CN115961042A - Application of IGFBP1 gene or CHAF1A gene as gastric adenocarcinoma prognostic molecular marker - Google Patents

Abstract

The invention provides an application of an IGFBP1 gene or a CHAF1A gene as a gastric adenocarcinoma prognosis molecular marker, belonging to the fields of gene technology and medicine. The invention establishes that IGFBP1 and CHAF1A can be respectively used as representative target markers of early stage and advanced stage of gastric adenocarcinoma through QPCR and IHC, and the two molecules have great clinical application potential as accurate treatment targets and prognostic markers of gastric adenocarcinoma. The research method and the result of the invention are helpful for identifying gastric adenocarcinoma patients with different risks, guiding targeted effective treatment, possibly promoting the development of new drugs and improving the accurate medical level of gastric adenocarcinoma.

Description

Application of IGFBP1 gene or CHAF1A gene as a molecular marker for prognosis of gastric adenocarcinoma

Technical Field

The present invention relates to the field of gene technology and medicine, and in particular to the application of IGFBP1 gene or CHAF1A gene as a molecular marker for gastric adenocarcinoma prognosis.

Background Art

Gastric cancer is one of the most common malignancies in the world. Although many advances have been made in the treatment of gastric cancer in the past few decades, most gastric cancer tumors show strong treatment resistance ^[1] , which leads to a huge health burden.

Among gastric cancers, gastric adenocarcinoma is the most common histological type (approximately 95%), and clinical guidelines describe the differences in treatment strategies and outcomes for gastric adenocarcinoma at different clinical pathological stages ^[2] . Ideally, patients with early gastric adenocarcinoma undergo local resection through endoscopy, while patients with advanced gastric adenocarcinoma require surgery and multidisciplinary combined treatment ^[2] . The 5-year survival rate of early gastric adenocarcinoma (according to the TNM staging classification of malignant tumors) can be as high as 95%, however, the median survival time of patients with advanced gastric adenocarcinoma is only 9 to 10 months ^[3] . Therefore, early detection of high-risk gastric adenocarcinoma patients and selection of appropriate treatment are crucial to prolonging the survival of these patients. New evidence shows that biomarkers can help improve the molecular classification accuracy, predict prognosis, and promote precision treatment in the gastric adenocarcinoma population ^[4] . For example, Jiang et al. found that ITGB1-DT was significantly upregulated in gastric adenocarcinoma tissues and was associated with T stage, treatment efficacy, and poor prognosis of gastric adenocarcinoma patients, while interfering with the expression of ITGB1-DT could inhibit the proliferation, invasion, and migration of gastric adenocarcinoma cells ^[5] . In addition, bioinformatics analysis can be used to screen key immune-related genes (IRGs) and pathways that are significantly associated with gastric adenocarcinoma treatment. For example, Xia et al. constructed an IRG risk regression model consisting of BMP8A, MMP12, NRG4, S100A9, and TUBB3, which were associated with the prognosis of gastric adenocarcinoma patients and may provide new markers for immunotherapy of gastric adenocarcinoma ^[6] . Nevertheless, the pathogenesis and important factors of early and advanced gastric adenocarcinoma have not been fully emphasized. Therefore, identifying new and promising targets and models is crucial to elucidate the mechanisms of gastric adenocarcinoma and provide candidate diagnostic options for patients with different gastric adenocarcinoma stages.

Summary of the invention

In order to solve the above problems, the present invention provides the use of IGFBP1 gene or CHAF1A gene as a molecular marker for gastric adenocarcinoma prognosis, providing a new idea for the precise treatment of gastric adenocarcinoma patients and the study of patient prognosis prediction.

In order to achieve the above object, the present invention provides the following technical solutions:

The present invention provides the use of IGFBP1 gene or CHAF1A gene as a molecular marker for gastric adenocarcinoma prognosis.

Preferably, the IGFBP1 gene is used as a molecular marker for the prognosis of early gastric adenocarcinoma, and the CHAF1A gene is used as a molecular marker for the prognosis of advanced gastric adenocarcinoma.

The present invention also provides a method for screening gastric adenocarcinoma prognostic molecular markers, comprising the following steps:

1) Data preparation and DEG identification: The mRNA expression and clinical data of 407 total gastric adenocarcinoma samples were downloaded from the TCGA database (Cancer Genome Atlas) (http://portal.gdc.cancer.gov/), including 32 adjacent tumor samples and 375 tumor samples. The mRNA expression and clinical data of early and advanced gastric adenocarcinoma were divided into two independent data sets. The early gastric adenocarcinoma samples included 21 adjacent tumor samples and 164 tumor samples, and the advanced gastric adenocarcinoma samples included 10 adjacent tumor samples and 188 tumor samples. These two data sets were obtained after deleting 24 gastric adenocarcinoma samples without clinical staging information from the 407 samples.

2) Based on the three groups of gastric adenocarcinoma samples, namely, overall, early and advanced gastric adenocarcinoma samples, the edgeR R language package was used to analyze the gene expression profiles according to the cut-off criteria of P adj < 0.05, |log2FC| > 1, to identify the differentially expressed genes DEGs, and generate the DEG visualization volcano maps of the three data sets;

3) Univariate COX regression analysis: The survival package of R software was used to perform univariate COX proportional hazard regression assessment on the differentially expressed gene DEGs in the overall, early, and advanced gastric adenocarcinoma groups, and mRNAs associated with the overall survival of patients in different stages were obtained according to the standard P < 0.05, and mRNAs-OS were obtained, which were related to the survival and prognosis of gastric adenocarcinoma patients;

4) PPI network construction and candidate key mRNA identification: The protein interaction network retrieval tool was used to analyze the interactions between mRNAs-OS, and the protein interaction data were obtained. Proteins with a minimum required interaction score ≥ 0.400 were selected to construct a protein-protein interaction network. The PPI network and its interaction score were imported into Cytoscape software, and CytoHubba was used to identify potential key mRNAs. The top 40 candidate core mRNAs-OS at each stage node were screened for further analysis;

5) Establishment of three COX proportional hazard regression models: Based on the identified core mRNAs-OS, multivariate COX proportional hazard regression analysis was performed using the “surminer” package of R to construct a prognostic model. Subsequently, a prognostic model consisting of mRNAs-OS associated with patient prognosis was constructed for overall, early, and advanced gastric adenocarcinoma, respectively. Among them, mRNA-PRO with P < 0.05 was considered an independent prognostic factor for gastric adenocarcinoma. According to the expression of mRNA-PRO, the risk score of each patient was calculated according to the model formula as follows: Risk score = Exp(m mRNA1)×β1+Exp(mRNA2)×β2+Exp(mRNA3)×β3+…+Exp(mRNAn)×βn. According to the median risk score, gastric adenocarcinoma patients were divided into high-risk group and low-risk group. The 5-year survival rates of high-risk and low-risk patients were calculated respectively. Risk score curves were drawn to distinguish the difference in risk scores between the two groups of patients. Survival status diagrams, risk heat maps, and survival curves were drawn. Thus, prognostic models for the three stages were established respectively. ROC curves represented by the area under the model curve were drawn to evaluate its accuracy and reliability in predicting the prognosis of patients in each stage.

6) Real-time quantitative polymerase chain reaction and immunohistochemical staining: 30 pairs of cDNA tissue chips, 84 pairs of tissue microarrays,

The expression levels of IGFBP1 and CHAF1A in gastric adenocarcinoma samples and matched adjacent normal tissues were detected by QPCR;

According to standard procedures, two independent pathologists performed immunohistochemical analysis in a blinded manner on 84 cancer and 84 matched adjacent adjacent tissues, and the expression levels of IGFBP1 and CHAF1A were semiquantitatively analyzed based on the proportion of positively stained cells and staining intensity.

Preferably, the primers used to amplify the IGFBP1 gene include an upstream primer and a downstream primer, the nucleotide sequence of the upstream primer is shown as SEQ ID No.1, and the nucleotide sequence of the downstream primer is shown as SEQ ID No.2.

Preferably, the primers used to amplify the CHAF1A gene include an upstream primer and a downstream primer, the nucleotide sequence of the upstream primer is shown as SEQ ID No.3, and the nucleotide sequence of the downstream primer is shown as SEQ ID No.4.

The beneficial effects of the present invention are:

The present invention divides gastric adenocarcinoma mRNA expression and clinical data into early and progressive groups, and performs bioinformatics analysis to obtain mRNA prognostic models related to early (including 9 mRNAs, including IGFBP1) and progressive (including 14 mRNAs, including CHAF1A) gastric adenocarcinoma, respectively. The AUC values of the two prognostic models are both high, 0.87 and 0.92, respectively, indicating that both prognostic models can accurately predict the prognosis of gastric adenocarcinoma patients. It was established by QPCR and IHC that IGFBP1 and CHAF1A can be used as representative targeted markers for early and progressive stages, respectively. These gene markers, especially IGFBP1 and CHAF1A, have great potential in becoming precise therapeutic targets and prognostic markers for gastric adenocarcinoma of different types. The research method and results of the present invention are thus helpful in identifying gastric adenocarcinoma patients at risk, guiding targeted and effective treatment, and may promote the development of new drugs and improve the precision medical level of gastric adenocarcinoma.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments are briefly introduced below.

Fig. 1 is the analysis procedure of the present invention;

Figure 2 is a volcano plot of DEGs, (A) DEGs of the overall stage gastric adenocarcinoma group, (B) DEGs of the early gastric adenocarcinoma group, (C) DEGs of the advanced gastric adenocarcinoma group, the abscissa represents the log2-transformed value of the differential expression fold change between gastric adenocarcinoma samples and adjacent paracancerous samples, the ordinate represents the -log10-transformed value of the FDR value, the light gray points on the right side of the dotted line represent significantly downregulated mRNAs, the dark gray points on the left side of the dotted line represent significantly upregulated mRNAs, and the black points represent mRNAs with no significant differential expression;

Figure 3 shows the Venn diagram of intersection DEGs and mRNAs-OS for overall stage, early stage, and advanced stage gastric adenocarcinoma, showing the Venn diagram of intersection DEGs (A) or intersection mRNAs-OS (B), and the terms on the circles represent the DEG or mRNAs-OS groups at different stages of gastric adenocarcinoma;

Figure 4 shows the construction of three prediction models. The upper left corner, lower left corner and lower right corner of the figure (3 sub-figures) respectively represent the total gastric adenocarcinoma sample group (A, B and C), the gastric adenocarcinoma samples in the early stage group (D, E and F) and the gastric adenocarcinoma samples in the advanced stage group (G, H and I). From top to bottom of each sub-figure are the risk score curves, survival status graphs and expression heat maps between the low-risk group and the high-risk group. (A, D and G) The color bar indicates the relative expression value level of mRNAs-PRO, dark gray indicates high expression, and light gray indicates low expression. (B, E and H) Survival curves of low-risk and high-risk populations, (C, E and I) ROC curves for predicting survival;

FIG5 shows the mRNA and protein expression levels of CHAF1A and IGFBP1 in gastric adenocarcinoma and normal samples evaluated by QPCR and immunohistochemistry (IHC), respectively.

DETAILED DESCRIPTION

The present invention provides the use of IGFBP1 gene or CHAF1A gene as a molecular marker for gastric adenocarcinoma prognosis.

In the present invention, the IGFBP1 gene is preferably used as a molecular marker for the prognosis of early gastric adenocarcinoma. In the present invention, the primers used to amplify the IGFBP1 gene include an upstream primer and a downstream primer, the nucleotide sequence of the upstream primer is shown in SEQ ID No. 1, and the nucleotide sequence of the downstream primer is shown in SEQ ID No. 2, which are specifically as follows:

SEQ ID No.1: 5'-GCATTTCTGCTCTTCCAAAG-3';

SEQ ID No. 2: 5'-GCAACATCACCACAGGTAG-3'.

In the present invention, the CHAF1A gene is preferably used as a molecular marker for the prognosis of advanced gastric adenocarcinoma. In the present invention, the primers used to amplify the CHAF1A gene include an upstream primer and a downstream primer, the nucleotide sequence of the upstream primer is shown in SEQ ID No.3, and the nucleotide sequence of the downstream primer is shown in SEQ ID No.4, which are specifically as follows:

SEQ ID No.3: 5'-AAAGGAGCAGGACAGTTGGA-3';

SEQ ID No. 4: 5'-CTGGAAGGGACTTGATTTGC-3'.

The present invention also provides a method for screening gastric adenocarcinoma prognostic molecular markers, comprising the following steps:

1) Data preparation and DEG identification: The mRNA expression and clinical data of 407 gastric adenocarcinoma samples were downloaded from the TCGA database (Cancer Genome Atlas) (http://portal.gdc.cancer.gov/), including 32 adjacent tumor samples and 375 tumor samples. The mRNA expression and clinical data of early and advanced gastric adenocarcinoma were divided into two independent data sets. The early gastric adenocarcinoma samples included 21 adjacent tumor samples and 164 tumor samples, and the advanced gastric adenocarcinoma samples included adjacent tumor samples and 188 tumor samples. These two data sets were obtained after deleting 24 gastric adenocarcinoma samples without clinical staging information from the 407 samples.

2) Based on the three groups of gastric adenocarcinoma samples, namely, overall, early and advanced gastric adenocarcinoma samples, the edgeR R language package was used to analyze the gene expression profiles according to the cut-off criteria of P adj < 0.05, |log2FC| > 1, to identify the differentially expressed genes DEGs, and generate the DEG visualization volcano maps of the three data sets;

3) Univariate COX regression analysis: The survival package of R software was used to perform univariate COX proportional hazard regression assessment on the differentially expressed gene DEGs in the overall, early, and advanced gastric adenocarcinoma groups, and mRNAs associated with the overall survival of patients in different stages were obtained according to the standard P < 0.05, and mRNAs-OS were obtained, which were related to the survival and prognosis of gastric adenocarcinoma patients;

4) PPI network construction and candidate key mRNA identification: The protein interaction network retrieval tool was used to analyze the interactions between mRNAs-OS, and the protein interaction data were obtained. Proteins with a minimum required interaction score ≥ 0.400 were selected to construct the protein-protein interaction network. The PPI network and its interaction score were imported into Cytoscape software, and CytoHubba was used to identify potential key mRNAs. The top 40 candidate core mRNAs-OS at each stage node were screened for further analysis;

5) Establishment of three COX proportional hazard regression models: Based on the identified core mRNAs-OS, multivariate COX proportional hazard regression analysis was performed using the “surminer” package of R to construct a prognostic model. Subsequently, a prognostic model consisting of mRNAs-OS associated with patient prognosis was constructed for overall, early, and advanced gastric adenocarcinoma, respectively. Among them, mRNA-PRO with P < 0.05 was considered an independent prognostic factor for gastric adenocarcinoma. According to the expression of mRNA-PRO, the risk score of each patient was calculated according to the model formula as follows: Risk score = Exp(m mRNA1)×β1+Exp(mRNA2)×β2+Exp(mRNA3)×β3+…+Exp(mRNAn)×βn. According to the median risk score, gastric adenocarcinoma patients were divided into high-risk group and low-risk group. The 5-year survival rates of high-risk and low-risk patients were calculated respectively. Risk score curves were drawn to distinguish the difference in risk scores between the two groups of patients. Survival status diagrams, risk heat maps, and survival curves were drawn. Thus, prognostic models for the three stages were established respectively. ROC curves represented by the area under the model curve were drawn to evaluate its accuracy and reliability in predicting the prognosis of patients in each stage.

6) Real-time quantitative polymerase chain reaction and immunohistochemical staining: 30 pairs of cDNA tissue chips, 84 pairs of tissue microarrays,

The expression levels of IGFBP1 and CHAF1A in gastric adenocarcinoma samples and matched adjacent normal tissues were detected by QPCR;

According to standard procedures, two independent pathologists performed immunohistochemical analysis in a blinded manner on 84 cancer and 84 matched adjacent adjacent tissues, and the expression levels of IGFBP1 and CHAF1A were semiquantitatively analyzed based on the proportion of positively stained cells and staining intensity.

In order to further illustrate the present invention, the present invention is described in detail below in conjunction with embodiments, but they should not be construed as limiting the scope of protection of the present invention.

Example 1

Establishment of molecular prognostic markers for early and advanced gastric adenocarcinoma.

1) Data preparation and DEG identification: The mRNA expression and clinical data of 407 total gastric adenocarcinoma samples were downloaded from the TCGA (Cancer Genome Atlas) database (http://portal.gdc.cancer.gov/), including 32 adjacent tumor samples and 375 tumor samples. The mRNA expression and clinical data of early (stage I and II) and advanced (stage III and IV) gastric adenocarcinoma were divided into two independent datasets. Early gastric adenocarcinoma samples included 21 adjacent tumor samples and 164 tumor samples, while advanced gastric adenocarcinoma samples included 10 adjacent tumor samples and 188 tumor samples. These two datasets were obtained after deleting 24 gastric adenocarcinoma samples without clinical staging information from the 407 samples. The data of all these gastric adenocarcinoma samples were downloaded in March 2021. Based on three groups of gastric adenocarcinoma samples, namely, overall, early-stage, and advanced gastric adenocarcinoma samples, the edgeRR language package (version 4.0.2) was used to analyze gene expression profiles and identify differentially expressed genes (DEGs) according to the cutoff criteria of Padj < 0.05 (corrected P value taking into account the false discovery rate (FDR)), |log2FC|> 1. A volcano map of DEG visualization was generated for these three datasets.

2) Univariate COX regression analysis: Univariate COX proportional hazard regression was performed to evaluate the DEGs in the overall, early, and advanced gastric adenocarcinoma groups using the survival package of R software. Based on the standard P < 0.05, mRNAs associated with overall survival of patients in different stages (mRNAs-OS) were obtained, which were related to the survival and prognosis of gastric adenocarcinoma patients.

3) PPI network construction and identification of candidate key mRNAs: To further identify all key mRNAs-OS in the three gastric adenocarcinoma stages, the protein interaction network retrieval tool (http://string-db.org) was used to analyze the interactions between mRNAs-OS and obtain protein interaction data. Proteins with a minimum required interaction score ≥ 0.400 were selected to construct a protein-protein interaction (PPI) network, and nodes with network interruptions were hidden. The PPI network and its interaction scores were then imported into Cytoscape software (Version 3.6.1, https://cytoscape.org/), and CytoHubba (a plug-in in Cytoscape software) was used to identify potential key mRNAs. The top 40 candidate core mRNA-OS at each stage node were screened out for further analysis.

4) Establishment of three COX proportional hazard regression models: Based on the identified core mRNAs-OS, multivariate COX proportional hazard regression analysis was performed using the “surminer” package of R to construct a prognostic model. Subsequently, a prognostic model consisting of mRNAs-OS (mRNAs-PRO) associated with patient prognosis was constructed for overall, early, and advanced gastric adenocarcinoma, respectively. Among them, mRNA-PRO with P < 0.05 was considered an independent prognostic factor for gastric adenocarcinoma. According to the expression of mRNA-PRO, the risk score of each patient was calculated according to the model formula as follows: risk score = Exp(mRNA1) × β1 + Exp(mRNA2) × β2 + Exp(mRNA3) × β3 + … + Exp(mRNAn) × βn. According to the median risk score, gastric adenocarcinoma patients were divided into high-risk group and low-risk group, and the 5-year survival rate of high-risk and low-risk patients was calculated respectively. Risk score curves were drawn to distinguish the difference in risk scores between the two groups of patients. Survival status graphs were drawn to show the survival status of each patient. Heat maps were drawn to show the differences in mRNA-PRO expression levels between high-risk and low-risk groups. Survival curves were drawn to show the 5-year survival rates of high-risk and low-risk groups. ROC curves represented by the area under the curve (AUC) of the model were drawn to evaluate its accuracy and reliability in predicting the prognosis of patients in each stage.

5) Real-time quantitative polymerase chain reaction (QPCR) and immunohistochemistry (IHC): 30 pairs of cDNA tissue chips (including 13 pairs of early-stage pairs and 17 pairs of advanced-stage pairs), 84 pairs of tissue microarrays (including 32 pairs of early-stage pairs and 52 pairs of advanced-stage pairs), the relevant clinical pathological information of these paired gastric adenocarcinoma and normal samples was provided by Shanghai Aoduo Biotechnology Co., Ltd. (Shanghai). According to the American Joint Committee on Cancer (AJCC) criteria, all patients were pathologically diagnosed with gastric adenocarcinoma. The samples were obtained after obtaining written consent according to the established protocol approved by the Institutional Review Board of the Biobank of Shanghai Aoduo Biotechnology Co., Ltd.

The expression levels of IGFBP1 and CHAF1A in gastric adenocarcinoma samples and matched paracancerous tissues were detected by QPCR (detailed clinical data are shown in Table 1), with GAPDH as a reference gene. Total RNA was extracted from tissues and cells by Trizol reagent (Sigma, USA). cDNA was obtained by reverse transcription of RNA using PrimeScriptTM RT Master Mix (Perfect RealTime) (Takara, Japan) according to the manufacturer's protocol. The expression of IGFBP1 and CHAF1A was determined by the protocol of ChamQ Universal SYBRqPCR Master Mix (Vazyme, Nanjing, China) according to the manufacturer's protocol. The primers used in this study are as follows: IGFBP1, forward: 5'-GCATTTCTGCTCTTCCAAAG-3', reverse: 5'-GCAACATCACCACAGGTAG-3'; CHAF1A, forward: 5'-AAAGGAGCAG GACAGTTGGA-3', reverse: 5'-CTGGAAGGGACTTGATTTGC-3'.

Table 1 Baseline characteristics of patients with gastric adenocarcinoma tested by QPCR

variable Early Progressive P-value gender 0.713 male 10 14 female 3 3 Age (years) 0.696 <60 3 5 ≥60 10 12 T stage 0.001 T1 1 0 T2 3 0 T3 9 9 T4 0 8 N-Stage <0.001 N0 6 3 N1 6 1 N2 1 7 N3 0 6 M stage 0.056 M0 13 14 M1 0 3

Immunohistochemical analysis was performed in 84 cancers and 84 matched paracancerous tissues by two independent pathologists in a blinded manner according to standard procedures (detailed clinical data are shown in Table 2 ). The expression levels of IGFBP1 and CHAF1A were analyzed semiquantitatively based on the proportion of positively stained cells and the staining intensity (Proteintech, China). The proportion was evaluated using a semiquantitative standard: 0, (no staining); 1, weak (<10%); 2, moderate (10–50%); 3, diffuse (>50%) stained cells. The staining intensity was also scored as 0 (negative); +1 (weak); +2 (moderate); and +3 (strong). Overall, the final expression score for each case, composed of the proportion and staining intensity, was 0+ (0), negative; 1+ (1 or 2), weakly positive; 2+ (3 or 4), moderately positive; and 3+ (5 or 6), strongly positive. Statistical analysis was performed using a software package (SPSS, version 19.0, Chicago, IL, USA). The clinicopathological characteristics and expression data of representative genes were analyzed using Person chi-square test and likelihood ratio test. P < 0.05 was considered statistically significant.

Table 2 Relationship between the expression levels of IGFBP1 and CHAF1A based on IHC detection and clinical pathological factors in patients with gastric adenocarcinoma Statistical analysis was performed using Pearson’s χ 2 test and likelihood ratio test

Differential expression analysis results

A total of 4627 DEGs were identified from overall stage gastric adenocarcinoma, consisting of 2445 up-regulated mRNAs and 2182 down-regulated mRNAs (Figure 2A and Table 3). In addition, 4715 DEGs were identified in early gastric adenocarcinoma, of which 2542 DEGs were significantly up-regulated and 2173 DEGs were significantly down-regulated (Figure 2B and Table 3). 3465 DEGs were detected from advanced gastric adenocarcinoma, consisting of 1493 DEG up-regulated mRNAs and 1972 down-regulated mRNAs (Figure 2C and Table 3).

Table 3 Differentially expressed genes in overall stage, early stage and advanced stage gastric adenocarcinoma (partial content is as follows)

The intersection of the overall stage and early stage, overall stage and advanced stage, and early stage and advanced stage gastric adenocarcinoma DEGs was obtained, and 4059, 2850, and 2540 intersection DEGs were obtained, respectively. There were 2526 intersection DEGs with significant differential expression in the three stages of gastric adenocarcinoma (Figure 3A, Table 4). Interestingly, six mRNAs including SCGB3A1, SRARP, MUC5B, GABRB1, CNMD, and KRT27 were upregulated in early gastric adenocarcinoma and downregulated in advanced gastric adenocarcinoma, while these mRNAs had no significant differences when the overall stage gastric adenocarcinoma samples were included (Table 4).

Table 4 Differentially expressed genes at the intersection of the three stages (partial content is as follows)

Cox proportional hazards regression model for DEGs

Univariate COX regression analysis identified 504, 430, and 193 mRNA-OS for overall stage, early stage, and advanced stage gastric adenocarcinoma (Table 5). Intersection analysis showed that there were 168, 104, and 15 intersection mRNA-OS between overall stage and early stage gastric adenocarcinoma, overall stage and advanced stage gastric adenocarcinoma, and early stage and advanced stage gastric adenocarcinoma, respectively (Figure 3B and Table 6). In addition, after intersecting mRNAs-OS in all three stages of gastric adenocarcinoma, a total of 14 intersection genes were obtained (Figure 3B and Table 6). In addition, CytoHubba plug-in analysis selected 40 candidate core mRNA-OS from overall stage, early stage, and advanced stage gastric adenocarcinoma, respectively (Table 7).

Table 5 Univariate risk proportional regression analysis of three stages (partial content is as follows)

Table 6 Intersection results of overall survival-related genes (mRNA-OS) in three stages (partial content is as follows)

Table 7 Top 40 key candidate genes for overall stage, early stage and advanced stage (partial content is as follows)

Further multivariate COX analysis constructed three prognostic models of 7, 9 and 14 mRNAs-PRO for the three stage groups, respectively (Figure 4 and Table 8). The survival risk score based on mRNAs-PRO for each stage was calculated by the model formula, and the patients were divided into low-risk group and high-risk group according to their median risk score. As shown in Figure 4A, 4D and 4G, the expression heat map, risk score curve and survival status diagram between the low-risk group and the high-risk group of the prognostic model based on 7, 9 and 14 mRNAs for overall stage, early and advanced gastric adenocarcinoma were drawn, respectively. The Kaplan-Meier survival curves of the high-risk group and the low-risk group for overall stage, early and advanced gastric adenocarcinoma are shown in Figure 4B, 4E and 4H. The AUC values of the ROC curves were 0.73, 0.87 and 0.92, respectively, indicating that these models can reliably and accurately predict the prognosis of patients with gastric adenocarcinoma (Figure 4C, 4F and 4I).

Table 8 Multivariate risk ratio regression analysis of patients with overall stage, early stage and advanced stage gastric adenocarcinoma

QPCR and immunohistochemistry (IHC) of IGFBP1 and CHAF1A in gastric adenocarcinoma samples

The relative mRNA levels of IGFBP1 and CHAF1A in overall stage, early and advanced gastric adenocarcinoma and matched adjacent tissues were evaluated by QPCR (Figure 5A). The results showed that the mRNA level of IGFBP 1 was significantly increased in early gastric adenocarcinoma compared with matched normal tissues (P = 0.018, Figure 5A), which was consistent with the bioinformatics findings. The mRNA level of CHAF1A was significantly increased in advanced gastric adenocarcinoma compared with matched normal tissues (P = 0.002, Figure 5A), which was consistent with the expected analysis. Therefore, IGFBP1 mRNA can be used as a predictor of early gastric adenocarcinoma, while CHAF1A can be used as a biomarker for advanced gastric adenocarcinoma.

In addition, representative IHC staining of IGFBP1 and CHAF1A at the protein level is shown in Figure 5B. Figure 5C shows that two representative targets exhibit cytoplasmic immunoreactivity in cancer tissues of gastric adenocarcinoma patients compared with paired adjacent normal tissues.

IGFBP1 showed stronger staining in early gastric adenocarcinoma samples than in paired normal tissues (P = 0.022, Figure 5C). For CHAF1A, it showed stronger staining in overall stage, early stage, and especially advanced gastric adenocarcinoma samples than in paired normal tissues (all P < 0.001, Figure 5C). Immunohistochemistry results showed that the protein levels of these two representative targets were basically consistent with the QPCR results and consistent with the calculation results. Therefore, IGFBP1 can be used as a biomarker for early gastric adenocarcinoma, while CHAF1A can be used as a biomarker for advanced gastric adenocarcinoma.

Among gastric cancers, gastric adenocarcinoma is the most common histological type (approximately 95%), and clinical guidelines describe the differences in treatment strategies and outcomes for gastric adenocarcinoma at different clinical pathological stages ^[2] . Ideally, patients with early-stage gastric adenocarcinoma undergo local resection through endoscopy, while patients with advanced gastric adenocarcinoma require surgery and multidisciplinary combined treatment ^[2] . The 5-year survival rate of early-stage gastric adenocarcinoma (according to the TNM staging classification for malignant tumors) is as high as 95%, however, the median survival time of patients with advanced gastric adenocarcinoma is only 9 to 10 months ^[3] . Therefore, early detection of high-risk gastric adenocarcinoma patients and selection of appropriate treatment are crucial to prolonging the survival of these patients. New evidence shows that biomarkers can help improve the molecular classification accuracy of gastric adenocarcinoma populations, improve prognosis prediction, and promote precision medicine ^[4] . mRNA is a single-stranded ribonucleic acid transcribed from a DNA chain. The genetic information it carries can guide protein synthesis and plays a central role in the pathogenesis of various cancers, including gastric adenocarcinoma. Many researchers emphasize that mRNA has diagnostic and prognostic value in clinical practice. In the study, the present invention divides gastric adenocarcinoma patients into different stages, and based on the bioinformatics analysis method, accurately predicts the prognosis of gastric adenocarcinoma patients of different stages by constructing a prognostic model (the early prognostic model includes 9 prognostic mRNAs: IGFBP1, etc., and the advanced prognostic model includes 14 prognostic mRNAs: CHAF1A, etc.). The functional annotation and pathway enrichment of mRNA markers indicate that gastric adenocarcinoma of different stages is dominated by different key mechanisms. The present invention also detects the gene and protein levels of representative biomarkers IGFBP1 and CHAF1A in clinical samples of early and advanced gastric adenocarcinoma by QPCR and IHC detection, respectively. The overall process of bioinformatics analysis is shown in Figure 1. The present invention emphasizes that emerging evidence supports that IGFBP1 and CHAF1A can be used as diagnostic biomarkers for patients with gastric adenocarcinoma of different stages, which may become the basis for future precision medicine strategies.

References:

1.Rihawi,K.,et al.,Tumor-Associated Macrophages and InflammatoryMicroenvironment in Gastric Cancer:Novel Translational Implications.Int JMolSci,2021.22(8).

2.Ajani,J.A.,et al.,Gastric adenocarcinoma. Nat Rev Dis Primers, 2017.3:p.17036.

3.Ajani,J.A.,et al.,Gastric Cancer,Version 3.2016,NCCN ClinicalPractice Guidelines in Oncology.J Natl Compr Canc Netw,2016.14(10):p.1286-1312.

4. Joshi, S.S. and B.D. Badgwell, Current treatment and recent progress ingastric cancer. CA Cancer J Clin, 2021.71(3):p.264-279.

5. Jiang, N., Q. Guo, and Q. Luo, Inhibition of ITGB1-DTexpression delays the growth and migration of stomach adenocarcinoma and improves the prognosis of cancer patients using the bioinformatics and cell model analysis. JGastrointest Oncol, 2022.13(2):p .615-629.

6.Xia,N.,et al.,[Immune-relatedgenes and their determined immune cell

microenvironment topredict the prognosis of gastric adenocarcinoma].

ZhonghuaYi Xue Za Zhi,2022.102(12):p.840-846.

Although the above embodiment describes the present invention in detail, it is only a part of the embodiments of the present invention, not all of the embodiments. People can also obtain other embodiments based on this embodiment without creativity, and these embodiments all fall within the protection scope of the present invention.

Claims (5)

1. Application of IGFBP1 gene or CHAF1A gene as a molecular marker for prognosis of gastric adenocarcinoma. 2. The use according to claim 1, characterized in that the IGFBP1 gene is used as a molecular marker for the prognosis of early gastric adenocarcinoma, and the CHAF1A gene is used as a molecular marker for the prognosis of advanced gastric adenocarcinoma. 3. A method for screening gastric adenocarcinoma prognostic molecular markers, characterized in that the gene expression and clinical data of early and advanced gastric adenocarcinoma samples are downloaded respectively to screen prognostic markers based on staging and stratification data, comprising the following steps: 1) Data preparation and DEG identification: The mRNA expression and clinical data of 407 total gastric adenocarcinoma samples were downloaded from the TCGA database (Cancer Genome Atlas) (http://portal.gdc.cancer.gov/), including 32 adjacent tumor samples and 375 tumor samples. The mRNA expression and clinical data of early and advanced gastric adenocarcinoma were divided into two independent data sets. The early gastric adenocarcinoma samples included 21 adjacent tumor samples and 164 tumor samples, and the advanced gastric adenocarcinoma samples included 10 adjacent tumor samples and 188 tumor samples. These two data sets were obtained after deleting 24 gastric adenocarcinoma samples without clinical staging information from the 407 samples. 2) Based on the three groups of gastric adenocarcinoma samples, namely, overall, early and advanced gastric adenocarcinoma samples, the edgeR R language package was used to analyze the gene expression profiles according to the cut-off criteria of P adj < 0.05, |log2FC| > 1, to identify the differentially expressed genes DEGs, and generate the DEG visualization volcano maps of the three data sets; 3) Univariate COX regression analysis: The survival package of R software was used to perform univariate COX proportional hazard regression assessment on the differentially expressed gene DEGs in the overall, early, and advanced gastric adenocarcinoma groups, and mRNAs associated with the overall survival of patients in different stages were obtained according to the standard P < 0.05, and mRNAs-OS were obtained, which were related to the survival and prognosis of gastric adenocarcinoma patients; 4) PPI network construction and candidate key mRNA identification: The protein interaction network retrieval tool was used to analyze the interactions between mRNAs-OS, and the protein interaction data were obtained. Proteins with a minimum required interaction score ≥ 0.400 were selected to construct the protein-protein interaction network. The PPI network and its interaction score were imported into Cytoscape software, and CytoHubba was used to identify potential key mRNAs. The top 40 candidate core mRNAs-OS at each stage node were screened for further analysis; 5) Establishment of three COX proportional hazard regression models: Based on the identified core mRNAs-OS, multivariate COX proportional hazard regression analysis was performed using the “surminer” package of R to construct a prognostic model. Subsequently, a prognostic model consisting of mRNAs-OS associated with patient prognosis was constructed for overall, early, and advanced gastric adenocarcinoma, respectively. Among them, mRNA-PRO with P < 0.05 was considered an independent prognostic factor for gastric adenocarcinoma. According to the expression of mRNA-PRO, the risk score of each patient was calculated according to the model formula as follows: Risk score = Exp(m mRNA1)×β1+Exp(mRNA2)×β2+Exp(mRNA3)×β3+…+Exp(mRNAn)×βn. According to the median risk score, gastric adenocarcinoma patients were divided into high-risk group and low-risk group. The 5-year survival rates of high-risk and low-risk patients were calculated respectively. Risk score curves were drawn to distinguish the difference in risk scores between the two groups of patients. Survival status diagrams, risk heat maps, and survival curves were drawn. Thus, prognostic models for the three stages were established respectively. ROC curves represented by the area under the model curve were drawn to evaluate its accuracy and reliability in predicting the prognosis of patients in each stage. 6) Real-time quantitative polymerase chain reaction and immunohistochemical staining: 30 pairs of cDNA tissue chips, 84 pairs of tissue microarrays, The expression levels of IGFBP1 and CHAF1A in gastric adenocarcinoma samples and matched adjacent normal tissues were detected by QPCR; According to standard procedures, two independent pathologists performed immunohistochemical analysis in a blinded manner on 84 cancer and 84 matched adjacent adjacent tissues, and the expression levels of IGFBP1 and CHAF1A were semiquantitatively analyzed based on the proportion of positively stained cells and staining intensity. 4. The use according to claim 1 or 2, characterized in that the primers used to amplify the IGFBP1 gene include an upstream primer and a downstream primer, the nucleotide sequence of the upstream primer is shown as SEQ ID No.1, and the nucleotide sequence of the downstream primer is shown as SEQ ID No.2. 5. The use according to claim 1 or 2, characterized in that the primers used to amplify the CHAF1A gene include an upstream primer and a downstream primer, the nucleotide sequence of the upstream primer is shown in SEQ ID No.3, and the nucleotide sequence of the downstream primer is shown in SEQ ID No.4.

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