A prognosis prediction system and a prognosis prediction chip for head and neck squamous cell carcinoma

Abstract

The application discloses a prognosis prediction system and a prognosis prediction chip for head and neck squamous cell carcinoma, wherein the prognosis prediction chip is a gene expression profile chip for detecting the expression amounts of nine genes AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A and DC1 of a human. The application obtains nine chromatin regulators most related to the prognosis of the head and neck squamous cell carcinoma through analysis, and the corresponding prediction system can predict the prognosis of the head and neck squamous cell carcinoma patient according to the expression amounts of the nine chromatin regulators, has strong robustness and can greatly improve the prediction accuracy of the overall survival rate of the head and neck squamous cell carcinoma.

Description

Technical Field

[0001] This invention relates to the field of biotechnology, and in particular to a prognostic prediction system for head and neck squamous cell carcinoma. Background Technology

[0002] Head and neck tumors mainly consist of three categories: neck tumors, ear, nose, and throat tumors, and oral and maxillofacial tumors. Data from authoritative institutions indicates that approximately 645,000 new cases of head and neck cancer are diagnosed globally each year. Because the symptoms of head and neck cancer are nonspecific in their early stages, they are easily confused with common head and neck diseases such as rhinitis and oral ulcers. This presents challenges to the early diagnosis and prognosis of head and neck cancers.

[0003] Currently, due to their unique anatomical location, head and neck tumors are primarily treated with surgery, supplemented by radiotherapy and chemotherapy, but the efficacy remains unsatisfactory. The rise of immunotherapy and targeted therapy has, to some extent, changed the treatment strategy for head and neck squamous cell carcinoma, improving survival rates. However, challenges remain in the prognosis of head and neck squamous cell carcinoma, necessitating an advanced method for accurate prediction.

[0004] Chromatin regulatory factors (CRs) are a class of proteases with specific functional domains. They are currently considered one of the most important regulatory factors in tumors. Studies have shown that abnormal expression of CRs is associated with various biological processes, such as inflammation, apoptosis, autophagy, and proliferation. Therefore, dysregulation of CRs is highly likely to affect tumor development. Thus, CRs hold promise as a novel therapeutic target for head and neck tumors. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a prognostic prediction system for head and neck squamous cell carcinoma.

[0006] The objective of this invention is achieved through the following technical solution:

[0007] A prognostic prediction chip for head and neck squamous cell carcinoma, wherein the prognostic prediction chip is a gene expression profile chip for detecting the expression levels of nine genes in humans: AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A, and DC1.

[0008] A further improvement is that the prognostic prediction chip is a gene expression profiling chip for detecting the expression levels of thirty-nine genes in humans: ACTR2, AHCTF1, AICDA, ARID4B, BRCA1, BRD8, BRWD3, CBX1, CBX7, CDK3, DPF1, ELP6, EXOSC4, EXOSC5, EYA1, FOXP3, HDAC1, HIF1AN, HNF1A, IKZF1, IKZF3, KDM5A, MBD4, MDC1, MORF4L2, NFRKB, PCGF1, PRDM16, PRKAG1, PRKCB, PRR12, RNF2, SETD1B, TAF5, TSSK6, UHRF2, WSB2, YWHAZ, and ZNF532.

[0009] Further improvements include a data input unit, a prognostic prediction unit, and a data output unit. The data input unit is used to input the expression levels of nine genes (AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A, and DC1) of the individuals to be predicted. The prognostic prediction unit is used to use the data input from the data input unit to form a prediction model and obtain the annual survival rate of the individuals to be predicted. The data output unit is used to output the annual survival rate of the individuals to be predicted.

[0010] A further improvement is made to the method for obtaining the prediction model as follows:

[0011] Normal and lesion samples were collected, and the expression levels of nine genes (AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A, and DC1) in the normal and lesion samples were detected. The samples were then input into the "timeROC" package of the R software for training, thus obtaining the prediction model.

[0012] Further improvements were made by selecting the nine genes AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A, and DC1 using the following screening method:

[0013] 1.1) Download case samples of head and neck squamous cell carcinoma from the public database The Cancer Genome Atlas from the website: https: / / portal.gdc.cancer.gov. The case samples contain one normal sample and one lesion sample; and obtain the genes related to chromosomal regulatory factors.

[0014] 1.2) Extract the expression matrix of genes related to regulatory factors of each chromosome in the case samples;

[0015] 1.3) The expression differential analysis of the expression matrix of chromosomal regulatory factor-related genes was performed using the R language "limma" package. The selection criteria were: |logFC|>1 and adj.p-value<0.05. The p-values ​​were corrected using the Benjamini-Hochberg method. Finally, the 50 most significant differentially expressed genes were displayed using a heatmap.

[0016] 1.4) Use the R language "survival" package to perform univariate regression analysis on the 50 most significant differentially expressed genes;

[0017] 1.5) The results of the univariate regression analysis were analyzed using the "glmnet" package in R language. The most significant genes that affect the occurrence of head and neck squamous cell carcinoma above the preset threshold were selected, namely, nine genes: AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A, and DC1.

[0018] The beneficial effects of this invention are as follows:

[0019] By comparing gene expression in diseased and normal tissues of patients with head and neck tumors, key genes for head and neck lesions were identified using bioinformatics methods. Based on this, a signature-based model was constructed to predict overall survival for head and neck tumors. The study found that this system has strong robustness and can significantly improve the prediction accuracy of overall survival for head and neck squamous cell carcinoma. Attached Figure Description

[0020] The invention will be further illustrated with reference to the accompanying drawings, but the contents of the drawings do not constitute any limitation on the invention.

[0021] Figure 1 The expression heatmap shows the top 50 most significantly expressed genes among the differentially expressed genes, where N represents normal samples and T represents the diseased samples in the treatment group.

[0022] Figure 2 This is a forest plot for univariate regression.

[0023] Figure 3 LASSO path diagram;

[0024] Figure 4 Confidence plot for the LASSO model;

[0025] Figure 5 The model predicts 1-year, 3-year, and 5-year survival for patients with head and neck squamous cell carcinoma.

[0026] Figure 6 For KM survival analysis curves;

[0027] Figure 7 Survival status curve based on optimal cutoff value

[0028] Figure 8 Survival state dot plot based on optimal cutoff value

[0029] Figure 9 Expression heatmaps of core modeling genes;

[0030] Figure 10 Univariate regression forest plot for clinical factors;

[0031] Figure 11 Multivariate regression forest plot for clinical factors;

[0032] Figure 12 Nodal plot for the prediction model

[0033] Figure 13 The calibration curves for the prediction model at 1, 3, and 5 years are shown.

[0034] Figure 14 GO and KEGG enrichment maps of differentially expressed genes

[0035] Figure 15 The graph shows the immune infiltration analysis results for different algorithms. Detailed Implementation

[0036] To make the purpose, technical solution, and advantages of the invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and examples.

[0037] Example 1

[0038] like Figure 1 The method for establishing a prognostic prediction system for head and neck squamous cell carcinoma based on chromosomal regulatory factors, as shown, includes the following steps:

[0039] Step (1): Download case samples of head and neck squamous cell carcinoma from the public database The Cancer Genome Atlas (TCGA) at https: / / portal.gdc.cancer.gov. The sample contains 44 normal samples and 502 lesion samples. 870 genes related to chromosomal regulatory factors were identified from relevant literature.

[0040] Step (2): Extract the expression matrix of genes related to chromosomal regulatory factors based on the R language environment.

[0041] Step (3): Using the R language package "limma", differential expression analysis was performed on the expression matrix of genes related to chromosomal regulatory factors. The screening criteria were: |logFC|>1 and adj.p-value<0.05. The Benjamini-Hochberg method was used to correct the p-value (FC: fold change, adj.p-value: adjusted p-value). Differentially expressed genes were screened, and a total of 470 differentially expressed chromosomal regulatory genes were identified. The 50 most significant differentially expressed chromosomal regulatory genes are shown in the heatmap as follows: Figure 1 As shown.

[0042] Step (4): Use the R language "survival" package to perform univariate regression analysis on differentially chromosomal regulatory genes. The main steps are: excluding normal samples and analyzing lesion samples, it was found that: ACTR2, AHCTF1, AICDA, ARID4B, BRCA1, BRD8, BRWD3, CBX1, CBX7, CDK3, DPF1, ELP6, EXOSC4, EXOSC5, EYA1, FOXP3, HDAC1, HIF1AN, HNF1A, IKZF1, IKZF3, KDM5A, MBD4, MDC1, MORF4L2, NFRKB, PCGF1, PRDM16, PRKAG1, PRKCB, PRR12, RNF2, SETD1B, TAF5, TSSK6, UHRF2, WSB2, YWHAZ, and ZNF532 showed statistical significance (p<0.05). Figure 2 As shown

[0043] Step (5): Use the R language "glmnet" package to perform LASSO analysis on the results of the univariate regression analysis, and select the most significant genes affecting the occurrence of head and neck squamous cell carcinoma. The key processing method is as follows: When λ = 0.014, nine genes, AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A, and DC1, are most associated with the prognosis of patients with head and neck squamous cell carcinoma. The results are as follows. Figure 3 As shown in Figure 4. Table 1 shows the regression coefficients of LASSO for the characteristic genes.

[0044] Table 1 LASSO regression coefficients

[0045] Gene name Regression coefficient AHCTF1 0.033180392 AICDA -0.10339806 BRD8 0.300394481 BRWD3 -0.015603402 FOXP3 -0.073921435 HNF1A -0.040985406 IKZF3 -0.023265253 KDM5A 0.008804684 MDC1 3.57E-05

[0046] Step (6): Using the "timeROC" package in R software, predict the 1-, 3-, and 5-year survival rates of the prognostic model. For example... Figure 5As shown, the one-year survival rate was 0.637; the three-year survival rate was 0.638; and the five-year survival rate was 0.626. In this step, the key treatment step utilized X-tile software to obtain the optimal cutoff value for the risk score: 2.4. Based on 2.4, patients were divided into high-risk and low-risk groups. Furthermore, the survival status of the high-risk group was worse than that of the low-risk group. Figure 6 As shown. The survival status of the high-risk and low-risk groups is as follows. Figure 7 As shown in Figure 8. The heatmap of the modeling genes is in... Figure 9 Displayed

[0047] Step (7): To further demonstrate the validity of the signature, this study conducted univariate and multivariate regression analyses on clinical factors, such as... Figure 10 As shown in Figure 11, the risk scores were statistically significant in both univariate and multivariate analyses (P<0.05).

[0048] Step (8): Finally, we constructed the predicted nomogram model. The predicted nomogram model was drawn using the R language "rms" package. Figure 12 As shown.

[0049] Step (9): To further verify the accuracy of the model, we plotted a calibration curve for the model and found that it has high stability. Figure 13 As shown.

[0050] Step (10): To interpret the physiological function of the model, we performed Gene Ontology (GO) pathway and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on genes related to differentially expressed chromosomal regulators. The key step in the GO and KEGG enrichment analyses was performed using the R language "org.Hs.eg.db". Enrichments were found in covalent chromatin modification, histone modification, peptidyl-lysine modification, nuclear chromatin, histoneacetyltransferase complex, histone binding, transcription corepressor activity, histoneacetyltransferase activity, cell cycle, basal transcription factors, and lysine degradation. These are all associated with tumors, such as... Figure 14 As shown

[0051] Step (11): To further explain the immune function of the model, we performed an immune infiltration analysis and found that T cells had a high level of immune infiltration, such as Figure 15 As shown.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the scope of protection of the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A prognosis predicting chip for head and neck squamous cell carcinoma, characterized by, The prognostic chip is used for detecting human... Gene expression profiling chip for nine genes: AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A, and DC1.

2. A prognosis prediction system for head and neck squamous cell carcinoma, characterized by, The prognostic prediction system includes: Data input unit, prognostic prediction unit, and data output unit; The data input unit is used to input the expression levels of nine genes, namely AHCTF1, AICDA, BRD8, BRWD3, FOXP3, HNF1A, IKZF3, KDM5A, and DC1, of the person to be predicted. The prognosis prediction unit is used to input the data input from the data input unit into the prediction model to obtain the survival rate of the person to be predicted for each year. The data output unit is used to output the survival rate of the person to be predicted for each year.

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