Cervical cancer patient prognosis model construction method based on senescence-related genes

Abstract

The invention discloses a cervical cancer patient prognosis model construction method based on senescence-related genes, which is characterized in that the senescence-related genes are collected from five databases of Aging Altas, Cell Age, GenAge, LongevityMap and SenMayo, clinical data and gene expression data related to cervical cancer are downloaded from a TCGA database, and 4313 prognosis-related genes are calculated by using a Kaplan-Meier method; the method comprises the following steps: carrying out intersection analysis on senescence-related genes and prognosis-related genes, screening out 28 cervical cancer senescence prognosis-related genes, typing cervical cancer patients by using Consensuses ClusterPlus according to an SSGs expression mode, and constructing a prognosis model by using Lasso regression analysis, and has the advantage that the model can provide simple and effective patient prognosis evaluation and treatment scheme selection guidance for clinic.

Description

Technical Field

[0001] This invention relates to the field of bioinformatics, specifically to a method for constructing a prognostic model for cervical cancer patients based on aging-related genes. Background Technology

[0002] Cervical cancer is one of the most common malignant tumors among women worldwide and a leading cause of cancer death in women. Although regular cervical screening and HPV vaccination can effectively prevent cervical cancer, its incidence and mortality rates remain high in developing countries. Traditional cervical cancer treatments include surgery, radiotherapy, and chemotherapy, but these have limited effectiveness for patients with advanced or recurrent cervical cancer, resulting in low five-year survival rates. Therefore, developing new treatment strategies and identifying new therapeutic targets are crucial for improving the prognosis of cervical cancer patients. Aging-related genes are of significant value in cervical cancer research. As we age, the function of human cells gradually declines, which is closely related to the regulation of the expression of aging-related genes. In cervical cancer, these genes may affect the growth and spread of tumor cells by regulating biological processes such as cell cycle, apoptosis, and cellular senescence. Studies have shown that certain aging-related genes may play a role in promoting or inhibiting tumor development in cervical cancer. Therefore, in-depth research into the role of aging-related genes in cervical cancer can not only help us better understand the pathogenesis of cervical cancer but also provide strong scientific evidence for developing new diagnostic biomarkers and treatment strategies. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for constructing a prognostic model for cervical cancer patients based on aging-related genes with strong predictive ability and high accuracy.

[0004] The technical solution adopted by this invention to solve the above-mentioned technical problems is as follows: a method for constructing a prognostic model for cervical cancer patients based on aging-related genes, the method comprising the following steps:

[0005] Step 1: Screening for senescence-related genes: Download senescence-related genes from five senescence-related databases: Aging Atlas, CellAge, GenAge, LongevityMap, and SenMayo. If a gene appears in three or more databases, it is identified as a senescence-related gene (SRG).

[0006] Step 2: Download and analyze cervical cancer data: Download cervical cancer-related clinical data and gene expression data from the TCGA database, and use the Kaplan-Meier method to calculate prognosis-related genes. A total of 4313 cervical cancer prognosis-related genes (SAGs) were obtained, of which 2214 genes with high expression were associated with good prognosis and 2099 genes with high expression were associated with poor prognosis.

[0007] Step 3: Perform gene intersection analysis: Perform intersection analysis between SRGs and SAGs to obtain 28 cervical cancer senescence-Survival genes (SSGs), and analyze the expression levels of these genes in cervical cancer samples and their correlation with prognosis.

[0008] Step 4: Patient subtyping based on SSGs: ConsensusClusterPlus was used to subtype all cervical cancer patients according to the expression patterns of SSGs, dividing them into two types with different prognoses;

[0009] Step 5: Constructing a prognostic model: Based on the comparison results with normal cervical tissue, nine genes with high expression levels and associated with poor prognosis (MMP1, CXCL8, MMP3, MIF, IL1A, IL1B, CXCL1, SERPINE1, MYC) and three genes with low expression levels but associated with good prognosis (COQ7, CLU, IGF1) were selected. Lasso regression analysis was used to construct a cervical cancer prognostic model. The risk score formula for the model is: Risk score = [0.0001×MMP1] + [0.0009×CXCL8] + [2.0496×MIF] + [0.0004×IL1B] + [(-0.0187×COQ7)] + [(-0.0008×MMP1] + [(-0.0187×COQ7)] + [(-0.0008×MMP1] + [(-0.0008×MMP1] + [(-0.0009 ...

[0010] CLU).

[0011] Compared with the prior art, the advantages of the present invention are as follows: The present invention provides a method for constructing a prognostic model for cervical cancer patients based on aging-related genes. The cervical cancer prognostic model based on aging-related genes can perform prognostic stratification for all cervical cancer patients by calculating risk scores, showing good risk stratification effect, easy to apply in clinical practice, and has broad practical value. Attached Figure Description

[0012] Figure 1 This is an overall flowchart of the present invention;

[0013] Figure 2The intersection of aging-related genes in the Aging Atlas, CellAge, GenAge, LongevityMap, and SenMayo datasets was taken, and 215 genes that appeared in three (including three) were finally retained.

[0014] Figure 3 The intersection of cervical cancer prognosis-associated genes (SAGs) and senescence-related genes (SRGs) was calculated, and 28 cervical cancer senescence-survival genes (SSGs) were ultimately retained.

[0015] Figure 4 After cervical cancer was classified into two clusters using ConsensusClusterPlus (A), there was a significant difference in prognosis between the two clusters, with cluster 1 having a worse prognosis than cluster 2 (B).

[0016] Figure 5 A prognostic model (AB) was constructed for Lasso regression analysis. TCGA-CESC patients were assigned risk scores based on the prognostic model of this invention. Patients were then divided into high-risk and low-risk groups based on the median risk score. KM survival analysis and ROC curve plotting were performed, yielding survival curves (C) and ROC curves for 3-year, 5-year, and 10-year survival of cervical cancer patients (D). Simultaneously, the sensitivity of each patient to the chemotherapy drug Methotrexate was predicted using the "pRRophetic" program in the R package, revealing that high-risk patients were resistant to Methotrexate (E). Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Specific Implementation

[0019] This invention provides a method for constructing a prognostic model for cervical cancer patients based on aging-related genes, such as... Figure 1 As shown, it includes the following steps:

[0020] 1. Screening for aging-related genes

[0021] Download aging-related genes from five aging-related databases: Aging Atlas, CellAge, GenAge, LongevityMap, and SenMayo. Figure 2As shown, if a gene appears in three or more databases, it is identified as a senescence-related gene (SRG).

[0022] 2. Download and analyze cervical cancer data.

[0023] Clinical data and gene expression data related to cervical cancer were downloaded from the TCGA database. The Kaplan-Meier method was used to calculate prognostic-related genes, resulting in 4313 cervical cancer prognostic-associated genes (SAGs). Among them, high expression of 2214 genes was associated with a good prognosis, and high expression of 2099 genes was associated with a poor prognosis.

[0024] 3. Perform gene intersection analysis

[0025] Perform intersection analysis between SRGs and SAGs, such as Figure 3 As shown, 28 cervical cancer senescence-Survival genes (SSGs) were identified, and their expression levels in cervical cancer samples and their correlation with prognosis were analyzed.

[0026] 4. Patient classification based on SSGs

[0027] like Figure 4 As shown, ConsensusClusterPlus was used to classify all cervical cancer patients according to the expression patterns of SSGs, resulting in two different prognostic types, with Cluster1 having a better prognosis than Cluster2.

[0028] 5. Construct a prognostic model

[0029] Based on the results of comparison with normal cervical tissue, nine genes with high expression levels and associated with poor prognosis (MMP1, CXCL8, MMP3, MIF, IL1A, IL1B, CXCL1, SERPINE1, MYC) and three genes with low expression levels but associated with good prognosis (COQ7, CLU, IGF1) were selected. Figure 5 As shown in Figure AE, a prognostic model for cervical cancer was constructed using Lasso regression analysis. The risk score formula for the model is: Risk Score = [0.0001×MMP1] + [0.0009×CXCL8] + [2.0496×MIF] + [0.0004×IL1B] + [(-0.0187×COQ7)] + [(-0.0008×CLU)]. Patients were divided into high-risk and low-risk groups based on the median risk score. The results showed that the survival time of patients in the high-risk group was significantly shorter than that of patients in the low-risk group, and they were resistant to methodrexate.

[0030] The foregoing description is not intended to limit the invention, nor is the invention limited to the examples given. Any changes, modifications, additions, or substitutions made by those skilled in the art within the scope of the invention should also be considered within the protection scope of the invention.

Claims

1. A method for constructing a prognostic model for cervical cancer patients based on aging-related genes, characterized in that: Aging-related genes were collected from five databases: AgingAltas, CellAge, GenAge, LongevityMap, and SenMayo. Clinical data and gene expression data related to cervical cancer were downloaded from the TCGA database. The Kaplan-Meier method was used to calculate 4313 prognostic-related genes. Intersection analysis of aging-related genes and prognostic-related genes was performed to screen out 28 cervical cancer aging prognostic genes. ConsensusClusterPlus was used to classify cervical cancer patients according to SSG expression patterns. Based on these SSGs, a prognostic model was constructed using Lasso regression analysis. Finally, a prognostic risk model was determined through multivariate Cox regression analysis. The prognostic risk model calculation formula is as follows: Risk score = [0.0001×MMP1] + [0.0009×CXCL8] + [2.0496×MIF] + [0.0004×IL1B] + [(-0.0187×COQ7)] + [(-0.0008×CLU)].

2. The method for constructing a prognostic model for cervical cancer patients based on aging-related genes according to claim 1, characterized in that, The aging-related gene models include: MMP1, CXCL8, MIF, IL1B, COQ7, and CLU.

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