlation between serum B4GALT1 protein level and treatment and prognosis of small cell lung cancer patients.

[0026] Note: A is the protein expression amount of serum sample B4GALT1 between different groups of 106 small cell patients (Normal; SCLC; therapy); B is the protein expression amount of serum sample B4GALT1 between different treatment methods (chemotherapy; chemoradiotherapy; chemotherapy immunotherapy and other treatment methods); C is the ROC curve; D is the protein expression amount of serum sample B4GALT1 between different curative effects in patients receiving treatment; wherein, Normal is the normal control group; SCLC is the small cell lung cancer group; therapy is the treatment group; chemotherapy is chemotherapy, chemoradiotherapy is chemotherapy combined with radiotherapy; chemotherapy immunotherapy is chemotherapy combined with immunotherapy; CR is complete remission; PR is partial remission; SD is stable disease; PD is disease progression. DETAILED DESCRIPTION

[0027] The specific embodiments of the present application will be further described below. It should be noted that the description of these embodiments is used to help understand the present application, but does not constitute a limitation on the present application. In addition, the technical features involved in the following described embodiments can be combined with each other as long as they do not conflict with each other.

[0028] The experimental methods in the following examples are all conventional methods unless otherwise specified. The experimental materials used in the following examples are all commercially available unless otherwise specified.

[0029] Example 1 Serum proteomics of small cell lung cancer

[0030] A. Experimental methods

[0031] 1) Protein extraction

[0032] Low-abundance protein extraction in blood was performed using the EasyPeptide Deep Low Abundance Enrichment Kit. First, the magnetic beads were evenly dispersed, and the magnetic microparticle suspension was separated by magnetic separation to discard the supernatant. After washing the magnetic beads, the supernatant was discarded by magnetic separation. After resuspending the magnetic beads, plasma was added for incubation. After incubation, magnetic particle washing solution was added, and the low-abundance proteins enriched on the magnetic beads were washed by shaking.

[0033] 2) Protein digestion

[0034] Reduction alkylation reagent was added to the magnetic beads and heated. After the sample returned to room temperature, enzyme was added for digestion; after digestion, termination reagent was added for mixing to terminate the enzyme reaction. After centrifugation, the supernatant was collected for subsequent desalting.

[0035] 3) Peptide desalting and quantification

[0036] After activation, impurity removal, and equilibration of the Cartridge desalting column, all samples were loaded into the desalting column and centrifuged. Then the desalting column was washed. After eluting the samples, concentration was performed. After resuspending the samples with the sample buffer, NANODROP ONE was used for peptide quantification.

[0037] 4) DIA mass spectrometry detection

[0038] An equal amount of peptides was taken for mass spectrometry detection. Vanquish Neo was used for peptide separation, the analysis column was ES906 (150 μm x 15 cm, Thermo, USA), mobile phase A was water (2% acetonitrile + 0.1% formic acid), mobile phase B was water (80% acetonitrile + 0.1% formic acid), the chromatographic gradient was 60 SPD, and the flow rate was 500 nL / min. The sample separated by the upgraded high-performance liquid chromatography was analyzed by Orbitrap Astral mass spectrometer. The settings were 1.5 kV, and the MS scan range (m / z) was 100-1700.

[0039] Comparison of means between two groups was performed using T test (normal + homoscedastic), Welch t'test (normal + not homoscedastic), Wilcoxon rank sum test (not normal, non-parametric test). Multiple groups overall: One-way ANOVA (normal + homoscedastic); Welch one-way ANOVA (normal + not homoscedastic); Kruskal-Wallis test (not normal, non-parametric test). Statistical significance was considered for a two-sided P value less than 0.05.

[0040] B. Results of the test

[0041] DIA quantitative proteomics technology was used to analyze 25 serum samples (including 5 healthy control samples and 15 small cell lung cancer samples). The quality control results showed that a total of 11363 peptide segments and 1222 protein segments were identified, and proteomics analysis was performed. First, the correlation between biological replicates of different sample groups was calculated, and the obtained results were clustered. The results showed that the samples within the same group had high correlation Figure 1 A). The volcano plot showed that the relative expression of a total of 246 proteins was statistically different (FC≥1.2 or FC≤1, P<0.05) Figure 1 B). GO enrichment analysis of differential proteins found that they were mainly enriched in chromatin remodeling, protein-DNA complex organization structure, chromosome structure and pore complex, etc. Figure 1 C). KEGG enrichment analysis was also performed, and it was found that the differential proteins were mainly enriched in alcoholic liver disease, various types of N-glycan biosynthesis and metabolic pathways, etc. Figure 1 D). Reactcome was mainly enriched in the regulation of SLITs and ROBOs expression, the formation of free 40S subunit and DNA division caused by cell apoptosis, etc. Figure 1 E).

[0042] Example 2 Multi-omics analysis

[0043] A. Test method

[0044] All data were uploaded to the Majorbio cloud platform (cloud.majorbio.com) for analysis. The t.test function in R was used to calculate the significance P value and fold change (FC) between groups. Proteins with a significant test p < 0.05 and a fold change greater than 1.2 were considered differentially expressed proteins. The GO database (gene ontology, http: / / geneontology.org / ) was used to analyze the functional clustering of all differential proteins from three aspects: biological process, cellular component, and molecular function; the KEGG (Kyoto encyclopedia of gene and genomes, http: / / www.genome.jp / kegg / / ) pathway database was used to analyze the metabolic pathways involved in differential proteins. Protein-protein interaction analysis was performed using String v11.5.

[0045] R packages: survival [3.5-7], survminer [0.4.9], ggplot2 [3.4.4]

[0046] Processing process:

[0047] The proportional hazards assumption test was performed using the survival package, and the survival regression was fitted. The results were visualized using the survminer package and the ggplot2 package.

[0048] The median value of gene expression was selected as the grouping cut-off value for grouping.

[0049] Statistical method: Cox regression

[0050] Prognosis type: OS [Overall Survival]

[0051] Data filtering strategy: remove genes with the same expression value + remove NA values.

[0052] The mean values of variables between the two groups were compared using T test (satisfy normal + variance homogeneity), Welch t'test (satisfy normal + not satisfy variance homogeneity), Wilcoxon rank sum test (not satisfy normal, non-parametric test). Multiple groups: One-way ANOVA (satisfy normal + variance homogeneity); Welch one-way ANOVA (satisfy normal + not satisfy variance homogeneity); Kruskal-Wallis test (not satisfy normal, non-parametric test). Bilateral P value less than 0.05 was considered statistically significant.

[0053] B. Test results

[0054] Analysis was performed using published data from 2024 in Cell titled “Proteogenomic characterization of small cell lung cancer identifies biological insights and subtype-specific therapeutic strategies” (OMIX database: OMIX002489, https: / / ngdc.cncb.ac.cn / omix).

[0055] The data included 110 tumors and paired specimens and were subjected to mass spectrometry-based proteomics analysis. The expression values were log-transformed, and the differential proteins in the dataset were subjected to Kaplan Meier Plotter survival analysis. The patient cohort was divided into two groups based on the median value of protein expression.

[0056] Analysis was performed in combination with published data from Atay S, 2023 in Cancers (Basel) titled “A 15-Gene-Based Risk Signature for Predicting Overall Survival in SCLC Patients Who Have Undergone Surgical Resection”. Comprehensive transcriptome analysis was performed on three gene expression datasets (GSE30219, GSE43346, and GSE149507), and 1734 upregulated genes and 2907 downregulated genes were found.

[0057] As shown in Figure 2 , in combination with protein, transcriptome, and serum proteomics analysis, B4GALT1 was found to be a gene associated with the survival of small cell lung cancer patients, and small cell lung cancer patients with high expression of B4GALT1 protein had shorter survival time than those with low expression.

[0058] Example 3 Role of B4GALT1 in the prognosis of small cell lung cancer patients

[0059] A. Test method

[0060] The protein expression of serum B4GALT1 was determined by enzyme-linked immunosorbent assay.

[0061] The mean comparison of variables between two groups uses T test (satisfies normality + variance homogeneity), Welch t'test (satisfies normality + does not satisfy variance homogeneity), Wilcoxon rank sum test (does not satisfy normality, non-parametric test). Multiple groups overall: One-way ANOVA (satisfies normality + variance homogeneity); Welch one-way ANOVA (satisfies normality + does not satisfy variance homogeneity); Kruskal-Wallis test (does not satisfy normality, non-parametric test). Bilateral P value less than 0.05 is considered statistically significant.

[0062] Select 106 clinical cases, record the serum samples of small cell patients and clinical relevant information, divide the patients into healthy group (n=24 cases), untreated group (SCLC=17 cases), chemotherapy group (n=17 cases), radiotherapy and chemotherapy group (n=23 cases), chemotherapy and immunotherapy group (n=18 cases), other (radiotherapy and chemotherapy combined with targeted therapy, n=7 cases); a total of 65 cases in the therapy group; among them, the healthy group is a healthy population, and the blood routine and five indicators of the marker are normal.

[0063] B. Test results

[0064] During the test, 106 samples were randomly allocated to ensure that each group was covered, and each time they were divided into two parts for data measurement. Figure 3 The data is the data result of all 106 samples.

[0065] As shown in Figure 3 A, the protein expression of B4GALT1 in the serum of patients was detected and compared, and it was found that the content in the serum of small cell lung cancer patients was higher than that of healthy controls, and the expression of patients after treatment was also higher than that of healthy controls (healthy control: 13.981 ng / ml, SCLC: 45.354 ng / ml, therapy: 40.987 ng / ml).

[0066] As shown in Figure 3 B, the protein expression of B4GALT1 in the serum of patients in different treatment methods was higher than that of the healthy control group, but there was no statistical difference in the protein expression of B4GALT1 among each other (chemotherapy group: 37.634 ng / ml, radiotherapy and chemotherapy group: 43.752 ng / ml, chemotherapy and immunotherapy group: 39.964 ng / ml, other: 42.676 ng / ml).

[0067] To determine the accuracy of serum B4GALT1 in disease diagnosis, ROC analysis was performed. The AUC of B4GALT1 was 0.995 (95% CI: 0.990-1.000), 0.999 (95% CI: 0.998-1.000), 0.999 (95% CI: 0.998-1.000), 0.999 (95% CI: 0.998-1.000), 0.999 (95% CI: 0.998-1.000) and 0.999 (95% CI: 0.998-1.000) respectively. Figure 3C). The cut-off value of B4GALT1 is 29.207 ng / ml. After these cut-offs, the sensitivity and specificity of B4GALT1 are 1 and 0.95833, respectively. As Figure 3 As shown in D, high expression of serum B4GALT1 protein expression is associated with poor efficacy of radiotherapy and chemotherapy in patients with small cell lung cancer, that is, when the serum B4GALT1 protein expression of the subject is greater than or equal to 29.207 ng / ml, the prognosis of the subject is poor; when the serum B4GALT1 protein expression of the subject is less than 29.207 ng / ml, the prognosis of the subject is poor.

Claims

1.A small cell lung cancer prognostic marker, the marker comprising B4GALT1. 2.The marker of claim 1, when the protein expression amount of B4GALT1 in the biological sample of the subject is greater than or equal to 29.207 ng / ml, the subject has a poor prognosis. 3.The marker of claim 1, wherein the biological sample of the subject is blood; and the blood sample of the subject is at least one of peripheral blood, plasma and / or serum. 4.Use of the biomarker of claim 1 in the preparation of a reagent for predicting the efficacy of small cell lung cancer patients. 5.The use of claim 4, when the protein expression amount of B4GALT1 in the biological sample of the patient is greater than or equal to 29.207 ng / ml, the patient has a poor prognosis. 6.The use of claim 4, wherein the biological sample of the patient is blood; and the blood sample of the subject is at least one of peripheral blood, plasma and / or serum. 7.A kit for predicting the efficacy of small cell lung cancer patients, the kit comprising a reagent for detecting the expression amount of the marker of the first aspect. 8.The kit of claim 7, when the protein expression amount of B4GALT1 in the biological sample of the patient is greater than or equal to 29.207 ng / ml, the patient has a poor prognosis. 9.The kit of claim 7, wherein the biological sample of the patient is blood; and the blood sample of the subject is at least one of peripheral blood, plasma and / or serum. 10.The kit of claim 7, wherein the kit is selected from an ELISA detection kit, a colloidal gold detection kit and / or a flow detection kit; and the diagnostic method of the kit comprises a direct method, an indirect method, a double antibody sandwich method and / or a competition method.

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